tqcq/llama.cpp
0
0
Fork 0
mirror of https://github.com/ggml-org/llama.cpp.git synced 2025-06-27 12:05:03 +00:00
Code Issues Packages Projects Releases Wiki Activity

cont : gate the ggml_set_rows usage with env var

Browse Source 
ggml-ci
This commit is contained in:
Georgi Gerganov
2025-06-21 10:37:06 +03:00
parent 79dac3c861
commit db2bb378b1
2 changed files with 53 additions and 25 deletions
Download Patch File Download Diff File Expand all files Collapse all files

73
src/llama-kv-cache-unified.cpp
View File

@ -130,6 +130,13 @@ llama_kv_cache_unified::llama_kv_cache_unified(
const char * LLAMA_KV_CACHE_DEBUG = getenv("LLAMA_KV_CACHE_DEBUG");
debug = LLAMA_KV_CACHE_DEBUG ? atoi(LLAMA_KV_CACHE_DEBUG) : 0;
const char * LLAMA_SET_ROWS = getenv("LLAMA_SET_ROWS");
supports_set_rows = LLAMA_SET_ROWS ? atoi(LLAMA_SET_ROWS) : 0;
if (!supports_set_rows) {
LLAMA_LOG_WARN("%s: LLAMA_SET_ROWS=0, using old ggml_cpy() method for backwards compatibility\n", __func__);
}
}
void llama_kv_cache_unified::clear(bool data) {
@ -751,15 +758,21 @@ ggml_tensor * llama_kv_cache_unified::cpy_k(ggml_context * ctx, ggml_tensor * k_
auto * k = layers[ikv].k;
const int64_t n_embd_k_gqa = k->ne[0];
const int64_t n_tokens = k_cur->ne[2];
if (kv_idxs) {
return ggml_set_rows(ctx, k, ggml_reshape_2d(ctx, k_cur, k->ne[0], n_tokens), kv_idxs);
k_cur = ggml_reshape_2d(ctx, k_cur, k->ne[0], n_tokens);
if (kv_idxs && supports_set_rows) {
return ggml_set_rows(ctx, k, k_cur, kv_idxs);
}
// TODO: fallback to old ggml_cpy() method for backwards compatibility
// will be removed when ggml_set_rows() is adopted by all backends
ggml_tensor * k_view = ggml_view_1d(ctx, k,
n_tokens*hparams.n_embd_k_gqa(il),
ggml_row_size(k->type, hparams.n_embd_k_gqa(il))*head_cur);
n_tokens*n_embd_k_gqa,
ggml_row_size(k->type, n_embd_k_gqa)*head_cur);
return ggml_cpy(ctx, k_cur, k_view);
}
@ -769,37 +782,43 @@ ggml_tensor * llama_kv_cache_unified::cpy_v(ggml_context * ctx, ggml_tensor * v_
auto * v = layers[ikv].v;
const int64_t n_embd_v_gqa = v->ne[0];
const int64_t n_tokens = v_cur->ne[2];
v_cur = ggml_reshape_2d(ctx, v_cur, hparams.n_embd_v_gqa(il), n_tokens);
v_cur = ggml_reshape_2d(ctx, v_cur, n_embd_v_gqa, n_tokens);
if (kv_idxs && supports_set_rows) {
if (!v_trans) {
return ggml_set_rows(ctx, v, v_cur, kv_idxs);
}
// note: the V cache is transposed when not using flash attention
v_cur = ggml_transpose(ctx, v_cur);
// the row becomes a single element and we repeat the KV indices d_head times
ggml_tensor * v_view = ggml_reshape_3d(ctx, v, 1, v->ne[1], v->ne[0]);
v_cur = ggml_cont_3d(ctx, v_cur, 1, v_cur->ne[0], v_cur->ne[1]);
// TODO: this repeat can be avoided if ggml_set_rows() supports broadcast
kv_idxs = ggml_repeat_4d(ctx, kv_idxs, v_cur->ne[1], v_cur->ne[2], 1, 1);
return ggml_set_rows(ctx, v_view, v_cur, kv_idxs);
}
// TODO: fallback to old ggml_cpy() method for backwards compatibility
// will be removed when ggml_set_rows() is adopted by all backends
ggml_tensor * v_view = nullptr;
if (!v_trans) {
if (kv_idxs) {
return ggml_set_rows(ctx, v, v_cur, kv_idxs);
}
v_view = ggml_view_1d(ctx, v,
n_tokens*hparams.n_embd_v_gqa(il),
ggml_row_size(v->type, hparams.n_embd_v_gqa(il))*head_cur);
n_tokens*n_embd_v_gqa,
ggml_row_size(v->type, n_embd_v_gqa)*head_cur);
} else {
v_cur = ggml_transpose(ctx, v_cur);
// note: the V cache is transposed when not using flash attention
if (kv_idxs) {
// the row becomes a single element and we repeat the KV indices d_head times
// TODO: this seems not very optimal - can we do something better?
v_view = ggml_reshape_3d(ctx, v, 1, v->ne[1], v->ne[0]);
v_cur = ggml_cont_3d(ctx, v_cur, 1, v_cur->ne[0], v_cur->ne[1]);
kv_idxs = ggml_repeat_4d(ctx, kv_idxs, v_cur->ne[1], v_cur->ne[2], 1, 1);
return ggml_set_rows(ctx, v_view, v_cur, kv_idxs);
}
v_view = ggml_view_2d(ctx, v, n_tokens, hparams.n_embd_v_gqa(il),
v_view = ggml_view_2d(ctx, v, n_tokens, n_embd_v_gqa,
(v->ne[1])*ggml_element_size(v),
(head_cur)*ggml_element_size(v));
}
@ -808,6 +827,10 @@ ggml_tensor * llama_kv_cache_unified::cpy_v(ggml_context * ctx, ggml_tensor * v_
}
void llama_kv_cache_unified::set_input_kv_idxs(ggml_tensor * dst, const llama_ubatch * ubatch, uint32_t head_cur) const {
if (!supports_set_rows) {
return;
}
const uint32_t n_tokens = ubatch->n_tokens;
GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer));

5
src/llama-kv-cache-unified.h
View File

@ -158,8 +158,13 @@ private:
// SWA
const uint32_t n_swa = 0;
// env: LLAMA_KV_CACHE_DEBUG
int debug = 0;
// env: LLAMA_SET_ROWS (temporary)
// ref: https://github.com/ggml-org/llama.cpp/pull/14285
int supports_set_rows = false;
const llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
std::vector<ggml_context_ptr> ctxs;