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llama : add RobertaForSequenceClassification reranker support (#13875)

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This commit is contained in:
Sigbjørn Skjæret
2025-05-29 08:15:01 +02:00
committed by GitHub
parent 1b8fb8152d
commit 6385b843a8
6 changed files with 24 additions and 8 deletions
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17
convert_hf_to_gguf.py
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@ -3695,6 +3695,10 @@ class BertModel(TextModel):
self.gguf_writer.add_causal_attention(False)
self._try_set_pooling_type()
if cls_out_labels := self.hparams.get("id2label"):
key_name = gguf.Keys.Classifier.OUTPUT_LABELS.format(arch = gguf.MODEL_ARCH_NAMES[self.model_arch])
self.gguf_writer.add_array(key_name, [v for k, v in sorted(cls_out_labels.items())])
def set_vocab(self):
tokens, toktypes, tokpre = self.get_vocab_base()
self.vocab_size = len(tokens)
@ -3745,12 +3749,13 @@ class BertModel(TextModel):
if name.startswith("cls.seq_relationship"):
return []
# For BertForSequenceClassification (direct projection layer)
if name == "classifier.weight":
name = "classifier.out_proj.weight"
if self.hparams.get("id2label"):
# For BertForSequenceClassification (direct projection layer)
if name == "classifier.weight":
name = "classifier.out_proj.weight"
if name == "classifier.bias":
name = "classifier.out_proj.bias"
if name == "classifier.bias":
name = "classifier.out_proj.bias"
return [(self.map_tensor_name(name), data_torch)]
@ -3846,7 +3851,7 @@ class BertModel(TextModel):
self.gguf_writer.add_add_eos_token(True)
@ModelBase.register("RobertaModel")
@ModelBase.register("RobertaModel", "RobertaForSequenceClassification")
class RobertaModel(BertModel):
model_arch = gguf.MODEL_ARCH.BERT

3
gguf-py/gguf/constants.py
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@ -177,6 +177,9 @@ class Keys:
EMBEDDING_LENGTH = "{arch}.convnext.embedding_length"
BLOCK_COUNT = "{arch}.convnext.block_count"
class Classifier:
OUTPUT_LABELS = "{arch}.classifier.output_labels"
class Tokenizer:
MODEL = "tokenizer.ggml.model"
PRE = "tokenizer.ggml.pre"

2
src/llama-arch.cpp
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@ -174,6 +174,8 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_CONVNEXT_EMBEDDING_LENGTH, "%s.convnext.embedding_length" },
{ LLM_KV_CONVNEXT_BLOCK_COUNT, "%s.convnext.block_count" },
{ LLM_KV_CLASSIFIER_OUTPUT_LABELS, "%s.classifier.output_labels" },
{ LLM_KV_TOKENIZER_MODEL, "tokenizer.ggml.model" },
{ LLM_KV_TOKENIZER_PRE, "tokenizer.ggml.pre" },
{ LLM_KV_TOKENIZER_LIST, "tokenizer.ggml.tokens" },

2
src/llama-arch.h
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@ -213,6 +213,8 @@ enum llm_kv {
LLM_KV_CONVNEXT_EMBEDDING_LENGTH,
LLM_KV_CONVNEXT_BLOCK_COUNT,
LLM_KV_CLASSIFIER_OUTPUT_LABELS,
// deprecated:
LLM_KV_TOKENIZER_PREFIX_ID,
LLM_KV_TOKENIZER_SUFFIX_ID,

3
src/llama-hparams.h
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@ -131,6 +131,9 @@ struct llama_hparams {
bool attn_soft_cap = false;
bool use_kq_norm = true;
// for Classifiers
uint32_t n_cls_out = 1;
// llama4
uint32_t n_moe_layer_step = 0;
uint32_t n_no_rope_layer_step = 4;

5
src/llama-model.cpp
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@ -683,6 +683,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
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);
ml.get_arr_n(LLM_KV_CLASSIFIER_OUTPUT_LABELS, hparams.n_cls_out, false);
switch (hparams.n_layer) {
case 3:
@ -2121,8 +2122,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
cls = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, TENSOR_NOT_REQUIRED);
cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"), {n_embd}, TENSOR_NOT_REQUIRED);
cls_out = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, 1}, TENSOR_NOT_REQUIRED);
cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"), {1}, TENSOR_NOT_REQUIRED);
cls_out = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, hparams.n_cls_out}, TENSOR_NOT_REQUIRED);
cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"), {hparams.n_cls_out}, TENSOR_NOT_REQUIRED);
}
tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);