[Bug]: VLLM 0.5.3.post1 [rank0]: RuntimeError: NCCL错误：未处理的CUDA错误(使用NCCL_DEBUG=INFO运行以获取详细信息)

当前环境：

The output of `python collect_env.py`
Collecting environment information...
PyTorch version: 2.3.1+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
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 3.30.1
Libc version: glibc-2.35

Python version: 3.10.12 (main, Mar 22 2024, 16:50:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.15.0-1053-nvidia-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA H100 80GB HBM3
GPU 1: NVIDIA H100 80GB HBM3
GPU 2: NVIDIA H100 80GB HBM3
GPU 3: NVIDIA H100 80GB HBM3
GPU 4: NVIDIA H100 80GB HBM3
GPU 5: NVIDIA H100 80GB HBM3
GPU 6: NVIDIA H100 80GB HBM3
GPU 7: NVIDIA H100 80GB HBM3

Nvidia driver version: 550.90.07
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.6
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:                      52 bits physical, 57 bits virtual
Byte Order:                         Little Endian
CPU(s):                             224
On-line CPU(s) list:                0-223
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) Platinum 8480C
CPU family:                         6
Model:                              143
Thread(s) per core:                 2
Core(s) per socket:                 56
Socket(s):                          2
Stepping:                           8
CPU max MHz:                        3800.0000
CPU min MHz:                        800.0000
BogoMIPS:                           4000.00
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 tsc_known_freq 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 cat_l2 cdp_l3 invpcid_single intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi 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 avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          5.3 MiB (112 instances)
L1i cache:                          3.5 MiB (112 instances)
L2 cache:                           224 MiB (112 instances)
L3 cache:                           210 MiB (2 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-55,112-167
NUMA node1 CPU(s):                  56-111,168-223
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 Retbleed:             Not affected
Vulnerability Spec rstack overflow: 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

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] torch==2.3.1
[pip3] torchvision==0.18.1
[pip3] transformers==4.43.1
[pip3] triton==2.3.1
[conda] Could not collect
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	GPU1	GPU2	GPU3	GPU4	GPU5	GPU6	GPU7	NIC0	NIC1	NIC2	NIC3	NIC4	NIC5	NIC6	NIC7	NIC8	NIC9	NIC10	NIC11	CPU AffinityNUMA Affinity	GPU NUMA ID
GPU0	 X 	NV18	NV18	NV18	NV18	NV18	NV18	NV18	PXB	NODE	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS	0-55,112-167	0		N/A
GPU1	NV18	 X 	NV18	NV18	NV18	NV18	NV18	NV18	NODE	NODE	NODE	PXB	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS	0-55,112-167	0		N/A
GPU2	NV18	NV18	 X 	NV18	NV18	NV18	NV18	NV18	NODE	NODE	NODE	NODE	PXB	NODE	SYS	SYS	SYS	SYS	SYS	SYS	0-55,112-167	0		N/A
GPU3	NV18	NV18	NV18	 X 	NV18	NV18	NV18	NV18	NODE	NODE	NODE	NODE	NODE	PXB	SYS	SYS	SYS	SYS	SYS	SYS	0-55,112-167	0		N/A
GPU4	NV18	NV18	NV18	NV18	 X 	NV18	NV18	NV18	SYS	SYS	SYS	SYS	SYS	SYS	PXB	NODE	NODE	NODE	NODE	NODE	56-111,168-223	1		N/A
GPU5	NV18	NV18	NV18	NV18	NV18	 X 	NV18	NV18	SYS	SYS	SYS	SYS	SYS	SYS	NODE	NODE	NODE	PXB	NODE	NODE	56-111,168-223	1		N/A
GPU6	NV18	NV18	NV18	NV18	NV18	NV18	 X 	NV18	SYS	SYS	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	PXB	NODE	56-111,168-223	1		N/A
GPU7	NV18	NV18	NV18	NV18	NV18	NV18	NV18	 X 	SYS	SYS	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	NODE	PXB	56-111,168-223	1		N/A
NIC0	PXB	NODE	NODE	NODE	SYS	SYS	SYS	SYS	 X 	NODE	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS
NIC1	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	NODE	 X 	PIX	NODE	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS
NIC2	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	NODE	PIX	 X 	NODE	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS
NIC3	NODE	PXB	NODE	NODE	SYS	SYS	SYS	SYS	NODE	NODE	NODE	 X 	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS
NIC4	NODE	NODE	PXB	NODE	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	 X 	NODE	SYS	SYS	SYS	SYS	SYS	SYS
NIC5	NODE	NODE	NODE	PXB	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	NODE	 X 	SYS	SYS	SYS	SYS	SYS	SYS
NIC6	SYS	SYS	SYS	SYS	PXB	NODE	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS	 X 	NODE	NODE	NODE	NODE	NODE
NIC7	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS	NODE	 X 	PIX	NODE	NODE	NODE
NIC8	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS	NODE	PIX	 X 	NODE	NODE	NODE
NIC9	SYS	SYS	SYS	SYS	NODE	PXB	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS	NODE	NODE	NODE	 X 	NODE	NODE
NIC10	SYS	SYS	SYS	SYS	NODE	NODE	PXB	NODE	SYS	SYS	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	 X 	NODE
NIC11	SYS	SYS	SYS	SYS	NODE	NODE	NODE	PXB	SYS	SYS	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	NODE	 X

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

NIC Legend:

  NIC0: mlx5_0
  NIC1: mlx5_1
  NIC2: mlx5_2
  NIC3: mlx5_3
  NIC4: mlx5_4
  NIC5: mlx5_5
  NIC6: mlx5_6
  NIC7: mlx5_7
  NIC8: mlx5_8
  NIC9: mlx5_9
  NIC10: mlx5_10
  NIC11: mlx5_11

