[Misc] Clean up processing logic (#37541)

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

vllm/model_executor/models/ernie45_vl.py

@@ -1221,49 +1221,33 @@ class Ernie4_5_VLDummyInputsBuilder(BaseDummyInputsBuilder[Ernie4_5_VLProcessing
         num_videos: int,
         overrides: VideoDummyOptions | None = None,
     ):
-        if overrides:
-            if overrides.num_frames:
-                if overrides.num_frames > num_frames:
-                    logger.warning(
-                        "video.num_frames override (%d) exceeds model's "
-                        "maximum number of frames (%d), will be ignored",
-                        overrides.num_frames,
-                        num_frames,
-                    )
-                num_frames = min(num_frames, overrides.num_frames)
-            if overrides.width:
-                if overrides.width > width:
-                    logger.warning(
-                        "video.width override (%d) exceeds model's "
-                        "maximum width (%d), will be ignored",
-                        overrides.width,
-                        width,
-                    )
-                width = min(width, overrides.width)
-            if overrides.height:
-                if overrides.height > height:
-                    logger.warning(
-                        "video.height override (%d) exceeds model's "
-                        "maximum height (%d), will be ignored",
-                        overrides.height,
-                        height,
-                    )
-                height = min(height, overrides.height)
-        num_frames = max(num_frames, 2)  # ernie4.5-vl requires at least 2 frames
-
-        video = np.full((num_frames, width, height, 3), 255, dtype=np.uint8)
+        # ernie4.5-vl requires at least 2 frames
+        num_frames = max(num_frames, 2)
+        if overrides and overrides.num_frames:
+            overrides.num_frames = max(overrides.num_frames, 2)
+
+        videos = super()._get_dummy_videos(
+            width=width,
+            height=height,
+            num_frames=num_frames,
+            num_videos=num_videos,
+            overrides=overrides,
+        )
+        videos = [v.copy() for v in videos]
+
         video_items = []
-        for i in range(num_videos):
+        for video in videos:
+            video_num_frames = video.shape[0]
             video_metadata = {
                 "fps": 2.0,
-                "duration": num_frames / 2.0,
-                "total_num_frames": num_frames,
-                "frames_indices": [i for i in range(num_frames)],
+                "duration": video_num_frames / 2.0,
+                "total_num_frames": video_num_frames,
+                "frames_indices": list(range(video_num_frames)),
                 "video_backend": "opencv",
                 "do_sample_frames": False,
             }
-            video_item = (video.copy(), video_metadata)
-            video_items.append(video_item)
+            video_items.append((video, video_metadata))
+
         return video_items
 
 

vllm/model_executor/models/glm4_1v.py

@@ -1206,49 +1206,32 @@ class Glm4vDummyInputsBuilder(BaseDummyInputsBuilder[Glm4vProcessingInfo]):
         num_videos: int,
         overrides: VideoDummyOptions | None = None,
     ) -> list[VideoItem]:
-        if overrides:
-            if overrides.num_frames:
-                if overrides.num_frames > num_frames:
-                    logger.warning(
-                        "video.num_frames override (%d) exceeds model's "
-                        "maximum number of frames (%d), will be ignored",
-                        overrides.num_frames,
-                        num_frames,
-                    )
-                num_frames = min(num_frames, overrides.num_frames)
-            if overrides.width:
-                if overrides.width > width:
-                    logger.warning(
-                        "video.width override (%d) exceeds model's "
-                        "maximum width (%d), will be ignored",
-                        overrides.width,
-                        width,
-                    )
-                width = min(width, overrides.width)
-            if overrides.height:
-                if overrides.height > height:
-                    logger.warning(
-                        "video.height override (%d) exceeds model's "
-                        "maximum height (%d), will be ignored",
-                        overrides.height,
-                        height,
-                    )
-                height = min(height, overrides.height)
-
-        num_frames = max(num_frames, 2)  # GLM 4.6V requires 2 frames
-        video = np.full((num_frames, width, height, 3), 255, dtype=np.uint8)
+        # GLM 4.6V requires at least 2 frames
+        num_frames = max(num_frames, 2)
+        if overrides and overrides.num_frames:
+            overrides.num_frames = max(overrides.num_frames, 2)
+
+        videos = super()._get_dummy_videos(
+            width=width,
+            height=height,
+            num_frames=num_frames,
+            num_videos=num_videos,
+            overrides=overrides,
+        )
+        videos = [v.copy() for v in videos]
+
         video_items = []
-        for i in range(num_videos):
+        for video in videos:
+            video_num_frames = video.shape[0]
             video_metadata = {
                 "fps": 2.0,
-                "duration": num_frames / 2.0,
-                "total_num_frames": num_frames,
-                "frames_indices": [i for i in range(num_frames)],
+                "duration": video_num_frames / 2.0,
+                "total_num_frames": video_num_frames,
+                "frames_indices": list(range(video_num_frames)),
                 "video_backend": "opencv",
                 "do_sample_frames": False,
             }
-            video_item = (video.copy(), video_metadata)
-            video_items.append(video_item)
+            video_items.append((video, video_metadata))
 
