[Feature] Add support for naver/splade-v3 (BERT-based sparse embedding model) (#26339)
Signed-off-by: gjgjos <gjgjos@naver.com> Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>
This commit is contained in:
gjgjos
@@ -572,6 +572,220 @@ def _decode_token_type_ids(input_ids: torch.Tensor) -> torch.Tensor:
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return token_type_ids
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class BertMLMHead(nn.Module):
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def __init__(
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self, hidden_size: int, vocab_size: int, layer_norm_eps: float = 1e-12
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):
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super().__init__()
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self.dense = nn.Linear(hidden_size, hidden_size)
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self.activation = nn.GELU()
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self.layer_norm = nn.LayerNorm(hidden_size, eps=layer_norm_eps)
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self.decoder = nn.Linear(hidden_size, vocab_size, bias=True)
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def tie_weights_with_embeddings(self, embeddings_weight: torch.Tensor):
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self.decoder.weight = embeddings_weight
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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x = self.dense(hidden_states)
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x = self.activation(x)
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x = self.layer_norm(x)
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logits = self.decoder(x)
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return logits
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class SPLADESparsePooler(Pooler):
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"""
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SPLADE sparse pooling:
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logits = mlm_head(hidden_states)
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-> log1p(relu(logits))
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-> (max|sum over L)
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-> [V]
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Padding is masked with an attention mask,
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[CLS]/[SEP] is removed (selected),
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and then pooled.
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"""
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def __init__(
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self,
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mlm_head: nn.Module,
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cls_token_id: Optional[int] = 101,
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sep_token_id: Optional[int] = 102,
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pooling: str = "max",
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remove_cls_sep: bool = True,
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):
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super().__init__()
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assert pooling in ("max", "sum")
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self.mlm_head = mlm_head
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self.cls_token_id = cls_token_id
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self.sep_token_id = sep_token_id
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self.pooling = pooling
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self.remove_cls_sep = remove_cls_sep
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def get_supported_tasks(self) -> Set[PoolingTask]:
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return {"embed"}
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def get_pooling_updates(self, task: PoolingTask) -> PoolingParamsUpdate:
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return PoolingParamsUpdate(requires_token_ids=True)
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def forward(
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self,
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hidden_states: torch.Tensor,
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pooling_metadata: PoolingMetadata,
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) -> torch.Tensor:
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assert isinstance(hidden_states, torch.Tensor) and hidden_states.dim() == 2
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lens_tensor: torch.Tensor = pooling_metadata.prompt_lens
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lens: list[int] = lens_tensor.tolist()
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B: int = len(lens)
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token_ids = pooling_metadata.prompt_token_ids
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offset = 0
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pooled_list: list[torch.Tensor] = []
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for i in range(B):
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L = int(lens[i])
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hs = hidden_states[offset : offset + L]
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start_idx = 0
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end_idx = L
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if self.remove_cls_sep and token_ids is not None:
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if (
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self.cls_token_id is not None
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and token_ids[i, 0].item() == self.cls_token_id
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):
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start_idx = 1
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if (
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self.sep_token_id is not None
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and token_ids[i, L - 1].item() == self.sep_token_id
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):
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end_idx = max(start_idx, L - 1)
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if end_idx <= start_idx:
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V = int(self.mlm_head.decoder.out_features)
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pooled_list.append(hs.new_zeros((V,)))
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offset += L
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continue
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logits_i = self.mlm_head(hs[start_idx:end_idx])
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scores_i = torch.log1p(torch.relu(logits_i))
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if self.pooling == "sum":
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pooled_i = scores_i.sum(dim=0)
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else: # "max"
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pooled_i = scores_i.max(dim=0).values
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pooled_list.append(pooled_i.contiguous())
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offset += L
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return torch.stack(pooled_list, dim=0).contiguous()
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@default_pooling_type("CLS")
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class BertSpladeSparseEmbeddingModel(BertEmbeddingModel):
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"""
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BertEmbeddingModel + SPLADE sparse embedding.
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- Make logits by self.mlm_head
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- pooler: SPLADESparsePooler(mlm_head...)
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"""
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def __init__(
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self, *, vllm_config: VllmConfig, prefix: str = "", splade_pooling: str = "max"
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):
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super().__init__(vllm_config=vllm_config, prefix=prefix)
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cfg = vllm_config.model_config.hf_config
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# MLM head
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self.mlm_head = BertMLMHead(
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hidden_size=cfg.hidden_size,
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vocab_size=cfg.vocab_size,
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layer_norm_eps=getattr(cfg, "layer_norm_eps", 1e-12),
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)
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self._splade_pooling = splade_pooling
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pooler_config = vllm_config.model_config.pooler_config
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assert pooler_config is not None
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self.pooler = self._build_pooler(pooler_config)
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def _build_pooler(self, pooler_config: PoolerConfig) -> Pooler:
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cfg = self.model.config
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if not hasattr(self, "mlm_head"):
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self.mlm_head = BertMLMHead(
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hidden_size=cfg.hidden_size,
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vocab_size=cfg.vocab_size,
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layer_norm_eps=getattr(cfg, "layer_norm_eps", 1e-12),
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)
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pooling_mode = getattr(self, "_splade_pooling", "max")
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cls_id = getattr(cfg, "cls_token_id", None)
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sep_id = getattr(cfg, "sep_token_id", None)
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return DispatchPooler(
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{
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"encode": Pooler.for_encode(pooler_config),
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"embed": SPLADESparsePooler(
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mlm_head=self.mlm_head,
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cls_token_id=cls_id,
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sep_token_id=sep_id,
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pooling=pooling_mode, # "max" or "sum"
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remove_cls_sep=True,
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),
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}
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)
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
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if not hasattr(self, "mlm_head"):
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cfg = self.model.config
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self.mlm_head = BertMLMHead(
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hidden_size=cfg.hidden_size,
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vocab_size=cfg.vocab_size,
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layer_norm_eps=getattr(cfg, "layer_norm_eps", 1e-12),
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)
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def _strip(name: str) -> str:
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for p in ("model.", "bert."):
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if name.startswith(p):
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name = name[len(p) :]
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return name
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weights_list = list(weights)
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model_side: list[tuple[str, torch.Tensor]] = []
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mlm_side: list[tuple[str, torch.Tensor]] = []
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for k, w in weights_list:
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name = _strip(k)
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if name.startswith("cls.predictions."):
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mlm_side.append((name, w))
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else:
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model_side.append((name, w))
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loaded: set[str] = set()
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loaded_model = self.model.load_weights(model_side)
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loaded.update({"model." + n for n in loaded_model})
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if mlm_side:
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name_map = {
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"cls.predictions.transform.dense.weight": "mlm_head.dense.weight",
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"cls.predictions.transform.dense.bias": "mlm_head.dense.bias",
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("cls.predictions.transform.LayerNorm.weight"): (
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"mlm_head.layer_norm.weight"
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),
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("cls.predictions.transform.LayerNorm.bias"): (
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"mlm_head.layer_norm.bias"
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),
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"cls.predictions.decoder.weight": "mlm_head.decoder.weight",
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"cls.predictions.decoder.bias": "mlm_head.decoder.bias",
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}
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remapped = [(name_map[n], w) for n, w in mlm_side if n in name_map]
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if remapped:
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loaded_mlm = AutoWeightsLoader(self).load_weights(remapped)
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loaded.update(loaded_mlm)
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return loaded
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@default_pooling_type("CLS")
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class BertForSequenceClassification(nn.Module, SupportsCrossEncoding, SupportsQuant):
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"""A model that uses Bert to provide embedding functionalities.
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