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vllm-project / vllm

A high-throughput and memory-efficient inference and serving engine for LLMs
https://docs.vllm.ai
Apache License 2.0
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[Bug]: zmq.error.ZMQError: No such device #7138

Closed xyfZzz closed 2 months ago

xyfZzz commented 2 months ago

Your current environment

Collecting environment information...
/app/apps/anaconda3/envs/vllm_053p1_main/lib/python3.9/site-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.16) or
 chardet (5.2.0)/charset_normalizer (2.0.4) doesn't match a supported version!
  warnings.warn("urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported "
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 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.31

Python version: 3.9.12 (main, Apr  5 2022, 06:56:58)  [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-5.4.0-148-generic-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 12.4.131
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA A100-SXM4-80GB
Nvidia driver version: 550.90.07
cuDNN version: Probably one of the following:
/usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn.so.8.2.4
/usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.2.4
/usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.2.4
/usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.2.4
/usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.2.4
/usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.2.4
/usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.2.4
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
Byte Order:                      Little Endian
Address sizes:                   46 bits physical, 48 bits virtual
CPU(s):                          16
On-line CPU(s) list:             0-15
Thread(s) per core:              2
Core(s) per socket:              8
Socket(s):                       1
NUMA node(s):                    1
Vendor ID:                       GenuineIntel
CPU family:                      6
Model:                           106
Model name:                      Intel(R) Xeon(R) Platinum 8369B CPU @ 2.90GHz
Stepping:                        6
CPU MHz:                         2900.000
BogoMIPS:                        5800.00
Hypervisor vendor:               KVM
Virtualization type:             full
L1d cache:                       384 KiB
L1i cache:                       256 KiB
L2 cache:                        10 MiB
L3 cache:                        48 MiB
NUMA node0 CPU(s):               0-15
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 Retbleed:          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; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected
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 sysca
ll nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid s
se4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stib
p ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_
ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves wbnoinvd arat avx512vbmi avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx51
2_vpopcntdq rdpid arch_capabilities

Versions of relevant libraries:
[pip3] flake8==3.8.2
[pip3] flake8-bugbear==22.9.23
[pip3] flake8-comprehensions==3.10.0
[pip3] flake8-executable==2.1.2
[pip3] flake8-pyi==20.5.0
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.21.5
[pip3] numpydoc==1.2
[pip3] nvidia-nccl-cu11==2.20.5
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] pytorch-crf==0.7.2
[pip3] pyzmq==22.3.0
[pip3] sentence-transformers==2.2.2
[pip3] torch==2.4.0+cu121
[pip3] torchaudio==0.12.1+cu116
[pip3] torchnet==0.0.4
[pip3] torchstat==0.0.7
[pip3] torchsummary==1.5.1
[pip3] torchvision==0.19.0+cu118
[pip3] transformers==4.43.2
[pip3] transformers-stream-generator==0.0.4
[pip3] triton==3.0.0
[conda] blas                      1.0                         mkl  
[conda] mkl                       2021.4.0           h06a4308_640  
[conda] mkl-service               2.4.0            py39h7f8727e_0  
[conda] mkl_fft                   1.3.1            py39hd3c417c_0  
[conda] mkl_random                1.2.2            py39h51133e4_0  
[conda] numpy                     1.21.5           py39he7a7128_1  
[conda] numpy-base                1.21.5           py39hf524024_1  
[conda] numpydoc                  1.2                pyhd3eb1b0_0  
[conda] nvidia-nccl-cu11          2.20.5                   pypi_0    pypi
[conda] nvidia-nccl-cu12          2.20.5                   pypi_0    pypi
[conda] pytorch-crf               0.7.2                    pypi_0    pypi
[conda] pyzmq                     22.3.0           py39h295c915_2  
[conda] sentence-transformers     2.2.2                    pypi_0    pypi
[conda] torch                     2.4.0+cu121              pypi_0    pypi
[conda] torchaudio                0.12.1+cu116             pypi_0    pypi
[conda] torchnet                  0.0.4                    pypi_0    pypi
[conda] torchstat                 0.0.7                    pypi_0    pypi
[conda] torchsummary              1.5.1                    pypi_0    pypi
[conda] torchvision               0.19.0+cu118             pypi_0    pypi
[conda] transformers              4.43.2                   pypi_0    pypi
[conda] transformers-stream-generator 0.0.4                    pypi_0    pypi
[conda] triton                    3.0.0                    pypi_0    pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.5.3.post1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X  0-15    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

🐛 Describe the bug

Built vLLM from source (main branch).

INFO 08-05 11:28:26 model_runner.py:732] Loading model weights took 26.4277 GB
INFO 08-05 11:28:30 gpu_executor.py:102] # GPU blocks: 3343, # CPU blocks: 327
INFO 08-05 11:28:31 model_runner.py:1024] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not st
atic. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.
INFO 08-05 11:28:31 model_runner.py:1028] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider d
ecreasing `gpu_memory_utilization` or enforcing eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.
INFO 08-05 11:28:41 model_runner.py:1225] Graph capturing finished in 10 secs.
INFO 08-05 11:28:41 block_manager_v1.py:247] Automatic prefix caching is enabled.
Process Process-1:
Traceback (most recent call last):
  File "/app/apps/anaconda3/envs/vllm_053p1_main/lib/python3.9/multiprocessing/process.py", line 315, in _bootstrap
    self.run()
  File "/app/apps/anaconda3/envs/vllm_053p1_main/lib/python3.9/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/mnt/xie/libs/vllm/vllm/entrypoints/openai/rpc/server.py", line 215, in run_rpc_server
    server = AsyncEngineRPCServer(async_engine_args, usage_context, port)
  File "/mnt/xie/libs/vllm/vllm/entrypoints/openai/rpc/server.py", line 33, in __init__
    self.socket.bind(f"tcp://localhost:{port}")
  File "/app/apps/anaconda3/envs/vllm_053p1_main/lib/python3.9/site-packages/zmq/sugar/socket.py", line 214, in bind
    super().bind(addr)
  File "zmq/backend/cython/socket.pyx", line 540, in zmq.backend.cython.socket.Socket.bind
  File "zmq/backend/cython/checkrc.pxd", line 28, in zmq.backend.cython.checkrc._check_rc
zmq.error.ZMQError: No such device
youkaichao commented 2 months ago

can you try https://github.com/vllm-project/vllm/issues/7118#issuecomment-2267611045 ?

xyfZzz commented 2 months ago

can you try #7118 (comment) ?

Thanks, I replaced localhost with 127.0.0.1 and it worked.