[Model] support new model ovis2.5 (#23084)

Signed-off-by: myselvess <244285088@qq.com>
Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>
Co-authored-by: Isotr0py <2037008807@qq.com>
Co-authored-by: Isotr0py <mozf@mail2.sysu.edu.cn>

docs/models/supported_models.md

@@ -641,6 +641,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `MolmoForCausalLM` | Molmo | T + I<sup>+</sup> | `allenai/Molmo-7B-D-0924`, `allenai/Molmo-7B-O-0924`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `NVLM_D_Model` | NVLM-D 1.0 | T + I<sup>+</sup> | `nvidia/NVLM-D-72B`, etc. | | ✅︎ | ✅︎ |
 | `Ovis` | Ovis2, Ovis1.6 | T + I<sup>+</sup> | `AIDC-AI/Ovis2-1B`, `AIDC-AI/Ovis1.6-Llama3.2-3B`, etc. | | ✅︎ | ✅︎ |
+| `Ovis2_5` | Ovis2.5 | T + I<sup>+</sup> + V | `AIDC-AI/Ovis2.5-9B`, etc. | | | ✅︎ |
 | `PaliGemmaForConditionalGeneration` | PaliGemma, PaliGemma 2 | T + I<sup>E</sup> | `google/paligemma-3b-pt-224`, `google/paligemma-3b-mix-224`, `google/paligemma2-3b-ft-docci-448`, etc. | | ✅︎ | ⚠️ |
 | `Phi3VForCausalLM` | Phi-3-Vision, Phi-3.5-Vision | T + I<sup>E+</sup> | `microsoft/Phi-3-vision-128k-instruct`, `microsoft/Phi-3.5-vision-instruct`, etc. | | ✅︎ | ✅︎ |
 | `Phi4MMForCausalLM` | Phi-4-multimodal | T + I<sup>+</sup> / T + A<sup>+</sup> / I<sup>+</sup> + A<sup>+</sup> | `microsoft/Phi-4-multimodal-instruct`, etc. | ✅︎ | ✅︎ | ✅︎ |

examples/offline_inference/vision_language.py

@@ -1105,6 +1105,38 @@ def run_ovis(questions: list[str], modality: str) -> ModelRequestData:
     )
 
 
+# Ovis2_5
+def run_ovis2_5(questions: list[str], modality: str) -> ModelRequestData:
+    model_name = "AIDC-AI/Ovis2.5-2B"
+
+    engine_args = EngineArgs(
+        model=model_name,
+        max_model_len=4096,
+        max_num_seqs=2,
+        trust_remote_code=True,
+        dtype="half",
+        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"{placeholder}\n{question}"}]
+        for question in questions
+    ]
+    prompts = tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompts=prompts,
+    )
+
+
 # PaliGemma
 def run_paligemma(questions: list[str], modality: str) -> ModelRequestData:
     assert modality == "image"
@@ -1579,6 +1611,7 @@ model_example_map = {
     "nemotron_vl": run_nemotron_vl,
     "NVLM_D": run_nvlm_d,
     "ovis": run_ovis,
+    "ovis2_5": run_ovis2_5,
     "paligemma": run_paligemma,
     "paligemma2": run_paligemma2,
     "phi3_v": run_phi3v,

examples/offline_inference/vision_language_multi_image.py

@@ -680,6 +680,36 @@ def load_ovis(question: str, image_urls: list[str]) -> ModelRequestData:
     )
 
 
+# ovis2_5
+def load_ovis2_5(question: str, image_urls: list[str]) -> ModelRequestData:
+    model_name = "AIDC-AI/Ovis2.5-2B"
+
+    engine_args = EngineArgs(
+        model=model_name,
+        max_model_len=8192,
+        max_num_seqs=2,
+        trust_remote_code=True,
+        dtype="half",
+        limit_mm_per_prompt={"image": len(image_urls)},
+    )
+
+    placeholders = "\n".join(
+        f"Image-{i}: <image>\n" for i, _ in enumerate(image_urls, start=1)
+    )
+    messages = [{"role": "user", "content": f"{placeholders}\n{question}"}]
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+    prompt = tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompt=prompt,
+        image_data=[fetch_image(url) for url in image_urls],
+    )
+
+
 def load_pixtral_hf(question: str, image_urls: list[str]) -> ModelRequestData:
     model_name = "mistral-community/pixtral-12b"
 
