[Bug]: RuntimeError "cancelled" when using pipeline parallelism with Qwen3-14B

[slwang-ustc]

Your current environment

The output of python collect_env.py

Collecting environment information...
==============================
        System Info
==============================
OS                           : Ubuntu 22.04.5 LTS (x86_64)
GCC version                  : (Ubuntu 12.3.0-1ubuntu1~22.04.2) 12.3.0
Clang version                : Could not collect
CMake version                : version 3.22.1
Libc version                 : glibc-2.35

==============================
       PyTorch Info
==============================
PyTorch version              : 2.8.0+cu128
Is debug build               : False
CUDA used to build PyTorch   : 12.8
ROCM used to build PyTorch   : N/A

==============================
      Python Environment
==============================
Python version               : 3.12.11 (main, Sep  2 2025, 14:20:58) [Clang 20.1.4 ] (64-bit runtime)
Python platform              : Linux-6.8.0-48-generic-x86_64-with-glibc2.35

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 12.8.93
CUDA_MODULE_LOADING set to   : LAZY
GPU models and configuration : 
GPU 0: NVIDIA A100 80GB PCIe
GPU 1: NVIDIA A100 80GB PCIe
GPU 2: NVIDIA A100 80GB PCIe
GPU 3: NVIDIA A100 80GB PCIe

Nvidia driver version        : 580.82.07
cuDNN version                : Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.7.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.7.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.7.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.7.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.7.1
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.7.1
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.7.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.7.1
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):                               144
On-line CPU(s) list:                  0-143
Vendor ID:                            GenuineIntel
Model name:                           Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz
CPU family:                           6
Model:                                106
Thread(s) per core:                   2
Core(s) per socket:                   36
Socket(s):                            2
Stepping:                             6
CPU max MHz:                          3500.0000
CPU min MHz:                          800.0000
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 art 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 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:                            3.4 MiB (72 instances)
L1i cache:                            2.3 MiB (72 instances)
L2 cache:                             90 MiB (72 instances)
L3 cache:                             108 MiB (2 instances)
NUMA node(s):                         2
NUMA node0 CPU(s):                    0-35,72-107
NUMA node1 CPU(s):                    36-71,108-143
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

==============================
Versions of relevant libraries
==============================
[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-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-nccl-cu12==2.27.3
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] pyzmq==27.1.0
[pip3] torch==2.8.0+cu128
[pip3] torchaudio==2.8.0+cu128
[pip3] torchvision==0.23.0+cu128
[pip3] transformers==4.57.1
[pip3] triton==3.4.0
[conda] No relevant packages

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.11.0
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
        GPU0    GPU1    GPU2    GPU3    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NV12    SYS     SYS     0-35,72-107     0               N/A
GPU1    NV12     X      SYS     SYS     0-35,72-107     0               N/A
GPU2    SYS     SYS      X      NV12    36-71,108-143   1               N/A
GPU3    SYS     SYS     NV12     X      36-71,108-143   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

==============================
     Environment Variables
==============================
LD_LIBRARY_PATH=/data/slwang/cuda-12.8/lib64:/data/slwang/cuda-12.8/lib64:
CUDA_HOME=/data/slwang/cuda-12.8
CUDA_HOME=/data/slwang/cuda-12.8
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY

🐛 Describe the bug

Reproduction Steps

1. Start vLLM serve with pipeline parallelism:

CUDA_VISIBLE_DEVICES=0,1,2,3 vllm serve /data/hfhub/Qwen3/Qwen3-14B/ --port 8011 --gpu-memory-utilization 0.9 --tensor-parallel-size 1 --pipeline-parallel-size 4 --max-num-batched-tokens 2048 --max-num-seqs 2048

2. Run benchmark test:

vllm bench serve --base-url http://localhost:8011 --model /data/hfhub/Qwen3/Qwen3-14B/ --dataset-name random --random-input-len 128 --random-output-len 100 --request-rate 40 --num-prompts 20000 --save-result --save-detailed --append-result --result-dir /data/slwang/vllm_bench_results/Qwen3-14B-A100-t1-p4/ --result-filename 40qps-batch2048-burstiness0.1.json --seed 100 --percentile-metrics ttft,tpot,itl,e2el --metric-percentiles 90,95,99 --ignore-eos --burstiness 0.1

