[Bug]: vllm 0.8.2 have severe quality problem

[aabbccddwasd]

Your current environment

The output of `python collect_env.py`

Collecting environment information...
PyTorch version: 2.5.1
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: Could not collect
Libc version: glibc-2.35

Python version: 3.12.9 | packaged by Anaconda, Inc. | (main, Feb  6 2025, 18:56:27) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-133-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.8.61
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 2080 Ti
GPU 1: NVIDIA GeForce RTX 2080 Ti
GPU 2: NVIDIA GeForce RTX 2080 Ti
GPU 3: NVIDIA GeForce RTX 2080 Ti
GPU 4: NVIDIA GeForce RTX 2080 Ti
GPU 5: NVIDIA GeForce RTX 2080 Ti

Nvidia driver version: 550.144.03
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, 48 bits virtual
Byte Order:                           Little Endian
CPU(s):                               56
On-line CPU(s) list:                  0-55
Vendor ID:                            GenuineIntel
Model name:                           Intel(R) Xeon(R) CPU E5-2680 v4 @ 2.40GHz
CPU family:                           6
Model:                                79
Thread(s) per core:                   2
Core(s) per socket:                   14
Socket(s):                            2
Stepping:                             1
CPU max MHz:                          3300.0000
CPU min MHz:                          1200.0000
BogoMIPS:                             4800.00
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 arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor 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 cdp_l3 invpcid_single pti intel_ppin ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a rdseed adx smap intel_pt xsaveopt cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts md_clear flush_l1d
Virtualization:                       VT-x
L1d cache:                            896 KiB (28 instances)
L1i cache:                            896 KiB (28 instances)
L2 cache:                             7 MiB (28 instances)
L3 cache:                             70 MiB (2 instances)
NUMA node(s):                         2
NUMA node0 CPU(s):                    0-13,28-41
NUMA node1 CPU(s):                    14-27,42-55
Vulnerability Gather data sampling:   Not affected
Vulnerability Itlb multihit:          KVM: Mitigation: VMX disabled
Vulnerability L1tf:                   Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
Vulnerability Mds:                    Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Meltdown:               Mitigation; PTI
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 and seccomp
Vulnerability Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:             Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP conditional; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Mitigation; Clear CPU buffers; SMT vulnerable

