vllm - 💡(How to fix) Fix [Bug]: Qwen3-1.7B silent correctness regression in vLLM 0.21.0: TP=2/4 and Triton attention produce wrong answer

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Qwen/Qwen3-1.7B on vLLM 0.21.0 gives a wrong answer to the simple arithmetic prompt 2 ** 10 when using tensor parallelism. The model outputs 512 or 81 instead of 1024. When TP=1, the result is correct.

This is a regression from vLLM 0.19.1, which produces correct results on all configs.

Root Cause

Qwen/Qwen3-1.7B on vLLM 0.21.0 gives a wrong answer to the simple arithmetic prompt 2 ** 10 when using tensor parallelism. The model outputs 512 or 81 instead of 1024. When TP=1, the result is correct.

This is a regression from vLLM 0.19.1, which produces correct results on all configs.

Fix Action

Fix / Workaround

============================== CPU Info

Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 52 bits physical, 57 bits virtual Byte Order: Little Endian CPU(s): 128 On-line CPU(s) list: 0-127 Vendor ID: AuthenticAMD Model name: AMD EPYC 9554 64-Core Processor CPU family: 25 Model: 17 Thread(s) per core: 2 Core(s) per socket: 64 Socket(s): 1 Stepping: 1 Frequency boost: enabled CPU max MHz: 3762.9880 CPU min MHz: 1500.0000 BogoMIPS: 6190.73 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid overflow_recov succor smca fsrm flush_l1d Virtualization: AMD-V L1d cache: 2 MiB (64 instances) L1i cache: 2 MiB (64 instances) L2 cache: 64 MiB (64 instances) L3 cache: 256 MiB (8 instances) NUMA node(s): 1 NUMA node0 CPU(s): 0-127 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: Mitigation; safe RET 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; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected

Code Example

==============================
        System Info
==============================
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

==============================
       PyTorch Info
==============================
PyTorch version              : 2.11.0+cu130
Is debug build               : False
CUDA used to build PyTorch   : 13.0
ROCM used to build PyTorch   : N/A
XPU 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-5.15.0-122-generic-x86_64-with-glibc2.35
    
==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : Could not collect
CUDA_MODULE_LOADING set to   : 
GPU models and configuration : 
GPU 0: NVIDIA L40S
GPU 1: NVIDIA L40S
GPU 2: NVIDIA L40S
GPU 3: NVIDIA L40S

