[Bugfix] Fix models and tests for transformers v5

docs/models/supported_models.md

@@ -674,7 +674,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | Architecture | Models | Inputs | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/parallelism_scaling.md) |
 |--------------|--------|--------|-------------------|----------------------|---------------------------|
 | `AriaForConditionalGeneration` | Aria | T + I<sup>+</sup> | `rhymes-ai/Aria` | | |
-| `AudioFlamingo3ForConditionalGeneration` | AudioFlamingo3 | T + A<sup>+</sup> | `nvidia/audio-flamingo-3-hf`, `nvidia/music-flamingo-2601-hf` | ✅︎ | ✅︎ |
+| `AudioFlamingo3ForConditionalGeneration` | AudioFlamingo3 | T + A | `nvidia/audio-flamingo-3-hf`, `nvidia/music-flamingo-2601-hf` | ✅︎ | ✅︎ |
 | `AyaVisionForConditionalGeneration` | Aya Vision | T + I<sup>+</sup> | `CohereLabs/aya-vision-8b`, `CohereLabs/aya-vision-32b`, etc. | | ✅︎ |
 | `BagelForConditionalGeneration` | BAGEL | T + I<sup>+</sup> | `ByteDance-Seed/BAGEL-7B-MoT` | ✅︎ | ✅︎ |
 | `BeeForConditionalGeneration` | Bee-8B | T + I<sup>E+</sup> | `Open-Bee/Bee-8B-RL`, `Open-Bee/Bee-8B-SFT` | | ✅︎ |

tests/models/multimodal/generation/test_qwen2_vl.py

@@ -198,10 +198,10 @@ def batch_make_video_embeddings(
         videos += video_batch
 
     # video to pixel values
-    image_processor = processor.image_processor
+    video_processor = processor.video_processor
 
-    preprocess_result = image_processor.preprocess(
-        images=None, videos=videos, return_tensors="pt"
+    preprocess_result = video_processor.preprocess(
+        videos=videos, return_tensors="pt"
     ).data
     pixel_values = preprocess_result["pixel_values_videos"]
     video_grid_thw = preprocess_result["video_grid_thw"]
@@ -222,7 +222,7 @@ def get_image_embeds(model):
     embed_counter = 0
     for video_batch in video_batches_:
         cur_batch_video_count = len(video_batch)
-        merge_size = image_processor.merge_size
+        merge_size = video_processor.merge_size
         cur_batch_embed_len = sum(
             grid_thw.prod(-1) // merge_size // merge_size
             for grid_thw in video_grid_thw[

tests/models/multimodal/pooling/test_llava_next.py

@@ -81,7 +81,7 @@ def _run_test(
         # Patch the issue where image_token_id
         # exceeds the maximum allowed vocab size
         hf_model.model.resize_token_embeddings(
-            hf_model.model.language_model.vocab_size + 1
+            hf_model.model.model.language_model.vocab_size + 1
         )
 
         all_inputs = hf_model.get_inputs(input_texts, images=input_images)

tests/models/multimodal/test_mapping.py

@@ -33,7 +33,9 @@ def create_dummy_model(repo: str, model_arch: str) -> PreTrainedModel:
         model = model_cls._from_config(config)
     # TODO(hmellor): Remove this once Transformers has fixed tied weights on meta device
     # https://github.com/huggingface/transformers/issues/43522
-    if getattr(config.get_text_config(), "tie_word_embeddings", False):
+    if getattr(config.get_text_config(), "tie_word_embeddings", False) or getattr(
+        config, "tie_word_embeddings", False
+    ):
         model.tie_weights()
     return model
 

vllm/model_executor/models/audioflamingo3.py

@@ -236,7 +236,7 @@ def get_data_parser(self):
         )
 
     def get_supported_mm_limits(self) -> Mapping[str, int | None]:
-        return {"audio": None}
+        return {"audio": 1}
 
 
 class AudioFlamingo3DummyInputsBuilder(

vllm/model_executor/models/hunyuan_vision.py

@@ -692,7 +692,7 @@ class HunYuanVLDummyInputsBuilder(BaseDummyInputsBuilder[HunYuanVLProcessingInfo
     def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
         num_images = mm_counts.get("image", 0)
 
