Lumpiasty/llama.cpp
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hparams : refactor hparams.n_layer (#24060)

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* hparams : refactor hparams.n_layer

* cont : remove `n_layer_kv()`, use n_layer_all instead

* cont : type consistency

* pi : update SYSTEM.md

* models : fix Step3.5 MTP

* cont : remove duplicate switch cases

* cont : explicitly set `false` to extra layers for `is_swa` and `is_recr`

* cont : fix nextn layer count handling

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
This commit is contained in:
Georgi Gerganov
2026-06-05 11:09:36 +03:00
committed by GitHub
co-authored by Sigbjørn Skjæret
parent 3ecfb150a4
commit 7acb4e8cd2
129 changed files with 412 additions and 431 deletions
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src/llama-hparams.cpp
+38 -45
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@@ -7,31 +7,38 @@
void llama_hparams::set_swa_pattern(uint32_t n_pattern, bool dense_first) {
if (dense_first) {
for (uint32_t il = 0; il < n_layer; ++il) {
for (uint32_t il = 0; il < n_layer(); ++il) {
is_swa_impl[il] = n_pattern == 0 || (il % n_pattern != 0);
}
} else {
for (uint32_t il = 0; il < n_layer; ++il) {
for (uint32_t il = 0; il < n_layer(); ++il) {
is_swa_impl[il] = n_pattern == 0 || (il % n_pattern < (n_pattern - 1));
}
}
for (uint32_t il = n_layer(); il < n_layer_all; ++il) {
is_swa_impl[il] = false;
}
}
// TODO: implement
//void llama_hparams::set_recr_pattern(uint32_t n_pattern, bool dense_first) {
// if (dense_first) {
// for (uint32_t il = 0; il < n_layer; ++il) {
// is_recr_impl[il] = n_pattern == 0 || (il % n_pattern != 0);
// }
// } else {
// for (uint32_t il = 0; il < n_layer; ++il) {
// is_recr_impl[il] = n_pattern == 0 || (il % n_pattern < (n_pattern - 1));
// }
// }
//}
void llama_hparams::set_recr_pattern(uint32_t n_pattern, bool dense_first) {
if (dense_first) {
for (uint32_t il = 0; il < n_layer(); ++il) {
is_recr_impl[il] = n_pattern == 0 || (il % n_pattern != 0);
}
} else {
for (uint32_t il = 0; il < n_layer(); ++il) {
is_recr_impl[il] = n_pattern == 0 || (il % n_pattern < (n_pattern - 1));
}
}
for (uint32_t il = n_layer(); il < n_layer_all; ++il) {
is_recr_impl[il] = false;
}
}
bool llama_hparams::is_swa_any() const {
for (uint32_t il = 0; il < n_layer; ++il) {
for (uint32_t il = 0; il < n_layer_all; ++il) {
if (is_swa_impl[il]) {
return true;
}
@@ -41,7 +48,7 @@ bool llama_hparams::is_swa_any() const {
}
uint32_t llama_hparams::n_head(uint32_t il) const {
if (il < n_layer) {
if (il < n_layer_all) {
return n_head_arr[il];
}
@@ -49,7 +56,7 @@ uint32_t llama_hparams::n_head(uint32_t il) const {
}
uint32_t llama_hparams::n_head_kv(uint32_t il) const {
if (il < n_layer) {
if (il < n_layer_all) {
return n_head_kv_arr[il];
}
@@ -57,7 +64,7 @@ uint32_t llama_hparams::n_head_kv(uint32_t il) const {
}
uint32_t llama_hparams::n_ff(uint32_t il) const {
if (il < n_layer) {
if (il < n_layer_all) {
return n_ff_arr[il];
}
@@ -76,7 +83,7 @@ uint32_t llama_hparams::n_gqa(uint32_t il) const {
}
uint32_t llama_hparams::n_rot(uint32_t il) const {
if (il < n_layer) {
if (il < n_layer_all) {
return is_swa(il) ? n_rot_swa : n_rot_full;
}
