hparams : refactor hparams.n_layer (#24060)

* 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
parent 3ecfb150a4
commit 7acb4e8cd2
129 changed files with 412 additions and 431 deletions
+8 -14
View File
@@ -9,18 +9,17 @@ void llama_model_mimo2::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl, hparams.n_layer);
ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl, hparams.n_layer());
float value_scale = 0.0f;
if (ml.get_key(LLM_KV_ATTENTION_VALUE_SCALE, value_scale, false) && value_scale != 1.0f) {
hparams.f_attn_value_scale = value_scale;
}
ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.nextn_predict_layers, false);
GGML_ASSERT(hparams.nextn_predict_layers < hparams.n_layer && "nextn_predict_layers must be < n_layer");
hparams.n_layer_kv_from_start = hparams.n_layer - hparams.nextn_predict_layers;
ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_impl");
switch (hparams.n_layer - hparams.nextn_predict_layers) {
switch (hparams.n_layer()) {
case 48: type = LLM_TYPE_310B_A15B; break;
default: type = LLM_TYPE_UNKNOWN;
}
@@ -35,16 +34,14 @@ void llama_model_mimo2::load_arch_tensors(llama_model_loader &) {
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
const uint32_t n_nextn = hparams.nextn_predict_layers;
for (int i = 0; i < n_layer; ++i) {
for (int i = 0; i < n_layer_all; ++i) {
auto & layer = layers[i];
uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i);
uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i);
uint32_t n_head = hparams.n_head(i);
// NextN/MTP layers (the last n_nextn blocks) are preserved but disabled pending support
const bool is_nextn = (n_nextn > 0) && (static_cast<uint32_t>(i) >= n_layer - n_nextn);
const bool is_nextn = i >= n_layer;
const int skip = is_nextn ? TENSOR_SKIP : 0;
create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, skip);
@@ -93,10 +90,7 @@ llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_param
const float v_scale = hparams.f_attn_value_scale;
// The last hparams.nextn_predict_layers blocks are MTP heads, currently inactive
const int n_transformer_layers = n_layer - hparams.nextn_predict_layers;
for (int il = 0; il < n_transformer_layers; ++il) {
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * inpSA = inpL;
uint32_t n_head_l = hparams.n_head(il);
@@ -174,7 +168,7 @@ llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_param
}
}
if (il == n_transformer_layers - 1 && inp_out_ids) {
if (il == n_layer - 1 && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}