Error Log

(EngineCore_DP0 pid=69817) Process EngineCore_DP0:
(EngineCore_DP0 pid=69817) Traceback (most recent call last):
(EngineCore_DP0 pid=69817)   File "/home/slwang/.local/share/uv/python/cpython-3.12.11-linux-x86_64-gnu/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
(EngineCore_DP0 pid=69817)     self.run()
(EngineCore_DP0 pid=69817)   File "/home/slwang/.local/share/uv/python/cpython-3.12.11-linux-x86_64-gnu/lib/python3.12/multiprocessing/process.py", line 108, in run
(EngineCore_DP0 pid=69817)     self._target(*self._args, **self._kwargs)
(EngineCore_DP0 pid=69817)   File "/data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/vllm/v1/engine/core.py", line 712, in run_engine_core
(EngineCore_DP0 pid=69817)     raise e
(EngineCore_DP0 pid=69817)   File "/data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/vllm/v1/engine/core.py", line 701, in run_engine_core
(EngineCore_DP0 pid=69817)     engine_core.run_busy_loop()
(EngineCore_DP0 pid=69817)   File "/data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/vllm/v1/engine/core.py", line 728, in run_busy_loop
(EngineCore_DP0 pid=69817)     self._process_engine_step()
(EngineCore_DP0 pid=69817)   File "/data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/vllm/v1/engine/core.py", line 754, in _process_engine_step
(EngineCore_DP0 pid=69817)     outputs, model_executed = self.step_fn()
(EngineCore_DP0 pid=69817)                               ^^^^^^^^^^^^^^
(EngineCore_DP0 pid=69817)   File "/data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/vllm/v1/engine/core.py", line 346, in step_with_batch_queue
(EngineCore_DP0 pid=69817)     model_output = self.execute_model_with_error_logging(
(EngineCore_DP0 pid=69817)                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore_DP0 pid=69817)   File "/data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/vllm/v1/engine/core.py", line 270, in execute_model_with_error_logging
(EngineCore_DP0 pid=69817)     raise err
(EngineCore_DP0 pid=69817)   File "/data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/vllm/v1/engine/core.py", line 261, in execute_model_with_error_logging
(EngineCore_DP0 pid=69817)     return model_fn(scheduler_output)
(EngineCore_DP0 pid=69817)            ^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore_DP0 pid=69817)   File "/data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/vllm/v1/engine/core.py", line 347, in <lambda>
(EngineCore_DP0 pid=69817)     lambda _: future.result(), scheduler_output)
(EngineCore_DP0 pid=69817)               ^^^^^^^^^^^^^^^
(EngineCore_DP0 pid=69817)   File "/home/slwang/.local/share/uv/python/cpython-3.12.11-linux-x86_64-gnu/lib/python3.12/concurrent/futures/_base.py", line 456, in result
(EngineCore_DP0 pid=69817)     return self.__get_result()
(EngineCore_DP0 pid=69817)            ^^^^^^^^^^^^^^^^^^^
(EngineCore_DP0 pid=69817)   File "/home/slwang/.local/share/uv/python/cpython-3.12.11-linux-x86_64-gnu/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result
(EngineCore_DP0 pid=69817)     raise self._exception
(EngineCore_DP0 pid=69817)   File "/home/slwang/.local/share/uv/python/cpython-3.12.11-linux-x86_64-gnu/lib/python3.12/concurrent/futures/thread.py", line 59, in run
(EngineCore_DP0 pid=69817)     result = self.fn(*self.args, **self.kwargs)
(EngineCore_DP0 pid=69817)              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore_DP0 pid=69817)   File "/data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/vllm/v1/executor/multiproc_executor.py", line 244, in get_response
(EngineCore_DP0 pid=69817)     status, result = w.worker_response_mq.dequeue(
(EngineCore_DP0 pid=69817)                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore_DP0 pid=69817)   File "/data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/vllm/distributed/device_communicators/shm_broadcast.py", line 511, in dequeue
(EngineCore_DP0 pid=69817)     with self.acquire_read(timeout, cancel, indefinite) as buf:
(EngineCore_DP0 pid=69817)          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore_DP0 pid=69817)   File "/home/slwang/.local/share/uv/python/cpython-3.12.11-linux-x86_64-gnu/lib/python3.12/contextlib.py", line 137, in __enter__
(EngineCore_DP0 pid=69817)     return next(self.gen)
(EngineCore_DP0 pid=69817)            ^^^^^^^^^^^^^^
(EngineCore_DP0 pid=69817)   File "/data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/vllm/distributed/device_communicators/shm_broadcast.py", line 455, in acquire_read
(EngineCore_DP0 pid=69817)     raise RuntimeError("cancelled")

