[Model] Support Qwen3-VL Model Series (#24727)

Signed-off-by: Roger Wang <hey@rogerw.io>
Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>
Co-authored-by: Huang Jie <92386084+JJJYmmm@users.noreply.github.com>
Co-authored-by: 松灵 <26085463+wulipc@users.noreply.github.com>
Co-authored-by: Isotr0py <mozf@mail2.sysu.edu.cn>

docs/models/supported_models.md

@@ -661,6 +661,8 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `Qwen2VLForConditionalGeneration` | QVQ, Qwen2-VL | T + I<sup>E+</sup> + V<sup>E+</sup> | `Qwen/QVQ-72B-Preview`, `Qwen/Qwen2-VL-7B-Instruct`, `Qwen/Qwen2-VL-72B-Instruct`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `Qwen2_5_VLForConditionalGeneration` | Qwen2.5-VL | T + I<sup>E+</sup> + V<sup>E+</sup> | `Qwen/Qwen2.5-VL-3B-Instruct`, `Qwen/Qwen2.5-VL-72B-Instruct`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `Qwen2_5OmniThinkerForConditionalGeneration` | Qwen2.5-Omni | T + I<sup>E+</sup> + V<sup>E+</sup> + A<sup>+</sup> | `Qwen/Qwen2.5-Omni-3B`, `Qwen/Qwen2.5-Omni-7B` | ✅︎ | ✅︎ | ✅︎ |
+| `Qwen3VLForConditionalGeneration` | Qwen3-VL | T + I<sup>E+</sup> + V<sup>E+</sup> | `Qwen/Qwen3-VL-4B-Instruct`, etc. | ✅︎ | ✅︎ | ✅︎ |
+| `Qwen3VLMoeForConditionalGeneration` | Qwen3-VL-MOE | T + I<sup>E+</sup> + V<sup>E+</sup> | `Qwen/Qwen3-VL-30B-A3B-Instruct`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `RForConditionalGeneration` | R-VL-4B | T + I<sup>E+</sup> | `YannQi/R-4B` | | ✅︎ | ✅︎ |
 | `SkyworkR1VChatModel` | Skywork-R1V-38B | T + I | `Skywork/Skywork-R1V-38B` | | ✅︎ | ✅︎ |
 | `SmolVLMForConditionalGeneration` | SmolVLM2 | T + I | `SmolVLM2-2.2B-Instruct` | ✅︎ | | ✅︎ |

examples/offline_inference/vision_language.py

@@ -1437,6 +1437,80 @@ def run_qwen2_5_omni(questions: list[str], modality: str):
     )
 
 
+# Qwen3-VL-Dense
+def run_qwen3_vl(questions: list[str], modality: str) -> ModelRequestData:
+    model_name = "Qwen/Qwen3-VL-4B-Instruct"
+
+    engine_args = EngineArgs(
+        model=model_name,
+        max_model_len=4096,
+        max_num_seqs=5,
+        mm_processor_kwargs={
+            "min_pixels": 28 * 28,
+            "max_pixels": 1280 * 28 * 28,
+            "fps": 1,
+        },
+        limit_mm_per_prompt={modality: 1},
+    )
+
+    if modality == "image":
+        placeholder = "<|image_pad|>"
+    elif modality == "video":
+        placeholder = "<|video_pad|>"
+
+    prompts = [
+        (
+            "<|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"
+        )
+        for question in questions
+    ]
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompts=prompts,
+    )
+
+
+# Qwen3-VL-MOE
+def run_qwen3_vl_moe(questions: list[str], modality: str) -> ModelRequestData:
+    model_name = "Qwen/Qwen3-VL-30B-A3B-Instruct"
+
+    engine_args = EngineArgs(
+        model=model_name,
+        max_model_len=4096,
+        max_num_seqs=5,
+        mm_processor_kwargs={
+            "min_pixels": 28 * 28,
+            "max_pixels": 1280 * 28 * 28,
+            "fps": 1,
+        },
+        limit_mm_per_prompt={modality: 1},
+    )
+
+    if modality == "image":
+        placeholder = "<|image_pad|>"
+    elif modality == "video":
+        placeholder = "<|video_pad|>"
+
+    prompts = [
+        (
+            "<|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"
+        )
+        for question in questions
+    ]
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompts=prompts,
+    )
+
+
 # R-4B
 def run_r_vl(questions: list[str], modality: str) -> ModelRequestData:
     assert modality == "image"
@@ -1645,6 +1719,8 @@ model_example_map = {
     "qwen2_vl": run_qwen2_vl,
     "qwen2_5_vl": run_qwen2_5_vl,
     "qwen2_5_omni": run_qwen2_5_omni,
+    "qwen3_vl": run_qwen3_vl,
+    "qwen3_vl_moe": run_qwen3_vl_moe,
     "rvl": run_r_vl,
     "skywork_chat": run_skyworkr1v,
     "smolvlm": run_smolvlm,
@@ -1658,6 +1734,8 @@ MODELS_NEED_VIDEO_METADATA = [
     "glm4_1v",
     "glm4_5v",
     "glm4_5v_fp8",
+    "qwen3_vl",
+    "qwen3_vl_moe",
 ]
 
 

tests/models/multimodal/processing/test_common.py

@@ -31,6 +31,7 @@ def glm4_1v_patch_mm_data(mm_data: MultiModalDataDict) -> MultiModalDataDict:
     """
     # Ensure video metadata is included
     if "video" in mm_data:
+        # GLM4.1V doesn't support multiple videos
         video = mm_data["video"]
         num_frames = len(video)
         mm_data["video"] = (video, {
@@ -44,6 +45,34 @@ def glm4_1v_patch_mm_data(mm_data: MultiModalDataDict) -> MultiModalDataDict:
     return mm_data
 
 
+def qwen3_vl_patch_mm_data(mm_data: MultiModalDataDict) -> MultiModalDataDict:
+    """
+    Patch the multimodal data for Qwen3-VL model.
+    """
+
+    def create_metadata(frames: np.ndarray):
+        num_frames = len(frames)
+        return {
+            "total_num_frames": num_frames,
+            "fps": 2.0,
+            "duration": num_frames / 2.0,
+            "video_backend": "opencv",
+            "frames_indices": list(range(num_frames)),
+            "do_sample_frames": True,
+        }
+
+    # Ensure video metadata is included
+    if "video" in mm_data:
+        video = mm_data["video"]
+        if isinstance(video, list):
+            # multiple videos
+            mm_data["video"] = [(vid, create_metadata(vid)) for vid in video]
+        else:
+            # single video
+            mm_data["video"] = (video, create_metadata(video))
+    return mm_data
+
+
 def _test_processing_correctness(
     model_id_or_arch: str,
     hit_rate: float,
@@ -182,8 +211,10 @@ _IGNORE_MM_KEYS = {
 }
 
 MM_DATA_PATCHES = {
-    # GLM4.1V requires video metadata to be included in the input
+    # GLM4.1V and Qwen3-VL requires video metadata to be included in the input
     "glm4v": glm4_1v_patch_mm_data,
+    "qwen3_vl": qwen3_vl_patch_mm_data,
+    "qwen3_vl_moe": qwen3_vl_patch_mm_data,
 }
 
 
@@ -326,6 +357,8 @@ def _test_processing_correctness_one(
     "Qwen/Qwen2.5-VL-3B-Instruct",
     "Qwen/Qwen2-Audio-7B-Instruct",
     "Qwen/Qwen2.5-Omni-3B",
+    "Qwen/Qwen3-VL-4B-Instruct",
+    "Qwen/Qwen3-VL-30B-A3B-Instruct",
     "YannQi/R-4B",
     "Skywork/Skywork-R1V-38B",
     "HuggingFaceTB/SmolVLM2-2.2B-Instruct",

tests/models/registry.py

@@ -557,6 +557,12 @@ _MULTIMODAL_EXAMPLE_MODELS = {
                                                           max_model_len=4096),
     "Qwen2_5OmniModel": _HfExamplesInfo("Qwen/Qwen2.5-Omni-3B"),
     "Qwen2_5OmniForConditionalGeneration": _HfExamplesInfo("Qwen/Qwen2.5-Omni-7B-AWQ"),  # noqa: E501
+    "Qwen3VLForConditionalGeneration": _HfExamplesInfo("Qwen/Qwen3-VL-4B-Instruct", # noqa: E501
+                                                        max_model_len=4096,
+                                                        min_transformers_version="4.57"),  # noqa: E501
+    "Qwen3VLMoeForConditionalGeneration": _HfExamplesInfo("Qwen/Qwen3-VL-30B-A3B-Instruct", # noqa: E501
+                                                        max_model_len=4096,
+                                                        min_transformers_version="4.57"),
     "RForConditionalGeneration": _HfExamplesInfo("YannQi/R-4B",
                                                  trust_remote_code=True),
     "SkyworkR1VChatModel": _HfExamplesInfo("Skywork/Skywork-R1V-38B",

vllm/model_executor/layers/rotary_embedding/__init__.py

@@ -103,6 +103,8 @@ def get_rope(
                     is_neox_style,
                     dtype,
                     mrope_section=rope_scaling["mrope_section"],
+                    mrope_interleaved=rope_scaling.get("mrope_interleaved",
+                                                       False),
                 )
             else:
                 rotary_emb = RotaryEmbedding(

vllm/model_executor/layers/rotary_embedding/mrope.py

@@ -177,6 +177,18 @@ def triton_mrope(
     return q, k
 
 
+def apply_interleaved_rope(x: torch.Tensor,
+                           mrope_section: list[int]) -> torch.Tensor:
+    """Apply interleaved MRoPE to 3D rotary embeddings.
+    Reorganizes frequency layout from chunked [TTT...HHH...WWW] to
+    interleaved [THTHWHTHW...TT], preserving frequency continuity.
+    """
+    x_t = x[0].clone()
+    x_t[..., 1:mrope_section[1] * 3:3] = x[1, ..., 1:mrope_section[1] * 3:3]
+    x_t[..., 2:mrope_section[2] * 3:3] = x[2, ..., 2:mrope_section[2] * 3:3]
+    return x_t
+
+
 class MRotaryEmbedding(RotaryEmbedding):
     """Rotary Embedding with Multimodal Sections."""
 
