[Model] Update pooling model interface (#21058)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

vllm/model_executor/models/jina_vl.py

@@ -13,9 +13,8 @@ from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.pooler import Pooler, PoolingType
-from vllm.model_executor.pooling_metadata import PoolingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
-from vllm.sequence import IntermediateTensors, PoolerOutput
+from vllm.sequence import IntermediateTensors
 
 from .interfaces import (SupportsCrossEncoding, SupportsMultiModal,
                          SupportsScoreTemplate)
@@ -72,6 +71,8 @@ class JinaVLForSequenceClassification(Qwen2VLForConditionalGeneration,
                                       SupportsCrossEncoding,
                                       SupportsMultiModal,
                                       SupportsScoreTemplate):
+
+    is_pooling_model = True
     weight_mapper = WeightsMapper(
         orig_to_new_prefix={
             "score.0.": "score.dense.",
@@ -95,7 +96,7 @@ class JinaVLForSequenceClassification(Qwen2VLForConditionalGeneration,
 
         self.score = JinaVLScorer(config)
 
-        self._pooler = Pooler.from_config_with_defaults(
+        self.pooler = Pooler.from_config_with_defaults(
             pooler_config,
             pooling_type=PoolingType.LAST,
             normalize=False,
@@ -137,14 +138,6 @@ class JinaVLForSequenceClassification(Qwen2VLForConditionalGeneration,
         logits = self.score(hidden_states) - self.LOGIT_BIAS
         return logits
 
-    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]]):
-
         loader = AutoWeightsLoader(self)
         return loader.load_weights(weights, mapper=self.weight_mapper)