[Performance]: vllm量化和非量化的性能对比

[xllrun]

Proposal to improve performance

No response

Report of performance regression

No response

Misc discussion on performance

使用qwen32b模型，输入tokens 3000，输出tokens 50左右，在单张A800并发量在11，最大响应时间达到10s，使用awq将qwen32b模型量化为4bit,输入tokens 3000，输出tokens 50左右，无论是显卡利用率设置为0.5还是0.95在单张A800并发量也是在11，最大响应时间达到10s，想知道这是为什么，是什么参数设置不对？理论上量化后显存占用小了，并发数会增加啊

Your current environment (if you think it is necessary)

Collecting environment information... PyTorch version: 2.2.2+cpu Is debug build: False CUDA used to build PyTorch: Could not collect ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64) GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 Clang version: Could not collect CMake version: version 3.16.3 Libc version: glibc-2.31

Python version: 3.10.14 (main, May 6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.15.0-1050-azure-x86_64-with-glibc2.31 Is CUDA available: False CUDA runtime version: 12.2.140 CUDA_MODULE_LOADING set to: N/A GPU models and configuration: GPU 0: NVIDIA A100 80GB PCIe 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: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian Address sizes: 48 bits physical, 48 bits virtual CPU(s): 24 On-line CPU(s) list: 0-23 Thread(s) per core: 1 Core(s) per socket: 24 Socket(s): 1 NUMA node(s): 1 Vendor ID: AuthenticAMD CPU family: 25 Model: 1 Model name: AMD EPYC 7V13 64-Core Processor Stepping: 1 CPU MHz: 2445.438 BogoMIPS: 4890.87 Hypervisor vendor: Microsoft Virtualization type: full L1d cache: 768 KiB L1i cache: 768 KiB L2 cache: 12 MiB L3 cache: 96 MiB NUMA node0 CPU(s): 0-23 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, no microcode Vulnerability Spec store bypass: Vulnerable Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Retpolines, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected 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 aperfmperf pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core invpcid_single vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr rdpru arat umip vaes vpclmulqdq rdpid fsrm

Versions of relevant libraries: [pip3] numpy==1.26.4 [pip3] torch==2.2.2+cpu [pip3] transformers==4.44.0 [conda] numpy 1.26.4 pypi_0 pypi [conda] torch 2.2.2+cpu pypi_0 pypi [conda] transformers 4.44.0 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 NIC0 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X SYS 0-23 0 N/A NIC0 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_an0

[jyyang621]

能问一下怎么启的量化模型吗

[jyyang621]

能问一下怎么启的量化模型吗

[xllrun]

能问一下怎么启的量化模型吗

python -m vllm.entrypoints.openai.api_server --model "**" --served-model-name "" --gpu_memory_utilization 0.5 --quantization awq

[TragedyN]

并发量也和max_num_batched_tokens设置有关

[github-actions[bot]]

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