@@ -189,6 +201,7 @@ class MRotaryEmbedding(RotaryEmbedding):
         is_neox_style: bool,
         dtype: torch.dtype,
         mrope_section: Optional[list[int]] = None,
+        mrope_interleaved: Optional[bool] = False,
     ) -> None:
         # In Qwen2.5-VL, the maximum index value is related to the duration of
         # the input video. We enlarge max_position_embeddings to 4 times to get
@@ -198,6 +211,7 @@ class MRotaryEmbedding(RotaryEmbedding):
                          base, is_neox_style, dtype)
 
         self.mrope_section = mrope_section
+        self.mrope_interleaved = mrope_interleaved
         if self.mrope_section:
             assert sum(self.mrope_section) == rotary_dim // 2
 
@@ -225,17 +239,20 @@ class MRotaryEmbedding(RotaryEmbedding):
         cos, sin = cos_sin.chunk(2, dim=-1)
         if positions.ndim == 2:
             assert self.mrope_section
-
-            cos = torch.cat([
-                m[i]
-                for i, m in enumerate(cos.split(self.mrope_section, dim=-1))
-            ],
-                            dim=-1)
-            sin = torch.cat([
-                m[i]
-                for i, m in enumerate(sin.split(self.mrope_section, dim=-1))
-            ],
-                            dim=-1)
+            if self.mrope_interleaved:
+                cos = apply_interleaved_rope(cos, self.mrope_section)
+                sin = apply_interleaved_rope(sin, self.mrope_section)
+            else:
+                cos = torch.cat([
+                    m[i] for i, m in enumerate(
+                        cos.split(self.mrope_section, dim=-1))
+                ],
+                                dim=-1)
+                sin = torch.cat([
+                    m[i] for i, m in enumerate(
+                        sin.split(self.mrope_section, dim=-1))
+                ],
+                                dim=-1)
 
         query_shape = query.shape
         query = query.view(num_tokens, -1, self.head_size)
@@ -265,6 +282,10 @@ class MRotaryEmbedding(RotaryEmbedding):
         assert positions.ndim == 1 or positions.ndim == 2
         assert key is not None
 
+        if self.mrope_interleaved:
+            # TODO: add triton implementation to support mrope-interleaved
+            return self.forward_native(positions, query, key)
+
         num_tokens = positions.shape[-1]
         cos_sin = self.cos_sin_cache[positions]
         cos, sin = cos_sin.chunk(2, dim=-1)
@@ -388,6 +409,15 @@ class MRotaryEmbedding(RotaryEmbedding):
                 context_len=context_len,
                 seq_len=seq_len,
             )
+        elif hf_config.model_type in ["qwen3_vl", "qwen3_vl_moe"]:
+            return cls._qwen3vl_get_input_positions_tensor(
+                input_tokens=input_tokens,
+                hf_config=hf_config,
+                image_grid_thw=image_grid_thw,
+                video_grid_thw=video_grid_thw,
+                context_len=context_len,
+                seq_len=seq_len,
+            )
         elif hf_config.model_type in ["ernie4_5_moe_vl", "ernie4_5_vl"]:
             return cls._ernie_get_input_positions_tensor(
                 input_tokens=input_tokens,
@@ -526,6 +556,98 @@ class MRotaryEmbedding(RotaryEmbedding):
                                 len(input_tokens)).item()
         return llm_positions, mrope_position_delta
 
+    @classmethod
+    def _qwen3vl_get_input_positions_tensor(
+        cls,
+        input_tokens: list[int],
+        hf_config: PretrainedConfig,
+        image_grid_thw: Union[list[list[int]], torch.Tensor],
+        video_grid_thw: Union[list[list[int]], torch.Tensor],
+        context_len: int = 0,
+        seq_len: Optional[int] = None,
+    ) -> tuple[torch.Tensor, int]:
+        """Get mrope input positions and delta value."""
+
+        video_grid_thw = [[1, h, w] for t, h, w in video_grid_thw
+                          for _ in range(t)]
+
+        image_token_id = hf_config.image_token_id
+        video_token_id = hf_config.video_token_id
+        vision_start_token_id = hf_config.vision_start_token_id
+        spatial_merge_size = hf_config.vision_config.spatial_merge_size
+
+        input_tokens_tensor = torch.tensor(input_tokens)
+        vision_start_indices = torch.argwhere(
+            input_tokens_tensor == vision_start_token_id).squeeze(1)
+        vision_tokens = input_tokens_tensor[vision_start_indices + 1]
+        image_nums = (vision_tokens == image_token_id).sum()
+        video_nums = (vision_tokens == video_token_id).sum()
+        llm_pos_ids_list: list = []
+
+        st = 0
+        remain_images, remain_videos = image_nums, video_nums
+
+        image_index, video_index = 0, 0
+        for _ in range(image_nums + video_nums):
+            if image_token_id in input_tokens and remain_images > 0:
+                ed_image = input_tokens.index(image_token_id, st)
+            else:
+                ed_image = len(input_tokens) + 1
+            if video_token_id in input_tokens and remain_videos > 0:
+                ed_video = input_tokens.index(video_token_id, st)
+            else:
+                ed_video = len(input_tokens) + 1
+            if ed_image < ed_video:
+                t, h, w = (
+                    image_grid_thw[image_index][0],
+                    image_grid_thw[image_index][1],
+                    image_grid_thw[image_index][2],
+                )
+                image_index += 1
+                remain_images -= 1
+                ed = ed_image
+            else:
+                t, h, w = (
+                    video_grid_thw[video_index][0],
+                    video_grid_thw[video_index][1],
+                    video_grid_thw[video_index][2],
+                )
+                video_index += 1
+                remain_videos -= 1
+                ed = ed_video
+
+            llm_grid_t, llm_grid_h, llm_grid_w = \
+                t, h // spatial_merge_size, w // spatial_merge_size
+            text_len = ed - st
+
+            st_idx = llm_pos_ids_list[-1].max() + 1 if len(
+                llm_pos_ids_list) > 0 else 0
+            llm_pos_ids_list.append(
+                torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+            t_index = torch.arange(llm_grid_t).view(-1, 1).expand(
+                -1, llm_grid_h * llm_grid_w).flatten()
+            h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(
+                llm_grid_t, -1, llm_grid_w).flatten()
+            w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(
+                llm_grid_t, llm_grid_h, -1).flatten()
+            llm_pos_ids_list.append(
+                torch.stack([t_index, h_index, w_index]) + text_len + st_idx)
+            st = ed + llm_grid_t * llm_grid_h * llm_grid_w
+
+        if st < len(input_tokens):
+            st_idx = llm_pos_ids_list[-1].max() + 1 if len(
+                llm_pos_ids_list) > 0 else 0
+            text_len = len(input_tokens) - st
+            llm_pos_ids_list.append(
+                torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+        llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
+        mrope_position_delta = (llm_positions.max() + 1 -
+                                len(input_tokens)).item()
+        llm_positions = llm_positions[:, context_len:seq_len]
+        return llm_positions, mrope_position_delta
+
     @classmethod
     def _ernie_get_input_positions_tensor(
         cls,

vllm/model_executor/models/qwen2.py

@@ -285,7 +285,7 @@ class Qwen2Model(nn.Module):
                  decoder_layer_type: type[nn.Module] = Qwen2DecoderLayer):
         super().__init__()
 
-        config = vllm_config.model_config.hf_config
+        config = vllm_config.model_config.hf_config.get_text_config()
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
 

vllm/model_executor/models/qwen2_vl.py

@@ -83,7 +83,7 @@ from .vision import get_vit_attn_backend
 logger = init_logger(__name__)
 
 # For profile run
-_MAX_FRAMES_PER_VIDEO = 16
+_MAX_FRAMES_PER_VIDEO = 600
 
 # === Vision Inputs === #
 

vllm/model_executor/models/qwen3_moe.py

@@ -378,7 +378,7 @@ class Qwen3MoeModel(nn.Module):
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
 