Nvidia driver version        : 595.71.05
cuDNN version                : Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.7
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:                        52 bits physical, 57 bits virtual
Byte Order:                           Little Endian
CPU(s):                               128
On-line CPU(s) list:                  0-127
Vendor ID:                            AuthenticAMD
Model name:                           AMD EPYC 9554 64-Core Processor
CPU family:                           25
Model:                                17
Thread(s) per core:                   2
Core(s) per socket:                   64
Socket(s):                            1
Stepping:                             1
Frequency boost:                      enabled
CPU max MHz:                          3762.9880
CPU min MHz:                          1500.0000
BogoMIPS:                             6190.73
Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid overflow_recov succor smca fsrm flush_l1d
Virtualization:                       AMD-V
L1d cache:                            2 MiB (64 instances)
L1i cache:                            2 MiB (64 instances)
L2 cache:                             64 MiB (64 instances)
L3 cache:                             256 MiB (8 instances)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-127
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:   Mitigation; safe RET
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; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.8.post1
[pip3] numpy==2.3.5
[pip3] nvidia-cublas==13.1.0.3
[pip3] nvidia-cuda-cupti==13.0.85
[pip3] nvidia-cuda-nvrtc==13.0.88
[pip3] nvidia-cuda-runtime==13.0.96
[pip3] nvidia-cudnn-cu13==9.19.0.56
[pip3] nvidia-cudnn-frontend==1.18.0
[pip3] nvidia-cufft==12.0.0.61
[pip3] nvidia-cufile==1.15.1.6
[pip3] nvidia-curand==10.4.0.35
[pip3] nvidia-cusolver==12.0.4.66
[pip3] nvidia-cusparse==12.6.3.3
[pip3] nvidia-cusparselt-cu13==0.8.0
[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-cu13==2.28.9
[pip3] nvidia-nvjitlink==13.0.88
[pip3] nvidia-nvshmem-cu13==3.4.5
[pip3] nvidia-nvtx==13.0.85
[pip3] pyzmq==27.1.0
[pip3] tokenspeed-triton==3.7.10.post20260505
[pip3] torch==2.11.0
[pip3] torch_c_dlpack_ext==0.1.5
[pip3] torchaudio==2.11.0
[pip3] torchvision==0.26.0
[pip3] transformers==5.9.0
[pip3] triton==3.6.0
[conda] flashinfer-python                           0.6.8.post1          pypi_0           pypi
[conda] numpy                                       2.3.5                pypi_0           pypi
[conda] nvidia-cublas                               13.1.0.3             pypi_0           pypi
[conda] nvidia-cuda-cupti                           13.0.85              pypi_0           pypi
[conda] nvidia-cuda-nvrtc                           13.0.88              pypi_0           pypi
[conda] nvidia-cuda-runtime                         13.0.96              pypi_0           pypi
[conda] nvidia-cudnn-cu13                           9.19.0.56            pypi_0           pypi
[conda] nvidia-cudnn-frontend                       1.18.0               pypi_0           pypi
[conda] nvidia-cufft                                12.0.0.61            pypi_0           pypi
[conda] nvidia-cufile                               1.15.1.6             pypi_0           pypi
[conda] nvidia-curand                               10.4.0.35            pypi_0           pypi
[conda] nvidia-cusolver                             12.0.4.66            pypi_0           pypi
[conda] nvidia-cusparse                             12.6.3.3             pypi_0           pypi
[conda] nvidia-cusparselt-cu13                      0.8.0                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-cu13                            2.28.9               pypi_0           pypi
[conda] nvidia-nvjitlink                            13.0.88              pypi_0           pypi
[conda] nvidia-nvshmem-cu13                         3.4.5                pypi_0           pypi
[conda] nvidia-nvtx                                 13.0.85              pypi_0           pypi
[conda] pyzmq                                       27.1.0               pypi_0           pypi
[conda] tokenspeed-triton                           3.7.10.post20260505  pypi_0           pypi
[conda] torch                                       2.11.0               pypi_0           pypi
[conda] torch-c-dlpack-ext                          0.1.5                pypi_0           pypi
[conda] torchaudio                                  2.11.0               pypi_0           pypi
[conda] torchvision                                 0.26.0               pypi_0           pypi
[conda] transformers                                5.9.0                pypi_0           pypi
[conda] triton                                      3.6.0                pypi_0           pypi

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.21.0
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled; XPU: Disabled
GPU Topology:
        GPU0    GPU1    GPU2    GPU3    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      PHB     NODE    NODE    0-127   0               N/A
GPU1    PHB      X      NODE    NODE    0-127   0               N/A
GPU2    NODE    NODE     X      PHB     0-127   0               N/A
GPU3    NODE    NODE    PHB      X      0-127   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
==============================
CUDA_HOME=/usr/local/cuda
CUDA_HOME=/usr/local/cuda
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor

---

import os; os.environ["VLLM_USE_FLASHINFER_SAMPLER"] = "0"
from vllm import LLM, SamplingParams

MODEL = "Qwen/Qwen3-1.7B"
PROMPT = "In Python, what is the output of: 2 ** 10? Answer with just the number."
sp = SamplingParams(temperature=0.0, top_p=1.0, seed=42, max_tokens=32)

for seed in [42, 0, 100]:
    sp.seed = seed
    a = LLM(model=MODEL, enforce_eager=True).generate([PROMPT], sp)[0].outputs[0].text
    b = LLM(model=MODEL, enforce_eager=True, tensor_parallel_size=2).generate([PROMPT], sp)[0].outputs[0].text
    c = LLM(model=MODEL, enforce_eager=True, attention_backend="TRITON_ATTN").generate([PROMPT], sp)[0].outputs[0].text
    print(f"seed={seed}: TP=1='{a.strip()}' || TP=2='{b.strip()}' || Triton='{c.strip()}'")

