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llama.cpp/ggml/src/ggml-sycl/dmmv.cpp
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Todd Malsbary 8efbf65dbd sycl : Add DMMV ESIMD Q3_K kernel (#26251) 
* Add DMMV Q4_K and Q6_K ESIMD kernels

Configure cmake build with -DGGML_SYCL_ESIMD=ON to enable.

Signed-off-by: Todd Malsbary <todd.malsbary@intel.com>

* Refactor ESIMD kernels to share common code

Signed-off-by: Todd Malsbary <todd.malsbary@intel.com>

* Move control of ESIMD from compile to runtime

Signed-off-by: Todd Malsbary <todd.malsbary@intel.com>

* Use ESIMD by default when available

Signed-off-by: Todd Malsbary <todd.malsbary@intel.com>

* Fix possible error when using ESIMD by default

While not an issue in the current version, this will become an
issue when additional QK ESIMD kernels are added (such as Q2_K).

Signed-off-by: Todd Malsbary <todd.malsbary@intel.com>

* Add explicit unroll to ESIMD kernels

Signed-off-by: Todd Malsbary <todd.malsbary@intel.com>

* Tidy up ESIMD kernels a bit

Signed-off-by: Todd Malsbary <todd.malsbary@intel.com>

* Add DMMV Q3_K ESIMD kernel

Signed-off-by: Todd Malsbary <todd.malsbary@intel.com>

---------

Signed-off-by: Todd Malsbary <todd.malsbary@intel.com>
2026-08-13 11:32:36 +03:00

