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

Signed-off-by: Roger Wang <[email protected]>
Signed-off-by: Isotr0py <[email protected]>
Co-authored-by: Huang Jie <[email protected]>
Co-authored-by: 松灵 <[email protected]>
Co-authored-by: Isotr0py <[email protected]>

Author: 4 people

docs/models/supported_models.md

@@ -667,6 +667,8 @@ 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

@@ -1499,6 +1499,80 @@ 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"
@@ -1709,6 +1783,8 @@ 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,
@@ -1718,6 +1794,15 @@ def run_tarsier2(questions: list[str], modality: str) -> ModelRequestData:
 }
 
 
+MODELS_NEED_VIDEO_METADATA = [
+    "glm4_1v",
+    "glm4_5v",
+    "glm4_5v_fp8",
+    "qwen3_vl",
+    "qwen3_vl_moe",
+]
+
+
 def get_multi_modal_input(args):
     """
     return {

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"]
         mm_data["video"] = (video, {
             "total_num_frames": len(video),
@@ -41,6 +42,34 @@ 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,
@@ -181,8 +210,10 @@ 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,
 }
 
 
@@ -328,6 +359,8 @@ 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

@@ -559,6 +559,12 @@ 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/attention/layer.py

@@ -30,6 +30,15 @@
 USE_XFORMERS_OPS = None
 
 
+
+def check_upstream_fa_availability(dtype: torch.dtype):
+    if dtype in (torch.float16, torch.bfloat16) and current_platform.is_cuda(
+    ) and current_platform.has_device_capability(80):
+        from transformers.utils import is_flash_attn_2_available
+        return is_flash_attn_2_available()
+    return False
+
+
 def check_xformers_availability():
     global USE_XFORMERS_OPS
     if USE_XFORMERS_OPS is not None:

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(