initial

Signed-off-by: Roger Wang <ywang@roblox.com>

examples/offline_inference/vision_language.py

@@ -530,6 +530,39 @@ def run_qwen2_vl(question: str, modality: str):
     return llm, prompt, stop_token_ids
 
 
+# Qwen2-VL
+def run_qwen2_5_vl(question: str, modality: str):
+
+    model_name = "Qwen/Qwen2.5-VL-3B-Instruct"
+
+    llm = LLM(
+        model=model_name,
+        max_model_len=4096,
+        max_num_seqs=5,
+        mm_processor_kwargs={
+            "min_pixels": 28 * 28,
+            "max_pixels": 256 * 28 * 28,
+        },
+        disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
+        limit_mm_per_prompt={
+            "image": 1,
+            "video": 0
+        },
+    )
+
+    if modality == "image":
+        placeholder = "<|image_pad|>"
+    elif modality == "video":
+        placeholder = "<|video_pad|>"
+
+    prompt = ("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
+              f"<|im_start|>user\n<|vision_start|>{placeholder}<|vision_end|>"
+              f"{question}<|im_end|>\n"
+              "<|im_start|>assistant\n")
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
+
+
 model_example_map = {
     "aria": run_aria,
     "blip-2": run_blip2,
@@ -556,6 +589,7 @@ model_example_map = {
     "pixtral_hf": run_pixtral_hf,
     "qwen_vl": run_qwen_vl,
     "qwen2_vl": run_qwen2_vl,
+    "qwen2_5_vl": run_qwen2_5_vl,
 }
 
 

vllm/model_executor/models/qwen2_5_vl.py

@@ -0,0 +1,748 @@
+# Adapted from
+# https://github.com/huggingface/transformers/blob/main/src/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py
+# Copyright 2025 The vLLM team.
+# Copyright 2025 The Qwen Team and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only Qwen2-VL model compatible with HuggingFace weights."""
+from functools import cached_property, partial
+from typing import (Any, Callable, Iterable, List, Literal, Mapping, Optional,
+                    Set, Tuple, Type, TypedDict, Union)
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from transformers import BatchFeature
+from transformers.models.qwen2_5_vl import (Qwen2_5_VLImageProcessor,
+                                            Qwen2_5_VLProcessor)
+from transformers.models.qwen2_5_vl.configuration_qwen2_5_vl import (
+    Qwen2_5_VLConfig, Qwen2_5_VLVisionConfig)
+from transformers.models.qwen2_5_vl.image_processing_qwen2_5_vl import (
+    smart_resize)
+from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import (
+    Qwen2_5_VisionTransformerPretrainedModel)
+
+from vllm.attention import AttentionMetadata
+from vllm.config import VllmConfig
+from vllm.distributed import parallel_state, tensor_model_parallel_all_gather
+from vllm.distributed import utils as dist_utils
+from vllm.logger import init_logger
+from vllm.model_executor import SamplingMetadata
+from vllm.model_executor.layers.activation import QuickGELU
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.quantization.gptq import GPTQConfig
+from vllm.model_executor.layers.quantization.gptq_marlin import (
+    GPTQMarlinConfig)
+from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.module_mapping import MultiModelKeys
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (ImageItem, ModalityData,
+                                    MultiModalFieldConfig, MultiModalKwargs,
+                                    VideoItem)
+from vllm.multimodal.parse import (ImageSize, ModalityDataItems,
+                                   MultiModalDataItems, MultiModalDataParser)
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, PromptReplacement)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
+from vllm.platforms import _Backend
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.config import uses_mrope
+
+from .interfaces import SupportsLoRA, SupportsMultiModal, SupportsPP
+from .utils import (AutoWeightsLoader, WeightsMapper,
+                    init_vllm_registered_model, maybe_prefix,
+                    merge_multimodal_embeddings)
+from .vision import get_vit_attn_backend
+
+logger = init_logger(__name__)
+
+# For profile run
+_MAX_FRAMES_PER_VIDEO = 16
+
+# === Vision Inputs === #
+
+
+class Qwen2_5_VLImagePixelInputs(TypedDict):
+    type: Literal["pixel_values"]
+    pixel_values: torch.Tensor
+    """Shape:
+    `(num_patches, num_channels * patch_size * patch_size)`
+    """
+
+    image_grid_thw: torch.Tensor
+    """Shape: `(num_images, 3)`
+    This should be in `(grid_t, grid_h, grid_w)` format.