-        config = vllm_config.model_config.hf_config
+        config = vllm_config.model_config.hf_config.get_text_config()
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
         parallel_config = vllm_config.parallel_config

vllm/model_executor/models/qwen3_vl.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2025 The vLLM team.
+# Copyright 2025 The Qwen Team.
+# Copyright 2025 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 Qwen3VL model compatible with HuggingFace weights."""
+from collections.abc import Iterable, Mapping, Sequence
+from functools import partial
+from typing import Any, Callable, Optional, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import BatchFeature
+from transformers.models.qwen2_vl import Qwen2VLImageProcessorFast
+from transformers.models.qwen2_vl.image_processing_qwen2_vl import smart_resize
+from transformers.models.qwen3_vl import (Qwen3VLProcessor,
+                                          Qwen3VLVideoProcessor)
+from transformers.models.qwen3_vl.configuration_qwen3_vl import (
+    Qwen3VLConfig, Qwen3VLVisionConfig)
+from transformers.video_utils import VideoMetadata
+
+from vllm.attention.layer import check_upstream_fa_availability
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import VllmConfig
+from vllm.distributed import get_pp_group
+from vllm.logger import init_logger
+from vllm.model_executor import SamplingMetadata
+from vllm.model_executor.layers.activation import _ACTIVATION_REGISTRY
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+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.vocab_parallel_embedding import ParallelLMHead
+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 (MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalKwargsItem,
+                                    MultiModalKwargsItems, VideoItem)
+from vllm.multimodal.parse import (ImageSize, MultiModalDataItems,
+                                   MultiModalDataParser)
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        PromptReplacement, PromptUpdate,
+                                        PromptUpdateDetails)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.platforms import _Backend
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.config import uses_mrope
+from vllm.utils import is_list_of
+
+from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
+                         SupportsMultiModal, SupportsPP)
+from .qwen2_5_vl import (Qwen2_5_VisionAttention,
+                         Qwen2_5_VisionRotaryEmbedding,
+                         Qwen2_5_VLImageEmbeddingInputs, Qwen2_5_VLImageInputs,
+                         Qwen2_5_VLImagePixelInputs,
+                         Qwen2_5_VLVideoEmbeddingInputs, Qwen2_5_VLVideoInputs,
+                         Qwen2_5_VLVideoPixelInputs)
+from .qwen2_vl import Qwen2VLProcessingInfo
+from .qwen3 import Qwen3ForCausalLM, Qwen3Model
+from .utils import (AutoWeightsLoader, PPMissingLayer, WeightsMapper,
+                    maybe_prefix, merge_multimodal_embeddings)
+from .vision import get_vit_attn_backend
+
+logger = init_logger(__name__)
+
+
+class Qwen3_VisionPatchEmbed(nn.Module):
+
+    def __init__(
+        self,
+        patch_size: int = 14,
+        temporal_patch_size: int = 2,
+        in_channels: int = 3,
+        hidden_size: int = 1152,
+    ) -> None:
+        super().__init__()
+        self.patch_size = patch_size
+        self.temporal_patch_size = temporal_patch_size
+        self.hidden_size = hidden_size
+
+        kernel_size = (temporal_patch_size, patch_size, patch_size)
+        self.proj = nn.Conv3d(in_channels,
+                              hidden_size,
+                              kernel_size=kernel_size,
+                              stride=kernel_size,
+                              bias=True)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        L, C = x.shape
+        x = x.view(L, -1, self.temporal_patch_size, self.patch_size,
+                   self.patch_size)
+        x = self.proj(x).view(L, self.hidden_size)
+        return x
+
+
+class Qwen3_VisionMLP(nn.Module):
+
+    def __init__(self,
+                 in_features: int,
+                 hidden_features: int,
+                 bias: bool = False,
+                 act_fn: Callable[[torch.Tensor], torch.Tensor] = F.silu,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+        self.linear_fc1 = ColumnParallelLinear(in_features,
+                                               hidden_features,
+                                               bias=bias,
+                                               quant_config=quant_config,
+                                               return_bias=False,
+                                               prefix=f"{prefix}.linear_fc1")
+        self.linear_fc2 = RowParallelLinear(hidden_features,
+                                            in_features,
+                                            bias=bias,
+                                            quant_config=quant_config,
+                                            return_bias=False,
+                                            prefix=f"{prefix}.linear_fc2")
+        self.act_fn = act_fn
+
+    def forward(self, x: torch.Tensor):
+        mlp_output = self.linear_fc2(self.act_fn(self.linear_fc1(x)))
+        return mlp_output
+
+
+class Qwen3_VisionBlock(nn.Module):
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_hidden_dim: int,
+        act_fn: Callable[[torch.Tensor], torch.Tensor] = F.silu,
+        norm_layer: Optional[Callable[[int], nn.Module]] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        if norm_layer is None:
+            norm_layer = partial(nn.LayerNorm, eps=1e-6)
+        self.norm1 = norm_layer(dim)
+        self.norm2 = norm_layer(dim)
+        self.attn = Qwen2_5_VisionAttention(embed_dim=dim,
+                                            num_heads=num_heads,
+                                            projection_size=dim,
+                                            quant_config=quant_config,
+                                            prefix=f"{prefix}.attn")
+        self.mlp = Qwen3_VisionMLP(dim,
+                                   mlp_hidden_dim,
+                                   act_fn=act_fn,
+                                   bias=True,
+                                   quant_config=quant_config,
+                                   prefix=f"{prefix}.mlp")
+
+    def forward(
+            self,
+            x: torch.Tensor,
+            cu_seqlens: torch.Tensor,
+            rotary_pos_emb: torch.Tensor,
+            max_seqlen: Optional[int] = None,  # Only used for Flash Attention
+            seqlens: Optional[list[int]] = None,  # Only used for xFormers
+    ) -> torch.Tensor:
+        x = x + self.attn(self.norm1(x),
+                          cu_seqlens=cu_seqlens,
+                          rotary_pos_emb=rotary_pos_emb,
+                          max_seqlen=max_seqlen,
+                          seqlens=seqlens)
+
+        x = x + self.mlp(self.norm2(x))
+        return x
+
+
+class Qwen3_VisionPatchMerger(nn.Module):
+
+    def __init__(
+        self,
+        d_model: int,
+        context_dim: int,
+        norm_layer: Optional[Callable[[int], nn.Module]] = None,
+        spatial_merge_size: int = 2,
+        use_postshuffle_norm: bool = False,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = context_dim * (spatial_merge_size**2)
+
+        self.use_postshuffle_norm = use_postshuffle_norm
+        if self.use_postshuffle_norm:
+            context_dim = self.hidden_size
+
+        if norm_layer is None:
+            norm_layer = partial(nn.LayerNorm, eps=1e-6)
+        self.use_postshuffle_norm = use_postshuffle_norm
+        self.norm = norm_layer(
+            self.hidden_size if use_postshuffle_norm else context_dim)
+        self.linear_fc1 = ColumnParallelLinear(self.hidden_size,
+                                               self.hidden_size,
+                                               bias=True,
+                                               quant_config=quant_config,
+                                               prefix=f"{prefix}.linear_fc1")
+        self.act_fn = nn.GELU()
+        self.linear_fc2 = RowParallelLinear(self.hidden_size,
+                                            d_model,
+                                            bias=True,
+                                            quant_config=quant_config,
+                                            prefix=f"{prefix}.linear_fc2")
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if self.use_postshuffle_norm:
+            x = self.norm(x.view(-1, self.hidden_size))
+        else:
+            x = self.norm(x).view(-1, self.hidden_size)
+
+        x_parallel, _ = self.linear_fc1(x)
+        x_parallel = self.act_fn(x_parallel)
+        out, _ = self.linear_fc2(x_parallel)
+        return out
+
+
+class Qwen3_VisionTransformer(nn.Module):
+
+    def __init__(
+        self,
+        vision_config: Qwen3VLVisionConfig,
+        norm_eps: float = 1e-6,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = vision_config.hidden_size
+        self.num_heads = vision_config.num_heads
+        self.num_position_embeddings = vision_config.num_position_embeddings
+        self.patch_size = vision_config.patch_size
+        self.spatial_merge_size = vision_config.spatial_merge_size
+        self.spatial_merge_unit = self.spatial_merge_size**2
+        self.temporal_patch_size = vision_config.temporal_patch_size
+        self.deepstack_visual_indexes = vision_config.deepstack_visual_indexes
+
+        self.patch_embed = Qwen3_VisionPatchEmbed(
+            patch_size=self.patch_size,
+            temporal_patch_size=self.temporal_patch_size,
+            in_channels=vision_config.in_channels,
+            hidden_size=self.hidden_size,
+        )
+
+        self.pos_embed = nn.Embedding(self.num_position_embeddings,
+                                      self.hidden_size)
+
+        norm_layer = partial(nn.LayerNorm, eps=norm_eps)
+        head_dim = self.hidden_size // self.num_heads
+        self.rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding(head_dim // 2)
+
+        self.blocks = nn.ModuleList([
+            Qwen3_VisionBlock(
+                dim=self.hidden_size,
+                num_heads=self.num_heads,
+                mlp_hidden_dim=vision_config.intermediate_size,
+                act_fn=_ACTIVATION_REGISTRY[vision_config.hidden_act],
+                norm_layer=norm_layer,
+                quant_config=quant_config,
+                prefix=f"{prefix}.blocks.{layer_idx}")
+            for layer_idx in range(vision_config.depth)
+        ])
+
+        self.merger = Qwen3_VisionPatchMerger(
+            d_model=vision_config.out_hidden_size,
+            context_dim=self.hidden_size,
+            norm_layer=norm_layer,
+            spatial_merge_size=self.spatial_merge_size,
+            quant_config=quant_config,
+            prefix=f"{prefix}.merger",
+        )
+
+        self.deepstack_merger_list = nn.ModuleList([
+            Qwen3_VisionPatchMerger(
+                d_model=vision_config.out_hidden_size,
+                context_dim=self.hidden_size,
+                spatial_merge_size=self.spatial_merge_size,
+                use_postshuffle_norm=True,
+                norm_layer=norm_layer,
+                quant_config=quant_config,
+                prefix=f"{prefix}.deepstack_merger_list.{layer_idx}")
+            for layer_idx in range(len(self.deepstack_visual_indexes))
+        ])
+
+        self.attn_backend = get_vit_attn_backend(
+            head_size=head_dim, dtype=torch.get_default_dtype())
+        if self.attn_backend != _Backend.FLASH_ATTN and \
+            check_upstream_fa_availability(
+                torch.get_default_dtype()):
+            self.attn_backend = _Backend.FLASH_ATTN
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return self.patch_embed.proj.weight.dtype
+
+    @property
+    def device(self) -> torch.device:
+        return self.patch_embed.proj.weight.device
+
+    def rot_pos_emb(self, grid_thw):
+        pos_ids = []
+        for t, h, w in grid_thw:
+            hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
+            hpos_ids = hpos_ids.reshape(
+                h // self.spatial_merge_size,
+                self.spatial_merge_size,
+                w // self.spatial_merge_size,
+                self.spatial_merge_size,
+            )
+            hpos_ids = hpos_ids.permute(0, 2, 1, 3)
+            hpos_ids = hpos_ids.flatten()
+
