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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9
src/llama-arch.cpp
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@@ -219,6 +219,11 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_CLASSIFIER_OUTPUT_LABELS, "%s.classifier.output_labels" },
{ LLM_KV_SHORTCONV_L_CACHE, "%s.shortconv.l_cache" },
// sentence-transformers dense modules feature dims
{ LLM_KV_DENSE_2_FEAT_IN, "%s.dense_2_feat_in" },
{ LLM_KV_DENSE_2_FEAT_OUT, "%s.dense_2_feat_out" },
{ LLM_KV_DENSE_3_FEAT_IN, "%s.dense_3_feat_in" },
{ LLM_KV_DENSE_3_FEAT_OUT, "%s.dense_3_feat_out" },
{ LLM_KV_TOKENIZER_MODEL, "tokenizer.ggml.model" },
{ LLM_KV_TOKENIZER_PRE, "tokenizer.ggml.pre" },
@@ -1071,6 +1076,8 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_DENSE_2_OUT, "dense_2" },
{ LLM_TENSOR_DENSE_3_OUT, "dense_3" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" },
@@ -2281,6 +2288,8 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
{LLM_TENSOR_OUTPUT, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
{LLM_TENSOR_CLS, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
{LLM_TENSOR_CLS_OUT, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
{LLM_TENSOR_DENSE_2_OUT, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, // Dense layer output
{LLM_TENSOR_DENSE_3_OUT, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, // Dense layer output
{LLM_TENSOR_OUTPUT_NORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
{LLM_TENSOR_DEC_OUTPUT_NORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
{LLM_TENSOR_ENC_OUTPUT_NORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},