Error Analysis

The error occurs in the shared memory broadcast communication layer during inter-process communication. Specifically, the read operation from the worker response message queue is being cancelled, which suggests a timeout or communication failure between the pipeline parallel stages.

The error stack trace shows:

1. The issue originates from shm_broadcast.py during the dequeue operation
2. The acquire_read method raises a RuntimeError("cancelled")
3. This happens in the context of pipeline parallelism with 4 stages (--pipeline-parallel-size 4)

Additional Context

• Using 4 A100 GPUs with pipeline parallelism (tensor parallelism = 1)
• GPU memory utilization set to 90%
• Model: Qwen3-14B
• The error occurs during benchmark testing with 40 QPS and burstiness of 0.1

Questions

1. Is this a known issue with pipeline parallelism in the current vLLM version?

Additional Information

• The server starts successfully but fails during benchmark load testing
• • Running this benchmark sometimes crashes and sometimes doesn't

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.

[princepride]

@DarkLight1337 I reproduced this error, please assign this task to me, I will try to solve it😊.

[njhill]

@slwang-ustc this exception should only be raised when vllm is shutting down, which could be triggered by some other failure. Could you share more of the log, specifically the part prior to this traceback. It's likely there would be other messages indicating what failed.

One possibility is that one of the processes died or was killed e.g. due to an OOM condition.

[slwang-ustc]

@njhill @princepride the log:

[rank0]:[E1127 10:59:12.036119079 ProcessGroupNCCL.cpp:2068] [PG ID 4 PG GUID 19 Rank 0] Process group watchdog thread terminated with exception: CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with TORCH_USE_CUDA_DSA to enable device-side assertions.

Exception raised from c10_cuda_check_implementation at /pytorch/c10/cuda/CUDAException.cpp:42 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits, std::allocator >) + 0x80 (0x753407259eb0 in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libc10.so)
frame #1: + 0x111c7 (0x7534072ec1c7 in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libc10_cuda.so)
frame #2: c10d::ProcessGroupNCCL::WorkNCCL::finishedGPUExecutionInternal() const + 0x50 (0x7533a9c30640 in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
frame #3: c10d::ProcessGroupNCCL::WorkNCCL::isCompleted() + 0x68 (0x7533a9c3fe28 in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
frame #4: c10d::ProcessGroupNCCL::Watchdog::runLoop() + 0x978 (0x7533a9c42f48 in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
frame #5: c10d::ProcessGroupNCCL::Watchdog::run() + 0xd2 (0x7533a9c44ec2 in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
frame #6: + 0xdc253 (0x75338cedc253 in /lib/x86_64-linux-gnu/libstdc++.so.6)
frame #7: + 0x94ac3 (0x753408294ac3 in /lib/x86_64-linux-gnu/libc.so.6)
frame #8: + 0x126850 (0x753408326850 in /lib/x86_64-linux-gnu/libc.so.6)

terminate called after throwing an instance of 'c10::DistBackendError'
what(): [PG ID 4 PG GUID 19 Rank 0] Process group watchdog thread terminated with exception: CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with TORCH_USE_CUDA_DSA to enable device-side assertions.

