[Bugfix] Loosen type check to avoid errors in V1 (#15021)

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

vllm/model_executor/models/blip2.py

@@ -25,7 +25,7 @@ from vllm.sequence import IntermediateTensors
 
 from .blip import BlipVisionModel
 from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
-from .utils import (AutoWeightsLoader, init_vllm_registered_model,
+from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
                     maybe_prefix, merge_multimodal_embeddings)
 
 # We use this internally as placeholders since there is no image token
@@ -565,12 +565,11 @@ class Blip2ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
             return None
 
         if pixel_values is not None:
-            if not isinstance(pixel_values, torch.Tensor):
+            if not isinstance(pixel_values, (torch.Tensor, list)):
                 raise ValueError("Incorrect type of pixel values. "
                                  f"Got type: {type(pixel_values)}")
 
-            # Remove the N dimension until multiple images are supported.
-            pixel_values = pixel_values.squeeze(1)
+            pixel_values = flatten_bn(pixel_values, concat=True)
 
             return Blip2ImagePixelInputs(
                 type="pixel_values",
@@ -578,12 +577,11 @@ class Blip2ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
             )
 
         if image_embeds is not None:
-            if not isinstance(image_embeds, torch.Tensor):
+            if not isinstance(image_embeds, (torch.Tensor, list)):
                 raise ValueError("Incorrect type of image embeddings. "
                                  f"Got type: {type(image_embeds)}")
 
-            # Remove the N dimension until multiple images are supported.
-            image_embeds = image_embeds.squeeze(1)
+            image_embeds = flatten_bn(image_embeds, concat=True)
 
             return Blip2ImageEmbeddingInputs(
                 type="image_embeds",

vllm/model_executor/models/chameleon.py

@@ -39,7 +39,7 @@ from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
 from vllm.sequence import IntermediateTensors
 
 from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
-from .utils import (is_pp_missing_parameter,
+from .utils import (flatten_bn, is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix, merge_multimodal_embeddings)
 
@@ -972,12 +972,11 @@ class ChameleonForConditionalGeneration(nn.Module, SupportsMultiModal,
         if pixel_values is None:
             return None
 
-        if not isinstance(pixel_values, torch.Tensor):
+        if not isinstance(pixel_values, (torch.Tensor, list)):
             raise ValueError("Incorrect type of pixel values. "
                              f"Got type: {type(pixel_values)}")
 
-        # Remove the N dimension until multiple images are supported.
-        pixel_values = pixel_values.squeeze(1)
+        pixel_values = flatten_bn(pixel_values, concat=True)
 
         return ChameleonImagePixelInputs(
             type="pixel_values",

vllm/model_executor/models/deepseek_vl2.py

@@ -478,7 +478,7 @@ class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
                     flatten_bn(images_spatial_crop, concat=True)))
 
         if image_embeds is not None:
-            if not isinstance(image_embeds, torch.Tensor):
+            if not isinstance(image_embeds, (torch.Tensor, list)):
                 raise ValueError("Incorrect type of image embeddings. "
                                  f"Got type: {type(image_embeds)}")
 

vllm/model_executor/models/glm4v.py

@@ -578,7 +578,7 @@ class GLM4VForCausalLM(ChatGLMBaseModel, SupportsLoRA, SupportsPP,
         pixel_values = kwargs.pop("pixel_values", None)
 
         if pixel_values is not None:
-            if not isinstance(pixel_values, torch.Tensor):
+            if not isinstance(pixel_values, (torch.Tensor, list)):
                 raise ValueError("Incorrect type of pixel values. "
                                  f"Got type: {type(pixel_values)}")
 

vllm/model_executor/models/internvl.py

@@ -838,7 +838,7 @@ class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP):
             return None
 
         if image_embeds is not None:
-            if not isinstance(image_embeds, torch.Tensor):
+            if not isinstance(image_embeds, (torch.Tensor, list)):
                 raise ValueError("Incorrect type of image embeddings. "
                                  f"Got type: {type(image_embeds)}")
 
@@ -856,7 +856,9 @@ class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP):
                 raise ValueError("Incorrect type of pixel values. "
                                  f"Got type: {type(pixel_values_flat)}")
 
-            assert isinstance(image_num_patches, (torch.Tensor, list))
+            if not isinstance(image_num_patches, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of image_num_patches. "
+                                 f"Got type: {type(pixel_values_flat)}")
 
             return InternVLImagePixelInputs(
                 type="pixel_values",

vllm/model_executor/models/llava_next_video.py

@@ -349,21 +349,18 @@ class LlavaNextVideoForConditionalGeneration(nn.Module, SupportsMultiModal,
                 List[b, Tensor(nb_frames, nb_channels, height, width)]
         }
         """
-        pixel_values = kwargs.pop("pixel_values_videos", None)
+        pixel_values_videos = kwargs.pop("pixel_values_videos", None)
 
