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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
parent 3ecfb150a4
commit 7acb4e8cd2
129 changed files with 412 additions and 431 deletions
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src/models/deepseek2.cpp
+5 -6
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@@ -5,7 +5,7 @@ void llama_model_deepseek2::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_VOCAB_SIZE, n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, n_vocab, false);
// lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B, Kanana-2-30B-A3B
const bool is_lite = (hparams.n_layer == 27 || hparams.n_layer == 26 || (hparams.n_layer == 48 && n_vocab == 128256));
const bool is_lite = (hparams.n_layer() == 27 || hparams.n_layer() == 26 || (hparams.n_layer() == 48 && n_vocab == 128256));
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false);
@@ -23,7 +23,7 @@ void llama_model_deepseek2::load_arch_hparams(llama_model_loader & ml) {
if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
// for compatibility with existing DeepSeek V2 and V2.5 GGUFs
// that have no expert_gating_func model parameter set
if ((hparams.n_layer == 47 || hparams.n_layer == 48) && n_vocab == 154880) {
if ((hparams.n_layer() == 47 || hparams.n_layer() == 48) && n_vocab == 154880) {
// GLM 4.7 Lite
hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
} else {
@@ -43,7 +43,7 @@ void llama_model_deepseek2::load_arch_hparams(llama_model_loader & ml) {
hparams.f_attn_temp_offset = 0.0f;
switch (hparams.n_layer) {
switch (hparams.n_layer()) {
case 27: type = LLM_TYPE_16B; break;
case 47: type = LLM_TYPE_30B_A3B; break;
case 60: type = LLM_TYPE_236B; break;
@@ -191,8 +191,7 @@ llama_model_deepseek2::graph::graph(const llama_model & model, const llm_graph_p
ggml_tensor * inp_out_ids = build_inp_out_ids();
int effective_n_layers = hparams.n_layer - hparams.nextn_predict_layers;
for (int il = 0; il < effective_n_layers; ++il) {
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * inpSA = inpL;
// norm
@@ -366,7 +365,7 @@ llama_model_deepseek2::graph::graph(const llama_model & model, const llm_graph_p
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
}
}
if (il == effective_n_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);
}