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vllm-project / vllm

A high-throughput and memory-efficient inference and serving engine for LLMs
https://docs.vllm.ai
Apache License 2.0
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[Installation]: #8255

Closed ndao600 closed 1 week ago

ndao600 commented 1 week ago

Your current environment

The output of `python collect_env.py`

Collecting environment information... /home/miniconda3/envs/vllm/lib/python3.12/site-packages/torch/cuda/init.py:128: UserWarning: CUDA initialization: Unexpected error from cudaGetDeviceCount(). Did you run some cuda functions before calling NumCudaDevices() that might have already set an error? Error 2: out of memory (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:108.) return torch._C._cuda_getDeviceCount() > 0 PyTorch version: 2.4.0+cu121 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: version 3.22.1 Libc version: glibc-2.35

Python version: 3.12.4 | packaged by Anaconda, Inc. | (main, Jun 18 2024, 15:12:24) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.15.153.1-microsoft-standard-WSL2-x86_64-with-glibc2.35 Is CUDA available: False CUDA runtime version: 12.5.82 CUDA_MODULE_LOADING set to: N/A GPU models and configuration: GPU 0: NVIDIA RTX 4000 Ada Generation GPU 1: NVIDIA RTX 4000 Ada Generation GPU 2: NVIDIA RTX 4000 Ada Generation GPU 3: NVIDIA RTX 4000 Ada Generation GPU 4: NVIDIA RTX 4000 Ada Generation GPU 5: NVIDIA RTX 4000 Ada Generation GPU 6: NVIDIA RTX 4000 Ada Generation GPU 7: NVIDIA RTX 4000 Ada Generation

Nvidia driver version: 555.99 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: 48 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 48 On-line CPU(s) list: 0-47 Vendor ID: AuthenticAMD Model name: AMD Ryzen Threadripper 7960X 24-Cores CPU family: 25 Model: 24 Thread(s) per core: 2 Core(s) per socket: 24 Socket(s): 1 Stepping: 1 BogoMIPS: 8387.54 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 tsc_reliable nonstop_tsc cpuid extd_apicid pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core invpcid_single ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx512_bf16 clzero xsaveerptr arat npt nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload avx512vbmi umip avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid fsrm Virtualization: AMD-V Hypervisor vendor: Microsoft Virtualization type: full L1d cache: 768 KiB (24 instances) L1i cache: 768 KiB (24 instances) L2 cache: 24 MiB (24 instances) L3 cache: 32 MiB (1 instance) 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: Mitigation; safe RET 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; Retpolines, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling, PBRSB-eIBRS 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==12.1.3.1 [pip3] nvidia-cuda-cupti-cu12==12.1.105 [pip3] nvidia-cuda-nvrtc-cu12==12.1.105 [pip3] nvidia-cuda-runtime-cu12==12.1.105 [pip3] nvidia-cudnn-cu12==9.1.0.70 [pip3] nvidia-cufft-cu12==11.0.2.54 [pip3] nvidia-curand-cu12==10.3.2.106 [pip3] nvidia-cusolver-cu12==11.4.5.107 [pip3] nvidia-cusparse-cu12==12.1.0.106 [pip3] nvidia-ml-py==12.560.30 [pip3] nvidia-nccl-cu12==2.20.5 [pip3] nvidia-nvjitlink-cu12==12.6.68 [pip3] nvidia-nvtx-cu12==12.1.105 [pip3] pyzmq==26.2.0 [pip3] torch==2.4.0 [pip3] torchvision==0.19.0 [pip3] transformers==4.44.2 [pip3] triton==3.0.0 [conda] numpy 1.26.4 pypi_0 pypi [conda] nvidia-cublas-cu12 12.1.3.1 pypi_0 pypi [conda] nvidia-cuda-cupti-cu12 12.1.105 pypi_0 pypi [conda] nvidia-cuda-nvrtc-cu12 12.1.105 pypi_0 pypi [conda] nvidia-cuda-runtime-cu12 12.1.105 pypi_0 pypi [conda] nvidia-cudnn-cu12 9.1.0.70 pypi_0 pypi [conda] nvidia-cufft-cu12 11.0.2.54 pypi_0 pypi [conda] nvidia-curand-cu12 10.3.2.106 pypi_0 pypi [conda] nvidia-cusolver-cu12 11.4.5.107 pypi_0 pypi [conda] nvidia-cusparse-cu12 12.1.0.106 pypi_0 pypi [conda] nvidia-ml-py 12.560.30 pypi_0 pypi [conda] nvidia-nccl-cu12 2.20.5 pypi_0 pypi [conda] nvidia-nvjitlink-cu12 12.6.68 pypi_0 pypi [conda] nvidia-nvtx-cu12 12.1.105 pypi_0 pypi [conda] pyzmq 26.2.0 pypi_0 pypi [conda] torch 2.4.0 pypi_0 pypi [conda] torchvision 0.19.0 pypi_0 pypi [conda] transformers 4.44.2 pypi_0 pypi [conda] triton 3.0.0 pypi_0 pypi ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: N/A vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X SYS SYS SYS SYS SYS SYS SYS N/A GPU1 SYS X SYS SYS SYS SYS SYS SYS N/A GPU2 SYS SYS X SYS SYS SYS SYS SYS N/A GPU3 SYS SYS SYS X SYS SYS SYS SYS N/A GPU4 SYS SYS SYS SYS X SYS SYS SYS N/A GPU5 SYS SYS SYS SYS SYS X SYS SYS N/A GPU6 SYS SYS SYS SYS SYS SYS X SYS N/A GPU7 SYS SYS SYS SYS SYS SYS SYS X 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

