[Model] Support Qwen3-VL Model Series

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 @@ def run_tarsier2(questions: list[str], modality: str) -> ModelRequestData:
     "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 @@ def run_tarsier2(questions: list[str], modality: str) -> ModelRequestData:
     "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 @@ def _test_processing_correctness(
 }
 
 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 @@ def check_available_online(
                                                           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 @@ def __init__(
         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 @@ def __init__(
                          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 @@ def forward_native(
         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 @@ def forward_cuda(
         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 @@ def get_input_positions_tensor(
                 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 @@ def _glm4v_get_input_positions_tensor(
                                 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 @@ def __init__(self,
                  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