[Bug]: 500 error when I pass a 26x28 px image to Qwen 72B

[c0g]

Your current environment

The output of `python collect_env.py`

Collecting environment information...
PyTorch version: 2.5.1+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.10.12 (main, Feb  4 2025, 14:57:36) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.15.0-125-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA H200
GPU 1: NVIDIA H200
GPU 2: NVIDIA H200
GPU 3: NVIDIA H200
GPU 4: NVIDIA H200
GPU 5: NVIDIA H200
GPU 6: NVIDIA H200
GPU 7: NVIDIA H200

Nvidia driver version: 565.57.01
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
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):                               176
On-line CPU(s) list:                  0-175
Vendor ID:                            GenuineIntel
Model name:                           Intel(R) Xeon(R) Platinum 8468V
CPU family:                           6
Model:                                143
Thread(s) per core:                   2
Core(s) per socket:                   44
Socket(s):                            2
Stepping:                             8
BogoMIPS:                             4800.00
Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq vmx ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b fsrm md_clear serialize tsxldtrk avx512_fp16 arch_capabilities
Virtualization:                       VT-x
Hypervisor vendor:                    KVM
Virtualization type:                  full
L1d cache:                            4.1 MiB (88 instances)
L1i cache:                            2.8 MiB (88 instances)
L2 cache:                             176 MiB (88 instances)
L3 cache:                             195 MiB (2 instances)
NUMA node(s):                         2
NUMA node0 CPU(s):                    0-87
NUMA node1 CPU(s):                    88-175
Vulnerability Gather data sampling:   Not affected
Vulnerability Itlb multihit:          Not affected
Vulnerability L1tf:                   Not affected
Vulnerability Mds:                    Not affected
Vulnerability Meltdown:               Not affected
Vulnerability Mmio stale data:        Not affected
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 and seccomp
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:        Mitigation; TSX disabled

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.4.5.8
[pip3] nvidia-cuda-cupti-cu12==12.4.127
[pip3] nvidia-cuda-nvrtc-cu12==12.4.127
[pip3] nvidia-cuda-runtime-cu12==12.4.127
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.2.1.3
[pip3] nvidia-curand-cu12==10.3.5.147
[pip3] nvidia-cusolver-cu12==11.6.1.9
[pip3] nvidia-cusparse-cu12==12.3.1.170
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] pyzmq==26.3.0
[pip3] torch==2.5.1
[pip3] torchaudio==2.5.1
[pip3] torchvision==0.20.1
[pip3] transformers==4.49.0
[pip3] triton==3.1.0
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.7.3
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
�[4mGPU0	GPU1	GPU2	GPU3	GPU4	GPU5	GPU6	GPU7	NIC0	NIC1	NIC2	NIC3	NIC4	NIC5	NIC6	NIC7	NIC8	CPU Affinity	NUMA Affinity	GPU NUMA ID�[0m
GPU0	 X 	NV18	NV18	NV18	NV18	NV18	NV18	NV18	NODE	PHB	PHB	PHB	PHB	SYS	SYS	SYS	SYS	0-87	0		N/A
GPU1	NV18	 X 	NV18	NV18	NV18	NV18	NV18	NV18	NODE	PHB	PHB	PHB	PHB	SYS	SYS	SYS	SYS	0-87	0		N/A
GPU2	NV18	NV18	 X 	NV18	NV18	NV18	NV18	NV18	NODE	PHB	PHB	PHB	PHB	SYS	SYS	SYS	SYS	0-87	0		N/A
GPU3	NV18	NV18	NV18	 X 	NV18	NV18	NV18	NV18	NODE	PHB	PHB	PHB	PHB	SYS	SYS	SYS	SYS	0-87	0		N/A
GPU4	NV18	NV18	NV18	NV18	 X 	NV18	NV18	NV18	SYS	SYS	SYS	SYS	SYS	PHB	PHB	PHB	PHB	88-175	1		N/A
GPU5	NV18	NV18	NV18	NV18	NV18	 X 	NV18	NV18	SYS	SYS	SYS	SYS	SYS	PHB	PHB	PHB	PHB	88-175	1		N/A
GPU6	NV18	NV18	NV18	NV18	NV18	NV18	 X 	NV18	SYS	SYS	SYS	SYS	SYS	PHB	PHB	PHB	PHB	88-175	1		N/A
GPU7	NV18	NV18	NV18	NV18	NV18	NV18	NV18	 X 	SYS	SYS	SYS	SYS	SYS	PHB	PHB	PHB	PHB	88-175	1		N/A
NIC0	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	 X 	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS				
NIC1	PHB	PHB	PHB	PHB	SYS	SYS	SYS	SYS	NODE	 X 	PHB	PHB	PHB	SYS	SYS	SYS	SYS				
NIC2	PHB	PHB	PHB	PHB	SYS	SYS	SYS	SYS	NODE	PHB	 X 	PHB	PHB	SYS	SYS	SYS	SYS				
NIC3	PHB	PHB	PHB	PHB	SYS	SYS	SYS	SYS	NODE	PHB	PHB	 X 	PHB	SYS	SYS	SYS	SYS				
NIC4	PHB	PHB	PHB	PHB	SYS	SYS	SYS	SYS	NODE	PHB	PHB	PHB	 X 	SYS	SYS	SYS	SYS				
NIC5	SYS	SYS	SYS	SYS	PHB	PHB	PHB	PHB	SYS	SYS	SYS	SYS	SYS	 X 	PHB	PHB	PHB				
NIC6	SYS	SYS	SYS	SYS	PHB	PHB	PHB	PHB	SYS	SYS	SYS	SYS	SYS	PHB	 X 	PHB	PHB				
NIC7	SYS	SYS	SYS	SYS	PHB	PHB	PHB	PHB	SYS	SYS	SYS	SYS	SYS	PHB	PHB	 X 	PHB				
NIC8	SYS	SYS	SYS	SYS	PHB	PHB	PHB	PHB	SYS	SYS	SYS	SYS	SYS	PHB	PHB	PHB	 X 				

