[Bug]: "address already in use" while deploying pipeline parallel

[Tatiats7]

Your current environment

The output of `python collect_env.py`

PyTorch version: 2.4.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 24.04.1 LTS (x86_64)
GCC version: (Ubuntu 13.2.0-23ubuntu4) 13.2.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.39

Python version: 3.12.3 (main, Sep 11 2024, 14:17:37) [GCC 13.2.0] (64-bit runtime)
Is CUDA available: True
CUDA runtime version: 12.6.77
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA L40S
GPU 1: NVIDIA L40S
GPU 2: NVIDIA L40S
GPU 3: NVIDIA L40S
GPU 4: NVIDIA L40S
GPU 5: NVIDIA L40S
GPU 6: NVIDIA L40S
GPU 7: NVIDIA L40S

Nvidia driver version: 560.35.03
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:                        48 bits physical, 48 bits virtual
Byte Order:                           Little Endian
CPU(s):                               192
On-line CPU(s) list:                  0-191
Vendor ID:                            AuthenticAMD
Model name:                           AMD EPYC 7R13 Processor
CPU family:                           25
Model:                                1
Thread(s) per core:                   2
Core(s) per socket:                   48
Socket(s):                            2
Stepping:                             1
BogoMIPS:                             5299.99
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 tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext perfctr_core ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid
Hypervisor vendor:                    KVM
Virtualization type:                  full
L1d cache:                            3 MiB (96 instances)
L1i cache:                            3 MiB (96 instances)
L2 cache:                             48 MiB (96 instances)
L3 cache:                             384 MiB (12 instances)
NUMA node(s):                         2
NUMA node0 CPU(s):                    0-47,96-143
NUMA node1 CPU(s):                    48-95,144-191
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:   Vulnerable: Safe RET, no microcode
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; Retpolines; IBPB conditional; IBRS_FW; 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] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.1.3.1
[pip3] nvidia-cuda-cupti-cu12==12.1.105
[pip3] nvidia-cuda-nvrtc-cu12==12.1.105
[pip3] nvidia-cuda-runtime-cu12==12.1.105
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.0.2.54
[pip3] nvidia-curand-cu12==10.3.2.106
[pip3] nvidia-cusolver-cu12==11.4.5.107
[pip3] nvidia-cusparse-cu12==12.1.0.106
[pip3] nvidia-ml-py==12.560.30
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] nvidia-nvjitlink-cu12==12.6.77
[pip3] nvidia-nvtx-cu12==12.1.105
[pip3] pyzmq==26.2.0
[pip3] torch==2.4.0
[pip3] torchvision==0.19.0
[pip3] transformers==4.45.2
[pip3] triton==3.0.0
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.6.1.dev238+ge2c6e0a82
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    GPU1    GPU2    GPU3    GPU4    GPU5    GPU6    GPU7    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NODE    NODE    NODE    SYS     SYS     SYS     SYS     0-47,96-143     0               N/A
GPU1    NODE     X      NODE    NODE    SYS     SYS     SYS     SYS     0-47,96-143     0               N/A
GPU2    NODE    NODE     X      NODE    SYS     SYS     SYS     SYS     0-47,96-143     0               N/A
GPU3    NODE    NODE    NODE     X      SYS     SYS     SYS     SYS     0-47,96-143     0               N/A
GPU4    SYS     SYS     SYS     SYS      X      NODE    NODE    NODE    48-95,144-191   1               N/A
GPU5    SYS     SYS     SYS     SYS     NODE     X      NODE    NODE    48-95,144-191   1               N/A
GPU6    SYS     SYS     SYS     SYS     NODE    NODE     X      NODE    48-95,144-191   1               N/A
GPU7    SYS     SYS     SYS     SYS     NODE    NODE    NODE     X      48-95,144-191   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

Model Input Dumps

No response

🐛 Describe the bug

the command:

vllm serve MODELPATH --dtype auto --api-key TOKEN --tensor-parallel-size 4 --pipeline-parallel-size 2 --gpu-memory-utilization 0.90 --enable_prefix_caching --max_model_len 8192

returns the error:
INFO: Waiting for application startup.
INFO: Application startup complete.
ERROR: [Errno 98] error while attempting to bind on address ('0.0.0.0', 8000): address already in use
INFO: Waiting for application shutdown.
INFO: Application shutdown complete.
(VllmWorkerProcess pid=29772) INFO 10-21 15:37:38 multiproc_worker_utils.py:244] Worker exiting
(VllmWorkerProcess pid=29774) INFO 10-21 15:37:38 multiproc_worker_utils.py:244] Worker exiting
(VllmWorkerProcess pid=29771) INFO 10-21 15:37:38 multiproc_worker_utils.py:244] Worker exiting
(VllmWorkerProcess pid=29773) INFO 10-21 15:37:38 multiproc_worker_utils.py:244] Worker exiting
/usr/lib/python3.12/multiprocessing/resource_tracker.py:254: UserWarning: resource_tracker: There appear to be 1 leaked shared_memory objects to clean up at shutdown
warnings.warn('resource_tracker: There appear to be %d '
ERROR 10-21 15:37:40 multiproc_worker_utils.py:120] Worker VllmWorkerProcess pid 29772 died, exit code: 1
INFO 10-21 15:37:40 multiproc_worker_utils.py:124] Killing local vLLM worker processes

When I'm not using pipeline parallel, there is no issue

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]

This should be fixed in the latest version already by #8491. Please update your vLLM version.

[Tatiats7]

This should be fixed in the latest version already by #8491. Please update your vLLM version.

Thank you, it was helpful! It works for vLLM version 0.6.3.post1.