+            wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
+            wpos_ids = wpos_ids.reshape(
+                h // self.spatial_merge_size,
+                self.spatial_merge_size,
+                w // self.spatial_merge_size,
+                self.spatial_merge_size,
+            )
+            wpos_ids = wpos_ids.permute(0, 2, 1, 3)
+            wpos_ids = wpos_ids.flatten()
+            pos_ids.append(
+                torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
+        pos_ids = torch.cat(pos_ids, dim=0)
+        max_grid_size = grid_thw[:, 1:].max()
+        rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
+        rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
+        return rotary_pos_emb
+
+    def fast_pos_embed_interpolate(self, grid_thw):
+        num_grid_per_side = int(self.num_position_embeddings**0.5)
+
+        idx_list = [[] for _ in range(4)]
+        weight_list = [[] for _ in range(4)]
+
+        for t, h, w in grid_thw:
+            h_idxs = torch.linspace(0,
+                                    num_grid_per_side - 1,
+                                    h,
+                                    dtype=torch.float32)
+            w_idxs = torch.linspace(0,
+                                    num_grid_per_side - 1,
+                                    w,
+                                    dtype=torch.float32)
+
+            h_idxs_floor = h_idxs.to(torch.long)
+            w_idxs_floor = w_idxs.to(torch.long)
+            h_idxs_ceil = torch.clamp(h_idxs.to(torch.long) + 1,
+                                      max=num_grid_per_side - 1)
+            w_idxs_ceil = torch.clamp(w_idxs.to(torch.long) + 1,
+                                      max=num_grid_per_side - 1)
+
+            dh = h_idxs - h_idxs_floor
+            dw = w_idxs - w_idxs_floor
+
+            idx_list[0].extend(((h_idxs_floor * num_grid_per_side)[None].T +
+                                w_idxs_floor[None]).flatten().tolist() * t)
+            idx_list[1].extend(((h_idxs_floor * num_grid_per_side)[None].T +
+                                w_idxs_ceil[None]).flatten().tolist() * t)
+            idx_list[2].extend(((h_idxs_ceil * num_grid_per_side)[None].T +
+                                w_idxs_floor[None]).flatten().tolist() * t)
+            idx_list[3].extend(((h_idxs_ceil * num_grid_per_side)[None].T +
+                                w_idxs_ceil[None]).flatten().tolist() * t)
+
+            weight_list[0].extend(
+                ((1 - dh)[None].T * (1 - dw)[None]).flatten().tolist() * t)
+            weight_list[1].extend(
+                ((1 - dh)[None].T * dw[None]).flatten().tolist() * t)
+            weight_list[2].extend(
+                (dh[None].T * (1 - dw)[None]).flatten().tolist() * t)
+            weight_list[3].extend(
+                (dh[None].T * dw[None]).flatten().tolist() * t)
+
+        device = self.pos_embed.weight.device
+        dtype = self.pos_embed.weight.dtype
+
+        p0 = self.pos_embed(
+            torch.tensor(
+                idx_list[0], dtype=torch.long, device=device)) * torch.tensor(
+                    weight_list[0], dtype=dtype, device=device)[:, None]
+        p1 = self.pos_embed(
+            torch.tensor(
+                idx_list[1], dtype=torch.long, device=device)) * torch.tensor(
+                    weight_list[1], dtype=dtype, device=device)[:, None]
+        p2 = self.pos_embed(
+            torch.tensor(
+                idx_list[2], dtype=torch.long, device=device)) * torch.tensor(
+                    weight_list[2], dtype=dtype, device=device)[:, None]
+        p3 = self.pos_embed(
+            torch.tensor(
+                idx_list[3], dtype=torch.long, device=device)) * torch.tensor(
+                    weight_list[3], dtype=dtype, device=device)[:, None]
+
+        patch_pos_embeds = p0 + p1 + p2 + p3
+        patch_pos_embeds = patch_pos_embeds.split(
+            [t * h * w for t, h, w in grid_thw])
+        patch_pos_embeds_permute = []
+        m_size = self.spatial_merge_size
+        for pos_embed, (t, h, w) in zip(patch_pos_embeds, grid_thw):
+            pos_embed = pos_embed.view(t, h // m_size, m_size, w // m_size,
+                                       m_size, -1).permute(0, 1, 3, 2, 4,
+                                                           5).flatten(0, 4)
+            patch_pos_embeds_permute.append(pos_embed)
+        patch_pos_embeds = torch.cat(patch_pos_embeds_permute)
+        return patch_pos_embeds
+
+    def compute_attn_mask_seqlen(
+        self,
+        cu_seqlens: torch.Tensor,
+    ) -> tuple[Optional[int], Optional[list[int]]]:
+        max_seqlen, seqlens = None, None
+        if self.attn_backend == _Backend.FLASH_ATTN:
+            max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
+        elif self.attn_backend == _Backend.XFORMERS:
+            seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
+        return max_seqlen, seqlens
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        grid_thw: list[list[int]],
+    ) -> torch.Tensor:
+        hidden_states = x.to(device=self.device, dtype=self.dtype)
+        hidden_states = self.patch_embed(hidden_states)
+
+        pos_embeds = self.fast_pos_embed_interpolate(grid_thw)
+        hidden_states = hidden_states + pos_embeds
+        rotary_pos_emb = self.rot_pos_emb(grid_thw)
+
+        cu_seqlens = torch.repeat_interleave(
+            grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]).cumsum(
+                dim=0,
+                dtype=grid_thw.dtype
+                if torch.jit.is_tracing() else torch.int32,
+            )
+        cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
+
+        hidden_states = hidden_states.unsqueeze(1)
+        rotary_pos_emb = rotary_pos_emb.to(hidden_states.device)
+        max_seqlen, seqlens = self.compute_attn_mask_seqlen(cu_seqlens)
+
+        deepstack_feature_lists = []
+        for layer_num, blk in enumerate(self.blocks):
+            hidden_states = blk(hidden_states,
+                                cu_seqlens=cu_seqlens,
+                                rotary_pos_emb=rotary_pos_emb,
+                                max_seqlen=max_seqlen,
+                                seqlens=seqlens)
+            if layer_num in self.deepstack_visual_indexes:
+                deepstack_merger_idx = self.deepstack_visual_indexes.index(
+                    layer_num)
+                deepstack_feature = self.deepstack_merger_list[
+                    deepstack_merger_idx](hidden_states)
+                deepstack_feature_lists.append(deepstack_feature)
+        hidden_states = self.merger(hidden_states)
+        hidden_states = torch.cat(
+            [hidden_states] + deepstack_feature_lists,
+            dim=1)  # [seq_len, hidden_size * (1 + depth_of_deepstack)]
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("attn.qkv.", "attn.q.", "q"),
+            ("attn.qkv.", "attn.k.", "k"),
+            ("attn.qkv.", "attn.v.", "v"),
+        ]
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: set[str] = set()
+
+        for name, loaded_weight in weights:
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class Qwen3VLProcessingInfo(Qwen2VLProcessingInfo):
+
+    def get_hf_config(self):
+        return self.ctx.get_hf_config(Qwen3VLConfig)
+
+    def get_hf_processor(self, **kwargs: object) -> Qwen3VLProcessor:
+        return self.ctx.get_hf_processor(
+            Qwen3VLProcessor,
+            use_fast=kwargs.pop("use_fast", True),
+            **kwargs,
+        )
+
+    def get_tokenizer(self):
+        return self.ctx.tokenizer
+
+    def get_image_processor(self,
+                            **kwargs: object) -> Qwen2VLImageProcessorFast:
+        return self.get_hf_processor(**kwargs).image_processor
+
+    def get_video_processor(self, **kwargs: object) -> Qwen3VLVideoProcessor:
+        return self.get_hf_processor(**kwargs).video_processor
+
+    def _get_vision_info(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        num_frames: int = 2,
+        do_resize: bool = True,
+        image_processor: Optional[Qwen2VLImageProcessorFast],
+    ) -> 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.size["shortest_edge"],
+                max_pixels=image_processor.size["longest_edge"],
+            )
+            preprocessed_size = ImageSize(width=resized_width,
+                                          height=resized_height)
+        else:
+            preprocessed_size = ImageSize(width=image_width,
+                                          height=image_height)
+
+        padded_num_frames = num_frames + num_frames % temporal_patch_size
+
+        grid_t = max(padded_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 _calculate_timestamps(self, indices: list[int] | torch.Tensor,
+                              video_fps: float, merge_size: int):
+        if not isinstance(indices, list):
+            indices = indices.tolist()
+        if len(indices) % merge_size != 0:
+            # don't update metadata's frames_indices directly
+            indices = indices + [indices[-1]
+                                 ] * (merge_size - len(indices) % merge_size)
+        timestamps = [idx / video_fps for idx in indices]
+        timestamps = [(timestamps[i] + timestamps[i + merge_size - 1]) / 2
+                      for i in range(0, len(timestamps), merge_size)]
+        return timestamps
+
+    def _get_video_second_idx(
+            self,
+            metadata: dict[str, Any],
+            out_item: MultiModalKwargsItem,
+            do_sample_frames: Optional[bool] = None,
+            sampled_fps: Optional[float] = None) -> list[int]:
+        video_processor = self.get_video_processor()
+        merge_size = video_processor.merge_size
+        indices = metadata["frames_indices"]
+
+        # metadata["fps"] refers to the true fps of the input video.
+        video_fps = metadata["fps"]
+        if do_sample_frames is None:
+            do_sample_frames = metadata.get("do_sample_frames", False)
+
+        # If video frames are sampled in HF processor (instead of vLLM
+        # video loader), we need to re-calculate the indices from original
+        # metadata.
+        if do_sample_frames:
+            # here video_fps is the fps of the sampled video, and
+            # metadata["fps"] refers to the fps of the original video.
+            video_fps = sampled_fps if sampled_fps else video_processor.fps
+            total_num_frames = metadata["total_num_frames"]
+            num_frames = int(total_num_frames / metadata["fps"] * video_fps)
+            num_frames = min(
+                min(max(num_frames, video_processor.min_frames),
+                    video_processor.max_frames), total_num_frames)
+            indices = np.linspace(0, total_num_frames - 1,
+                                  num_frames).round().astype(int).tolist()
+        timestamps = self._calculate_timestamps(indices, video_fps, merge_size)
+        return timestamps
+
+
+class Qwen3VLDummyInputsBuilder(BaseDummyInputsBuilder[Qwen3VLProcessingInfo]):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_images = mm_counts.get("image", 0)
+        num_videos = mm_counts.get("video", 0)
+
+        image_token = "<|vision_start|><|image_pad|><|vision_end|>"
+        video_token = "<|vision_start|><|video_pad|><|vision_end|>"
+
+        return image_token * num_images + video_token * num_videos
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        num_images = mm_counts.get("image", 0)
+        num_videos = mm_counts.get("video", 0)
+
+        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_counts)
+        return {
+            "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,
+            ),
+        }
+
+    def _get_dummy_videos(
+        self,
+        *,
+        width: int,
+        height: int,
+        num_frames: int,
+        num_videos: int,
+    ) -> list[VideoItem]:
+        num_frames = max(num_frames, 2)
+        video = np.full((num_frames, width, height, 3), 255, dtype=np.uint8)
+        video_items = []
+        for i in range(num_videos):
+            video_metadata = {
+                "fps": 2.0,
+                "duration": num_frames / 2.0,
+                "total_num_frames": num_frames,
+                "frames_indices": [i for i in range(num_frames)],
+                "video_backend": "opencv",
+                "do_sample_frames": False,
+            }
+            video_item = (video.copy(), video_metadata)
+            video_items.append(video_item)
+        return video_items
