[Model][2/N] Improve all pooling task | Support multi-vector retrieval (#25370)

Signed-off-by: wang.yuqi <noooop@126.com>

vllm/entrypoints/openai/api_server.py

@@ -1748,16 +1748,19 @@ async def init_app_state(
         else None
     )
     state.openai_serving_pooling = (
+        (
             OpenAIServingPooling(
                 engine_client,
                 state.openai_serving_models,
+                supported_tasks=supported_tasks,
                 request_logger=request_logger,
                 chat_template=resolved_chat_template,
                 chat_template_content_format=args.chat_template_content_format,
                 trust_request_chat_template=args.trust_request_chat_template,
                 log_error_stack=args.log_error_stack,
             )
-        if "encode" in supported_tasks
+        )
+        if ("token_embed" in supported_tasks or "token_classify" in supported_tasks)
         else None
     )
     state.openai_serving_embedding = (

vllm/entrypoints/openai/protocol.py

@@ -1682,7 +1682,7 @@ class IOProcessorRequest(OpenAIBaseModel, Generic[T]):
     When using plugins IOProcessor plugins, the actual input is processed
     by the plugin itself. Hence, we use a generic type for the request data
     """
-    softmax: bool = True
+    activation: bool = False
 
     embed_dtype: str = Field(
         default="float32",
@@ -1693,7 +1693,7 @@ class IOProcessorRequest(OpenAIBaseModel, Generic[T]):
     )
 
     def to_pooling_params(self):
-        return PoolingParams(task="encode", softmax=self.softmax)
+        return PoolingParams(task="token_classify", activation=self.activation)
 
 
 class IOProcessorResponse(OpenAIBaseModel, Generic[T]):

vllm/entrypoints/openai/serving_pooling.py

@@ -35,6 +35,7 @@ from vllm.entrypoints.renderer import RenderConfig
 from vllm.entrypoints.utils import _validate_truncation_size
 from vllm.logger import init_logger
 from vllm.outputs import PoolingOutput, PoolingRequestOutput
+from vllm.tasks import SupportedTask
 from vllm.utils import merge_async_iterators
 
 logger = init_logger(__name__)
@@ -62,6 +63,7 @@ class OpenAIServingPooling(OpenAIServing):
         engine_client: EngineClient,
         models: OpenAIServingModels,
         *,
+        supported_tasks: tuple[SupportedTask, ...],
         request_logger: RequestLogger | None,
         chat_template: str | None,
         chat_template_content_format: ChatTemplateContentFormatOption,
@@ -75,6 +77,7 @@ class OpenAIServingPooling(OpenAIServing):
             log_error_stack=log_error_stack,
         )
 
+        self.supported_tasks = supported_tasks
         self.chat_template = chat_template
         self.chat_template_content_format: Final = chat_template_content_format
         self.trust_request_chat_template = trust_request_chat_template
@@ -178,8 +181,17 @@ class OpenAIServingPooling(OpenAIServing):
         try:
             pooling_params = request.to_pooling_params()
 
+            if "token_embed" in self.supported_tasks:
+                pooling_task = "token_embed"
+            elif "token_classify" in self.supported_tasks:
+                pooling_task = "token_classify"
+            else:
+                return self.create_error_response(
+                    f"pooling_task must be one of {self.supported_tasks}."
+                )
+
             try:
-                pooling_params.verify("encode", self.model_config)
+                pooling_params.verify(pooling_task, self.model_config)
             except ValueError as e:
                 return self.create_error_response(str(e))
 

vllm/model_executor/layers/pooler.py

@@ -64,66 +64,6 @@ class PoolingParamsUpdate:
         params.requires_token_ids = self.requires_token_ids
 
 
-class Pooler(nn.Module, ABC):
-    """The interface required for all poolers used in pooling models in vLLM."""
-
-    @staticmethod
-    def for_encode(pooler_config: PoolerConfig):
-        if pooler_config.pooling_type == "STEP":
-            return StepPooler()
-
-        resolved_config = ResolvedPoolingConfig(
-            task="encode", pooling_type=PoolingType.ALL
-        )
-
-        return SimplePooler.from_config(resolved_config)
-
-    @staticmethod
-    def for_embed(pooler_config: PoolerConfig):
-        resolved_config = ResolvedPoolingConfig.from_config(
-            task="embed",
-            pooler_config=pooler_config,
-        )
-
-        return SimplePooler.from_config(resolved_config)
-
-    @staticmethod
-    def for_classify(
-        pooler_config: PoolerConfig,
-        classifier: ClassifierFn | None,
-    ):
-        resolved_config = ResolvedPoolingConfig.from_config(
-            task="classify",
-            pooler_config=pooler_config,
-        )
-
-        pooling = PoolingMethod.from_pooling_type(resolved_config.pooling_type)
-
-        return ClassifierPooler(
-            pooling=pooling,
-            classifier=classifier,
-        )
-
-    @abstractmethod
-    def get_supported_tasks(self) -> Set[PoolingTask]:
-        """Determine which pooling tasks are supported."""
-        raise NotImplementedError
-
-    def get_pooling_updates(self, task: PoolingTask) -> PoolingParamsUpdate:
-        """
-        Construct the updated pooling parameters to use for a supported task.
-        """
-        return PoolingParamsUpdate()
-
-    @abstractmethod
-    def forward(
-        self,
-        hidden_states: list[torch.Tensor] | torch.Tensor,
-        pooling_metadata: PoolingMetadata,
-    ) -> PoolerOutput:
-        raise NotImplementedError
-
-
 def get_prompt_lens(
     hidden_states: torch.Tensor | list[torch.Tensor],
     pooling_metadata: PoolingMetadata,
@@ -237,7 +177,7 @@ class PoolingMethod(nn.Module, ABC):
 
