vllm - 💡(How to fix) Fix [Usage]: Qwen3-VL inference on video complains of lack of metadata [3 comments, 3 participants]

The output of `python collect_env.py`

==============================
        System Info
==============================
OS                           : Ubuntu 24.04.4 LTS (x86_64)
GCC version                  : (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0
Clang version                : Could not collect
CMake version                : version 3.27.6
Libc version                 : glibc-2.39

==============================
       PyTorch Info
==============================
PyTorch version              : 2.9.0+cu128
Is debug build               : False
CUDA used to build PyTorch   : 12.8
ROCM used to build PyTorch   : N/A

==============================
      Python Environment
==============================
Python version               : 3.12.13 | packaged by Anaconda, Inc. | (main, Mar 19 2026, 20:20:58) [GCC 14.3.0] (64-bit runtime)
Python platform              : Linux-6.8.0-107-generic-x86_64-with-glibc2.39

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 12.1.66
CUDA_MODULE_LOADING set to   : 
GPU models and configuration : GPU 0: NVIDIA GeForce RTX 3080 Laptop GPU
Nvidia driver version        : 580.126.09
cuDNN version                : Probably one of the following:
/usr/local/cuda-10.2/targets/x86_64-linux/lib/libcudnn.so.8.2.1
/usr/local/cuda-10.2/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.2.1
/usr/local/cuda-10.2/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.2.1
/usr/local/cuda-10.2/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.2.1
/usr/local/cuda-10.2/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.2.1
/usr/local/cuda-10.2/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.2.1
/usr/local/cuda-10.2/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.2.1
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn.so.8.7.0
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.7.0
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.7.0
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.7.0
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.7.0
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.7.0
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.7.0
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn.so.8.9.5
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.9.5
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.9.5
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.9.5
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.9.5
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.9.5
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.9.5
/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn.so.8
/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8
/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn_adv_train.so.8
/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8
/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8
/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8
/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn_ops_train.so.8
/usr/local/cuda-12.8/targets/x86_64-linux/lib/libcudnn.so.9
/usr/local/cuda-12.8/targets/x86_64-linux/lib/libcudnn_adv.so.9
/usr/local/cuda-12.8/targets/x86_64-linux/lib/libcudnn_cnn.so.9
/usr/local/cuda-12.8/targets/x86_64-linux/lib/libcudnn_engines_precompiled.so.9
/usr/local/cuda-12.8/targets/x86_64-linux/lib/libcudnn_engines_runtime_compiled.so.9
/usr/local/cuda-12.8/targets/x86_64-linux/lib/libcudnn_graph.so.9
/usr/local/cuda-12.8/targets/x86_64-linux/lib/libcudnn_heuristic.so.9
/usr/local/cuda-12.8/targets/x86_64-linux/lib/libcudnn_ops.so.9
HIP runtime version          : N/A
MIOpen runtime version       : N/A
Is XNNPACK available         : True

==============================
          CPU Info
==============================
Architecture:                            x86_64
CPU op-mode(s):                          32-bit, 64-bit
Address sizes:                           39 bits physical, 48 bits virtual
Byte Order:                              Little Endian
Vendor ID:                               GenuineIntel

