}
}
+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) {
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:
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:
}
#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) {
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;
#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) {
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);
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.
switch (type) {
case GGML_TYPE_Q4_0:
return true;
+ case GGML_TYPE_Q4_K:
+ return !g_ggml_sycl_prioritize_dmmv;
default:
return false;
}
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;
}
}
-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(
*(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) {
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;
}
// 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;
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);
// 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);
}
}
}
}
+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,
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);
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
}
__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];
};
};
+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
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];
- // 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 block_q4_K * bq4_K = (const block_q4_K *) vbq;
- const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
- u[2*i+0] = q8[0];
- u[2*i+1] = q8[4];
- }
+ 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;
- 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
overview / pkg / ggml / sources / llama.cpp / commitdiff