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README
Apache-2.0

fastllm

English Document

介绍

fastllm是c++实现,后端无依赖(仅依赖CUDA,无需依赖PyTorch)的高性能大模型推理库。

可实现MOE模型混合推理,eypc 9374f*2 + 24路DDR5 4800 + 4090 24G,推理DeepSeek R1 671B INT4模型单路可达20+tps。

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| 快速开始 | DeepSeek部署指南 | 版本日志 |

亮点功能

  • 🚀 DeepSeek混合推理,消费级单卡即可多并发部署,后续将支持多卡提速
  • 🚀 双CPU仅占用1份内存,部署DeepSeek R1 671b int4 共占用内存340G
  • 🚀 支持多NUMA节点加速
  • 🚀 支持动态Batch,流式输出
  • 🚀 支持多卡部署,支持GPU + CPU混合部署
  • 🚀 前后端分离设计,便于支持新的计算设备
  • 🚀 后端纯c++实现,便于跨平台移植,可在安卓上直接编译
  • 🚀 支持Python自定义模型结构

快速开始

安装

  • PIP安装 由于目前pypi限制库大小,安装包中不含cuda依赖,安装ftllm之前建议先手动安装cuda12以上版本 (已安装cuda可跳过)
wget https://developer.download.nvidia.com/compute/cuda/12.8.1/local_installers/cuda_12.8.1_570.124.06_linux.run
sudo sh cuda_12.8.1_570.124.06_linux.run

Linux系统可尝试直接pip安装,命令如下:

pip install ftllm

(若使用时报错,可参考ftllm报错 )

  • 源码安装

若pip安装失败或有其它特殊需求,可以用源码编译安装 源码安装后如果需要卸载,方法和PIP安装一样

pip uninstall ftllm

建议使用cmake编译,需要提前安装gcc,g++ (建议9.4以上), make, cmake (建议3.23以上)

GPU编译需要提前安装好CUDA编译环境,建议使用尽可能新的CUDA版本

使用如下命令编译

bash install.sh -DUSE_CUDA=ON -D CMAKE_CUDA_COMPILER=$(which nvcc) # 编译GPU版本
# bash install.sh -DUSE_CUDA=ON -DCUDA_ARCH=89 -D CMAKE_CUDA_COMPILER=$(which nvcc) # 可以指定CUDA架构,如4090使用89架构
# bash install.sh # 仅编译CPU版本

其他不同平台的编译可参考文档 TFACC平台

运行demo程序

以Qwen/Qwen2-0.5B-Instruct模型为例,可以运行一个较小模型测试安装是否成功

命令行聊天:

ftllm run Qwen/Qwen2-0.5B-Instruct

webui:

ftllm webui Qwen/Qwen2-0.5B-Instruct

api server (openai风格):

ftllm server Qwen/Qwen2-0.5B-Instruct

启动本地模型

可以启动本地下载好的Hugging Face模型,假设本地模型路径为 /mnt/Qwen/Qwen2-0.5B-Instruct/ 则可以用如下命令启动(webui, server类似)

ftllm run /mnt/Qwen/Qwen2-0.5B-Instruct/

模糊启动

如果记不住模型名,可以输入大概的模型名(不保证能匹配成功) 例如:

ftllm run qwen2-7b-awq
ftllm run deepseek-v3-0324-int4

设置缓存目录

模型会下载到缓存目录(默认~/.cache),可以通过环境变量 FASTLLM_CACHEDIR 来设置缓存目录,例如在Linux下:

export FASTLLM_CACHEDIR=/mnt/

常用参数

以下是运行 ftllm 模块时常用的参数说明:

通用参数
  • --device:

    • 描述: 指定模型运行的计算设备。
    • 常用值: cpucudanumamulticuda
    • 示例: --device cpu--device cuda
    • 使用多显卡: --device multicuda:0,1
    • 使用显卡+CPU: --device multicuda:0,cpu
    • 按比例使用多显卡+CPU: --device multicuda:0:4,1:5,cpu:1 (cuda:0计算4/10, cuda:1计算5/10, cpu计算1/10)
  • --moe_device:

    • 描述: 指定 MOE(Mixture of Experts)层的计算设备。
    • 常用值: cpucudanuma
    • 示例: --moe_device cpu
    • 说明: 一般和device指定为不同的设备实现混合推理,例如 --device cuda --moe_device cpu来实现MOE模型的单卡+CPU混合推理。 --device cuda --moe_device numa 来实现MOE模型的单卡+多NUMA节点加速推理
  • -t--threads:

    • 描述: 设置使用的CPU线程数。
    • 示例: -t 27
  • --dtype:

    • 描述: 指定模型的数据类型。
    • 可选值: int4 或其他支持的数据类型。
    • 示例: --dtype int4
    • 说明: 使用原始模型时,指定此参数可以在线量化模型。例如下述命令会将DeepSeek-R1在线量化为int4后运行。
    ftllm run deepseek-ai/DeepSeek-R1 --dtype int4
    

    若使用的模型已经是量化好的模型(例如AWQ模型,Fastllm导出的量化模型等),建议不指定该参数

  • --moe_experts:

    • 描述: 指定 MOE(Mixture of Experts)层使用的专家数。不设定则根据模型配置设定。减少专家数可以提高推理速度,但可能降低推理准确度
    • 示例: --moe_experts 6
  • --port:

    • 描述: 指定服务运行的端口号。
    • 示例: --port 8080

模型下载

可以使用如下命令将模型下载到本地

ftllm download deepseek-ai/DeepSeek-R1

模型导出

如果使用量化加载模型(如--dtype int4),那么每次读取模型时会在线量化,读取速度较慢。

ftllm.export 是一个用于导出和转换模型权重的工具。它支持将模型权重转换为不同的数据类型。以下是如何使用 ftllm.export 的详细说明。

命令格式

python3 -m ftllm.export -p <模型路径> -o <输出路径> --dtype <数据类型> -t <线程数>

示例命令

python3 -m ftllm.export -p /mnt/DeepSeek-V3 -o /mnt/DeepSeek-V3-INT4 --dtype int4 -t 16

加载导出后的模型

导出后的模型使用方法和原始模型类似,使用导出模型时--dtype参数将被忽略

例如

ftllm run /mnt/DeepSeek-V3-INT4/
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