         return video_items
 

vllm/model_executor/models/h2ovl.py

@@ -8,14 +8,13 @@
 # Copyright (c) 2024 H2O.AI
 # Licensed under Apache 2.0 License [see LICENSE for details]
 # --------------------------------------------------------
-from collections.abc import Mapping, Sequence
 
 import torch
 from transformers import PretrainedConfig
 
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.multimodal import MULTIMODAL_REGISTRY
-from vllm.multimodal.inputs import MultiModalKwargsItems
+from vllm.multimodal.inputs import BatchedTensorInputs
 from vllm.multimodal.parse import (
     ImageEmbeddingItems,
     ImageProcessorItems,
@@ -25,7 +24,6 @@ from vllm.multimodal.processing.processor import (
     MultiModalProcessingInfo,
     ProcessorInputs,
     PromptReplacement,
-    PromptUpdate,
     TimingContext,
 )
 from vllm.transformers_utils.processors.h2ovl import H2OVLImageProcessor, H2OVLProcessor
@@ -86,15 +84,12 @@ class H2OVLProcessingInfo(BaseInternVLProcessingInfo):
 
 
 class H2OVLMultiModalProcessor(BaseInternVLMultiModalProcessor[H2OVLProcessingInfo]):
-    def _get_prompt_updates(
+    def _get_prompt_repl_image(
         self,
         mm_items: MultiModalDataItems,
-        hf_processor_mm_kwargs: Mapping[str, object],
-        out_mm_kwargs: MultiModalKwargsItems,
-    ) -> Sequence[PromptUpdate]:
-        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
-
-        out_mm_data = out_mm_kwargs.get_data()
+        hf_processor: H2OVLProcessor,
+        out_mm_data: BatchedTensorInputs,
+    ):
         if "image_num_patches" in out_mm_data:
             image_num_patches = out_mm_data["image_num_patches"]
             assert isinstance(image_num_patches, torch.Tensor)
@@ -130,13 +125,11 @@ class H2OVLMultiModalProcessor(BaseInternVLMultiModalProcessor[H2OVLProcessingIn
 
             return hf_processor.get_image_repl(num_patches, num_features=feature_size)
 
-        return [
-            PromptReplacement(
+        return PromptReplacement(
             modality="image",
             target="<image>",
             replacement=get_replacement_internvl,
         )
-        ]
 
     def _cached_apply_hf_processor(
         self,

vllm/model_executor/models/internvl.py

@@ -27,6 +27,7 @@ from vllm.model_executor.models.intern_vit import (
 from vllm.model_executor.models.module_mapping import MultiModelKeys
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import (
+    BatchedTensorInputs,
     MultiModalDataDict,
     MultiModalFieldConfig,
     MultiModalKwargsItems,
@@ -238,11 +239,7 @@ class BaseInternVLMultiModalProcessor(BaseMultiModalProcessor[_I]):
 
         return processed_outputs
 
-    def _get_mm_fields_config(
-        self,
-        hf_inputs: BatchFeature,
-        hf_processor_mm_kwargs: Mapping[str, object],
-    ) -> Mapping[str, MultiModalFieldConfig]:
+    def _get_image_fields_config(self, hf_inputs: BatchFeature):
         image_num_patches = hf_inputs.get("image_num_patches", torch.empty(0))
         num_images = len(image_num_patches)
 
@@ -255,15 +252,19 @@ class BaseInternVLMultiModalProcessor(BaseMultiModalProcessor[_I]):
             image_token_id=MultiModalFieldConfig.shared("image", num_images),
         )
 
