[VLM] Initialize video input support for InternVL models (#18499)

Signed-off-by: Isotr0py <2037008807@qq.com>
Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>

docs/models/supported_models.md

@@ -527,7 +527,7 @@ Specified using `--task generate`.
 | `GraniteSpeechForConditionalGeneration`      | Granite Speech                                                           | T + A                                                                 | `ibm-granite/granite-speech-3.3-8b`                                                                                                                     | ✅︎                     | ✅︎                          | ✅︎                    |
 | `H2OVLChatModel`                             | H2OVL                                                                    | T + I<sup>E+</sup>                                                    | `h2oai/h2ovl-mississippi-800m`, `h2oai/h2ovl-mississippi-2b`, etc.                                                                                      | ✅︎                     | ✅︎\*                        |                       |
 | `Idefics3ForConditionalGeneration`           | Idefics3                                                                 | T + I                                                                 | `HuggingFaceM4/Idefics3-8B-Llama3` etc.                                                                                                                 | ✅︎                     | ✅︎                          |                       |
-| `InternVLChatModel`                          | InternVL 3.0, InternVideo 2.5, InternVL 2.5, Mono-InternVL, InternVL 2.0 | T + I<sup>E+</sup>                                                    | `OpenGVLab/InternVL3-9B`, `OpenGVLab/InternVideo2_5_Chat_8B`, `OpenGVLab/InternVL2_5-4B`, `OpenGVLab/Mono-InternVL-2B`, `OpenGVLab/InternVL2-4B`, etc.  | ✅︎                     | ✅︎                          |                       |
+| `InternVLChatModel`                          | InternVL 3.0, InternVideo 2.5, InternVL 2.5, Mono-InternVL, InternVL 2.0 | T + I<sup>E+</sup> + (V<sup>E+</sup>)                                                    | `OpenGVLab/InternVL3-9B`, `OpenGVLab/InternVideo2_5_Chat_8B`, `OpenGVLab/InternVL2_5-4B`, `OpenGVLab/Mono-InternVL-2B`, `OpenGVLab/InternVL2-4B`, etc.  | ✅︎                     | ✅︎                          |                       |
 | `KimiVLForConditionalGeneration`             | Kimi-VL-A3B-Instruct, Kimi-VL-A3B-Thinking                               | T + I<sup>+</sup>                                                     | `moonshotai/Kimi-VL-A3B-Instruct`, `moonshotai/Kimi-VL-A3B-Thinking`                                                                                    | ✅︎                     |                             |                       |
 | `Llama4ForConditionalGeneration`             | Llama 4                                                                  | T + I<sup>+</sup>                                                     | `meta-llama/Llama-4-Scout-17B-16E-Instruct`, `meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8`, `meta-llama/Llama-4-Maverick-17B-128E-Instruct`, etc. | ✅︎                     | ✅︎                          |                       |
 | `LlavaForConditionalGeneration`              | LLaVA-1.5                                                                | T + I<sup>E+</sup>                                                    | `llava-hf/llava-1.5-7b-hf`, `TIGER-Lab/Mantis-8B-siglip-llama3` (see note), etc.                                                                        | ✅︎                     | ✅︎                          |                       |
@@ -577,6 +577,9 @@ Specified using `--task generate`.
 
     This limitation exists because the model's mixed attention pattern (bidirectional for images, causal otherwise) is not yet supported by vLLM's attention backends.
 