@@ -1155,6 +1185,7 @@ model_example_map = {
     "mllama": load_mllama,
     "NVLM_D": load_nvlm_d,
     "ovis": load_ovis,
+    "ovis2_5": load_ovis2_5,
     "phi3_v": load_phi3v,
     "phi4_mm": load_phi4mm,
     "phi4_multimodal": load_phi4_multimodal,

tests/models/multimodal/generation/test_common.py

@@ -11,6 +11,7 @@ from pathlib import PosixPath
 import pytest
 from transformers import (AutoModel, AutoModelForImageTextToText,
                           AutoModelForTextToWaveform, AutoModelForVision2Seq)
+from transformers.utils import is_flash_attn_2_available
 
 from vllm.platforms import current_platform
 from vllm.utils import identity
@@ -621,6 +622,26 @@ VLM_TEST_SETTINGS = {
         hf_model_kwargs={"llm_attn_implementation": "sdpa"},
         patch_hf_runner=model_utils.ovis_patch_hf_runner,
     ),
+    "ovis2_5": VLMTestInfo(
+        models=["AIDC-AI/Ovis2.5-2B"],
+        test_type=(
+            VLMTestType.IMAGE,
+            VLMTestType.MULTI_IMAGE,
+            VLMTestType.VIDEO
+        ),
+        prompt_formatter=lambda img_prompt: f"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n", # noqa: E501
+        img_idx_to_prompt=lambda idx: "<image>\n", # noqa: E501
+        video_idx_to_prompt=lambda idx: "<video>\n",
+        max_model_len=4096,
+        max_num_seqs=2,
+        dtype="half",
+        num_logprobs=10,
+        patch_hf_runner=model_utils.ovis2_5_patch_hf_runner,
+        marks=[pytest.mark.skipif(
+            not is_flash_attn_2_available(),
+            reason="HF model needs `flash_attn` installed"
+        )],
+    ),
     "phi3v": VLMTestInfo(
         models=["microsoft/Phi-3.5-vision-instruct"],
         test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),

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

@@ -10,6 +10,7 @@ from typing import Optional, Union
 
 import numpy as np
 import numpy.typing as npt
+import PIL.Image
 import pytest
 import regex as re
 import torch
@@ -810,6 +811,63 @@ def ovis_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
     return hf_model
 
 
+def ovis2_5_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
+    """Patches and returns an instance of the HfRunner to use for Ovis2."""
+    hf_model.model.get_output_embeddings = lambda: \
+        hf_model.model.llm.get_output_embeddings()
+
+    def processor(*args, text="", images=None, videos=None, **kwargs):
+        if images is None:
+            images = []
+        else:
+            images = [images] if isinstance(images, Image) else images
+        if videos is None:
+            videos = []
+        else:
+            videos = [videos] if isinstance(videos, np.ndarray) else videos
+            videos = [[PIL.Image.fromarray(frame) for frame in vid]
+                      for vid in videos]
+
+        prompt_start_and_end = {
+            "qwen2": ("<|im_start|>user\n", "<|im_end|>\n"),
+            "llama":
+            ("<|start_header_id|>user<|end_header_id|>\n\n", "<|eot_id|>"),
+            "gemma2": ("<start_of_turn>user\n", "<end_of_turn>\n"),
+        }
+        for start, end in prompt_start_and_end.values():
+            if start in text and end in text:
+                text = text.split(start)[1].split(end)[0]
+                break
+
+        images_message = [{"type": "image", "image": img} for img in images]
+        videos_message = [{"type": "video", "video": vid} for vid in videos]
+
+        messages = [{
+            "role":
+            "user",
+            "content": [
+                *images_message,
+                *videos_message,
+                {
+                    "type": "text",
+                    "text": text
+                },
+            ],
+        }]
+
+        input_ids, pixel_values, grid_thws = hf_model.model.preprocess_inputs(
+            messages=messages, enable_thinking=True)
+        inputs = {
+            "inputs": input_ids,
+            "pixel_values": pixel_values,
+            "grid_thws": grid_thws,
+        }
+        return BatchFeature(data=inputs, tensor_type="pt")
+
+    hf_model.processor = processor
+    return hf_model
+
+
 def qwen2_5_omni_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
     """Patches and returns an instance of the HfRunner for Qwen2.5-Omni."""
     thinker = hf_model.model.thinker