 class CLSPool(PoolingMethod):
     def get_supported_tasks(self) -> Set[PoolingTask]:
-        return {"encode", "embed", "classify", "score"}
+        return {"token_embed", "token_classify", "embed", "classify", "score"}
 
     def forward_all(
         self,
@@ -253,7 +193,7 @@ class CLSPool(PoolingMethod):
 
 class LastPool(PoolingMethod):
     def get_supported_tasks(self) -> Set[PoolingTask]:
-        return {"encode", "embed", "classify", "score"}
+        return {"token_embed", "token_classify", "embed", "classify", "score"}
 
     def forward_all(
         self,
@@ -265,7 +205,7 @@ class LastPool(PoolingMethod):
 
 class AllPool(PoolingMethod):
     def get_supported_tasks(self) -> Set[PoolingTask]:
-        return {"encode"}
+        return {"token_embed", "token_classify"}
 
     def forward_all(
         self,
@@ -284,7 +224,7 @@ class AllPool(PoolingMethod):
 
 class MeanPool(PoolingMethod):
     def get_supported_tasks(self) -> Set[PoolingTask]:
-        return {"encode", "embed", "classify", "score"}
+        return {"token_embed", "token_classify", "embed", "classify", "score"}
 
     def forward_all(
         self,
@@ -398,6 +338,82 @@ class LambdaPoolerActivation(PoolerActivation):
         return self.fn(pooled_data)
 
 
+class Pooler(nn.Module, ABC):
+    """The interface required for all poolers used in pooling models in vLLM."""
+
+    @staticmethod
+    def for_token_embed(pooler_config: PoolerConfig):
+        head = TokenEmbeddingPoolerHead()
+
+        if pooler_config.pooling_type == "STEP":
+            return StepPooler(head=head)
+
+        return AllPooler(head=head)
+
+    @staticmethod
+    def for_token_classify(
+        pooler_config: PoolerConfig,
+        classifier: ClassifierFn | None = None,
+        act_fn: PoolerActivation | str | None = None,
+    ):
+        head = TokenClassifierPoolerHead(classifier=classifier, act_fn=act_fn)
+
+        if pooler_config.pooling_type == "STEP":
+            return StepPooler(head=head)
+
+        return AllPooler(head=head)
+
+    @staticmethod
+    def for_embed(pooler_config: PoolerConfig):
+        resolved_config = ResolvedPoolingConfig.from_config(
+            task="embed",
+            pooler_config=pooler_config,
+        )
+
+        pooling = PoolingMethod.from_pooling_type(resolved_config.pooling_type)
+        head = EmbeddingPoolerHead()
+
+        return SimplePooler(pooling=pooling, head=head)
+
+    @staticmethod
+    def for_classify(
+        pooler_config: PoolerConfig,
+        classifier: ClassifierFn | None,
+        act_fn: PoolerActivation | str | None = None,
+    ):
+        resolved_config = ResolvedPoolingConfig.from_config(
+            task="classify",
+            pooler_config=pooler_config,
+        )
+
+        pooling = PoolingMethod.from_pooling_type(resolved_config.pooling_type)
+
+        return ClassifierPooler(
+            pooling=pooling,
+            classifier=classifier,
+            act_fn=act_fn,
+        )
+
+    @abstractmethod
+    def get_supported_tasks(self) -> Set[PoolingTask]:
+        """Determine which pooling tasks are supported."""
+        raise NotImplementedError
+
+    def get_pooling_updates(self, task: PoolingTask) -> PoolingParamsUpdate:
+        """
+        Construct the updated pooling parameters to use for a supported task.
+        """
+        return PoolingParamsUpdate()
+
+    @abstractmethod
+    def forward(
+        self,
+        hidden_states: list[torch.Tensor] | torch.Tensor,
+        pooling_metadata: PoolingMetadata,
+    ) -> PoolerOutput:
+        raise NotImplementedError
+
+
 class PoolerHead(nn.Module):
     def __init__(self, activation: PoolerActivation) -> None:
         super().__init__()
@@ -416,7 +432,6 @@ class EmbeddingPoolerHead(PoolerHead):
         super().__init__(activation=PoolerNormalize())
 
         # Load ST projector if available
-
         vllm_config = get_current_vllm_config()
         self.projector: nn.Module | None = (
             _load_st_projector(vllm_config.model_config) if vllm_config else None
@@ -471,39 +486,6 @@ class EmbeddingPoolerHead(PoolerHead):
         return pooled_data
 
