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Collecting environment information...
PyTorch version: 2.2.1+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 3.29.2
Libc version: glibc-2.35
Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.14.0-284.59.1.el9_2.x86_64-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 L40S
GPU 1: NVIDIA L40S
Nvidia driver version: 550.54.14
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: 52 bits physical, 57 bits virtual
Byte Order: Little Endian
CPU(s): 128
On-line CPU(s) list: 0-127
Vendor ID: AuthenticAMD
Model name: AMD EPYC 9334 32-Core Processor
CPU family: 25
Model: 17
Thread(s) per core: 2
Core(s) per socket: 32
Socket(s): 2
Stepping: 1
Frequency boost: enabled
CPU max MHz: 3910.2529
CPU min MHz: 1500.0000
BogoMIPS: 5399.76
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 pcid sse4_1 sse4_2 x2apic 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 invpcid_single hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif x2avic v_spec_ctrl avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid overflow_recov succor smca fsrm flush_l1d
Virtualization: AMD-V
L1d cache: 2 MiB (64 instances)
L1i cache: 2 MiB (64 instances)
L2 cache: 64 MiB (64 instances)
L3 cache: 256 MiB (8 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-31,64-95
NUMA node1 CPU(s): 32-63,96-127
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
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, 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-nccl-cu12==2.19.3
[pip3] torch==2.2.1
[pip3] triton==2.2.0
[pip3] vllm-nccl-cu12==2.18.1.0.3.0
[conda] Could not collectROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.4.1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0 GPU1 NIC0 NIC1 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X SYS SYS SYS 32-63,96-127 1 N/A
GPU1 SYS X SYS SYS 32-63,96-127 1 N/A
NIC0 SYS SYS X PIX
NIC1 SYS SYS PIX 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
您希望如何使用vllm
我想在一个已经安装了nvidia GPU operator的openshift集群上运行一个mixtral-8x7b-instruct的推理。当我运行以下yaml文件时,我得到以下冻结的日志输出:
apiVersion: apps/v1
kind: Deployment
metadata:
name: mixtral-8x7b-instruct-vllm-pod
spec:
replicas: 1
selector:
matchLabels:
app: mixtral-8x7b-instruct-vllm-pod
template:
metadata:
labels:
app: mixtral-8x7b-instruct-vllm-pod
spec:
containers:
- name: mixtral-8x7b-instruct-vllm-pod
image: vllm/vllm-openai:v0.2.7
args: ["--model", "mistralai/Mixtral-8x7B-Instruct-v0.1", "--tensor-parallel-size", "2", "--dtype", "half"]
ports:
- containerPort: 8000
volumeMounts:
- name: huggingface-cache
mountPath: /root/.cache/huggingface
env:
- name: HUGGING_FACE_HUB_TOKEN
value: xxxxxxx
resources:
limits:
nvidia.com/gpu: "2"
volumes:
- name: huggingface-cache
persistentVolumeClaim:
claimName: example-pv-filesystem
hostIPC: true
注意 如果我使用较小的mistral模型和一个GPU,它按预期工作。只有在添加2个或更多GPU时,它才会冻结。
9条答案
按热度按时间edqdpe6u1#
v0.2.7版本相当旧;您可以尝试使用当前的v0.4.1版本吗?
此外,从容器内部的'collect_env.py'输出也会很有帮助,例如:
$ kubectl exec -- python3 -c 'import requests; exec(requests.get(" https://raw.githubusercontent.com/vllm-project/vllm/main/collect_env.py").text.text) )'
flvlnr442#
v0.2.7相当旧;你能尝试使用当前的v0.4.1吗?
此外,从容器内部的'collect_env.py'输出也会很有帮助,例如:
$ kubectl exec -- python3 -c 'import requests; exec(requests.get(" https://raw.githubusercontent.com/vllm-project/vllm/main/collect_env.py").text .text))'
感谢您的回复!
我应该提到我测试了标签latest、v0.4.1、v0.4.0、v0.3.2和v0.2.7,因为这与#4455有关的一个问题。
然而,在vllm/vllm-openai:latest pod内,我运行了collect_env.py
bttbmeg03#
vllm 0.4.1 + qwen-14b-chat the yaml as below:
dojqjjoe4#
好的,我按照你的示例进行了一些修改
图片
vllm/vllm-openai:0.4.1
不存在,但vllm/vllm-openai:v0.4.1
存在应用程序无法启动,我得到了错误
但是当我注解掉命令并在pod内搜索
/mnt/models/models--mistralai--Mixtral-8x7B-Instruct-v0.1
时,我可以找到该目录y0u0uwnf5#
我遇到了同样的问题
@jayteaftw ,你在这方面有进展吗?
ou6hu8tu6#
我正在遇到相同的问题
在这个问题上你有什么进展吗@jayteaftw ?
我不明白为什么我们会有同样的问题,但是的,我的这个问题仍然存在,即使是在4.0.3版本中。
xkrw2x1b7#
@jayteaftw
我看到RH有一个ubi vllm镜像,它对我有效。你可能也想试试这个。
quay.io/rh-aiservices-bu/vllm-openai-ubi9:0.4.2
这将帮助你从huggingface下载镜像,所以对于你的情况,在
container.args
中设置--model mistralai/Mixtral-8x7B-Instruct-v0.1
2o7dmzc58#
@jayteaftw 我看到RH有一个ubi vllm镜像,它对我有效,你可能也想试试这个。
quay.io/rh-aiservices-bu/vllm-openai-ubi9:0.4.2
这将帮助你从huggingface下载镜像,所以对于你的情况,在
container.args
中设置--model mistralai/Mixtral-8x7B-Instruct-v0.1
感谢你的建议。然而,当我尝试在Openshift上运行他们的镜像时,我仍然遇到了同样的问题。当使用超过1个GPU时,它会卡住。我甚至尝试从源代码编译并将其更改为使用新的0.4.3版本,但结果仍然是一样的
djp7away9#
遇到了与OP相同的问题,尝试过0.4.2和0.4.3版本,但都没有成功。希望VLLM能提供一些关于正确实现的反馈