Exception raised from c10_cuda_check_implementation at /pytorch/c10/cuda/CUDAException.cpp:42 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits, std::allocator >) + 0x80 (0x753407259eb0 in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libc10.so)
frame #1: + 0x111c7 (0x7534072ec1c7 in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libc10_cuda.so)
frame #2: c10d::ProcessGroupNCCL::WorkNCCL::finishedGPUExecutionInternal() const + 0x50 (0x7533a9c30640 in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
frame #3: c10d::ProcessGroupNCCL::WorkNCCL::isCompleted() + 0x68 (0x7533a9c3fe28 in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
frame #4: c10d::ProcessGroupNCCL::Watchdog::runLoop() + 0x978 (0x7533a9c42f48 in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
frame #5: c10d::ProcessGroupNCCL::Watchdog::run() + 0xd2 (0x7533a9c44ec2 in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
frame #6: + 0xdc253 (0x75338cedc253 in /lib/x86_64-linux-gnu/libstdc++.so.6)
frame #7: + 0x94ac3 (0x753408294ac3 in /lib/x86_64-linux-gnu/libc.so.6)
frame #8: + 0x126850 (0x753408326850 in /lib/x86_64-linux-gnu/libc.so.6)

Exception raised from run at /pytorch/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:2074 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits, std::allocator >) + 0x80 (0x753407259eb0 in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libc10.so)
frame #1: + 0xe1c1a1 (0x7533a9c1c1a1 in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
frame #2: + 0x9468e6 (0x7533a97468e6 in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
frame #3: + 0xdc253 (0x75338cedc253 in /lib/x86_64-linux-gnu/libstdc++.so.6)
frame #4: + 0x94ac3 (0x753408294ac3 in /lib/x86_64-linux-gnu/libc.so.6)
frame #5: + 0x126850 (0x753408326850 in /lib/x86_64-linux-gnu/libc.so.6)

(APIServer pid=1993052) INFO: 127.0.0.1:60706 - "POST /v1/completions HTTP/1.1" 200 OK
(APIServer pid=1993052) INFO: 127.0.0.1:60712 - "POST /v1/completions HTTP/1.1" 200 OK
(APIServer pid=1993052) INFO: 127.0.0.1:60716 - "POST /v1/completions HTTP/1.1" 200 OK
(APIServer pid=1993052) INFO: 127.0.0.1:60722 - "POST /v1/completions HTTP/1.1" 200 OK
(APIServer pid=1993052) INFO: 127.0.0.1:60732 - "POST /v1/completions HTTP/1.1" 200 OK
(APIServer pid=1993052) INFO: 127.0.0.1:60738 - "POST /v1/completions HTTP/1.1" 200 OK
(APIServer pid=1993052) INFO: 127.0.0.1:60748 - "POST /v1/completions HTTP/1.1" 200 OK
(APIServer pid=1993052) INFO: 127.0.0.1:60758 - "POST /v1/completions HTTP/1.1" 200 OK
(APIServer pid=1993052) INFO: 127.0.0.1:60774 - "POST /v1/completions HTTP/1.1" 200 OK
(EngineCore_DP0 pid=1993574) ERROR 11-27 10:59:13 [multiproc_executor.py:154] Worker proc VllmWorker-0 died unexpectedly, shutting down executor.
(Worker_PP1 pid=1993761) INFO 11-27 10:59:13 [multiproc_executor.py:558] Parent process exited, terminating worker
(Worker_PP2 pid=1993762) INFO 11-27 10:59:13 [multiproc_executor.py:558] Parent process exited, terminating worker
(Worker_PP3 pid=1993763) INFO 11-27 10:59:13 [multiproc_executor.py:558] Parent process exited, terminating worker
(APIServer pid=1993052) INFO: 127.0.0.1:60788 - "POST /v1/completions HTTP/1.1" 200 OK
(APIServer pid=1993052) INFO: 127.0.0.1:60794 - "POST /v1/completions HTTP/1.1" 200 OK
[rank2]:[W1127 10:59:13.378242278 TCPStore.cpp:125] [c10d] recvValue failed on SocketImpl(fd=90, addr=[localhost]:43844, remote=[localhost]:38683): Connection reset by peer
Exception raised from recvBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:679 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits, std::allocator >) + 0x80 (0x7f0fd2ed9eb0 in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libc10.so)
frame #1: + 0x5d694d1 (0x7f0fb63694d1 in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
frame #2: + 0x5d6a933 (0x7f0fb636a933 in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
frame #3: + 0x5d6b47a (0x7f0fb636b47a in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
frame #4: c10d::TCPStore::check(std::vector<std::__cxx11::basic_string<char, std::char_traits, std::allocator >, std::allocator<std::__cxx11::basic_string<char, std::char_traits, std::allocator > > > const&) + 0x31e (0x7f0fb636619e in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
frame #5: c10d::ProcessGroupNCCL::HeartbeatMonitor::runLoop() + 0x398 (0x7f0f7583db18 in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
frame #6: + 0xdc253 (0x7f0f58adc253 in /lib/x86_64-linux-gnu/libstdc++.so.6)
frame #7: + 0x94ac3 (0x7f0fd3e94ac3 in /lib/x86_64-linux-gnu/libc.so.6)
frame #8: + 0x126850 (0x7f0fd3f26850 in /lib/x86_64-linux-gnu/libc.so.6)