Versions of relevant libraries:
[pip3] numpy==1.26.3
[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-cusparselt-cu12==0.6.2
[pip3] nvidia-ml-py==12.570.86
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] onnxruntime==1.20.1
[pip3] pyzmq==26.2.0
[pip3] rapidocr-onnxruntime==1.3.24
[pip3] sentence-transformers==3.4.1
[pip3] torch==2.5.1
[pip3] torchaudio==2.5.1
[pip3] torchvision==0.20.1
[pip3] transformers==4.49.0.dev0
[pip3] triton==3.1.0
[conda] blas                      1.0                         mkl  
[conda] cuda-cccl                 12.8.55                       0    nvidia
[conda] cuda-cccl_linux-64        12.8.55                       0    nvidia
[conda] cuda-command-line-tools   12.4.1                        0    nvidia
[conda] cuda-compiler             12.8.0                        0    nvidia
[conda] cuda-crt-dev_linux-64     12.8.61                       0    nvidia
[conda] cuda-crt-tools            12.8.61                       0    nvidia
[conda] cuda-cudart               12.4.127                      0    nvidia
[conda] cuda-cudart-dev           12.4.127                      0    nvidia
[conda] cuda-cudart-dev_linux-64  12.8.57                       0    nvidia
[conda] cuda-cudart-static        12.8.57                       0    nvidia
[conda] cuda-cudart-static_linux-64 12.8.57                       0    nvidia
[conda] cuda-cudart_linux-64      12.8.57                       0    nvidia
[conda] cuda-cuobjdump            12.8.55                       0    nvidia
[conda] cuda-cupti                12.4.127                      0    nvidia
[conda] cuda-cuxxfilt             12.8.55                       0    nvidia
[conda] cuda-documentation        12.4.127                      0    nvidia
[conda] cuda-driver-dev           12.8.57                       0    nvidia
[conda] cuda-driver-dev_linux-64  12.8.57                       0    nvidia
[conda] cuda-gdb                  12.8.55                       0    nvidia
[conda] cuda-libraries            12.4.1                        0    nvidia
[conda] cuda-libraries-dev        12.6.2                        0    nvidia
[conda] cuda-libraries-static     12.8.0                        0    nvidia
[conda] cuda-nsight               12.8.55                       0    nvidia
[conda] cuda-nvcc                 12.8.61                       0    nvidia
[conda] cuda-nvcc-dev_linux-64    12.8.61                       0    nvidia
[conda] cuda-nvcc-impl            12.8.61                       0    nvidia
[conda] cuda-nvcc-tools           12.8.61                       0    nvidia
[conda] cuda-nvcc_linux-64        12.8.61                       0    nvidia
[conda] cuda-nvdisasm             12.8.55                       0    nvidia
[conda] cuda-nvml-dev             12.8.55                       0    nvidia
[conda] cuda-nvprof               12.8.57                       0    nvidia
[conda] cuda-nvprune              12.8.55                       0    nvidia
[conda] cuda-nvrtc                12.4.127                      0    nvidia
[conda] cuda-nvrtc-dev            12.4.127                      0    nvidia
[conda] cuda-nvrtc-static         12.8.61                       0    nvidia
[conda] cuda-nvtx                 12.4.127                      0    nvidia
[conda] cuda-nvvm-dev_linux-64    12.8.61                       0    nvidia
[conda] cuda-nvvm-impl            12.8.61                       0    nvidia
[conda] cuda-nvvm-tools           12.8.61                       0    nvidia
[conda] cuda-nvvp                 12.8.57                       0    nvidia
[conda] cuda-opencl               12.8.55                       0    nvidia
[conda] cuda-opencl-dev           12.8.55                       0    nvidia
[conda] cuda-profiler-api         12.8.55                       0    nvidia
[conda] cuda-runtime              12.4.1                        0    nvidia
[conda] cuda-sanitizer-api        12.8.55                       0    nvidia
[conda] cuda-toolkit              12.4.1                        0    nvidia
[conda] cuda-tools                12.4.1                        0    nvidia
[conda] cuda-version              12.8                          3    nvidia
[conda] cuda-visual-tools         12.6.2                        0    nvidia
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] gds-tools                 1.13.0.11                     0    nvidia
[conda] libcublas                 12.4.5.8                      0    nvidia
[conda] libcublas-dev             12.4.5.8                      0    nvidia
[conda] libcublas-static          12.8.3.14                     0    nvidia
[conda] libcufft                  11.2.1.3                      0    nvidia
[conda] libcufft-dev              11.2.1.3                      0    nvidia
[conda] libcufft-static           11.3.3.41                     0    nvidia
[conda] libcufile                 1.13.0.11                     0    nvidia
[conda] libcufile-dev             1.13.0.11                     0    nvidia
[conda] libcufile-static          1.13.0.11                     0    nvidia
[conda] libcurand                 10.3.9.55                     0    nvidia
[conda] libcurand-dev             10.3.9.55                     0    nvidia
[conda] libcurand-static          10.3.9.55                     0    nvidia
[conda] libcusolver               11.6.1.9                      0    nvidia
[conda] libcusolver-dev           11.6.1.9                      0    nvidia
[conda] libcusolver-static        11.7.2.55                     0    nvidia
[conda] libcusparse               12.3.1.170                    0    nvidia
[conda] libcusparse-dev           12.3.1.170                    0    nvidia
[conda] libcusparse-static        12.5.7.53                     0    nvidia
[conda] libjpeg-turbo             2.0.0                h9bf148f_0    pytorch
[conda] libnpp                    12.2.5.30                     0    nvidia
[conda] libnpp-dev                12.2.5.30                     0    nvidia
[conda] libnpp-static             12.3.3.65                     0    nvidia
[conda] libnvfatbin               12.8.55                       0    nvidia
[conda] libnvfatbin-dev           12.8.55                       0    nvidia
[conda] libnvfatbin-static        12.8.55                       0    nvidia
[conda] libnvjitlink              12.4.127                      0    nvidia
[conda] libnvjitlink-dev          12.4.127                      0    nvidia
[conda] libnvjitlink-static       12.8.61                       1    nvidia
[conda] libnvjpeg                 12.3.1.117                    0    nvidia
[conda] libnvjpeg-dev             12.3.1.117                    0    nvidia
[conda] libnvjpeg-static          12.3.5.57                     0    nvidia
[conda] mkl                       2023.1.0         h213fc3f_46344  
[conda] mkl-service               2.4.0           py312h5eee18b_2  
[conda] mkl_fft                   1.3.11          py312h5eee18b_0  
[conda] mkl_random                1.2.8           py312h526ad5a_0  
[conda] nsight-compute            2025.1.0.14                   0    nvidia
[conda] numpy                     1.26.3                   pypi_0    pypi
[conda] nvidia-ml-py              12.570.86                pypi_0    pypi
[conda] pytorch                   2.5.1           py3.12_cuda12.4_cudnn9.1.0_0    pytorch
[conda] pytorch-cuda              12.4                 hc786d27_7    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] pyzmq                     26.2.1                   pypi_0    pypi
[conda] sentence-transformers     3.4.1                    pypi_0    pypi
[conda] torchaudio                2.5.1               py312_cu124    pytorch
[conda] torchtriton               3.1.0                     py312    pytorch
[conda] torchvision               0.20.1              py312_cu124    pytorch
[conda] transformers              4.49.0.dev0              pypi_0    pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.7.2
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0	GPU1	GPU2	GPU3	GPU4	GPU5	CPU Affinity	NUMA Affinity	GPU NUMA ID
GPU0	 X 	NV2	SYS	SYS	SYS	SYS	0-13,28-41	0		N/A
GPU1	NV2	 X 	SYS	SYS	SYS	SYS	0-13,28-41	0		N/A
GPU2	SYS	SYS	 X 	NV2	PHB	PHB	14-27,42-55	1		N/A
GPU3	SYS	SYS	NV2	 X 	PHB	PHB	14-27,42-55	1		N/A
GPU4	SYS	SYS	PHB	PHB	 X 	NV2	14-27,42-55	1		N/A
GPU5	SYS	SYS	PHB	PHB	NV2	 X 	14-27,42-55	1		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

