[Bug]: RuntimeError: No CUDA GPUs are available in transformers v4.48.0 or above when running Ray RLHF example #13597
Description
Your current environment
The output of `python collect_env.py`
Collecting environment information...
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: CentOS Linux 7 (Core) (x86_64)
GCC version: (GCC) 11.4.0
Clang version: 3.4.2 (tags/RELEASE_34/dot2-final)
CMake version: version 3.30.5
Libc version: glibc-2.35
Python version: 3.10.12 (main, Jul 5 2023, 18:54:27) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-3.10.0-1127.el7.x86_64-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.4.131
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA A800-SXM4-80GB
GPU 1: NVIDIA A800-SXM4-80GB
GPU 2: NVIDIA A800-SXM4-80GB
GPU 3: NVIDIA A800-SXM4-80GB
GPU 4: NVIDIA A800-SXM4-80GB
GPU 5: NVIDIA A800-SXM4-80GB
GPU 6: NVIDIA A800-SXM4-80GB
GPU 7: NVIDIA A800-SXM4-80GB
Nvidia driver version: 525.105.17
cuDNN version: Probably one of the following:
/usr/lib64/libcudnn.so.8.0.5
/usr/lib64/libcudnn_adv_infer.so.8.0.5
/usr/lib64/libcudnn_adv_train.so.8.0.5
/usr/lib64/libcudnn_cnn_infer.so.8.0.5
/usr/lib64/libcudnn_cnn_train.so.8.0.5
/usr/lib64/libcudnn_ops_infer.so.8.0.5
/usr/lib64/libcudnn_ops_train.so.8.0.5
/usr/local/cuda/targets/x86_64-linux/lib/libcudnn.so.8.9.5
/usr/local/cuda/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.9.5
/usr/local/cuda/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.9.5
/usr/local/cuda/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.9.5
/usr/local/cuda/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.9.5
/usr/local/cuda/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.9.5
/usr/local/cuda/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.9.5
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
CPU(s): 128
On-line CPU(s) list: 0-127
Thread(s) per core: 2
Core(s) per socket: 32
Socket(s): 2
NUMA node(s): 2
Vendor ID: GenuineIntel
CPU family: 6
Model: 106
Model name: Intel(R) Xeon(R) Platinum 8338C CPU @ 2.60GHz
Stepping: 6
CPU MHz: 3500.000
CPU max MHz: 3500.0000
CPU min MHz: 800.0000
BogoMIPS: 5200.00
Virtualization: VT-x
L1d cache: 48K
L1i cache: 32K
L2 cache: 1280K
L3 cache: 49152K
NUMA node0 CPU(s): 0-31,64-95
NUMA node1 CPU(s): 32-63,96-127
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 aperfmperf eagerfpu 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 epb cat_l3 invpcid_single intel_pt ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq md_clear pconfig spec_ctrl intel_stibp flush_l1d arch_capabilities
Versions of relevant libraries:
[pip3] gpytorch==1.13
[pip3] numpy==1.26.3
[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-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] pynvml==12.0.0
[pip3] pyzmq==26.2.0
[pip3] sentence-transformers==3.2.1
[pip3] torch==2.5.1
[pip3] torchaudio==2.5.1
[pip3] torchvision==0.20.1
[pip3] transformer-engine-torch==1.11.0
[pip3] transformers==4.48.0
[pip3] transformers-stream-generator==0.0.5
[pip3] triton==3.1.0
[conda] gpytorch 1.13 pypi_0 pypi