+
+
+class Qwen3VLMultiModalProcessor(BaseMultiModalProcessor[Qwen3VLProcessingInfo]
+                                 ):
+
+    def _get_data_parser(self) -> MultiModalDataParser:
+        return MultiModalDataParser(video_needs_metadata=True)
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        mm_data = dict(mm_data)
+        processor = self.info.get_hf_processor(**mm_kwargs)
+
+        # Separate video processing from image processing. Because the videos
+        # are processed into serval image patches
+        if ("videos" in mm_data and isinstance(mm_data["videos"], list)
+                and len(mm_data["videos"]) > 0):
+            video_grid_thw_lst = []
+            pixel_values_videos_lst = []
+
+            for item_idx, item in enumerate(mm_data.pop("videos", [])):
+                video_array, metadata = item
+
+                # NOTE: @JJJYmmm new attr metadata.frames_indices indicates
+                # the sampled frames indices of pre-sampled videos, which is
+                # used to calculate the timestamps. Make sure that
+                # do_sample_frames in mm_kwargs is false for presampled videos.
+
+                # NOTE: a copy of is created to update do_sample_frames,
+                # otherwise mm_hash for the object will be incorrect.
+                video_mm_kwargs = dict(**mm_kwargs)
+                if "do_sample_frames" not in video_mm_kwargs:
+                    # qwen_vl_utils already has "do_sample_frames" in
+                    # mm_kwargs, don't overwrite it.
+                    video_mm_kwargs["do_sample_frames"] = metadata.get(
+                        "do_sample_frames", False)
+
+                metadata = VideoMetadata(**{
+                    k: metadata[k]
+                    for k in metadata if k != "do_sample_frames"
+                })
+
+                video_mm_data = dict()
+                video_mm_data["videos"] = [[video_array]]
+                video_mm_data["video_metadata"] = [[metadata]]
+
+                video_outputs = super()._call_hf_processor(
+                    prompt="<|vision_start|><|video_pad|><|vision_end|>",
+                    mm_data=video_mm_data,
+                    mm_kwargs=video_mm_kwargs,
+                    tok_kwargs=tok_kwargs,
+                )
+                input_ids = video_outputs.pop("input_ids")
+                video_placeholder = processor.tokenizer.batch_decode(
+                    input_ids)[0]
+                prompt = prompt.replace(
+                    "<|vision_start|><|video_pad|><|vision_end|>",
+                    video_placeholder,
+                    1,
+                )
+
+                video_grid_thw_lst.append(video_outputs["video_grid_thw"])
+                pixel_values_videos_lst.append(
+                    video_outputs["pixel_values_videos"])
+            video_outputs = dict(
+                pixel_values_videos=torch.cat(pixel_values_videos_lst),
+                video_grid_thw=torch.cat(video_grid_thw_lst),
+            )
+        else:
+            video_outputs = dict()
+
+        processed_outputs = super()._call_hf_processor(
+            prompt=prompt,
+            mm_data=mm_data,
+            mm_kwargs=mm_kwargs,
+            tok_kwargs=tok_kwargs,
+        )
+        combined_outputs = dict(
+            processed_outputs,
+            **video_outputs,
+        )
+        return BatchFeature(combined_outputs)
+
+    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_grid_sizes = image_grid_thw.prod(-1)
+
+        video_grid_thw = hf_inputs.get("video_grid_thw", torch.empty((0, 3)))
+        video_grid_sizes = video_grid_thw.prod(-1)
+
+        return dict(
+            pixel_values=MultiModalFieldConfig.flat_from_sizes(
+                "image", image_grid_sizes),
+            image_embeds=MultiModalFieldConfig.flat_from_sizes(
+                "image", image_grid_sizes),
+            image_grid_thw=MultiModalFieldConfig.batched("image"),
+            pixel_values_videos=MultiModalFieldConfig.flat_from_sizes(
+                "video", video_grid_sizes),
+            video_embeds=MultiModalFieldConfig.flat_from_sizes(
+                "video", video_grid_sizes),
+            video_grid_thw=MultiModalFieldConfig.batched("video"),
+        )
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, Any],
+        out_mm_kwargs: MultiModalKwargsItems,
+    ) -> Sequence[PromptUpdate]:
+        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()
+        hf_config = self.info.get_hf_config()
+
+        video_token_id = hf_config.video_token_id
+        vision_start_token_id = hf_config.vision_start_token_id
+        vision_end_token_id = hf_config.vision_end_token_id
+
+        merge_length = image_processor.merge_size**2
+
+        def get_image_replacement_qwen3vl(item_idx: int):
+            out_item = out_mm_kwargs["image"][item_idx]
+            grid_thw = out_item["image_grid_thw"].data
+            assert isinstance(grid_thw, torch.Tensor)
+
+            num_tokens = int(grid_thw.prod()) // merge_length
+            return [hf_processor.image_token_id] * num_tokens
+
+        def get_video_replacement_qwen3vl(item_idx: int):
+            out_item = out_mm_kwargs["video"][item_idx]
+            grid_thw = out_item["video_grid_thw"].data
+            assert isinstance(grid_thw, torch.Tensor)
+
+            video, metadata = mm_items["video"][item_idx]
+            do_sample_frames = hf_processor_mm_kwargs.get("do_sample_frames")
+            sampled_fps = hf_processor_mm_kwargs.get("fps")
+            if is_list_of(sampled_fps, float):
+                sampled_fps = sampled_fps[item_idx]
+            timestamps = self.info._get_video_second_idx(
+                metadata, out_item, do_sample_frames, sampled_fps)
+
+            assert len(timestamps) == grid_thw[0], (
+                f"The timestamps length({len(timestamps)}) should be equal "
+                f"video length ({grid_thw[0]}).")
+
+            frames_idx_token = [
+                tokenizer.encode(f"<{curr_time:.1f} seconds>",
+                                 add_special_tokens=False)
+                for curr_time in timestamps
+            ]
+            num_tokens_per_frame = int(grid_thw[1:].prod()) // merge_length
+            placeholder = []
+            for frame_idx in frames_idx_token:
+                placeholder.extend(frame_idx)
+                placeholder.extend([vision_start_token_id] +
+                                   [video_token_id] * num_tokens_per_frame +
+                                   [vision_end_token_id])
+            return PromptUpdateDetails.select_token_id(placeholder,
+                                                       video_token_id)
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target=hf_processor.image_token,
+                replacement=get_image_replacement_qwen3vl,
+            ),
+
+            # NOTE: We match string on purpose since searching sequence of
+            # token ids takes more time.
+            PromptReplacement(
+                modality="video",
+                target="<|vision_start|><|video_pad|><|vision_end|>",
+                replacement=get_video_replacement_qwen3vl,
+            ),
+        ]
+
+
+@support_torch_compile(
+    dynamic_arg_dims={
+        "input_ids": 0,
+        # positions is of shape (3, seq_len) if mrope is enabled for qwen2-vl,
+        # otherwise (seq_len, ).
+        "positions": -1,
+        "intermediate_tensors": 0,
+        "inputs_embeds": 0,
+        # the same shape as input_embeds
+        "deepstack_input_embeds": 0
+    })
+class Qwen3LLMModel(Qwen3Model):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+        if not get_pp_group().is_first_rank:
+            assert self.start_layer >= len(
+                vllm_config.model_config.hf_config.vision_config.
+                deepstack_visual_indexes), (
+                    "start_layer should be greater than or equal to "
+                    "len(deepstack_visual_indexes)")
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        # args for deepstack
+        deepstack_input_embeds: Optional[IntermediateTensors] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+        for layer_idx, layer in enumerate(
+                self.layers[self.start_layer:self.end_layer]):
+            layer_idx = layer_idx + self.start_layer
+
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                residual,
+            )
+
+            if deepstack_input_embeds is not None and \
+                    layer_idx in range(0, len(deepstack_input_embeds)):
+                hidden_states = hidden_states + deepstack_input_embeds[
+                    f"deepstack_input_embeds_{layer_idx}"]
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+
+class Qwen3LLMForCausalLM(Qwen3ForCausalLM):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super(Qwen3ForCausalLM, self).__init__()
+        config = vllm_config.model_config.hf_config.text_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        self.config = config
+        self.lora_config = lora_config
+
+        self.quant_config = quant_config
+        self.model = Qwen3LLMModel(vllm_config=vllm_config, prefix=prefix)
+
+        if get_pp_group().is_last_rank:
+            if config.tie_word_embeddings:
+                self.lm_head = self.model.embed_tokens
+            else:
+                self.lm_head = ParallelLMHead(config.vocab_size,
+                                              config.hidden_size,
+                                              quant_config=quant_config,
+                                              prefix="lm_head")
+        else:
+            self.lm_head = PPMissingLayer()
+
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+
+@MULTIMODAL_REGISTRY.register_processor(Qwen3VLMultiModalProcessor,
+                                        info=Qwen3VLProcessingInfo,
+                                        dummy_inputs=Qwen3VLDummyInputsBuilder)
+class Qwen3VLForConditionalGeneration(nn.Module, SupportsMultiModal,
+                                      SupportsLoRA, SupportsPP):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+    # To ensure correct weight loading and mapping.
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            "model.visual.": "visual.",
+            "lm_head.": "language_model.lm_head.",
+            "model.language_model.": "language_model.model.",
+        })
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("image"):
+            return "<|vision_start|><|image_pad|><|vision_end|>"
+        if modality.startswith("video"):
+            return "<|vision_start|><|video_pad|><|vision_end|>"
+
+        raise ValueError("Only image or video modality is supported")
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "model"):
+        super().__init__()
+        config: Qwen3VLConfig = 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 = Qwen3_VisionTransformer(
+            config.vision_config,
+            norm_eps=getattr(config, "rms_norm_eps", 1e-6),
+            quant_config=self._maybe_ignore_quant_config(quant_config),
+            prefix=maybe_prefix(prefix, "visual"),
+        )
+
+        self.language_model = Qwen3LLMForCausalLM(vllm_config=vllm_config,
+                                                  prefix=maybe_prefix(
+                                                      prefix,
+                                                      "language_model"))
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+        self.use_deepstack = hasattr(config.vision_config,
+                                     'deepstack_visual_indexes')
+        self.deepstack_num_level = len(
+            config.vision_config.deepstack_visual_indexes
+        ) if self.use_deepstack else 0
+        # register buffer for deepstack
+        self.deepstack_input_embeds = [
+            torch.zeros(vllm_config.scheduler_config.max_num_batched_tokens,
+                        config.text_config.hidden_size)
+            for _ in range(self.deepstack_num_level)
+        ] if self.use_deepstack else None
+
+    def _get_deepstack_input_embeds(self,
+                                    num_tokens: int) -> IntermediateTensors:
+        # get deepstack_input_embeds from buffer, and clear the buffer
+        return IntermediateTensors({
+            f"deepstack_input_embeds_{idx}":
+            self.deepstack_input_embeds[idx][:num_tokens]
+            for idx in range(self.deepstack_num_level)
+        })
+
+    def _set_deepstack_input_embeds(
+            self, deepstack_input_embeds: torch.Tensor) -> None:
+        # set deepstack_input_embeds to buffer
+        num_tokens = deepstack_input_embeds.size(1)
+        if num_tokens > self.deepstack_input_embeds[0].size(0):
+            self.deepstack_input_embeds = [
+                torch.zeros(num_tokens,
+                            self.config.text_config.hidden_size,
+                            device=self.deepstack_input_embeds[0].device,
+                            dtype=self.deepstack_input_embeds[0].dtype)
+                for _ in range(self.deepstack_num_level)
+            ]
+        for idx in range(self.deepstack_num_level):
+            self.deepstack_input_embeds[idx][:num_tokens].copy_(
+                deepstack_input_embeds[idx])
+
+    def _clear_deepstack_input_embeds(self, num_tokens: int) -> None:
+        # clear deepstack_input_embeds in buffer
+        if num_tokens > 0:
+            for idx in range(self.deepstack_num_level):
+                self.deepstack_input_embeds[idx][:num_tokens].zero_()
+
+    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.
+        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_VLImageInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+        image_grid_thw = kwargs.pop("image_grid_thw", None)
+
+        if pixel_values is None and image_embeds 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)
+
+        if image_embeds is not None:
+            image_embeds = self._validate_and_reshape_mm_tensor(
+                image_embeds, "image embeds")
+            image_grid_thw = self._validate_and_reshape_mm_tensor(
+                image_grid_thw, "image grid_thw")
+
+            if not isinstance(image_embeds, torch.Tensor):
+                raise ValueError("Incorrect type of image embeddings. "
+                                 f"Got type: {type(image_embeds)}")
+            return Qwen2_5_VLImageEmbeddingInputs(
+                type="image_embeds",
+                image_embeds=image_embeds,
+                image_grid_thw=image_grid_thw)
+
+    def _parse_and_validate_video_input(
+            self, **kwargs: object) -> Optional[Qwen2_5_VLVideoInputs]:
+        pixel_values_videos = kwargs.pop("pixel_values_videos", None)
+        video_embeds = kwargs.pop("video_embeds", None)
+        video_grid_thw = kwargs.pop("video_grid_thw", None)
+        second_per_grid_ts = kwargs.pop("second_per_grid_ts", None)
+
+        if pixel_values_videos is None and video_embeds 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,
+                second_per_grid_ts=second_per_grid_ts,
+            )
+
+        if video_embeds is not None:
+            video_embeds = self._validate_and_reshape_mm_tensor(
+                video_embeds, "video embeds")
+            video_grid_thw = self._validate_and_reshape_mm_tensor(
+                video_grid_thw, "video grid_thw")
+
+            if not isinstance(video_embeds, torch.Tensor):
+                raise ValueError("Incorrect type of video embeddings. "
+                                 f"Got type: {type(video_embeds)}")
+            return Qwen2_5_VLVideoEmbeddingInputs(
+                type="video_embeds",
+                video_embeds=video_embeds,
+                video_grid_thw=video_grid_thw)
+
+    def _process_image_input(
+            self,
+            image_input: Qwen2_5_VLImageInputs) -> tuple[torch.Tensor, ...]:
+
+        grid_thw = image_input["image_grid_thw"]
+        assert grid_thw.ndim == 2
+        grid_thw_list = grid_thw.tolist()
+
+        if image_input["type"] == "image_embeds":
+            image_embeds = image_input["image_embeds"].type(self.visual.dtype)
+        else:
+            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.
+        # Using prod on grid_thw_list instead of grid_thw.prod avoids CUDA sync
+        merge_size = self.visual.spatial_merge_size
+        sizes = (torch.tensor(grid_thw_list, dtype=torch.long).prod(-1) //
+                 (merge_size * merge_size)).tolist()
+        return image_embeds.split(sizes)
+
+    def _process_video_input(
+            self,
+            video_input: Qwen2_5_VLVideoInputs) -> tuple[torch.Tensor, ...]:
+
+        grid_thw = video_input["video_grid_thw"]
+        assert grid_thw.ndim == 2
+        grid_thw_list = grid_thw.tolist()
+
+        if video_input["type"] == "video_embeds":
+            video_embeds = video_input["video_embeds"].type(self.visual.dtype)
+        else:
+            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.
+        # Using prod on grid_thw_list instead of grid_thw.prod avoids CUDA sync
+        merge_size = self.visual.spatial_merge_size
+        sizes = (torch.tensor(grid_thw_list, dtype=torch.long).prod(-1) //
+                 (merge_size * merge_size)).tolist()
+        return video_embeds.split(sizes)
+
+    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
+        mm_input_by_modality = {}
+        for input_key in kwargs:
+            if input_key in ("pixel_values", "image_embeds"
+                             ) and "image" not in mm_input_by_modality:
+                mm_input_by_modality[
+                    "image"] = self._parse_and_validate_image_input(**kwargs)
+            if input_key in ("pixel_values_videos", "video_embeds"
+                             ) and "video" not in mm_input_by_modality:
+                mm_input_by_modality[
+                    "video"] = self._parse_and_validate_video_input(**kwargs)
+        return mm_input_by_modality
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def get_multimodal_embeddings(
+            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+
+        mm_input_by_modality = self._parse_and_validate_multimodal_inputs(
+            **kwargs)
+        if not mm_input_by_modality:
+            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 mm_input_by_modality:
+            multimodal_input = mm_input_by_modality[modality]
+            if modality == "image":
+                vision_embeddings = self._process_image_input(multimodal_input)
+                multimodal_embeddings += vision_embeddings
+            if modality == "video":
+                video_embeddings = self._process_video_input(multimodal_input)
+                multimodal_embeddings += video_embeddings
+        return multimodal_embeddings
+
+    def _compute_deepstack_embeds(
+            self, input_ids: torch.Tensor, inputs_embeds: torch.Tensor,
+            multimodal_embeddings: MultiModalEmbeddings) -> torch.Tensor:
+        visual_lens = [
+            x.shape[0] if isinstance(x, torch.Tensor) else len(x)
+            for x in multimodal_embeddings
+        ]
+        multimodal_embeddings_cat = torch.cat(multimodal_embeddings, dim=0)
+
+        visual_dim = multimodal_embeddings_cat.shape[-1] // (
+            self.deepstack_num_level + 1)
+
+        main_dim, multi_dim = visual_dim, visual_dim * self.deepstack_num_level
+        multimodal_embeddings_main, multimodal_embeddings_multiscale = torch.split(  # noqa:E501
+            multimodal_embeddings_cat, [main_dim, multi_dim],
+            dim=-1)
+
+        multimodal_embeddings = torch.split(multimodal_embeddings_main,
+                                            visual_lens,
+                                            dim=0)
+        multimodal_embeddings_multiscale = torch.split(
+            multimodal_embeddings_multiscale, visual_lens, dim=0)
+
+        deepstack_input_embeds = inputs_embeds.new_zeros(
+            inputs_embeds.size(0),
+            self.deepstack_num_level * inputs_embeds.size(1))
+
+        deepstack_input_embeds = merge_multimodal_embeddings(
+            input_ids,
+            deepstack_input_embeds,
+            multimodal_embeddings_multiscale,
+            placeholder_token_id=[
+                self.config.image_token_id, self.config.video_token_id
+            ],
+        )
+        deepstack_input_embeds = deepstack_input_embeds.view(
+            inputs_embeds.shape[0], self.deepstack_num_level,
+            visual_dim).contiguous()
+        deepstack_input_embeds = deepstack_input_embeds.permute(
+            1, 0, 2).contiguous()
+        return deepstack_input_embeds, multimodal_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        deepstack_input_embeds = None
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None and self.use_deepstack:
+            deepstack_input_embeds, multimodal_embeddings = self._compute_deepstack_embeds(  # noqa:E501
+                input_ids, inputs_embeds, multimodal_embeddings)
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids, inputs_embeds, multimodal_embeddings,
+                [self.config.image_token_id, self.config.video_token_id])
+
+        if self.use_deepstack:
+            if deepstack_input_embeds is None:
+                deepstack_input_embeds = torch.zeros_like(
+                    inputs_embeds).unsqueeze(0).repeat(
+                        self.deepstack_num_level, 1, 1).contiguous()
+            self._set_deepstack_input_embeds(deepstack_input_embeds)
+
+        return inputs_embeds
+
+    def get_input_embeddings_v0(
+        self,
+        input_ids: torch.Tensor,
+        image_input: Optional[Qwen2_5_VLImageInputs] = None,
+        video_input: Optional[Qwen2_5_VLVideoInputs] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.get_input_embeddings(input_ids)
+
+        if self.use_deepstack:
+            visual_dim = inputs_embeds.shape[-1]
+            deepstack_input_embeds = None
+            if image_input is not None or video_input is not None:
+                deepstack_input_embeds = torch.zeros_like(
+                    inputs_embeds).unsqueeze(1).repeat(
+                        1, self.deepstack_num_level, 1).flatten(1)
+
+        if image_input is not None:
+            image_embeds = self._process_image_input(image_input)
+            if self.use_deepstack:
+                image_embeds = torch.cat(image_embeds)
+
+                image_embeds, image_embeds_multiscale = image_embeds.split(
+                    [visual_dim, visual_dim * self.deepstack_num_level],
+                    dim=-1)
+
+                deepstack_input_embeds = merge_multimodal_embeddings(
+                    input_ids,
+                    deepstack_input_embeds,
+                    image_embeds_multiscale,
+                    placeholder_token_id=self.config.image_token_id,
+                )
+
+            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)
+            if self.use_deepstack:
+                video_embeds = torch.cat(video_embeds)
+
+                video_embeds, video_embeds_multiscale = video_embeds.split(
+                    [visual_dim, visual_dim * self.deepstack_num_level],
+                    dim=-1)
+
+                deepstack_input_embeds = merge_multimodal_embeddings(
+                    input_ids,
+                    deepstack_input_embeds,
+                    video_embeds_multiscale,
+                    placeholder_token_id=self.config.video_token_id,
+                )
+
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                video_embeds,
+                placeholder_token_id=self.config.video_token_id,
+            )
+
+        if self.use_deepstack and deepstack_input_embeds is not None:
+            deepstack_input_embeds = deepstack_input_embeds.view(
+                inputs_embeds.shape[0], self.deepstack_num_level,
+                visual_dim).permute(1, 0, 2).contiguous()
+            self._set_deepstack_input_embeds(deepstack_input_embeds)
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        """Run forward pass for Qwen3VL.
+
+        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 Qwen3VL
+                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
+
+        if self.use_deepstack and inputs_embeds is not None and get_pp_group(
+        ).is_first_rank:
+            deepstack_input_embeds = self._get_deepstack_input_embeds(
+                inputs_embeds.size(0))
+        else:
+            deepstack_input_embeds = None
+
+        hidden_states = self.language_model.model(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+            # args for deepstack
+            deepstack_input_embeds=deepstack_input_embeds,
+        )
+
+        if inputs_embeds is not None and get_pp_group().is_first_rank:
+            self._clear_deepstack_input_embeds(inputs_embeds.size(0))
+
+        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 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="model.visual.merger",
+            tower_model="model.visual.",
+        )