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#include "convert.hpp"
#include "dmmv.hpp"
#include "dequantize.hpp"
#include "presets.hpp"
#if defined(__INTEL_LLVM_COMPILER)
#if __has_include(<sycl/ext/oneapi/bfloat16.hpp>)
#include <sycl/ext/oneapi/bfloat16.hpp>
#define GGML_SYCL_DMMV_HAS_BF16
#endif
#include <sycl/ext/intel/esimd.hpp>
#include "esimd.hpp"
#define GGML_SYCL_DMMV_HAS_ESIMD
#endif
static void convert_f16(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){
const sycl::half *x = (const sycl::half *)vx;
// automatic half -> float type cast if dfloat == float
v.x() = x[ib + iqs + 0];
v.y() = x[ib + iqs + 1];
}
#ifdef GGML_SYCL_DMMV_HAS_BF16
static void convert_bf16(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){
const sycl::ext::oneapi::bfloat16 *x = (const sycl::ext::oneapi::bfloat16 *)vx;
// automatic bfloat16 -> float type cast if dfloat == float
v.x() = x[ib + iqs + 0];
v.y() = x[ib + iqs + 1];
}
#endif
static void convert_f32(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){
const float * x = (const float *) vx;
// automatic half -> float type cast if dfloat == float
v.x() = x[ib + iqs + 0];
v.y() = x[ib + iqs + 1];
}
template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
static void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows,
const sycl::nd_item<3> &item_ct1) {
// qk = quantized weights per x block
// qr = number of quantized weights per data value in x block
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 tid = item_ct1.get_local_id(2);
const int iter_stride = 2*GGML_SYCL_DMMV_X;
const int vals_per_iter = iter_stride / WARP_SIZE; // num quantized vals per thread and i iter
const int y_offset = qr == 1 ? 1 : qk/2;
// partial sum for each thread
#ifdef GGML_SYCL_F16
sycl::half2 tmp = {0.0f, 0.0f}; // two sums for f16 to take advantage of half2 intrinsics
#else
float tmp = 0.0f;
#endif // GGML_SYCL_F16
for (int i = 0; i < ncols; i += iter_stride) {
const int col = i + vals_per_iter*tid;
const int ib = (row*ncols + col)/qk; // x block index
const int iqs = (col%qk)/qr; // x quant index
const int iybs = col - col%qk; // y block start index
// processing >2 values per i iter is faster for fast GPUs
#pragma unroll
for (int j = 0; j < vals_per_iter; j += 2) {
// process 2 vals per j iter
// dequantize
// for qr = 2 the iqs needs to increase by 1 per j iter because 2 weights per data val
dfloat2 v;
dequantize_kernel(vx, ib, iqs + j/qr, v);
// matrix multiplication
// for qr = 2 the y index needs to increase by 1 per j iter because of y_offset = qk/2
#ifdef GGML_SYCL_F16
dfloat2 t1{y[iybs + iqs + j / qr + 0],
y[iybs + iqs + j / qr + y_offset]};
tmp += v * t1;
#else
tmp += v.x() * y[iybs + iqs + j / qr + 0];
tmp += v.y() * y[iybs + iqs + j / qr + y_offset];
#endif // GGML_SYCL_F16
}
}
// sum up partial sums and write back result
const int mask_start = ncols > GGML_SYCL_DMMV_X ? WARP_SIZE >> 1 : WARP_SIZE >> 2;
for (int mask = mask_start; mask > 0; mask >>= 1) {
tmp +=
dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
}
if (tid == 0) {
#ifdef GGML_SYCL_F16
dst[row] = tmp.x() + tmp.y();
#else
dst[row] = tmp;
#endif // GGML_SYCL_F16
}
}
template <int qk, int qr, dequantize_kernel_t_reorder dequantize_kernel_reorder>
static void dequantize_mul_mat_vec_reorder(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows,
const sycl::nd_item<3> &item_ct1) {
// qk = quantized weights per x block
// qr = number of quantized weights per data value in x block
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 tid = item_ct1.get_local_id(2);
const int ncols_left = ncols % (QK4_0*WARP_SIZE);
const int ncols_align = ncols - ncols_left;
const int iter_stride = 8*2*GGML_SYCL_DMMV_X;
const int vals_per_iter = iter_stride / WARP_SIZE; // num quantized vals per thread and i iter //64/16=4, 512/16/2= 16
const int y_offset = qr == 1 ? 1 : qk/2;
// partial sum for each thread
#ifdef GGML_SYCL_F16
sycl::half2 tmp = {0.0f, 0.0f}; // two sums for f16 to take advantage of half2 intrinsics
#else
float tmp = 0.0f;
#endif // GGML_SYCL_F16
const char *d_ptr = (const char*)vx+ncols*nrows/2;
int i=0;
for (i = 0; i < ncols_align; i += iter_stride) {
const int col = i + vals_per_iter*tid;
const int ib = (row*ncols + col)/qk; // x block index
const int iqs = (col%qk)/qr; // x quant index
const int iybs = col - col%qk; // y block start index
// processing >2 values per i iter is faster for fast GPUs
#pragma unroll
for (int j = 0; j < vals_per_iter; j += 2) {
// process 2 vals per j iter
// dequantize
// for qr = 2 the iqs needs to increase by 1 per j iter because 2 weights per data val
dfloat2 v;
dequantize_kernel_reorder((const void *)d_ptr, ib, (const void *)vx, ib * QK4_0 / 2 +iqs+j/qr, v);
// matrix multiplication
// for qr = 2 the y index needs to increase by 1 per j iter because of y_offset = qk/2
#ifdef GGML_SYCL_F16
dfloat2 t1{y[iybs + iqs + j / qr + 0],
y[iybs + iqs + j / qr + y_offset]};
tmp += v * t1;
#else
tmp += v.x() * y[iybs + iqs + j / qr + 0];
tmp += v.y() * y[iybs + iqs + j / qr + y_offset];
#endif // GGML_SYCL_F16
}
}
for (; i < ncols; i += iter_stride) {
if (tid>=ncols_left/QK4_0) continue;
const int col = i + vals_per_iter*tid;
const int ib = (row*ncols + col)/qk; // x block index
const int iqs = (col%qk)/qr; // x quant index
const int iybs = col - col%qk; // y block start index
// processing >2 values per i iter is faster for fast GPUs
#pragma unroll
for (int j = 0; j < vals_per_iter; j += 2) {
// process 2 vals per j iter
// dequantize
// for qr = 2 the iqs needs to increase by 1 per j iter because 2 weights per data val
dfloat2 v;
dequantize_kernel_reorder((const void *)d_ptr, ib, (const void *)vx, ib * QK4_0 / 2 +iqs+j/qr, v);
// matrix multiplication
// for qr = 2 the y index needs to increase by 1 per j iter because of y_offset = qk/2
#ifdef GGML_SYCL_F16
dfloat2 t1{y[iybs + iqs + j / qr + 0],
y[iybs + iqs + j / qr + y_offset]};
tmp += v * t1;
#else
tmp += v.x() * y[iybs + iqs + j / qr + 0];
tmp += v.y() * y[iybs + iqs + j / qr + y_offset];
#endif // GGML_SYCL_F16
}
}
// sum up partial sums and write back result
const int mask_start = ncols > GGML_SYCL_DMMV_X ? WARP_SIZE >> 1 : WARP_SIZE >> 2;
for (int mask = mask_start; mask > 0; mask >>= 1) {
tmp +=
dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
}
if (tid == 0) {
#ifdef GGML_SYCL_F16
dst[row] = tmp.x() + tmp.y();
#else
dst[row] = tmp;
#endif // GGML_SYCL_F16
}
}
static void convert_mul_mat_vec_f16_sycl(const void *vx, const dfloat *y,
float *dst, const int ncols,
const int nrows,
dpct::queue_ptr stream) {
GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 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);
{
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->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)]] {
dequantize_mul_mat_vec<1, 1, convert_f16>(vx, y, dst, ncols,
nrows, item_ct1);
});
}
}
#ifdef GGML_SYCL_DMMV_HAS_BF16
static void convert_mul_mat_vec_bf16_sycl(const void *vx, const dfloat *y,
float *dst, const int ncols,
const int nrows,
dpct::queue_ptr stream) {
// The qk=1 kernel iterates with stride 2*GGML_SYCL_DMMV_X, so ncols must be a
// multiple of that — not just GGML_SYCL_DMMV_X — to avoid out-of-bounds reads.
GGML_ASSERT(ncols % (2*GGML_SYCL_DMMV_X) == 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->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)]] {
dequantize_mul_mat_vec<1, 1, convert_bf16>(vx, y, dst, ncols,
nrows, item_ct1);
});
}
}
#endif
static void dequantize_mul_mat_vec_q2_k(const void *__restrict__ vx,
const float *__restrict__ yy,
float *__restrict__ dst,
const int ncols, int nrows,
const sycl::nd_item<3> &item_ct1) {
static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
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 num_blocks_per_row = ncols / QK_K;
const int ib0 = row*num_blocks_per_row;
const block_q2_K * x = (const block_q2_K *)vx + ib0;
float tmp = 0; // partial sum for thread in warp
#if QK_K == 256
const int tid =
item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...7 or 0...15
const int ix =
item_ct1.get_local_id(2) % K_QUANTS_PER_ITERATION; // 0 or 0,1
const int step = 16/K_QUANTS_PER_ITERATION;
const int in = tid % step; // 0...15 or 0...7
const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15 or 0...14 in steps of 2
uint32_t aux[4];
const uint8_t * d = (const uint8_t *)aux;
const uint8_t * m = (const uint8_t *)(aux + 2);
for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
const float dall = x[i].dm[0];
const float dmin = x[i].dm[1];
for (int im = 0; im < 2; ++im) {
const int q_offset = 32*im + l0;
const int s_offset = 8*im;
const int y_offset = 128*im + l0;
const float * y = yy + i * QK_K + y_offset;
const uint8_t * q = x[i].qs + q_offset;
const uint32_t * a = (const uint32_t *)(x[i].scales + s_offset);
aux[0] = a[0] & 0x0f0f0f0f;
aux[1] = a[1] & 0x0f0f0f0f;
aux[2] = (a[0] >> 4) & 0x0f0f0f0f;
