EngineX-Ascend/enginex-ascend-910-llama.cpp
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model : jina-embeddings-v3 support (#13693)

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* initial jina-embeddings-v3 support

* initial jina-embeddings-v3 support

* initial jina-embeddings-v3 support

* fix vocab parsing with only tokenizer.json

* set mask token lstrip attribute

* additional unk_token_id fallback just in case [no ci]

* revert vocab_size() change [no ci]

* merge tensor loading into general bert

* rope

* add lora embedding and loading (non-functional)

* export separate lora ggufs instead

* add adapter metadata api

* use std::string

* convert_hf_to_lora compatibility

* fix assert

* apply suggestions from review

* apply suggestion from review
This commit is contained in:
Sigbjørn Skjæret
2025-08-28 15:49:50 +02:00
committed by GitHub
parent 55042b3692
commit 84ab83cc0b
14 changed files with 245 additions and 23 deletions
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35
src/llama-model.cpp
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@@ -47,6 +47,7 @@ const char * llm_type_name(llm_type type) {
case LLM_TYPE_410M: return "410M";
case LLM_TYPE_450M: return "450M";
case LLM_TYPE_475M: return "475M";
case LLM_TYPE_558M: return "558M";
case LLM_TYPE_700M: return "700M";
case LLM_TYPE_770M: return "770M";
case LLM_TYPE_780M: return "780M";
@@ -772,6 +773,18 @@ void llama_model::load_hparams(llama_model_loader & ml) {
default: type = LLM_TYPE_UNKNOWN;
}
} break;
case LLM_ARCH_JINA_BERT_V3:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
switch (hparams.n_layer) {
case 24:
type = LLM_TYPE_558M; break;
default: type = LLM_TYPE_UNKNOWN;
}
} break;
case LLM_ARCH_NOMIC_BERT:
case LLM_ARCH_NOMIC_BERT_MOE:
{
@@ -2631,6 +2644,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
case LLM_ARCH_BERT:
case LLM_ARCH_NOMIC_BERT:
case LLM_ARCH_NOMIC_BERT_MOE:
case LLM_ARCH_JINA_BERT_V3:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
type_embd = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_token_types}, TENSOR_NOT_REQUIRED);
@@ -2666,24 +2680,22 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
}
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
layer.attn_out_norm = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);
layer.attn_out_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd}, 0);
if (hparams.moe_every_n_layers > 0 && i % hparams.moe_every_n_layers == 1) {
layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff, n_expert}, 0);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}, 0);
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
} else {
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
if (arch == LLM_ARCH_BERT || arch == LLM_ARCH_NOMIC_BERT_MOE) {
layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0);
layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0);
} else {
if (arch == LLM_ARCH_NOMIC_BERT) {
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
}
}
@@ -7461,7 +7473,7 @@ struct llm_build_bert : public llm_graph_context {
}
// RoPE
if (model.arch == LLM_ARCH_NOMIC_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE) {
if (model.arch == LLM_ARCH_NOMIC_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE || model.arch == LLM_ARCH_JINA_BERT_V3) {
Qcur = ggml_rope_ext(
ctx0, Qcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
@@ -7520,7 +7532,7 @@ struct llm_build_bert : public llm_graph_context {
0.0f,
LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il);
cb(cur, "ffn_moe_out", il);
} else if (model.arch == LLM_ARCH_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE) {
} else if (model.arch == LLM_ARCH_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE || model.arch == LLM_ARCH_JINA_BERT_V3) {
cur = build_ffn(cur,
model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
NULL, NULL, NULL,
@@ -18241,6 +18253,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
// switch statement
case LLM_ARCH_BERT:
case LLM_ARCH_JINA_BERT_V2:
case LLM_ARCH_JINA_BERT_V3:
case LLM_ARCH_NOMIC_BERT:
case LLM_ARCH_NOMIC_BERT_MOE:
case LLM_ARCH_NEO_BERT:
@@ -18395,6 +18408,7 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
} break;
case LLM_ARCH_BERT:
case LLM_ARCH_JINA_BERT_V2:
case LLM_ARCH_JINA_BERT_V3:
case LLM_ARCH_NOMIC_BERT:
case LLM_ARCH_NOMIC_BERT_MOE:
{
@@ -18885,6 +18899,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_GROK:
case LLM_ARCH_DBRX:
case LLM_ARCH_BERT:
case LLM_ARCH_JINA_BERT_V3:
case LLM_ARCH_NOMIC_BERT:
case LLM_ARCH_NOMIC_BERT_MOE:
case LLM_ARCH_STABLELM: