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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72
src/llama-adapter.cpp
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@@ -163,13 +163,38 @@ static void llama_adapter_lora_init_impl(llama_model & model, const char * path_
// check metadata
{
const gguf_context * gguf_ctx = ctx_gguf.get();
LLAMA_LOG_INFO("%s: Dumping metadata keys/values.\n", __func__);
// get metadata as string
for (int i = 0; i < gguf_get_n_kv(gguf_ctx); i++) {
gguf_type type = gguf_get_kv_type(gguf_ctx, i);
const std::string type_name =
type == GGUF_TYPE_ARRAY
? format("%s[%s,%zu]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(gguf_ctx, i)), gguf_get_arr_n(gguf_ctx, i))
: gguf_type_name(type);
const char * name = gguf_get_key(gguf_ctx, i);
const std::string value = gguf_kv_to_str(gguf_ctx, i);
if (type != GGUF_TYPE_ARRAY) {
adapter.gguf_kv.emplace(name, value);
}
const size_t MAX_VALUE_LEN = 40;
std::string print_value = value.size() > MAX_VALUE_LEN ? format("%s...", value.substr(0, MAX_VALUE_LEN - 3).c_str()) : value;
replace_all(print_value, "\n", "\\n");
LLAMA_LOG_INFO("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), print_value.c_str());
}
auto get_kv_str = [&](const std::string & key) -> std::string {
int id = gguf_find_key(ctx_gguf.get(), key.c_str());
return id < 0 ? "" : std::string(gguf_get_val_str(ctx_gguf.get(), id));
int id = gguf_find_key(gguf_ctx, key.c_str());
return id < 0 ? "" : std::string(gguf_get_val_str(gguf_ctx, id));
};
auto get_kv_f32 = [&](const std::string & key) -> float {
int id = gguf_find_key(ctx_gguf.get(), key.c_str());
return id < 0 ? 0.0f : gguf_get_val_f32(ctx_gguf.get(), id);
int id = gguf_find_key(gguf_ctx, key.c_str());
return id < 0 ? 0.0f : gguf_get_val_f32(gguf_ctx, id);
};
LLM_KV llm_kv = LLM_KV(LLM_ARCH_UNKNOWN);
@@ -383,6 +408,45 @@ llama_adapter_lora * llama_adapter_lora_init(llama_model * model, const char * p
return nullptr;
}
int32_t llama_adapter_meta_val_str(const llama_adapter_lora * adapter, const char * key, char * buf, size_t buf_size) {
const auto & it = adapter->gguf_kv.find(key);
if (it == adapter->gguf_kv.end()) {
if (buf_size > 0) {
buf[0] = '\0';
}
return -1;
}
return snprintf(buf, buf_size, "%s", it->second.c_str());
}
int32_t llama_adapter_meta_count(const llama_adapter_lora * adapter) {
return (int)adapter->gguf_kv.size();
}
int32_t llama_adapter_meta_key_by_index(const llama_adapter_lora * adapter, int i, char * buf, size_t buf_size) {
if (i < 0 || i >= (int)adapter->gguf_kv.size()) {
if (buf_size > 0) {
buf[0] = '\0';
}
return -1;
}
auto it = adapter->gguf_kv.begin();
std::advance(it, i);
return snprintf(buf, buf_size, "%s", it->first.c_str());
}
int32_t llama_adapter_meta_val_str_by_index(const llama_adapter_lora * adapter, int32_t i, char * buf, size_t buf_size) {
if (i < 0 || i >= (int)adapter->gguf_kv.size()) {
if (buf_size > 0) {
buf[0] = '\0';
}
return -1;
}
auto it = adapter->gguf_kv.begin();
std::advance(it, i);
return snprintf(buf, buf_size, "%s", it->second.c_str());
}
void llama_adapter_lora_free(llama_adapter_lora * adapter) {
delete adapter;
}