 
-class RewardPoolerHead(PoolerHead):
-    def __init__(self) -> None:
-        super().__init__(activation=PoolerClassify(static_num_labels=False))
-
-        vllm_config = get_current_vllm_config()
-        self.head_dtype = vllm_config.model_config.head_dtype
-
-    def forward(
-        self,
-        pooled_data: list[torch.Tensor] | torch.Tensor,
-        pooling_metadata: PoolingMetadata,
-    ):
-        if isinstance(pooled_data, list):
-            pooled_data = [p.to(self.head_dtype) for p in pooled_data]
-        else:
-            pooled_data = pooled_data.to(self.head_dtype)
-
-        pooling_params = get_pooling_params(pooling_metadata)
-
-        # for softmax
-        flags = [p.softmax for p in pooling_params]
-        if len(set(flags)) == 1:
-            if flags[0]:
-                pooled_data = self.activation(pooled_data)
-        else:
-            pooled_data = [
-                self.activation(vecs) if f else vecs
-                for vecs, f in zip(pooled_data, flags)
-            ]
-
-        return pooled_data
-
-
 class SimplePooler(Pooler):
     """A layer that pools specific information from hidden states.
 
@@ -513,20 +495,6 @@ class SimplePooler(Pooler):
     3. Returns structured results as `PoolerOutput`.
     """
 
-    @classmethod
-    def from_config(
-        cls,
-        pooler_config: ResolvedPoolingConfig,
-    ) -> "SimplePooler":
-        pooling = PoolingMethod.from_pooling_type(pooler_config.pooling_type)
-        if pooler_config.task == "embed":
-            head = EmbeddingPoolerHead()
-        elif pooler_config.task == "encode":
-            head = RewardPoolerHead()
-        else:
-            raise NotImplementedError(f"Unknown task: {pooler_config.task}")
-        return cls(pooling, head)
-
     def __init__(self, pooling: PoolingMethod, head: PoolerHead) -> None:
         super().__init__()
 
@@ -549,58 +517,6 @@ class SimplePooler(Pooler):
         return pooled_data
 
 
-class StepPooler(Pooler):
-    def __init__(
-        self,
-    ) -> None:
-        super().__init__()
-
-        self.pooling = AllPool()
-        self.head = RewardPoolerHead()
-
-    def extract_states(
-        self,
-        hidden_states: torch.Tensor | list[torch.Tensor],
-        pooling_metadata: PoolingMetadata,
-    ) -> list[torch.Tensor] | torch.Tensor:
-        pooled_data_lst = self.pooling(hidden_states, pooling_metadata)
-        prompt_token_ids = get_prompt_token_ids(pooling_metadata)
-
-        pooled_data = list[torch.Tensor]()
-
-        pooling_params = get_pooling_params(pooling_metadata)
-
-        for data, token_id, pooling_param in zip(
-            pooled_data_lst, prompt_token_ids, pooling_params
-        ):
-            step_tag_id = pooling_param.step_tag_id
-            returned_token_ids = pooling_param.returned_token_ids
-
-            if returned_token_ids is not None and len(returned_token_ids) > 0:
-                data = data[:, returned_token_ids]
-
-            if step_tag_id is not None:
-                data = data[token_id == step_tag_id]
-            pooled_data.append(data)
-
-        return pooled_data
-
-    def get_supported_tasks(self) -> Set[PoolingTask]:
-        return {"encode"}
-
-    def get_pooling_updates(self, task: PoolingTask) -> PoolingParamsUpdate:
-        return PoolingParamsUpdate(requires_token_ids=True)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor | list[torch.Tensor],
-        pooling_metadata: PoolingMetadata,
-    ) -> PoolerOutput:
-        pooled_data = self.extract_states(hidden_states, pooling_metadata)
-        pooled_data = self.head(pooled_data, pooling_metadata)
-        return pooled_data
-
-
 class ClassifierPooler(Pooler):
     """A pooling layer for classification tasks.
 
@@ -611,26 +527,46 @@ class ClassifierPooler(Pooler):
     """
 
     @staticmethod
-    def act_fn_for_seq_cls(config: ModelConfig):
-        return get_classification_activation_function(config.hf_config)
+    def act_fn_for_seq_cls(model_config: ModelConfig):
+        return get_classification_activation_function(model_config.hf_config)
 
     @staticmethod
-    def act_fn_for_cross_encoder(config: ModelConfig):
-        return get_cross_encoder_activation_function(config.hf_config)
+    def act_fn_for_cross_encoder(model_config: ModelConfig):
+        return get_cross_encoder_activation_function(model_config.hf_config)
+
+    @staticmethod
+    def resolve_act_fn(
+        model_config: ModelConfig,
+        static_num_labels: bool = True,
+        act_fn: PoolerActivation | str | None = None,
+    ):
+        if isinstance(act_fn, str):
+            if act_fn == "classify":
+                return ClassifierPooler.act_fn_for_seq_cls(model_config)
+            elif act_fn == "score":
+                return ClassifierPooler.act_fn_for_cross_encoder(model_config)
+            else:
+                raise ValueError(f"act_fn [{act_fn=}] not supported.")
+        elif act_fn is None:
+            return PoolerClassify(static_num_labels=static_num_labels)
+        else:
+            assert callable(act_fn)
+            return act_fn
 
     def __init__(
         self,
         pooling: PoolingFn,
         classifier: ClassifierFn | None,
-        act_fn: PoolerActivation | None = None,
+        act_fn: PoolerActivation | str | None = None,
     ) -> None:
         super().__init__()
 
         vllm_config = get_current_vllm_config()
-
         self.pooling = pooling
         self.classifier = classifier
-        self.act_fn = act_fn or PoolerClassify()
+        self.act_fn = self.resolve_act_fn(
+            vllm_config.model_config, static_num_labels=True, act_fn=act_fn
+        )
         self.logit_bias: float | None = (
             vllm_config.model_config.pooler_config.logit_bias
         )
@@ -672,6 +608,150 @@ class ClassifierPooler(Pooler):
         return scores
 