+    """
+
+
+class Qwen2_5_VLVideoPixelInputs(TypedDict):
+    type: Literal["pixel_values_videos"]
+    pixel_values_videos: torch.Tensor
+    """Shape:
+    `(num_patches,
+      num_channels * temporal_patch_size * patch_size * patch_size)`
+    """
+
+    video_grid_thw: torch.Tensor
+    """Shape: `(num_videos, 3)`
+
+    This should be in `(grid_t, grid_h, grid_w)` format.
+    """
+
+
+class Qwen2_5_VLProcessingInfo(BaseProcessingInfo):
+
+    def get_hf_config(self):
+        return self.ctx.get_hf_config(Qwen2_5_VLConfig)
+
+    def get_hf_processor(
+        self,
+        *,
+        min_pixels: Optional[int] = None,
+        max_pixels: Optional[int] = None,
+    ) -> Qwen2_5_VLProcessor:
+        hf_processor = self.ctx.get_hf_processor(Qwen2_5_VLProcessor)
+        image_processor = hf_processor.image_processor  # type: ignore
+        assert isinstance(image_processor, Qwen2_5_VLImageProcessor)
+
+        if min_pixels:
+            image_processor.min_pixels = min_pixels
+        if max_pixels:
+            image_processor.max_pixels = max_pixels
+        if max_pixels or min_pixels:
+            image_processor.size = {
+                "min_pixels": image_processor.min_pixels,
+                "max_pixels": image_processor.max_pixels,
+            }
+
+        return hf_processor
+
+    def get_image_processor(
+        self,
+        *,
+        min_pixels: Optional[int] = None,
+        max_pixels: Optional[int] = None,
+    ):
+        hf_processor = self.get_hf_processor(min_pixels=min_pixels,
+                                             max_pixels=max_pixels)
+        image_processor = hf_processor.image_processor  # type: ignore
+        assert isinstance(image_processor, Qwen2_5_VLImageProcessor)
+        return image_processor
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None, "video": None}
+
+    def get_mm_max_tokens_per_item(self, seq_len: int) -> Mapping[str, int]:
+        return {
+            "image": self.get_max_image_tokens(),
+            "video": self.get_max_video_tokens(seq_len),
+        }
+
+    def _get_vision_info(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        num_frames: int = 1,
+        do_resize: bool = True,
+        image_processor: Optional[Qwen2_5_VLImageProcessor],
+    ) -> tuple[ImageSize, int]:
+        if image_processor is None:
+            image_processor = self.get_image_processor()
+
+        hf_config = self.get_hf_config()
+        vision_config = hf_config.vision_config
+        patch_size = vision_config.patch_size
+        merge_size = vision_config.spatial_merge_size
+        temporal_patch_size = vision_config.temporal_patch_size
+
+        if do_resize:
+            resized_height, resized_width = smart_resize(
+                height=image_height,
+                width=image_width,
+                factor=patch_size * merge_size,
+                min_pixels=image_processor.min_pixels,
+                max_pixels=image_processor.max_pixels,
+            )
+            preprocessed_size = ImageSize(width=resized_width,
+                                          height=resized_height)
+        else:
+            preprocessed_size = ImageSize(width=image_width,
+                                          height=image_height)
+
+        grid_t = max(num_frames // temporal_patch_size, 1)
+        grid_h = preprocessed_size.height // patch_size
+        grid_w = preprocessed_size.width // patch_size
+
+        num_patches = grid_t * grid_h * grid_w
+        num_vision_tokens = num_patches // (merge_size**2)
+
+        return preprocessed_size, num_vision_tokens
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        image_processor: Optional[Qwen2_5_VLImageProcessor],
+    ) -> int:
+        _, num_image_tokens = self._get_vision_info(
+            image_width=image_width,
+            image_height=image_height,
+            image_processor=image_processor,
+        )
+        return num_image_tokens
+
+    def get_num_video_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        num_frames: int,
+        image_processor: Optional[Qwen2_5_VLImageProcessor],
+    ) -> int:
