[Bugfix] Make siglip/clip compatible with transformers v5 (#37200)

Signed-off-by: raushan <raushan@huggingface.co>

tests/models/multimodal/pooling/test_clip.py

@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import pytest
+import torch
 from transformers import CLIPModel
 
 from ....conftest import IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner
@@ -50,13 +51,16 @@ def _run_test(
             if "pixel_values" in inputs:
                 pooled_output = hf_model.model.get_image_features(
                     pixel_values=inputs.pixel_values,
-                ).squeeze(0)
+                )
             else:
                 pooled_output = hf_model.model.get_text_features(
                     input_ids=inputs.input_ids,
                     attention_mask=inputs.attention_mask,
-                ).squeeze(0)
+                )
 
+            if not isinstance(pooled_output, torch.Tensor):
+                pooled_output = pooled_output.pooler_output
+            pooled_output = pooled_output.squeeze(0)
             all_outputs.append(pooled_output.tolist())
 
         hf_outputs = all_outputs

tests/models/multimodal/pooling/test_siglip.py

@@ -4,6 +4,7 @@
 from typing import Any
 
 import pytest
+import torch
 from transformers import SiglipModel
 
 from ....conftest import IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner
@@ -68,12 +69,15 @@ def _run_test(
             if "pixel_values" in inputs:
                 pooled_output = hf_model.model.get_image_features(
                     pixel_values=inputs.pixel_values,
-                ).squeeze(0)
+                )
             else:
                 pooled_output = hf_model.model.get_text_features(
                     input_ids=inputs.input_ids,
-                ).squeeze(0)
+                )
 
+            if not isinstance(pooled_output, torch.Tensor):
+                pooled_output = pooled_output.pooler_output
+            pooled_output = pooled_output.squeeze(0)
             all_outputs.append(pooled_output.tolist())
 
         hf_outputs = all_outputs