 
+class TokenEmbeddingPoolerHead(EmbeddingPoolerHead):
+    def forward(
+        self, pooled_data: torch.Tensor, pooling_param: PoolingParams
+    ) -> torch.Tensor:
+        pooled_data = pooled_data.to(self.head_dtype)
+        # pooled_data shape: [n_tokens, hidden_dimension]
+
+        # Apply ST projector
+        if self.projector is not None:
+            pooled_data = self.projector(pooled_data)
+        # pooled_data shape: [n_tokens, embedding_dimension]
+
+        # for matryoshka representation
+        pooled_data = pooled_data[..., : pooling_param.dimensions]
+
+        # for normalize
+        if pooling_param.normalize:
+            pooled_data = self.activation(pooled_data)
+
+        # pooled_data shape: [n_tokens, embedding_dimension]
+        return pooled_data
+
+
+class TokenClassifierPoolerHead(nn.Module):
+    def __init__(
+        self,
+        classifier: ClassifierFn | None,
+        act_fn: PoolerActivation | str | None = None,
+    ) -> None:
+        super().__init__()
+        vllm_config = get_current_vllm_config()
+
+        self.classifier = classifier
+        self.act_fn = ClassifierPooler.resolve_act_fn(
+            vllm_config.model_config, static_num_labels=False, act_fn=act_fn
+        )
+        self.logit_bias: float | None = (
+            vllm_config.model_config.pooler_config.logit_bias
+        )
+        self.head_dtype = vllm_config.model_config.head_dtype
+
+    def get_supported_tasks(self) -> Set[PoolingTask]:
+        return {"token_classify"}
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        pooling_param: PoolingParams,
+    ) -> torch.Tensor:
+        hidden_states = hidden_states.to(self.head_dtype)
+        # hidden_states shape: [n_token, hidden_size]
+
+        if self.classifier is not None:
+            scores = self.classifier(hidden_states)
+        else:
+            scores = hidden_states
+        # scores shape: [n_token, num_labels]
+
+        if self.logit_bias is not None:
+            scores -= self.logit_bias
+
+        if pooling_param.activation:
+            scores = self.act_fn(scores)
+
+        # scores shape: [n_token, num_labels]
+        return scores
+
+
+class AllPooler(Pooler):
+    def __init__(self, head: nn.Module | PoolerHead) -> None:
+        super().__init__()
+
+        self.pooling = AllPool()
+        self.head = head
+
+    def get_supported_tasks(self) -> Set[PoolingTask]:
+        return {"token_embed", "token_classify"}
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        pooling_metadata: PoolingMetadata,
+    ) -> PoolerOutput:
+        pooled_data = self.pooling(hidden_states, pooling_metadata)
+        pooling_params = get_pooling_params(pooling_metadata)
+        assert len(pooled_data) == len(pooling_params)
+
+        pooled_data = [self.head(d, p) for d, p in zip(pooled_data, pooling_params)]
+        return pooled_data
+
+
+class StepPooler(Pooler):
+    def __init__(self, head: nn.Module | PoolerHead) -> None:
+        super().__init__()
+
+        self.pooling = AllPool()
+        self.head = head
+
+    def extract_states(
+        self,
+        hidden_states: torch.Tensor | list[torch.Tensor],
+        pooling_metadata: PoolingMetadata,
+    ) -> torch.Tensor | list[torch.Tensor]:
+        pooled_data_lst = self.pooling(hidden_states, pooling_metadata)
+        prompt_token_ids = get_prompt_token_ids(pooling_metadata)
+
+        pooled_data = list[torch.Tensor]()
+
+        pooling_params = get_pooling_params(pooling_metadata)
+
+        for data, token_id, pooling_param in zip(
+            pooled_data_lst, prompt_token_ids, pooling_params
+        ):
+            step_tag_id = pooling_param.step_tag_id
+            returned_token_ids = pooling_param.returned_token_ids
+
+            if returned_token_ids is not None and len(returned_token_ids) > 0:
+                data = data[:, returned_token_ids]
+
+            if step_tag_id is not None:
+                data = data[token_id == step_tag_id]
+            pooled_data.append(data)
+
+        return pooled_data
+
+    def get_supported_tasks(self) -> Set[PoolingTask]:
+        return {"token_embed", "token_classify"}
+
+    def get_pooling_updates(self, task: PoolingTask) -> PoolingParamsUpdate:
+        return PoolingParamsUpdate(requires_token_ids=True)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor | list[torch.Tensor],
+        pooling_metadata: PoolingMetadata,
+    ) -> PoolerOutput:
+        pooled_data = self.extract_states(hidden_states, pooling_metadata)
+        pooling_params = get_pooling_params(pooling_metadata)
+        assert len(pooled_data) == len(pooling_params)
+
+        pooled_data = [self.head(d, p) for d, p in zip(pooled_data, pooling_params)]
+        return pooled_data
+
+
 class DispatchPooler(Pooler):
     """Dispatches calls to a sub-pooler based on the pooling task."""
 

