[Models] Kimi-K2.5 (#33131)

Signed-off-by: wanglinian <wanglinian@stu.pku.edu.cn>
Signed-off-by: wangln19 <96399074+wangln19@users.noreply.github.com>
Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>
Signed-off-by: youkaichao <youkaichao@gmail.com>
Signed-off-by: Roger Wang <hey@rogerw.io>
Co-authored-by: wanglinian <wanglinian@stu.pku.edu.cn>
Co-authored-by: wangln19 <96399074+wangln19@users.noreply.github.com>
Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com>
Co-authored-by: Isotr0py <mozf@mail2.sysu.edu.cn>
Co-authored-by: Nick Hill <nickhill123@gmail.com>
Co-authored-by: youkaichao <youkaichao@gmail.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
(cherry picked from commit b539f988e1)

docs/models/supported_models.md

@@ -686,6 +686,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `KeyeForConditionalGeneration` | Keye-VL-8B-Preview | T + I<sup>E+</sup> + V<sup>E+</sup> | `Kwai-Keye/Keye-VL-8B-Preview` | ✅︎ | ✅︎ |
 | `KeyeVL1_5ForConditionalGeneration` | Keye-VL-1_5-8B | T + I<sup>E+</sup> + V<sup>E+</sup> | `Kwai-Keye/Keye-VL-1_5-8B` | ✅︎ | ✅︎ |
 | `KimiVLForConditionalGeneration` | Kimi-VL-A3B-Instruct, Kimi-VL-A3B-Thinking | T + I<sup>+</sup> | `moonshotai/Kimi-VL-A3B-Instruct`, `moonshotai/Kimi-VL-A3B-Thinking` | | ✅︎ |
+| `KimiK25ForConditionalGeneration` | Kimi-K2.5 | T + I<sup>+</sup> | `moonshotai/Kimi-K2.5` | | ✅︎ |
 | `LightOnOCRForConditionalGeneration`  | LightOnOCR-1B  | T + I<sup>+</sup> | `lightonai/LightOnOCR-1B`, etc | ✅︎ | ✅︎ |
 | `Lfm2VlForConditionalGeneration` | LFM2-VL | T + I<sup>+</sup> | `LiquidAI/LFM2-VL-450M`, `LiquidAI/LFM2-VL-3B`, `LiquidAI/LFM2-VL-8B-A1B`, etc. | ✅︎ | ✅︎ |
 | `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. | ✅︎ | ✅︎ |

tests/models/registry.py

@@ -771,6 +771,11 @@ _MULTIMODAL_EXAMPLE_MODELS = {
             )
         },
     ),
+    "KimiK25ForConditionalGeneration": _HfExamplesInfo(
+        "moonshotai/Kimi-K2.5",
+        trust_remote_code=True,
+        is_available_online=False,
+    ),
     "LightOnOCRForConditionalGeneration": _HfExamplesInfo(
         "lightonai/LightOnOCR-1B-1025"
     ),

vllm/entrypoints/chat_utils.py

@@ -46,6 +46,9 @@ from vllm.multimodal.inputs import (
     MultiModalBatchedField,
     MultiModalFlatField,
     MultiModalSharedField,
+    VisionChunk,
+    VisionChunkImage,
+    VisionChunkVideo,
 )
 from vllm.multimodal.processing import BaseMultiModalProcessor
 from vllm.multimodal.utils import MEDIA_CONNECTOR_REGISTRY, MediaConnector
@@ -336,7 +339,9 @@ ChatTemplateContentFormatOption = Literal["auto", "string", "openai"]
 ChatTemplateContentFormat = Literal["string", "openai"]
 
 
-ModalityStr = Literal["image", "audio", "video", "image_embeds", "audio_embeds"]
+ModalityStr = Literal[
+    "image", "audio", "video", "image_embeds", "audio_embeds", "vision_chunk"
+]
 _T = TypeVar("_T")
 
 
@@ -449,6 +454,78 @@ def _get_embeds_data(
     raise NotImplementedError(type(data_items))
 
 
+def rebuild_mm_uuids_from_mm_data(
+    mm_uuids: MultiModalUUIDDict,
+    mm_data: MultiModalDataDict,
+) -> MultiModalUUIDDict:
+    """Rebuild mm_uuids after vision_chunk processing.
+
+    When videos are split into chunks, the original UUIDs need to be updated
+    to reflect the new UUIDs generated for each chunk.
+
+    Args:
+        mm_uuids: Original UUIDs dictionary
+        mm_data: Processed multimodal data with vision_chunk items
+
+    Returns:
+        Updated UUIDs dictionary with chunk UUIDs
+    """
+    vision_chunks = mm_data.get("vision_chunk")
+    if vision_chunks is None:
+        return mm_uuids
+
+    new_uuids = dict(mm_uuids)
+    vision_chunk_uuids = []
+
+    for item in vision_chunks:
+        # vision_chunk items are always dicts (VisionChunkImage/VisionChunkVideo)
+        assert isinstance(item, dict)
+        uuid_val = item.get("uuid")
+        if uuid_val is not None:
+            vision_chunk_uuids.append(uuid_val)
+
+    if vision_chunk_uuids:
+        new_uuids["vision_chunk"] = vision_chunk_uuids
+
+    return new_uuids
+
+
+def build_video_prompts_from_mm_data(
+    mm_data: MultiModalDataDict,
+) -> list[str]:
+    """Build video prompts from vision_chunk data.
+
+    Collects prompts from video chunks and groups them by video_idx.
+
+    Args:
+        mm_data: Processed multimodal data with vision_chunk items
+
+    Returns:
+        List of video prompts, one per video.
+    """
+    vision_chunks = mm_data.get("vision_chunk")
+    if vision_chunks is None:
+        return []
+
+    # Group chunks by video_idx
+    video_prompts_dict: dict[int, list[str]] = defaultdict(list)
+
+    for item in vision_chunks:
+        # vision_chunk items are always dicts (VisionChunkImage/VisionChunkVideo)
+        assert isinstance(item, dict)
+        if item.get("type") == "video_chunk":
+            video_idx = item.get("video_idx", 0)
+            prompt = item.get("prompt", "")
+            video_prompts_dict[video_idx].append(prompt)
+
+    # Build prompts in video order
+    video_prompts = []
+    for video_idx in sorted(video_prompts_dict.keys()):
+        video_prompts.append("".join(video_prompts_dict[video_idx]))
+
+    return video_prompts
+
+
 class BaseMultiModalItemTracker(ABC, Generic[_T]):
     """
     Tracks multi-modal items in a given request and ensures that the number
@@ -462,6 +539,13 @@ class BaseMultiModalItemTracker(ABC, Generic[_T]):
         self._model_config = model_config
 
         self._items_by_modality = defaultdict[str, list[_T]](list)
+        # Track original modality for each vision_chunk item (image or video)
+        self._modality_order = defaultdict[str, list[str]](list)
+
+    @cached_property
+    def use_unified_vision_chunk_modality(self) -> bool:
+        """Check if model uses unified vision_chunk modality for images/videos."""
+        return getattr(self._model_config.hf_config, "use_unified_vision_chunk", False)
 
     @property
     def model_config(self) -> ModelConfig:
@@ -499,11 +583,31 @@ class BaseMultiModalItemTracker(ABC, Generic[_T]):
         media.
         """
         input_modality = modality.replace("_embeds", "")
-        num_items = len(self._items_by_modality[modality]) + 1
+        original_modality = modality
+        use_vision_chunk = (
+            self.use_unified_vision_chunk_modality
+            and original_modality in ["video", "image"]
+        )
+
+        # If use_unified_vision_chunk_modality is enabled,
+        # map image/video to vision_chunk
+        if use_vision_chunk:
+            # To avoid validation fail
+            # because models with use_unified_vision_chunk_modality=True
+            # will only accept vision_chunk modality.
+            input_modality = "vision_chunk"
+            num_items = len(self._items_by_modality[input_modality]) + 1
+        else:
+            num_items = len(self._items_by_modality[original_modality]) + 1
 
         self.mm_processor.validate_num_items(input_modality, num_items)
 
-        self._items_by_modality[modality].append(item)
+        # Track original modality for vision_chunk items
+        if use_vision_chunk:
+            self._items_by_modality[input_modality].append(item)  # type: ignore
+            self._modality_order["vision_chunk"].append(original_modality)
+        else:
+            self._items_by_modality[original_modality].append(item)
 
         return self.model_cls.get_placeholder_str(modality, num_items)
 