aux[3] = (a[1] >> 4) & 0x0f0f0f0f;
float sum1 = 0, sum2 = 0;
for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
sum1 += y[l+ 0] * d[0] * ((q[l+ 0] >> 0) & 3)
+ y[l+32] * d[2] * ((q[l+ 0] >> 2) & 3)
+ y[l+64] * d[4] * ((q[l+ 0] >> 4) & 3)
+ y[l+96] * d[6] * ((q[l+ 0] >> 6) & 3)
+ y[l+16] * d[1] * ((q[l+16] >> 0) & 3)
+ y[l+48] * d[3] * ((q[l+16] >> 2) & 3)
+ y[l+80] * d[5] * ((q[l+16] >> 4) & 3)
+y[l+112] * d[7] * ((q[l+16] >> 6) & 3);
sum2 += y[l+ 0] * m[0] + y[l+32] * m[2] + y[l+64] * m[4] + y[ l+96] * m[6]
+ y[l+16] * m[1] + y[l+48] * m[3] + y[l+80] * m[5] + y[l+112] * m[7];
}
tmp += dall * sum1 - dmin * sum2;
}
}
#else
const int tid = item_ct1.get_local_id(2) /
(2 * K_QUANTS_PER_ITERATION); // 0...15 or 0...7
const int ix = item_ct1.get_local_id(2) %
(2 * K_QUANTS_PER_ITERATION); // 0....1 or 0...3
const int offset = tid * K_QUANTS_PER_ITERATION;
uint32_t uaux[2];
const uint8_t * d = (const uint8_t *)uaux;
for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
const float * y = yy + i * QK_K + offset;
const uint8_t * q = x[i].qs + offset;
const uint32_t * s = (const uint32_t *)x[i].scales;
uaux[0] = s[0] & 0x0f0f0f0f;
uaux[1] = (s[0] >> 4) & 0x0f0f0f0f;
const sycl::float2 dall =
x[i].dm.convert<float, sycl::rounding_mode::automatic>();
float sum1 = 0, sum2 = 0;
for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
const uint8_t ql = q[l];
sum1 += y[l+ 0] * d[0] * ((ql >> 0) & 3)
+ y[l+16] * d[1] * ((ql >> 2) & 3)
+ y[l+32] * d[2] * ((ql >> 4) & 3)
+ y[l+48] * d[3] * ((ql >> 6) & 3);
sum2 += y[l+0] * d[4] + y[l+16] * d[5] + y[l+32] * d[6] + y[l+48] * d[7];
}
tmp += dall.x() * sum1 - dall.y() * sum2;
}
#endif
// 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 dequantize_mul_mat_vec_q2_k_reorder(const void *__restrict__ vx,
const float *__restrict__ yy,
float *__restrict__ dst,
const int ncols, int nrows,
const sycl::nd_item<3> &item_ct1) {
static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
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 num_blocks_per_row = ncols / QK_K;
const int ib0 = row*num_blocks_per_row;
// SOA base pointers for the reordered layout:
// [qs: nb * (QK_K/4)] [scales: nb * (QK_K/16)] [dm: nb * sizeof(half2)]
const int nb = nrows * num_blocks_per_row;
const uint8_t * qs_base = (const uint8_t *)vx;
const uint8_t * scales_base = qs_base + (size_t)nb * (QK_K / 4);
const sycl::half2 * dm_base = (const sycl::half2 *)(scales_base + (size_t)nb * (QK_K / 16));
float tmp = 0; // partial sum for thread in warp
#if QK_K == 256
const int tid =
item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...7 or 0...15
const int ix =
item_ct1.get_local_id(2) % K_QUANTS_PER_ITERATION; // 0 or 0,1
const int step = 16/K_QUANTS_PER_ITERATION;
const int in = tid % step; // 0...15 or 0...7
const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15 or 0...14 in steps of 2
uint32_t aux[4];
const uint8_t * d = (const uint8_t *)aux;
const uint8_t * m = (const uint8_t *)(aux + 2);
for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
const int bi = ib0 + i;
const sycl::half2 dm_val = dm_base[bi];
const float dall = dm_val[0];
const float dmin = dm_val[1];
for (int im = 0; im < 2; ++im) {
const int q_offset = 32*im + l0;
const int s_offset = 8*im;
const int y_offset = 128*im + l0;
const float * y = yy + i * QK_K + y_offset;
const uint8_t * q = qs_base + bi * (QK_K / 4) + q_offset;
const uint32_t * a = (const uint32_t *)(scales_base + bi * (QK_K / 16) + s_offset);
aux[0] = a[0] & 0x0f0f0f0f;
aux[1] = a[1] & 0x0f0f0f0f;
aux[2] = (a[0] >> 4) & 0x0f0f0f0f;
aux[3] = (a[1] >> 4) & 0x0f0f0f0f;
float sum1 = 0, sum2 = 0;
for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
sum1 += y[l+ 0] * d[0] * ((q[l+ 0] >> 0) & 3)
+ y[l+32] * d[2] * ((q[l+ 0] >> 2) & 3)
+ y[l+64] * d[4] * ((q[l+ 0] >> 4) & 3)
+ y[l+96] * d[6] * ((q[l+ 0] >> 6) & 3)
+ y[l+16] * d[1] * ((q[l+16] >> 0) & 3)
+ y[l+48] * d[3] * ((q[l+16] >> 2) & 3)
+ y[l+80] * d[5] * ((q[l+16] >> 4) & 3)
+y[l+112] * d[7] * ((q[l+16] >> 6) & 3);
sum2 += y[l+ 0] * m[0] + y[l+32] * m[2] + y[l+64] * m[4] + y[ l+96] * m[6]
+ y[l+16] * m[1] + y[l+48] * m[3] + y[l+80] * m[5] + y[l+112] * m[7];
}
tmp += dall * sum1 - dmin * sum2;
}
}
#else
GGML_UNUSED(vx);
GGML_UNUSED(yy);
GGML_UNUSED(ncols);
GGML_UNUSED(item_ct1);
GGML_ABORT("Q2_K reorder DMMV not supported for QK_K != 256");
#endif
// 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 dequantize_mul_mat_vec_q3_k(const void *__restrict__ vx,
const float *__restrict__ yy,
float *__restrict__ dst,
const int ncols, 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 num_blocks_per_row = ncols / QK_K;
const int ib0 = row*num_blocks_per_row;
const block_q3_K * x = (const block_q3_K *)vx + ib0;
float tmp = 0; // partial sum for thread in warp
#if QK_K == 256
const uint16_t kmask1 = 0x0303;
const uint16_t kmask2 = 0x0f0f;
const int tid =
item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...7 or 0...15
const int ix =
item_ct1.get_local_id(2) % K_QUANTS_PER_ITERATION; // 0 or 0,1
const int n = K_QUANTS_PER_ITERATION; // iterations in the inner loop
const int step = 16/K_QUANTS_PER_ITERATION;
const int in = tid % step; // 0...15 or 0...7
const int l0 = n*in; // 0...15 or 0...14 in steps of 2
uint16_t utmp[4];
const int8_t * s = (const int8_t *)utmp;
for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
const uint8_t * h = x[i].hmask + l0;
const float d = x[i].d;
for (int im = 0; im < 2; ++im) {
const int q_offset = 32*im + l0;
const int y_offset = 128*im + l0;
const uint16_t s_shift = 4*im;
const uint8_t m = 1 << (4*im);
const float * y = yy + i * QK_K + y_offset;
const uint8_t * q = x[i].qs + q_offset;
const uint16_t * a = (const uint16_t *)x[i].scales;
utmp[0] = ((a[0] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 0)) & kmask1) << 4);
utmp[1] = ((a[1] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 0)) & kmask1) << 4);
utmp[2] = ((a[2] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 2)) & kmask1) << 4);
utmp[3] = ((a[3] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 2)) & kmask1) << 4);
float sum = 0;
for (int l = 0; l < n; ++l) {
sum += y[l+ 0] * (s[0] - 32) * (((q[l] >> 0) & 3) - (h[l] & (m << 0) ? 0 : 4))
+ y[l+32] * (s[2] - 32) * (((q[l] >> 2) & 3) - (h[l] & (m << 1) ? 0 : 4))
+ y[l+64] * (s[4] - 32) * (((q[l] >> 4) & 3) - (h[l] & (m << 2) ? 0 : 4))
+ y[l+96] * (s[6] - 32) * (((q[l] >> 6) & 3) - (h[l] & (m << 3) ? 0 : 4));
sum += y[l+16] * (s[1] - 32) * (((q[l+16] >> 0) & 3) - (h[l+16] & (m << 0) ? 0 : 4))
+ y[l+48] * (s[3] - 32) * (((q[l+16] >> 2) & 3) - (h[l+16] & (m << 1) ? 0 : 4))
+ y[l+80] * (s[5] - 32) * (((q[l+16] >> 4) & 3) - (h[l+16] & (m << 2) ? 0 : 4))
+ y[l+112] * (s[7] - 32) * (((q[l+16] >> 6) & 3) - (h[l+16] & (m << 3) ? 0 : 4));
}
tmp += d * sum;
}
}
#else
const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...15 or 0...7
const int ix = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION); // 0....1 or 0...3
const int offset = tid * K_QUANTS_PER_ITERATION; // 0...15 or 0...14
const int in = offset/8; // 0 or 1
const int im = offset%8; // 0...7
for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
const float * y = yy + i * QK_K + offset;
const uint8_t * q = x[i].qs + offset;
const uint8_t * s = x[i].scales;
const float dall = (float)x[i].d;
float sum = 0;
for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
const uint8_t hl = x[i].hmask[im+l] >> in;
const uint8_t ql = q[l];
sum += y[l+ 0] * dall * ((s[0] & 0xF) - 8) * ((int8_t)((ql >> 0) & 3) - ((hl >> 0) & 1 ? 0 : 4))
+ y[l+16] * dall * ((s[0] >> 4) - 8) * ((int8_t)((ql >> 2) & 3) - ((hl >> 2) & 1 ? 0 : 4))
+ y[l+32] * dall * ((s[1] & 0xF) - 8) * ((int8_t)((ql >> 4) & 3) - ((hl >> 4) & 1 ? 0 : 4))
+ y[l+48] * dall * ((s[1] >> 4) - 8) * ((int8_t)((ql >> 6) & 3) - ((hl >> 6) & 1 ? 0 : 4));
}
tmp += sum;
}
#endif
// 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 dequantize_mul_mat_vec_q3_k_reorder(const void *__restrict__ vx,
const float *__restrict__ yy,
float *__restrict__ dst,
const int ncols, 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 num_blocks_per_row = ncols / QK_K;
const int ib0 = row*num_blocks_per_row;
// SOA base pointers for the reordered layout:
// [qs: nb * (QK_K/4)] [hmask: nb * (QK_K/8)] [scales: nb * 12] [d: nb * sizeof(half)]
const int nb = nrows * num_blocks_per_row;
const uint8_t * qs_base = (const uint8_t *)vx;
const uint8_t * hmask_base = qs_base + (size_t)nb * (QK_K / 4);
const uint8_t * scales_base = hmask_base + (size_t)nb * (QK_K / 8);
const sycl::half * d_base = (const sycl::half *)(scales_base + (size_t)nb * 12);
float tmp = 0; // partial sum for thread in warp
#if QK_K == 256
const uint16_t kmask1 = 0x0303;
const uint16_t kmask2 = 0x0f0f;
const int tid =
item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...7 or 0...15
const int ix =
item_ct1.get_local_id(2) % K_QUANTS_PER_ITERATION; // 0 or 0,1
const int n = K_QUANTS_PER_ITERATION; // iterations in the inner loop