How you are installing vllm

pip install -vvv vllm

pip install vllm I was using vllm 0.5.4, which was running fine without any issues. I just upgraded to 0.6.0 and I got the above message. I have tried to delate conda env and re-installed, set cuda visible devices, cuda arch without any success. Here is the error message when I try to vllm serve: Traceback (most recent call last): File "/home/miniconda3/envs/vllm/bin/vllm", line 5, in from vllm.scripts import main File "/home/miniconda3/envs/vllm/lib/python3.10/site-packages/vllm/init.py", line 3, in from vllm.engine.arg_utils import AsyncEngineArgs, EngineArgs File "/home/miniconda3/envs/vllm/lib/python3.10/site-packages/vllm/engine/arg_utils.py", line 11, in from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig, File "/home/miniconda3/envs/vllm/lib/python3.10/site-packages/vllm/config.py", line 12, in from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS File "/home/miniconda3/envs/vllm/lib/python3.10/site-packages/vllm/model_executor/layers/quantization/init.py", line 3, in from vllm.model_executor.layers.quantization.aqlm import AQLMConfig File "/home/miniconda3/envs/vllm/lib/python3.10/site-packages/vllm/model_executor/layers/quantization/aqlm.py", line 11, in from vllm import customops as ops File "/home/miniconda3/envs/vllm/lib/python3.10/site-packages/vllm/_custom_ops.py", line 10, in from vllm.platforms import current_platform File "/home/miniconda3/envs/vllm/lib/python3.10/site-packages/vllm/platforms/init.py", line 51, in from .cuda import CudaPlatform File "/home/miniconda3/envs/vllm/lib/python3.10/site-packages/vllm/platforms/cuda.py", line 79, in warn_if_different_devices() File "/home/miniconda3/envs/vllm/lib/python3.10/site-packages/vllm/platforms/cuda.py", line 41, in wrapper return fn(*args, *kwargs) File "/home/miniconda3/envs/vllm/lib/python3.10/site-packages/vllm/platforms/cuda.py", line 66, in warn_if_different_devices device_names = [get_physical_device_name(i) for i in range(device_ids)] File "/home/miniconda3/envs/vllm/lib/python3.10/site-packages/vllm/platforms/cuda.py", line 66, in device_names = [get_physical_device_name(i) for i in range(device_ids)] File "/home/miniconda3/envs/vllm/lib/python3.10/site-packages/vllm/platforms/cuda.py", line 41, in wrapper return fn(args, **kwargs) File "/home/miniconda3/envs/vllm/lib/python3.10/site-packages/vllm/platforms/cuda.py", line 59, in get_physical_device_name return pynvml.nvmlDeviceGetName(handle) File "/home/miniconda3/envs/vllm/lib/python3.10/site-packages/pynvml.py", line 2182, in wrapper return res.decode() UnicodeDecodeError: 'utf-8' codec can't decode byte 0xf8 in position 0: invalid start byte

