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Merge branch 'upstream' into concedo_experimental

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# Conflicts:
#	.github/workflows/build.yml
#	ci/run.sh
#	examples/model-conversion/Makefile
#	examples/model-conversion/README.md
#	examples/model-conversion/logits.cpp
#	examples/model-conversion/requirements.txt
#	examples/model-conversion/scripts/embedding/convert-model.sh
#	examples/model-conversion/scripts/embedding/run-converted-model.sh
#	examples/model-conversion/scripts/embedding/run-original-model.py
#	examples/model-conversion/scripts/utils/semantic_check.py
#	ggml/src/ggml-cann/common.h
#	ggml/src/ggml-cann/ggml-cann.cpp
#	ggml/src/ggml-cpu/kleidiai/kernels.cpp
#	ggml/src/ggml-cpu/kleidiai/kernels.h
#	ggml/src/ggml-cpu/kleidiai/kleidiai.cpp
#	ggml/src/ggml-sycl/common.hpp
#	ggml/src/ggml-sycl/dpct/helper.hpp
#	ggml/src/ggml-sycl/ggml-sycl.cpp
#	ggml/src/ggml-sycl/softmax.cpp
#	ggml/src/ggml-sycl/softmax.hpp
#	requirements/requirements-all.txt
#	tests/test-chat-parser.cpp
#	tools/server/README.md
This commit is contained in:
Concedo
2025-10-09 23:46:56 +08:00
parent 5396e62b56 8328fd4bae
commit 5b6ba02167
27 changed files with 421 additions and 440 deletions
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src/llama-model.cpp
+23 -3
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@@ -1223,12 +1223,21 @@ 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;
@@ -3744,6 +3753,11 @@ 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];
@@ -19955,6 +19969,12 @@ 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();
}