-    def _get_prompt_updates(
+    def _get_mm_fields_config(
         self,
-        mm_items: MultiModalDataItems,
+        hf_inputs: BatchFeature,
         hf_processor_mm_kwargs: Mapping[str, object],
-        out_mm_kwargs: MultiModalKwargsItems,
-    ) -> Sequence[PromptUpdate]:
-        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return self._get_image_fields_config(hf_inputs)
 
-        out_mm_data = out_mm_kwargs.get_data()
+    def _get_prompt_repl_image(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor: InternVLProcessor,
+        out_mm_data: BatchedTensorInputs,
+    ):
         if "image_num_patches" in out_mm_data:
             image_num_patches = out_mm_data["image_num_patches"]
             assert isinstance(image_num_patches, torch.Tensor)
@@ -296,12 +297,23 @@ class BaseInternVLMultiModalProcessor(BaseMultiModalProcessor[_I]):
 
             return hf_processor.get_image_repl(num_patches, num_features=feature_size)
 
-        return [
-            PromptReplacement(
+        return PromptReplacement(
             modality="image",
             target="<image>",
             replacement=get_replacement_internvl,
         )
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargsItems,
+    ) -> Sequence[PromptUpdate]:
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+        out_mm_data = out_mm_kwargs.get_data()
+
+        return [
+            self._get_prompt_repl_image(mm_items, hf_processor, out_mm_data),
         ]
 
 
@@ -455,44 +467,35 @@ class InternVLMultiModalProcessor(
 
         return processed_outputs
 
-    def _get_mm_fields_config(
-        self,
-        hf_inputs: BatchFeature,
-        hf_processor_mm_kwargs: Mapping[str, object],
-    ) -> Mapping[str, MultiModalFieldConfig]:
-        image_fields = super()._get_mm_fields_config(hf_inputs, hf_processor_mm_kwargs)
-        if self.info.ctx_video_token:
+    def _get_video_fields_config(self, hf_inputs: BatchFeature):
         video_num_patches = hf_inputs.get("video_num_patches", torch.empty(0))
         num_videos = len(video_num_patches)
-            video_fields = dict(
+
+        return dict(
             pixel_values_flat_video=MultiModalFieldConfig.flat_from_sizes(
                 "video", video_num_patches
             ),
             video_num_patches=MultiModalFieldConfig.batched("video"),
             video_token_id=MultiModalFieldConfig.shared("video", num_videos),
         )
-        else:
-            video_fields = {}
-
-        return image_fields | video_fields
 
-    def _get_prompt_updates(
+    def _get_mm_fields_config(
         self,
-        mm_items: MultiModalDataItems,
+        hf_inputs: BatchFeature,
         hf_processor_mm_kwargs: Mapping[str, object],
-        out_mm_kwargs: MultiModalKwargsItems,
-    ) -> Sequence[PromptUpdate]:
-        prompt_repl = super()._get_prompt_updates(
-            mm_items=mm_items,
-            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
-            out_mm_kwargs=out_mm_kwargs,
-        )
-        if self.info.ctx_video_token is None:
-            return prompt_repl
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        fields = self._get_image_fields_config(hf_inputs)
+        if self.info.ctx_video_token:
+            fields |= self._get_video_fields_config(hf_inputs)
 
-        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+        return fields
 
-        out_mm_data = out_mm_kwargs.get_data()
+    def _get_prompt_repl_video(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor: InternVLProcessor,
+        out_mm_data: BatchedTensorInputs,
+    ):
         if "video_num_patches" in out_mm_data:
             video_num_patches = out_mm_data["video_num_patches"]
             assert isinstance(video_num_patches, torch.Tensor)
@@ -507,14 +510,30 @@ class InternVLMultiModalProcessor(
 
             return hf_processor.get_video_repl(num_patches)
 
-        return [
-            *prompt_repl,
-            PromptReplacement(
+        return PromptReplacement(
             modality="video",
             target="<video>",
             replacement=get_video_replacement_internvl,
-            ),
+        )
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargsItems,
+    ) -> Sequence[PromptUpdate]:
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+        out_mm_data = out_mm_kwargs.get_data()
+
+        prompt_repls = [
+            self._get_prompt_repl_image(mm_items, hf_processor, out_mm_data),
         ]
+        if self.info.ctx_video_token is not None:
+            prompt_repls.append(
+                self._get_prompt_repl_video(mm_items, hf_processor, out_mm_data)
+            )
+
+        return prompt_repls
 