@@ -515,6 +619,7 @@ class BaseMultiModalItemTracker(ABC, Generic[_T]):
 def _resolve_items(
     items_by_modality: dict[str, list[tuple[object, str | None]]],
     mm_processor: BaseMultiModalProcessor,
+    vision_chunk_modality_order: dict[str, list[str]],
 ) -> tuple[MultiModalDataDict, MultiModalUUIDDict]:
     if "image" in items_by_modality and "image_embeds" in items_by_modality:
         raise ValueError("Mixing raw image and embedding inputs is not allowed")
@@ -546,6 +651,74 @@ def _resolve_items(
     if "video" in items_by_modality:
         mm_data["video"] = [data for data, uuid in items_by_modality["video"]]
         mm_uuids["video"] = [uuid for data, uuid in items_by_modality["video"]]
+    if "vision_chunk" in items_by_modality:
+        # Process vision_chunk items - extract from (data, modality) tuples
+        # and convert to VisionChunk types with proper UUID handling
+        vision_chunk_items = items_by_modality["vision_chunk"]
+        modality_order = vision_chunk_modality_order.get("vision_chunk", [])
+        mm_uuids["vision_chunk"] = [
+            uuid for data, uuid in items_by_modality["vision_chunk"]
+        ]
+
+        # Filter out None items (from asyncio.sleep(0) placeholders)
+        filtered_items = [
+            (idx, item)
+            for idx, item in enumerate(vision_chunk_items)
+            if item is not None
+        ]
+
+        assert len(filtered_items) == len(modality_order), (
+            f"vision_chunk items ({len(filtered_items)}) and "
+            f"modality_order ({len(modality_order)}) must have same length"
+        )
+
+        processed_chunks: list[VisionChunk] = []
+        video_idx = 0
+        for i, (idx, item) in enumerate(filtered_items):
+            inner_modality = modality_order[i]
+            data, uuid = item
+            uuid_val = uuid if idx < len(mm_uuids["vision_chunk"]) else None
+            if inner_modality == "image":
+                # Cast data to proper type for image
+                # Use .media (PIL.Image) directly to avoid redundant
+                # bytes→PIL conversion in media_processor
+                if hasattr(data, "media"):
+                    image_data = data.media  # type: ignore[union-attr]
+                    processed_chunks.append(
+                        VisionChunkImage(type="image", image=image_data, uuid=uuid_val)
+                    )
+                else:
+                    processed_chunks.append(data)  # type: ignore[arg-type]
+            elif inner_modality == "video":
+                # For video, we may need to split into chunks
+                # if processor supports it
+                # For now, just wrap as a video chunk placeholder
+                if hasattr(mm_processor, "split_video_chunks") and data is not None:
+                    try:
+                        video_uuid = uuid_val or random_uuid()
+                        # video await result is (video_data, video_meta) tuple
+                        if isinstance(data, tuple) and len(data) >= 1:
+                            video_data = data[0]
+                        else:
+                            video_data = data
+                        video_chunks = mm_processor.split_video_chunks(video_data)
+                        for i, vc in enumerate(video_chunks):
+                            processed_chunks.append(
+                                VisionChunkVideo(
+                                    type="video_chunk",
+                                    video_chunk=vc["video_chunk"],
+                                    uuid=f"{video_uuid}-{i}",
+                                    video_idx=video_idx,
+                                    prompt=vc["prompt"],
+                                )
+                            )
+                        video_idx += 1
+                    except Exception as e:
+                        logger.warning("Failed to split video chunks: %s", e)
+                        processed_chunks.append(data)  # type: ignore[arg-type]
+                else:
+                    processed_chunks.append(data)  # type: ignore[arg-type]
+        mm_data["vision_chunk"] = processed_chunks
 
     return mm_data, mm_uuids
 
@@ -557,7 +730,9 @@ class MultiModalItemTracker(BaseMultiModalItemTracker[tuple[object, str | None]]
         if not self._items_by_modality:
             return None, None
 
-        return _resolve_items(dict(self._items_by_modality), self.mm_processor)
+        return _resolve_items(
+            dict(self._items_by_modality), self.mm_processor, self._modality_order
+        )
 
     def create_parser(self) -> "BaseMultiModalContentParser":
         return MultiModalContentParser(self)
@@ -577,7 +752,9 @@ class AsyncMultiModalItemTracker(
             for modality, coros in self._items_by_modality.items()
         }
 
-        return _resolve_items(resolved_items_by_modality, self.mm_processor)
+        return _resolve_items(
+            resolved_items_by_modality, self.mm_processor, self._modality_order
+        )
 
     def create_parser(self) -> "BaseMultiModalContentParser":
         return AsyncMultiModalContentParser(self)

vllm/envs.py

@@ -782,6 +782,7 @@ environment_variables: dict[str, Callable[[], Any]] = {
     ),
     # Backend for Video IO
     # - "opencv": Default backend that uses OpenCV stream buffered backend.
+    # - "identity": Returns raw video bytes for model processor to handle.
     #
     # Custom backend implementations can be registered
     # via `@VIDEO_LOADER_REGISTRY.register("my_custom_video_loader")` and

