[Usage]: when i set --tensor-parallel-size 4 ，openai server dose not work . Report a new Exception

[Geek-Peng]

Your current environment

The output of `python collect_env.py`

PyTorch version: 2.5.1+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.27.9
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-3.10.0-1160.42.2.el7.x86_64-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.3.107
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA A100-SXM4-40GB
GPU 1: NVIDIA A100-SXM4-40GB
GPU 2: NVIDIA A100-SXM4-40GB
GPU 3: NVIDIA A100-SXM4-40GB

Nvidia driver version: 470.57.02
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:
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):                          112
On-line CPU(s) list:             0-111
Vendor ID:                       GenuineIntel
Model name:                      Intel(R) Xeon(R) Platinum 8350C CPU @ 2.60GHz
CPU family:                      6
Model:                           106
Thread(s) per core:              2
Core(s) per socket:              28
Socket(s):                       2
Stepping:                        6
BogoMIPS:                        5187.80
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 syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology eagerfpu pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb 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 arat avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq spec_ctrl
Hypervisor vendor:               KVM
Virtualization type:             full
L1d cache:                       2.6 MiB (56 instances)
L1i cache:                       1.8 MiB (56 instances)
L2 cache:                        70 MiB (56 instances)
L3 cache:                        48 MiB (1 instance)
NUMA node(s):                    2
NUMA node0 CPU(s):               0-55
NUMA node1 CPU(s):               56-111
Vulnerability Itlb multihit:     Processor vulnerable
Vulnerability L1tf:              Mitigation; PTE Inversion
Vulnerability Mds:               Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Meltdown:          Vulnerable
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1:        Mitigation; Load fences, usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:        Vulnerable
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] flake8==7.1.1
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.4
[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-dali-cuda120==1.32.0
[pip3] nvidia-ml-py==12.560.30
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] nvidia-pyindex==1.0.9
[pip3] onnx==1.15.0rc2
[pip3] onnxruntime==1.19.2
[pip3] optree==0.10.0
[pip3] pynvml==11.4.1
[pip3] pytorch-quantization==2.1.2
[pip3] pyzmq==25.1.2
[pip3] sentence-transformers==3.0.1
[pip3] torch==2.5.1
[pip3] torch-tensorrt==2.2.0a0
[pip3] torchdata==0.7.0a0
[pip3] torchtext==0.17.0a0
[pip3] torchvision==0.20.1
[pip3] transformers==4.46.1
[pip3] triton==3.1.0
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: N/A (dev)
vLLM Build Flags:
CUDA Archs: 5.2 6.0 6.1 7.0 7.2 7.5 8.0 8.6 8.7 9.0+PTX; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    GPU1    GPU2    GPU3    CPU Affinity    NUMA Affinity
GPU0     X      NV12    NV12    NV12    0-55    0
GPU1    NV12     X      NV12    NV12    56-111  1
GPU2    NV12    NV12     X      NV12    56-111  1
GPU3    NV12    NV12    NV12     X      56-111  1

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

How would you like to use vllm

I want to run inference of a Meta-Llama-3.1-70B-Instruct-quantized.w8a16. the shell is :

CUDA_VISIBLE_DEVICES=0,1,2,3 python -m vllm.entrypoints.openai.api_server --model Meta-Llama-3.1-70B-Instruct-quantized.w8a8 --pipeline-parallel-size 4 --gpu-memory-utilization 0.9 --port 2198 --host 0.0.0.0 --served-model-name lianshan-llm-v1 

and i meet the fllow error:

INFO 11-20 12:36:49 custom_all_reduce_utils.py:204] generating GPU P2P access cache in /root/.cache/vllm/gpu_p2p_access_cache_for_0,1,2,3.json
[rank0]: Traceback (most recent call last):
[rank0]:   File "/vllm/vllm/distributed/device_communicators/custom_all_reduce_utils.py", line 227, in gpu_p2p_access_check
[rank0]:     returned.check_returncode()
[rank0]:   File "/usr/lib/python3.10/subprocess.py", line 457, in check_returncode
[rank0]:     raise CalledProcessError(self.returncode, self.args, self.stdout,
[rank0]: subprocess.CalledProcessError: Command '['/usr/bin/python', '/lpai/volumes/data-yyj/gzkp/vllm/vllm/distributed/device_communicators/custom_all_reduce_utils.py']' returned non-zero exit status 1.