3
src/llama-adapter.h
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@@ -67,6 +67,9 @@ struct llama_adapter_lora {
float alpha;
// gguf metadata
std::unordered_map<std::string, std::string> gguf_kv;
llama_adapter_lora() = default;
~llama_adapter_lora() = default;

21
src/llama-arch.cpp
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@@ -22,6 +22,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_NOMIC_BERT_MOE, "nomic-bert-moe" },
{ LLM_ARCH_NEO_BERT, "neo-bert" },
{ LLM_ARCH_JINA_BERT_V2, "jina-bert-v2" },
{ LLM_ARCH_JINA_BERT_V3, "jina-bert-v3" },
{ LLM_ARCH_BLOOM, "bloom" },
{ LLM_ARCH_STABLELM, "stablelm" },
{ LLM_ARCH_QWEN, "qwen" },
@@ -234,8 +235,10 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_TOKENIZER_FIM_REP_ID, "tokenizer.ggml.fim_rep_token_id" },
{ LLM_KV_TOKENIZER_FIM_SEP_ID, "tokenizer.ggml.fim_sep_token_id" },
{ LLM_KV_ADAPTER_TYPE, "adapter.type" },
{ LLM_KV_ADAPTER_LORA_ALPHA, "adapter.lora.alpha" },
{ LLM_KV_ADAPTER_TYPE, "adapter.type" },
{ LLM_KV_ADAPTER_LORA_ALPHA, "adapter.lora.alpha" },
{ LLM_KV_ADAPTER_LORA_TASK_NAME, "adapter.lora.task_name" },
{ LLM_KV_ADAPTER_LORA_PROMPT_PREFIX, "adapter.lora.prompt_prefix" },
// deprecated
{ LLM_KV_TOKENIZER_PREFIX_ID, "tokenizer.ggml.prefix_token_id" },
@@ -575,6 +578,20 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_CLS, "cls" },
},
},
{
LLM_ARCH_JINA_BERT_V3,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
{ LLM_TENSOR_TOKEN_TYPES, "token_types" },
{ LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" },
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" },
},
},
{
LLM_ARCH_BLOOM,
{

3
src/llama-arch.h
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@@ -26,6 +26,7 @@ enum llm_arch {
LLM_ARCH_NOMIC_BERT_MOE,
LLM_ARCH_NEO_BERT,
LLM_ARCH_JINA_BERT_V2,
LLM_ARCH_JINA_BERT_V3,
LLM_ARCH_BLOOM,
LLM_ARCH_STABLELM,
LLM_ARCH_QWEN,
@@ -230,6 +231,8 @@ enum llm_kv {
LLM_KV_ADAPTER_TYPE,
LLM_KV_ADAPTER_LORA_ALPHA,
LLM_KV_ADAPTER_LORA_TASK_NAME,
LLM_KV_ADAPTER_LORA_PROMPT_PREFIX,
LLM_KV_POSNET_EMBEDDING_LENGTH,
LLM_KV_POSNET_BLOCK_COUNT,

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:

1
src/llama-model.h
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@@ -40,6 +40,7 @@ enum llm_type {
LLM_TYPE_450M,
LLM_TYPE_475M,
LLM_TYPE_537M,
LLM_TYPE_558M,
LLM_TYPE_700M,
LLM_TYPE_770M,
LLM_TYPE_780M,

2
src/llama-vocab.cpp
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@@ -2470,7 +2470,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
// set attributes by model/tokenizer/architecture name
if (false
|| _contains_any(tokenizer_pre, {"jina-v2-de", "jina-v2-es", "jina-v2-code"})
|| _contains_any(general_arch, {"nomic-bert-moe"})
|| _contains_any(general_arch, {"nomic-bert-moe", "jina-bert-v3"})
) {
if (token_to_id.count("<mask>") == 0) {
LLAMA_LOG_WARN("%s: Mask token is missing in vocab, please reconvert model!\n", __func__);