vllm/model_executor/models/kimi_k25.py

@@ -0,0 +1,581 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# ruff: noqa: E501
+"""
+Kimi-K2.5 Model Implementation for vLLM.
+
+Kimi-K2.5 extends Kimi-K2 with vision support
+
+This module defines:
+- KimiK25ProcessingInfo/KimiK25MultiModalProcessor: Processing logic
+- KimiK25ForConditionalGeneration: Main model class
+"""
+
+import copy
+from collections.abc import Iterable, Mapping, Sequence
+from dataclasses import dataclass
+from typing import Annotated, Any, Literal
+
+import torch
+from torch import nn
+from transformers import BatchFeature
+from transformers.processing_utils import ProcessorMixin
+
+from vllm.config import VllmConfig
+from vllm.config.multimodal import BaseDummyOptions
+from vllm.distributed import get_pp_group
+from vllm.logger import init_logger
+from vllm.model_executor.layers.fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    maybe_remap_kv_scale_name,
+)
+from vllm.model_executor.models.deepseek_v2 import DeepseekV2Model
+from vllm.model_executor.models.interfaces import SupportsMultiModal, SupportsPP
+from vllm.model_executor.models.kimi_k25_vit import (
+    KimiK25MultiModalProjector,
+    MoonViT3dPretrainedModel,
+    vision_tower_forward,
+)
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (
+    MultiModalDataDict,
+    MultiModalFieldConfig,
+    MultiModalKwargsItems,
+    NestedTensors,
+    VisionChunk,
+    VisionChunkImage,
+    VisionChunkVideo,
+)
+from vllm.multimodal.parse import MultiModalDataItems, VisionChunkProcessorItems
+from vllm.multimodal.processing import (
+    BaseDummyInputsBuilder,
+    BaseMultiModalProcessor,
+    BaseProcessingInfo,
+    InputProcessingContext,
+    PromptReplacement,
+    PromptUpdate,
+)
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.configs import KimiK25Config
+from vllm.transformers_utils.processor import cached_get_image_processor
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
+
+from .utils import PPMissingLayer, is_pp_missing_parameter, maybe_prefix
+
+logger = init_logger(__name__)
+
+
+# Dummy input dimensions for profiling.
+@dataclass
+class MaxImageTokenMeta:
+    width: int = 3000
+    height: int = 3000
+
+
+class KimiK25MediaPixelInputs(TensorSchema):
+    """
+    Media input schema for K2-VL model.
+
+    Dimensions:
+        - np: Number of patches (flattened from all media items)
+        - ps: Patch size
+        - nm: Number of media items
+    """
+
+    type: Literal["pixel_values"] = "pixel_values"
+
+    pixel_values: Annotated[
+        torch.Tensor | list[torch.Tensor],
+        TensorShape("np", 3, "ps", "ps"),
+    ]
+
+    grid_thws: Annotated[torch.Tensor, TensorShape("nm", 3)]
+
+
+class MoonshotKimiVAutoProcessor(ProcessorMixin):
+    attributes = ["tokenizer"]
+    tokenizer_class = "AutoTokenizer"
+
+    def __init__(self, media_processor=None, tokenizer=None):
+        super().__init__(tokenizer)
+        self.media_processor = media_processor
+
+    # We do not support str input for text here
+    def __call__(
+        self,
+        vision_chunks: list[VisionChunk] | None = None,
+        *,
+        text: list[int],
+        **kwargs,
+    ) -> BatchFeature:
+        """
+        Args:
+            vision_chunks: List of VisionChunk items to be processed.
+                For image: VisionChunkImage with type='image', image=PIL.Image
+                For video_chunk: VisionChunkVideo with type='video_chunk', video_chunk=list[PIL.Image]
+            text: The token ids to be fed to a model (required).
+        Returns:
+            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+            - **input_ids** -- list of token ids to be fed to a model.
+            - **pixel_values** -- Pixel values to be fed to a model. Returned when `vision_chunks` is not `None`.
+            - **grid_thws** -- list of image 3D grid in LLM. Returned when `vision_chunks` is not `None`.
+        """
+        mm_inputs = {}
+        if vision_chunks is not None:
+            assert isinstance(vision_chunks, list)
+            mm_inputs = self.media_processor.preprocess(vision_chunks)
+        # XXX: _apply_hf_processor_text_mm will call tolist() on input_ids
+        return BatchFeature(
+            data={
+                "input_ids": torch.tensor([text]),
+                **mm_inputs,
+            }
+        )
+
+
+class KimiK25ProcessingInfo(BaseProcessingInfo):
+    """Processing information for Kimi-K2.5 model.
+
+    Provides configuration and utilities for processing both
+    images and video-chunks.
+    """
+
+    def __init__(self, ctx: InputProcessingContext) -> None:
+        super().__init__(ctx)
+        self.hf_config = self.get_hf_config()
+        self.media_token_id = self.hf_config.media_placeholder_token_id
+        media_processor = cached_get_image_processor(
+            self.ctx.model_config.model, trust_remote_code=True
+        )
+        self.media_processor = media_processor
+        self.hf_processor = MoonshotKimiVAutoProcessor(
+            media_processor=self.media_processor,
+            tokenizer=self.get_tokenizer(),
+        )
+        self.media_tokens_calculator = self.media_processor.media_tokens_calculator
+
+    def get_hf_processor(self):
+        return self.hf_processor
+
+    def get_hf_config(self):
+        return self.ctx.get_hf_config(KimiK25Config)
+
+    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
+        # None means unlimited
+        return {"vision_chunk": None}
+
+
+class KimiK25DummyInputsBuilder(BaseDummyInputsBuilder[KimiK25ProcessingInfo]):
+    """Builds dummy inputs for Kimi-K2.5 model profiling."""
+
+    def __init__(self, info: KimiK25ProcessingInfo) -> None:
+        super().__init__(info)
+        self.media_token_id = self.info.media_token_id
+        self.frame_per_chunk = self.info.media_processor.num_frames_per_chunk
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> list[int]:
+        num_media = mm_counts.get("vision_chunk", 0)
+        return [self.media_token_id] * num_media
+
+    def get_dummy_mm_items(self):
+        dummy_videos = self._get_dummy_images(
+            height=MaxImageTokenMeta.height,
+            width=MaxImageTokenMeta.width,
+            num_images=self.frame_per_chunk,
+        )
+
+        video_chunk_dummy_item = VisionChunkVideo(
+            type="video_chunk", video_chunk=dummy_videos
+        )
+        video_chunk_num_tokens = self.info.media_tokens_calculator(
+            video_chunk_dummy_item
+        )
+
+        image_dummy_item = VisionChunkImage(
+            type="image",
+            image=self._get_dummy_images(
+                height=MaxImageTokenMeta.height,
+                width=MaxImageTokenMeta.width,
+                num_images=1,
+            )[0],
+        )
+        image_num_tokens = self.info.media_tokens_calculator(image_dummy_item)
+        # return the larger one
+        if video_chunk_num_tokens >= image_num_tokens:
+            return [video_chunk_dummy_item]
+        else:
+            return [image_dummy_item]
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+        mm_options: Mapping[str, BaseDummyOptions] | None = None,
+    ) -> MultiModalDataDict:
+        # TODO: Support mm_options for vision_chunk to allow user configuration
+        dummy_items = self.get_dummy_mm_items()
+        return {"vision_chunk": dummy_items}
+
+
+class KimiK25MultiModalProcessor(BaseMultiModalProcessor[KimiK25ProcessingInfo]):
+    """Multi-modal processor for Kimi-K2.5.
+
+    Handles both image and video-chunk modalities.
+    """
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        """Indicates how to slice media input into multiple items.
+
+        pixel_values: [N, 3, patch_size, patch_size], all patches collected from B medias
+        grid_thws: [B,3], each item: [N_t, N_h ,N_w], indicates the grid size in time/height/width direction
+                    for current item.
+
+        by multiplying [N_t, N_h ,N_w], we get the number of patches for each media item, thus we can slice
+        pixel_values by pixel_values[start:start + N_t*N_h*N_w] to get patches of one item.
+
+        """
+        grid_thws = hf_inputs.get("grid_thws", torch.empty((0, 3)))
+        grid_sizes = grid_thws.prod(-1)
+
+        return dict(
+            pixel_values=MultiModalFieldConfig.flat_from_sizes(
+                "vision_chunk", grid_sizes
+            ),
+            grid_thws=MultiModalFieldConfig.batched("vision_chunk"),
+        )
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, Any],
+        out_mm_kwargs: MultiModalKwargsItems,
+    ) -> Sequence[PromptUpdate]:
+        hf_config = self.info.get_hf_config()
+        media_token_id = hf_config.media_placeholder_token_id
+
+        def get_replacement(item_idx: int):
+            media = mm_items.get_items("vision_chunk", (VisionChunkProcessorItems,))
+            num_media_token = self.info.media_tokens_calculator(media[item_idx])
+            return [media_token_id] * num_media_token
+
+        return [
+            PromptReplacement(
+                modality="vision_chunk",
+                target=[media_token_id],
+                replacement=get_replacement,
+            ),
+        ]
+
+    def split_video_chunks(self, video):
+        return self.info.media_processor.split_video_chunks(video)
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    KimiK25MultiModalProcessor,
+    info=KimiK25ProcessingInfo,
+    dummy_inputs=KimiK25DummyInputsBuilder,
+)
+class KimiK25ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
+    """Kimi-K2.5 model for conditional generation.
+
+    Supports both image and video-chunk modalities.
+    Video-chunks are temporal segments (typically 4 frames) that are
+    processed with temporal pooling.
+    """
+
+    supports_encoder_tp_data = True
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
+        # Kimi-K2.5 uses video_chunk for all media types
+        if modality == "image":
+            return "<|media_begin|>image<|media_content|><|media_pad|><|media_end|>"
+        elif modality == "video":
