[Bug]: vLLM engine v1 with Qwen/Qwen3-Reranker-0.6B crashes with long input #24327
Description
Your current environment
The output of python collect_env.py
Collecting environment information...
==============================
System Info
==============================
OS : Ubuntu 20.04.6 LTS (x86_64)
GCC version : (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version : Could not collect
CMake version : version 3.16.3
Libc version : glibc-2.31
==============================
PyTorch Info
==============================
PyTorch version : 2.7.1+cu126
Is debug build : False
CUDA used to build PyTorch : 12.6
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.11.13 | packaged by conda-forge | (main, Jun 4 2025, 14:48:23) [GCC 13.3.0] (64-bit runtime)
Python platform : Linux-6.1.100+-x86_64-with-glibc2.31
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : Could not collect
CUDA_MODULE_LOADING set to : LAZY
GPU models and configuration : GPU 0: NVIDIA A100-SXM4-40GB
Nvidia driver version : 550.90.07
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
Byte Order: Little Endian
Address sizes: 46 bits physical, 48 bits virtual
CPU(s): 12
On-line CPU(s) list: 0-11
Thread(s) per core: 2
Core(s) per socket: 6
Socket(s): 1
NUMA node(s): 1
Vendor ID: GenuineIntel
CPU family: 6
Model: 85
Model name: Intel(R) Xeon(R) CPU @ 2.20GHz
Stepping: 7
CPU MHz: 2200.216
BogoMIPS: 4400.43
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 192 KiB
L1i cache: 192 KiB
L2 cache: 6 MiB
L3 cache: 38.5 MiB
NUMA node0 CPU(s): 0-11
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: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Mitigation; Enhanced IBRS
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: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
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 rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat avx512_vnni md_clear arch_capabilities
==============================
Versions of relevant libraries
==============================
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.6.4.1
[pip3] nvidia-cuda-cupti-cu12==12.6.80
[pip3] nvidia-cuda-nvrtc-cu12==12.6.77
[pip3] nvidia-cuda-runtime-cu12==12.6.77
[pip3] nvidia-cudnn-cu12==9.5.1.17
[pip3] nvidia-cufft-cu12==11.3.0.4
[pip3] nvidia-cufile-cu12==1.11.1.6
[pip3] nvidia-curand-cu12==10.3.7.77
[pip3] nvidia-cusolver-cu12==11.7.1.2
[pip3] nvidia-cusparse-cu12==12.5.4.2
[pip3] nvidia-cusparselt-cu12==0.6.3
[pip3] nvidia-nccl-cu12==2.26.2
[pip3] nvidia-nvjitlink-cu12==12.6.85
[pip3] nvidia-nvtx-cu12==12.6.77
[pip3] pyzmq==27.0.2
[pip3] torch==2.7.1
[pip3] torchaudio==2.7.1
[pip3] torchvision==0.22.1
[pip3] transformers==4.56.1
[pip3] triton==3.3.1
[conda] numpy 2.2.6 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.6.4.1 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu12 12.6.80 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu12 12.6.77 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu12 12.6.77 pypi_0 pypi
[conda] nvidia-cudnn-cu12 9.5.1.17 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.3.0.4 pypi_0 pypi
[conda] nvidia-cufile-cu12 1.11.1.6 pypi_0 pypi
[conda] nvidia-curand-cu12 10.3.7.77 pypi_0 pypi
[conda] nvidia-cusolver-cu12 11.7.1.2 pypi_0 pypi
[conda] nvidia-cusparse-cu12 12.5.4.2 pypi_0 pypi
[conda] nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.26.2 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.6.85 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.6.77 pypi_0 pypi
[conda] pyzmq 27.0.2 pypi_0 pypi
[conda] torch 2.7.1 pypi_0 pypi
[conda] torchaudio 2.7.1 pypi_0 pypi
[conda] torchvision 0.22.1 pypi_0 pypi
[conda] transformers 4.56.1 pypi_0 pypi
[conda] triton 3.3.1 pypi_0 pypi
==============================
vLLM Info
==============================
ROCM Version : Could not collect
Neuron SDK Version : N/A
vLLM Version : 0.10.1.1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X 0-11 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/nvidia/lib:/usr/local/nvidia/lib64
NVIDIA_VISIBLE_DEVICES=all
NVIDIA_DRIVER_CAPABILITIES=compute,utility
CUDA_DEVICE_ORDER=PCI_BUS_ID
NCCL_CUMEM_ENABLE=0
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY
🐛 Describe the bug
Logs
(vllm) user ➜ vllm serve Qwen/Qwen3-Reranker-0.6B --task score --port 8080
INFO 09-05 13:22:09 [init.py:241] Automatically detected platform cuda.