-        hf_processor = self.info.get_hf_processor()
+        hf_processor = self.info.get_hf_processor(typ=HunYuanVLProcessor)
         image_token: str = hf_processor.image_token
 
         return image_token * num_images

vllm/model_executor/models/isaac.py

@@ -13,7 +13,7 @@
 import torch.nn.functional as F
 from einops import rearrange
 from transformers.image_processing_utils import BatchFeature
-from transformers.tokenization_utils import TensorType
+from transformers.utils import TensorType
 from typing_extensions import TypedDict, Unpack
 
 from vllm.config import VllmConfig

vllm/model_executor/models/minimax_vl_01.py

@@ -230,8 +230,8 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
         self.vision_feature_layer = config.vision_feature_layer
         self.vocab_size = config.text_config.vocab_size
         self.pad_token_id = -1
-        if self.config.pad_token_id is not None:
-            self.pad_token_id = self.config.pad_token_id
+        if self.config.text_config.pad_token_id is not None:
+            self.pad_token_id = self.config.text_config.pad_token_id
 
         self.make_empty_intermediate_tensors = (
             self.language_model.make_empty_intermediate_tensors

vllm/transformers_utils/processors/bagel.py

@@ -6,10 +6,18 @@
 from transformers import AutoProcessor
 from transformers.feature_extraction_utils import BatchFeature
 from transformers.image_utils import ImageInput
-from transformers.processing_utils import ProcessorMixin
+from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
 from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
 
 
+class BagelProcessorKwargs(ProcessingKwargs, total=False):  # type: ignore[call-arg]
+    _defaults = {
+        "images_kwargs": {
+            "return_tensors": "pt",
+        },
+    }
+
+
 class BagelProcessor(ProcessorMixin):
     """
     Constructs a BAGEL processor which wraps a
@@ -27,34 +35,32 @@ def __call__(
         | list[TextInput]
         | list[PreTokenizedInput] = None,
         images: ImageInput = None,
-        **kwargs,
+        **kwargs: Unpack[BagelProcessorKwargs],
     ):
         """
         Main method to prepare for the model one or several sequences(s) and image(s).
         """
+        output_kwargs = self._merge_kwargs(
+            BagelProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
         if images is not None:
             # Process images with the image processor
-            # Ensure return_tensors is set to "pt" for PyTorch tensors
-            image_kwargs = {**kwargs}
-            if "return_tensors" not in image_kwargs:
-                image_kwargs["return_tensors"] = "pt"
-            pixel_values = self.image_processor(images, **image_kwargs)
+            pixel_values = self.image_processor(
+                images, **output_kwargs["images_kwargs"]
+            )
         else:
-            pixel_values = None
+            pixel_values = {}
 
-        text_inputs = self.tokenizer(text, **kwargs) if text is not None else None
+        text_inputs = (
+            self.tokenizer(text, **output_kwargs["text_kwargs"])
+            if text is not None
+            else {}
+        )
 
-        if pixel_values is not None and text_inputs is not None:
-            # Combine text and image inputs into BatchFeature
-            combined = dict(text_inputs)
-            combined["pixel_values"] = pixel_values["pixel_values"]
-            return BatchFeature(combined)
-        elif pixel_values is not None:
-            return pixel_values
-        elif text_inputs is not None:
-            return BatchFeature(dict(text_inputs))
-        else:
-            return BatchFeature({})
+        return BatchFeature(data={**pixel_values, **text_inputs})
 
     def batch_decode(self, *args, **kwargs):
         """

vllm/transformers_utils/processors/hunyuan_vl.py

@@ -23,7 +23,6 @@ def __init__(
         self,
         image_processor=None,
         tokenizer=None,
-        video_processor=None,
         chat_template=None,
         **kwargs,
     ):
@@ -42,9 +41,7 @@ def __init__(
         )
         self.pad_id = 120002  # self.tokenizer.pad_token_id
 
-        super().__init__(
-            image_processor, tokenizer, video_processor, chat_template=chat_template
-        )
+        super().__init__(image_processor, tokenizer, chat_template=chat_template)
 