tests/models/multimodal/processing/test_common.py

@@ -162,6 +162,7 @@ def _test_processing_correctness(
 _ADD_SPECIAL_TOKENS_OVERRIDES = {
     "mllama": False,
     "ovis": False,
+    "ovis2_5": False,
     "paligemma": False,
     "ultravox": False,
     "whisper": False,
@@ -301,6 +302,7 @@ def _test_processing_correctness_one(
     "AIDC-AI/Ovis1.6-Gemma2-9B",
     "AIDC-AI/Ovis1.6-Llama3.2-3B",
     "AIDC-AI/Ovis2-1B",
+    "AIDC-AI/Ovis2.5-2B",
     "google/paligemma-3b-mix-224",
     "google/paligemma2-3b-ft-docci-448",
     "microsoft/Phi-3.5-vision-instruct",

tests/models/registry.py

@@ -464,6 +464,9 @@ _MULTIMODAL_EXAMPLE_MODELS = {
                             transformers_version_reason="HF model is not compatible",  # noqa: E501
                             extras={"1.6-llama": "AIDC-AI/Ovis1.6-Llama3.2-3B",
                                     "1.6-gemma": "AIDC-AI/Ovis1.6-Gemma2-9B"}),  # noqa: E501
+    "Ovis2_5": _HfExamplesInfo("AIDC-AI/Ovis2.5-2B", trust_remote_code=True,
+                            max_transformers_version="4.53",
+                            transformers_version_reason="HF model is not compatible"),  # noqa: E501
     "PaliGemmaForConditionalGeneration": _HfExamplesInfo("google/paligemma-3b-mix-224",  # noqa: E501
                                                          extras={"v2": "google/paligemma2-3b-ft-docci-448"}),  # noqa: E501
     "Phi3VForCausalLM": _HfExamplesInfo("microsoft/Phi-3-vision-128k-instruct",

vllm/model_executor/models/registry.py

@@ -231,6 +231,7 @@ _MULTIMODAL_MODELS = {
     "MolmoForCausalLM": ("molmo", "MolmoForCausalLM"),
     "NVLM_D": ("nvlm_d", "NVLM_D_Model"),
     "Ovis": ("ovis", "Ovis"),
+    "Ovis2_5": ("ovis2_5", "Ovis2_5"),
     "PaliGemmaForConditionalGeneration": ("paligemma", "PaliGemmaForConditionalGeneration"),  # noqa: E501
     "Phi3VForCausalLM": ("phi3v", "Phi3VForCausalLM"),
     "Phi4MMForCausalLM": ("phi4mm", "Phi4MMForCausalLM"),

vllm/transformers_utils/processors/__init__.py

@@ -11,5 +11,6 @@ reasons:
 from vllm.transformers_utils.processors.deepseek_vl2 import (
     DeepseekVLV2Processor)
 from vllm.transformers_utils.processors.ovis import OvisProcessor
+from vllm.transformers_utils.processors.ovis2_5 import Ovis2_5Processor
 