const int step = 16/K_QUANTS_PER_ITERATION;
const int in = tid % step; // 0...15 or 0...7
const int l0 = n*in; // 0...15 or 0...14 in steps of 2
uint16_t utmp[4];
const int8_t * s = (const int8_t *)utmp;
for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
const int bi = ib0 + i;
const uint8_t * h = hmask_base + bi * (QK_K / 8) + l0;
const float d = d_base[bi];
for (int im = 0; im < 2; ++im) {
const int q_offset = 32*im + l0;
const int y_offset = 128*im + l0;
const uint16_t s_shift = 4*im;
const uint8_t m = 1 << (4*im);
const float * y = yy + i * QK_K + y_offset;
const uint8_t * q = qs_base + bi * (QK_K / 4) + q_offset;
const uint16_t * a = (const uint16_t *)(scales_base + bi * 12);
utmp[0] = ((a[0] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 0)) & kmask1) << 4);
utmp[1] = ((a[1] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 0)) & kmask1) << 4);
utmp[2] = ((a[2] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 2)) & kmask1) << 4);
utmp[3] = ((a[3] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 2)) & kmask1) << 4);
float sum = 0;
for (int l = 0; l < n; ++l) {
sum += y[l+ 0] * (s[0] - 32) * (((q[l] >> 0) & 3) - (h[l] & (m << 0) ? 0 : 4))
+ y[l+32] * (s[2] - 32) * (((q[l] >> 2) & 3) - (h[l] & (m << 1) ? 0 : 4))
+ y[l+64] * (s[4] - 32) * (((q[l] >> 4) & 3) - (h[l] & (m << 2) ? 0 : 4))
+ y[l+96] * (s[6] - 32) * (((q[l] >> 6) & 3) - (h[l] & (m << 3) ? 0 : 4));
sum += y[l+16] * (s[1] - 32) * (((q[l+16] >> 0) & 3) - (h[l+16] & (m << 0) ? 0 : 4))
+ y[l+48] * (s[3] - 32) * (((q[l+16] >> 2) & 3) - (h[l+16] & (m << 1) ? 0 : 4))
+ y[l+80] * (s[5] - 32) * (((q[l+16] >> 4) & 3) - (h[l+16] & (m << 2) ? 0 : 4))
+ y[l+112] * (s[7] - 32) * (((q[l+16] >> 6) & 3) - (h[l+16] & (m << 3) ? 0 : 4));
}
tmp += d * sum;
}
}
#else
GGML_UNUSED(vx);
GGML_UNUSED(yy);
GGML_UNUSED(ncols);
GGML_UNUSED(item_ct1);
GGML_ABORT("Q3_K reorder DMMV not supported for QK_K != 256");
#endif
// 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 dequantize_mul_mat_vec_q4_k(const void *__restrict__ vx,
const float *__restrict__ yy,
float *__restrict__ dst,
const int ncols, 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 num_blocks_per_row = ncols / QK_K;
const int ib0 = row*num_blocks_per_row;
const block_q4_K * x = (const block_q4_K *)vx + ib0;
#if QK_K == 256
const uint16_t kmask1 = 0x3f3f;
const uint16_t kmask2 = 0x0f0f;
const uint16_t kmask3 = 0xc0c0;
const int tid =
item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...7 or 0...15
const int ix =
item_ct1.get_local_id(2) % K_QUANTS_PER_ITERATION; // 0 or 0,1
const int step = 8/K_QUANTS_PER_ITERATION; // 8 or 4
const int il_base = tid/step; // 0 or 1 (was 0...3)
const int ir = tid - step*il_base; // 0...7 or 0...3
const int n = 2 * K_QUANTS_PER_ITERATION; // 2 or 4
const int in = il_base%2;
const int l0 = n*(2*ir + in);
uint16_t aux[4];
const uint8_t * sc = (const uint8_t *)aux;
#if K_QUANTS_PER_ITERATION == 2
uint32_t q32[4];
const uint8_t * q4 = (const uint8_t *)q32;
#else
uint16_t q16[4];
const uint8_t * q4 = (const uint8_t *)q16;
#endif
float tmp = 0; // partial sum for thread in warp
for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
const float dall = x[i].dm[0];
const float dmin = x[i].dm[1];
for (int im = 0; im < 2; ++im) {
const int q_offset = 32*im + l0;
const int y_offset = 64*im + l0;
const float * y1 = yy + i*QK_K + y_offset;
const float * y2 = y1 + 128;
const uint16_t * a = (const uint16_t *)x[i].scales;
aux[0] = a[im+0] & kmask1;
aux[1] = a[im+2] & kmask1;
aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
#if K_QUANTS_PER_ITERATION == 2
const uint32_t * q1 = (const uint32_t *)(x[i].qs + q_offset);
const uint32_t * q2 = q1 + 16;
q32[0] = q1[0] & 0x0f0f0f0f;
q32[1] = q1[0] & 0xf0f0f0f0;
q32[2] = q2[0] & 0x0f0f0f0f;
q32[3] = q2[0] & 0xf0f0f0f0;
sycl::float4 s = {0.f, 0.f, 0.f, 0.f};
float smin = 0;
for (int l = 0; l < 4; ++l) {
s.x() += y1[l] * q4[l + 0]; s.y() += y1[l + 32] * q4[l + 4];
s.z() += y2[l] * q4[l + 8]; s.w() += y2[l + 32] * q4[l + 12];
smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
}
tmp += dall * (s.x() * sc[0] + s.y() * sc[1] * 1.f / 16.f +
s.z() * sc[4] + s.w() * sc[5] * 1.f / 16.f) -
dmin * smin;
#else
const uint16_t * q1 = (const uint16_t *)(x[i].qs + q_offset);
const uint16_t * q2 = q1 + 32;
q16[0] = q1[0] & 0x0f0f;
q16[1] = q1[0] & 0xf0f0;
q16[2] = q2[0] & 0x0f0f;
q16[3] = q2[0] & 0xf0f0;
sycl::float4 s = {0.f, 0.f, 0.f, 0.f};
float smin = 0;
for (int l = 0; l < 2; ++l) {
s.x() += y1[l] * q4[l+0]; s.y() += y1[l+32] * q4[l+2];
s.z() += y2[l] * q4[l+4]; s.w() += y2[l+32] * q4[l+6];
smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
}
tmp += dall * (s.x() * sc[0] + s.y() * sc[1] * 1.f/16.f + s.z() * sc[4] + s.w() * sc[5] * 1.f/16.f) - dmin * smin;
#endif
}
}
#else
const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...15
const int ix = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION);
const int step = tid * K_QUANTS_PER_ITERATION;
uint16_t aux16[2];
const uint8_t * s = (const uint8_t *)aux16;
float tmp = 0;
for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
const uint8_t * q = x[i].qs + step;
const float * y = yy + i*QK_K + step;
const uint16_t * a = (const uint16_t *)x[i].scales;
aux16[0] = a[0] & 0x0f0f;
aux16[1] = (a[0] >> 4) & 0x0f0f;
const float d = (float)x[i].dm[0];
const float m = (float)x[i].dm[1];
float sum = 0.f;
for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
sum += y[j+ 0] * (d * s[0] * (q[j+ 0] & 0xF) - m * s[2])
+ y[j+16] * (d * s[0] * (q[j+16] & 0xF) - m * s[2])
+ y[j+32] * (d * s[1] * (q[j+ 0] >> 4) - m * s[3])
+ y[j+48] * (d * s[1] * (q[j+16] >> 4) - m * s[3]);
}
tmp += sum;
}
#endif
// 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 (tid == 0) {
dst[row] = tmp;
}
}
static void dequantize_mul_mat_vec_q4_k_reorder(const void *__restrict__ vx,
const float *__restrict__ yy,
float *__restrict__ dst,
const int ncols, 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 num_blocks_per_row = ncols / QK_K;
const int ib0 = row*num_blocks_per_row;
// SOA base pointers for the reordered layout:
// [qs: nb * QK_K/2] [scales: nb * K_SCALE_SIZE] [dm: nb * sizeof(half2)]
const int nb = nrows * num_blocks_per_row;
const uint8_t * qs_base = (const uint8_t *)vx;
const uint8_t * scales_base = qs_base + (size_t)nb * (QK_K / 2);
const sycl::half2 * dm_base = (const sycl::half2 *)(scales_base + (size_t)nb * K_SCALE_SIZE);
#if QK_K == 256
const uint16_t kmask1 = 0x3f3f;
const uint16_t kmask2 = 0x0f0f;
const uint16_t kmask3 = 0xc0c0;
const int tid =
item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...7 or 0...15
const int ix =
item_ct1.get_local_id(2) % K_QUANTS_PER_ITERATION; // 0 or 0,1
const int step = 8/K_QUANTS_PER_ITERATION; // 8 or 4
const int il_base = tid/step; // 0 or 1 (was 0...3)
const int ir = tid - step*il_base; // 0...7 or 0...3
const int n = 2 * K_QUANTS_PER_ITERATION; // 2 or 4
const int in = il_base%2;
const int l0 = n*(2*ir + in);
uint16_t aux[4];
const uint8_t * sc = (const uint8_t *)aux;
#if K_QUANTS_PER_ITERATION == 2
uint32_t q32[4];
const uint8_t * q4 = (const uint8_t *)q32;
#else
uint16_t q16[4];
const uint8_t * q4 = (const uint8_t *)q16;
#endif
float tmp = 0; // partial sum for thread in warp
for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
const int bi = ib0 + i;
const sycl::half2 dm_val = dm_base[bi];
const float dall = dm_val[0];
const float dmin = dm_val[1];
for (int im = 0; im < 2; ++im) {
const int q_offset = 32*im + l0;
const int y_offset = 64*im + l0;
const float * y1 = yy + i*QK_K + y_offset;
const float * y2 = y1 + 128;
const uint16_t * a = (const uint16_t *)(scales_base + bi * K_SCALE_SIZE);
aux[0] = a[im+0] & kmask1;
aux[1] = a[im+2] & kmask1;
aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
#if K_QUANTS_PER_ITERATION == 2
const uint32_t * q1 = (const uint32_t *)(qs_base + bi * (QK_K / 2) + q_offset);
const uint32_t * q2 = q1 + 16;
q32[0] = q1[0] & 0x0f0f0f0f;
q32[1] = q1[0] & 0xf0f0f0f0;
q32[2] = q2[0] & 0x0f0f0f0f;
q32[3] = q2[0] & 0xf0f0f0f0;
sycl::float4 s = {0.f, 0.f, 0.f, 0.f};
float smin = 0;
for (int l = 0; l < 4; ++l) {
s.x() += y1[l] * q4[l + 0]; s.y() += y1[l + 32] * q4[l + 4];
s.z() += y2[l] * q4[l + 8]; s.w() += y2[l + 32] * q4[l + 12];
smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
}
tmp += dall * (s.x() * sc[0] + s.y() * sc[1] * 1.f / 16.f +
s.z() * sc[4] + s.w() * sc[5] * 1.f / 16.f) -
dmin * smin;
#else
const uint16_t * q1 = (const uint16_t *)(qs_base + bi * (QK_K / 2) + q_offset);
const uint16_t * q2 = q1 + 32;
q16[0] = q1[0] & 0x0f0f;
q16[1] = q1[0] & 0xf0f0;
q16[2] = q2[0] & 0x0f0f;
q16[3] = q2[0] & 0xf0f0;
sycl::float4 s = {0.f, 0.f, 0.f, 0.f};
float smin = 0;
for (int l = 0; l < 2; ++l) {
s.x() += y1[l] * q4[l+0]; s.y() += y1[l+32] * q4[l+2];
s.z() += y2[l] * q4[l+4]; s.w() += y2[l+32] * q4[l+6];
smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
}