---

seed=42: TP=1='1024' || TP=2='1024' || Triton='1024'
seed=0:  TP=1='1024' || TP=2='1024' || Triton='1024'
seed=100:TP=1='1024' || TP=2='1024' || Triton='1024'

---

seed=42: TP=1='1024' || TP=2='512' || Triton='81'
seed=0:  TP=1='1024' || TP=2='512' || Triton='81'
seed=100:TP=1='1024' || TP=2='512' || Triton='81'
RAW_BUFFERClick to expand / collapse

Your current environment

<details> <summary>The output of <code>python collect_env.py</code></summary>

==============================
        System Info
==============================
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

==============================
       PyTorch Info
==============================
PyTorch version              : 2.11.0+cu130
Is debug build               : False
CUDA used to build PyTorch   : 13.0
ROCM used to build PyTorch   : N/A
XPU 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-5.15.0-122-generic-x86_64-with-glibc2.35
    
==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : Could not collect
CUDA_MODULE_LOADING set to   : 
GPU models and configuration : 
GPU 0: NVIDIA L40S
GPU 1: NVIDIA L40S
GPU 2: NVIDIA L40S
GPU 3: NVIDIA L40S

Nvidia driver version        : 595.71.05
cuDNN version                : Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.7
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:                        52 bits physical, 57 bits virtual
Byte Order:                           Little Endian
CPU(s):                               128
On-line CPU(s) list:                  0-127
Vendor ID:                            AuthenticAMD
Model name:                           AMD EPYC 9554 64-Core Processor
CPU family:                           25
Model:                                17
Thread(s) per core:                   2
Core(s) per socket:                   64
Socket(s):                            1
Stepping:                             1
Frequency boost:                      enabled
CPU max MHz:                          3762.9880
CPU min MHz:                          1500.0000
BogoMIPS:                             6190.73
Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid overflow_recov succor smca fsrm flush_l1d
Virtualization:                       AMD-V
L1d cache:                            2 MiB (64 instances)
L1i cache:                            2 MiB (64 instances)
L2 cache:                             64 MiB (64 instances)
L3 cache:                             256 MiB (8 instances)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-127
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:   Mitigation; safe RET
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; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.8.post1
[pip3] numpy==2.3.5
[pip3] nvidia-cublas==13.1.0.3
[pip3] nvidia-cuda-cupti==13.0.85
[pip3] nvidia-cuda-nvrtc==13.0.88
[pip3] nvidia-cuda-runtime==13.0.96
[pip3] nvidia-cudnn-cu13==9.19.0.56
[pip3] nvidia-cudnn-frontend==1.18.0
[pip3] nvidia-cufft==12.0.0.61
[pip3] nvidia-cufile==1.15.1.6
[pip3] nvidia-curand==10.4.0.35
[pip3] nvidia-cusolver==12.0.4.66
[pip3] nvidia-cusparse==12.6.3.3
[pip3] nvidia-cusparselt-cu13==0.8.0
[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-cu13==2.28.9
[pip3] nvidia-nvjitlink==13.0.88
[pip3] nvidia-nvshmem-cu13==3.4.5
[pip3] nvidia-nvtx==13.0.85
[pip3] pyzmq==27.1.0
[pip3] tokenspeed-triton==3.7.10.post20260505
[pip3] torch==2.11.0
[pip3] torch_c_dlpack_ext==0.1.5
[pip3] torchaudio==2.11.0
[pip3] torchvision==0.26.0
[pip3] transformers==5.9.0
[pip3] triton==3.6.0
[conda] flashinfer-python                           0.6.8.post1          pypi_0           pypi
[conda] numpy                                       2.3.5                pypi_0           pypi
[conda] nvidia-cublas                               13.1.0.3             pypi_0           pypi
[conda] nvidia-cuda-cupti                           13.0.85              pypi_0           pypi
[conda] nvidia-cuda-nvrtc                           13.0.88              pypi_0           pypi
[conda] nvidia-cuda-runtime                         13.0.96              pypi_0           pypi
[conda] nvidia-cudnn-cu13                           9.19.0.56            pypi_0           pypi
[conda] nvidia-cudnn-frontend                       1.18.0               pypi_0           pypi
[conda] nvidia-cufft                                12.0.0.61            pypi_0           pypi
[conda] nvidia-cufile                               1.15.1.6             pypi_0           pypi
[conda] nvidia-curand                               10.4.0.35            pypi_0           pypi
[conda] nvidia-cusolver                             12.0.4.66            pypi_0           pypi
[conda] nvidia-cusparse                             12.6.3.3             pypi_0           pypi
[conda] nvidia-cusparselt-cu13                      0.8.0                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-cu13                            2.28.9               pypi_0           pypi
[conda] nvidia-nvjitlink                            13.0.88              pypi_0           pypi
[conda] nvidia-nvshmem-cu13                         3.4.5                pypi_0           pypi
[conda] nvidia-nvtx                                 13.0.85              pypi_0           pypi
[conda] pyzmq                                       27.1.0               pypi_0           pypi
[conda] tokenspeed-triton                           3.7.10.post20260505  pypi_0           pypi
[conda] torch                                       2.11.0               pypi_0           pypi
[conda] torch-c-dlpack-ext                          0.1.5                pypi_0           pypi
[conda] torchaudio                                  2.11.0               pypi_0           pypi
[conda] torchvision                                 0.26.0               pypi_0           pypi
[conda] transformers                                5.9.0                pypi_0           pypi
[conda] triton                                      3.6.0                pypi_0           pypi

