[Bug]: VLLM (0.7.0) will report gpu missing on the hosting node in Ray

[pcpLiu]

Your current environment

The output of `python collect_env.py`

root@626dba5b7671:/# python collect_env.py 
INFO 01-31 10:16:44 __init__.py:183] Automatically detected platform cuda.
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: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.35

Python version: 3.10.12 (main, Jan 17 2025, 14:35:34) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.8.0-51-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 4090
Nvidia driver version: 550.90.07
cuDNN version: Could not collect
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:                        43 bits physical, 48 bits virtual
Byte Order:                           Little Endian
CPU(s):                               64
On-line CPU(s) list:                  0-63
Vendor ID:                            AuthenticAMD
Model name:                           AMD EPYC 7532 32-Core Processor
CPU family:                           23
Model:                                49
Thread(s) per core:                   2
Core(s) per socket:                   32
Socket(s):                            1
Stepping:                             0
Frequency boost:                      enabled
CPU max MHz:                          2400.0000
CPU min MHz:                          1500.0000
BogoMIPS:                             4799.86
Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sev sev_es
Virtualization:                       AMD-V
L1d cache:                            1 MiB (32 instances)
L1i cache:                            1 MiB (32 instances)
L2 cache:                             16 MiB (32 instances)
L3 cache:                             256 MiB (16 instances)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-63
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: Not affected
Vulnerability Retbleed:               Mitigation; untrained return thunk; SMT enabled with STIBP protection
Vulnerability Spec rstack overflow:   Mitigation; Safe RET
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; Retpolines; IBPB conditional; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

Versions of relevant libraries:
[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-ml-py==12.570.86
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] pyzmq==26.2.1
[pip3] torch==2.5.1
[pip3] torchvision==0.20.1
[pip3] transformers==4.48.2
[pip3] triton==3.1.0
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.7.0
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0	CPU Affinity	NUMA Affinity	GPU NUMA ID
GPU0	 X 	0-63	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

NVIDIA_DRIVER_CAPABILITIES=all
LD_LIBRARY_PATH=/usr/local/lib/python3.10/dist-packages/cv2/../../lib64:
NCCL_CUMEM_ENABLE=0
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY

Model Input Dumps

No response

🐛 Describe the bug

Context

I'm using Ray Serve to deploy a vLLM App. It was working well til recently we upgrade version of vLLM to 0.7.0 and adapted to the API change.

Hardware Env

We have a instance with six 4090 GPUs. We deployed a Ray cluster on it with one head node and 5 worker nodes. All are docker containers. Each container is attached to a gpu.

Issue

The core issue is that whenever the vLLM app tries to load the model from the disk, it fails to find GPU to the container where the APP is hosted.

• All containers have exactly the same ENV
• We can use vllm cli to run the model directly without any issue.
• We tried to re-deploy many times. Each time the APP could be hosted on an arbitrary node. Whenever a node becomes the hosting node, it will throw error of that it does not have GPU. But in the cases when that node is not the hosted node, it can work well and load model weights smoothly.
• We are now trying to downgrade the vllm version but want to get an idea if this is a bug or our usage issue. Thanks!

Ray Error log

The error line seems

 ValueError: Current node has no GPU available. current_node_resource={'node:172.17.0.6_group_0_5ea4bf00a38e2ed9e9af4d4e2c3d2c000000': 0.001, 'accelerator_type:G': 1.0, 'node:172.17.0.6_group_5ea4bf00a38e2ed9e9af4d4e2c3d2c000000': 0.001, 'CPU': 63.0, 'memory': 10593529856.0, 'object_store_memory': 4540084224.0, 'node:172.17.0.6': 0.999, 'bundle_group_0_5ea4bf00a38e2ed9e9af4d4e2c3d2c000000': 999.999, 'bundle_group_5ea4bf00a38e2ed9e9af4d4e2c3d2c000000': 999.999}. vLLM engine cannot start without GPU. Make sure you have at least 1 GPU available in a node current_node_id='6f6229bb91687736efbb6174c5885ad0ad7f5aa6fb53ad20afaee93a' current_ip='172.17.0.6'.

Full error log: https://gist.github.com/pcpLiu/0aea4772b3273a2e9a6427c77eb25354

App code

Python package version

vllm==0.7.0
ray==2.41.0
ray[serve]==2.41.0

https://gist.github.com/pcpLiu/65fc7fd487c2afd91ed34b27d8eb5b0a

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