 
 @MULTIMODAL_REGISTRY.register_processor(

vllm/model_executor/models/molmo2.py

@@ -1913,22 +1913,32 @@ class Molmo2DummyInputsBuilder(BaseDummyInputsBuilder[Molmo2ProcessingInfo]):
         height: int,
         num_frames: int,
         num_videos: int,
+        overrides: VideoDummyOptions | None = None,
     ) -> list[VideoItem]:
-        video = np.full((num_frames, height, width, 3), 255, dtype=np.uint8)
+        videos = super()._get_dummy_videos(
+            width=width,
+            height=height,
+            num_frames=num_frames,
+            num_videos=num_videos,
+            overrides=overrides,
+        )
+        videos = [v.copy() for v in videos]
+
         video_items = []
-        for i in range(num_videos):
+        for video in videos:
+            video_num_frames = video.shape[0]
             video_metadata = {
                 "fps": 2.0,
-                "duration": num_frames / 2.0,
-                "total_num_frames": num_frames,
-                "frames_indices": list(range(num_frames)),
+                "duration": video_num_frames / 2.0,
+                "total_num_frames": video_num_frames,
+                "frames_indices": list(range(video_num_frames)),
                 "video_backend": "decord",
                 "do_sample_frames": False,
                 "height": height,
                 "width": width,
             }
-            video_item = (video.copy(), video_metadata)
-            video_items.append(video_item)
+            video_items.append((video, video_metadata))
+
         return video_items
 
 

vllm/model_executor/models/nvlm_d.py

@@ -7,7 +7,7 @@
 # Copyright (c) 2024 NVIDIA
 # Licensed under Apache 2.0 License [see LICENSE for details]
 # --------------------------------------------------------
-from collections.abc import Mapping, Sequence
+from collections.abc import Mapping
 
 import torch
 import torch.nn as nn
@@ -16,7 +16,10 @@ from transformers import PretrainedConfig
 from vllm.config.multimodal import BaseDummyOptions
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.multimodal import MULTIMODAL_REGISTRY
-from vllm.multimodal.inputs import MultiModalDataDict, MultiModalKwargsItems
+from vllm.multimodal.inputs import (
+    BatchedTensorInputs,
+    MultiModalDataDict,
+)
 from vllm.multimodal.parse import (
     ImageEmbeddingItems,
     ImageProcessorItems,
@@ -24,7 +27,6 @@ from vllm.multimodal.parse import (
 )
 from vllm.multimodal.processing import (
     PromptReplacement,
-    PromptUpdate,
     PromptUpdateDetails,
 )
 from vllm.transformers_utils.processors.internvl import InternVLImageProcessor
@@ -100,15 +102,12 @@ class NVLMDummyInputsBuilder(BaseInternVLDummyInputsBuilder[NVLMProcessingInfo])
 
 
 class NVLMMultiModalProcessor(BaseInternVLMultiModalProcessor[NVLMProcessingInfo]):
-    def _get_prompt_updates(
+    def _get_prompt_repl_image(
         self,
         mm_items: MultiModalDataItems,
-        hf_processor_mm_kwargs: Mapping[str, object],
-        out_mm_kwargs: MultiModalKwargsItems,
-    ) -> Sequence[PromptUpdate]:
-        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
-
-        out_mm_data = out_mm_kwargs.get_data()
+        hf_processor: NVLMProcessor,
+        out_mm_data: BatchedTensorInputs,
+    ):
         if "image_num_patches" in out_mm_data:
             image_num_patches = out_mm_data["image_num_patches"]
             assert isinstance(image_num_patches, torch.Tensor)
@@ -146,13 +145,11 @@ class NVLMMultiModalProcessor(BaseInternVLMultiModalProcessor[NVLMProcessingInfo
             )
 
         # See note in dummy data regarding why we have the extra newline
-        return [
-            PromptReplacement(
+        return PromptReplacement(
             modality="image",
             target="<image>\n",
             replacement=get_replacement_nvlm,
         )
-        ]
 
