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sycl: reordered Q4_K MMVQ (llama/13109)

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Svetlozar Georgiev 2025-05-15 16:35:44 +01:00 committed by Georgi Gerganov
parent d807c497a4
commit 8081e7a23d
7 changed files with 280 additions and 84 deletions
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31
ggml/src/ggml-sycl/convert.cpp
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@ -183,6 +183,24 @@ static void dequantize_row_q4_K_sycl(const void *vx, dst_t *y, const int64_t k,
}
}
template <typename dst_t>
static void dequantize_row_q4_K_sycl_reorder(const void * vx, dst_t * y, const int64_t k, dpct::queue_ptr stream) {
const int64_t nb = k / QK_K;
const size_t local_size = 32;
const size_t global_size = nb * local_size;
dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 });
stream->submit([&](sycl::handler & cgh) {
sycl::local_accessor<uint8_t, 1> scale_local_acc(sycl::range<1>(12), cgh);
cgh.parallel_for(sycl::nd_range<1>(sycl::range<1>(global_size), sycl::range<1>(local_size)),
[=](sycl::nd_item<1> item_ct1) {
dequantize_block_q4_K_reorder(vx, y, get_pointer(scale_local_acc), item_ct1, nb);
});
});
}
template <typename dst_t>
static void dequantize_row_q5_K_sycl(const void *vx, dst_t *y, const int64_t k,
dpct::queue_ptr stream) {
@ -504,7 +522,11 @@ to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor * dst) {
case GGML_TYPE_Q3_K:
return dequantize_row_q3_K_sycl;
case GGML_TYPE_Q4_K:
return dequantize_row_q4_K_sycl;
if (dst->src[0]->extra && ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) {
return dequantize_row_q4_K_sycl_reorder;
} else {
return dequantize_row_q4_K_sycl;
}
case GGML_TYPE_Q5_K:
return dequantize_row_q5_K_sycl;
case GGML_TYPE_Q6_K:
@ -556,7 +578,12 @@ to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type, ggml_tensor *dst) {
case GGML_TYPE_Q3_K:
return dequantize_row_q3_K_sycl;
case GGML_TYPE_Q4_K:
return dequantize_row_q4_K_sycl;
if (dst->src[0]->extra &&
((ggml_tensor_extra_gpu*)dst->src[0]->extra)->optimized_feature.reorder) {
return dequantize_row_q4_K_sycl_reorder;
} else {
return dequantize_row_q4_K_sycl;
}
case GGML_TYPE_Q5_K:
return dequantize_row_q5_K_sycl;
case GGML_TYPE_Q6_K:

80
ggml/src/ggml-sycl/dequantize.hpp
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@ -357,6 +357,28 @@ static inline void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8
}
#endif
template <typename dst_t>
inline void dequantize_q4_K_common(dst_t * __restrict__ y, const uint8_t * __restrict__ qs_ptr, const float dall,
const float dmin, uint8_t * __restrict__ scales_local, int il, int ir) {
const int is = 2 * il;
constexpr int n = 4;
uint8_t sc, m;
get_scale_min_k4(is + 0, scales_local, sc, m);
const float d1 = dall * sc;
const float m1 = dmin * m;
get_scale_min_k4(is + 1, scales_local, sc, m);
const float d2 = dall * sc;
const float m2 = dmin * m;
sycl::vec<uint8_t, n> q_vec = vec_aligned_load<uint8_t, n>(qs_ptr + 32 * il + n * ir);
for (int l = 0; l < n; ++l) {
y[l + 0] = d1 * (q_vec[l] & 0xF) - m1;
y[l + 32] = d2 * (q_vec[l] >> 4) - m2;
}
}
template<typename dst_t>
static void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy,
uint8_t* scales_local, const sycl::nd_item<3> &item_ct1) {
@ -365,36 +387,22 @@ static void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restri
const int64_t i = item_ct1.get_group(2);
#if QK_K == 256
// assume 32 threads
const int64_t tid = item_ct1.get_local_id(2);
const int64_t il = tid/8;
const int64_t ir = tid%8;
const int64_t is = 2*il;
const int64_t n = 4;
const int64_t il = tid / 8;
const int64_t ir = tid % 8;
dst_t * y = yy + i*QK_K + 64*il + n*ir;
dst_t * y = yy + i * QK_K + 64 * il + 4 * ir;
const sycl::half2 dm = x[i].dm;
const float dall = dm[0];
const float dmin = dm[1];
if (tid < 12)
if (tid < 12) {
scales_local[tid] = x[i].scales[tid];
item_ct1.barrier(sycl::access::fence_space::local_space);
uint8_t sc, m;
get_scale_min_k4(is + 0, scales_local, sc, m);
const float d1 = dall * sc;
const float m1 = dmin * m;
get_scale_min_k4(is + 1, scales_local, sc, m);
const float d2 = dall * sc;
const float m2 = dmin * m;
sycl::vec<uint8_t, n> q_vec = vec_aligned_load<uint8_t, n>(x[i].qs + 32*il + n*ir);
for (int l = 0; l < n; ++l) {
y[l + 0] = d1 * (q_vec[l] & 0xF) - m1;
y[l +32] = d2 * (q_vec[l] >> 4) - m2;
}
item_ct1.barrier(sycl::access::fence_space::local_space);
dequantize_q4_K_common(y, x[i].qs, dall, dmin, scales_local, il, ir);
#else
const int64_t tid = item_ct1.get_local_id(2);
const uint8_t * q = x[i].qs;
@ -406,6 +414,36 @@ static void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restri
#endif
}
template <typename dst_t>
static void dequantize_block_q4_K_reorder(const void * __restrict__ vx, dst_t * __restrict__ yy, uint8_t * scales_local,
const sycl::nd_item<1> & item_ct1, int64_t nb) {
const int64_t i = item_ct1.get_group(0); // block index
const int64_t tid = item_ct1.get_local_id(0); // thread index within block
const int64_t il = tid / 8;
const int64_t ir = tid % 8;
dst_t * y = yy + i * QK_K + 64 * il + 4 * ir;
const uint8_t * base = static_cast<const uint8_t *>(vx);
const size_t qs_offset = i * (QK_K / 2);
const size_t scales_offset = nb * (QK_K / 2) + i * K_SCALE_SIZE;
const size_t dm_offset = nb * (QK_K / 2) + nb * K_SCALE_SIZE + i * sizeof(ggml_half2);
const uint8_t * qs_ptr = base + qs_offset;
const uint8_t * scales_ptr = base + scales_offset;
ggml_half2 dm_values = *reinterpret_cast<const ggml_half2 *>(base + dm_offset);
const float dall = dm_values.x();
const float dmin = dm_values.y();
if (tid < 12) {
scales_local[tid] = scales_ptr[tid];
}
item_ct1.barrier(sycl::access::fence_space::local_space);
dequantize_q4_K_common(y, qs_ptr, dall, dmin, scales_local, il, ir);
}
template<typename dst_t>
static void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restrict__ yy,
const sycl::nd_item<3> &item_ct1) {

8
ggml/src/ggml-sycl/dmmv.cpp
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@ -1129,7 +1129,13 @@ void ggml_sycl_op_dequantize_mul_mat_vec(
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:
dequantize_mul_mat_vec_q4_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
if ((ggml_tensor_extra_gpu *) dst->src[0]->extra &&
((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) {
// reorder is currently not supported for dmmv
GGML_ABORT("Unimplemented dequantize case case for q4_k reorder");
} 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:
dequantize_mul_mat_vec_q5_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);