vllm/model_executor/models/adapters.py

@@ -250,7 +250,7 @@ def as_embedding_model(cls: _T) -> _T:
 
             self.pooler = DispatchPooler(
                 {
-                    "encode": Pooler.for_encode(pooler_config),
+                    "token_embed": Pooler.for_token_embed(pooler_config),
                     "embed": Pooler.for_embed(pooler_config),
                 },
             )
@@ -279,11 +279,8 @@ def as_seq_cls_model(cls: _T) -> _T:
     # Lazy import
     from vllm.model_executor.layers.linear import ReplicatedLinear
     from vllm.model_executor.layers.pooler import (
-        ClassifierPooler,
         DispatchPooler,
         Pooler,
-        PoolingMethod,
-        PoolingType,
     )
     from vllm.model_executor.models.interfaces import SupportsCrossEncoding
     from vllm.sequence import IntermediateTensors
@@ -302,42 +299,29 @@ def as_seq_cls_model(cls: _T) -> _T:
                 model_config.hidden_size,
                 config.num_labels,
                 bias=False,
-                params_dtype=torch.float32,
+                params_dtype=vllm_config.model_config.head_dtype,
                 quant_config=quant_config,
+                return_bias=False,
                 prefix=maybe_prefix(prefix, "score"),
             )
 
             pooler_config = vllm_config.model_config.pooler_config
             assert pooler_config is not None
 
-            pooling_type_str = pooler_config.pooling_type
-            assert pooling_type_str is not None
-            pooling_type = PoolingType[pooling_type_str]
-
             self.pooler = DispatchPooler(
                 {
-                    "encode": Pooler.for_encode(pooler_config),
-                    "classify": ClassifierPooler(
-                        pooling=PoolingMethod.from_pooling_type(pooling_type),
-                        classifier=self._classifier,
-                        act_fn=ClassifierPooler.act_fn_for_seq_cls(
-                            vllm_config.model_config
-                        ),
+                    "token_classify": Pooler.for_token_classify(
+                        pooler_config, classifier=self.score
                     ),
-                    "score": ClassifierPooler(
-                        pooling=PoolingMethod.from_pooling_type(pooling_type),
-                        classifier=self._classifier,
-                        act_fn=ClassifierPooler.act_fn_for_cross_encoder(
-                            vllm_config.model_config
+                    "classify": Pooler.for_classify(
+                        pooler_config, classifier=self.score, act_fn="classify"
                     ),
+                    "score": Pooler.for_classify(
+                        pooler_config, classifier=self.score, act_fn="score"
                     ),
                 }
             )
 
-        def _classifier(self, x: torch.Tensor):
-            x, _ = self.score(x.float())
-            return x
-
         def forward(
             self,
             input_ids: torch.Tensor,
@@ -393,7 +377,11 @@ def as_reward_model(cls: _T) -> _T:
             assert pooler_config is not None
 
             self.pooler = DispatchPooler(
-                {"encode": Pooler.for_encode(pooler_config)},
+                {
+                    "token_classify": Pooler.for_token_classify(
+                        pooler_config=pooler_config
+                    )
+                }
             )
 
     ModelForReward.__name__ = _get_pooling_model_name(cls.__name__, "ForReward")

vllm/model_executor/models/bert.py

@@ -521,7 +521,7 @@ class BertEmbeddingModel(nn.Module, SupportsQuant):
     def _build_pooler(self, pooler_config: PoolerConfig) -> Pooler:
         return DispatchPooler(
             {
-                "encode": Pooler.for_encode(pooler_config),
+                "token_embed": Pooler.for_token_embed(pooler_config),
                 "embed": Pooler.for_embed(pooler_config),
             }
         )
@@ -724,7 +724,7 @@ class BertSpladeSparseEmbeddingModel(BertEmbeddingModel):
 
         return DispatchPooler(
             {
-                "encode": Pooler.for_encode(pooler_config),
+                "token_embed": Pooler.for_token_embed(pooler_config),
                 "embed": SPLADESparsePooler(
                     mlm_head=self.mlm_head,
                     cls_token_id=cls_id,
@@ -821,20 +821,16 @@ class BertForSequenceClassification(nn.Module, SupportsCrossEncoding, SupportsQu
 
         self.pooler = DispatchPooler(
             {
-                "encode": Pooler.for_encode(pooler_config),
+                "token_classify": Pooler.for_token_classify(
+                    pooler_config, classifier=self.classifier
+                ),
                 "classify": ClassifierPooler(
                     pooling=self.bert.pooler,
                     classifier=self.classifier,
-                    act_fn=ClassifierPooler.act_fn_for_seq_cls(
-                        vllm_config.model_config
-                    ),
+                    act_fn="classify",
                 ),
                 "score": ClassifierPooler(
-                    pooling=self.bert.pooler,
-                    classifier=self.classifier,
-                    act_fn=ClassifierPooler.act_fn_for_cross_encoder(
-                        vllm_config.model_config
-                    ),
+                    pooling=self.bert.pooler, classifier=self.classifier, act_fn="score"
                 ),
             }
         )
@@ -891,7 +887,9 @@ class BertForTokenClassification(nn.Module):
 
