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hparams : add n_embd_inp() to support extended embed (#16928)

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* add n_embd_full to support extended embed

* don't change output

* rename to n_embd_inp

* restore n_embd where applicable
This commit is contained in:
Sigbjørn Skjæret
2025-11-07 19:27:58 +01:00
committed by GitHub
parent 16bcc1259d
commit 9008027aa3
9 changed files with 29 additions and 28 deletions
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23
src/llama-model.cpp
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@@ -276,8 +276,8 @@ static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w
} break;
case GGML_OP_IM2COL:
{
const int n_embd = hparams.n_embd;
ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, n_embd, w->ne[1], 1, 1);
const int n_embd_inp = hparams.n_embd_inp();
ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, n_embd_inp, w->ne[1], 1, 1);
op_tensor = ggml_im2col(ctx, w, b, 1, 0, 0, 0, 1, 0, false, GGML_TYPE_F16);
} break;
case GGML_OP_SCALE:
@@ -1039,9 +1039,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
case 64: type = LLM_TYPE_32B; break;
default: type = LLM_TYPE_UNKNOWN;
}
// since vision model stacks deepstack features along feature dim
// we also create a fake "n_embd" for text model to be the main embd + deepstack embds
hparams.n_embd *= hparams.n_deepstack_layers + 1;
} break;
case LLM_ARCH_QWEN3MOE:
{
@@ -1065,9 +1062,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
case 94: type = LLM_TYPE_235B_A22B; break;
default: type = LLM_TYPE_UNKNOWN;
}
// since vision model stacks deepstack features along feature dim
// we also create a fake "n_embd" for text model to be the main embd + deepstack embds
hparams.n_embd *= hparams.n_deepstack_layers + 1;
} break;
case LLM_ARCH_PHI2:
{
@@ -3341,10 +3335,6 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
case LLM_ARCH_QWEN3:
case LLM_ARCH_QWEN3VL:
{
// for model loading, the weights only have the main embd
// so we need to divide by the number of deepstack layers + 1
// n_embd is const int so we declare a new variable
int64_t n_embd = hparams.n_embd / (hparams.n_deepstack_layers + 1);
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
// output
@@ -3380,10 +3370,6 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
case LLM_ARCH_QWEN3MOE:
case LLM_ARCH_QWEN3VLMOE:
{
// for model loading, the weights only have the main embd
// so we need to divide by the number of deepstack layers + 1
// n_embd is const int so we declare a new variable
int64_t n_embd = hparams.n_embd / (hparams.n_deepstack_layers + 1);
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
// output
@@ -6535,6 +6521,7 @@ void llama_model::print_info() const {
if (!hparams.vocab_only) {
LLAMA_LOG_INFO("%s: n_ctx_train = %u\n", __func__, hparams.n_ctx_train);
LLAMA_LOG_INFO("%s: n_embd = %u\n", __func__, hparams.n_embd);
LLAMA_LOG_INFO("%s: n_embd_inp = %u\n", __func__, hparams.n_embd_inp());
LLAMA_LOG_INFO("%s: n_layer = %u\n", __func__, hparams.n_layer);
LLAMA_LOG_INFO("%s: n_head = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_head(il); }, hparams.n_layer).c_str());
LLAMA_LOG_INFO("%s: n_head_kv = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_head_kv(il); }, hparams.n_layer).c_str());
@@ -7380,6 +7367,10 @@ int32_t llama_model_n_embd(const llama_model * model) {
return model->hparams.n_embd;
}
int32_t llama_model_n_embd_inp(const llama_model * model) {
return model->hparams.n_embd_inp();
}
int32_t llama_model_n_layer(const llama_model * model) {
return model->hparams.n_layer;
}