80
ggml/src/ggml-sycl/ggml-sycl.cpp
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@ -352,7 +352,7 @@ ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer,
assert(tensor->view_src->buffer->buft == buffer->buft);
return GGML_STATUS_SUCCESS;
}
if (tensor->type == GGML_TYPE_Q4_0 && !g_ggml_sycl_disable_optimize) {
if ((tensor->type == GGML_TYPE_Q4_0 || tensor->type == GGML_TYPE_Q4_K) && !g_ggml_sycl_disable_optimize) {
ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{};
tensor->extra = extra;
ctx->tensor_extras.push_back(extra); //used to release it when destroy ctx.
@ -2900,6 +2900,8 @@ inline bool ggml_sycl_supports_reorder_mul_mat_sycl(enum ggml_type type) {
switch (type) {
case GGML_TYPE_Q4_0:
return true;
case GGML_TYPE_Q4_K:
return !g_ggml_sycl_prioritize_dmmv;
default:
return false;
}
@ -2917,6 +2919,7 @@ inline bool ggml_sycl_supports_reorder_dmmv(enum ggml_type type) {
inline bool ggml_sycl_supports_reorder_mmvq(enum ggml_type type) {
switch (type) {
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_K:
return true;
default:
return false;
@ -2942,16 +2945,16 @@ static bool ggml_sycl_supports_dmmv(enum ggml_type type) {
}
}
static void reorder_qw(char *data_device, const int ncols, const int nrows,
size_t size, size_t offset, dpct::queue_ptr stream) {
auto tmp_buf = sycl::malloc_shared<char>(size, *stream);
static void reorder_qw_q4_0(uint8_t * data_device, const int ncols, const int nrows, size_t size, size_t offset,
dpct::queue_ptr stream) {
auto * tmp_buf = sycl::malloc_shared<uint8_t>(size, *stream);
SYCL_CHECK(
CHECK_TRY_ERROR((*stream).memcpy(tmp_buf, data_device, size)
.wait()));
GGML_ASSERT((size % sizeof(block_q4_0) == 0));
GGML_ASSERT((offset % sizeof(block_q4_0) == 0));
int offset_blks = offset / sizeof(block_q4_0);
auto qs_ptr = (uint8_t*)data_device + offset_blks * QK4_0 / 2;
auto qs_ptr = data_device + offset_blks * QK4_0 / 2;
auto d_ptr = (sycl::half*)(qs_ptr + ncols * nrows / 2) + offset_blks;
stream->parallel_for(
@ -2965,18 +2968,59 @@ static void reorder_qw(char *data_device, const int ncols, const int nrows,
*(qs_ptr + ib * QK4_0 / 2 + j) = x[ib].qs[j];
}
*(d_ptr + ib) = x[ib].d;
});
}).wait_and_throw();
sycl::free(tmp_buf, *stream);
}
static void reorder_qw_q4_k(uint8_t * data_device, size_t size, size_t offset, dpct::queue_ptr stream) {
GGML_ASSERT(size % sizeof(block_q4_K) == 0);
GGML_ASSERT(offset % sizeof(block_q4_K) == 0);
const int nblocks = size / sizeof(block_q4_K);
auto * tmp_buf = sycl::malloc_shared<uint8_t>(size, *stream);
SYCL_CHECK(CHECK_TRY_ERROR((*stream).memcpy(tmp_buf, data_device, size).wait()));
auto * qs_ptr = data_device;
auto * scales_ptr = qs_ptr + QK_K / 2 * nblocks;
auto * dm_ptr = (sycl::half2 *) (scales_ptr + K_SCALE_SIZE * nblocks);
stream->parallel_for(nblocks, [=](auto i) {
const block_q4_K * x = (const block_q4_K *) tmp_buf;
const int ib = i;
for (int j = 0; j < QK_K / 2; ++j) {
qs_ptr[ib * (QK_K / 2) + j] = x[ib].qs[j];
}
for (int j = 0; j < K_SCALE_SIZE; ++j) {
scales_ptr[ib * K_SCALE_SIZE + j] = x[ib].scales[j];
}
dm_ptr[ib] = x[ib].dm;
}).wait_and_throw();
sycl::free(tmp_buf, *stream);
}
static void reorder_qw(const ggml_tensor * src0, dpct::queue_ptr stream) {
char*data_device = (char*)src0->data;
uint8_t * data_device = (uint8_t *) src0->data;
size_t ncols = src0->ne[0];
size_t nrows = src0->ne[1];
size_t size = ggml_nbytes(src0);
reorder_qw(data_device, ncols, nrows, size, 0, stream);
switch (src0->type) {
case GGML_TYPE_Q4_0:
reorder_qw_q4_0(data_device, ncols, nrows, size, 0, stream);
break;
case GGML_TYPE_Q4_K:
reorder_qw_q4_k(data_device, size, 0, stream);
break;
default:
GGML_ABORT("reorder_qw() called with unsupported type");
break;
}
}
static bool should_reorder_tensor(ggml_backend_sycl_context& ctx, const ggml_tensor * dst) {
@ -3019,8 +3063,18 @@ static void opt_for_reorder(ggml_backend_sycl_context * ctx, const ggml_tensor *
extra->optimized_feature.reorder = true; // Used to decode/dequan in next steps and avoid re-reordering
}
static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
static bool can_use_dequantize_mul_mat_vec(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
return ggml_sycl_supports_dmmv(src0->type) && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 &&
src0->ne[0] % GGML_SYCL_DMMV_X == 0 && src1->ne[1] == 1;
}
static bool can_use_mul_mat_vec_q(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
return ggml_is_quantized(src0->type) && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 &&
src1->ne[1] <= MMVQ_MAX_BATCH_SIZE;
}
static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
const bool split = ggml_backend_buffer_is_sycl_split(src0->buffer);
int64_t min_compute_capability = INT_MAX;
@ -3043,13 +3097,9 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor
}
// check data types and tensor shapes for custom matrix multiplication kernels:
bool use_dequantize_mul_mat_vec = ggml_sycl_supports_dmmv(src0->type)
&& src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
&& src0->ne[0] % GGML_SYCL_DMMV_X == 0 && src1->ne[1] == 1;
bool use_dequantize_mul_mat_vec = can_use_dequantize_mul_mat_vec(src0, src1, dst);
bool use_mul_mat_vec_q = ggml_is_quantized(src0->type)
&& src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
&& src1->ne[1] <= MMVQ_MAX_BATCH_SIZE;
bool use_mul_mat_vec_q = can_use_mul_mat_vec_q(src0, src1, dst);
bool use_mul_mat_q = ggml_sycl_supports_mmq(src0->type)
&& src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;