+            # return a placeholder, to be replaced in the future.
+            return "<|kimi_k25_video_placeholder|>"
+
+        raise ValueError(f"Unsupported modality: {modality}")
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        model_config = vllm_config.model_config
+        config: KimiK25Config = model_config.hf_config
+        self.config = config
+        quant_config = vllm_config.quant_config
+
+        # Check for MoonViT config compatibility
+        self.use_data_parallel = (
+            model_config.multimodal_config.mm_encoder_tp_mode == "data"
+        )
+        self.hidden_size = config.text_config.hidden_size
+        self.device = torch.cuda.current_device()
+        # Build vision tower directly with KimiK25VisionConfig
+        self.vision_tower = MoonViT3dPretrainedModel(
+            config.vision_config,
+            prefix=maybe_prefix(prefix, "vision_tower"),
+        )
+        self.vision_tower = self.vision_tower.to(
+            device=self.device, dtype=model_config.dtype
+        )
+
+        self.mm_projector = KimiK25MultiModalProjector(
+            config=config.vision_config,
+            use_data_parallel=self.use_data_parallel,
+            prefix=maybe_prefix(prefix, "mm_projector"),
+        )
+        self.mm_projector = self.mm_projector.to(
+            device=self.device, dtype=model_config.dtype
+        )
+
+        self.quant_config = quant_config
+        sub_vllm_config = copy.deepcopy(vllm_config)
+        sub_vllm_config.model_config.hf_config = (
+            sub_vllm_config.model_config.hf_config.text_config
+        )
+        self.language_model = DeepseekV2Model(
+            vllm_config=sub_vllm_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
+        if get_pp_group().is_last_rank:
+            self.lm_head = ParallelLMHead(
+                config.vocab_size,
+                config.text_config.hidden_size,
+                prefix=maybe_prefix(prefix, "lm_head"),
+            )
+        else:
+            self.lm_head = PPMissingLayer()
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors
+        )
+        logit_scale = getattr(config, "logit_scale", 1.0)
+        self.logits_processor = LogitsProcessor(config.vocab_size, scale=logit_scale)
+        self.media_placeholder: int = self.config.media_placeholder_token_id
+
+    def _parse_and_validate_media_input(
+        self, **kwargs: object
+    ) -> KimiK25MediaPixelInputs | None:
+        pixel_values = kwargs.pop("pixel_values", None)
+        grid_thws = kwargs.pop("grid_thws", None)
+        if pixel_values is None:
+            return None
+
+        if isinstance(pixel_values, list):
+            pixel_values = torch.cat(pixel_values, dim=0)
+
+        if len(pixel_values.shape) == 5 or len(pixel_values.shape) == 3:
+            pixel_values = pixel_values.reshape(
+                pixel_values.shape[0] * pixel_values.shape[1], *pixel_values.shape[2:]
+            )
+
+        # The batch dimension of pixel_values has been flattened into shape[0]
+        target_dtype = next(self.vision_tower.parameters()).dtype
+        pixel_values = pixel_values.to(target_dtype)
+        assert isinstance(grid_thws, torch.Tensor), (
+            f"expect grid_thws to be a tensor, get {type(grid_thws)}"
+        )
+        # In some cases (e.g. with merger), grid_thws has an extra middle dimension
+        grid_thws = grid_thws.reshape(-1, grid_thws.shape[-1])
+        assert grid_thws.ndim == 2 and grid_thws.size(1) == 3, (
+            f"unexpected shape for grid_thws: {grid_thws.shape}"
+        )
+
+        return KimiK25MediaPixelInputs(
+            type="pixel_values",
+            pixel_values=pixel_values,
+            grid_thws=grid_thws,
+        )
+
+    def _process_media_input(
+        self, media_input: KimiK25MediaPixelInputs
+    ) -> list[torch.Tensor]:
+        # NOTE(moyan): This forward will automatically batch the forward pass internally
+        media_features = vision_tower_forward(
+            self.vision_tower,
+            media_input["pixel_values"],
+            media_input["grid_thws"],
+            mm_projector=self.mm_projector,
+            use_data_parallel=self.use_data_parallel,
+        )
+        return media_features
+
+    def embed_multimodal(self, **kwargs: object) -> NestedTensors | None:
+        # Validate the multimodal input keyword arguments
+        media_input = self._parse_and_validate_media_input(**kwargs)
+        if media_input is None:
+            return None
+
+        # Run multimodal inputs through encoder and projector
+        vision_embeddings = self._process_media_input(media_input)
+        return vision_embeddings
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        **kwargs: object,
+    ) -> IntermediateTensors:
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+        hidden_states = self.language_model(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+
+        return hidden_states
+
+    def compute_logits(self, hidden_states: torch.Tensor, **kwargs) -> torch.Tensor:
+        logits = self.logits_processor(self.lm_head, hidden_states, **kwargs)
+        return logits
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        # Params for weights, fp8 weight scales, fp8 activation scales
+        # (param_name, weight_name, expert_id, shard_id)
+        config = self.config.text_config
+        if not getattr(config, "n_routed_experts", None):
+            return []
+        return SharedFusedMoE.make_expert_params_mapping(
+            self,
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=config.n_routed_experts,
+            num_redundant_experts=0,
+        )
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        config = self.config.text_config
+        _KEYS_TO_MODIFY_MAPPING = {
+            "language_model.lm_head": "lm_head",
+            "language_model.model": "language_model",
+            # mm_projector -> mm_projector mapping
+            # "mm_projector": "mm_projector",
+            "mm_projector.proj.0": "mm_projector.linear_1",
+            "mm_projector.proj.2": "mm_projector.linear_2",
+        }
+        stacked_params_mapping = [
+            (".gate_up_proj", ".gate_proj", 0),
+            (".gate_up_proj", ".up_proj", 1),
+        ]
+        if getattr(config, "kv_lora_rank", None) and getattr(
+            config, "q_lora_rank", None
+        ):
+            stacked_params_mapping += [
+                (".fused_qkv_a_proj", ".q_a_proj", 0),
+                (".fused_qkv_a_proj", ".kv_a_proj_with_mqa", 1),
+            ]
+        expert_params_mapping = self.get_expert_mapping()
+
+        params_dict = dict(self.named_parameters())
+
+        for args in weights:
+            name, loaded_weight = args[:2]
+            kwargs = args[2] if len(args) > 2 else {}
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            spec_layer = get_spec_layer_idx_from_weight_name(config, name)
+            if spec_layer is not None:
+                continue  # skip spec decode layers for main model
+
+            if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
+                continue
+
+            for key_to_modify, new_key in _KEYS_TO_MODIFY_MAPPING.items():
+                if key_to_modify in name:
+                    name = name.replace(key_to_modify, new_key)
+
+            use_default_weight_loading = False
+            if "vision" in name:
+                if self.vision_tower is not None:
+                    use_default_weight_loading = True
+            else:
+                for param_name, weight_name, shard_id in stacked_params_mapping:
+                    if weight_name not in name:
+                        continue
+                    if ("mlp.experts." in name) and name not in params_dict:
+                        continue
+                    name = name.replace(weight_name, param_name)
+                    if name.endswith(".bias") and name not in params_dict:
+                        continue
+
+                    if is_pp_missing_parameter(name, self):
+                        continue
+
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(param, loaded_weight, shard_id, **kwargs)
+                    break
+                else:
+                    for _, (
+                        param_name,
+                        weight_name,
+                        expert_id,
+                        shard_id,
+                    ) in enumerate(expert_params_mapping):
+                        if weight_name not in name:
+                            continue
+                        name = name.replace(weight_name, param_name)
+
+                        if is_pp_missing_parameter(name, self):
+                            continue
+
+                        param = params_dict[name]
+                        weight_loader = param.weight_loader
+                        weight_loader(
+                            param,
+                            loaded_weight,
+                            name,
+                            expert_id=expert_id,
+                            shard_id=shard_id,
+                            **kwargs,
+                        )
+                        break
+                    else:
+                        use_default_weight_loading = True
+
+            if use_default_weight_loading:
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    continue
+
+                if is_pp_missing_parameter(name, self):
+                    continue
+
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight, **kwargs)
+
+
+def get_spec_layer_idx_from_weight_name(
+    config: KimiK25Config, weight_name: str
+) -> int | None:
+    if hasattr(config, "num_nextn_predict_layers") and (
+        config.num_nextn_predict_layers > 0
+    ):
+        layer_idx = config.num_hidden_layers
+        for i in range(config.num_nextn_predict_layers):
+            # might start with language_model.model.layers
+            if f"model.layers.{layer_idx + i}." in weight_name:
+                return layer_idx + i
+    return None