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.21.0
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled; XPU: Disabled
GPU Topology:
        GPU0    GPU1    GPU2    GPU3    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      PHB     NODE    NODE    0-127   0               N/A
GPU1    PHB      X      NODE    NODE    0-127   0               N/A
GPU2    NODE    NODE     X      PHB     0-127   0               N/A
GPU3    NODE    NODE    PHB      X      0-127   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
==============================
CUDA_HOME=/usr/local/cuda
CUDA_HOME=/usr/local/cuda
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor
</details>

🐛 Describe the bug

Description

Qwen/Qwen3-1.7B on vLLM 0.21.0 gives a wrong answer to the simple arithmetic prompt 2 ** 10 when using tensor parallelism. The model outputs 512 or 81 instead of 1024. When TP=1, the result is correct.

This is a regression from vLLM 0.19.1, which produces correct results on all configs.

Reproduction

import os; os.environ["VLLM_USE_FLASHINFER_SAMPLER"] = "0"
from vllm import LLM, SamplingParams

MODEL = "Qwen/Qwen3-1.7B"
PROMPT = "In Python, what is the output of: 2 ** 10? Answer with just the number."
sp = SamplingParams(temperature=0.0, top_p=1.0, seed=42, max_tokens=32)

for seed in [42, 0, 100]:
    sp.seed = seed
    a = LLM(model=MODEL, enforce_eager=True).generate([PROMPT], sp)[0].outputs[0].text
    b = LLM(model=MODEL, enforce_eager=True, tensor_parallel_size=2).generate([PROMPT], sp)[0].outputs[0].text
    c = LLM(model=MODEL, enforce_eager=True, attention_backend="TRITON_ATTN").generate([PROMPT], sp)[0].outputs[0].text
    print(f"seed={seed}: TP=1='{a.strip()}' || TP=2='{b.strip()}' || Triton='{c.strip()}'")

Expected output (all 3 seeds)

seed=42: TP=1='1024' || TP=2='1024' || Triton='1024'
seed=0:  TP=1='1024' || TP=2='1024' || Triton='1024'
seed=100:TP=1='1024' || TP=2='1024' || Triton='1024'

Actual output (vLLM 0.21.0)

seed=42: TP=1='1024' || TP=2='512' || Triton='81'
seed=0:  TP=1='1024' || TP=2='512' || Triton='81'
seed=100:TP=1='1024' || TP=2='512' || Triton='81'

Affected vs. Clean configs

Affected (wrong)OutputClean (correct)Output
TP=2512TP=1 baseline1024
TP=4512FlashAttn1024
Triton attention81cudagraph1024
TP=2 + prefix_cache512prefix_cache1024
TP=2 + chunked_prefill512chunked_prefill1024
TP=4 + prefix_cache512block_size=321024

Cross-version confirmation

vLLMTP=1TP=2Triton
0.19.11024 OK1024 OKn/a
0.21.01024 OK512 WRONG81 WRONG

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