+!!! note
+    Only `InternVLChatModel` with Qwen2.5 text backbone (`OpenGVLab/InternVL3-2B`, `OpenGVLab/InternVL2.5-1B` etc) has video inputs support currently.
+
 !!! note
     `h2oai/h2ovl-mississippi-2b` will be available in V1 once we support head size 80.
 

examples/offline_inference/vision_language.py

@@ -330,22 +330,26 @@ def run_smolvlm(questions: list[str], modality: str) -> ModelRequestData:
 
 # InternVL
 def run_internvl(questions: list[str], modality: str) -> ModelRequestData:
-    assert modality == "image"
 
-    model_name = "OpenGVLab/InternVL2-2B"
+    model_name = "OpenGVLab/InternVL3-2B"
 
     engine_args = EngineArgs(
         model=model_name,
         trust_remote_code=True,
-        max_model_len=4096,
+        max_model_len=8192,
         limit_mm_per_prompt={modality: 1},
     )
 
+    if modality == "image":
+        placeholder = "<image>"
+    elif modality == "video":
+        placeholder = "<video>"
+
     tokenizer = AutoTokenizer.from_pretrained(model_name,
                                               trust_remote_code=True)
     messages = [[{
         'role': 'user',
-        'content': f"<image>\n{question}"
+        'content': f"{placeholder}\n{question}"
     }] for question in questions]
     prompts = tokenizer.apply_chat_template(messages,
                                             tokenize=False,
@@ -357,6 +361,9 @@ def run_internvl(questions: list[str], modality: str) -> ModelRequestData:
     # https://huggingface.co/OpenGVLab/InternVL2-2B/blob/main/conversation.py
     stop_tokens = ["<|endoftext|>", "<|im_start|>", "<|im_end|>", "<|end|>"]
     stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens]
+    stop_token_ids = [
+        token_id for token_id in stop_token_ids if token_id is not None
+    ]
 
     return ModelRequestData(
         engine_args=engine_args,

tests/models/multimodal/generation/test_common.py

@@ -349,6 +349,17 @@ VLM_TEST_SETTINGS = {
         use_tokenizer_eos=True,
         patch_hf_runner=model_utils.internvl_patch_hf_runner,
     ),
+    "intern_vl-video": VLMTestInfo(
+        models=[
+            "OpenGVLab/InternVL3-1B",
+        ],
+        test_type=VLMTestType.VIDEO,
+        prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>Assistant\n", # noqa: E501
+        video_idx_to_prompt=lambda idx: "<video>",
+        max_model_len=8192,
+        use_tokenizer_eos=True,
+        patch_hf_runner=model_utils.internvl_patch_hf_runner,
+    ),
     "kimi_vl": VLMTestInfo(
         models=["moonshotai/Kimi-VL-A3B-Instruct"],
         test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),

tests/models/multimodal/generation/vlm_utils/model_utils.py

@@ -7,6 +7,8 @@ import types
 from pathlib import PosixPath
 from typing import Optional, Union
 
+import numpy as np
+import numpy.typing as npt
 import regex as re
 import torch
 from PIL.Image import Image
@@ -495,30 +497,74 @@ def internvl_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
             self.max_num = self.config.max_dynamic_patch
             self.image_size = self.vision_config.image_size
 
