LLM-Inference/ik_llama.cpp
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Indexer topk: on the CPU repack Q8_0 indexer cache (#2285)

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Kawrakow
2026-08-10 18:45:43 +02:00
committed by GitHub
parent 7ebbb906d2
commit b8b3034b0f
1 changed files with 53 additions and 6 deletions
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ggml/src/iqk/iqk_mul_mat.cpp
+53 -6
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@@ -1960,7 +1960,15 @@ size_t iqk_idx_topk_work_buffer_size(const struct ggml_tensor * dst, int nthread
auto k = dst->src[0];
auto q = dst->src[1];
if (q->ne[2] >= nthread) {
return iqk_idx_topk_work_wbs_per_thread(dst, nthread) * nthread;
size_t common_size = 0;
auto requant_type = MulMat::is_dequant_better(k->type, q->ne[1]);
if (requant_type != k->type) {
int nr = MulMat::num_rows(requant_type);
if (k->ne[1] % nr == 0 && k->ne[1] % k_indexer_chunks == 0 && k_indexer_chunks % nr == 0) {
common_size = ggml_row_size(requant_type, k->ne[0]) * k->ne[1];
}
}
return common_size + iqk_idx_topk_work_wbs_per_thread(dst, nthread) * nthread;
}
size_t size = 0;
auto tt = ggml_internal_get_type_traits(k->type);
@@ -1999,10 +2007,27 @@ bool iqk_indexer_topk(struct ggml_tensor * dst, void * work_buffer, barrier_t ba
if (m->type != GGML_TYPE_F32 && m->type != GGML_TYPE_F16) return false;
auto work_size = iqk_idx_topk_work_wbs_per_thread(dst, nth);
auto work = (char *)work_buffer + ith*work_size;
auto work_all = (char *)work_buffer;
ggml_from_float_t from_float = nullptr;
auto tt = ggml_internal_get_type_traits(k->type);
auto k_type = k->type;
int num_k_rows = 1;
if (q->ne[2] >= nth) {
auto requant_type = MulMat::is_dequant_better(k_type, q->ne[1]);
if (requant_type != k_type) {
int nr = MulMat::num_rows(requant_type);
if (k->ne[1] % nr == 0 && k->ne[1] % k_indexer_chunks == 0 && k_indexer_chunks % nr == 0) {
k_type = requant_type;
num_k_rows = nr;
}
//else if (ith == 0) {
// printf("Not repacking K from %s to %s because %d, %d, %d\n", ggml_type_name(k->type), ggml_type_name(requant_type),
// k->ne[1] % nr == 0, k->ne[1] % k_indexer_chunks == 0, k_indexer_chunks % nr == 0);
//}
}
}
auto tt = ggml_internal_get_type_traits(k_type);
size_t quantize_size = 0;
auto q_type = q->type;
@@ -2016,10 +2041,32 @@ bool iqk_indexer_topk(struct ggml_tensor * dst, void * work_buffer, barrier_t ba
}
MulMat mm;
if (!MulMat::prepare(int(k->type), int(q_type), k->ne[0], mm, q->ne[1])) {
if (!MulMat::prepare(int(k_type), int(q_type), k->ne[0], mm, q->ne[1])) {
return false;
}
auto k_data = k->data;
auto k_nb1 = k->nb[1];
if (k_type != k->type) {
auto row_size = ggml_row_size(k_type, k->ne[0]);
k_data = work_all;
work_all += row_size * k->ne[1];
int nk_tot = k->ne[1] / num_k_rows;
int npt = (nk_tot + nth - 1)/nth;
int first = npt*ith;
int last = std::min(nk_tot, first + npt);
if (last > first) {
if (!iqk_convert_repack(int(k->type), k->ne[0], (const char *)k->data + first*num_k_rows*k->nb[1], k->nb[1],
(char *)k_data + first*num_k_rows*row_size, k->ne[0], (last - first)*num_k_rows)) {
GGML_ABORT("Fatal error");
}
}
k_nb1 = row_size;
barrier(barrier_data);
}
auto work = work_all + ith*work_size;
if (q->ne[2] >= nth) {
auto kq = (float *)(work + quantize_size);
auto score = kq + k_indexer_chunks*q->ne[1];
@@ -2033,7 +2080,7 @@ bool iqk_indexer_topk(struct ggml_tensor * dst, void * work_buffer, barrier_t ba
auto this_w = (const float *)((const char *)w->data + w->nb[1]*iq);
bool done = false;
#ifdef __AVX2__
if (k->type == GGML_TYPE_F16 && q->type == GGML_TYPE_F32 && k->ne[1] % 32 == 0 && q->ne[1] % 8 == 0) {
if (k_type == GGML_TYPE_F16 && q->type == GGML_TYPE_F32 && k->ne[1] % 32 == 0 && q->ne[1] % 8 == 0) {
auto k_repacked = (float *)(sorted + k->ne[1]);
auto kq_local = k_repacked + 32*k->ne[0];
for (int ik = 0; ik < (int)k->ne[1]; ik += 32) {
@@ -2073,7 +2120,7 @@ bool iqk_indexer_topk(struct ggml_tensor * dst, void * work_buffer, barrier_t ba
for (int i_step = 0; i_step < n_step; ++i_step) {
int nk = std::min(k_indexer_chunks, int(k->ne[1]) - i_step*k_indexer_chunks);
DataInfo info{kq, this_q, (size_t)nk, (size_t)row_size_q, 0, 1, nullptr, 0};
mm.mul_mat_NxM(k->ne[0], (const char *)k->data + i_step*k_indexer_chunks*k->nb[1], k->nb[1], info, nk, q->ne[1]);
mm.mul_mat_NxM(k->ne[0], (const char *)k_data + i_step*k_indexer_chunks*k_nb1, k_nb1, info, nk, q->ne[1]);
auto kq_i = kq;
auto this_score = score + i_step*k_indexer_chunks;