+        _, num_video_tokens = self._get_vision_info(
+            image_width=image_width,
+            image_height=image_height,
+            num_frames=num_frames,
+            image_processor=image_processor,
+        )
+        return num_video_tokens
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        max_image_size, _ = self._get_vision_info(
+            image_width=9999999,
+            image_height=9999999,
+            image_processor=None,
+        )
+        return max_image_size
+
+    def get_max_image_tokens(self) -> int:
+        target_width, target_height = self.get_image_size_with_most_features()
+
+        return self.get_num_image_tokens(
+            image_width=target_width,
+            image_height=target_height,
+            image_processor=None,
+        )
+
+    def _get_max_video_frames(self, max_tokens: int) -> int:
+        target_width, target_height = self.get_image_size_with_most_features()
+
+        num_frames = 0
+
+        while True:
+            next_num_frames = num_frames + 1
+            next_max_tokens = self.get_num_video_tokens(
+                image_width=target_width,
+                image_height=target_height,
+                num_frames=next_num_frames,
+                image_processor=None,
+            )
+
+            if next_max_tokens > max_tokens:
+                break
+
+            num_frames = next_num_frames
+
+        return num_frames
+
+    def get_num_frames_with_most_features(self, seq_len: int) -> int:
+        mm_config = self.ctx.get_mm_config()
+        max_images = mm_config.limit_per_prompt.get("image", 1)
+        max_videos = mm_config.limit_per_prompt.get("video", 1)
+
+        max_image_tokens = self.get_max_image_tokens() * max_images
+        max_total_frames = self._get_max_video_frames(seq_len -
+                                                      max_image_tokens)
+        num_frames = min(max(max_total_frames // max(max_videos, 1), 1),
+                         _MAX_FRAMES_PER_VIDEO)
+
+        # Temporary workaround for https://github.com/huggingface/transformers/issues/35412
+        if num_frames > 1 and num_frames % 2 == 1:
+            num_frames += 1
+
+        return num_frames
+
+    def get_max_video_tokens(self, seq_len: int) -> int:
+        target_width, target_height = self.get_image_size_with_most_features()
+
+        return self.get_num_video_tokens(
+            image_width=target_width,
+            image_height=target_height,
+            num_frames=self.get_num_frames_with_most_features(seq_len),
+            image_processor=None,
+        )
+
+
+class Qwen2_5_VLDummyInputsBuilder(
+        BaseDummyInputsBuilder[Qwen2_5_VLProcessingInfo]):
+
+    def get_dummy_processor_inputs(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> ProcessorInputs:
+        num_images = mm_counts.get("image", 0)
+        num_videos = mm_counts.get("video", 0)
+
+        hf_processor = self.info.get_hf_processor()
+        image_token: str = hf_processor.image_token
+        video_token: str = hf_processor.video_token
+
+        target_width, target_height = \
+            self.info.get_image_size_with_most_features()
+        target_num_frames = \
+            self.info.get_num_frames_with_most_features(seq_len)
+
+        mm_data = {
+            "image":
+            self._get_dummy_images(width=target_width,
+                                   height=target_height,
+                                   num_images=num_images),
+            "video":
+            self._get_dummy_videos(
+                width=target_width,
+                height=target_height,
+                num_frames=target_num_frames,
+                num_videos=num_videos,
+            )
+        }
+
+        return ProcessorInputs(
+            prompt_text=image_token * num_images + video_token * num_videos,
+            mm_data=mm_data,
+        )
+
+
+class Qwen2_5_VLMultiModalProcessor(
+        BaseMultiModalProcessor[Qwen2_5_VLProcessingInfo]):
+
+    def _get_prompt_replacements(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, Any],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> list[PromptReplacement]:
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+        image_processor = self.info.get_image_processor(
+            **hf_processor_mm_kwargs)
+        tokenizer = self.info.get_tokenizer()
+        vocab = tokenizer.get_vocab()
+
+        # NOTE: Only Qwen2_5_VLProcessor in transformers 4.47.0 has
+        # image_token and video_token registered
+        placeholder = {
+            "image": vocab[hf_processor.image_token],
+            "video": vocab[hf_processor.video_token],
+        }
+
+        merge_length = image_processor.merge_size**2
+
+        def get_replacement_Qwen2_5_VL(item_idx: int, modality: str):
+            grid_thw = out_mm_kwargs[f"{modality}_grid_thw"][item_idx]
+            assert isinstance(grid_thw, torch.Tensor)
+
+            num_tokens = int(grid_thw.prod()) // merge_length
+            return [placeholder[modality]] * num_tokens
+
+        return [
+            PromptReplacement(
+                modality=modality,
+                target=[placeholder[modality]],
+                replacement=partial(get_replacement_Qwen2_5_VL,
+                                    modality=modality),
+            ) for modality in ("image", "video")
+        ]
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        image_grid_thw = hf_inputs.get("image_grid_thw", torch.empty((0, 3)))
+        image_slice_idxs = [0] + image_grid_thw.prod(-1).cumsum_(0).tolist()
+        image_slices = [
+            slice(image_slice_idxs[i], image_slice_idxs[i + 1])
+            for i in range(len(image_grid_thw))
+        ]
+
+        video_grid_thw = hf_inputs.get("video_grid_thw", torch.empty((0, 3)))
+        video_slice_idxs = [0] + video_grid_thw.prod(-1).cumsum_(0).tolist()
+        video_slices = [
+            slice(video_slice_idxs[i], video_slice_idxs[i + 1])
+            for i in range(len(video_grid_thw))
+        ]
+
+        return dict(
+            pixel_values=MultiModalFieldConfig.flat("image", image_slices),
+            image_grid_thw=MultiModalFieldConfig.batched("image"),
+            pixel_values_videos=MultiModalFieldConfig.flat(
+                "video", video_slices),
+            video_grid_thw=MultiModalFieldConfig.batched("video"),
+        )
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    Qwen2_5_VLMultiModalProcessor,
+    info=Qwen2_5_VLProcessingInfo,
+    dummy_inputs=Qwen2_5_VLDummyInputsBuilder)
+class Qwen2_5_VLForConditionalGeneration(nn.Module, SupportsMultiModal,
+                                         SupportsLoRA, SupportsPP):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    # LoRA specific attributes
+    supported_lora_modules = [
+        "qkv_proj",
+        "o_proj",
+        "gate_up_proj",
+        "down_proj",
+        # vision tower
+        "qkv",
+        "attn.proj",  # Distinguish patch_embed.proj
+        "fc1",
+        "fc2",
+        # projector
+        "mlp.0",
+        "mlp.2"
+    ]
+    embedding_modules = {}
+    embedding_padding_modules = []
+
+    # To ensure correct weight loading and mapping.
+    hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={
+        "lm_head.": "language_model.lm_head.",
+        "model.": "language_model.model.",
+    })
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config: Qwen2_5_VLConfig = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+
+        self.config = config
+        self.multimodal_config = multimodal_config
+
+        self.visual = Qwen2_5_VisionTransformerPretrainedModel._from_config(
+            config.vision_config)
+
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+            architectures=["Qwen2ForCausalLM"],
+        )
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+    @cached_property
+    def sampler(self):
+        if hasattr(self.language_model, "sampler"):
+            return self.language_model.sampler
+
+        return get_sampler()
+
+    def _maybe_ignore_quant_config(self, quant_config: QuantizationConfig):
+        # GPTQ configs do not have a list of ignored modules, however AutoGPTQ
+        # seems to avoid vision encoder sections for some models.