 
 @MULTIMODAL_REGISTRY.register_processor(

vllm/model_executor/models/qwen3_vl.py

@@ -931,20 +931,30 @@ class Qwen3VLDummyInputsBuilder(BaseDummyInputsBuilder[Qwen3VLProcessingInfo]):
         height: int,
         num_frames: int,
         num_videos: int,
+        overrides: VideoDummyOptions | None = None,
     ) -> list[VideoItem]:
-        video = np.full((num_frames, width, height, 3), 255, dtype=np.uint8)
+        videos = super()._get_dummy_videos(
+            width=width,
+            height=height,
+            num_frames=num_frames,
+            num_videos=num_videos,
+            overrides=overrides,
+        )
+        videos = [v.copy() for v in videos]
+
         video_items = []
-        for i in range(num_videos):
+        for video in videos:
+            video_num_frames = video.shape[0]
             video_metadata = {
                 "fps": 2.0,
-                "duration": num_frames / 2.0,
-                "total_num_frames": num_frames,
-                "frames_indices": [i for i in range(num_frames)],
+                "duration": video_num_frames / 2.0,
+                "total_num_frames": video_num_frames,
+                "frames_indices": list(range(video_num_frames)),
                 "video_backend": "opencv",
                 "do_sample_frames": False,
             }
-            video_item = (video.copy(), video_metadata)
-            video_items.append(video_item)
+            video_items.append((video, video_metadata))
+
         return video_items
 
 

vllm/model_executor/models/skyworkr1v.py

@@ -7,12 +7,12 @@
 # Copyright (c) 2025 Skywork
 # Licensed under The MIT License [see LICENSE for details]
 # --------------------------------------------------------
-from collections.abc import Iterable, Mapping, Sequence
+from collections.abc import Iterable, Mapping
 from typing import Annotated, Literal, TypeAlias
 
 import torch
 import torch.nn as nn
-from transformers import BatchFeature, PretrainedConfig
+from transformers import PretrainedConfig
 
 from vllm.config import VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
@@ -24,24 +24,8 @@ from vllm.model_executor.models.intern_vit import (
     InternVisionPatchModel,
 )
 from vllm.multimodal import MULTIMODAL_REGISTRY
-from vllm.multimodal.inputs import (
-    MultiModalDataDict,
-    MultiModalFieldConfig,
-    MultiModalKwargsItems,
-)
-from vllm.multimodal.parse import (
-    ImageEmbeddingItems,
-    ImageProcessorItems,
-    ImageSize,
-    MultiModalDataItems,
-)
-from vllm.multimodal.processing import (
-    BaseDummyInputsBuilder,
-    BaseMultiModalProcessor,
-    BaseProcessingInfo,
-    PromptReplacement,
-    PromptUpdate,
-)
+from vllm.multimodal.inputs import MultiModalDataDict
+from vllm.multimodal.processing import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.processors.internvl import (
     InternVLImageProcessor,
@@ -50,6 +34,11 @@ from vllm.transformers_utils.processors.internvl import (
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
 from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
+from .internvl import (
+    BaseInternVLDummyInputsBuilder,
+    BaseInternVLMultiModalProcessor,
+    BaseInternVLProcessingInfo,
+)
 from .utils import AutoWeightsLoader, init_vllm_registered_model, maybe_prefix
 
 
@@ -98,7 +87,7 @@ SkyworkR1VImageInputs: TypeAlias = (
 )
 
 
-class SkyworkR1VProcessingInfo(BaseProcessingInfo):
+class SkyworkR1VProcessingInfo(BaseInternVLProcessingInfo):
     def get_image_processor(self, **kwargs):
         config = self.get_hf_config()
         vision_config = config.vision_config
@@ -128,46 +117,6 @@ class SkyworkR1VProcessingInfo(BaseProcessingInfo):
             image_seq_length=image_seq_length,
         )
 
-    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
-        return {"image": None}
-
-    def get_num_image_tokens(
-        self,
-        *,
-        image_width: int,
-        image_height: int,
-        processor: InternVLProcessor,
-    ) -> int:
-        return processor.get_num_image_tokens(
-            image_width=image_width,
-            image_height=image_height,
-        )
-
-    def get_image_size_with_most_features(self) -> ImageSize:
-        processor = self.get_hf_processor()
-        image_processor = processor.image_processor
-
-        base_size = image_processor.image_size
-        target_ratios = processor.resolve_target_ratios()
-
-        largest_feature_size, largest_feature_pinpoint = 0, None
-        for wr, hr in target_ratios:
-            width, height = base_size * wr, base_size * hr
-
-            feat_size = self.get_num_image_tokens(
-                image_width=width,
-                image_height=height,
-                processor=processor,
-            )
-            if feat_size > largest_feature_size:
-                largest_feature_size = feat_size
-                largest_feature_pinpoint = ImageSize(width=width, height=height)
-
-        if largest_feature_size == 0 or largest_feature_pinpoint is None:
-            raise ValueError("Cannot have a largest feature size of 0!")
-
-        return largest_feature_pinpoint
-
 
 class SkyworkR1VDummyInputsBuilder(BaseDummyInputsBuilder[SkyworkR1VProcessingInfo]):
     def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
@@ -196,102 +145,10 @@ class SkyworkR1VDummyInputsBuilder(BaseDummyInputsBuilder[SkyworkR1VProcessingIn
         }
 
