EngineX-Ascend/enginex-ascend-910-llama.cpp
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model: EmbeddingGemma Adding Support for SentenceTransformers Dense Modules (#16367)

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* model: EmbeddingGemma sentence-transformers dense linear projections support

* model: add support for EmbeddingGemma SentenceTransformers dense linear projections

Adding support for the Dense modules used in EmbeddingGemma models.
EmbeddingGemma is a SentenceTransformers model with additional modules beyond the base Transformer backbone.

See: https://developers.googleblog.com/en/gemma-explained-embeddinggemma-architecture-and-recipe/

* model: add support for EmbeddingGemma SentenceTransformers dense linear projections

- converting model with dense-layers is optional
- introduced dense config params

* Update convert_hf_to_gguf.py

Co-authored-by: Daniel Bevenius <daniel.bevenius@gmail.com>

* fixed formatting issues

* Update src/llama-graph.cpp

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* - removed pooling_type_opt, always allow overriding pooling_type
- asserts checking dense features dims

* fix python lint

* fix ubuntu gcc build warning

* - fixed thread-safety test
- moved asserts to load_hparams

* - tidying up code
- simplifying graph-context expecting both dense weights

* minor : add TODO

---------

Co-authored-by: Daniel Bevenius <daniel.bevenius@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Saba Fallah
2025-10-09 08:39:18 +02:00
committed by GitHub
parent 12bbc3fa50
commit e08db42595
12 changed files with 170 additions and 7 deletions
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26
src/llama-model.cpp
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@@ -1218,12 +1218,21 @@ void llama_model::load_hparams(llama_model_loader & ml) {
hparams.set_swa_pattern(6);
hparams.causal_attn = false; // embeddings do not use causal attention
hparams.rope_freq_base_train_swa = 10000.0f;
hparams.rope_freq_base_train_swa = 10000.0f;
hparams.rope_freq_scale_train_swa = 1.0f;
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type);
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type);
//applied only if model converted with --sentence-transformers-dense-modules
ml.get_key(LLM_KV_DENSE_2_FEAT_IN, hparams.dense_2_feat_in, false);
ml.get_key(LLM_KV_DENSE_2_FEAT_OUT, hparams.dense_2_feat_out, false);
ml.get_key(LLM_KV_DENSE_3_FEAT_IN, hparams.dense_3_feat_in, false);
ml.get_key(LLM_KV_DENSE_3_FEAT_OUT, hparams.dense_3_feat_out, false);
GGML_ASSERT((hparams.dense_2_feat_in == 0 || hparams.dense_2_feat_in == hparams.n_embd) && "dense_2_feat_in must be equal to n_embd");
GGML_ASSERT((hparams.dense_3_feat_out == 0 || hparams.dense_3_feat_out == hparams.n_embd) && "dense_3_feat_out must be equal to n_embd");
switch (hparams.n_layer) {
case 24: type = LLM_TYPE_0_3B; break;
@@ -3686,6 +3695,11 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
}
// Dense linear weights
dense_2_out_layers = create_tensor(tn(LLM_TENSOR_DENSE_2_OUT, "weight"), {n_embd, hparams.dense_2_feat_out}, TENSOR_NOT_REQUIRED);
dense_3_out_layers = create_tensor(tn(LLM_TENSOR_DENSE_3_OUT, "weight"), {hparams.dense_3_feat_in, n_embd}, TENSOR_NOT_REQUIRED);
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
@@ -19893,6 +19907,12 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
// add on pooling layer
llm->build_pooling(cls, cls_b, cls_out, cls_out_b);
// if the gguf model was converted with --sentence-transformers-dense-modules
// there will be two additional dense projection layers
// dense linear projections are applied after pooling
// TODO: move reranking logic here and generalize
llm->build_dense_out(dense_2_out_layers, dense_3_out_layers);
return llm->res->get_gf();
}