[rank2]:[W1127 10:59:13.393738727 ProcessGroupNCCL.cpp:1783] [PG ID 0 PG GUID 0 Rank 2] Failed to check the "should dump" flag on TCPStore, (maybe TCPStore server has shut down too early), with error: Connection reset by peer
(APIServer pid=1993052) INFO: 127.0.0.1:60802 - "POST /v1/completions HTTP/1.1" 200 OK
(APIServer pid=1993052) INFO: 127.0.0.1:60818 - "POST /v1/completions HTTP/1.1" 200 OK
(APIServer pid=1993052) INFO: 127.0.0.1:60832 - "POST /v1/completions HTTP/1.1" 200 OK
(APIServer pid=1993052) INFO: 127.0.0.1:60848 - "POST /v1/completions HTTP/1.1" 200 OK
(APIServer pid=1993052) INFO: 127.0.0.1:60860 - "POST /v1/completions HTTP/1.1" 200 OK
(APIServer pid=1993052) INFO: 127.0.0.1:60866 - "POST /v1/completions HTTP/1.1" 200 OK
(APIServer pid=1993052) INFO: 127.0.0.1:60876 - "POST /v1/completions HTTP/1.1" 200 OK
[rank1]:[W1127 10:59:13.675273339 TCPStore.cpp:125] [c10d] recvValue failed on SocketImpl(fd=90, addr=[localhost]:43854, remote=[localhost]:38683): Failed to recv, got 0 bytes. Connection was likely closed. Did the remote server shutdown or crash?
Exception raised from recvBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:682 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits, std::allocator >) + 0x80 (0x71b91d759eb0 in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libc10.so)
frame #1: + 0x5d694d1 (0x71b900d694d1 in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
frame #2: + 0x5d6a8cd (0x71b900d6a8cd in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
frame #3: + 0x5d6b47a (0x71b900d6b47a in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
frame #4: c10d::TCPStore::check(std::vector<std::__cxx11::basic_string<char, std::char_traits, std::allocator >, std::allocator<std::__cxx11::basic_string<char, std::char_traits, std::allocator > > > const&) + 0x31e (0x71b900d6619e in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
frame #5: c10d::ProcessGroupNCCL::HeartbeatMonitor::runLoop() + 0x398 (0x71b8c023db18 in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
frame #6: + 0xdc253 (0x71b8a34dc253 in /lib/x86_64-linux-gnu/libstdc++.so.6)
frame #7: + 0x94ac3 (0x71b91e894ac3 in /lib/x86_64-linux-gnu/libc.so.6)
frame #8: + 0x126850 (0x71b91e926850 in /lib/x86_64-linux-gnu/libc.so.6)

