Description
Your current environment
INFO 06-20 10:55:55 [init.py:244] Automatically detected platform cuda.
Collecting environment information...
System Info
==============================
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 : version 4.0.2
Libc version : glibc-2.35
==============================
PyTorch Info
PyTorch version : 2.7.0+cu126
Is debug build : False
CUDA used to build PyTorch : 12.6
ROCM used to build PyTorch : N/A
==============================
Python Environment
Python version : 3.12.10 | packaged by conda-forge | (main, Apr 10 2025, 22:21:13) [GCC 13.3.0] (64-bit runtime)
Python platform : Linux-5.10.134-16.1.al8.x86_64-x86_64-with-glibc2.35
==============================
CUDA / GPU Info
Is CUDA available : True
CUDA runtime version : Could not collect
CUDA_MODULE_LOADING set to : LAZY
GPU models and configuration : GPU 0: NVIDIA H20
Nvidia driver version : 550.54.15
cuDNN version : Could not collect
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True
==============================
CPU Info
架构： x86_64
CPU 运行模式： 32-bit, 64-bit
Address sizes: 52 bits physical, 57 bits virtual
字节序： Little Endian
CPU: 24
在线 CPU 列表： 0-23
厂商 ID： GenuineIntel
型号名称： Intel(R) Xeon(R) Platinum 8469C
CPU 系列： 6
型号： 143
每个核的线程数： 2
每个座的核数： 12
座： 1
步进： 8
CPU 最大 MHz： 3800.0000
CPU 最小 MHz： 800.0000
BogoMIPS： 5200.00
标记： fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ibrs_enhanced fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd ida arat hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm uintr md_clear serialize tsxldtrk amx_bf16 avx512_fp16 amx_tile amx_int8 arch_capabilities
超管理器厂商： KVM
虚拟化类型： 完全
L1d 缓存： 576 KiB (12 instances)
L1i 缓存： 384 KiB (12 instances)
L2 缓存： 24 MiB (12 instances)
L3 缓存： 97.5 MiB (1 instance)
NUMA 节点： 1
NUMA 节点0 CPU： 0-23
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 store bypass: Vulnerable
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced IBRS, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
==============================
Versions of relevant libraries
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.6.4.1
[pip3] nvidia-cuda-cupti-cu12==12.6.80
[pip3] nvidia-cuda-nvrtc-cu12==12.6.77
[pip3] nvidia-cuda-runtime-cu12==12.6.77
[pip3] nvidia-cudnn-cu12==9.5.1.17
[pip3] nvidia-cufft-cu12==11.3.0.4
[pip3] nvidia-cufile-cu12==1.11.1.6
[pip3] nvidia-curand-cu12==10.3.7.77
[pip3] nvidia-cusolver-cu12==11.7.1.2
[pip3] nvidia-cusparse-cu12==12.5.4.2
[pip3] nvidia-cusparselt-cu12==0.6.3
[pip3] nvidia-ml-py==12.575.51
[pip3] nvidia-nccl-cu12==2.26.2
[pip3] nvidia-nvjitlink-cu12==12.6.85
[pip3] nvidia-nvtx-cu12==12.6.77
[pip3] pytorch-lightning==2.5.1.post0
[pip3] pyzmq==26.2.0
[pip3] torch==2.7.0
[pip3] torchaudio==2.7.0
[pip3] torchmetrics==0.10.3
[pip3] torchvision==0.22.0
[pip3] transformers==4.52.4
[pip3] triton==3.3.0
[conda] numpy 1.26.4 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.6.4.1 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu12 12.6.80 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu12 12.6.77 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu12 12.6.77 pypi_0 pypi
[conda] nvidia-cudnn-cu12 9.5.1.17 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.3.0.4 pypi_0 pypi
[conda] nvidia-cufile-cu12 1.11.1.6 pypi_0 pypi
[conda] nvidia-curand-cu12 10.3.7.77 pypi_0 pypi
[conda] nvidia-cusolver-cu12 11.7.1.2 pypi_0 pypi
[conda] nvidia-cusparse-cu12 12.5.4.2 pypi_0 pypi
[conda] nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi
[conda] nvidia-ml-py 12.575.51 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.26.2 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.6.85 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.6.77 pypi_0 pypi
[conda] pytorch-lightning 2.5.1.post0 pypi_0 pypi
[conda] pyzmq 26.4.0 pypi_0 pypi
[conda] torch 2.7.0 pypi_0 pypi
[conda] torchaudio 2.7.0 pypi_0 pypi
[conda] torchmetrics 0.10.3 pypi_0 pypi
[conda] torchvision 0.22.0 pypi_0 pypi
[conda] transformers 4.52.4 pypi_0 pypi
[conda] triton 3.3.0 pypi_0 pypi
==============================
vLLM Info
ROCM Version : Could not collect
Neuron SDK Version : N/A
vLLM Version : 0.9.1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X 0-23 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
==============================
Environment Variables
NCCL_CUMEM_ENABLE=0
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY
🐛 Describe the bug
AsyncLLMEngine stucks in V1, but works in V0.
predict.py
from vllm import SamplingParams
from vllm.lora.request import LoRARequest
import time
# from loguru import logger
import torch
async def async_gen(model, text, uid):
results_generator = model.generate(
text,
SamplingParams(
n=1, # Number of output sequences to return for each prompt.
top_k=1, # Float that controls the cumulative probability of the top tokens to consider.
temperature=0, # randomness of the sampling
seed=777, # Seed for reprodicibility
skip_special_tokens=False, # Whether to skip special tokens in the output.
max_tokens=1, # Maximum number of tokens to generate per output sequence.
],
logprobs=25
),
uid,
lora_request=LoRARequest("clue_type", 1, "./checkpoint-300")
)
final_output = None
async for request_output in results_generator:
final_output = request_output
return final_output
async def predict_clue_type(model, tokenizer, inputs, uid):
text = inputs["text"]
responses = await async_gen(model, text, uid)
return {"success": True, "prob": prob, "message": ""}
main.py
@app.post("/v1/models/{model_name}:predict")
async def predict(request: Item): # async def predict(request: Item):
inputs = request.dict()
st = time.time()
md5 = hashlib.md5()
md5.update(json.dumps(inputs, indent=4, ensure_ascii=False).encode("utf-8"))
uid = md5.hexdigest()
random_uid = uuid.uuid4()
try:
res = await predict_clue_type(engine, tokenizer, inputs, random_uid)
except:
error_mess = json.dumps(
{ "uid": uid,
"input": inputs,
"error": traceback.format_exc()
}, indent = 4)
#logger.error(error_mess)
print(error_mess)
res = {"success": False, "message": error_mess, "prob": -1}
finally:
et = time.time()
cost_time = round(et - st, 1)
#logger.info(f"Predict {uid} costs : {cost_time}s")
print(f"Predict {uid} costs : {cost_time}s")
return res
@app.on_event("startup")
async def startup_event():
st = time.time()
global tokenizer, engine
tokenizer = AutoTokenizer.from_pretrained('./Qwen3-8B')
engine = AsyncLLMEngine.from_engine_args(
AsyncEngineArgs(
model="./Qwen3-8B",
tensor_parallel_size=1,
gpu_memory_utilization=0.5,
trust_remote_code=True,
dtype="bfloat16" if is_bfloat16_supported() else "float16",
enforce_eager=True,
max_model_len=4096,
disable_log_stats=True,
max_logprobs=25,
enable_lora=True,
max_lora_rank=64,
disable_log_requests=True
)
)
et = time.time()
cost_time = round(et - st, 1)
print(f"Initalization costs : {cost_time}s")
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