[conda] numpy 1.26.3 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.4.5.8 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-cudnn-cu12 9.1.0.70 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.2.1.3 pypi_0 pypi
[conda] nvidia-curand-cu12 10.3.5.147 pypi_0 pypi
[conda] nvidia-cusolver-cu12 11.6.1.9 pypi_0 pypi
[conda] nvidia-cusparse-cu12 12.3.1.170 pypi_0 pypi
[conda] nvidia-ml-py 12.560.30 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.21.5 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.4.127 pypi_0 pypi
[conda] pynvml 12.0.0 pypi_0 pypi
[conda] pyzmq 26.2.0 pypi_0 pypi
[conda] sentence-transformers 3.2.1 pypi_0 pypi
[conda] torch 2.5.1 pypi_0 pypi
[conda] torchaudio 2.5.1 pypi_0 pypi
[conda] torchvision 0.20.1 pypi_0 pypi
[conda] transformer-engine-torch 1.11.0 pypi_0 pypi
[conda] transformers 4.48.0 pypi_0 pypi
[conda] transformers-stream-generator 0.0.5 pypi_0 pypi
[conda] triton 3.1.0 pypi_0 pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.7.2
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 CPU Affinity NUMA Affinity
GPU0 X NV8 NV8 NV8 NV8 NV8 NV8 NV8 PXB PXB NODE NODE NODE NODE SYS SYS SYS SYS NODE 0-31,64-95 0
GPU1 NV8 X NV8 NV8 NV8 NV8 NV8 NV8 PXB PXB NODE NODE NODE NODE SYS SYS SYS SYS NODE 0-31,64-95 0
GPU2 NV8 NV8 X NV8 NV8 NV8 NV8 NV8 NODE NODE PXB PXB NODE NODE SYS SYS SYS SYS NODE 0-31,64-95 0
GPU3 NV8 NV8 NV8 X NV8 NV8 NV8 NV8 NODE NODE PXB PXB NODE NODE SYS SYS SYS SYS NODE 0-31,64-95 0
GPU4 NV8 NV8 NV8 NV8 X NV8 NV8 NV8 SYS SYS SYS SYS SYS SYS PXB PXB NODE NODE SYS 32-63,96-127 1
GPU5 NV8 NV8 NV8 NV8 NV8 X NV8 NV8 SYS SYS SYS SYS SYS SYS PXB PXB NODE NODE SYS 32-63,96-127 1
GPU6 NV8 NV8 NV8 NV8 NV8 NV8 X NV8 SYS SYS SYS SYS SYS SYS NODE NODE PXB PXB SYS 32-63,96-127 1
GPU7 NV8 NV8 NV8 NV8 NV8 NV8 NV8 X SYS SYS SYS SYS SYS SYS NODE NODE PXB PXB SYS 32-63,96-127 1
NIC0 PXB PXB NODE NODE SYS SYS SYS SYS X PIX NODE NODE NODE NODE SYS SYS SYS SYS NODE
NIC1 PXB PXB NODE NODE SYS SYS SYS SYS PIX X NODE NODE NODE NODE SYS SYS SYS SYS NODE
NIC2 NODE NODE PXB PXB SYS SYS SYS SYS NODE NODE X PIX NODE NODE SYS SYS SYS SYS NODE
NIC3 NODE NODE PXB PXB SYS SYS SYS SYS NODE NODE PIX X NODE NODE SYS SYS SYS SYS NODE
NIC4 NODE NODE NODE NODE SYS SYS SYS SYS NODE NODE NODE NODE X PIX SYS SYS SYS SYS NODE
NIC5 NODE NODE NODE NODE SYS SYS SYS SYS NODE NODE NODE NODE PIX X SYS SYS SYS SYS NODE
NIC6 SYS SYS SYS SYS PXB PXB NODE NODE SYS SYS SYS SYS SYS SYS X PIX NODE NODE SYS
NIC7 SYS SYS SYS SYS PXB PXB NODE NODE SYS SYS SYS SYS SYS SYS PIX X NODE NODE SYS
NIC8 SYS SYS SYS SYS NODE NODE PXB PXB SYS SYS SYS SYS SYS SYS NODE NODE X PIX SYS
NIC9 SYS SYS SYS SYS NODE NODE PXB PXB SYS SYS SYS SYS SYS SYS NODE NODE PIX X SYS
NIC10 NODE NODE NODE NODE SYS SYS SYS SYS NODE NODE NODE NODE NODE NODE SYS SYS SYS SYS 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_6
NIC5: mlx5_7
NIC6: mlx5_8
NIC7: mlx5_9
NIC8: mlx5_10
NIC9: mlx5_11
NIC10: mlx5_bond_0
NCCL_SOCKET_IFNAME=eth0,bond0,bond4
CUDA_HOME=/usr/local/cuda
CUDA_HOME=/usr/local/cuda
NCCL_IB_GID_INDEX=3
