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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/bailingmoe2.cpp
+8 -13
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@@ -9,17 +9,13 @@ void llama_model_bailingmoe2::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func);
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");
ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
// TODO: when MTP is implemented, this should probably be updated if needed
hparams.n_layer_kv_from_start = hparams.n_layer - hparams.nextn_predict_layers;
GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_impl");
switch (hparams.n_layer) {
switch (hparams.n_layer()) {
case 20: type = LLM_TYPE_16B_A1B; break;
case 21: type = LLM_TYPE_16B_A1B; break;
case 32: type = LLM_TYPE_100B_A6B; break;
case 33: type = LLM_TYPE_100B_A6B; break;
default: type = LLM_TYPE_UNKNOWN;
}
}
@@ -39,9 +35,9 @@ void llama_model_bailingmoe2::load_arch_tensors(llama_model_loader &) {
GGML_ASSERT(n_expert > 0 && "n_expert must be > 0 for bailingmoe2");
GGML_ASSERT(n_expert_used > 0 && "n_expert_used must be > 0 for bailingmoe2");
for (int i = 0; i < n_layer; ++i) {
for (int i = 0; i < n_layer_all; ++i) {
int flags = 0;
if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
if (i >= n_layer) {
// skip all tensors in the NextN layers
flags |= TENSOR_SKIP;
}
@@ -78,7 +74,7 @@ void llama_model_bailingmoe2::load_arch_tensors(llama_model_loader &) {
}
// NextN/MTP tensors (preserved but unused) - conditionally load for last nextn_predict_layers
if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
if (i >= n_layer) {
layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);
layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED | flags);
layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
@@ -112,8 +108,7 @@ llama_model_bailingmoe2::graph::graph(const llama_model & model, const llm_graph
ggml_tensor * inp_out_ids = build_inp_out_ids();
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;
// norm
@@ -146,7 +141,7 @@ llama_model_bailingmoe2::graph::graph(const llama_model & model, const llm_graph
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
}
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);
}