+        # See: https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4
+        if isinstance(quant_config, (GPTQConfig, GPTQMarlinConfig)):
+            return None
+        return quant_config
+
+    def _validate_and_reshape_mm_tensor(self, mm_input: object,
+                                        name: str) -> torch.Tensor:
+        if not isinstance(mm_input, (torch.Tensor, list)):
+            raise ValueError(f"Incorrect type of {name}. "
+                             f"Got type: {type(mm_input)}")
+        if isinstance(mm_input, torch.Tensor):
+            if mm_input.ndim == 2:
+                return mm_input
+            if mm_input.ndim != 3:
+                raise ValueError(f"{name} should be 2D or batched 3D tensor. "
+                                 f"Got ndim: {mm_input.ndim} "
+                                 f"(shape={mm_input.shape})")
+            return torch.concat(list(mm_input))
+        else:
+            return torch.concat(mm_input)
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[Qwen2_5_VLImagePixelInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        image_grid_thw = kwargs.pop("image_grid_thw", None)
+
+        if pixel_values is None:
+            return None
+
+        if pixel_values is not None:
+            pixel_values = self._validate_and_reshape_mm_tensor(
+                pixel_values, "image pixel values")
+            image_grid_thw = self._validate_and_reshape_mm_tensor(
+                image_grid_thw, "image grid_thw")
+
+            if not isinstance(pixel_values, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of image pixel values. "
+                                 f"Got type: {type(pixel_values)}")
+
+            return Qwen2_5_VLImagePixelInputs(type="pixel_values",
+                                              pixel_values=pixel_values,
+                                              image_grid_thw=image_grid_thw)
+        raise
+
+    def _parse_and_validate_video_input(
+            self, **kwargs: object) -> Optional[Qwen2_5_VLVideoPixelInputs]:
+        pixel_values_videos = kwargs.pop("pixel_values_videos", None)
+        video_grid_thw = kwargs.pop("video_grid_thw", None)
+
+        if pixel_values_videos is None:
+            return None
+
+        if pixel_values_videos is not None:
+            pixel_values_videos = self._validate_and_reshape_mm_tensor(
+                pixel_values_videos, "video pixel values")
+            video_grid_thw = self._validate_and_reshape_mm_tensor(
+                video_grid_thw, "video grid_thw")
+
+            return Qwen2_5_VLVideoPixelInputs(
+                type="pixel_values_videos",
+                pixel_values_videos=pixel_values_videos,
+                video_grid_thw=video_grid_thw,
+            )
+        raise
+
+    def _process_image_input(
+            self, image_input: Qwen2_5_VLImagePixelInputs
+    ) -> tuple[torch.Tensor, ...]:
+
+        grid_thw = image_input["image_grid_thw"]
+        assert grid_thw.ndim == 2
+
+        pixel_values = image_input["pixel_values"].type(self.visual.dtype)
+        image_embeds = self.visual(pixel_values, grid_thw=grid_thw)
+
+        # Split concatenated embeddings for each image item.
+        merge_size = self.visual.spatial_merge_size
+        sizes = grid_thw.prod(-1) // merge_size // merge_size
+
+        return image_embeds.split(sizes.tolist())
+
+    def _process_video_input(
+            self, video_input: Qwen2_5_VLVideoPixelInputs
+    ) -> tuple[torch.Tensor, ...]:
+
+        grid_thw = video_input["video_grid_thw"]
+        assert grid_thw.ndim == 2
+
+        pixel_values_videos = video_input["pixel_values_videos"].type(
+            self.visual.dtype)
+        video_embeds = self.visual(pixel_values_videos, grid_thw=grid_thw)
+
+        # Split concatenated embeddings for each video item.
+        merge_size = self.visual.spatial_merge_size
+        sizes = grid_thw.prod(-1) // merge_size // merge_size
+
+        return video_embeds.split(sizes.tolist())
+
+    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
+        modalities = {}
+
+        # Preserve the order of modalities if there are multiple of them
+        # from the order of kwargs.
+        for input_key in kwargs:
+            if input_key in ("pixel_values",
+                             "image_embeds") and "images" not in modalities:
+                modalities["images"] = self._parse_and_validate_image_input(
+                    **kwargs)
+            if input_key in ("pixel_values_videos",
+                             "video_embeds") and "videos" not in modalities:
+                modalities["videos"] = self._parse_and_validate_video_input(
+                    **kwargs)
+
+        return modalities
+
+    def get_multimodal_embeddings(
+            self, **kwargs) -> Optional[tuple[torch.Tensor, ...]]:
+
+        modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
+        if not modalities:
+            return None
+
+        # The result multimodal_embeddings is tuple of tensors, with each
+        # tensor correspoending to a multimodal data item (image or video).
+        multimodal_embeddings: tuple[torch.Tensor, ...] = ()
+
+        # NOTE: It is important to iterate over the keys in this dictionary
+        # to preserve the order of the modalities.