         self.pooler = DispatchPooler(
             {
-                "encode": Pooler.for_encode(pooler_config),
+                "token_classify": Pooler.for_token_classify(
+                    pooler_config=pooler_config
+                ),
             }
         )
 

vllm/model_executor/models/bert_with_rope.py

@@ -695,20 +695,16 @@ class GteNewForSequenceClassification(nn.Module, SupportsCrossEncoding):
 
         self.pooler = DispatchPooler(
             {
-                "encode": Pooler.for_encode(pooler_config),
+                "token_classify": Pooler.for_token_classify(
+                    pooler_config, classifier=self.classifier
+                ),
                 "classify": ClassifierPooler(
                     pooling=self.new.pooler,
                     classifier=self.classifier,
-                    act_fn=ClassifierPooler.act_fn_for_seq_cls(
-                        vllm_config.model_config
-                    ),
+                    act_fn="classify",
                 ),
                 "score": ClassifierPooler(
-                    pooling=self.new.pooler,
-                    classifier=self.classifier,
-                    act_fn=ClassifierPooler.act_fn_for_cross_encoder(
-                        vllm_config.model_config
-                    ),
+                    pooling=self.new.pooler, classifier=self.classifier, act_fn="score"
                 ),
             }
         )

vllm/model_executor/models/clip.py

@@ -837,7 +837,7 @@ class CLIPEmbeddingModel(nn.Module, SupportsMultiModal, SupportsQuant):
 
         self.pooler = DispatchPooler(
             {
-                "encode": Pooler.for_encode(pooler_config),
+                "token_embed": Pooler.for_token_embed(pooler_config),
                 "embed": Pooler.for_embed(pooler_config),
             }
         )

vllm/model_executor/models/gpt2.py

@@ -353,8 +353,15 @@ class GPT2ForSequenceClassification(nn.Module, SupportsCrossEncoding):
 
         self.pooler = DispatchPooler(
             {
-                "encode": Pooler.for_encode(pooler_config),
-                "classify": Pooler.for_classify(pooler_config, classifier=self.score),
+                "token_classify": Pooler.for_token_classify(
+                    pooler_config, classifier=self.score
+                ),
+                "classify": Pooler.for_classify(
+                    pooler_config, classifier=self.score, act_fn="classify"
+                ),
+                "score": Pooler.for_classify(
+                    pooler_config, classifier=self.score, act_fn="score"
+                ),
             }
         )
 

vllm/model_executor/models/gritlm.py

@@ -239,7 +239,7 @@ class GritLM(LlamaForCausalLM):
         if pooler_config is not None:
             self.pooler = DispatchPooler(
                 {
-                    "encode": Pooler.for_encode(pooler_config),
+                    "token_embed": Pooler.for_token_embed(pooler_config),
                     "embed": GritLMPooler(vllm_config.model_config),
                 }
             )

vllm/model_executor/models/internlm2.py

@@ -444,7 +444,7 @@ class InternLM2ForRewardModel(InternLM2ForCausalLM):
         assert pooler_config is not None
 
         self.pooler = DispatchPooler(
-            {"encode": Pooler.for_encode(pooler_config)},
+            {"token_classify": Pooler.for_token_classify(pooler_config)}
         )
 
     def forward(

vllm/model_executor/models/jamba.py

@@ -604,10 +604,14 @@ class JambaForSequenceClassification(JambaForCausalLM):
 
         self.pooler = DispatchPooler(
             {
-                "encode": Pooler.for_encode(pooler_config),
+                "token_classify": Pooler.for_token_classify(
+                    pooler_config, classifier=self.score
+                ),
                 "classify": Pooler.for_classify(
-                    pooler_config,
-                    classifier=self.score,
+                    pooler_config, classifier=self.score, act_fn="classify"
+                ),
+                "score": Pooler.for_classify(
+                    pooler_config, classifier=self.score, act_fn="score"
                 ),
             }
         )

vllm/model_executor/models/jina_vl.py

@@ -97,9 +97,15 @@ class JinaVLForSequenceClassification(
         self.score = JinaVLScorer(vllm_config.model_config)
         self.pooler = DispatchPooler(
             {
-                "encode": Pooler.for_encode(pooler_config),
-                "classify": Pooler.for_classify(pooler_config, classifier=self.score),
-                "score": Pooler.for_classify(pooler_config, classifier=self.score),
+                "token_classify": Pooler.for_token_classify(
+                    pooler_config, classifier=self.score
+                ),
+                "classify": Pooler.for_classify(
+                    pooler_config, classifier=self.score, act_fn="classify"
+                ),
+                "score": Pooler.for_classify(
+                    pooler_config, classifier=self.score, act_fn="score"
+                ),
             }
         )
 

vllm/model_executor/models/modernbert.py

@@ -322,20 +322,14 @@ class ModernBertForSequenceClassification(nn.Module, SupportsCrossEncoding):
 
