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[Bug] bench_serving can not bench sglang with api_key auth #3869

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kebe7jun opened this issue Feb 26, 2025 · 0 comments · May be fixed by #3870
Open
5 tasks done

[Bug] bench_serving can not bench sglang with api_key auth #3869

kebe7jun opened this issue Feb 26, 2025 · 0 comments · May be fixed by #3870

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@kebe7jun
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Checklist

  • 1. I have searched related issues but cannot get the expected help.
  • 2. The bug has not been fixed in the latest version.
  • 3. Please note that if the bug-related issue you submitted lacks corresponding environment info and a minimal reproducible demo, it will be challenging for us to reproduce and resolve the issue, reducing the likelihood of receiving feedback.
  • 4. If the issue you raised is not a bug but a question, please raise a discussion at https://github.com/sgl-project/sglang/discussions/new/choose Otherwise, it will be closed.
  • 5. Please use English, otherwise it will be closed.

Describe the bug

When sglang is started with the --api-key parameter, an authentication error will be reported when using bench_serving test, even if the OPENAI_API_KEY environment variable is added.

Reproduction

  1. Start sglang server with: --model /data/llms/Qwen2.5-7B-Instruct --served-model-name=model --tp 2 --enable-p2p-check --api-key kebe
  2. Run bench serving OPENAI_API_KEY=kebe python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompts 3000 --random-input 1024 --random-output 1024 --random-range-ratio 0.5 --tokenizer Qwen/Qwen2.5-7B-Instruct

Environment

2025-02-26 11:39:27,619 - INFO - flashinfer.jit: Prebuilt kernels not found, using JIT backend
INFO 02-26 11:39:29 init.py:190] Automatically detected platform cuda.
Python: 3.12.8 | packaged by conda-forge | (main, Dec 5 2024, 14:24:40) [GCC 13.3.0]
CUDA available: True
GPU 0,1,2,3,4,5,6,7: NVIDIA GeForce RTX 3090
GPU 0,1,2,3,4,5,6,7 Compute Capability: 8.6
CUDA_HOME: /root/miniconda3/envs/vllm-dev
NVCC: Cuda compilation tools, release 11.8, V11.8.89
CUDA Driver Version: 560.28.03
PyTorch: 2.5.1+cu124
sglang: 0.4.3.post2
sgl_kernel: 0.0.3.post6
flashinfer: 0.2.2
triton: 3.1.0
transformers: 4.48.3
torchao: 0.8.0
numpy: 1.26.4
aiohttp: 3.11.13
fastapi: 0.115.8
hf_transfer: 0.1.9
huggingface_hub: 0.29.1
interegular: 0.3.3
modelscope: 1.23.1
orjson: 3.10.15
packaging: 24.2
psutil: 5.9.0
pydantic: 2.10.6
multipart: 0.0.20
zmq: 26.2.0
uvicorn: 0.34.0
uvloop: 0.21.0
vllm: 0.7.2
openai: 1.64.0
tiktoken: 0.9.0
anthropic: 0.47.2
decord: 0.6.0
NVIDIA Topology:
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 NIC0 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X PXB PXB PXB SYS SYS SYS SYS PXB 0-27,56-83 0 N/A
GPU1 PXB X PXB PXB SYS SYS SYS SYS PIX 0-27,56-83 0 N/A
GPU2 PXB PXB X PIX SYS SYS SYS SYS PXB 0-27,56-83 0 N/A
GPU3 PXB PXB PIX X SYS SYS SYS SYS PXB 0-27,56-83 0 N/A
GPU4 SYS SYS SYS SYS X PXB PXB PXB SYS 28-55,84-111 1 N/A
GPU5 SYS SYS SYS SYS PXB X PXB PXB SYS 28-55,84-111 1 N/A
GPU6 SYS SYS SYS SYS PXB PXB X PIX SYS 28-55,84-111 1 N/A
GPU7 SYS SYS SYS SYS PXB PXB PIX X SYS 28-55,84-111 1 N/A
NIC0 PXB PIX PXB PXB SYS SYS SYS SYS 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_bond_0

ulimit soft: 1024

@kebe7jun kebe7jun linked a pull request Feb 26, 2025 that will close this issue
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