vllm/model_executor/models/kimi_k25_vit.py

@@ -0,0 +1,678 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Vision tower implementation for Kimi-K2.5 model.
+
+This module provides the vision encoder components for Kimi-K2.5,
+including 3D patch embedding, RoPE position embedding, and
+temporal pooling for video chunks.
+"""
+
+from collections.abc import Sequence
+from copy import deepcopy
+from typing import Any
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers.activations import GELUActivation
+
+from vllm.distributed import divide, get_tensor_model_parallel_world_size
+from vllm.logger import init_logger
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.attention.mm_encoder_attention import MMEncoderAttention
+from vllm.model_executor.layers.linear import (
+    ColumnParallelLinear,
+    QKVParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.models.utils import maybe_prefix
+from vllm.model_executor.models.vision import (
+    is_vit_use_data_parallel,
+    run_dp_sharded_mrope_vision_model,
+)
+from vllm.transformers_utils.configs.kimi_k25 import KimiK25VisionConfig
+
+logger = init_logger(__name__)
+
+
+def _apply_rope_input_validation(x, freqs_cis):
+    assert x.ndim == freqs_cis.ndim + 1, (x.shape, freqs_cis.shape)
+    assert x.shape[:-2] == freqs_cis.shape[:-1], (x.shape, freqs_cis.shape)
+    assert x.shape[-1] == 2 * freqs_cis.shape[-1], (x.shape, freqs_cis.shape)
+    assert freqs_cis.dtype == torch.complex64, freqs_cis.dtype
+
+
+def get_rope_shape_decorate(func):
+    _get_rope_shape_first_call_flag = set()
+
+    def wrapper(org, interpolation_mode, shape):
+        key = (org.requires_grad, torch.is_grad_enabled(), interpolation_mode)
+        if key not in _get_rope_shape_first_call_flag:
+            _get_rope_shape_first_call_flag.add(key)
+            _ = func(org, interpolation_mode, shape=(64, 64))
+        return func(org, interpolation_mode, shape)
+
+    return wrapper
+
+
+@get_rope_shape_decorate
+@torch.compile(dynamic=True)
+def get_rope_shape(org, interpolation_mode, shape):
+    return (
+        F.interpolate(
+            org.permute((2, 0, 1)).unsqueeze(0),
+            size=shape,
+            mode=interpolation_mode,
+        )
+        .squeeze(0)
+        .permute((1, 2, 0))
+        .flatten(end_dim=1)
+    )
+
+
+def apply_rope(
+    xq: torch.Tensor, xk: torch.Tensor, freqs_cis: torch.Tensor
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Args: (The leading dimensions of all inputs should be the same)
+        xq: query, tensor of shape (..., num_heads, head_dim)
+        xk: key, tensor of shape (..., num_heads, head_dim)
+        freqs_cis: tensor of shape (..., head_dim/2), dtype=torch.complex64.
+    Returns:
+        xq_out, xk_out: tensors of shape (..., num_heads, head_dim)
+    """
+    _apply_rope_input_validation(xq, freqs_cis)
+    _apply_rope_input_validation(xk, freqs_cis)
+
+    freqs_cis = freqs_cis.unsqueeze(-2)  # ..., 1, head_dim/2
+    # ..., num_heads, head_dim/2
+    xq_ = torch.view_as_complex(xq.float().view(*xq.shape[:-1], -1, 2))
+    xk_ = torch.view_as_complex(xk.float().view(*xq.shape[:-1], -1, 2))
+    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(-2)  # ..., num_heads, head_dim
+    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(-2)  # ..., num_heads, head_dim
+    return xq_out.type_as(xq), xk_out.type_as(xk)
+
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+    """Generate 1D sincos positional embedding from grid positions."""
+    assert embed_dim % 2 == 0
+    omega = np.arange(embed_dim // 2, dtype=np.float32)
+    omega /= embed_dim / 2.0
+    omega = 1.0 / 10000**omega  # (D/2,)
+
+    pos = pos.reshape(-1)  # (M,)
+    out = np.einsum("m,d->md", pos, omega)  # (M, D/2), outer product
+
+    emb_sin = np.sin(out)  # (M, D/2)
+    emb_cos = np.cos(out)  # (M, D/2)
+
+    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+    return emb
+
+
+def get_1d_sincos_pos_embed(embed_dim, t_size, cls_token=False):
+    """Generate 1D sincos positional embedding."""
+    grid_t = np.arange(t_size, dtype=np.float32)
+    pos_embed = get_1d_sincos_pos_embed_from_grid(embed_dim, grid_t)
+    if cls_token:
+        pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
+    return pos_embed
+
+
+class Learnable2DInterpPosEmbDivided_fixed(nn.Module):
+    """2D learnable position embedding with temporal extension."""
+
+    def __init__(
+        self,
+        height: int,
+        width: int,
+        num_frames: int,
+        dim: int,
+        interpolation_mode: str = "bicubic",
+    ) -> None:
+        super().__init__()
+        self.height = height
+        self.width = width
+        self.num_frames = num_frames
+        self.dim = dim
+        self.interpolation_mode = interpolation_mode
+        self.weight = nn.Parameter(torch.empty(height, width, dim))
+        self.register_buffer(
+            "time_weight",
+            torch.from_numpy(get_1d_sincos_pos_embed(self.dim, self.num_frames))
+            .float()
+            .unsqueeze(1),
+            persistent=False,
+        )
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.normal_(self.weight)
+
+    def forward(self, x: torch.Tensor, grid_thws: torch.Tensor) -> torch.Tensor:
+        pos_embs = []
+        for t, h, w in grid_thws.tolist():
+            assert t <= self.num_frames, f"t:{t} > self.num_frames:{self.num_frames}"
+            if (h, w) == self.weight.shape[:-1]:
+                pos_emb_2d = self.weight.flatten(end_dim=1)
+            else:
+                pos_emb_2d = get_rope_shape(
+                    self.weight,
+                    interpolation_mode=self.interpolation_mode,
+                    shape=(h, w),
+                )
+
+            if t == 1:
+                pos_emb_3d = pos_emb_2d
+            else:
+                pos_emb_3d = (
+                    pos_emb_2d.unsqueeze(0).repeat(t, 1, 1) + self.time_weight[0:t]
+                )
+
+            pos_embs.append(pos_emb_3d.reshape(-1, pos_emb_3d.shape[-1]))
+
+        out = x + torch.cat(pos_embs)
+        return out
+
+
+class MoonVision3dPatchEmbed(nn.Module):
+    """3D patch embedding for vision tower."""
+
+    def __init__(
+        self,
+        out_dim: int,
+        in_dim: int = 3,
+        patch_size: int | tuple[int, int] = (14, 14),
+        pos_emb_height: int = 14,
+        pos_emb_width: int = 14,
+        pos_emb_time: int = 4,
+        pos_emb_type: str = "divided_fixed",
+    ):
+        super().__init__()
+        assert isinstance(patch_size, int | Sequence), (
+            f"Invalid patch_size type: {type(patch_size)}"
+        )
+        if isinstance(patch_size, int):
+            patch_size = (patch_size, patch_size)
+        assert len(patch_size) == 2, (
+            f"Expected patch_size to be a tuple of 2, got {patch_size}"
+        )
+        self.patch_size = patch_size
+
+        self.proj = nn.Conv2d(
+            in_dim, out_dim, kernel_size=patch_size, stride=patch_size
+        )
+
+        if pos_emb_type == "divided_fixed":
+            self.pos_emb = Learnable2DInterpPosEmbDivided_fixed(
+                height=pos_emb_height,
+                width=pos_emb_width,
+                num_frames=pos_emb_time,
+                dim=out_dim,
+            )
+        else:
+            raise NotImplementedError(f"Not support pos_emb_type: {pos_emb_type}")
+
+    def forward(self, x: torch.Tensor, grid_thws: torch.Tensor) -> torch.Tensor:
+        x = self.proj(x).view(x.size(0), -1)
+        # apply positional embedding
+        x = self.pos_emb(x, grid_thws)
+        return x
+
+
+class Rope2DPosEmbRepeated(nn.Module):
+    """2D rotary position embedding with multi-resolution support."""
+
+    def __init__(self, dim: int, max_height: int, max_width: int, theta_base=10000):
+        super().__init__()
+        self.dim = dim
+        assert self.dim % 4 == 0, "dim must be divisible by 4"
+        self.max_height = max_height
+        self.max_width = max_width
+        self.theta_base = theta_base
+
+    def extra_repr(self):
+        return (
+            f"dim={self.dim}, max_height={self.max_height}, "
+            f"max_width={self.max_width}, theta_base={self.theta_base}"
+        )
+
+    def _precompute_freqs_cis(self, device: torch.device) -> torch.Tensor:
+        """Calculate the cis(freqs) for each position in the 2D grid."""
+        N = self.max_height * self.max_width
+        flat_pos = torch.arange(0, N).float().to(device)
+        x_pos = flat_pos % self.max_width
+        y_pos = flat_pos // self.max_width
+        dim_range = (
+            torch.arange(0, self.dim, 4)[: (self.dim // 4)].float().to(device)
+        )  # C/4
+        freqs = 1.0 / (self.theta_base ** (dim_range / self.dim))
+        x_freqs = torch.outer(x_pos, freqs).float()  # N, C/4
+        y_freqs = torch.outer(y_pos, freqs).float()  # N, C/4
+        x_cis = torch.polar(torch.ones_like(x_freqs), x_freqs)  # N, C/4
+        y_cis = torch.polar(torch.ones_like(y_freqs), y_freqs)  # N, C/4
+        # N, C/4, 2
+        freqs_cis = torch.cat(
+            [x_cis.unsqueeze(dim=-1), y_cis.unsqueeze(dim=-1)], dim=-1
+        )
+        # max_height, max_width, C/2
+        freqs_cis = freqs_cis.reshape(self.max_height, self.max_width, -1)
+        return freqs_cis
+
+    def get_freqs_cis(
+        self, grid_thws: torch.Tensor, device: torch.device
+    ) -> torch.Tensor:
+        """
+        Args:
+            grid_thws (torch.Tensor): grid time, height and width
+
+        Returns:
+            freqs_cis: tensor of shape (sum(t * height * width), dim//2)
+        """
+        if not hasattr(self, "freqs_cis"):
+            self.register_buffer(
+                "freqs_cis", self._precompute_freqs_cis(device), persistent=False
+            )
+
+        shapes = grid_thws.tolist()
+        assert all(
+            1 <= h <= self.max_height and 1 <= w <= self.max_width for t, h, w in shapes
+        ), (
+            shapes,
+            self.max_height,
+            self.max_width,
+        )
+        freqs_cis = torch.cat(
+            [
+                self.freqs_cis[:h, :w].reshape(-1, self.dim // 2).repeat(t, 1)
+                for t, h, w in shapes
+            ],
+            dim=0,
+        )
+        return freqs_cis
+
+
+class MLP2(nn.Module):
+    """Two-layer MLP with tensor parallel support."""
+
+    def __init__(
+        self,
+        dims: list[int],
+        activation,
+        bias: bool = True,
+        prefix: str = "",
+        use_data_parallel: bool = False,
+    ):
+        super().__init__()
+        assert len(dims) == 3
+        self.use_data_parallel = use_data_parallel
+        self.fc0 = ColumnParallelLinear(
+            dims[0],
+            dims[1],
+            bias=bias,
+            prefix=maybe_prefix(prefix, "fc0"),
+            disable_tp=self.use_data_parallel,
+        )
+        self.fc1 = RowParallelLinear(
+            dims[1],
+            dims[2],
+            bias=bias,
+            prefix=maybe_prefix(prefix, "fc1"),
+            disable_tp=self.use_data_parallel,
+        )
+        self.activation = activation
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x, _ = self.fc0(x)
+        x = self.activation(x)
+        x, _ = self.fc1(x)