 
-class SkyworkR1VMultiModalProcessor(BaseMultiModalProcessor[SkyworkR1VProcessingInfo]):
-    def _call_hf_processor(
-        self,
-        prompt: str,
-        mm_data: Mapping[str, object],
-        mm_kwargs: Mapping[str, object],
-        tok_kwargs: Mapping[str, object],
-    ) -> BatchFeature:
-        processed_outputs = super()._call_hf_processor(
-            prompt=prompt,
-            mm_data=mm_data,
-            mm_kwargs=mm_kwargs,
-            tok_kwargs=tok_kwargs,
-        )
-
-        hf_processor = self.info.get_hf_processor(**mm_kwargs)
-        image_token_id = hf_processor.ctx_image_token_id
-
-        # Since there may be extra tokens in the feature placeholders,
-        # we need to pass the image token ID to the model to select the
-        # tokens to merge from the vision encoder outputs
-        processed_outputs["image_token_id"] = torch.tensor(image_token_id)
-
-        return processed_outputs
-
-    def _get_mm_fields_config(
-        self,
-        hf_inputs: BatchFeature,
-        hf_processor_mm_kwargs: Mapping[str, object],
-    ) -> Mapping[str, MultiModalFieldConfig]:
-        image_num_patches = hf_inputs.get("image_num_patches", torch.empty(0))
-        num_images = len(image_num_patches)
-
-        return dict(
-            pixel_values_flat=MultiModalFieldConfig.flat_from_sizes(
-                "image", image_num_patches
-            ),
-            image_num_patches=MultiModalFieldConfig.batched("image"),
-            image_embeds=MultiModalFieldConfig.batched("image"),
-            image_token_id=MultiModalFieldConfig.shared("image", num_images),
-        )
-
-    def _get_prompt_updates(
-        self,
-        mm_items: MultiModalDataItems,
-        hf_processor_mm_kwargs: Mapping[str, object],
-        out_mm_kwargs: MultiModalKwargsItems,
-    ) -> Sequence[PromptUpdate]:
-        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
-
-        out_mm_data = out_mm_kwargs.get_data()
-        if "image_num_patches" in out_mm_data:
-            image_num_patches = out_mm_data["image_num_patches"]
-            assert isinstance(image_num_patches, torch.Tensor)
-            image_num_patches = image_num_patches.tolist()
-        elif "image_embeds" in out_mm_data:
-            # TODO: Use image size information in dictionary embedding inputs
-            # to compute num_patches (similar to Qwen2-VL)
-            image_num_patches = [None] * len(out_mm_data["image_embeds"])
-        else:
-            image_num_patches = []
-
-        def get_replacement_skyworkr1v(item_idx: int):
-            images = mm_items.get_items(
-                "image", (ImageEmbeddingItems, ImageProcessorItems)
-            )
-
-            if isinstance(images, ImageEmbeddingItems):
-                feature_size = images.get_feature_size(item_idx)
-            else:
-                image_size = images.get_image_size(item_idx)
-                feature_size = self.info.get_num_image_tokens(
-                    image_width=image_size.width,
-                    image_height=image_size.height,
-                    processor=hf_processor,
-                )
-
-            num_patches = image_num_patches[item_idx]
-            if num_patches is not None:
-                assert isinstance(num_patches, int)
-
-            return hf_processor.get_image_repl(num_patches, num_features=feature_size)
-
-        return [
-            PromptReplacement(
-                modality="image",
-                target="<image>",
-                replacement=get_replacement_skyworkr1v,
-            )
-        ]
-
-
 @MULTIMODAL_REGISTRY.register_processor(
-    SkyworkR1VMultiModalProcessor,
+    BaseInternVLMultiModalProcessor,
     info=SkyworkR1VProcessingInfo,
-    dummy_inputs=SkyworkR1VDummyInputsBuilder,
+    dummy_inputs=BaseInternVLDummyInputsBuilder,
 )
 class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP):
     @classmethod