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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17
src/llama-graph.cpp
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@@ -1853,6 +1853,23 @@ llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const {
return (llm_graph_input_mem_hybrid *) res->add_input(std::move(inp));
}
void llm_graph_context::build_dense_out(
ggml_tensor * dense_2,
ggml_tensor * dense_3) const {
if (!cparams.embeddings || dense_2 == nullptr || dense_3 == nullptr) {
return;
}
ggml_tensor * cur = res->t_embd_pooled != nullptr ? res->t_embd_pooled : res->t_embd;
GGML_ASSERT(cur != nullptr && "missing t_embd_pooled/t_embd");
cur = ggml_mul_mat(ctx0, dense_2, cur);
cur = ggml_mul_mat(ctx0, dense_3, cur);
cb(cur, "result_embd_pooled", -1);
res->t_embd_pooled = cur;
ggml_build_forward_expand(gf, cur);
}
void llm_graph_context::build_pooling(
ggml_tensor * cls,
ggml_tensor * cls_b,