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Fix the pooling method of BGE embedding model #2926

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merged 8 commits into from
Jan 24, 2024
Merged

Fix the pooling method of BGE embedding model #2926

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ee21ecb
Update model_adapter.py
staoxiao Sep 22, 2023
39fc0b6
Update model_worker.py
staoxiao Sep 22, 2023
389a889
Merge pull request #1 from lm-sys/main
staoxiao Jan 16, 2024
b013949
Update model_adapter.py
staoxiao Jan 16, 2024
ba31c84
Update model_worker.py
staoxiao Jan 16, 2024
2679768
Update model_worker.py
staoxiao Jan 16, 2024
056ffb3
Update model_worker.py
staoxiao Jan 17, 2024
f3bce43
reformat
staoxiao Jan 17, 2024
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2 changes: 2 additions & 0 deletions fastchat/model/model_adapter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1702,6 +1702,8 @@ def load_model(self, model_path: str, from_pretrained_kwargs: dict):
model.config.max_sequence_length = min(
model.config.max_position_embeddings, tokenizer.model_max_length
)
model.use_cls_pooling = True
model.eval()
return model, tokenizer

def get_default_conv_template(self, model_path: str) -> Conversation:
Expand Down
51 changes: 45 additions & 6 deletions fastchat/serve/model_worker.py
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Original file line number Diff line number Diff line change
Expand Up @@ -164,9 +164,13 @@ def __process_embed_chunk(self, input_ids, attention_mask, **model_type_dict):
data = model_output.hidden_states[-1].transpose(0, 1)
else:
data = model_output.hidden_states[-1]
mask = attention_mask.unsqueeze(-1).expand(data.size()).float()
masked_embeddings = data * mask
sum_embeddings = torch.sum(masked_embeddings, dim=1)

if hasattr(self.model, "use_cls_pooling") and self.model.use_cls_pooling:
sum_embeddings = data[:, 0]
else:
mask = attention_mask.unsqueeze(-1).expand(data.size()).float()
masked_embeddings = data * mask
sum_embeddings = torch.sum(masked_embeddings, dim=1)
token_num = torch.sum(attention_mask).item()

return sum_embeddings, token_num
Expand Down Expand Up @@ -211,10 +215,14 @@ def get_embeddings(self, params):
base64_encode = params.get("encoding_format", None)

if self.embed_in_truncate:
chunk_embeddings, token_num = self.__process_embed_chunk(
embedding, token_num = self.__process_embed_chunk(
input_ids, attention_mask, **model_type_dict
)
embedding = chunk_embeddings / token_num
if (
not hasattr(self.model, "use_cls_pooling")
or not self.model.use_cls_pooling
):
embedding = embedding / token_num
normalized_embeddings = F.normalize(embedding, p=2, dim=1)
ret["token_num"] = token_num
else:
Expand All @@ -224,10 +232,41 @@ def get_embeddings(self, params):
chunk_input_ids = input_ids[:, i : i + self.context_len]
chunk_attention_mask = attention_mask[:, i : i + self.context_len]

# add cls token and mask to get cls embedding
if (
hasattr(self.model, "use_cls_pooling")
and self.model.use_cls_pooling
):
cls_tokens = (
torch.zeros(
(chunk_input_ids.size(0), 1),
dtype=chunk_input_ids.dtype,
device=chunk_input_ids.device,
)
+ tokenizer.cls_token_id
)
chunk_input_ids = torch.cat(
[cls_tokens, chunk_input_ids], dim=-1
)
mask = torch.ones(
(chunk_attention_mask.size(0), 1),
dtype=chunk_attention_mask.dtype,
device=chunk_attention_mask.device,
)
chunk_attention_mask = torch.cat(
[mask, chunk_attention_mask], dim=-1
)

chunk_embeddings, token_num = self.__process_embed_chunk(
chunk_input_ids, chunk_attention_mask, **model_type_dict
)
all_embeddings.append(chunk_embeddings)
if (
hasattr(self.model, "use_cls_pooling")
and self.model.use_cls_pooling
):
all_embeddings.append(chunk_embeddings * token_num)
else:
all_embeddings.append(chunk_embeddings)
all_token_num += token_num

all_embeddings_tensor = torch.stack(all_embeddings)
Expand Down
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