tmp += dall * (s.x() * sc[0] + s.y() * sc[1] * 1.f/16.f + s.z() * sc[4] + s.w() * sc[5] * 1.f/16.f) - dmin * smin;
#endif
}
}
#else
const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...15
const int ix = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION);
const int step = tid * K_QUANTS_PER_ITERATION;
uint16_t aux16[2];
const uint8_t * s = (const uint8_t *)aux16;
float tmp = 0;
for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
const int bi = ib0 + i;
const uint8_t * q = qs_base + bi * (QK_K / 2) + step;
const float * y = yy + i*QK_K + step;
const uint16_t * a = (const uint16_t *)(scales_base + bi * K_SCALE_SIZE);
aux16[0] = a[0] & 0x0f0f;
aux16[1] = (a[0] >> 4) & 0x0f0f;
const sycl::half2 dm_val = dm_base[bi];
const float d = (float)dm_val[0];
const float m = (float)dm_val[1];
float sum = 0.f;
for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
sum += y[j+ 0] * (d * s[0] * (q[j+ 0] & 0xF) - m * s[2])
+ y[j+16] * (d * s[0] * (q[j+16] & 0xF) - m * s[2])
+ y[j+32] * (d * s[1] * (q[j+ 0] >> 4) - m * s[3])
+ y[j+48] * (d * s[1] * (q[j+16] >> 4) - m * s[3]);
}
tmp += sum;
}
#endif
// 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 (tid == 0) {
dst[row] = tmp;
}
}
static void dequantize_mul_mat_vec_q5_k(const void *__restrict__ vx,
const float *__restrict__ yy,
float *__restrict__ dst,
const int ncols, 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 num_blocks_per_row = ncols / QK_K;
const int ib0 = row*num_blocks_per_row;
const block_q5_K * x = (const block_q5_K *)vx + ib0;
float tmp = 0; // partial sum for thread in warp
#if QK_K == 256
const uint16_t kmask1 = 0x3f3f;
const uint16_t kmask2 = 0x0f0f;
const uint16_t kmask3 = 0xc0c0;
const int tid = item_ct1.get_local_id(2) / 2; // 0...7
const int ix = item_ct1.get_local_id(2) % 2;
const int il_base = tid/4; // 0 or 1 (was 0...3)
const int ir = tid - 4*il_base;// 0...3
const int n = 2;
const int in = il_base%2;
const int l0 = n*(2*ir + in);
uint16_t aux[4];
const uint8_t * sc = (const uint8_t *)aux;
uint16_t q16[8];
const uint8_t * q4 = (const uint8_t *)q16;
for (int i = ix; i < num_blocks_per_row; i += 2) {
const uint8_t * qh = x[i].qh + l0;
const float dall = x[i].dm[0];
const float dmin = x[i].dm[1];
for (int im = 0; im < 2; ++im) {
const int q_offset = 32*im + l0;
const int y_offset = 64*im + l0;
const uint8_t hm1 = 1 << (2*im);
const uint8_t hm2 = hm1 << 4;
const uint8_t * ql1 = x[i].qs + q_offset;
const float * y1 = yy + i*QK_K + y_offset;
const float * y2 = y1 + 128;
const uint16_t * a = (const uint16_t *)x[i].scales;
aux[0] = a[im+0] & kmask1;
aux[1] = a[im+2] & kmask1;
aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
sycl::float4 sum = {0.f, 0.f, 0.f, 0.f};
float smin = 0;
const uint16_t * q1 = (const uint16_t *)ql1;
const uint16_t * q2 = q1 + 32;
q16[0] = q1[0] & 0x0f0f;
q16[1] = q1[8] & 0x0f0f;
q16[2] = (q1[0] >> 4) & 0x0f0f;
q16[3] = (q1[8] >> 4) & 0x0f0f;
q16[4] = q2[0] & 0x0f0f;
q16[5] = q2[8] & 0x0f0f;
q16[6] = (q2[0] >> 4) & 0x0f0f;
q16[7] = (q2[8] >> 4) & 0x0f0f;
for (int l = 0; l < n; ++l) {
sum.x() +=
y1[l + 0] * (q4[l + 0] + (qh[l + 0] & (hm1 << 0) ? 16 : 0)) +
y1[l + 16] * (q4[l + 2] + (qh[l + 16] & (hm1 << 0) ? 16 : 0));
sum.y() +=
y1[l + 32] * (q4[l + 4] + (qh[l + 0] & (hm1 << 1) ? 16 : 0)) +
y1[l + 48] * (q4[l + 6] + (qh[l + 16] & (hm1 << 1) ? 16 : 0));
sum.z() +=
y2[l + 0] * (q4[l + 8] + (qh[l + 0] & (hm2 << 0) ? 16 : 0)) +
y2[l + 16] * (q4[l + 10] + (qh[l + 16] & (hm2 << 0) ? 16 : 0));
sum.w() +=
y2[l + 32] * (q4[l + 12] + (qh[l + 0] & (hm2 << 1) ? 16 : 0)) +
y2[l + 48] * (q4[l + 14] + (qh[l + 16] & (hm2 << 1) ? 16 : 0));
smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3]
+ (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7];
}
tmp += dall * (sum.x() * sc[0] + sum.y() * sc[1] + sum.z() * sc[4] +
sum.w() * sc[5]) -
dmin * smin;
}
}
#else
const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...15
const int ix = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION);
const int step = tid * K_QUANTS_PER_ITERATION;
const int im = step/8;
const int in = step%8;
for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
const uint8_t * q = x[i].qs + step;
const int8_t * s = x[i].scales;
const float * y = yy + i*QK_K + step;
const float d = x[i].d;
float sum = 0.f;
for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
const uint8_t h = x[i].qh[in+j] >> im;
sum += y[j+ 0] * d * s[0] * ((q[j+ 0] & 0xF) - ((h >> 0) & 1 ? 0 : 16))
+ y[j+16] * d * s[1] * ((q[j+16] & 0xF) - ((h >> 2) & 1 ? 0 : 16))
+ y[j+32] * d * s[2] * ((q[j+ 0] >> 4) - ((h >> 4) & 1 ? 0 : 16))
+ y[j+48] * d * s[3] * ((q[j+16] >> 4) - ((h >> 6) & 1 ? 0 : 16));
}
tmp += sum;
}
#endif
// 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 dequantize_mul_mat_vec_q5_k_reorder(const void *__restrict__ vx,
const float *__restrict__ yy,
float *__restrict__ dst,
const int ncols, 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 num_blocks_per_row = ncols / QK_K;
const int ib0 = row*num_blocks_per_row;
// SOA base pointers for the reordered layout:
// [qs: nb * QK_K/2] [qh: nb * QK_K/8] [scales: nb * K_SCALE_SIZE] [dm: nb * sizeof(half2)]
const int nb = nrows * num_blocks_per_row;
const uint8_t * qs_base = (const uint8_t *)vx;
const uint8_t * qh_base = qs_base + (size_t)nb * (QK_K / 2);
const uint8_t * scales_base = qh_base + (size_t)nb * (QK_K / 8);
const sycl::half2 * dm_base = (const sycl::half2 *)(scales_base + (size_t)nb * K_SCALE_SIZE);
float tmp = 0; // partial sum for thread in warp
#if QK_K == 256
const uint16_t kmask1 = 0x3f3f;
const uint16_t kmask2 = 0x0f0f;
const uint16_t kmask3 = 0xc0c0;
const int tid = item_ct1.get_local_id(2) / 2; // 0...15
const int ix = item_ct1.get_local_id(2) % 2;
const int il_base = tid/4; // 0...3
const int ir = tid - 4*il_base;// 0...3
const int n = 2;
const int in = il_base%2;
const int l0 = n*(2*ir + in);
uint16_t aux[4];
const uint8_t * sc = (const uint8_t *)aux;
uint16_t q16[8];
const uint8_t * q4 = (const uint8_t *)q16;
for (int i = ix; i < num_blocks_per_row; i += 2) {
const int bi = ib0 + i;
const uint8_t * qh = qh_base + bi * (QK_K / 8) + l0;
const sycl::half2 dm_val = dm_base[bi];
const float dall = dm_val[0];
const float dmin = dm_val[1];
for (int im = 0; im < 2; ++im) {
const int q_offset = 32*im + l0;
const int y_offset = 64*im + l0;
const uint8_t hm1 = 1 << (2*im);
const uint8_t hm2 = hm1 << 4;
const uint8_t * ql1 = qs_base + bi * (QK_K / 2) + q_offset;
const float * y1 = yy + i*QK_K + y_offset;
const float * y2 = y1 + 128;
const uint16_t * a = (const uint16_t *)(scales_base + bi * K_SCALE_SIZE);
aux[0] = a[im+0] & kmask1;
aux[1] = a[im+2] & kmask1;
aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
sycl::float4 sum = {0.f, 0.f, 0.f, 0.f};
float smin = 0;
const uint16_t * q1 = (const uint16_t *)ql1;
const uint16_t * q2 = q1 + 32;
q16[0] = q1[0] & 0x0f0f;
q16[1] = q1[8] & 0x0f0f;
q16[2] = (q1[0] >> 4) & 0x0f0f;
q16[3] = (q1[8] >> 4) & 0x0f0f;
q16[4] = q2[0] & 0x0f0f;
q16[5] = q2[8] & 0x0f0f;
q16[6] = (q2[0] >> 4) & 0x0f0f;
q16[7] = (q2[8] >> 4) & 0x0f0f;
for (int l = 0; l < n; ++l) {
sum.x() +=
y1[l + 0] * (q4[l + 0] + (qh[l + 0] & (hm1 << 0) ? 16 : 0)) +
y1[l + 16] * (q4[l + 2] + (qh[l + 16] & (hm1 << 0) ? 16 : 0));
sum.y() +=
y1[l + 32] * (q4[l + 4] + (qh[l + 0] & (hm1 << 1) ? 16 : 0)) +
y1[l + 48] * (q4[l + 6] + (qh[l + 16] & (hm1 << 1) ? 16 : 0));
sum.z() +=
y2[l + 0] * (q4[l + 8] + (qh[l + 0] & (hm2 << 0) ? 16 : 0)) +
y2[l + 16] * (q4[l + 10] + (qh[l + 16] & (hm2 << 0) ? 16 : 0));
sum.w() +=
y2[l + 32] * (q4[l + 12] + (qh[l + 0] & (hm2 << 1) ? 16 : 0)) +
y2[l + 48] * (q4[l + 14] + (qh[l + 16] & (hm2 << 1) ? 16 : 0));
smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3]
+ (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7];
}
tmp += dall * (sum.x() * sc[0] + sum.y() * sc[1] + sum.z() * sc[4] +
sum.w() * sc[5]) -
dmin * smin;
}
}
#else
// The reordered Q5_K layout is only produced for QK_K == 256.
#endif
// 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 dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows,
const sycl::nd_item<3> &item_ct1) {
static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
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 num_blocks_per_row = ncols / QK_K;
const int ib0 = row*num_blocks_per_row;
const block_q6_K * x = (const block_q6_K *)vx + ib0;
#if QK_K == 256
const int tid =
item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...7 or 0...15
const int ix =
item_ct1.get_local_id(2) % K_QUANTS_PER_ITERATION; // 0 or 0, 1
const int step = 16/K_QUANTS_PER_ITERATION; // 16 or 8
const int in = tid % step; // 0...15 or 0...7
#if K_QUANTS_PER_ITERATION == 1
const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15
const int is = 0;
#else
const int l0 = 4 * in; // 0, 4, 8, ..., 28
const int is = in / 4;
#endif
float tmp = 0; // partial sum for thread in warp
for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
const float d = x[i].d;