+        for modality in modalities:
+            if modality == "images":
+                image_input = modalities["images"]
+                vision_embeddings = self._process_image_input(image_input)
+                multimodal_embeddings += vision_embeddings
+            if modality == "videos":
+                video_input = modalities["videos"]
+                video_embeddings = self._process_video_input(video_input)
+                multimodal_embeddings += video_embeddings
+
+        return multimodal_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[tuple[torch.Tensor, ...]] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids, inputs_embeds, multimodal_embeddings,
+                [self.config.image_token_id, self.config.video_token_id])
+        return inputs_embeds
+
+    def get_input_embeddings_v0(
+        self,
+        input_ids: torch.Tensor,
+        image_input: Optional[tuple[torch.Tensor, ...]] = None,
+        video_input: Optional[tuple[torch.Tensor, ...]] = None,
+    ) -> torch.Tensor:
+
+        inputs_embeds = self.get_input_embeddings(input_ids)
+        if image_input is not None:
+            image_embeds = self._process_image_input(image_input)
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                image_embeds,
+                placeholder_token_id=self.config.image_token_id,
+            )
+
+        if video_input is not None:
+            video_embeds = self._process_video_input(video_input)
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                video_embeds,
+                placeholder_token_id=self.config.video_token_id,
+            )
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        """Run forward pass for Qwen2.5-VL.
+
+        Args:
+            input_ids: Flattened (concatenated) input_ids corresponding to a
+                batch.
+            positions: Flattened (concatenated) position ids corresponding to a
+                batch.
+                **NOTE**: If mrope is enabled (default setting for Qwen2-VL
+                opensource models), the shape will be `(3, seq_len)`,
+                otherwise it will be `(seq_len,).
+            pixel_values: Pixel values to be fed to a model.
+                `None` if no images are passed.
+            image_grid_thw: Tensor `(n_images, 3)` of image 3D grid in LLM.
+                `None` if no images are passed.
+            pixel_values_videos: Pixel values of videos to be fed to a model.
+                `None` if no videos are passed.
+            video_grid_thw: Tensor `(n_videos, 3)` of video 3D grid in LLM.
+                `None` if no videos are passed.
+        """
+
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner from
+        # `get_multimodal_embeddings` and `get_input_embeddings`, this
+        # condition is only for v0 compatibility.
+        elif inputs_embeds is None:
+            image_input = self._parse_and_validate_image_input(**kwargs)
+            video_input = self._parse_and_validate_video_input(**kwargs)
+
+            if image_input is None and video_input is None:
+                inputs_embeds = None
+            else:
+                if uses_mrope(self.config):
+                    assert positions.ndim == 2 and positions.size(0) == 3, (
+                        "multimodal section rotary embedding requires "
+                        f"(3, seq_len) positions, but got {positions.size()}")
+                inputs_embeds = self.get_input_embeddings_v0(
+                    input_ids,
+                    image_input=image_input,
+                    video_input=video_input)
+                input_ids = None
+
+        hidden_states = self.language_model.model(
+            input_ids=input_ids,
+            positions=positions,
+            kv_caches=kv_caches,
+            attn_metadata=attn_metadata,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def sample(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        return self.language_model.sample(logits, sampling_metadata)
+
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> Set[str]:
+
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+
+    def get_mm_mapping(self) -> MultiModelKeys:
+        """
+        Get the module prefix in multimodal models
+        """
+        return MultiModelKeys.from_string_field(
+            language_model="language_model",
+            connector="visual.",
+            tower_model="visual.merger.")

vllm/model_executor/models/registry.py

@@ -171,6 +171,7 @@ _MULTIMODAL_MODELS = {
     "PixtralForConditionalGeneration": ("pixtral", "PixtralForConditionalGeneration"),  # noqa: E501
     "QWenLMHeadModel": ("qwen", "QWenLMHeadModel"),
     "Qwen2VLForConditionalGeneration": ("qwen2_vl", "Qwen2VLForConditionalGeneration"),  # noqa: E501
+    "Qwen2_5_VLForConditionalGeneration": ("qwen2_5_vl", "Qwen2_5_VLForConditionalGeneration"),  # noqa: E501
     "Qwen2AudioForConditionalGeneration": ("qwen2_audio", "Qwen2AudioForConditionalGeneration"),  # noqa: E501
     "UltravoxModel": ("ultravox", "UltravoxModel"),
     # [Encoder-decoder]