Description
Your current environment
INFO 06-20 10:37:01 [init.py:243] Automatically detected platform cuda.
Collecting environment information...
uv is set
System Info
==============================
OS : Ubuntu 24.04.2 LTS (x86_64)
GCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
Clang version : Could not collect
CMake version : version 4.0.3
Libc version : glibc-2.39
==============================
PyTorch Info
PyTorch version : 2.7.1+cu128
Is debug build : False
CUDA used to build PyTorch : 12.8
ROCM used to build PyTorch : N/A
==============================
Python Environment
Python version : 3.11.13 (main, Jun 4 2025, 17:37:17) [Clang 20.1.4 ] (64-bit runtime)
Python platform : Linux-6.11.0-26-generic-x86_64-with-glibc2.39
==============================
CUDA / GPU Info
Is CUDA available : True
CUDA runtime version : 12.8.61
CUDA_MODULE_LOADING set to : LAZY
GPU models and configuration :
GPU 0: NVIDIA GeForce RTX 5090
GPU 1: NVIDIA GeForce RTX 5090
Nvidia driver version : 570.86.10
cuDNN version : Could not collect
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True
==============================
CPU Info
架构： x86_64
CPU 运行模式： 32-bit, 64-bit
Address sizes: 46 bits physical, 48 bits virtual
字节序： Little Endian
CPU: 32
在线 CPU 列表： 0-31
厂商 ID： GenuineIntel
型号名称： Intel(R) Core(TM) i9-14900KS
CPU 系列： 6
型号： 183
每个核的线程数： 2
每个座的核数： 24
座： 1
步进： 1
CPU(s) scaling MHz: 32%
CPU 最大 MHz： 6300.0000
CPU 最小 MHz： 800.0000
BogoMIPS： 6374.40
标记： 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 sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect user_shstk avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi vnmi umip pku ospke waitpkg gfni vaes vpclmulqdq rdpid movdiri movdir64b fsrm md_clear serialize pconfig arch_lbr ibt flush_l1d arch_capabilities
虚拟化： VT-x
L1d 缓存： 896 KiB (24 instances)
L1i 缓存： 1.3 MiB (24 instances)
L2 缓存： 32 MiB (12 instances)
L3 缓存： 36 MiB (1 instance)
NUMA 节点： 1
NUMA 节点0 CPU： 0-31
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: Mitigation; Clear Register File
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 BHI_DIS_S
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
==============================
Versions of relevant libraries
[pip3] numpy==2.1.2
[pip3] nvidia-cublas-cu12==12.8.3.14
[pip3] nvidia-cuda-cupti-cu12==12.8.57
[pip3] nvidia-cuda-nvrtc-cu12==12.8.61
[pip3] nvidia-cuda-runtime-cu12==12.8.57
[pip3] nvidia-cudnn-cu12==9.7.1.26
[pip3] nvidia-cufft-cu12==11.3.3.41
[pip3] nvidia-cufile-cu12==1.13.0.11
[pip3] nvidia-curand-cu12==10.3.9.55
[pip3] nvidia-cusolver-cu12==11.7.2.55
[pip3] nvidia-cusparse-cu12==12.5.7.53
[pip3] nvidia-cusparselt-cu12==0.6.3
[pip3] nvidia-ml-py==12.575.51
[pip3] nvidia-nccl-cu12==2.26.2
[pip3] nvidia-nvjitlink-cu12==12.8.61
[pip3] nvidia-nvtx-cu12==12.8.55
[pip3] pynvml==12.0.0
[pip3] pyzmq==27.0.0
[pip3] torch==2.7.1+cu128
[pip3] torchaudio==2.7.1+cu128
[pip3] torchcodec==0.4.0
[pip3] torchdata==0.11.0
[pip3] torchvision==0.22.1+cu128
[pip3] transformers==4.51.3
[pip3] triton==3.3.1
[conda] Could not collect
==============================
vLLM Info
ROCM Version : Could not collect
Neuron SDK Version : N/A
vLLM Version : 0.9.1.dev0+g587387724.d20250617 (git sha: 5873877, date: 20250617)
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
�[4mGPU0 GPU1 CPU Affinity NUMA Affinity GPU NUMA ID�[0m
GPU0 X PHB 0-31 0 N/A
GPU1 PHB X 0-31 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
==============================
Environment Variables
NCCL_CUMEM_ENABLE=0
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY
🐛 Describe the bug
I tried using the command to install vllm, and it runs without issues on a single GPU. However, when running on multiple GPUs (setting tensor_parallel_size=2), it throws an error.