     def __call__(
         self,

vllm/transformers_utils/processors/ovis.py

@@ -43,9 +43,7 @@ class OvisProcessorKwargs(ProcessingKwargs, total=False):  # type: ignore[call-a
             "padding": False,
         },
         "images_kwargs": {
-            "max_partition": 9,
-            "covering_threshold": 0.9,
-            "convert_to_rgb": True,
+            "do_convert_rgb": True,
             "return_tensors": "pt",
         },
     }
@@ -143,6 +141,10 @@ def __call__(
                 - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
                 - **second_per_grid_ts** -- List of video seconds per time grid. Returned when `videos` is not `None`.
         """
+
+        max_partition = kwargs.pop("max_partition", 9)
+        covering_threshold = kwargs.pop("covering_threshold", 0.9)
+
         output_kwargs = self._merge_kwargs(
             OvisProcessorKwargs,
             tokenizer_init_kwargs=self.tokenizer.init_kwargs,
@@ -159,7 +161,10 @@ def __call__(
             # Process each image
             for image in images if isinstance(images, list) else [images]:
                 pixel_values, image_placeholders, grid = self.preprocess_image(
-                    image=image, **output_kwargs["images_kwargs"]
+                    image=image,
+                    max_partition=max_partition,
+                    covering_threshold=covering_threshold,
+                    **output_kwargs["images_kwargs"],
                 )
                 processed_images.append(pixel_values)
                 image_placeholders_list.append(image_placeholders)
@@ -300,7 +305,7 @@ def preprocess_image(
         image: PIL.Image.Image,
         max_partition,
         covering_threshold,
-        convert_to_rgb,
+        do_convert_rgb,
         return_tensors,
     ):
         def _preprocess(img: PIL.Image.Image, side):
@@ -394,7 +399,7 @@ def _get_best_grid(img, side):
                 # pick the partition with maximum covering_ratio and break the tie using #sub_images
                 return sorted(all_grids, key=lambda x: (-x[1], x[0][0] * x[0][1]))[0][0]
 
-        if convert_to_rgb:
+        if do_convert_rgb:
             image = convert_image_mode(image, "RGB")
 
         sides = self.get_image_size()

vllm/transformers_utils/processors/ovis2_5.py

@@ -24,14 +24,10 @@ class Ovis2_5ProcessorKwargs(ProcessingKwargs, total=False):  # type: ignore[cal
             "padding": False,
         },
         "images_kwargs": {
-            "convert_to_rgb": True,
-            "min_pixels": MIN_PIXELS,
-            "max_pixels": MAX_PIXELS,
+            "do_convert_rgb": True,
         },
         "videos_kwargs": {
-            "convert_to_rgb": True,
-            "min_pixels": MIN_PIXELS,
-            "max_pixels": MAX_PIXELS,
+            "do_convert_rgb": True,
         },
     }
 
@@ -160,6 +156,9 @@ def __call__(
                 - **second_per_grid_ts** -- list of video seconds per time grid.
                   Returned when `videos` is not `None`.
         """
+        min_pixels = kwargs.pop("min_pixels", MIN_PIXELS)
+        max_pixels = kwargs.pop("max_pixels", MAX_PIXELS)
+
         output_kwargs = self._merge_kwargs(
             Ovis2_5ProcessorKwargs,
             tokenizer_init_kwargs=self.tokenizer.init_kwargs,
@@ -175,7 +174,10 @@ def __call__(
             # Process each image
             for image in images if isinstance(images, list) else [images]:
                 pixel_values, image_placeholders, grid = self.preprocess_multidata(
-                    images=image, **output_kwargs["images_kwargs"]
+                    images=image,
+                    min_pixels=min_pixels,
+                    max_pixels=max_pixels,
+                    **output_kwargs["images_kwargs"],
                 )
                 processed_images.append(pixel_values)
                 image_placeholders_list.append(image_placeholders)
@@ -194,7 +196,10 @@ def __call__(
             # Process each video
             for video in videos if isinstance(videos, list) else [videos]:
                 pixel_values, video_placeholders, grid = self.preprocess_multidata(
-                    video=video, **output_kwargs["videos_kwargs"]
+                    video=video,
+                    min_pixels=min_pixels,
+                    max_pixels=max_pixels,
+                    **output_kwargs["videos_kwargs"],
                 )
                 processed_videos.append(pixel_values)
                 videos_placeholders_list.append(video_placeholders)
@@ -378,7 +383,7 @@ def preprocess_multidata(
         self,
         images: PIL.Image.Image | list[PIL.Image.Image] | None = None,
         video: list[PIL.Image.Image] | np.ndarray | None = None,
-        convert_to_rgb: bool | None = True,
+        do_convert_rgb: bool | None = True,
         min_pixels: int = MIN_PIXELS,
         max_pixels: int = MAX_PIXELS,
         return_tensors: str | None = "pt",
@@ -404,7 +409,7 @@ def preprocess_multidata(
             min_pixels if min_pixels is not None else MIN_PIXELS,
         )
         images = [
-            image.convert("RGB") if convert_to_rgb and image.mode != "RGB" else image
+            image.convert("RGB") if do_convert_rgb and image.mode != "RGB" else image
             for image in images
         ]
 
@@ -420,9 +425,9 @@ def preprocess_multidata(
                 max_pixels=max_pixels,
             )
             new_size = dict(height=resized_height, width=resized_width)
-            image_pt = self.image_processor.preprocess(
-                image, size=new_size, return_tensors="np"
-            )["pixel_values"][0]
+            image_pt = self.image_processor.preprocess(image, size=new_size)[
+                "pixel_values"
+            ][0]
 
             processed_images.append(image_pt)