LD_LIBRARY_PATH=/usr/local/miniconda3/lib/python3.10/site-packages/nvidia/cudnn/lib/:/usr/local/cuda-12.4/compat:/usr/mpi4_gdr/lib:/usr/local/cuda/lib64/:/usr/local/cuda/extras/CUPTI/lib64:/usr/local/cuda/lib64:
NVIDIA_VISIBLE_DEVICES=all
NVIDIA_DRIVER_CAPABILITIES=compute,utility
NCCL_SHM_DISABLE=0
CUDA_DEVICE_MAX_CONNECTIONS=1
NCCL_IB_TC=160
NCCL_IB_DISABLE=0
NCCL_IB_HCA=^=mlx5_bond_0
VLLM_WORKER_MULTIPROC_METHOD=spawn
NCCL_IB_CUDA_SUPPORT=1
NCCL_DEBUG=INFO
NCCL_NET_GDR_LEVEL=2
NCCL_CUMEM_ENABLE=0
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY
🐛 Describe the bug
Hi for all!
I failed to run the vLLM project RLHF example script. The code is exactly same as the vLLM docs page: https://docs.vllm.ai/en/latest/getting_started/examples/rlhf.html
The error messages are:
(MyLLM pid=70946) ERROR 02-20 15:38:34 worker_base.py:574] Error executing method 'init_device'. This might cause deadlock in distributed execution.
(MyLLM pid=70946) ERROR 02-20 15:38:34 worker_base.py:574] Traceback (most recent call last):
(MyLLM pid=70946) ERROR 02-20 15:38:34 worker_base.py:574] File "/usr/local/miniconda3/lib/python3.10/site-packages/vllm/worker/worker_base.py", line 566, in execute_method
(MyLLM pid=70946) ERROR 02-20 15:38:34 worker_base.py:574] return run_method(target, method, args, kwargs)
(MyLLM pid=70946) ERROR 02-20 15:38:34 worker_base.py:574] File "/usr/local/miniconda3/lib/python3.10/site-packages/vllm/utils.py", line 2220, in run_method
(MyLLM pid=70946) ERROR 02-20 15:38:34 worker_base.py:574] return func(*args, **kwargs)
(MyLLM pid=70946) ERROR 02-20 15:38:34 worker_base.py:574] File "/usr/local/miniconda3/lib/python3.10/site-packages/vllm/worker/worker.py", line 155, in init_device
(MyLLM pid=70946) ERROR 02-20 15:38:34 worker_base.py:574] torch.cuda.set_device(self.device)
(MyLLM pid=70946) ERROR 02-20 15:38:34 worker_base.py:574] File "/usr/local/miniconda3/lib/python3.10/site-packages/torch/cuda/__init__.py", line 478, in set_device
(MyLLM pid=70946) ERROR 02-20 15:38:34 worker_base.py:574] torch._C._cuda_setDevice(device)
(MyLLM pid=70946) ERROR 02-20 15:38:34 worker_base.py:574] File "/usr/local/miniconda3/lib/python3.10/site-packages/torch/cuda/__init__.py", line 319, in _lazy_init
(MyLLM pid=70946) ERROR 02-20 15:38:34 worker_base.py:574] torch._C._cuda_init()
(MyLLM pid=70946) ERROR 02-20 15:38:34 worker_base.py:574] RuntimeError: No CUDA GPUs are available
(MyLLM pid=70946) Exception raised in creation task: The actor died because of an error raised in its creation task, ray::MyLLM.__init__() (pid=70946, ip=11.163.37.230, actor_id=202b48118215566c51057a0101000000, repr=<test_ray_vllm_rlhf.MyLLM object at 0x7fb7453669b0>)
(MyLLM pid=70946) File "/data/cfs/workspace/test_ray_vllm_rlhf.py", line 96, in __init__
(MyLLM pid=70946) super().__init__(*args, **kwargs)
(MyLLM pid=70946) File "/usr/local/miniconda3/lib/python3.10/site-packages/vllm/utils.py", line 1051, in inner
(MyLLM pid=70946) return fn(*args, **kwargs)
(MyLLM pid=70946) File "/usr/local/miniconda3/lib/python3.10/site-packages/vllm/entrypoints/llm.py", line 242, in __init__