[rank0]: The above exception was the direct cause of the following exception:

[rank0]: Traceback (most recent call last):
[rank0]:   File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
[rank0]:     return _run_code(code, main_globals, None,
[rank0]:   File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
[rank0]:     exec(code, run_globals)
[rank0]:   File "/vllm/vllm/entrypoints/openai/api_server.py", line 676, in <module>
[rank0]:     uvloop.run(run_server(args))
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/uvloop/__init__.py", line 82, in run
[rank0]:     return loop.run_until_complete(wrapper())
[rank0]:   File "uvloop/loop.pyx", line 1517, in uvloop.loop.Loop.run_until_complete
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/uvloop/__init__.py", line 61, in wrapper
[rank0]:     return await main
[rank0]:   File "/vllm/vllm/entrypoints/openai/api_server.py", line 643, in run_server
[rank0]:     async with build_async_engine_client(args) as engine_client:
[rank0]:   File "/usr/lib/python3.10/contextlib.py", line 199, in __aenter__
[rank0]:     return await anext(self.gen)
[rank0]:   File "/vllm/vllm/entrypoints/openai/api_server.py", line 107, in build_async_engine_client
[rank0]:     async with build_async_engine_client_from_engine_args(
[rank0]:   File "/usr/lib/python3.10/contextlib.py", line 199, in __aenter__
[rank0]:     return await anext(self.gen)
[rank0]:   File "/vllm/vllm/entrypoints/openai/api_server.py", line 141, in build_async_engine_client_from_engine_args
[rank0]:     engine_client = await asyncio.get_running_loop().run_in_executor(
[rank0]:   File "/usr/lib/python3.10/concurrent/futures/thread.py", line 58, in run
[rank0]:     result = self.fn(*self.args, **self.kwargs)
[rank0]:   File "/vllm/vllm/engine/async_llm_engine.py", line 682, in from_engine_args
[rank0]:     engine = cls(
[rank0]:   File "vllm/vllm/engine/async_llm_engine.py", line 577, in __init__
[rank0]:     self.engine = self._engine_class(*args, **kwargs)
[rank0]:   File "/vllm/vllm/engine/async_llm_engine.py", line 263, in __init__
[rank0]:     super().__init__(*args, **kwargs)
[rank0]:   File "/vllm/vllm/engine/llm_engine.py", line 347, in __init__
[rank0]:     self.model_executor = executor_class(vllm_config=vllm_config, )
[rank0]:   File "/vllm/vllm/executor/multiproc_gpu_executor.py", line 215, in __init__
[rank0]:     super().__init__(*args, **kwargs)
[rank0]:   File "/vllm/vllm/executor/distributed_gpu_executor.py", line 26, in __init__
[rank0]:     super().__init__(*args, **kwargs)
[rank0]:   File "/vllm/vllm/executor/executor_base.py", line 36, in __init__
[rank0]:     self._init_executor()
[rank0]:   File "/vllm/vllm/executor/multiproc_gpu_executor.py", line 110, in _init_executor
[rank0]:     self._run_workers("init_device")
[rank0]:   File "/vllm/vllm/executor/multiproc_gpu_executor.py", line 192, in _run_workers
[rank0]:     driver_worker_output = driver_worker_method(*args, **kwargs)
[rank0]:   File "/vllm/vllm/worker/worker.py", line 148, in init_device
[rank0]:     init_worker_distributed_environment(self.parallel_config, self.rank,
[rank0]:   File "/vllm/vllm/worker/worker.py", line 465, in init_worker_distributed_environment
[rank0]:     ensure_model_parallel_initialized(parallel_config.tensor_parallel_size,
[rank0]:   File "/vllm/vllm/distributed/parallel_state.py", line 1091, in ensure_model_parallel_initialized
[rank0]:     initialize_model_parallel(tensor_model_parallel_size,
[rank0]:   File "/vllm/vllm/distributed/parallel_state.py", line 1055, in initialize_model_parallel
[rank0]:     _TP = init_model_parallel_group(group_ranks,
[rank0]:   File "/vllm/vllm/distributed/parallel_state.py", line 896, in init_model_parallel_group
[rank0]:     return GroupCoordinator(
[rank0]:   File "/vllm/vllm/distributed/parallel_state.py", line 233, in __init__
[rank0]:     self.ca_comm = CustomAllreduce(
[rank0]:   File "/vllm/vllm/distributed/device_communicators/custom_all_reduce.py", line 140, in __init__
[rank0]:     if not _can_p2p(rank, world_size):
[rank0]:   File "/vllm/vllm/distributed/device_communicators/custom_all_reduce.py", line 35, in _can_p2p
[rank0]:     if not gpu_p2p_access_check(rank, i):
[rank0]:   File "/vllm/vllm/distributed/device_communicators/custom_all_reduce_utils.py", line 230, in gpu_p2p_access_check
[rank0]:     raise RuntimeError(
[rank0]: RuntimeError: Error happened when batch testing peer-to-peer access from (0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3) to (0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3):
[rank0]: Traceback (most recent call last):
[rank0]:   File "/vllm/vllm/distributed/device_communicators/custom_all_reduce_utils.py", line 14, in <module>
[rank0]:     import vllm.envs as envs
[rank0]: ModuleNotFoundError: No module named 'vllm'

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