+        return x
+
+
+class MoonViTEncoderLayer(nn.Module):
+    """Single encoder layer for MoonViT with TP/DP support."""
+
+    def __init__(
+        self,
+        num_heads: int,
+        hidden_dim: int,
+        mlp_dim: int,
+        prefix: str = "",
+        *,
+        activation=F.gelu,
+        attn_bias: bool = False,
+    ):
+        super().__init__()
+        self.use_data_parallel = is_vit_use_data_parallel()
+
+        self.num_heads = num_heads
+        self.hidden_dim = hidden_dim
+        self.hidden_size_per_attention_head = self.hidden_dim // self.num_heads
+        self.tp_size = (
+            1 if self.use_data_parallel else get_tensor_model_parallel_world_size()
+        )
+        self.num_attention_heads_per_partition = divide(num_heads, self.tp_size)
+
+        self.norm0 = nn.LayerNorm(hidden_dim)
+        self.norm1 = nn.LayerNorm(hidden_dim)
+        self.mlp = MLP2(
+            [hidden_dim, mlp_dim, hidden_dim],
+            activation,
+            prefix=f"{prefix}.mlp",
+            use_data_parallel=self.use_data_parallel,
+        )
+        self.wqkv = QKVParallelLinear(
+            hidden_size=hidden_dim,
+            head_size=self.hidden_size_per_attention_head,
+            total_num_heads=num_heads,
+            total_num_kv_heads=num_heads,
+            bias=attn_bias,
+            prefix=f"{prefix}.wqkv",
+            disable_tp=self.use_data_parallel,
+        )
+        self.wo = RowParallelLinear(
+            hidden_dim,
+            hidden_dim,
+            bias=attn_bias,
+            prefix=f"{prefix}.wo",
+            disable_tp=self.use_data_parallel,
+        )
+        self.attn = MMEncoderAttention(
+            num_heads=self.num_attention_heads_per_partition,
+            head_size=self.hidden_size_per_attention_head,
+            scale=self.hidden_size_per_attention_head**-0.5,
+            prefix=f"{prefix}.attn",
+        )
+
+    def attention_qkvpacked(
+        self,
+        x: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        rope_freqs_cis: torch.Tensor | None = None,
+    ):
+        """Compute self-attention with packed QKV.
+
+        Args:
+            x (torch.Tensor): (seqlen, hidden_dim)
+            cu_seqlens (torch.Tensor): cumulative sequence lengths
+        """
+        seq_length = x.size(0)
+        xqkv, _ = self.wqkv(x)
+
+        qkv_shape = xqkv.size()[:-1] + (
+            3,
+            self.num_attention_heads_per_partition,
+            self.hidden_size_per_attention_head,
+        )
+        # xqkv: (seqlen, 3, nheads, headdim)
+        xqkv = xqkv.view(*qkv_shape)
+        xq, xk, xv = torch.unbind(xqkv, dim=-3)
+
+        xq, xk = apply_rope(xq, xk, rope_freqs_cis)
+
+        max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
+        attn_out = self.attn(
+            xq.unsqueeze(0),
+            xk.unsqueeze(0),
+            xv.unsqueeze(0),
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+        )
+        attn_out = attn_out.reshape(
+            seq_length,
+            self.num_attention_heads_per_partition
+            * self.hidden_size_per_attention_head,
+        )
+        attn_out, _ = self.wo(attn_out)
+        return attn_out
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        rope_freqs_cis: torch.Tensor | None = None,
+    ):
+        residual = hidden_states
+        hidden_states = self.norm0(hidden_states)
+
+        hidden_states = self.attention_qkvpacked(
+            hidden_states, cu_seqlens, rope_freqs_cis
+        )
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.norm1(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+class MoonViT3dEncoder(nn.Module):
+    """Full encoder stack for MoonViT 3D."""
+
+    def __init__(
+        self,
+        hidden_dim: int,
+        num_layers: int,
+        block_cfg: dict,
+        video_attn_type: str = "spatial_temporal",
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        assert video_attn_type == "spatial_temporal", (
+            f'video_attn_type must be "spatial_temporal", got {video_attn_type}'
+        )
+        self.video_attn_type = video_attn_type
+        self.rope_2d = Rope2DPosEmbRepeated(
+            block_cfg["hidden_dim"] // block_cfg["num_heads"], 512, 512
+        )
+        self.blocks = nn.ModuleList(
+            [
+                MoonViTEncoderLayer(
+                    **block_cfg,
+                    prefix=f"{prefix}.blocks.{layer_idx}",
+                )
+                for layer_idx in range(num_layers)
+            ]
+        )
+        self.final_layernorm = nn.LayerNorm(hidden_dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        grid_thws: torch.Tensor,
+    ) -> torch.Tensor:
+        rope_freqs_cis = self.rope_2d.get_freqs_cis(
+            grid_thws=grid_thws, device=hidden_states.device
+        )
+
+        lengths = torch.cat(
+            (
+                torch.zeros(1, dtype=grid_thws.dtype, device=grid_thws.device),
+                grid_thws[:, 0] * grid_thws[:, 1] * grid_thws[:, 2],
+            )
+        )
+
+        cu_seqlens = lengths.to(hidden_states.device).cumsum(dim=0, dtype=torch.int32)
+
+        for block in self.blocks:
+            hidden_states = block(
+                hidden_states, cu_seqlens, rope_freqs_cis=rope_freqs_cis
+            )
+
+        hidden_states = self.final_layernorm(hidden_states)
+
+        return hidden_states
+
+
+def tpool_patch_merger(
+    x: torch.Tensor,
+    grid_thws: torch.Tensor,
+    merge_kernel_size: tuple[int, int] = (2, 2),
+) -> list[torch.Tensor]:
+    """Temporal pooling patch merger."""
+    kh, kw = merge_kernel_size
+    lengths = (grid_thws[:, 0] * grid_thws[:, 1] * grid_thws[:, 2]).tolist()
+    seqs = x.split(lengths, dim=0)
+
+    outputs = []
+    for seq, (t, h, w) in zip(seqs, grid_thws.tolist()):
+        nh, nw = h // kh, w // kw
+        # Reshape: (t*h*w, d) -> (t, nh, kh, nw, kw, d)
+        v = seq.view(t, nh, kh, nw, kw, -1)
+        # Temporal pooling first (reduces tensor size before permute)
+        v = v.mean(dim=0)  # (nh, kh, nw, kw, d)
+        # Spatial rearrangement: (nh, kh, nw, kw, d) -> (nh, nw, kh, kw, d)
+        out = v.permute(0, 2, 1, 3, 4).reshape(nh * nw, kh * kw, -1)
+        outputs.append(out)
+
+    return outputs
+
+
+class MoonViT3dPretrainedModel(nn.Module):
+    """Main vision tower model.
+
+    Uses KimiK25VisionConfig directly from transformers_utils/configs/kimi_k25.py.
+    """
+
+    def __init__(
+        self,
+        config: KimiK25VisionConfig,
+        prefix: str = "",
+    ):
+        super().__init__()
+        config = deepcopy(config)
+        self.config = config  # Required for run_dp_sharded_mrope_vision_model
+        self.merge_kernel_size = config.merge_kernel_size
+        self.patch_size = config.patch_size
+        self.merge_type = config.merge_type
+
+        self.patch_embed = MoonVision3dPatchEmbed(
+            out_dim=config.hidden_size,
+            patch_size=config.patch_size,
+            pos_emb_height=config.init_pos_emb_height,
+            pos_emb_width=config.init_pos_emb_width,
+            pos_emb_time=config.init_pos_emb_time,
+            pos_emb_type=config.pos_emb_type,
+        )
+
+        self.encoder = MoonViT3dEncoder(
+            hidden_dim=config.hidden_size,
+            num_layers=config.num_hidden_layers,
+            block_cfg={
+                "num_heads": config.num_attention_heads,
+                "hidden_dim": config.hidden_size,
+                "mlp_dim": config.intermediate_size,
+                "activation": get_act_fn("gelu_pytorch_tanh"),
+                "attn_bias": True,
+            },
+            video_attn_type=config.video_attn_type,
+            prefix=maybe_prefix(prefix, "encoder"),
+        )
+
+    def forward(
+        self, pixel_values: torch.Tensor, grid_thws: torch.Tensor
+    ) -> torch.Tensor:
+        """
+        Args:
+            pixel_values (torch.Tensor): The input pixel values.
+            grid_thws (torch.Tensor): Temporal, height and width.
+
+        Returns:
+            torch.Tensor: The output tokens.
+        """
+        hidden_states = self.patch_embed(pixel_values, grid_thws)
+        hidden_states = self.encoder(hidden_states, grid_thws)
+        if (
+            self.merge_type == "sd2_tpool"
+        ):  # spatial downsampling 2x with temporal pooling all
+            hidden_states = tpool_patch_merger(
+                hidden_states, grid_thws, merge_kernel_size=self.merge_kernel_size
+            )
+        else:
+            raise NotImplementedError(f"Not support {self.merge_type}")
+
+        return hidden_states
+
+
+@torch.inference_mode()
+def mm_projector_forward(mm_projector: torch.nn.Module, vt_output: list[torch.Tensor]):
+    """Apply MM projector to vision tower outputs."""
+    num_embedding_list = [x.shape[0] for x in vt_output]
+    batched = torch.cat(vt_output, dim=0)
+    proj_out = mm_projector(batched)
+    proj_out = proj_out.reshape(-1, proj_out.shape[-1])
+    proj_out = torch.split(proj_out, num_embedding_list)
+    return proj_out
+
+
+@torch.inference_mode()
+def vision_tower_forward(
+    vision_tower: Any,
+    pixel_values: torch.Tensor,
+    grid_thw: torch.Tensor,
+    mm_projector: Any,
+    use_data_parallel: bool,
+) -> list[torch.Tensor]:
+    """DP-sharded vision tower forward with mrope.
+
+    Uses vLLM's standard data parallelism utility to shard the batch
+    across available GPUs, enabling parallel processing of vision features.
+    """
+    if use_data_parallel:
+        grid_thw_list = grid_thw.tolist()
+        vt_outputs = run_dp_sharded_mrope_vision_model(
+            vision_model=vision_tower,
+            pixel_values=pixel_values,
+            grid_thw_list=grid_thw_list,
+            rope_type="rope_2d",
+        )
+    else:
+        vt_outputs = vision_tower(pixel_values, grid_thw)
+    tensors = mm_projector_forward(mm_projector, list(vt_outputs))
+    return list(tensors)
+
+
+class KimiK25MultiModalProjector(nn.Module):
+    """Multi-modal projector with patch merging for Kimi-K2.5."""
+
+    def __init__(
+        self,
+        config: KimiK25VisionConfig,
+        use_data_parallel: bool = False,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.use_data_parallel = use_data_parallel
+
+        # Hidden size after patch merging
+        merge_h, merge_w = config.merge_kernel_size
+        self.hidden_size = config.hidden_size * merge_h * merge_w
+
+        self.pre_norm = torch.nn.LayerNorm(config.hidden_size, eps=1e-5)
+        self.linear_1 = ReplicatedLinear(
+            self.hidden_size,
+            self.hidden_size,
+            bias=True,
+            prefix=maybe_prefix(prefix, "linear_1"),
+        )
+        self.linear_2 = ReplicatedLinear(
+            self.hidden_size,
+            config.mm_hidden_size,
+            bias=True,
+            prefix=maybe_prefix(prefix, "linear_2"),
+        )
+        self.act = GELUActivation()
+
+    def forward(self, image_features: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.pre_norm(image_features).view(-1, self.hidden_size)
+        hidden_states, _ = self.linear_1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states, _ = self.linear_2(hidden_states)
+        return hidden_states