LD_LIBRARY_PATH=/home/aabbccddwasd/.local/lib/python3.12/site-packages/cv2/../../lib64::/usr/local/cuda-12.4/lib64
NCCL_CUMEM_ENABLE=0
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY

I just downgraded vllm to 0.7.2, so pytorch is 2.5.1, but I'm sure that the environment when I was using 0.8.2 is fine.

🐛 Describe the bug

the quality of model decreases incredibly, for example the model used to output well in 0.7.2 can't output correct LaTeX formulas at 0.8.2, besides it often stuck in a loop.
I noticed this problem when using Qwen2.5-VL-32B AWQ, firstly I thought it was something about quantization but soon I found fp16 version also performs badly, and I tested Qwen2.5-VL-72B and I found it can't output correctly as used to be. so this is definitely a problem with vllm.

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]

Can you try using V0 Engine (VLLM_USE_V1=0) and see if the problem still occurs? There was an issue with Qwen2.5-VL but it should have been fixed by #15200 which is in v0.8.2 release.

[manitadayon]

For me the 0.8.2 version immediately crashes for any model not only the Qwen. The prior versions of VLLM work just fine.

[aabbccddwasd]

Can you try using V0 Engine (VLLM_USE_V1=0) and see if the problem still occurs? There was an issue with Qwen2.5-VL but it should have been fixed by #15200 which is in v0.8.2 release.

I don't even used v1, 2080ti doesn't support it, this is a problem with v0...

[DarkLight1337]

Can you show the input you used so we can try to reproduce it? cc @Isotr0py

[hmellor]

@aabbccddwasd in v0.8.0 the default sampling parameters was changed to come from the model instead of vLLM's neutral (but arbitrary) defaults. If you can confirm if this is the issue by trying the old defaults with --generation-config vllm.

If this is the problem, you can see which sampling parameters were provided by the model creator by going to https://huggingface.co/ORGANISATION/MODEL/blob/main/generation_config.json. Then, I would recommend petitioning the model creator to provide better defaults with their model.

[DarkLight1337]

@hmellor can we add this to the troubleshooting guide? Seems quite a few people got confused by this

[Qubitium]

@hmellor I would go as far to log print the default sampling params on startup and note the source since this is hugely important for default generation.

[xumanba]

Can you try using V0 Engine (VLLM_USE_V1=0) and see if the problem still occurs? There was an issue with Qwen2.5-VL but it should have been fixed by #15200 which is in v0.8.2 release.

I don't even used v1, 2080ti doesn't support it, this is a problem with v0...

2080ti can not work with vllm now?

[aabbccddwasd]

Can you try using V0 Engine (VLLM_USE_V1=0) and see if the problem still occurs? There was an issue with Qwen2.5-VL but it should have been fixed by #15200 which is in v0.8.2 release.

I don't even used v1, 2080ti doesn't support it, this is a problem with v0...

2080ti can not work with vllm now?

it can work, but can't use v1 engine, it's because v1 requires flashattention which 2080ti doesn't support

[aabbccddwasd]

@aabbccddwasd in v0.8.0 the default sampling parameters was changed to come from the model instead of vLLM's neutral (but arbitrary) defaults. If you can confirm if this is the issue by trying the old defaults with --generation-config vllm.

If this is the problem, you can see which sampling parameters were provided by the model creator by going to https://huggingface.co/ORGANISATION/MODEL/blob/main/generation_config.json. Then, I would recommend petitioning the model creator to provide better defaults with their model.

--generation-config vllm doesn't help, still have severe problems as I said before.

[aabbccddwasd]

--generation-config vllm

also I noticed if I enable --generation-config vllm on v0.7.2 it will cause a very long delay between client send the request and server receive the request, usually 30 seconds