git clone https://github.com/vllm-project/vllm
cd vllm/
git checkout tags/v0.9.0 -b mybranch
conda install -c conda-forge libstdcxx-ng
pip install torch==2.7.0 --index-url https://download.pytorch.org/whl/cu128
python use_existing_torch.py
pip list |grep torch
uv pip install -r requirements/build.txt && uv pip install -r requirements/common.txt && MAX_JOBS=16 uv pip install -e . --no-build-isolation
File "/home/lc/EasyR1/verl/single_controller/ray/base.py", line 432, in func
return getattr(self.worker_dict[key], name)(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/lc/EasyR1/verl/single_controller/base/decorator.py", line 207, in inner
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/lc/EasyR1/verl/workers/fsdp_workers.py", line 366, in init_model
self._build_model_optimizer(
File "/home/lc/EasyR1/verl/workers/fsdp_workers.py", line 276, in _build_model_optimizer
fsdp_module = FSDP(
^^^^^
File "/home/lc/.venv/easyr1/lib/python3.11/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py", line 473, in __init__
_auto_wrap(
File "/home/lc/.venv/easyr1/lib/python3.11/site-packages/torch/distributed/fsdp/_wrap_utils.py", line 101, in _auto_wrap
_recursive_wrap(**recursive_wrap_kwargs, **root_kwargs) # type: ignore[arg-type]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/lc/.venv/easyr1/lib/python3.11/site-packages/torch/distributed/fsdp/wrap.py", line 533, in _recursive_wrap
wrapped_child, num_wrapped_params = _recursive_wrap(
^^^^^^^^^^^^^^^^
File "/home/lc/.venv/easyr1/lib/python3.11/site-packages/torch/distributed/fsdp/wrap.py", line 533, in _recursive_wrap
wrapped_child, num_wrapped_params = _recursive_wrap(
^^^^^^^^^^^^^^^^
File "/home/lc/.venv/easyr1/lib/python3.11/site-packages/torch/distributed/fsdp/wrap.py", line 533, in _recursive_wrap
wrapped_child, num_wrapped_params = _recursive_wrap(
^^^^^^^^^^^^^^^^
File "/home/lc/.venv/easyr1/lib/python3.11/site-packages/torch/distributed/fsdp/wrap.py", line 551, in _recursive_wrap
return _wrap(module, wrapper_cls, **kwargs), nonwrapped_numel
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/lc/.venv/easyr1/lib/python3.11/site-packages/torch/distributed/fsdp/wrap.py", line 480, in _wrap
return wrapper_cls(module, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/lc/.venv/easyr1/lib/python3.11/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py", line 499, in __init__
_init_param_handle_from_module(
File "/home/lc/.venv/easyr1/lib/python3.11/site-packages/torch/distributed/fsdp/_init_utils.py", line 615, in _init_param_handle_from_module
_sync_module_params_and_buffers(
File "/home/lc/.venv/easyr1/lib/python3.11/site-packages/torch/distributed/fsdp/_init_utils.py", line 1112, in _sync_module_params_and_buffers
_sync_params_and_buffers(
File "/home/lc/.venv/easyr1/lib/python3.11/site-packages/torch/distributed/utils.py", line 322, in _sync_params_and_buffers
dist._broadcast_coalesced(
torch.distributed.DistBackendError: NCCL error in: /pytorch/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:3356, unhandled cuda error (run with NCCL_DEBUG=INFO for details), NCCL version 2.26.2
ncclUnhandledCudaError: Call to CUDA function failed.
Last error:
Cuda failure 700 'an illegal memory access was encountered'
(WorkerDict pid=3290198)
(WorkerDict pid=3290198) [2025-06-20 10:15:41] lc-System-Product-Name:3290198:3290198 [0] enqueue.cc:1556 NCCL WARN Cuda failure 700 'an illegal memory access was encountered'
(WorkerDict pid=3291061)
(WorkerDict pid=3291061) [2025-06-20 10:15:41] lc-System-Product-Name:3291061:3291061 [0] enqueue.cc:1556 NCCL WARN Cuda failure 700 'an illegal memory access was encountered'
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