-__all__ = ["DeepseekVLV2Processor", "OvisProcessor"]
+__all__ = ["DeepseekVLV2Processor", "OvisProcessor", "Ovis2_5Processor"]

vllm/transformers_utils/processors/ovis2_5.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import math
+from functools import cached_property
+from typing import Optional, Union
+
+import numpy as np
+import PIL
+import torch
+from transformers import AutoProcessor, BatchFeature
+from transformers.image_utils import ImageInput
+from transformers.processing_utils import (ProcessingKwargs, ProcessorMixin,
+                                           Unpack)
+from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
+
+__all__ = ['Ovis2_5Processor']
+IMAGE_TOKEN = "<image>"
+VIDEO_TOKEN = "<video>"
+MIN_PIXELS = 448 * 448
+MAX_PIXELS = 1792 * 1792
+
+
+class Ovis2_5ProcessorKwargs(ProcessingKwargs,
+                             total=False):  # type: ignore[call-arg]
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+        },
+        "images_kwargs": {
+            'convert_to_rgb': True,
+            'min_pixels': MIN_PIXELS,
+            'max_pixels': MAX_PIXELS,
+        },
+        "videos_kwargs": {
+            'convert_to_rgb': True,
+            'min_pixels': MIN_PIXELS,
+            'max_pixels': MAX_PIXELS,
+        }
+    }
+
+
+class Ovis2_5Processor(ProcessorMixin):
+    r"""
+    Constructs a Ovis processor which wraps a Ovis image processor
+    and a Qwen2 tokenizer into a single processor.
+    [`OvisProcessor`] offers all the functionalities of 
+    [`Qwen2VLImageProcessor`] and [`Qwen2TokenizerFast`]. 
+    See the [`~OvisProcessor.__call__`] and [`~OvisProcessor.decode`]
+    for more information.
+    Args:
+        image_processor ([`Qwen2VLImageProcessor`], *optional*):
+            The image processor is a required input.
+        tokenizer ([`Qwen2TokenizerFast`], *optional*):
+            The tokenizer is a required input.
+        chat_template (`str`, *optional*): A Jinja template which will
+            be used to convert lists of messages in a chat into
+            a tokenizable string.
+    """
+
+    attributes = ["image_processor", "tokenizer"]
+    valid_kwargs = ["chat_template", "image_pad_token"]
+
+    image_processor_class = "AutoImageProcessor"
+    tokenizer_class = "AutoTokenizer"
+
+    def __init__(
+        self,
+        image_processor=None,
+        tokenizer=None,
+        chat_template=None,
+        image_pad_token=None,
+        patch_size=16,
+        hidden_stride=2,
+        temporal_patch_size=1,
+        **kwargs,
+    ):
+        self.image_token = IMAGE_TOKEN
+        self.video_token = VIDEO_TOKEN
+        self.image_pad_token = "<|image_pad|>"
+
+        self.patch_size = patch_size
+        self.hidden_stride = hidden_stride
+        self.temporal_patch_size = temporal_patch_size
+        super().__init__(image_processor,
+                         tokenizer,
+                         chat_template=chat_template)
+
+    @cached_property
+    def extra_special_tokens(self):
+        image_pad_token_id = self.tokenizer.get_vocab()[self.image_pad_token]
+        extra_special_tokens = {
+            "image_token": -200,
+            "video_token": -201,
+            "visual_atom": -300,
+            "image_start": -301,
+            "image_end": -302,
+            "video_start": -303,
+            "video_end": -304,
+            'image_pad': image_pad_token_id,
+        }
+        return extra_special_tokens
+
+    def __call__(
+        self,
+        images: ImageInput = None,
+        videos: Union[np.ndarray, list[ImageInput]] = None,
+        text: Union[TextInput, PreTokenizedInput, list[TextInput],
+                    list[PreTokenizedInput]] = None,
+        **kwargs: Unpack[Ovis2_5ProcessorKwargs],
+    ) -> BatchFeature:
+        """
+        Main method to prepare for the model one or several sequences(s)
+        and image(s). This method forwards the `text`and `kwargs` arguments
+        to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text`
+        is not `None` to encode the text. To prepare the vision inputs,
+        this method forwards the `vision_infos` and `kwrags` arguments to
+        Qwen2VLImageProcessor's [`~Qwen2VLImageProcessor.__call__`]
+        if `vision_infos` is not `None`.
+            Args:
+                images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`,
+                    `list[PIL.Image.Image]`, `list[np.ndarray]`,
+                    `list[torch.Tensor]`):
+                    The image or batch of images to be prepared.
+                    Each image can be a PIL image, NumPy array or PyTorch
+                    tensor. Both channels-first and channels-last formats
+                    are supported.
+                text (`str`, `list[str]`, `list[list[str]]`):