问题描述：
请描述这个bug。

答案：
无法生成答案，因为query中没有提供足够的信息来描述bug。
(VllmWorkerProcess pid=761) INFO 07-24 08:25:45 utils.py:784] Found nccl from library libnccl.so.2
(VllmWorkerProcess pid=757) INFO 07-24 08:25:45 utils.py:784] Found nccl from library libnccl.so.2
(VllmWorkerProcess pid=758) INFO 07-24 08:25:45 utils.py:784] Found nccl from library libnccl.so.2
(VllmWorkerProcess pid=759) INFO 07-24 08:25:45 utils.py:784] Found nccl from library libnccl.so.2
(VllmWorkerProcess pid=760) INFO 07-24 08:25:45 utils.py:784] Found nccl from library libnccl.so.2
(VllmWorkerProcess pid=761) INFO 07-24 08:26:01 multiproc_worker_utils.py:226] Exception in worker VllmWorkerProcess while processing method init_device: NCCL error: unhandled cuda error (run with NCCL_DEBUG=INFO for details), Traceback (most recent call last):
这段文本是一段错误日志，其中包含了多个进程(VllmWorkerProcess)在处理过程中遇到的错误。错误类型为NCCL error,表示这是一个与NVIDIA的NCCL库相关的错误。具体的错误信息是"unhandled cuda error",意味着在执行CUDA操作时遇到了未处理的错误。建议运行带有NCCL_DEBUG=INFO参数的命令以获取详细信息。
这个错误信息表示在使用VLLM(Visual Layout Language Model)时，出现了一个与CUDA相关的错误。具体来说，是在初始化设备时，NCCL(NVIDIA Collective Communications Library)库遇到了一个未处理的CUDA错误。为了解决这个问题，你可以尝试运行以下命令，以获取更详细的错误信息：

NCCL_DEBUG=INFO NCCL_SOCKET_IFNAME=eth0 NCCL_IB_DISABLE=1 python your_script.py

这将启用NCCL调试信息，并指定网络接口名称为eth0。请确保将your_script.py替换为你实际运行的脚本文件名。
这个错误信息表示在使用NCCL(NVIDIA Collective Communications Library)时遇到了未处理的CUDA错误。要解决这个问题，可以尝试以下方法：

更新显卡驱动程序和CUDA工具包到最新版本。
运行nccl-tests以检查是否存在已知问题。在终端中执行以下命令：

nccl-tests --verbose

如果上述方法都无法解决问题，可以尝试在运行程序时添加环境变量NCCL_DEBUG=INFO,以获取更详细的错误信息。例如：

export NCCL_DEBUG=INFO
python your_script.py

根据NCCL_DEBUG=INFO输出的详细错误信息，进一步排查问题。
这是一个关于PyTorch分布式训练中的NCCL错误。错误信息显示，NCCL(NVIDIA Collective Communications Library)遇到了一个未处理的CUDA错误。要解决这个问题，可以尝试以下方法：
确保你的显卡驱动是最新的。
检查你的CUDA和cuDNN版本是否与PyTorch兼容。你可以在PyTorch官网上查看支持的版本。
运行nccl-tests来检查NCCL是否正常工作。在终端中输入以下命令：

nccl-tests --verbose

如果测试失败，你可能需要重新安装NCCL库。

如果问题仍然存在，可以尝试使用NCCL_DEBUG=INFO环境变量来获取更详细的调试信息。在运行你的程序之前，设置环境变量：

export NCCL_DEBUG=INFO

然后运行你的程序，观察输出的调试信息，以便找到问题的根源。
这段文本是一段错误日志，主要涉及到了分布式训练过程中的 NCCL 错误。具体来说，这个错误是由于在执行 NCCL(NVIDIA Collective Communications Library)操作时发生了未处理的 CUDA 错误。为了解决这个问题，可以尝试在运行程序时添加 NCCL_DEBUG=INFO 参数以获取详细的错误信息。

[rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/executor/multiproc_gpu_executor.py", line 201, in **init**
[rank0]: super().**init**(*args, **kwargs)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/executor/distributed_gpu_executor.py", line 25, in **init**
[rank0]: super().**init**(*args, **kwargs)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/executor/executor_base.py", line 47, in **init**
[rank0]: self._init_executor()
[rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/executor/multiproc_gpu_executor.py", line 123, in _init_executor
[rank0]: self._run_workers("init_device")
[rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/executor/multiproc_gpu_executor.py", line 178, in _run_workers
[rank0]: driver_worker_output = driver_worker_method(*args, **kwargs)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker.py", line 132, in init_device
[rank0]: init_worker_distributed_environment(self.parallel_config, self.rank,
[rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker.py", line 346, in init_worker_distributed_environment
[rank0]: ensure_model_parallel_initialized(parallel_config.tensor_parallel_size,
[rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/distributed/parallel_state.py", line 923, in ensure_model_parallel_initialized
[rank0]: initialize_model_parallel(tensor_model_parallel_size,
[rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/distributed/parallel_state.py", line 889, in initialize_model_parallel
[rank0]: _TP = init_model_parallel_group(group_ranks,
[rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/distributed/parallel_state.py", line 732, in init_model_parallel_group
[rank0]: return GroupCoordinator(
[rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/distributed/parallel_state.py", line 176, in **init**
[rank0]: self.pynccl_comm = PyNcclCommunicator(
[rank0]: File "/usr/local/lib/python3.10/dist-packages/vllm/distributed

[Bug]: VLLM 0.5.3.post1 [rank0]: RuntimeError: NCCL错误：未处理的CUDA错误(使用NCCL_DEBUG=INFO运行以获取详细信息)

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