for (int im = 0; im < 2; ++im) {
const int ql_offset = 64*im + l0;
const int qh_offset = 32*im + l0;
const int s_offset = 8*im + is;
const int y_offset = 128*im + l0;
const float * y = yy + i * QK_K + y_offset;
const uint8_t * ql = x[i].ql + ql_offset;
const uint8_t * qh = x[i].qh + qh_offset;
const int8_t * s = x[i].scales + s_offset;
#if K_QUANTS_PER_ITERATION == 1
float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32)
+ y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32)
+ y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32)
+ y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32)
+ y[64] * s[4] * d * ((int8_t)((ql[ 0] >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32)
+ y[80] * s[5] * d * ((int8_t)((ql[16] >> 4) | ((qh[16] & 0x30) >> 0)) - 32)
+ y[96] * s[6] * d * ((int8_t)((ql[32] >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32)
+y[112] * s[7] * d * ((int8_t)((ql[48] >> 4) | ((qh[16] & 0xc0) >> 2)) - 32);
tmp += sum;
#else
float sum = 0;
for (int l = 0; l < 4; ++l) {
sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32)
+ y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32)
+ y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32)
+ y[l+96] * s[6] * d * ((int8_t)((ql[l+32] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32);
}
tmp += sum;
#endif
}
}
#else
const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...7
const int ix = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION); // 0...3
const int step = tid * K_QUANTS_PER_ITERATION;
float tmp = 0; // partial sum for thread in warp
for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
const float * y = yy + i * QK_K + step;
const uint8_t * ql = x[i].ql + step;
const uint8_t * qh = x[i].qh + step;
const int8_t * s = x[i].scales;
const float d = x[i+0].d;
float sum = 0;
for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
sum += y[j+ 0] * s[0] * d * ((int8_t)((ql[j+ 0] & 0xF) | ((qh[j] & 0x03) << 4)) - 32)
+ y[j+16] * s[1] * d * ((int8_t)((ql[j+16] & 0xF) | ((qh[j] & 0x0c) << 2)) - 32)
+ y[j+32] * s[2] * d * ((int8_t)((ql[j+ 0] >> 4) | ((qh[j] & 0x30) >> 0)) - 32)
+ y[j+48] * s[3] * d * ((int8_t)((ql[j+16] >> 4) | ((qh[j] & 0xc0) >> 2)) - 32);
}
tmp += sum;
}
#endif
// 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 (tid == 0) {
dst[row] = tmp;
}
}
static void dequantize_mul_mat_vec_q6_k_reorder(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows,
const sycl::nd_item<3> &item_ct1) {
static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
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 num_blocks_per_row = ncols / QK_K;
const int ib0 = row*num_blocks_per_row;
// SOA base pointers for the reordered layout:
// [ql: nb * QK_K/2] [qh: nb * QK_K/4] [scales: nb * QK_K/16] [d: nb * sizeof(half)]
const int nb = nrows * num_blocks_per_row;
const uint8_t * ql_base = (const uint8_t *)vx;
const uint8_t * qh_base = ql_base + (size_t)nb * (QK_K / 2);
const int8_t * scales_base = (const int8_t *)(qh_base + (size_t)nb * (QK_K / 4));
const sycl::half * d_base = (const sycl::half *)((const uint8_t *)scales_base + (size_t)nb * (QK_K / 16));
#if QK_K == 256
const int tid =
item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...7 or 0...15
const int ix =
item_ct1.get_local_id(2) % K_QUANTS_PER_ITERATION; // 0 or 0, 1
const int step = 16/K_QUANTS_PER_ITERATION; // 16 or 8
const int in = tid % step; // 0...15 or 0...7
#if K_QUANTS_PER_ITERATION == 1
const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15
const int is = 0;
#else
const int l0 = 4 * in; // 0, 4, 8, ..., 28
const int is = in / 4;
#endif
float tmp = 0; // partial sum for thread in warp
for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
const int bi = ib0 + i;
const float d = d_base[bi];
for (int im = 0; im < 2; ++im) {
const int ql_offset = 64*im + l0;
const int qh_offset = 32*im + l0;
const int s_offset = 8*im + is;
const int y_offset = 128*im + l0;
const float * y = yy + i * QK_K + y_offset;
const uint8_t * ql = ql_base + bi * (QK_K / 2) + ql_offset;
const uint8_t * qh = qh_base + bi * (QK_K / 4) + qh_offset;
const int8_t * s = scales_base + bi * (QK_K / 16) + s_offset;
#if K_QUANTS_PER_ITERATION == 1
float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32)
+ y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32)
+ y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32)
+ y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32)
+ y[64] * s[4] * d * ((int8_t)((ql[ 0] >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32)
+ y[80] * s[5] * d * ((int8_t)((ql[16] >> 4) | ((qh[16] & 0x30) >> 0)) - 32)
+ y[96] * s[6] * d * ((int8_t)((ql[32] >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32)
+y[112] * s[7] * d * ((int8_t)((ql[48] >> 4) | ((qh[16] & 0xc0) >> 2)) - 32);
tmp += sum;
#else
float sum = 0;
for (int l = 0; l < 4; ++l) {
sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32)
+ y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32)
+ y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32)
+ y[l+96] * s[6] * d * ((int8_t)((ql[l+32] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32);
}
tmp += sum;
#endif
}
}
#else
const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...7
const int ix = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION); // 0...3
const int step = tid * K_QUANTS_PER_ITERATION;
float tmp = 0; // partial sum for thread in warp
for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
const int bi = ib0 + i;
const float * y = yy + i * QK_K + step;
const uint8_t * ql = ql_base + bi * (QK_K / 2) + step;
const uint8_t * qh = qh_base + bi * (QK_K / 4) + step;
const int8_t * s = scales_base + bi * (QK_K / 16);
const float d = d_base[bi];
float sum = 0;
for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
sum += y[j+ 0] * s[0] * d * ((int8_t)((ql[j+ 0] & 0xF) | ((qh[j] & 0x03) << 4)) - 32)
+ y[j+16] * s[1] * d * ((int8_t)((ql[j+16] & 0xF) | ((qh[j] & 0x0c) << 2)) - 32)
+ y[j+32] * s[2] * d * ((int8_t)((ql[j+ 0] >> 4) | ((qh[j] & 0x30) >> 0)) - 32)
+ y[j+48] * s[3] * d * ((int8_t)((ql[j+16] >> 4) | ((qh[j] & 0xc0) >> 2)) - 32);
}
tmp += sum;
}
#endif
// 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 (tid == 0) {
dst[row] = tmp;
}
}
static void dequantize_mul_mat_vec_q4_0_sycl_reorder(const void *vx, const dfloat *y,
float *dst, const int ncols,
const int nrows,
dpct::queue_ptr stream) {
GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0);
const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
// the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
{
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->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)]] {
dequantize_mul_mat_vec_reorder<QK4_0, QR4_0, dequantize_q4_0_reorder>(
vx, y, dst, ncols, nrows, item_ct1);
});
}
}
static void dequantize_mul_mat_vec_q4_0_sycl(const void *vx, const dfloat *y,
float *dst, const int ncols,
const int nrows,
dpct::queue_ptr stream) {
GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0);
const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
// the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
{
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->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)]] {
dequantize_mul_mat_vec<QK4_0, QR4_0, dequantize_q4_0>(
vx, y, dst, ncols, nrows, item_ct1);
});
}
}
static void dequantize_mul_mat_vec_q1_0_sycl_reorder(const void *vx, const dfloat *y,
float *dst, const int ncols,
const int nrows,
dpct::queue_ptr stream) {
GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0);
const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
// the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
{
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->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)]] {
dequantize_mul_mat_vec_reorder<QK1_0, QR1_0, dequantize_q1_0_reorder>(
vx, y, dst, ncols, nrows, item_ct1);
});
}
}
static void dequantize_mul_mat_vec_q1_0_sycl(const void *vx, const dfloat *y,
float *dst, const int ncols,
const int nrows,
dpct::queue_ptr stream) {
GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0);
const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
// the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
{
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->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)]] {
dequantize_mul_mat_vec<QK1_0, QR1_0, dequantize_q1_0>(
vx, y, dst, ncols, nrows, item_ct1);
});
}
}
static void dequantize_mul_mat_vec_q4_1_sycl(const void *vx, const dfloat *y,
float *dst, const int ncols,
const int nrows,
dpct::queue_ptr stream) {
GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 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);
{
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->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)]] {
dequantize_mul_mat_vec<QK4_1, QR4_1, dequantize_q4_1>(
vx, y, dst, ncols, nrows, item_ct1);