[rank1]:[W1127 10:59:13.689880884 ProcessGroupNCCL.cpp:1783] [PG ID 0 PG GUID 0 Rank 1] Failed to check the "should dump" flag on TCPStore, (maybe TCPStore server has shut down too early), with error: Failed to recv, got 0 bytes. Connection was likely closed. Did the remote server shutdown or crash?
[rank3]:[W1127 10:59:13.695750671 TCPStore.cpp:125] [c10d] recvValue failed on SocketImpl(fd=90, addr=[localhost]:43860, remote=[localhost]:38683): Failed to recv, got 0 bytes. Connection was likely closed. Did the remote server shutdown or crash?
Exception raised from recvBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:682 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits, std::allocator >) + 0x80 (0x744d4cb8ceb0 in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libc10.so)
frame #1: + 0x5d694d1 (0x744d301694d1 in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
frame #2: + 0x5d6a8cd (0x744d3016a8cd in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
frame #3: + 0x5d6b47a (0x744d3016b47a in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
frame #4: c10d::TCPStore::check(std::vector<std::__cxx11::basic_string<char, std::char_traits, std::allocator >, std::allocator<std::__cxx11::basic_string<char, std::char_traits, std::allocator > > > const&) + 0x31e (0x744d3016619e in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
frame #5: c10d::ProcessGroupNCCL::HeartbeatMonitor::runLoop() + 0x398 (0x744cef63db18 in /data/slwang/uv_envs/vllm0110_cuda128/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
frame #6: + 0xdc253 (0x744cd28dc253 in /lib/x86_64-linux-gnu/libstdc++.so.6)
frame #7: + 0x94ac3 (0x744d4dc94ac3 in /lib/x86_64-linux-gnu/libc.so.6)
frame #8: + 0x126850 (0x744d4dd26850 in /lib/x86_64-linux-gnu/libc.so.6)

[rank3]:[W1127 10:59:13.710312385 ProcessGroupNCCL.cpp:1783] [PG ID 0 PG GUID 0 Rank 3] Failed to check the "should dump" flag on TCPStore, (maybe TCPStore server has shut down too early), with error: Failed to recv, got 0 bytes. Connection was likely closed. Did the remote server shutdown or crash?

[princepride]

Interesting, when I add the parameter: CUDA_VISIBLE_DEVICES=LAUNCH_BLOCKING=1 , The error message seems to have disappeared.

CUDA_VISIBLE_DEVICES=LAUNCH_BLOCKING=1 CUDA_VISIBLE_DEVICES=0,1,2,3 vllm serve /models/Qwen3-14B --port 8011 --gpu-memory-utilization 0.9 --tensor-parallel-size 1 --max-num-batched-tokens 2048 --max-num-seqs 2048 --pipeline-parallel-size 4

Maximum request concurrency: None
100%|██████████████████████████████████████████████| 20000/20000 [11:46<00:00, 28.30it/s]
tip: install termplotlib and gnuplot to plot the metrics
============ Serving Benchmark Result ============
Successful requests:                     19995     
Failed requests:                         5         
Request rate configured (RPS):           40.00     
Benchmark duration (s):                  706.68    
Total input tokens:                      2559360   
Total generated tokens:                  1999500   
Request throughput (req/s):              28.29     
Output token throughput (tok/s):         2829.43   
Peak output token throughput (tok/s):    34650.00  
Peak concurrent requests:                3142.00   
Total Token throughput (tok/s):          6451.09   
---------------Time to First Token----------------
Mean TTFT (ms):                          19286.85  
Median TTFT (ms):                        132.18    
P90 TTFT (ms):                           87045.20  
P95 TTFT (ms):                           163864.11 
P99 TTFT (ms):                           209677.53 
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          90.53     
Median TPOT (ms):                        33.92     
P90 TPOT (ms):                           408.57    
P95 TPOT (ms):                           598.18    
P99 TPOT (ms):                           623.40    
---------------Inter-token Latency----------------
Mean ITL (ms):                           92.09     
Median ITL (ms):                         26.18     
P90 ITL (ms):                            70.75     
P95 ITL (ms):                            91.26     
P99 ITL (ms):                            121.46    
----------------End-to-end Latency----------------
Mean E2EL (ms):                          28249.62  
Median E2EL (ms):                        3743.99   
P90 E2EL (ms):                           135353.47 
P95 E2EL (ms):                           187753.01 
P99 E2EL (ms):                           224588.91 
==================================================