(MyLLM pid=70946) self.llm_engine = self.engine_class.from_engine_args(
(MyLLM pid=70946) File "/usr/local/miniconda3/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 484, in from_engine_args
(MyLLM pid=70946) engine = cls(
(MyLLM pid=70946) File "/usr/local/miniconda3/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 273, in __init__
(MyLLM pid=70946) self.model_executor = executor_class(vllm_config=vllm_config, )
(MyLLM pid=70946) File "/usr/local/miniconda3/lib/python3.10/site-packages/vllm/executor/executor_base.py", line 262, in __init__
(MyLLM pid=70946) super().__init__(*args, **kwargs)
(MyLLM pid=70946) File "/usr/local/miniconda3/lib/python3.10/site-packages/vllm/executor/executor_base.py", line 51, in __init__
(MyLLM pid=70946) self._init_executor()
(MyLLM pid=70946) File "/usr/local/miniconda3/lib/python3.10/site-packages/vllm/executor/ray_distributed_executor.py", line 90, in _init_executor
(MyLLM pid=70946) self._init_workers_ray(placement_group)
(MyLLM pid=70946) File "/usr/local/miniconda3/lib/python3.10/site-packages/vllm/executor/ray_distributed_executor.py", line 355, in _init_workers_ray
(MyLLM pid=70946) self._run_workers("init_device")
(MyLLM pid=70946) File "/usr/local/miniconda3/lib/python3.10/site-packages/vllm/executor/ray_distributed_executor.py", line 476, in _run_workers
(MyLLM pid=70946) self.driver_worker.execute_method(sent_method, *args, **kwargs)
(MyLLM pid=70946) File "/usr/local/miniconda3/lib/python3.10/site-packages/vllm/worker/worker_base.py", line 575, in execute_method
(MyLLM pid=70946) raise e
(MyLLM pid=70946) File "/usr/local/miniconda3/lib/python3.10/site-packages/vllm/worker/worker_base.py", line 566, in execute_method
(MyLLM pid=70946) return run_method(target, method, args, kwargs)
(MyLLM pid=70946) File "/usr/local/miniconda3/lib/python3.10/site-packages/vllm/utils.py", line 2220, in run_method
(MyLLM pid=70946) return func(*args, **kwargs)
(MyLLM pid=70946) File "/usr/local/miniconda3/lib/python3.10/site-packages/vllm/worker/worker.py", line 155, in init_device
(MyLLM pid=70946) torch.cuda.set_device(self.device)
(MyLLM pid=70946) File "/usr/local/miniconda3/lib/python3.10/site-packages/torch/cuda/__init__.py", line 478, in set_device
(MyLLM pid=70946) torch._C._cuda_setDevice(device)
(MyLLM pid=70946) File "/usr/local/miniconda3/lib/python3.10/site-packages/torch/cuda/__init__.py", line 319, in _lazy_init
(MyLLM pid=70946) torch._C._cuda_init()
(MyLLM pid=70946) RuntimeError: No CUDA GPUs are available
I found in transformers==4.47.1 the script could run normally. However when I tried transformers==4.48.0, 4.48.1 and 4.49.0 I got the error messages above. Then I checked pip envs with pip list and found only transformers versions are different.
I've tried to change vllm version between 0.7.0 and 0.7.2, the behavior is the same.
I make a issue in transformers repo: huggingface/transformers#36295
Related issue in Ray project: #13230
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