});
}
}
static void dequantize_mul_mat_vec_q5_0_sycl(const void *vx, const dfloat *y,
float *dst, const int ncols,
const int nrows,
dpct::queue_ptr stream) {
GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 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);
{
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->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)]] {
dequantize_mul_mat_vec<QK5_0, QR5_0, dequantize_q5_0>(
vx, y, dst, ncols, nrows, item_ct1);
});
}
}
static void dequantize_mul_mat_vec_q5_1_sycl(const void *vx, const dfloat *y,
float *dst, const int ncols,
const int nrows,
dpct::queue_ptr stream) {
GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 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);
{
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->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)]] {
dequantize_mul_mat_vec<QK5_1, QR5_1, dequantize_q5_1>(
vx, y, dst, ncols, nrows, item_ct1);
});
}
}
static void dequantize_mul_mat_vec_q8_0_sycl_reorder(const void *vx, const dfloat *y,
float *dst, const int ncols,
const int nrows,
dpct::queue_ptr stream) {
GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 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);
{
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->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)]] {
// Q8_0 reorder layout: [all qs (ncols*nrows bytes)][all d values]
// Cannot reuse dequantize_mul_mat_vec_reorder template because it has
// Q4_0-specific constants hardcoded (d_ptr offset and qs stride).
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 tid = item_ct1.get_local_id(2);
const int iter_stride = 8*2*GGML_SYCL_DMMV_X;
const int vals_per_iter = iter_stride / WARP_SIZE;
const int ncols_left = ncols % (QK8_0*WARP_SIZE);
const int ncols_align = ncols - ncols_left;
#ifdef GGML_SYCL_F16
sycl::half2 tmp = {0.0f, 0.0f};
#else
float tmp = 0.0f;
#endif
const char *d_ptr = (const char*)vx + ncols*nrows; // d after all qs
int i = 0;
for (i = 0; i < ncols_align; i += iter_stride) {
const int col = i + vals_per_iter*tid;
const int ib = (row*ncols + col)/QK8_0;
const int iqs = col % QK8_0;
#pragma unroll
for (int j = 0; j < vals_per_iter; j += 2) {
dfloat2 v;
dequantize_q8_0_reorder((const void *)d_ptr, ib, (const void *)vx,
ib * QK8_0 + iqs + j, v);
#ifdef GGML_SYCL_F16
dfloat2 t1{y[col + j + 0], y[col + j + 1]};
tmp += v * t1;
#else
tmp += v.x() * y[col + j + 0];
tmp += v.y() * y[col + j + 1];
#endif
}
}
// handle remaining columns
for (; i < ncols; i += iter_stride) {
if (tid >= ncols_left/QK8_0) continue;
const int col = i + vals_per_iter*tid;
const int ib = (row*ncols + col)/QK8_0;
const int iqs = col % QK8_0;
#pragma unroll
for (int j = 0; j < vals_per_iter; j += 2) {
dfloat2 v;
dequantize_q8_0_reorder((const void *)d_ptr, ib, (const void *)vx,
ib * QK8_0 + iqs + j, v);
#ifdef GGML_SYCL_F16
dfloat2 t1{y[col + j + 0], y[col + j + 1]};
tmp += v * t1;
#else
tmp += v.x() * y[col + j + 0];
tmp += v.y() * y[col + j + 1];
#endif
}
}
// reduce
const int mask_start = ncols > GGML_SYCL_DMMV_X ? WARP_SIZE >> 1 : WARP_SIZE >> 2;
for (int mask = mask_start; mask > 0; mask >>= 1) {
tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
}
if (tid == 0) {
#ifdef GGML_SYCL_F16
dst[row] = tmp.x() + tmp.y();
#else
dst[row] = tmp;
#endif
}
});
}
}
static void dequantize_mul_mat_vec_q8_0_sycl(const void *vx, const dfloat *y,
float *dst, const int ncols,
const int nrows,
dpct::queue_ptr stream) {
GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 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);
{
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->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)]] {
dequantize_mul_mat_vec<QK8_0, QR8_0, dequantize_q8_0>(
vx, y, dst, ncols, nrows, item_ct1);
});
}
}
static void dequantize_mul_mat_vec_q2_K_sycl(const void *vx, const float *y,
float *dst, const int ncols,
const int nrows,
dpct::queue_ptr stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int ny = 2; // very slightly faster than 1 even when K_QUANTS_PER_ITERATION = 2
const int block_num_y = (nrows + ny - 1) / ny;
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, ny, WARP_SIZE);
stream->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)]] {
dequantize_mul_mat_vec_q2_k(vx, y, dst, ncols, nrows, item_ct1);
});
}
static void dequantize_mul_mat_vec_q2_K_sycl_reorder(const void *vx, const float *y,
float *dst, const int ncols,
const int nrows,
dpct::queue_ptr stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int ny = 2 / K_QUANTS_PER_ITERATION;
const int block_num_y = (nrows + ny - 1) / ny;
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, ny, WARP_SIZE);
stream->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)]] {
dequantize_mul_mat_vec_q2_k_reorder(vx, y, dst, ncols, nrows, item_ct1);
});
}
static void dequantize_mul_mat_vec_q3_K_sycl(const void *vx, const float *y,
float *dst, const int ncols,
const int nrows,
dpct::queue_ptr stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int ny = 2 / K_QUANTS_PER_ITERATION;
const int block_num_y = (nrows + ny - 1) / ny;
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, ny, WARP_SIZE);
stream->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)]] {
dequantize_mul_mat_vec_q3_k(vx, y, dst, ncols, nrows, item_ct1);
});
}
static void dequantize_mul_mat_vec_q3_K_sycl_reorder(const void *vx, const float *y,
float *dst, const int ncols,
const int nrows,
dpct::queue_ptr stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int ny = 2 / K_QUANTS_PER_ITERATION;
const int block_num_y = (nrows + ny - 1) / ny;
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, ny, WARP_SIZE);
stream->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)]] {
dequantize_mul_mat_vec_q3_k_reorder(vx, y, dst, ncols, nrows, item_ct1);
});
}
static void dequantize_mul_mat_vec_q4_K_sycl(const void *vx, const float *y,
float *dst, const int ncols,
const int nrows,
dpct::queue_ptr stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int ny = 2 / K_QUANTS_PER_ITERATION;
const int block_num_y = (nrows + ny - 1) / ny;
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, ny, WARP_SIZE);
stream->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)]] {
dequantize_mul_mat_vec_q4_k(vx, y, dst, ncols, nrows, item_ct1);
});
}
static void dequantize_mul_mat_vec_q5_K_sycl(const void *vx, const float *y,
float *dst, const int ncols,
const int nrows,
dpct::queue_ptr stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int ny = 2 / K_QUANTS_PER_ITERATION;
const int block_num_y = (nrows + ny - 1) / ny;
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, ny, WARP_SIZE);
stream->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)]] {
dequantize_mul_mat_vec_q5_k(vx, y, dst, ncols, nrows, item_ct1);
});
}
static void dequantize_mul_mat_vec_q6_K_sycl(const void *vx, const float *y,
float *dst, const int ncols,
const int nrows,
dpct::queue_ptr stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int ny = 2 / K_QUANTS_PER_ITERATION;
const int block_num_y = (nrows + ny - 1) / ny;
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, ny, WARP_SIZE);
stream->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)]] {
dequantize_mul_mat_vec_q6_k(vx, y, dst, ncols, nrows, item_ct1);
});
}
#ifdef GGML_SYCL_DMMV_HAS_ESIMD
using ggml_sycl_esimd::GGML_SYCL_DMMV_ESIMD_WG_SIZE;
// generic reordered dequantize-matvec: each work-group owns a pair of
// consecutive output rows and updates one 32-wide accumulator per row
template <ggml_type T>
ESIMD_INLINE void dequantize_mul_mat_vec_reorder_esimd(
const void * vx, const float * y, float * dst,
const int ncols, const int nrows,
sycl::local_accessor<float, 1> lmem,
const sycl::nd_item<1> & it) {
using namespace sycl::ext::intel::esimd;
using traits = ggml_sycl_esimd::esimd_reorder_q_traits<T>;
const int num_blocks_per_row = ncols / QK_K;
const size_t nb = (size_t) nrows * num_blocks_per_row;
const auto ps = traits::make_ptrs(vx, nb);
const int tid = it.get_local_id(0);
const int row_pair = it.get_group(0);
const int row0 = row_pair * 2; // two consecutive output rows
const bool has_row1 = row0 + 1 < nrows;
// one 32-wide accumulator per output row (small footprint, no spill)
simd<float, 32> acc0 = 0.0f;
simd<float, 32> acc1 = 0.0f;
for (int ib = tid; ib < num_blocks_per_row; ib += GGML_SYCL_DMMV_ESIMD_WG_SIZE) {
simd<float, 256> y_vec = block_load<float, 256>(y + (size_t) ib * QK_K);
const size_t bi0 = (size_t) (row0 + 0) * num_blocks_per_row + ib;
const size_t bi1 = (size_t) (row0 + 1) * num_blocks_per_row + ib;
traits::mac_pair(ps, bi0, ps, bi1, has_row1, y_vec, acc0, acc1);
}
lmem[tid * 2 + 0] = reduce<float>(acc0, std::plus<>{});
lmem[tid * 2 + 1] = reduce<float>(acc1, std::plus<>{});
it.barrier(sycl::access::fence_space::local_space);
if (tid == 0) {
float sum0 = 0.0f;
float sum1 = 0.0f;