vllm/model_executor/models/registry.py

@@ -359,6 +359,7 @@ _MULTIMODAL_MODELS = {
     ),
     "RForConditionalGeneration": ("rvl", "RForConditionalGeneration"),
     "KimiVLForConditionalGeneration": ("kimi_vl", "KimiVLForConditionalGeneration"),  # noqa: E501
+    "KimiK25ForConditionalGeneration": ("kimi_k25", "KimiK25ForConditionalGeneration"),  # noqa: E501
     "LightOnOCRForConditionalGeneration": (
         "lightonocr",
         "LightOnOCRForConditionalGeneration",

vllm/multimodal/inputs.py

@@ -20,6 +20,7 @@ from typing import (
 )
 
 import numpy as np
+from PIL.Image import Image
 from typing_extensions import NotRequired, TypeVar
 
 from vllm.utils.collection_utils import full_groupby, is_list_of
@@ -29,7 +30,6 @@ from vllm.utils.jsontree import json_map_leaves
 if TYPE_CHECKING:
     import torch
     import torch.types
-    from PIL.Image import Image
     from transformers.feature_extraction_utils import BatchFeature
 
     from .media import MediaWithBytes
@@ -105,6 +105,28 @@ The number of data items allowed per modality is restricted by
 """
 
 
+class VisionChunkImage(TypedDict):
+    """Represents an image wrapped as a vision chunk."""
+
+    type: Literal["image"]
+    image: Image
+    uuid: str | None
+
+
+class VisionChunkVideo(TypedDict):
+    """Represents a video chunk with metadata."""
+
+    type: Literal["video_chunk"]
+    video_chunk: list[Image]
+    uuid: str | None
+    prompt: str
+    video_idx: int
+
+
+VisionChunk = VisionChunkImage | VisionChunkVideo
+"""A vision chunk is either an image or a video chunk."""
+
+
 @final
 class MultiModalDataBuiltins(TypedDict, total=False):
     """Type annotations for modality types predefined by vLLM."""
@@ -118,6 +140,9 @@ class MultiModalDataBuiltins(TypedDict, total=False):
     audio: ModalityData[AudioItem]
     """The input audio(s)."""
 
+    vision_chunk: ModalityData[VisionChunk]
+    """The input visual atom(s) - unified modality for images and video chunks."""
+
 
 MultiModalDataDict: TypeAlias = Mapping[str, ModalityData[Any]]
 """

vllm/multimodal/parse.py

@@ -384,6 +384,13 @@ class VideoEmbeddingItems(EmbeddingItems):
         super().__init__(data, "video", expected_hidden_size)
 
 
+class VisionChunkProcessorItems(ProcessorBatchItems[Any]):
+    """Processor items for vision chunks (unified image and video chunks)."""
+
+    def __init__(self, data: Sequence[Any]) -> None:
+        super().__init__(data, "vision_chunk")
+
+
 _D = TypeVar("_D", bound=ModalityDataItems[Any, Any])
 
 
@@ -652,11 +659,23 @@ class MultiModalDataParser:
 
         return VideoProcessorItems(new_videos, metadata=metadata_lst)
 
+    def _parse_vision_chunk_data(
+        self,
+        data: ModalityData[Any],
+    ) -> ModalityDataItems[Any, Any] | None:
+        """Parse vision chunk data (unified image and video chunks)."""
+        if data is None or self._is_empty(data):
+            return None
+        if self.is_embeddings(data):
+            raise ValueError("Do not support embedding data for vision_chunk right now")
+        return VisionChunkProcessorItems(data)
+
     def _get_subparsers(self) -> Mapping[str, ModalityDataParser]:
         return {
             "audio": self._parse_audio_data,
             "image": self._parse_image_data,
             "video": self._parse_video_data,
+            "vision_chunk": self._parse_vision_chunk_data,
         }
 
     def parse_mm_data(self, mm_data: MultiModalDataDict) -> MultiModalDataItems:

vllm/multimodal/video.py

@@ -235,6 +235,27 @@ class VideoLoader:
 VIDEO_LOADER_REGISTRY = ExtensionManager()
 
 
+@VIDEO_LOADER_REGISTRY.register("identity")
+class IdentityVideoLoader(VideoLoader):
+    """IdentityVideoLoader returns raw video bytes without decoding.
+
+    This allows the model processor to handle video decoding and
+    is required for models like Kimi-K2.5 that need custom video chunk splitting.
+
+    NOTE: This is temporary for Kimi-K2.5 testing. Remember to change back
+    to opencv before release if needed.
+    """
+
+    @classmethod
+    def load_bytes(
+        cls,
+        data: bytes,
+        num_frames: int = -1,
+        **kwargs: Any,
+    ) -> tuple[Any, Any]:
+        return data, None
+
+
 @VIDEO_LOADER_REGISTRY.register("opencv")
 class OpenCVVideoBackend(VideoLoader):
     def get_cv2_video_api(self):

vllm/reasoning/__init__.py

@@ -53,8 +53,8 @@ _REASONING_PARSERS_TO_REGISTER = {
         "HunyuanA13BReasoningParser",
     ),
     "kimi_k2": (
-        "deepseek_r1_reasoning_parser",
-        "DeepSeekR1ReasoningParser",
+        "kimi_k2_reasoning_parser",
+        "KimiK2ReasoningParser",
     ),
     "minimax_m2": (
         "minimax_m2_reasoning_parser",

vllm/reasoning/kimi_k2_reasoning_parser.py

@@ -0,0 +1,80 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Sequence
+from typing import TYPE_CHECKING, Any
+
+from transformers import PreTrainedTokenizerBase
+
+from vllm.entrypoints.openai.engine.protocol import DeltaMessage
+from vllm.logger import init_logger
+from vllm.reasoning import ReasoningParser
+from vllm.reasoning.deepseek_r1_reasoning_parser import DeepSeekR1ReasoningParser
+
+from .identity_reasoning_parser import IdentityReasoningParser
+
+if TYPE_CHECKING:
+    from vllm.entrypoints.openai.chat_completion.protocol import (
+        ChatCompletionRequest,
+    )
+else:
+    ChatCompletionRequest = Any
+
+
+logger = init_logger(__name__)
+
+
+class KimiK2ReasoningParser(ReasoningParser):
+    """
+    Kimi K2 parser that delegates to either DeepSeekR1ReasoningParser or
+    IdentityReasoningParser based on `thinking` and `separate_reasoning`.
+
+    Unlike DeepSeekV3ReasoningParser which defaults to NOT thinking,
+    KimiK2ReasoningParser defaults to thinking mode (uses DeepSeekR1ReasoningParser).
+    """
+
+    def __init__(self, tokenizer: PreTrainedTokenizerBase, *args, **kwargs):
+        super().__init__(tokenizer, *args, **kwargs)
+
+        chat_kwargs = kwargs.pop("chat_template_kwargs", {}) or {}
+        # Key difference: default to True instead of False
+        thinking = bool(chat_kwargs.pop("thinking", True))
+
+        if thinking:
+            self._parser = DeepSeekR1ReasoningParser(tokenizer, *args, **kwargs)
+        else:
+            self._parser = IdentityReasoningParser(tokenizer, *args, **kwargs)
+
+    def is_reasoning_end(self, input_ids: Sequence[int]) -> bool:
+        return self._parser.is_reasoning_end(input_ids)
+
+    def is_reasoning_end_streaming(
+        self, input_ids: list[int], delta_ids: list[int]
+    ) -> bool:
+        return self._parser.is_reasoning_end_streaming(input_ids, delta_ids)
+
+    def extract_content_ids(self, input_ids: list[int]) -> list[int]:
+        return self._parser.extract_content_ids(input_ids)
+
+    def extract_reasoning(
+        self, model_output: str, request: "ChatCompletionRequest"
+    ) -> tuple[str | None, str | None]:
+        return self._parser.extract_reasoning(model_output, request)
+
+    def extract_reasoning_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+    ) -> DeltaMessage | None:
+        return self._parser.extract_reasoning_streaming(
+            previous_text,
+            current_text,
+            delta_text,
+            previous_token_ids,
+            current_token_ids,
+            delta_token_ids,
+        )

vllm/renderers/hf.py

@@ -20,9 +20,11 @@ from vllm.entrypoints.chat_utils import (
     ChatTemplateContentFormatOption,
     ChatTemplateResolutionError,
     ConversationMessage,
+    build_video_prompts_from_mm_data,
     load_chat_template,
     parse_chat_messages,
     parse_chat_messages_async,
+    rebuild_mm_uuids_from_mm_data,
 )
 from vllm.inputs import TextPrompt, TokensPrompt
 from vllm.logger import init_logger
@@ -547,6 +549,40 @@ class HfRenderer(RendererLike):
             **kwargs,
         )
 
