[Model] Support multiple images for qwen-vl (#8247)

Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com>
Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>
Co-authored-by: DarkLight1337 <tlleungac@connect.ust.hk>

vllm/model_executor/models/qwen.py

@@ -47,6 +47,7 @@ from vllm.multimodal.base import MultiModalInputs
 from vllm.multimodal.utils import cached_get_tokenizer
 from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, IntermediateTensors,
                            SequenceData)
+from vllm.utils import is_list_of
 
 from .utils import flatten_bn, is_pp_missing_parameter, make_layers
 
@@ -684,9 +685,12 @@ def input_processor_for_qwen(ctx: InputContext,
             raise ValueError(
                 f"Expected img embeds to be have 3 dimensions, got {num_dims}")
         num_images = 1 if num_dims == 2 else image_data.shape[0]
-    else:
-        # TODO - handle multiple image inputs once the API is solidified
+    elif isinstance(image_data, Image.Image):
         num_images = 1
+    elif is_list_of(image_data, Image.Image):
+        num_images = len(image_data)
+    else:
+        raise TypeError(f"Invalid image type: {type(image_data)}")
 
     if prompt is None:
         prompt = tokenizer.decode(prompt_token_ids)
@@ -767,11 +771,11 @@ def input_mapper_for_qwen(ctx: InputContext, data: object) -> MultiModalInputs:
                 f"[# images, {MAX_QWEN_IMG_TOKENS}, {img_emb_size}], but "
                 f"received shape [{data.shape}]")
         pixel_values = data
-
     else:
         transform = build_normalization_transform(image_size)
-        # TODO - handle multiple image inputs once the API is solidified
-        transformed_images = [transform(data)]
+        if not isinstance(data, (list, tuple)):
+            data = [data]
+        transformed_images = [transform(datum) for datum in data]
         pixel_values = torch.stack(transformed_images, dim=0)
     return MultiModalInputs({"pixel_values": pixel_values})