-        if pixel_values is None:
+        if pixel_values_videos is None:
             return None
 
-        if not (is_list_of(pixel_values,
-                           (torch.Tensor))  # different shape videos 
-                or isinstance(pixel_values,
-                              torch.Tensor)):  # same shape videos
-            raise ValueError("Incorrect type of pixel values. "
-                             f"Got type: {type(pixel_values)}")
+        if not isinstance(pixel_values_videos, (torch.Tensor, list)):
+            raise ValueError("Incorrect type of pixel_values_videos. "
+                             f"Got type: {type(pixel_values_videos)}")
 
         return LlavaNextVideoPixelInputs(
             type="pixel_values_videos",
-            data=pixel_values,
+            data=pixel_values_videos,
         )
 
     def _select_image_features(self, image_features: torch.Tensor, *,

vllm/model_executor/models/llava_onevision.py

@@ -574,10 +574,7 @@ class LlavaOnevisionForConditionalGeneration(nn.Module, SupportsMultiModal,
         if pixel_values_videos is None:
             return None
 
-        if not (is_list_of(pixel_values_videos,
-                           torch.Tensor)  # different shape videos 
-                or isinstance(pixel_values_videos,
-                              torch.Tensor)):  # same shape videos
+        if not isinstance(pixel_values_videos, (torch.Tensor, list)):
             raise ValueError("Incorrect type of pixel_values_videos. "
                              f"Got type: {type(pixel_values_videos)}")
 

vllm/model_executor/models/paligemma.py

@@ -23,7 +23,7 @@ from vllm.sequence import IntermediateTensors
 
 from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
 from .siglip import SiglipVisionModel
-from .utils import (AutoWeightsLoader, init_vllm_registered_model,
+from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
                     maybe_prefix, merge_multimodal_embeddings)
 from .vision import get_vision_encoder_info
 
@@ -270,12 +270,11 @@ class PaliGemmaForConditionalGeneration(nn.Module, SupportsMultiModal,
             return None
 
         if pixel_values is not None:
-            if not isinstance(pixel_values, torch.Tensor):
+            if not isinstance(pixel_values, (torch.Tensor, list)):
                 raise ValueError("Incorrect type of pixel values. "
                                  f"Got type: {type(pixel_values)}")
 
-            # Remove the N dimension until multiple images are supported.
-            pixel_values = pixel_values.squeeze(1)
+            pixel_values = flatten_bn(pixel_values, concat=True)
 
             return PaliGemmaImagePixelInputs(
                 type="pixel_values",
@@ -287,8 +286,7 @@ class PaliGemmaForConditionalGeneration(nn.Module, SupportsMultiModal,
                 raise ValueError("Incorrect type of image embeddings. "
                                  f"Got type: {type(image_embeds)}")
 
-            # Remove the N dimension until multiple images are supported.
-            image_embeds = image_embeds.squeeze(1)
+            image_embeds = flatten_bn(image_embeds, concat=True)
 
             return PaliGemmaImageEmbeddingInputs(
                 type="image_embeds",

vllm/model_executor/models/qwen_vl.py

@@ -711,7 +711,7 @@ class QwenVLForConditionalGeneration(QWenBaseModel, SupportsPP, SupportsLoRA,
         image_embeds = kwargs.pop("image_embeds", None)
 
         if pixel_values is not None:
-            if not isinstance(pixel_values, torch.Tensor):
+            if not isinstance(pixel_values, (torch.Tensor, list)):
                 raise ValueError("Incorrect type of pixel values. "
                                  f"Got type: {type(pixel_values)}")
 
@@ -722,13 +722,13 @@ class QwenVLForConditionalGeneration(QWenBaseModel, SupportsPP, SupportsLoRA,
             )
 
         if image_embeds is not None:
-            if not isinstance(image_embeds, torch.Tensor):
+            if not isinstance(image_embeds, (torch.Tensor, list)):
                 raise ValueError("Incorrect type of image embeddings. "
                                  f"Got type: {type(image_embeds)}")
 
             return QwenImageEmbeddingInputs(
                 type="image_embeds",
-                data=flatten_bn(image_embeds),
+                data=flatten_bn(image_embeds, concat=True),
             )
 
         return None