vllm [Bug]:在示例化LLM类后无法清理内存使用,

zmeyuzjn  于 6个月前  发布在  其他
关注(0)|答案(2)|浏览(49)

当前环境

创建了一个新的conda环境并运行
pip install vllm==0.4.2

> python collect_env.py

Collecting environment information...
PyTorch version: N/A
Is debug build: N/A
CUDA used to build PyTorch: N/A
ROCM used to build PyTorch: N/A

OS: Fedora Linux 37 (Workstation Edition) (x86_64)
GCC version: (GCC) 12.3.1 20230508 (Red Hat 12.3.1-1)
Clang version: Could not collect
CMake version: version 3.27.7
Libc version: glibc-2.36

Python version: 3.9.18 | packaged by conda-forge | (main, Aug 30 2023, 03:49:32)  [GCC 12.3.0] (64-bit runtime)
Python platform: Linux-6.5.12-100.fc37.x86_64-x86_64-with-glibc2.36
Is CUDA available: N/A
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3050 Ti Laptop GPU
Nvidia driver version: 545.29.06
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: N/A

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      39 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             20
On-line CPU(s) list:                0-19
Vendor ID:                          GenuineIntel
Model name:                         12th Gen Intel(R) Core(TM) i9-12900HK
CPU family:                         6
Model:                              154
Thread(s) per core:                 2
Core(s) per socket:                 14
Socket(s):                          1
Stepping:                           3
CPU(s) scaling MHz:                 45%
CPU max MHz:                        5000.0000
CPU min MHz:                        400.0000
BogoMIPS:                           5836.80
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 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 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 arch_lbr ibt flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          544 KiB (14 instances)
L1i cache:                          704 KiB (14 instances)
L2 cache:                           11.5 MiB (8 instances)
L3 cache:                           24 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-19
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: 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
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] No relevant packages
[conda] No relevant packagesROCM 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	CPU Affinity	NUMA Affinity	GPU NUMA ID
GPU0	 X 	0-19	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

🐛 描述bug

第二次运行f()时出现GPU OOM错误。

sd2nnvve

sd2nnvve1#

有趣的问题。我已经尝试使用以下代码复现这个问题:

def 

...
f()
for _ in range(100):
cleanup()
f()

():
    # your code here

def `cleanup`():
    # your code here

def `cuda context`():
    # your code here

但是我无法复现这个问题,尽管cleanup函数可以释放大部分内存,但它只反转了很小的一部分。我怀疑这小部分与cuda context有关,这是一个常见现象。

LLM模型是Llama-7B,GPU是A800,vllm最近从源代码构建。也许你可以克隆'main'分支并从源代码构建以测试是否可以再次复现此问题。

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