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.5.3
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.8.4.1
[pip3] nvidia-cuda-cupti-cu12==12.8.90
[pip3] nvidia-cuda-nvrtc-cu12==12.8.93
[pip3] nvidia-cuda-runtime-cu12==12.8.90
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-frontend==1.21.0
[pip3] nvidia-cufft-cu12==11.3.3.83
[pip3] nvidia-cufile-cu12==1.13.1.3
[pip3] nvidia-curand-cu12==10.3.9.90
[pip3] nvidia-cusolver-cu12==11.7.3.90
[pip3] nvidia-cusparse-cu12==12.5.8.93
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-cutlass-dsl==4.4.2
[pip3] nvidia-cutlass-dsl-libs-base==4.4.2
[pip3] nvidia-ml-py==13.595.45
[pip3] nvidia-nccl-cu12==2.27.5
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvshmem-cu12==3.3.20
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] pyzmq==27.1.0
[pip3] torch==2.9.0
[pip3] torchaudio==2.9.0
[pip3] torchvision==0.24.0
[pip3] transformers==4.57.6
[pip3] triton==3.5.0
[conda] flashinfer-python                    0.5.3                     pypi_0           pypi
[conda] numpy                                2.2.6                     pypi_0           pypi
[conda] nvidia-cublas-cu12                   12.8.4.1                  pypi_0           pypi
[conda] nvidia-cuda-cupti-cu12               12.8.90                   pypi_0           pypi
[conda] nvidia-cuda-nvrtc-cu12               12.8.93                   pypi_0           pypi
[conda] nvidia-cuda-runtime-cu12             12.8.90                   pypi_0           pypi
[conda] nvidia-cudnn-cu12                    9.10.2.21                 pypi_0           pypi
[conda] nvidia-cudnn-frontend                1.21.0                    pypi_0           pypi
[conda] nvidia-cufft-cu12                    11.3.3.83                 pypi_0           pypi
[conda] nvidia-cufile-cu12                   1.13.1.3                  pypi_0           pypi
[conda] nvidia-curand-cu12                   10.3.9.90                 pypi_0           pypi
[conda] nvidia-cusolver-cu12                 11.7.3.90                 pypi_0           pypi
[conda] nvidia-cusparse-cu12                 12.5.8.93                 pypi_0           pypi
[conda] nvidia-cusparselt-cu12               0.7.1                     pypi_0           pypi
[conda] nvidia-cutlass-dsl                   4.4.2                     pypi_0           pypi
[conda] nvidia-cutlass-dsl-libs-base         4.4.2                     pypi_0           pypi
[conda] nvidia-ml-py                         13.595.45                 pypi_0           pypi
[conda] nvidia-nccl-cu12                     2.27.5                    pypi_0           pypi
[conda] nvidia-nvjitlink-cu12                12.8.93                   pypi_0           pypi
[conda] nvidia-nvshmem-cu12                  3.3.20                    pypi_0           pypi
[conda] nvidia-nvtx-cu12                     12.8.90                   pypi_0           pypi
[conda] pyzmq                                27.1.0                    pypi_0           pypi
[conda] torch                                2.9.0                     pypi_0           pypi
[conda] torchaudio                           2.9.0                     pypi_0           pypi
[conda] torchvision                          0.24.0                    pypi_0           pypi
[conda] transformers                         4.57.6                    pypi_0           pypi
[conda] triton                               3.5.0                     pypi_0           pypi

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.11.2.dev453+g653591d5e (git sha: 653591d5e)
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
  	GPU0	CPU Affinity	NUMA Affinity	GPU NUMA ID
GPU0	 X 	0-15	0		N/A

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks

==============================
     Environment Variables
==============================
LD_LIBRARY_PATH=/usr/local/cuda-12.1/lib64:/usr/local/cuda-12.1/extras/CUPTI/lib64:/usr/local/cuda/lib64:
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1

---

from transformers import AutoProcessor
from vllm import LLM, SamplingParams
from qwen_vl_utils import process_vision_info

model_path = "Qwen/Qwen3-VL-2B-Instruct-FP8" 
video_path = "https://content.pexels.com/videos/free-videos.mp4"

llm = LLM(
    model=model_path,
    gpu_memory_utilization=0.9, 
    max_model_len=40000, # to fit in 16Gb VRAM
    enforce_eager=True,
    limit_mm_per_prompt={"video": 1},
)

sampling_params = SamplingParams(max_tokens=1024)

video_messages = [
    {
        "role": "system",
        "content": "You are a helpful assistant.",
    },
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Please describe this video. Indicate how many different scenes it has."},
            {
                "type": "video",
                "video": video_path,
                "total_pixels": 256 * 28 * 28, # 20480 * 28 * 28,
                "min_pixels": 16 * 28 * 28,
            },
        ]
    },
]

messages = video_messages
processor = AutoProcessor.from_pretrained(model_path)
prompt = processor.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)

image_inputs, video_inputs = process_vision_info(messages)
mm_data = {}
if video_inputs is not None:
    mm_data["video"] = video_inputs

llm_inputs = {
    "prompt": prompt,
    "multi_modal_data": mm_data,
}

outputs = llm.generate([llm_inputs], sampling_params=sampling_params)
for o in outputs:
    generated_text = o.outputs[0].text
    print(generated_text)

---

File "/home/jarvis/anaconda3/envs/Qwen3-VL/lib/python3.12/site-packages/vllm/multimodal/parse.py", line 511, in _parse_video_data
    raise ValueError(
ValueError: Video metadata is required but not found in mm input. Please check your video input in `multi_modal_data`

---

video_array, metadata = item