[Model] Update pooling model interface

tests/plugins/vllm_add_dummy_model/vllm_add_dummy_model/my_gemma_embedding.py

@@ -11,11 +11,13 @@
 from vllm.model_executor.layers.pooler import Pooler, PoolingType
 from vllm.model_executor.models.gemma2 import Gemma2Model
 from vllm.model_executor.models.utils import WeightsMapper, maybe_prefix
-from vllm.model_executor.pooling_metadata import PoolingMetadata
-from vllm.sequence import IntermediateTensors, PoolerOutput
+from vllm.sequence import IntermediateTensors
 
 
 class MyGemma2Embedding(nn.Module):
+
+    is_pooling_model = True
+
     hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={"model.": ""})
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
@@ -24,7 +26,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.model = Gemma2Model(vllm_config=vllm_config,
                                  prefix=maybe_prefix(prefix, "model"))
 
-        self._pooler = Pooler.from_config_with_defaults(
+        self.pooler = Pooler.from_config_with_defaults(
             vllm_config.model_config.pooler_config,
             pooling_type=PoolingType.LAST,
             normalize=True,
@@ -54,13 +56,6 @@ def forward(
         # Return all-zero embeddings
         return torch.zeros_like(hidden_states)
 
-    def pooler(
-        self,
-        hidden_states: torch.Tensor,
-        pooling_metadata: PoolingMetadata,
-    ) -> Optional[PoolerOutput]:
-        return self._pooler(hidden_states, pooling_metadata)
-
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
 
         weights = self.hf_to_vllm_mapper.apply(weights)

vllm/entrypoints/openai/protocol.py

@@ -1237,10 +1237,6 @@ class EmbeddingCompletionRequest(OpenAIBaseModel):
     user: Optional[str] = None
     truncate_prompt_tokens: Optional[Annotated[int, Field(ge=-1)]] = None
 
-    # --8<-- [start:embedding-pooling-params]
-    additional_data: Optional[Any] = None
-    # --8<-- [end:embedding-pooling-params]
-
     # --8<-- [start:embedding-extra-params]
     add_special_tokens: bool = Field(
         default=True,
@@ -1259,8 +1255,7 @@ class EmbeddingCompletionRequest(OpenAIBaseModel):
     # --8<-- [end:embedding-extra-params]
 
     def to_pooling_params(self):
-        return PoolingParams(dimensions=self.dimensions,
-                             additional_data=self.additional_data)
+        return PoolingParams(dimensions=self.dimensions)
 
 
 class EmbeddingChatRequest(OpenAIBaseModel):
@@ -1272,10 +1267,6 @@ class EmbeddingChatRequest(OpenAIBaseModel):
     user: Optional[str] = None
     truncate_prompt_tokens: Optional[Annotated[int, Field(ge=-1)]] = None
 
-    # --8<-- [start:chat-embedding-pooling-params]
-    additional_data: Optional[Any] = None
-    # --8<-- [end:chat-embedding-pooling-params]
-
     # --8<-- [start:chat-embedding-extra-params]
     add_special_tokens: bool = Field(
         default=False,
@@ -1323,8 +1314,7 @@ def check_generation_prompt(cls, data):
         return data
 
     def to_pooling_params(self):
-        return PoolingParams(dimensions=self.dimensions,
-                             additional_data=self.additional_data)
+        return PoolingParams(dimensions=self.dimensions)
 
 
 EmbeddingRequest = Union[EmbeddingCompletionRequest, EmbeddingChatRequest]
@@ -1340,10 +1330,6 @@ class ScoreRequest(OpenAIBaseModel):
     text_2: Union[list[str], str, ScoreMultiModalParam]
     truncate_prompt_tokens: Optional[Annotated[int, Field(ge=-1)]] = None
 
-    # --8<-- [start:score-pooling-params]
-    additional_data: Optional[Any] = None
-    # --8<-- [end:score-pooling-params]
-
     # --8<-- [start:score-extra-params]
 
     mm_processor_kwargs: Optional[dict[str, Any]] = Field(
@@ -1362,8 +1348,7 @@ class ScoreRequest(OpenAIBaseModel):
     # --8<-- [end:score-extra-params]
 
     def to_pooling_params(self, *, use_cross_encoder: bool = False):
-        return PoolingParams(use_cross_encoder=use_cross_encoder,
-                             additional_data=self.additional_data)
+        return PoolingParams(use_cross_encoder=use_cross_encoder)
 
 
 class RerankRequest(OpenAIBaseModel):
@@ -1373,10 +1358,6 @@ class RerankRequest(OpenAIBaseModel):
     top_n: int = Field(default_factory=lambda: 0)
     truncate_prompt_tokens: Optional[Annotated[int, Field(ge=-1)]] = None
 
-    # --8<-- [start:rerank-pooling-params]
-    additional_data: Optional[Any] = None
-    # --8<-- [end:rerank-pooling-params]
-
     # --8<-- [start:rerank-extra-params]
 
     mm_processor_kwargs: Optional[dict[str, Any]] = Field(
@@ -1395,8 +1376,7 @@ class RerankRequest(OpenAIBaseModel):
     # --8<-- [end:rerank-extra-params]
 
     def to_pooling_params(self, *, use_cross_encoder: bool = False):
-        return PoolingParams(use_cross_encoder=use_cross_encoder,
-                             additional_data=self.additional_data)
+        return PoolingParams(use_cross_encoder=use_cross_encoder)
 
 
 class RerankDocument(BaseModel):
@@ -1534,10 +1514,6 @@ class ClassificationRequest(OpenAIBaseModel):
     truncate_prompt_tokens: Optional[int] = None
     user: Optional[str] = None
 
-    # --8<-- [start:classification-pooling-params]
-    additional_data: Optional[Any] = None
-    # --8<-- [end:classification-pooling-params]
-
     # --8<-- [start:classification-extra-params]
     priority: int = Field(
         default=0,
@@ -1550,7 +1526,7 @@ class ClassificationRequest(OpenAIBaseModel):
     # --8<-- [end:classification-extra-params]
 
     def to_pooling_params(self):
-        return PoolingParams(additional_data=self.additional_data)
+        return PoolingParams()
 
 
 class ClassificationData(OpenAIBaseModel):