vllm/model_executor/models/qwen3_vl_moe.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2025 The vLLM team.
+# Copyright 2025 The Qwen Team.
+# Copyright 2025 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 Qwen3-VL-MoE model compatible with HuggingFace weights."""
+import typing
+from collections.abc import Iterable
+from typing import Callable, Optional, Union
+
+import torch
+from transformers.models.qwen3_vl_moe.configuration_qwen3_vl_moe import (
+    Qwen3VLMoeConfig)
+
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import VllmConfig
+from vllm.distributed import get_pp_group
+from vllm.logger import init_logger
+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.multimodal import MULTIMODAL_REGISTRY
+from vllm.sequence import IntermediateTensors
+
+from .qwen3_moe import Qwen3MoeForCausalLM, Qwen3MoeModel
+from .qwen3_vl import (Qwen3_VisionTransformer, Qwen3VLDummyInputsBuilder,
+                       Qwen3VLForConditionalGeneration,
+                       Qwen3VLMultiModalProcessor, Qwen3VLProcessingInfo)
+from .utils import is_pp_missing_parameter, maybe_prefix
+
+logger = init_logger(__name__)
+
+
+class Qwen3VLMoeProcessingInfo(Qwen3VLProcessingInfo):
+
+    def get_hf_config(self):
+        return self.ctx.get_hf_config(Qwen3VLMoeConfig)
+
+
+@support_torch_compile(
+    dynamic_arg_dims={
+        "input_ids": 0,
+        # positions is of shape (3, seq_len) if mrope is enabled for qwen2-vl,
+        # otherwise (seq_len, ).
+        "positions": -1,
+        "intermediate_tensors": 0,
+        "inputs_embeds": 0,
+        # the same shape as input_embeds
+        "deepstack_input_embeds": 0
+    })
+class Qwen3MoeLLMModel(Qwen3MoeModel):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+        if not get_pp_group().is_first_rank:
+            assert self.start_layer >= len(
+                vllm_config.model_config.hf_config.vision_config.
+                deepstack_visual_indexes), (
+                    "start_layer should be greater than or equal to "
+                    "len(deepstack_visual_indexes)")
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        deepstack_input_embeds: Optional[IntermediateTensors] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+        for layer_idx, layer in enumerate(
+                self.layers[self.start_layer:self.end_layer]):
+            layer_idx = layer_idx + self.start_layer
+
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                residual,
+            )
+
+            if deepstack_input_embeds is not None and \
+                    layer_idx in range(0, len(deepstack_input_embeds)):
+                hidden_states = hidden_states + deepstack_input_embeds[
+                    f"deepstack_input_embeds_{layer_idx}"]
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+    def load_fused_expert_weights(self, name: str, params_dict: dict,
+                                  loaded_weight: torch.Tensor, shard_id: str,
+                                  num_experts: int):
+        param = params_dict[name]
+        weight_loader = typing.cast(Callable[..., bool], param.weight_loader)
+        for expert_id in range(num_experts):
+            curr_expert_weight = loaded_weight[expert_id]
+            success = weight_loader(param,
+                                    curr_expert_weight,
+                                    name,
+                                    shard_id,
+                                    expert_id,
+                                    return_success=True)
+            if not success:
+                return False
+        return True
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        # Skip loading extra parameters for GPTQ/modelopt models.
+        ignore_suffixes = (".bias", "_bias", ".k_scale", "_k_scale",
+                           ".v_scale", "_v_scale", ".weight_scale",
+                           "_weight_scale", ".input_scale", "_input_scale")
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        expert_params_mapping = self.get_expert_mapping()
+        is_fused_expert = False
+        fused_expert_params_mapping = [
+            ("experts.w13_weight", "experts.gate_up_proj", 0, "w1"),
+            ("experts.w2_weight", "experts.down_proj", 0, "w2"),
+        ]
+        num_experts = self.config.num_experts
+        for name, loaded_weight in weights:
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if ("experts.gate_up_proj" in name
+                        or "experts.down_proj" in name):
+                    is_fused_expert = True
+                    expert_params_mapping = fused_expert_params_mapping
+
+                # Skip non-stacked layers and experts (experts handled below).
+                if weight_name not in name:
+                    continue
+                # We have mlp.experts[0].gate_proj in the checkpoint.
+                # Since we handle the experts below in expert_params_mapping,
+                # we need to skip here BEFORE we update the name, otherwise
+                # name will be updated to mlp.experts[0].gate_up_proj, which
+                # will then be updated below in expert_params_mapping
+                # for mlp.experts[0].gate_gate_up_proj, which breaks load.
+                if "mlp.experts" in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra parameters for GPTQ/modelopt models.
+                if name.endswith(ignore_suffixes) and name not in params_dict:
+                    continue
+                # Skip layers on other devices.
+                if is_pp_missing_parameter(name, self):
+                    continue
+                if name.endswith("scale"):
+                    # Remapping the name of FP8 kv-scale.
+                    name = maybe_remap_kv_scale_name(name, params_dict)
+                    if name is None:
+                        continue
+                if name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                if weight_loader == default_weight_loader:
+                    weight_loader(param, loaded_weight)
+                else:
+                    weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                is_expert_weight = False
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, expert_id, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+                    # Anyway, this is an expert weight and should not be
+                    # attempted to load as other weights later
+                    is_expert_weight = True
+                    name_mapped = name.replace(weight_name, param_name)
+                    if is_fused_expert:
+                        loaded_weight = loaded_weight.transpose(-1,
+                                                                -2)  # no bias
+                        if "experts.gate_up_proj" in name:
+                            loaded_weight = loaded_weight.chunk(2, dim=-2)
+                            success_w1 = self.load_fused_expert_weights(
+                                name_mapped, params_dict, loaded_weight[0],
+                                "w1", num_experts)
+                            success_w3 = self.load_fused_expert_weights(
+                                name_mapped, params_dict, loaded_weight[1],
+                                "w3", num_experts)
+                            success = success_w1 and success_w3
+                        else:
+                            # down_proj
+                            success = self.load_fused_expert_weights(
+                                name_mapped, params_dict, loaded_weight,
+                                shard_id, num_experts)
+                    else:
+                        if is_pp_missing_parameter(name_mapped, self):
+                            continue
+                        # Skip loading extra parameters for GPTQ/modelopt models
+                        if name_mapped.endswith(
+                                ignore_suffixes
+                        ) and name_mapped not in params_dict:
+                            continue
+                        param = params_dict[name_mapped]
+                        # We should ask the weight loader to return success or
+                        # not here since otherwise we may skip experts with
+                        # other available replicas.
+                        weight_loader = typing.cast(Callable[..., bool],
+                                                    param.weight_loader)
+                        success = weight_loader(param,
+                                                loaded_weight,
+                                                name_mapped,
+                                                shard_id=shard_id,
+                                                expert_id=expert_id,
+                                                return_success=True)
+                    if success:
+                        name = name_mapped
+                        break
+                else:
+                    if is_expert_weight:
+                        # We've checked that this is an expert weight
+                        # However it's not mapped locally to this rank
+                        # So we simply skip it
+                        continue
+                    # Skip loading extra parameters for GPTQ/modelopt models.
+                    if name.endswith(
+                            ignore_suffixes) and name not in params_dict:
+                        continue
+                    # Skip layers on other devices.
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    # Remapping the name of FP8 kv-scale.
+                    if name.endswith("kv_scale"):
+                        remapped_kv_scale_name = name.replace(
+                            ".kv_scale", ".attn.kv_scale")
+                        if remapped_kv_scale_name not in params_dict:
+                            logger.warning_once(
+                                "Found kv scale in the checkpoint (e.g. %s), but not found the expected name in the model (e.g. %s). kv-scale is not loaded.",  # noqa: E501
+                                name,
+                                remapped_kv_scale_name,
+                            )
+                            continue
+                        else:
+                            name = remapped_kv_scale_name
+                    param = params_dict[name]
+                    weight_loader = getattr(param, "weight_loader",
+                                            default_weight_loader)
+                    weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class Qwen3MoeLLMForCausalLM(Qwen3MoeForCausalLM):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super(Qwen3MoeForCausalLM, self).__init__()
+        self.config = vllm_config.model_config.hf_config.text_config
+        self.quant_config = vllm_config.quant_config
+        self.model = Qwen3MoeLLMModel(vllm_config=vllm_config,
+                                      prefix=maybe_prefix(prefix, "model"))
+        self.lm_head = ParallelLMHead(self.config.vocab_size,
+                                      self.config.hidden_size,
+                                      quant_config=self.quant_config)
+        if self.config.tie_word_embeddings:
+            self.lm_head.weight = self.model.embed_tokens.weight
+        self.logits_processor = LogitsProcessor(self.config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+
+@MULTIMODAL_REGISTRY.register_processor(Qwen3VLMultiModalProcessor,
+                                        info=Qwen3VLMoeProcessingInfo,
+                                        dummy_inputs=Qwen3VLDummyInputsBuilder)
+class Qwen3VLMoeForConditionalGeneration(Qwen3VLForConditionalGeneration):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super(Qwen3VLForConditionalGeneration, self).__init__()
+        config: Qwen3VLMoeConfig = 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 = Qwen3_VisionTransformer(
+            config.vision_config,
+            norm_eps=getattr(config, "rms_norm_eps", 1e-6),
+            quant_config=self._maybe_ignore_quant_config(quant_config),
+            prefix=maybe_prefix(prefix, "visual"),
+        )
+
+        self.language_model = Qwen3MoeLLMForCausalLM(vllm_config=vllm_config,
+                                                     prefix=maybe_prefix(
+                                                         prefix,
+                                                         "language_model"))
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+        self.use_deepstack = hasattr(config.vision_config,
+                                     'deepstack_visual_indexes')
+        self.deepstack_num_level = len(
+            config.vision_config.deepstack_visual_indexes
+        ) if self.use_deepstack else 0
+        # register buffer for deepstack
+        self.deepstack_input_embeds = [
+            torch.zeros(vllm_config.scheduler_config.max_num_batched_tokens,
+                        config.text_config.hidden_size)
+            for _ in range(self.deepstack_num_level)
+        ] if self.use_deepstack else None

