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sycl : set K_QUANTS_PER_ITERATION to 1 on DMMV path (#25063)

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* sycl: add supported types to ggml_sycl_supports_reorder_dmmv

The reordered feature is implemented in ggml_sycl_op_dequantize_mul_mat_vec,
but gated by ggml_sycl_supports_reorder_dmmv. This commit fixes the gate.

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

* sycl: set K_QUANTS_PER_ITERATION=1 to improve utilization

When combined with opening the reorder gate, this improves GPU
utilization on B70, giving a significant boost to tg t/s.

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

* sycl: replace QK_WARP_SIZE with WARP_SIZE for QK_5

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

* sycl: add missing types to ggml_backend_sycl_buffer_init_tensor

Without this, the extra field is not allocated and the reorder path
will not take effect.

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

---------

Signed-off-by: Todd Malsbary <todd.malsbary@intel.com>
This commit is contained in:
Todd Malsbary
2026-07-07 10:43:41 +03:00
committed by GitHub
parent 95e5254c0a
commit d209086157
3 changed files with 22 additions and 13 deletions
Download Patch File Download Diff File Expand all files Collapse all files
ggml/src/ggml-sycl/dmmv.cpp
+15 -12
View File
@@ -680,14 +680,14 @@ static void dequantize_mul_mat_vec_q4_k(const void *__restrict__ vx,
q16[2] = q2[0] & 0x0f0f; q16[2] = q2[0] & 0x0f0f;
q16[3] = q2[0] & 0xf0f0; q16[3] = q2[0] & 0xf0f0;
float4 s = {0.f, 0.f, 0.f, 0.f}; sycl::float4 s = {0.f, 0.f, 0.f, 0.f};
float smin = 0; float smin = 0;
for (int l = 0; l < 2; ++l) { for (int l = 0; l < 2; ++l) {
s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+2]; 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]; 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]; 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; 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 #endif
} }
@@ -835,14 +835,14 @@ static void dequantize_mul_mat_vec_q4_k_reorder(const void *__restrict__ vx,
q16[2] = q2[0] & 0x0f0f; q16[2] = q2[0] & 0x0f0f;
q16[3] = q2[0] & 0xf0f0; q16[3] = q2[0] & 0xf0f0;
float4 s = {0.f, 0.f, 0.f, 0.f}; sycl::float4 s = {0.f, 0.f, 0.f, 0.f};
float smin = 0; float smin = 0;
for (int l = 0; l < 2; ++l) { for (int l = 0; l < 2; ++l) {
s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+2]; 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]; 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]; 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; 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 #endif
} }
@@ -1126,7 +1126,7 @@ static void dequantize_mul_mat_vec_q5_k_reorder(const void *__restrict__ vx,
// sum up partial sums and write back result // sum up partial sums and write back result
#pragma unroll #pragma unroll
for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) { for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
tmp += tmp +=
dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
} }
@@ -1762,10 +1762,13 @@ static void dequantize_mul_mat_vec_q5_K_sycl_reorder(const void *vx, const float
const int nrows, const int nrows,
dpct::queue_ptr stream) { dpct::queue_ptr stream) {
GGML_ASSERT(ncols % QK_K == 0); GGML_ASSERT(ncols % QK_K == 0);
const sycl::range<3> block_dims(1, 1, QK_WARP_SIZE); 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( stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, block_dims), sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] { [=](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); dequantize_mul_mat_vec_q5_k_reorder(vx, y, dst, ncols, nrows, item_ct1);
}); });
} }
ggml/src/ggml-sycl/ggml-sycl.cpp
+6
View File
@@ -546,7 +546,9 @@ ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer,
switch (tensor->type) { switch (tensor->type) {
case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_0:
case GGML_TYPE_Q8_0: case GGML_TYPE_Q8_0:
case GGML_TYPE_Q3_K:
case GGML_TYPE_Q4_K: case GGML_TYPE_Q4_K:
case GGML_TYPE_Q5_K:
case GGML_TYPE_Q6_K:{ case GGML_TYPE_Q6_K:{
ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{}; ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{};
tensor->extra = extra; tensor->extra = extra;
@@ -3687,6 +3689,10 @@ inline bool ggml_sycl_supports_reorder_dmmv(enum ggml_type type) {
case GGML_TYPE_Q1_0: case GGML_TYPE_Q1_0:
case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_0:
case GGML_TYPE_Q8_0: case GGML_TYPE_Q8_0:
case GGML_TYPE_Q3_K:
case GGML_TYPE_Q4_K:
case GGML_TYPE_Q5_K:
case GGML_TYPE_Q6_K:
return true; return true;
default: default:
return false; return false;
ggml/src/ggml-sycl/presets.hpp
+1 -1
View File
@@ -62,7 +62,7 @@
#endif #endif
#ifndef K_QUANTS_PER_ITERATION #ifndef K_QUANTS_PER_ITERATION
#define K_QUANTS_PER_ITERATION 2 #define K_QUANTS_PER_ITERATION 1
#else #else
static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2"); static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
#endif #endif