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

NIC Legend:

  NIC0: mlx5_0
  NIC1: mlx5_1
  NIC2: mlx5_2
  NIC3: mlx5_3
  NIC4: mlx5_4
  NIC5: mlx5_5
  NIC6: mlx5_6
  NIC7: mlx5_7
  NIC8: mlx5_8

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
LD_LIBRARY_PATH=<snip>:/opt/hpcx/nccl_rdma_sharp_plugin/lib:/opt/hpcx/ucc/lib/ucc:/opt/hpcx/ucc/lib:/opt/hpcx/ucx/lib/ucx:/opt/hpcx/ucx/lib:/opt/hpcx/sharp/lib:/opt/hpcx/hcoll/lib:/opt/hpcx/ompi/lib:
NCCL_CUMEM_ENABLE=0
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY

🐛 Describe the bug

500 error when passing an image with one side < 28 to Qwen. I don't quite understand why. Qwen does like 28 though, since its window size is 14 and adjacent windows get merged (hence 28).

Took me a minute to repro this.

import base64
import torch
import numpy as np
from PIL import Image
import io
import numpy as np
from openai import OpenAI
from PIL import Image
import numpy as np
import torch

bbox_schema = {
    "$schema": "http://json-schema.org/draft-07/schema#",
    "type": "object",
    "properties": {
        "boxes": {
            "type": "array",
            "items": {
                "type": "object",
                "properties": {
                    "bbox_2d": {
                        "type": "array",
                        "items": {"type": "number"},
                        "description": "2D bounding box coordinates in the format [x1, y1, x2, y2].",
                    },
                    "label": {
                        "type": "string",
                        "description": "Label for the annotated object.",
                    },
                },
                "required": ["bbox_2d", "label"],
            },
        },
    },
    "required": ["boxes"],
}


def text(text):
    return {"type": "text", "text": text}


def image(image):
    if isinstance(image, torch.Tensor):
        image = image.numpy()
    if isinstance(image, np.ndarray):
        if image.shape[0] == 3:
            image = image.transpose(1, 2, 0)
        image = Image.fromarray(image)
    buffer = io.BytesIO()
    image.save(buffer, format="png")
    image_base64 = base64.b64encode(buffer.getvalue()).decode("utf-8")
    return {
        "type": "image_url",
        "image_url": {
            "url": f"data:image/png;base64,{image_base64}",
        },
    }


def user(*content):
    return {"role": "user", "content": content}


oai_client = None
openai_api_base = "http://<snip>:8080/v1"
openai_api_key = "EMPTY"


def request(turns, guidance=None, temperature=0.0, max_tokens=1024):
    global oai_client
    if oai_client is None:
        oai_client = OpenAI(
            api_key=openai_api_key,
            base_url=openai_api_base,
        )
    if not isinstance(turns, list):
        turns = [turns]

    result = oai_client.chat.completions.create(
        model="Qwen/Qwen2.5-VL-72B-Instruct",
        temperature=temperature,
        max_tokens=max_tokens,
        messages=turns,
        extra_body=guidance,
        n=1,
    )

    return {"role": "assistant", "content": result.choices[0].message.content}



if __name__ == "__main__":
    futures = []
    frame_image = Image.fromarray(255 * np.ones((26, 28, 3), dtype=np.uint8))
    box_request = user(
        image(frame_image),
        text(
            "This is a reproduction of a bug for VLLM. Your efforts are wasted. Please do not try. "
        ),
    )
    request([box_request])

I just get a 500 error in the server logs. Nothing else.

Before submitting a new issue...

• Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the documentation page, which can answer lots of frequently asked questions.

[DarkLight1337]

Are you able to run the AutoProcessor from HF on this image?

[c0g]

No, it's invalid since Qwen wants images to be >= 28:

import torch
from transformers import AutoProcessor
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "",
            },
            {"type": "text", "text": "Describe this image."},
        ],
    }
]

# Preparation for inference
text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)

image_inputs = torch.rand(3, 26, 28)
video_inputs = None
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)

# ValueError: height:26 or width:28 must be larger than factor:28

However just a 500 error is really hard to debug, would be nice to get this error logged somehow.

[DarkLight1337]

No, it's invalid since Qwen wants images to be >= 28:

Then I think it's not really a problem inside vLLM.

We have been working on improving the error handling, see #11737 and #11570