31
ggml/src/ggml-sycl/mmvq.cpp
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@ -24,6 +24,7 @@ static void mul_mat_vec_q_reorder(const void * __restrict__ vx, const void * __r
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);
@ -45,7 +46,7 @@ static void mul_mat_vec_q_reorder(const void * __restrict__ vx, const void * __r
// 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);
partial_sum += reorder_vec_dot_q_sycl()(vx, bx_offset, d_offset, &y[iby], iqs, nblocks);
}
}
@ -739,6 +740,27 @@ static void mul_mat_vec_q4_K_q8_1_sycl(const void *vx, const void *vy,
}
}
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,
@ -1035,7 +1057,12 @@ void ggml_sycl_op_mul_mat_vec_q(ggml_backend_sycl_context & ctx, const ggml_tens
mul_mat_vec_q3_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
break;
case GGML_TYPE_Q4_K:
mul_mat_vec_q4_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
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);

22
ggml/src/ggml-sycl/quants.hpp
View File

@ -56,6 +56,28 @@ template <> struct block_q_t<GGML_TYPE_Q4_0> {
static constexpr int block_to_q8_1_ratio() { return traits::qk / QK8_1; }
};
template <> struct block_q_t<GGML_TYPE_Q4_K> {
struct traits {
static constexpr uint32_t qk = QK_K;
static constexpr uint32_t qi = QI4_K;
static constexpr uint32_t qr = QR4_K;
static constexpr uint32_t vdr_mmvq = 2;
};
static constexpr int get_block_offset(const int block_index) { return block_index * (traits::qk / traits::qr); }
static constexpr int get_d_offset(int nrows, int ncols, const int block_index) {
auto nblocks = (nrows * (ncols / traits::qk));
return (nblocks * QK_K / 2) + (nblocks * K_SCALE_SIZE) + (block_index * sizeof(ggml_half2));
}
static constexpr int block_to_q8_1_ratio() { return traits::qk / QK8_1; }
constexpr size_t get_total_qs_bytes(int nblocks) { return nblocks * QK_K / 2; }
constexpr size_t get_dm_offset(int nblocks) { return get_total_qs_bytes(nblocks) + nblocks * K_SCALE_SIZE; }
};
} // namespace ggml_sycl_reordered
#endif // GGML_SYCL_QUANTS_HPP