+                    The sequence or batch of sequences to be encoded.
+                    Each sequence can be a string or a list of strings
+                    (pretokenized string). If the sequences are provided as
+                    list of strings (pretokenized), you must set
+                    `is_split_into_words=True` (to lift the ambiguity with
+                    a batch of sequences).
+                videos (`np.ndarray`, `torch.Tensor`, `list[np.ndarray]`,
+                    `list[torch.Tensor]`):
+                    The image or batch of videos to be prepared. Each video
+                    can be a 4D NumPy array or PyTorch tensor, or a nested
+                    list of 3D frames. Both channels-first and channels-last
+                    formats are supported.
+                return_tensors (`str` or [`~utils.TensorType`], *optional*):
+                    If set, will return tensors of a particular framework.
+                    Acceptable values are:
+                    - `'tf'`: Return TensorFlow `tf.constant` objects.
+                    - `'pt'`: Return PyTorch `torch.Tensor` objects.
+                    - `'np'`: Return NumPy `np.ndarray` objects.
+                    - `'jax'`: Return JAX `jnp.ndarray` objects.
+            Returns:
+                [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+                - **input_ids** -- list of token ids to be fed to a model.
+                  Returned when `text` is not `None`.
+                - **attention_mask** -- list of indices specifying which tokens 
+                  should be attended to by the model (when
+                  `return_attention_mask=True` or if *"attention_mask"* 
+                  is in `self.model_input_names` and if `text` is not `None`).
+                - **pixel_values** -- Pixel values to be fed to a model.
+                  Returned when `images` is not `None`.
+                - **pixel_values_videos** -- Pixel values of videos to be fed to
+                  a model. Returned when `videos` is not `None`.
+                - **image_grid_thw** -- list of image 3D grid in LLM. Returned
+                  when `images` is not `None`.
+                - **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`.
+        """
+        output_kwargs = self._merge_kwargs(
+            Ovis2_5ProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+        # Process all images first
+        visual_features = {}
+        output = BatchFeature()
+        if images is not None:
+            processed_images = []
+            image_placeholders_list = []
+            grids = []
+            # 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"]))
+                processed_images.append(pixel_values)
+                image_placeholders_list.append(image_placeholders)
+                grids.append(grid)
+
+            # assign all processed images
+            if processed_images:
+                visual_features["image_placeholders"] = image_placeholders_list
+            output["pixel_values"] = processed_images
+            output["grids"] = grids
+
+        if videos is not None:
+            processed_videos = []
+            videos_placeholders_list = []
+            grids = []
+            # 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"]))
+                processed_videos.append(pixel_values)
+                videos_placeholders_list.append(video_placeholders)
+                grids.append(grid)
+            # assign all processed videos
+            if processed_videos:
+                visual_features[
+                    "video_placeholders"] = videos_placeholders_list
+            output["video_pixel_values"] = processed_videos
+            output["video_grids"] = grids
+
+        # Process text input
+        if text is not None:
+            if not isinstance(text, list):
+                text = [text]
+            tokenized_batched_text = self._tokenize_with_visual_symbol(text)
+            image_token_id = self.get_token_value("image_token")
+            video_token_id = self.get_token_value("video_token")
+            replaced_ids_list = []
+            image_idx = 0
+            video_idx = 0
+            for ids_tensor in tokenized_batched_text:
+                has_image_tokens = (image_token_id in ids_tensor
+                                    and "image_placeholders" in visual_features
+                                    and image_idx < len(
+                                        visual_features["image_placeholders"]))
+                has_video_tokens = (video_token_id in ids_tensor
+                                    and "video_placeholders" in visual_features
+                                    and video_idx < len(
+                                        visual_features["video_placeholders"]))
+                if has_image_tokens or has_video_tokens:
+                    # Convert to list for easier manipulation
+                    ids_list = ids_tensor.tolist()
+                    new_ids = []
+
+                    # Replace placeholders
+                    for token_id in ids_list:
+                        if token_id == image_token_id:
+                            new_ids.extend(
+                                visual_features["image_placeholders"]
+                                [image_idx])
+                            image_idx += 1
+                        elif token_id == video_token_id:
+                            new_ids.extend(
+                                visual_features["video_placeholders"]
+                                [video_idx])
+                            video_idx += 1
+                        else:
+                            new_ids.append(token_id)
+                    # Convert back to tensor
+                    ids_tensor = torch.tensor(new_ids, dtype=torch.long)
+                replaced_ids_list.append(ids_tensor)
+            if replaced_ids_list:
+                replaced_and_tokenized_ids = torch.stack(replaced_ids_list)
+            else:
+                replaced_and_tokenized_ids = torch.tensor([], dtype=torch.long)
+            output["input_ids"] = replaced_and_tokenized_ids
+
+            return output
+        # If only images were provided
+        return BatchFeature(data=visual_features)
+
+    def _tokenize_with_visual_symbol(self,
+                                     text_list: list[str]) -> torch.LongTensor:
+        batch_token_ids = []
+        for text in text_list:
+            token_ids = []
+            video_token_id = self.get_token_value("video_token")
+            image_token_id = self.get_token_value("image_token")
+            video_split_texts = text.split(self.video_token)
+
+            for j, video_segment in enumerate(video_split_texts):
+                image_split_texts = video_segment.split(self.image_token)
+                text_chunks = [
+                    self.tokenizer(chunk, add_special_tokens=False).input_ids
+                    for chunk in image_split_texts
+                ]
+                segment_tokens = []
+                for i, chunk in enumerate(text_chunks):
+                    segment_tokens.extend(chunk)
+                    if i < len(text_chunks) - 1:
+                        segment_tokens.append(image_token_id)
+                token_ids.extend(segment_tokens)
+                if j < len(video_split_texts) - 1:
+                    token_ids.append(video_token_id)
+
+            batch_token_ids.append(token_ids)
+        return torch.tensor(batch_token_ids, dtype=torch.long)
+
+    # Copied from qwen2_vl
+    def smart_resize(self,
+                     height: int,
+                     width: int,
+                     factor: int = 28,
+                     min_pixels: int = MIN_PIXELS,
+                     max_pixels: int = MAX_PIXELS):
+        """Rescales the image so that the following conditions are met:
+        1. Both dimensions (height and width) are divisible by 'factor'.
+        2. The total number of pixels is within the range 
+            ['min_pixels', 'max_pixels'].
+        3. The aspect ratio of the image is maintained as closely as possible.
+        """
+        if height < factor or width < factor:
+            print(f"height:{height} or width:{width} must be "
+                  f"larger than factor:{factor}")
+            if height < width:
+                width = round(factor / height * width)
+                height = factor
+            else:
+                height = round(factor / width * height)
+                width = factor
+
+        elif max(height, width) / min(height, width) > 200:
+            print(f"absolute aspect ratio must be smaller than 200, "
+                  f"got {max(height, width) / min(height, width)}")
+            if height > width:
+                height = 200 * width
+            else:
+                width = 200 * height
+
+        h_bar = round(height / factor) * factor
+        w_bar = round(width / factor) * factor
+        if h_bar * w_bar > max_pixels:
+            beta = math.sqrt((height * width) / max_pixels)
+            h_bar = math.floor(height / beta / factor) * factor
+            w_bar = math.floor(width / beta / factor) * factor
+        elif h_bar * w_bar < min_pixels:
+            beta = math.sqrt(min_pixels / (height * width))
+            h_bar = math.ceil(height * beta / factor) * factor
+            w_bar = math.ceil(width * beta / factor) * factor
+        return h_bar, w_bar
+
+    def get_token_value(self, tok):
+        return self.extra_special_tokens[tok]
+
+    def construct_visual_indicators(self, grid, is_video: bool = False):
+        if is_video:
+            start_token = self.get_token_value('video_start')
+            end_token = self.get_token_value('video_end')
+        else:
+            start_token = self.get_token_value('image_start')
+            end_token = self.get_token_value('image_end')
+
+        image_placeholders = [start_token, self.get_token_value('visual_atom')]
+        if grid[0] * grid[1] > 1:
+            for r in range(grid[0]):
+                for c in range(grid[1]):
+                    image_placeholders.append(
+                        self.get_token_value('visual_atom'))
+
+        image_placeholders.append(end_token)
+        return image_placeholders
+
+    def construct_visual_placeholders(self, grid, is_video: bool = False):
+        visual_placeholders = self.construct_visual_indicators((1, 1),
+                                                               is_video)
+
+        image_atom_token_id = self.get_token_value('visual_atom')
+        # Extract the padding token ID from tokenizer
+        image_padding_token_id = self.get_token_value('image_pad')
+
+        num_image_atoms = grid[0] * grid[1] * grid[2]
+        num_image_atoms //= self.hidden_stride**2
+        num_image_atoms //= self.temporal_patch_size
+
+        # Create a new list with padding tokens inserted
+        padded_placeholder_tokens = []
+        for token in visual_placeholders:
+            if token == image_atom_token_id:
+                padded_placeholder_tokens.extend([image_padding_token_id] *
+                                                 num_image_atoms)
+            else:
+                padded_placeholder_tokens.append(image_padding_token_id)
+        return padded_placeholder_tokens
+
+    def preprocess_multidata(
+        self,
+        images: Optional[Union[PIL.Image.Image, list[PIL.Image.Image]]] = None,
+        video: Optional[Union[list[PIL.Image.Image], np.ndarray]] = None,
+        convert_to_rgb: Optional[bool] = True,
+        min_pixels: int = MIN_PIXELS,
+        max_pixels: int = MAX_PIXELS,
+        return_tensors: Optional[str] = 'pt',
+    ):
+        is_video = False
+        if images is not None:
+            if not isinstance(images, list):
+                images = [images]
+        elif video is not None:
+            is_video = True
+            # type of vidoe in dummy_mm_data is np.ndarray
+            if isinstance(video, np.ndarray):
+                images = []
+                for i in range(video.shape[0]):
+                    image = PIL.Image.fromarray(video[i].astype(np.uint8))
+                    images.append(image)
+            elif isinstance(video, list):
+                images = video
+        min_pixels = min(max_pixels if max_pixels is not None else MAX_PIXELS,
+                         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
+            for image in images
+        ]
+
+        width, height = images[0].size
+        resized_height, resized_width = height, width
+        processed_images = []
+        for image in images:
+            resized_height, resized_width = self.smart_resize(
+                height,
+                width,
+                factor=self.patch_size * self.hidden_stride,
+                min_pixels=min_pixels,
+                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]
+
+            processed_images.append(image_pt)
+
+        patches = np.array(processed_images)
+        if patches.shape[0] % self.temporal_patch_size != 0:
+            num_to_pad = self.temporal_patch_size - (patches.shape[0] %
+                                                     self.temporal_patch_size)
+            repeats = np.repeat(patches[-1][np.newaxis], num_to_pad, axis=0)
+            patches = np.concatenate([patches, repeats], axis=0)
+        channel = patches.shape[1]
+        grid_t = patches.shape[0] // self.temporal_patch_size
+        grid_h = resized_height // self.patch_size
+        grid_w = resized_width // self.patch_size
+
+        patches = patches.reshape(
+            grid_t,
+            self.temporal_patch_size,
+            channel,
+            grid_h // self.hidden_stride,
+            self.hidden_stride,
+            self.patch_size,
+            grid_w // self.hidden_stride,
+            self.hidden_stride,
+            self.patch_size,
+        )
+        patches = patches.transpose(0, 3, 6, 4, 7, 2, 1, 5, 8)
+        flatten_patches = patches.reshape(
+            grid_t * grid_h * grid_w, channel * self.temporal_patch_size *
+            self.patch_size * self.patch_size)
+
+        visual_placeholders = self.construct_visual_placeholders(
+            [grid_t, grid_h, grid_w], is_video)
+        return torch.tensor(
+            flatten_patches), visual_placeholders, torch.tensor(
+                [[grid_t, grid_h, grid_w]])
+
+
+AutoProcessor.register("Ovis2_5Processor", Ovis2_5Processor)