[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_moe.py

@@ -0,0 +1,344 @@
+# 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)),