@@ -98,7 +105,7 @@ uint32_t llama_hparams::n_embd_out() const {
}
uint32_t llama_hparams::n_embd_head_k(uint32_t il) const {
if (il < n_layer) {
if (il < n_layer_all) {
return is_swa(il) ? n_embd_head_k_swa : n_embd_head_k_full;
}
@@ -106,7 +113,7 @@ uint32_t llama_hparams::n_embd_head_k(uint32_t il) const {
}
uint32_t llama_hparams::n_embd_head_v(uint32_t il) const {
if (il < n_layer) {
if (il < n_layer_all) {
return is_swa(il) ? n_embd_head_v_swa : n_embd_head_v_full;
}
@@ -127,7 +134,7 @@ uint32_t llama_hparams::n_embd_v_gqa(uint32_t il) const {
bool llama_hparams::is_n_embd_k_gqa_variable() const {
const uint32_t val = n_embd_k_gqa();
for (uint32_t il = 0; il < n_layer; ++il) {
for (uint32_t il = 0; il < n_layer_all; ++il) {
if (val != n_embd_k_gqa(il)) {
return true;
}
@@ -138,7 +145,7 @@ bool llama_hparams::is_n_embd_k_gqa_variable() const {
bool llama_hparams::is_n_embd_v_gqa_variable() const {
const uint32_t val = n_embd_v_gqa();
for (uint32_t il = 0; il < n_layer; ++il) {
for (uint32_t il = 0; il < n_layer_all; ++il) {
if (val != n_embd_v_gqa(il)) {
return true;
}
@@ -149,7 +156,7 @@ bool llama_hparams::is_n_embd_v_gqa_variable() const {
uint32_t llama_hparams::n_embd_k_gqa_max() const {
uint32_t val = n_embd_k_gqa();
for (uint32_t il = 0; il < n_layer; ++il) {
for (uint32_t il = 0; il < n_layer_all; ++il) {
val = std::max(val, n_embd_k_gqa(il));
}
@@ -158,7 +165,7 @@ uint32_t llama_hparams::n_embd_k_gqa_max() const {
uint32_t llama_hparams::n_embd_v_gqa_max() const {
uint32_t val = n_embd_v_gqa();
for (uint32_t il = 0; il < n_layer; ++il) {
for (uint32_t il = 0; il < n_layer_all; ++il) {
val = std::max(val, n_embd_v_gqa(il));
}
@@ -207,11 +214,11 @@ uint32_t llama_hparams::n_embd_s() const {
}
bool llama_hparams::is_recr(uint32_t il) const {
if (il < n_layer) {
if (il < n_layer_all) {
return is_recr_impl[il];
}
GGML_ABORT("%s: il (%u) out of bounds (n_layer: %u)\n", __func__, il, n_layer);
GGML_ABORT("%s: il (%u) out of bounds (n_layer_all: %u)\n", __func__, il, n_layer_all);
}
uint32_t llama_hparams::n_pos_per_embd() const {
@@ -219,11 +226,11 @@ uint32_t llama_hparams::n_pos_per_embd() const {
}
bool llama_hparams::is_swa(uint32_t il) const {
if (il < n_layer) {
if (il < n_layer_all) {
return is_swa_impl[il];
}
GGML_ABORT("fatal error");
GGML_ABORT("%s: il (%u) out of bounds (n_layer_all: %u)\n", __func__, il, n_layer_all);
}
bool llama_hparams::is_mla() const {
@@ -242,12 +249,6 @@ uint32_t llama_hparams::n_embd_head_v_mla() const {
}
bool llama_hparams::has_kv(uint32_t il) const {
if (kv_only_nextn) {
// MTP head: only the trailing nextn_predict_layers blocks own a KV cache;
// the leading trunk blocks are not executed in this graph.
return nextn_predict_layers > 0 && il >= (n_layer - nextn_predict_layers);
}
if (n_layer_kv_from_start >= 0) {
if (il < (uint32_t) n_layer_kv_from_start) {
return true;
@@ -260,16 +261,8 @@ bool llama_hparams::has_kv(uint32_t il) const {
return true;
}
uint32_t llama_hparams::n_layer_kv() const {
uint32_t res = 0;
for (uint32_t il = 0; il < n_layer; ++il) {
if (has_kv(il)) {
res++;
}
}
return res;
uint32_t llama_hparams::n_layer() const {
return n_layer_all - n_layer_nextn;
}
bool llama_hparams::use_mrope() const {