-        def __call__(self, text: str, images: Union[Image, list[Image]],
-                     **kwargs):
+        def __call__(
+            self,
+            text: str,
+            images: Union[Image, list[Image]] = None,
+            videos: Union[npt.NDArray, list[npt.NDArray]] = None,
+            **kwargs,
+        ):
             from vllm.model_executor.models.internvl import (
                 IMG_CONTEXT, IMG_END, IMG_START,
-                image_to_pixel_values_internvl)
+                image_to_pixel_values_internvl, video_to_pixel_values_internvl)
             images = [images] if isinstance(images, Image) else images
-            pixel_values = [
-                image_to_pixel_values_internvl(
-                    image,
-                    input_size=self.image_size,
-                    min_num=self.min_num,
-                    max_num=self.max_num,
-                    use_thumbnail=self.use_thumbnail,
-                ) for image in images
-            ]
-            num_patches_list = [
-                pixel_value.shape[0] for pixel_value in pixel_values
-            ]
+            videos = [videos] if isinstance(videos, np.ndarray) else videos
+            if images is not None:
+                pixel_values_images = [
+                    image_to_pixel_values_internvl(
+                        image,
+                        input_size=self.image_size,
+                        min_num=self.min_num,
+                        max_num=self.max_num,
+                        use_thumbnail=self.use_thumbnail,
+                    ) for image in images
+                ]
+                num_patches_images = [
+                    pixel_value.shape[0] for pixel_value in pixel_values_images
+                ]
+            else:
+                pixel_values_images, num_patches_images = [], []
+
+            if videos is not None:
+                pixel_values_videos = [
+                    video_to_pixel_values_internvl(
+                        video,
+                        input_size=self.image_size,
+                        min_num=1,
+                        max_num=1,
+                        use_thumbnail=False,
+                    ) for video in videos
+                ]
+                num_patches_videos = [
+                    pixel_value.shape[0] for pixel_value in pixel_values_videos
+                ]
+            else:
+                pixel_values_videos, num_patches_videos = [], []
+
+            pixel_values = []
+            while ("<image>" in text) or ("<video>" in text):
+                image_index = text.find("<image>")
+                video_index = text.find("<video>")
+                if image_index == -1 or (video_index > -1
+                                         and video_index < image_index):
+                    num_patches = num_patches_videos.pop(0)
+                    pixel_values.append(pixel_values_videos.pop(0))
+                    context_tokens = IMG_START + \
+                        IMG_CONTEXT * self.num_image_token + IMG_END
+                    video_tokens = ''.join([
+                        f'Frame{i+1}: {context_tokens}'
+                        for i in range(num_patches)
+                    ])
+                    text = text.replace('<video>', video_tokens, 1)
+                else:
+                    num_patches = num_patches_images.pop(0)
+                    pixel_values.append(pixel_values_images.pop(0))
+                    context_tokens = IMG_CONTEXT * self.num_image_token \
+                        * num_patches
+                    image_tokens = IMG_START + context_tokens + IMG_END
+                    text = text.replace('<image>', image_tokens, 1)
             pixel_values = torch.cat(pixel_values, dim=0)
-            for num_patches in num_patches_list:
-                context_tokens = IMG_CONTEXT * self.num_image_token \
-                    * num_patches
-                image_tokens = IMG_START + context_tokens + IMG_END
-                text = text.replace('<image>', image_tokens, 1)
+
             prompt = self.tokenizer(text, return_tensors="pt")
             prompt.update({"pixel_values": pixel_values})
             return prompt

tests/models/multimodal/processing/test_common.py

@@ -258,6 +258,7 @@ def _test_processing_correctness_mistral(
     "ibm-granite/granite-speech-3.3-8b",
     "h2oai/h2ovl-mississippi-800m",
     "OpenGVLab/InternVL2-1B",
+    "OpenGVLab/InternVL3-1B",
     "HuggingFaceM4/Idefics3-8B-Llama3",
     "HuggingFaceTB/SmolVLM2-2.2B-Instruct",
     "moonshotai/Kimi-VL-A3B-Instruct",

tests/models/registry.py

@@ -334,7 +334,8 @@ _MULTIMODAL_EXAMPLE_MODELS = {
                                       max_transformers_version="4.48",  # noqa: E501
                                       transformers_version_reason="HF model is not compatible."),  # noqa: E501
     "InternVLChatModel": _HfExamplesInfo("OpenGVLab/InternVL2-1B",
-                                         extras={"2B": "OpenGVLab/InternVL2-2B"},  # noqa: E501
+                                         extras={"2B": "OpenGVLab/InternVL2-2B",
+                                                 "3.0": "OpenGVLab/InternVL3-1B"},  # noqa: E501
                                          trust_remote_code=True),
     "Idefics3ForConditionalGeneration": _HfExamplesInfo("HuggingFaceM4/Idefics3-8B-Llama3",  # noqa: E501
                                                         {"tiny": "HuggingFaceTB/SmolVLM-256M-Instruct"}),  # noqa: E501