Interestingly, I intermittently got this message when running other programs and never able to solve it UserWarning: CUDA initialization: Unexpected error from cudaGetDeviceCount(). Did you run some cuda functions before calling NumCudaDevices() that might have already set an error? Error 2: out of memory (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:108.) return torch._C._cuda_getDeviceCount() > 0

I would really appreciate the help to troubleshoot this.

Before submitting a new issue...

axel7083 commented 1 week ago

I am facing the same issue while running vllm/vllm-openai:latest inside podman.

Here is the logs

$: podman run --entrypoint=bash -it --device nvidia.com/gpu=all -p 8000:8000 --ipc=host vllm/vllm-openai:latest
/vllm-workspace# python3 -m vllm.entrypoints.openai.api_server --model mistralai/Mistral-7B-v0.1
Process SpawnProcess-1:
Traceback (most recent call last):
  File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/rpc/server.py", line 236, in run_rpc_server
    server = AsyncEngineRPCServer(async_engine_args, usage_context, rpc_path)
  File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/rpc/server.py", line 34, in __init__
    self.engine = AsyncLLMEngine.from_engine_args(
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 735, in from_engine_args
    engine = cls(
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 615, in __init__
    self.engine = self._init_engine(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 835, in _init_engine
    return engine_class(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 262, in __init__
    super().__init__(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py", line 305, in __init__
    self.model_executor = executor_class(
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/executor_base.py", line 47, in __init__
    self._init_executor()
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/gpu_executor.py", line 39, in _init_executor
    self.driver_worker.init_device()
  File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker.py", line 168, in init_device
    _check_if_gpu_supports_dtype(self.model_config.dtype)
  File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker.py", line 459, in _check_if_gpu_supports_dtype
    gpu_name = current_platform.get_device_name()
  File "/usr/local/lib/python3.10/dist-packages/vllm/platforms/cuda.py", line 107, in get_device_name
    return get_physical_device_name(physical_device_id)
  File "/usr/local/lib/python3.10/dist-packages/vllm/platforms/cuda.py", line 41, in wrapper
    return fn(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/platforms/cuda.py", line 59, in get_physical_device_name
    return pynvml.nvmlDeviceGetName(handle)
  File "/usr/local/lib/python3.10/dist-packages/pynvml.py", line 2182, in wrapper
    return res.decode()
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xf8 in position 0: invalid start byte
ERROR 09-09 06:00:53 api_server.py:186] RPCServer process died before responding to readiness probe

Debugging the container

inside the container I am able to reproduce the issue,

/vllm-workspace# python3
>>> import pynvml
>>> pynvml.nvmlInit()
>>> device_ids: int = pynvml.nvmlDeviceGetCount()
>>> assert device_ids > 0
>>> handle = pynvml.nvmlDeviceGetHandleByIndex(0)
>>> pynvml.nvmlDeviceGetName(handle)

Which correspond to the issue raised by the server when reaching the following line

https://github.com/vllm-project/vllm/blob/4ef41b84766670c1bd8079f58d35bf32b5bcb3ab/vllm/platforms/cuda.py#L59

Related issues

https://github.com/wandb/wandb/issues/7770

Problem seems to be linked to driver version 555.X (I am currently also having this)

Solution

Updating to the latest driver 560.X fixed the problem

ndao600 commented 1 week ago

Yes. I just updated the driver and it is not fixed. Thank you.