112
ggml/src/ggml-sycl/vecdotq.hpp
View File

@ -285,7 +285,7 @@ template <> struct reorder_vec_dot_q_sycl<GGML_TYPE_Q4_0> {
}
__dpct_inline__ float operator()(const void * __restrict__ vbq, const int ibx_offset, const int d_offset,
const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
const block_q8_1 * __restrict__ bq8_1, const int & iqs, int /* nblocks */) {
const uint8_t * bq4_0 = static_cast<const uint8_t *>(vbq) + ibx_offset;
const ggml_half d = *(reinterpret_cast<const ggml_half *>(static_cast<const uint8_t *>(vbq) + d_offset));
int v[q4_0_traits::vdr_mmvq];
@ -303,6 +303,67 @@ template <> struct reorder_vec_dot_q_sycl<GGML_TYPE_Q4_0> {
};
};
static inline float vec_dot_q4_K_q8_1_common(const int * __restrict__ q4, const uint16_t * __restrict__ scales,
const ggml_half2 & dm, const block_q8_1 * __restrict__ bq8_1,
const int & iqs) {
int v[2];
int u[2 * QR4_K];
float d8[QR4_K];
v[0] = q4[0];
v[1] = q4[4];
uint16_t aux[2];
const int j = (QR4_K * ((iqs / 2) / (QI8_1 / 2))) / 2;
if (j < 2) {
aux[0] = scales[j + 0] & 0x3f3f;
aux[1] = scales[j + 2] & 0x3f3f;
} else {
aux[0] = ((scales[j + 2] >> 0) & 0x0f0f) | ((scales[j - 2] & 0xc0c0) >> 2);
aux[1] = ((scales[j + 2] >> 4) & 0x0f0f) | ((scales[j - 0] & 0xc0c0) >> 2);
}
const uint8_t * sc = (const uint8_t *) aux;
const uint8_t * m = sc + 2;
const int bq8_offset = QR4_K * ((iqs / 2) / (QI8_1 / 2));
for (int i = 0; i < QR4_K; ++i) {
const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
d8[i] = bq8i->ds[0];
const int * q8 = (const int *) bq8i->qs + ((iqs / 2) % 4);
u[2 * i + 0] = q8[0];
u[2 * i + 1] = q8[4];
}
return vec_dot_q4_K_q8_1_impl_vmmq(v, u, sc, m, dm, d8);
}
template <> struct reorder_vec_dot_q_sycl<GGML_TYPE_Q4_K> {
static constexpr ggml_type gtype = GGML_TYPE_Q4_K;
using q4_k_block = ggml_sycl_reordered::block_q_t<GGML_TYPE_Q4_K>;
using q4_k_traits = typename q4_k_block::traits;
float operator()(const void * __restrict__ vbq, const int ibx_offset, const int d_offset,
const block_q8_1 * __restrict__ bq8_1, const int & iqs, int nblocks) {
const int ib = ibx_offset / (QK_K / 2);
const uint8_t * base = static_cast<const uint8_t *>(vbq);
const uint8_t * qs = base + ibx_offset;
const int total_qs_bytes = nblocks * (QK_K / 2);
const uint8_t * scs = base + total_qs_bytes + ib * K_SCALE_SIZE;
const ggml_half2 * dms = reinterpret_cast<const ggml_half2 *>(base + d_offset);
const int bq8_offset = QR4_K * ((iqs / 2) / (QI8_1 / 2));
const int * q4 = (const int *) (qs + 16 * bq8_offset + 4 * ((iqs / 2) % 4));
const uint16_t * scales = (const uint16_t *) scs;
return vec_dot_q4_K_q8_1_common(q4, scales, *dms, bq8_1, iqs);
}
};
#define VDR_Q4_0_Q8_1_MMVQ 2
#define VDR_Q4_0_Q8_1_MMQ 4
@ -649,52 +710,17 @@ vec_dot_q3_K_q8_1(const void *__restrict__ vbq,
return vec_dot_q3_K_q8_1_impl_mmvq(vl, vh, u, bq3_K->scales, scale_offset, d, d8);
}
static __dpct_inline__ float
vec_dot_q4_K_q8_1(const void *__restrict__ vbq,
const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
static __dpct_inline__ float vec_dot_q4_K_q8_1(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1,
const int & iqs) {
#ifndef GGML_QKK_64
const block_q4_K * bq4_K = (const block_q4_K *) vbq;
int v[2];
int u[2*QR4_K];
float d8[QR4_K];
const int bq8_offset = QR4_K * ((iqs / 2) / (QI8_1 / 2));
const int * q4 = (const int *) (bq4_K->qs + 16 * bq8_offset + 4 * ((iqs / 2) % 4));
const uint16_t * scales = (const uint16_t *) bq4_K->scales;
// iqs is in 0,2..30. bq8_offset = iqs/4 -> bq8_offset = 0, 2, 4, 6
const int bq8_offset = QR4_K * ((iqs/2) / (QI8_1/2));
// iqs = 0....3 -> bq8_offset = 0, want q4_offset = 0, 4, 8, 12
// iqs = 4....7 -> bq8_offset = 2, want q4_offset = 32, 36, 40, 44
// iqs = 8...11 -> bq8_offset = 4, want q4_offset = 64, 68, 72, 76
// iqs = 12..15 -> bq8_offset = 6, want q4_offset = 96, 100, 104, 108
const int * q4 = (const int *)(bq4_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
v[0] = q4[0];
v[1] = q4[4];
const uint16_t * scales = (const uint16_t *)bq4_K->scales;
uint16_t aux[2];
const int j = bq8_offset/2;
if (j < 2) {
aux[0] = scales[j+0] & 0x3f3f;
aux[1] = scales[j+2] & 0x3f3f;
} else {
aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
}
const uint8_t * sc = (const uint8_t *)aux;
const uint8_t * m = sc + 2;
for (int i = 0; i < QR4_K; ++i) {
const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
d8[i] = bq8i->ds[0];
const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
u[2*i+0] = q8[0];
u[2*i+1] = q8[4];
}
return vec_dot_q4_K_q8_1_impl_vmmq(v, u, sc, m, bq4_K->dm, d8);
return vec_dot_q4_K_q8_1_common(q4, scales, bq4_K->dm, bq8_1, iqs);
#else