WARNING 09-05 13:22:12 [init.py:1734] argument 'task' is deprecated
(APIServer pid=5410) INFO 09-05 13:22:12 [api_server.py:1805] vLLM API server version 0.10.1.1
(APIServer pid=5410) INFO 09-05 13:22:12 [utils.py:326] non-default args: {'model_tag': 'Qwen/Qwen3-Reranker-0.6B', 'port': 8080, 'model': 'Qwen/Qwen3-Reranker-0.6B', 'task': 'score'}
(APIServer pid=5410) INFO 09-05 13:22:20 [init.py:711] Resolved architecture: Qwen3ForCausalLM
(APIServer pid=5410) torch_dtype is deprecated! Use dtype instead!
(APIServer pid=5410) INFO 09-05 13:22:20 [init.py:1750] Using max model len 40960
(APIServer pid=5410) INFO 09-05 13:22:20 [arg_utils.py:1608] (Enabling) chunked prefill by default
(APIServer pid=5410) INFO 09-05 13:22:20 [arg_utils.py:1611] (Enabling) prefix caching by default
(APIServer pid=5410) INFO 09-05 13:22:20 [scheduler.py:222] Chunked prefill is enabled with max_num_batched_tokens=2048.
INFO 09-05 13:22:26 [init.py:241] Automatically detected platform cuda.
(EngineCore_0 pid=5465) INFO 09-05 13:22:28 [core.py:636] Waiting for init message from front-end.
(EngineCore_0 pid=5465) INFO 09-05 13:22:28 [core.py:74] Initializing a V1 LLM engine (v0.10.1.1) with config: model='Qwen/Qwen3-Reranker-0.6B', speculative_config=None, tokenizer='Qwen/Qwen3-Reranker-0.6B', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config={}, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=40960, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, decoding_config=DecodingConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_backend=''), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None), seed=0, served_model_name=Qwen/Qwen3-Reranker-0.6B, enable_prefix_caching=True, chunked_prefill_enabled=True, use_async_output_proc=False, pooler_config=PoolerConfig(pooling_type='LAST', normalize=None, dimensions=None, activation=None, softmax=None, step_tag_id=None, returned_token_ids=None, enable_chunked_processing=None, max_embed_len=None), compilation_config={"level":3,"debug_dump_path":"","cache_dir":"","backend":"","custom_ops":[],"splitting_ops":["vllm.unified_attention","vllm.unified_attention_with_output","vllm.mamba_mixer2"],"use_inductor":true,"compile_sizes":[],"inductor_compile_config":{"enable_auto_functionalized_v2":false},"inductor_passes":{},"cudagraph_mode":1,"use_cudagraph":true,"cudagraph_num_of_warmups":1,"cudagraph_capture_sizes":[512,504,496,488,480,472,464,456,448,440,432,424,416,408,400,392,384,376,368,360,352,344,336,328,320,312,304,296,288,280,272,264,256,248,240,232,224,216,208,200,192,184,176,168,160,152,144,136,128,120,112,104,96,88,80,72,64,56,48,40,32,24,16,8,4,2,1],"cudagraph_copy_inputs":false,"full_cuda_graph":false,"pass_config":{},"max_capture_size":512,"local_cache_dir":null}
(EngineCore_0 pid=5465) INFO 09-05 13:22:30 [parallel_state.py:1134] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, TP rank 0, EP rank 0
(EngineCore_0 pid=5465) WARNING 09-05 13:22:30 [topk_topp_sampler.py:61] FlashInfer is not available. Falling back to the PyTorch-native implementation of top-p & top-k sampling. For the best performance, please install FlashInfer.
(EngineCore_0 pid=5465) INFO 09-05 13:22:30 [gpu_model_runner.py:1953] Starting to load model Qwen/Qwen3-Reranker-0.6B...
(EngineCore_0 pid=5465) INFO 09-05 13:22:30 [gpu_model_runner.py:1985] Loading model from scratch...
(EngineCore_0 pid=5465) INFO 09-05 13:22:30 [cuda.py:328] Using Flash Attention backend on V1 engine.
(EngineCore_0 pid=5465) INFO 09-05 13:22:30 [weight_utils.py:296] Using model weights format ['*.safetensors']
(EngineCore_0 pid=5465) INFO 09-05 13:22:31 [weight_utils.py:349] No model.safetensors.index.json found in remote.
Loading safetensors checkpoint shards: 0% Completed | 0/1 [00:00<?, ?it/s]
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00, 2.66it/s]
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00, 2.65it/s]
The vLLM server crashes when processing reranking requests with long documents using the default V1 engine, but works correctly with the V0 engine.
vLLM Version: 0.10.1.1
Model: Qwen/Qwen3-Reranker-0.6B
Task: score (reranking)
Platform: CUDA
Python Version: 3.11.13
GPU: A100
Steps to Reproduce
-
Start vLLM server with V1 engine (default):
vllm serve Qwen/Qwen3-Reranker-0.6B --task score --port 8080 -
Send reranking request with long documents:
5 documents ~3,000 characters each
Expected Behavior: The server should process the reranking request successfully and return relevance scores.
Actual Behavior: The vLLM server crashes when processing the request.
Workaround: Using the V0 engine resolves the issue
VLLM_USE_V1=0 vllm serve Qwen/Qwen3-Reranker-0.6B --task score --port 8080
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