         self.pooler = DispatchPooler(
             {
-                "encode": Pooler.for_encode(pooler_config),
-                "classify": ClassifierPooler(
-                    pooling=self.pooling,
-                    classifier=self.classifier,
-                    act_fn=ClassifierPooler.act_fn_for_seq_cls(
-                        vllm_config.model_config
+                "token_classify": Pooler.for_token_classify(
+                    pooler_config, classifier=self.classifier
                 ),
+                "classify": ClassifierPooler(
+                    pooling=self.pooling, classifier=self.classifier, act_fn="classify"
                 ),
                 "score": ClassifierPooler(
-                    pooling=self.pooling,
-                    classifier=self.classifier,
-                    act_fn=ClassifierPooler.act_fn_for_cross_encoder(
-                        vllm_config.model_config
-                    ),
+                    pooling=self.pooling, classifier=self.classifier, act_fn="score"
                 ),
             }
         )
@@ -421,7 +415,9 @@ class ModernBertForTokenClassification(nn.Module):
 
         self.pooler = DispatchPooler(
             {
-                "encode": Pooler.for_encode(pooler_config),
+                "token_classify": Pooler.for_token_classify(
+                    pooler_config=pooler_config
+                ),
             }
         )
 

vllm/model_executor/models/qwen2_rm.py

@@ -107,7 +107,7 @@ class Qwen2ForRewardModel(Qwen2RewardBaseModel):
         assert pooler_config is not None
 
         self.pooler = DispatchPooler(
-            {"encode": Pooler.for_encode(pooler_config)},
+            {"token_classify": Pooler.for_token_classify(pooler_config)}
         )
 
 
@@ -120,4 +120,6 @@ class Qwen2ForProcessRewardModel(Qwen2RewardBaseModel):
         pooler_config = vllm_config.model_config.pooler_config
         assert pooler_config is not None
 
-        self.pooler = DispatchPooler({"encode": Pooler.for_encode(pooler_config)})
+        self.pooler = DispatchPooler(
+            {"token_classify": Pooler.for_token_classify(pooler_config)}
+        )

vllm/model_executor/models/roberta.py

@@ -105,15 +105,7 @@ class RobertaClassificationHead(nn.Module):
 
 @default_pooling_type("CLS")
 class RobertaEmbeddingModel(BertEmbeddingModel):
-    """A model that uses Roberta to provide embedding functionalities.
-
-    This class encapsulates the BertModel and provides an interface for
-    embedding operations and customized pooling functions.
-
-    Attributes:
-        model: An instance of BertModel used for forward operations.
-        _pooler: An instance of Pooler used for pooling operations.
-    """
+    """A model that uses Roberta to provide embedding functionalities."""
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__(vllm_config=vllm_config, prefix=prefix)
@@ -212,20 +204,14 @@ class RobertaForSequenceClassification(nn.Module, SupportsCrossEncoding):
 
         self.pooler = DispatchPooler(
             {
-                "encode": Pooler.for_encode(pooler_config),
-                "classify": ClassifierPooler(
-                    pooling=CLSPool(),
-                    classifier=self.classifier,
-                    act_fn=ClassifierPooler.act_fn_for_seq_cls(
-                        vllm_config.model_config
+                "token_classify": Pooler.for_token_classify(
+                    pooler_config=pooler_config, classifier=self.classifier
                 ),
+                "classify": ClassifierPooler(
+                    pooling=CLSPool(), classifier=self.classifier, act_fn="classify"
                 ),
                 "score": ClassifierPooler(
-                    pooling=CLSPool(),
-                    classifier=self.classifier,
-                    act_fn=ClassifierPooler.act_fn_for_cross_encoder(
-                        vllm_config.model_config
-                    ),
+                    pooling=CLSPool(), classifier=self.classifier, act_fn="score"
                 ),
             }
         )

vllm/model_executor/models/terratorch.py

@@ -250,7 +250,7 @@ class Terratorch(nn.Module, IsAttentionFree, SupportsMultiModal):
         assert pooler_config is not None
 
         self.pooler = DispatchPooler(
-            {"encode": Pooler.for_encode(pooler_config)},
+            {"token_classify": Pooler.for_token_classify(pooler_config)}
         )
 
     def get_input_embeddings(

vllm/model_executor/models/transformers_pooling.py

@@ -135,7 +135,7 @@ class TransformersEmbeddingModel(TransformersPoolingBase):
 
         self.pooler = DispatchPooler(
             {
-                "encode": Pooler.for_encode(pooler_config),
+                "token_embed": Pooler.for_token_embed(pooler_config),
                 "embed": Pooler.for_embed(pooler_config),
             }
         )
@@ -190,20 +190,14 @@ class TransformersForSequenceClassification(TransformersPoolingBase):
 
         self.pooler = DispatchPooler(
             {
-                "encode": Pooler.for_encode(pooler_config),
-                "classify": ClassifierPooler(
-                    pooling=CLSPool(),
-                    classifier=self.classifier,
-                    act_fn=ClassifierPooler.act_fn_for_seq_cls(
-                        vllm_config.model_config
+                "token_classify": Pooler.for_token_classify(
+                    pooler_config, classifier=self.classifier
                 ),
+                "classify": ClassifierPooler(
+                    pooling=CLSPool(), classifier=self.classifier, act_fn="classify"
                 ),
                 "score": ClassifierPooler(
-                    pooling=CLSPool(),
-                    classifier=self.classifier,
-                    act_fn=ClassifierPooler.act_fn_for_cross_encoder(
-                        vllm_config.model_config
-                    ),
+                    pooling=CLSPool(), classifier=self.classifier, act_fn="score"
                 ),
             }
         )