vllm/model_executor/models/registry.py

@@ -259,11 +259,13 @@ _MULTIMODAL_MODELS = {
     "Qwen2AudioForConditionalGeneration": ("qwen2_audio", "Qwen2AudioForConditionalGeneration"),  # noqa: E501
     "Qwen2_5OmniModel": ("qwen2_5_omni_thinker", "Qwen2_5OmniThinkerForConditionalGeneration"),  # noqa: E501
     "Qwen2_5OmniForConditionalGeneration": ("qwen2_5_omni_thinker", "Qwen2_5OmniThinkerForConditionalGeneration"),  # noqa: E501
-    "UltravoxModel": ("ultravox", "UltravoxModel"),
+    "Qwen3VLForConditionalGeneration": ("qwen3_vl", "Qwen3VLForConditionalGeneration"),  # noqa: E501
+    "Qwen3VLMoeForConditionalGeneration": ("qwen3_vl_moe", "Qwen3VLMoeForConditionalGeneration"),  # noqa: E501
     "SkyworkR1VChatModel": ("skyworkr1v", "SkyworkR1VChatModel"),
     "Step3VLForConditionalGeneration": ("step3_vl", "Step3VLForConditionalGeneration"),  # noqa: E501
     "TarsierForConditionalGeneration": ("tarsier", "TarsierForConditionalGeneration"),  # noqa: E501
     "Tarsier2ForConditionalGeneration": ("qwen2_vl", "Tarsier2ForConditionalGeneration"),  # noqa: E501
+    "UltravoxModel": ("ultravox", "UltravoxModel"),
     "VoxtralForConditionalGeneration": ("voxtral", "VoxtralForConditionalGeneration"),  # noqa: E501
     # [Encoder-decoder]
     "WhisperForConditionalGeneration": ("whisper", "WhisperForConditionalGeneration"),  # noqa: E501

vllm/multimodal/video.py

@@ -156,7 +156,7 @@ class OpenCVVideoBackend(VideoLoader):
         # can cause incorrect timestamp calculation without num_frames=-1.
         metadata = {
             "total_num_frames": num_frames,
-            "fps": original_fps,
+            "fps": num_frames / duration,
             "duration": duration,
             "video_backend": "opencv",
             "frames_indices": list(range(num_frames)),