+        # NOTE: use_unified_vision_chunk is currently specific to Kimi-K2.5
+        # model which uses unified vision chunks for both images and videos.
+        if (
+            getattr(model_config.hf_config, "use_unified_vision_chunk", False)
+            and mm_uuids is not None
+            and mm_data is not None
+        ):
+            mm_uuids = rebuild_mm_uuids_from_mm_data(mm_uuids, mm_data)
+
+            # get video placehoder, replace it with runtime video-chunk prompts
+            video_placeholder = getattr(
+                model_config.hf_config, "video_placeholder", None
+            )
+            if video_placeholder and isinstance(prompt_raw, str):
+                video_prompts = build_video_prompts_from_mm_data(mm_data)
+
+                # replace in order
+                prompt_raw_parts = prompt_raw.split(video_placeholder)
+                if len(prompt_raw_parts) == len(video_prompts) + 1:
+                    prompt_raw = "".join(
+                        [
+                            prompt_raw_parts[i] + video_prompts[i]
+                            for i in range(len(video_prompts))
+                        ]
+                    )
+                    prompt_raw += prompt_raw_parts[-1]
+                else:
+                    logger.warning(
+                        "Number of video placeholders (%d) does not match "
+                        "number of videos (%d) in the request.",
+                        len(prompt_raw_parts) - 1,
+                        len(video_prompts),
+                    )
+
         prompt = (
             TextPrompt(prompt=prompt_raw)
             if isinstance(prompt_raw, str)
@@ -587,6 +623,40 @@ class HfRenderer(RendererLike):
             **kwargs,
         )
 
+        # NOTE: use_unified_vision_chunk is currently specific to Kimi-K2.5
+        # model which uses unified vision chunks for both images and videos.
+        if (
+            getattr(model_config.hf_config, "use_unified_vision_chunk", False)
+            and mm_uuids is not None
+            and mm_data is not None
+        ):
+            mm_uuids = rebuild_mm_uuids_from_mm_data(mm_uuids, mm_data)
+
+            # get video placehoder, replace it with runtime video-chunk prompts
+            video_placeholder = getattr(
+                model_config.hf_config, "video_placeholder", None
+            )
+            if video_placeholder and isinstance(prompt_raw, str):
+                video_prompts = build_video_prompts_from_mm_data(mm_data)
+
+                # replace in order
+                prompt_raw_parts = prompt_raw.split(video_placeholder)
+                if len(prompt_raw_parts) == len(video_prompts) + 1:
+                    prompt_raw = "".join(
+                        [
+                            prompt_raw_parts[i] + video_prompts[i]
+                            for i in range(len(video_prompts))
+                        ]
+                    )
+                    prompt_raw += prompt_raw_parts[-1]
+                else:
+                    logger.warning(
+                        "Number of video placeholders (%d) does not match "
+                        "number of videos (%d) in the request.",
+                        len(prompt_raw_parts) - 1,
+                        len(video_prompts),
+                    )
+
         prompt = (
             TextPrompt(prompt=prompt_raw)
             if isinstance(prompt_raw, str)

vllm/transformers_utils/config.py

@@ -81,6 +81,7 @@ _CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = LazyConfigDict(
     isaac="IsaacConfig",
     kimi_linear="KimiLinearConfig",
     kimi_vl="KimiVLConfig",
+    kimi_k25="KimiK25Config",
     RefinedWeb="RWConfig",  # For tiiuae/falcon-40b(-instruct)
     RefinedWebModel="RWConfig",  # For tiiuae/falcon-7b(-instruct)
     jais="JAISConfig",

vllm/transformers_utils/configs/__init__.py

@@ -38,6 +38,7 @@ _CLASS_TO_MODULE: dict[str, str] = {
     "MoonViTConfig": "vllm.transformers_utils.configs.moonvit",
     "KimiLinearConfig": "vllm.transformers_utils.configs.kimi_linear",
     "KimiVLConfig": "vllm.transformers_utils.configs.kimi_vl",
+    "KimiK25Config": "vllm.transformers_utils.configs.kimi_k25",
     "NemotronConfig": "vllm.transformers_utils.configs.nemotron",
     "NemotronHConfig": "vllm.transformers_utils.configs.nemotron_h",
     "Olmo3Config": "vllm.transformers_utils.configs.olmo3",
@@ -77,6 +78,7 @@ __all__ = [
     "MoonViTConfig",
     "KimiLinearConfig",
     "KimiVLConfig",
+    "KimiK25Config",
     "NemotronConfig",
     "NemotronHConfig",
     "Olmo3Config",

vllm/transformers_utils/configs/kimi_k25.py

@@ -0,0 +1,129 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Kimi-K2.5 Model Configuration.
+
+This configuration supports video-chunk as an internal modality type.
+A video-chunk is the smallest independently processable unit of video.
+"""
+
+from transformers import DeepseekV3Config
+from transformers.configuration_utils import PretrainedConfig
+
+
+class KimiK25VisionConfig(PretrainedConfig):
+    model_type = "kimi_k25_vision"
+
+    def __init__(
+        self,
+        # Vision Tower
+        patch_size: int = 14,
+        init_pos_emb_height: int = 64,
+        init_pos_emb_width: int = 64,
+        init_pos_emb_time: int = 4,
+        pos_emb_type: str = "divided_fixed",
+        num_attention_heads: int = 16,
+        num_hidden_layers: int = 27,
+        hidden_size: int = 1152,
+        intermediate_size: int = 4304,
+        merge_kernel_size: tuple[int, int] = (2, 2),
+        video_attn_type: str = "spatial_temporal",
+        merge_type: str = "sd2_tpool",
+        # MM Projector
+        mm_projector_type: str = "patchmerger",
+        mm_hidden_size: int | None = None,
+        projector_hidden_act: str = "gelu",
+        projector_ln_eps: float = 1e-5,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        # Vision Tower
+        self.patch_size = patch_size
+        self.init_pos_emb_height = init_pos_emb_height
+        self.init_pos_emb_width = init_pos_emb_width
+        self.init_pos_emb_time = init_pos_emb_time
+        self.pos_emb_type = pos_emb_type
+        self.num_attention_heads = num_attention_heads
+        self.num_hidden_layers = num_hidden_layers
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.merge_kernel_size = merge_kernel_size
+        self.video_attn_type = video_attn_type
+        self.merge_type = merge_type
+        # MM Projector
+        self.mm_projector_type = mm_projector_type
+        if mm_hidden_size is not None:
+            self.mm_hidden_size = mm_hidden_size
+        else:
+            self.mm_hidden_size = hidden_size
+        self.projector_hidden_act = projector_hidden_act
+        self.projector_ln_eps = projector_ln_eps
+
+
+class KimiK25Config(PretrainedConfig):
+    """Kimi-K2.5 model configuration.
+
+    Kimi-K2.5 extends Kimi-K2 with vision support using video-chunks.
+    A video-chunk consists of multiple consecutive frames
+    that are processed together with temporal pooling.
+
+    Args:
+        vision_config: Configuration for the vision tower and projector.
+        text_config: Configuration for the text model (DeepseekV3).
+        ignore_index: The ignore index for the loss function.
+        media_placeholder_token_id: The token ID for media placeholders.
+        pad_token_id: The token ID for padding.
+    """
+
+    model_type = "kimi_k25"
+
+    def __init__(
+        self,
+        vision_config: dict | KimiK25VisionConfig | None = None,
+        text_config: dict | DeepseekV3Config | None = None,
+        ignore_index: int = -100,
+        media_placeholder_token_id: int = 163605,
+        pad_token_id: int = 0,
+        use_unified_vision_chunk: bool = False,
+        video_placeholder: str = "<|kimi_k25_video_placeholder|>",
+        **kwargs,
+    ):
+        # Vision config
+        if vision_config is None:
+            vision_config = KimiK25VisionConfig()
+        elif isinstance(vision_config, dict):
+            vision_config = KimiK25VisionConfig(**vision_config)
+        self.vision_config: KimiK25VisionConfig = vision_config
+
+        # Text config
+        if text_config is None:
+            text_config = DeepseekV3Config()
+        elif isinstance(text_config, dict):
+            text_config = DeepseekV3Config(**text_config)
+        self.text_config: DeepseekV3Config = text_config
+
+        # Set mm_hidden_size to text hidden size if not explicitly set
+        if self.vision_config.mm_hidden_size == self.vision_config.hidden_size:
+            self.vision_config.mm_hidden_size = self.text_config.hidden_size
+
+        # Other config
+        self.ignore_index = ignore_index
+        self.media_placeholder_token_id = media_placeholder_token_id
+        self.use_unified_vision_chunk = use_unified_vision_chunk
+        self.video_placeholder = video_placeholder
+
+        # Propagate quantization config from text model
+        if getattr(self.text_config, "quantization_config", None) is not None:
+            self.quantization_config = self.text_config.quantization_config
+
+        super().__init__(pad_token_id=pad_token_id, **kwargs)
+
+    @property
+    def hidden_size(self) -> int:
+        """Get hidden size from text config for compatibility."""
+        return self.text_config.hidden_size
+
+    @property
+    def vocab_size(self) -> int:
+        """Get vocab size from text config for compatibility."""
+        return self.text_config.vocab_size