vllm/pooling_params.py

@@ -10,7 +10,7 @@ from vllm.sampling_params import RequestOutputKind
 from vllm.tasks import PoolingTask
 
 if TYPE_CHECKING:
-    from vllm.config import ModelConfig
+    from vllm.config import ModelConfig, PoolerConfig
 
 
 class PoolingParams(
@@ -30,7 +30,6 @@ class PoolingParams(
             if model support matryoshka representation.
         activation: Whether to apply activation function to
             the classification outputs.
-        softmax: Whether to apply softmax to the reward outputs.
     """
 
     # --8<-- [start:common-pooling-params]
@@ -48,32 +47,19 @@ class PoolingParams(
     activation: bool | None = None
     # --8<-- [end:classification-pooling-params]
 
-    ## for reward models
-    softmax: bool | None = None
+    ## for step pooling models
     step_tag_id: int | None = None
     returned_token_ids: list[int] | None = None
 
+    ## Internal use only
     task: PoolingTask | None = None
-    """Internal use only."""
-
     requires_token_ids: bool = False
-    """Internal use only."""
-
     extra_kwargs: dict[str, Any] | None = None
-    """Internal use only."""
-
     output_kind: RequestOutputKind = RequestOutputKind.FINAL_ONLY
 
     @property
     def all_parameters(self) -> list[str]:
-        return [
-            "dimensions",
-            "normalize",
-            "activation",
-            "softmax",
-            "step_tag_id",
-            "returned_token_ids",
-        ]
+        return ["dimensions", "normalize", "activation"]
 
     @property
     def valid_parameters(self):
@@ -81,7 +67,8 @@ class PoolingParams(
             "embed": ["dimensions", "normalize"],
             "classify": ["activation"],
             "score": ["activation"],
-            "encode": ["softmax", "step_tag_id", "returned_token_ids"],
+            "token_embed": ["dimensions", "normalize"],
+            "token_classify": ["activation"],
         }
 
     def clone(self) -> "PoolingParams":
@@ -100,7 +87,6 @@ class PoolingParams(
         # NOTE: Task validation needs to done against the model instance,
         # which is not available in model config. So, it's not included
         # in this method
-
         self._merge_default_parameters(model_config)
         self._set_default_parameters(model_config)
         self._verify_valid_parameters()
@@ -125,8 +111,34 @@ class PoolingParams(
             if getattr(self, k, None) is None:
                 setattr(self, k, getattr(pooler_config, k))
 
+        self._verify_step_pooling(pooler_config, valid_parameters)
+
+    def _verify_step_pooling(
+        self, pooler_config: "PoolerConfig", valid_parameters: list[str]
+    ):
+        step_pooling_parameters = ["step_tag_id", "returned_token_ids"]
+        if pooler_config.pooling_type != "STEP":
+            invalid_parameters = []
+            for k in step_pooling_parameters:
+                if getattr(self, k, None) is not None:
+                    invalid_parameters.append(k)
+
+            if invalid_parameters:
+                raise ValueError(
+                    f"Task {self.task} only supports {valid_parameters} "
+                    f"parameters, does not support "
+                    f"{invalid_parameters} parameters"
+                )
+        else:
+            for k in step_pooling_parameters:
+                if getattr(pooler_config, k, None) is None:
+                    continue
+
+                if getattr(self, k, None) is None:
+                    setattr(self, k, getattr(pooler_config, k))
+
     def _set_default_parameters(self, model_config: Optional["ModelConfig"]):
-        if self.task == "embed":
+        if self.task in ["embed", "token_embed"]:
             if self.normalize is None:
                 self.normalize = True
 
@@ -150,13 +162,9 @@ class PoolingParams(
                 elif self.dimensions < 1:
                     raise ValueError("Dimensions must be greater than 0")
 
-        elif self.task in ["classify", "score"]:
+        elif self.task in ["classify", "score", "token_classify"]:
             if self.activation is None:
                 self.activation = True
-
-        elif self.task == "encode":
-            if self.softmax is None:
-                self.softmax = True
         else:
             raise ValueError(f"Unknown pooling task: {self.task}")
 
@@ -185,7 +193,6 @@ class PoolingParams(
             f"normalize={self.normalize}, "
             f"dimensions={self.dimensions}, "
             f"activation={self.activation}, "
-            f"softmax={self.softmax}, "
             f"step_tag_id={self.step_tag_id}, "
             f"returned_token_ids={self.returned_token_ids}, "
             f"requires_token_ids={self.requires_token_ids}, "

vllm/tasks.py

@@ -5,7 +5,7 @@ from typing import Literal, get_args
 GenerationTask = Literal["generate", "transcription"]
 GENERATION_TASKS = get_args(GenerationTask)
 
-PoolingTask = Literal["encode", "embed", "classify", "score"]
+PoolingTask = Literal["embed", "classify", "score", "token_embed", "token_classify"]
 POOLING_TASKS = get_args(PoolingTask)
 
 SupportedTask = Literal[GenerationTask, PoolingTask]