for (int p = 0; p < GGML_SYCL_DMMV_ESIMD_WG_SIZE; ++p) {
sum0 += lmem[p * 2 + 0];
sum1 += lmem[p * 2 + 1];
}
dst[row0 + 0] = sum0;
if (has_row1) {
dst[row0 + 1] = sum1;
}
}
}
static void dequantize_mul_mat_vec_q3_K_sycl_reorder_esimd(const void *vx, const float *y,
float *dst, const int ncols,
const int nrows,
dpct::queue_ptr stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int workgroups = (nrows + 1) / 2;
stream->submit([&](sycl::handler &h) {
sycl::local_accessor<float, 1> lmem(sycl::range<1>(GGML_SYCL_DMMV_ESIMD_WG_SIZE * 2), h);
h.parallel_for(
sycl::nd_range<1>(sycl::range<1>((size_t)workgroups * GGML_SYCL_DMMV_ESIMD_WG_SIZE), sycl::range<1>(GGML_SYCL_DMMV_ESIMD_WG_SIZE)),
[=](sycl::nd_item<1> it) [[intel::sycl_explicit_simd]] {
dequantize_mul_mat_vec_reorder_esimd<GGML_TYPE_Q3_K>(
vx, y, dst, ncols, nrows, lmem, it);
});
});
}
static void dequantize_mul_mat_vec_q4_K_sycl_reorder_esimd(const void *vx, const float *y,
float *dst, const int ncols,
const int nrows,
dpct::queue_ptr stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int workgroups = (nrows + 1) / 2;
stream->submit([&](sycl::handler &h) {
sycl::local_accessor<float, 1> lmem(sycl::range<1>(GGML_SYCL_DMMV_ESIMD_WG_SIZE * 2), h);
h.parallel_for(
sycl::nd_range<1>(sycl::range<1>((size_t)workgroups * GGML_SYCL_DMMV_ESIMD_WG_SIZE), sycl::range<1>(GGML_SYCL_DMMV_ESIMD_WG_SIZE)),
[=](sycl::nd_item<1> it) [[intel::sycl_explicit_simd]] {
dequantize_mul_mat_vec_reorder_esimd<GGML_TYPE_Q4_K>(
vx, y, dst, ncols, nrows, lmem, it);
});
});
}
static void dequantize_mul_mat_vec_q6_K_sycl_reorder_esimd(const void *vx, const float *y,
float *dst, const int ncols,
const int nrows,
dpct::queue_ptr stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int workgroups = (nrows + 1) / 2;
stream->submit([&](sycl::handler &h) {
sycl::local_accessor<float, 1> lmem(sycl::range<1>(GGML_SYCL_DMMV_ESIMD_WG_SIZE * 2), h);
h.parallel_for(
sycl::nd_range<1>(sycl::range<1>((size_t)workgroups * GGML_SYCL_DMMV_ESIMD_WG_SIZE), sycl::range<1>(GGML_SYCL_DMMV_ESIMD_WG_SIZE)),
[=](sycl::nd_item<1> it) [[intel::sycl_explicit_simd]] {
dequantize_mul_mat_vec_reorder_esimd<GGML_TYPE_Q6_K>(
vx, y, dst, ncols, nrows, lmem, it);
});
});
}
#endif // GGML_SYCL_DMMV_HAS_ESIMD
static void dequantize_mul_mat_vec_q4_K_sycl_reorder(const void *vx, const float *y,
float *dst, const int ncols,
const int nrows,
dpct::queue_ptr stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int ny = 2 / K_QUANTS_PER_ITERATION;
const int block_num_y = (nrows + ny - 1) / ny;
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, ny, WARP_SIZE);
stream->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)]] {
dequantize_mul_mat_vec_q4_k_reorder(vx, y, dst, ncols, nrows, item_ct1);
});
}
static void dequantize_mul_mat_vec_q5_K_sycl_reorder(const void *vx, const float *y,
float *dst, const int ncols,
const int nrows,
dpct::queue_ptr stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int ny = 2 / K_QUANTS_PER_ITERATION;
const int block_num_y = (nrows + ny - 1) / ny;
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, ny, WARP_SIZE);
stream->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)]] {
dequantize_mul_mat_vec_q5_k_reorder(vx, y, dst, ncols, nrows, item_ct1);
});
}
static void dequantize_mul_mat_vec_q6_K_sycl_reorder(const void *vx, const float *y,
float *dst, const int ncols,
const int nrows,
dpct::queue_ptr stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int ny = 2 / K_QUANTS_PER_ITERATION;
const int block_num_y = (nrows + ny - 1) / ny;
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, ny, WARP_SIZE);
stream->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)]] {
dequantize_mul_mat_vec_q6_k_reorder(vx, y, dst, ncols, nrows, item_ct1);
});
}
void ggml_sycl_op_dequantize_mul_mat_vec(
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_row_size,
const dpct::queue_ptr &stream) {
const int64_t ne00 = src0->ne[0];
const int64_t row_diff = row_high - row_low;
GGML_ASSERT(src1->type == GGML_TYPE_F32);
// on some GPUs it is faster to convert src1 to half and to use half precision intrinsics
#ifdef GGML_SYCL_F16
ggml_sycl_pool_alloc<sycl::half> src1_dfloat_a(ctx.pool());
sycl::half *src1_dfloat = nullptr; // dfloat == half
bool src1_convert_f16 =
src0->type == GGML_TYPE_Q1_0 ||
src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 ||
src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 ||
src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16 ||
src0->type == GGML_TYPE_BF16;
if (src1_convert_f16) {
scope_op_debug_print scope_dbg_print(__func__, "/to_fp16_sycl", dst, /*num_src=*/2,
" : converting src1 to fp16");
src1_dfloat = src1_dfloat_a.alloc(ne00);
const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type, dst);
GGML_ASSERT(to_fp16_sycl != nullptr);
to_fp16_sycl(src1_ddf_i, src1_dfloat, ne00, stream);
}
#else
const dfloat * src1_dfloat = (const dfloat *) src1_ddf_i; // dfloat == float, no conversion
#endif // GGML_SYCL_F16
switch (src0->type) {
case GGML_TYPE_Q1_0:
if ((ggml_tensor_extra_gpu*)dst->src[0]->extra &&
((ggml_tensor_extra_gpu*)dst->src[0]->extra)->optimized_feature.reorder) {
dequantize_mul_mat_vec_q1_0_sycl_reorder(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
} else {
dequantize_mul_mat_vec_q1_0_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
}
break;
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) {
dequantize_mul_mat_vec_q4_0_sycl_reorder(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
} else {
dequantize_mul_mat_vec_q4_0_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
}
break;
case GGML_TYPE_Q4_1:
dequantize_mul_mat_vec_q4_1_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
break;
case GGML_TYPE_Q5_0:
dequantize_mul_mat_vec_q5_0_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
break;
case GGML_TYPE_Q5_1:
dequantize_mul_mat_vec_q5_1_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
break;
case GGML_TYPE_Q8_0:
if ((ggml_tensor_extra_gpu *) dst->src[0]->extra &&
((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) {
dequantize_mul_mat_vec_q8_0_sycl_reorder(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
} else {
dequantize_mul_mat_vec_q8_0_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
}
break;
case GGML_TYPE_Q2_K:
if ((ggml_tensor_extra_gpu *) dst->src[0]->extra &&
((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) {
dequantize_mul_mat_vec_q2_K_sycl_reorder(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
} else {
dequantize_mul_mat_vec_q2_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
}
break;
case GGML_TYPE_Q3_K:
if ((ggml_tensor_extra_gpu *) dst->src[0]->extra &&
((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) {
#ifdef GGML_SYCL_DMMV_HAS_ESIMD
if (g_ggml_sycl_enable_esimd) {
dequantize_mul_mat_vec_q3_K_sycl_reorder_esimd(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
}
else
#endif
{
dequantize_mul_mat_vec_q3_K_sycl_reorder(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
}
} else {
dequantize_mul_mat_vec_q3_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, 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) {
#ifdef GGML_SYCL_DMMV_HAS_ESIMD
if (g_ggml_sycl_enable_esimd) {
dequantize_mul_mat_vec_q4_K_sycl_reorder_esimd(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
}
else
#endif
{
dequantize_mul_mat_vec_q4_K_sycl_reorder(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
}
} else {
dequantize_mul_mat_vec_q4_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
}
break;
case GGML_TYPE_Q5_K:
if ((ggml_tensor_extra_gpu *) dst->src[0]->extra &&
((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) {
dequantize_mul_mat_vec_q5_K_sycl_reorder(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
} else {
dequantize_mul_mat_vec_q5_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
}
break;
case GGML_TYPE_Q6_K:
if ((ggml_tensor_extra_gpu *) dst->src[0]->extra &&
((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) {
#ifdef GGML_SYCL_DMMV_HAS_ESIMD
if (g_ggml_sycl_enable_esimd) {
dequantize_mul_mat_vec_q6_K_sycl_reorder_esimd(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
}
else
#endif
{
dequantize_mul_mat_vec_q6_K_sycl_reorder(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
}
} else {
dequantize_mul_mat_vec_q6_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
}
break;
case GGML_TYPE_F16:
convert_mul_mat_vec_f16_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
break;
#ifdef GGML_SYCL_DMMV_HAS_BF16
case GGML_TYPE_BF16:
convert_mul_mat_vec_bf16_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
break;
#endif
default:
printf("ggml_sycl_op_dequantize_mul_mat_vec unsupported GGML_TYPE %d\n", src0->type);
GGML_ABORT("fatal error");
}
GGML_UNUSED(src1);
GGML_UNUSED(dst);
GGML_UNUSED(src1_ddq_i);
GGML_UNUSED(src1_ncols);
GGML_UNUSED(src1_padded_row_size);
GGML_UNUSED(ctx);
}
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