vllm/entrypoints/chat_utils.py

@@ -556,6 +556,8 @@ class BaseMultiModalItemTracker(ABC, Generic[_T]):
                 return "(<audio>./</audio>)"
             raise TypeError(f"Unknown model type: {model_type}")
         elif modality == "video":
+            if model_type == "internvl_chat":
+                return "<video>"
             if model_type in ("qwen2_vl", "qwen2_5_vl"):
                 return "<|vision_start|><|video_pad|><|vision_end|>"
             if model_type == "qwen2_5_omni":

vllm/model_executor/models/h2ovl.py

@@ -25,9 +25,10 @@ from vllm.transformers_utils.tokenizer import AnyTokenizer
 
 from .intern_vit import InternVisionModel
 from .internvl import (IMG_CONTEXT, IMG_END, IMG_START,
+                       BaseInternVLDummyInputsBuilder,
+                       BaseInternVLMultiModalProcessor,
                        BaseInternVLProcessingInfo, BaseInternVLProcessor,
-                       InternVLChatModel, InternVLDummyInputsBuilder,
-                       InternVLMultiModalProcessor, build_transform,
+                       InternVLChatModel, build_transform,
                        find_closest_aspect_ratio, get_internvl_target_ratios)
 
 
@@ -430,8 +431,8 @@ class H2OVLProcessingInfo(BaseInternVLProcessingInfo):
         )
 
 
-class H2OVLMultiModalProcessor(InternVLMultiModalProcessor[H2OVLProcessingInfo]
-                               ):
+class H2OVLMultiModalProcessor(
+        BaseInternVLMultiModalProcessor[H2OVLProcessingInfo]):
 
     def _get_prompt_updates(
         self,
@@ -514,7 +515,7 @@ class H2OVLMultiModalProcessor(InternVLMultiModalProcessor[H2OVLProcessingInfo]
 @MULTIMODAL_REGISTRY.register_processor(
     H2OVLMultiModalProcessor,
     info=H2OVLProcessingInfo,
-    dummy_inputs=InternVLDummyInputsBuilder)
+    dummy_inputs=BaseInternVLDummyInputsBuilder)
 class H2OVLChatModel(InternVLChatModel):
 
     def _init_vision_model(

vllm/model_executor/models/nvlm_d.py

@@ -22,9 +22,10 @@ from vllm.multimodal.processing import (PromptReplacement, PromptUpdate,
                                         PromptUpdateDetails)
 
 from .intern_vit import InternVisionModel
-from .internvl import (BaseInternVLProcessingInfo, BaseInternVLProcessor,
-                       InternVLChatModel, InternVLDummyInputsBuilder,
-                       InternVLMultiModalProcessor)
+from .internvl import (BaseInternVLDummyInputsBuilder,
+                       BaseInternVLMultiModalProcessor,
+                       BaseInternVLProcessingInfo, BaseInternVLProcessor,
+                       InternVLChatModel)
 
 IMG_PAD = "<|vision_pad|>"
 
@@ -84,7 +85,8 @@ class NVLMProcessingInfo(BaseInternVLProcessingInfo):
         )
 
 
-class NVLMDummyInputsBuilder(InternVLDummyInputsBuilder[NVLMProcessingInfo]):
+class NVLMDummyInputsBuilder(BaseInternVLDummyInputsBuilder[NVLMProcessingInfo]
+                             ):
 
     def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
         num_images = mm_counts.get("image", 0)
@@ -110,7 +112,8 @@ class NVLMDummyInputsBuilder(InternVLDummyInputsBuilder[NVLMProcessingInfo]):
         }
 
 
-class NVLMMultiModalProcessor(InternVLMultiModalProcessor[NVLMProcessingInfo]):
+class NVLMMultiModalProcessor(
+        BaseInternVLMultiModalProcessor[NVLMProcessingInfo]):
 
     def _get_prompt_updates(
         self,