[Model] Move vision_feature_select_strategy into resolve_visual_encoder_outputs (#25938)

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

vllm/model_executor/models/siglip.py

@@ -23,7 +23,8 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader, maybe_remap_kv_scale_name)
 
-from .vision import VisionEncoderInfo, resolve_visual_encoder_outputs
+from .vision import (VisionEncoderInfo, VisionFeatureSelectStrategy,
+                     resolve_visual_encoder_outputs)
 
 
 class SiglipEncoderInfo(VisionEncoderInfo[SiglipVisionConfig]):
@@ -415,28 +416,31 @@ class SiglipVisionTransformer(nn.Module):
     def forward(
         self,
         pixel_values: torch.Tensor,
-        interpolate_pos_encoding: bool = True,
-        feature_sample_layers: Optional[list[int]] = None,
+        *,
+        interpolate_pos_encoding: bool = False,
+        select_layers: Optional[list[int]] = None,
+        feature_select_strategy: Optional[VisionFeatureSelectStrategy] = None,
     ) -> torch.Tensor:
-
         hidden_states = self.embeddings(
             pixel_values,
             interpolate_pos_encoding=interpolate_pos_encoding,
         )
 
-        return_all_hidden_states = feature_sample_layers is not None
-
         # Produces either the last layer output or all of the hidden states,
-        # depending on if we have feature_sample_layers or not
+        # depending on if we have select_layers or not
         encoder_outputs = self.encoder(
             inputs_embeds=hidden_states,
-            return_all_hidden_states=return_all_hidden_states,
+            return_all_hidden_states=select_layers is not None,
         )
 
         # Handle post-norm (if applicable) and stacks feature layers if needed
         encoder_outputs = resolve_visual_encoder_outputs(
-            encoder_outputs, feature_sample_layers, self.post_layernorm,
-            self.config.num_hidden_layers)
+            encoder_outputs,
+            self.post_layernorm,
+            select_layers=select_layers,
+            max_possible_layers=self.config.num_hidden_layers,
+            feature_select_strategy=feature_select_strategy,
+        )
 
         # TODO: add this back when pooled_output is used in inference.
         # if self.use_head:
@@ -471,16 +475,22 @@ class SiglipVisionModel(nn.Module):
     def get_input_embeddings(self) -> nn.Module:
         return self.vision_model.embeddings.patch_embedding
 
+    @property
+    def dtype(self):
+        return self.get_input_embeddings().weight.dtype
+
     def forward(
         self,
         pixel_values: torch.Tensor,
         interpolate_pos_encoding: bool = False,
-        feature_sample_layers: Optional[list[int]] = None,
+        select_layers: Optional[list[int]] = None,
+        feature_select_strategy: Optional[VisionFeatureSelectStrategy] = None,
     ) -> torch.Tensor:
         return self.vision_model(
             pixel_values=pixel_values,
             interpolate_pos_encoding=interpolate_pos_encoding,
-            feature_sample_layers=feature_sample_layers,
+            select_layers=select_layers,
+            feature_select_strategy=feature_select_strategy,
         )
 
     def load_weights(self, weights: Iterable[tuple[str,