Your current environment
The output of python collect_env.py
Collecting environment information...
==============================
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 3.28.3
Libc version : glibc-2.39
==============================
PyTorch Info
==============================
PyTorch version : 2.10.0+cu128
Is debug build : False
CUDA used to build PyTorch : 12.8
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.11.0 (main, Jan 23 2026, 16:28:26) [GCC 9.5.0] (64-bit runtime)
Python platform : Linux-6.8.0-94-generic-x86_64-with-glibc2.39
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : 12.4.99
CUDA_MODULE_LOADING set to :
GPU models and configuration :
GPU 0: NVIDIA A100 80GB PCIe
GPU 1: NVIDIA A100 80GB PCIe
GPU 2: NVIDIA A100 80GB PCIe
Nvidia driver version : 550.163.01
cuDNN version : Could not collect
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: 46 bits physical, 57 bits virtual
Byte Order: Little Endian
CPU(s): 112
On-line CPU(s) list: 0-111
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Gold 6348 CPU @ 2.60GHz
CPU family: 6
Model: 106
Thread(s) per core: 2
Core(s) per socket: 28
Socket(s): 2
Stepping: 6
CPU(s) scaling MHz: 32%
CPU max MHz: 3500.0000
CPU min MHz: 800.0000
BogoMIPS: 5200.00
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect wbnoinvd dtherm ida arat pln pts vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 2.6 MiB (56 instances)
L1i cache: 1.8 MiB (56 instances)
L2 cache: 70 MiB (56 instances)
L3 cache: 84 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-27,56-83
NUMA node1 CPU(s): 28-55,84-111
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI SW loop, KVM SW loop
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Vulnerability Vmscape: Not affected
==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.4
[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==1.18.0
[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==4.4.1
[pip3] nvidia-cutlass-dsl-libs-base==4.4.1
[pip3] nvidia-ml-py==13.590.48
[pip3] nvidia-nccl-cu12==2.27.5
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvshmem-cu12==3.4.5
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] pyzmq==27.1.0
[pip3] torch==2.10.0
[pip3] torch_c_dlpack_ext==0.1.5
[pip3] torchaudio==2.10.0
[pip3] torchvision==0.25.0
[pip3] transformers==4.57.6
[pip3] triton==3.6.0
[conda] Could not collect
==============================
vLLM Info
==============================
ROCM Version : Could not collect
vLLM Version : 0.17.1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
�[4mGPU0 GPU1 GPU2 CPU Affinity NUMA Affinity GPU NUMA ID�[0m
GPU0 X PIX PXB 0-27,56-83 0 N/A
GPU1 PIX X PXB 0-27,56-83 0 N/A
GPU2 PXB PXB X 0-27,56-83 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-12.4/lib64:/usr/local/cuda-12.4/lib64:
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_{username}
🐛 Describe the bug
The vLLM serving cannot support video inputs with a list of base64-encoded extracted JPEG frames.
This usage is needed, since in my case the input video could be rather long and the resolusion might be large, I'd like to resize and sparsely sample frames before sending the request to vllm server.
A simple example:
import base64
import json
import os
import tempfile
import cv2
import requests
MODEL_NAME="Qwen/Qwen3.5-2B"
REQUEST_URL= "http://localhost:41091/v1/chat/completions"
video_url='https://videos.pexels.com/video-files/5992517/5992517-hd_1920_1080_30fps.mp4'
print(f"Downloading video from {video_url}...")
# 1. Download video
response = requests.get(video_url, stream=True)
if response.status_code == 200:
temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
for chunk in response.iter_content(chunk_size=8192):
temp_file.write(chunk)
temp_file.close()
video_path = temp_file.name
else:
print("Failed to download video")
exit(1)
print("Extracting frames...")
# 2. Extract frames, resize, base64 encode
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
print("Error opening video file")
exit(1)
fps = max(1, int(cap.get(cv2.CAP_PROP_FPS)))
video_base64_frames = []
frame_count = 0
while True:
ret, frame = cap.read()
if not ret:
break
# fps=1
if frame_count % fps == 0:
resized_frame = cv2.resize(frame, (480, 270))
# Encode to JPEG
_, buffer = cv2.imencode('.jpg', resized_frame)
# Base64 encode
b64_str = base64.b64encode(buffer).decode('utf-8')
video_base64_frames.append(b64_str)
frame_count += 1
cap.release()
os.remove(video_path)
print(f"Extracted {len(video_base64_frames)} frames.")
# 3. Request
print("Sending request to server...")
headers = {"Content-Type": "application/json"}
data = {
"model": MODEL_NAME,
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Describe the video content in detail."
},
{
"type": "video_url",
"video_url": {"url": f"data:video/jpeg;base64,{','.join(video_base64_frames)}"},
"fps": 1
}
]
}
],
"max_tokens": 128
}
resp = requests.post(REQUEST_URL, headers=headers, json=data)
print("Response details:")
print(f"Status Code: {resp.status_code}")
try:
print(json.dumps(resp.json(), indent=2))
except json.JSONDecodeError:
print(resp.text)Output:
Extracting frames...
Extracted 58 frames.
Sending request to server...
Response details:
Status Code: 400
{
"error": {
"message": "VideoMetadata.__init__() missing 1 required positional argument: 'total_num_frames'",
"type": "BadRequestError",
"param": null,
"code": 400
}
}
The may caused by this implementation: video.py#L74
The workaround in my environment could be:
def load_base64(
self, media_type: str, data: str
) -> tuple[npt.NDArray, dict[str, Any]]:
if media_type.lower() == "video/jpeg":
load_frame = partial(
self.image_io.load_base64,
"image/jpeg",
)
# return np.stack(
# [np.asarray(load_frame(frame_data)) for frame_data in data.split(",")]
# ), {}
#
# workaround:
#
frames = np.stack([np.asarray(load_frame(frame_data)) for frame_data in data.split(",")])
total = int(frames.shape[0])
fps = float(self.kwargs.get("fps", 1))
duration = (total / fps) if fps > 0 else 0.0
metadata = {
"total_num_frames": total,
"fps": fps,
"duration": duration,
"video_backend": "jpeg_sequence",
"frames_indices": list(range(total)),
"do_sample_frames": False,
}
return frames, metadata
return self.load_bytes(base64.b64decode(data))Do you have any suggestions on that case? Or if I may have missed something from the document about this use case...
Before submitting a new issue...
Your current environment
The output of
python collect_env.py🐛 Describe the bug
The vLLM serving cannot support video inputs with a list of base64-encoded extracted JPEG frames.
This usage is needed, since in my case the input video could be rather long and the resolusion might be large, I'd like to resize and sparsely sample frames before sending the request to vllm server.
A simple example:
Output:
Extracting frames... Extracted 58 frames. Sending request to server... Response details: Status Code: 400 { "error": { "message": "VideoMetadata.__init__() missing 1 required positional argument: 'total_num_frames'", "type": "BadRequestError", "param": null, "code": 400 } }The may caused by this implementation: video.py#L74
The workaround in my environment could be:
Do you have any suggestions on that case? Or if I may have missed something from the document about this use case...
Before submitting a new issue...