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Xlite轻量化推理运行时

介绍

xlite (GVirt前端)：轻量级Transformer模型运行时，支持多样性算力协同，当前支持在昇腾硬件上高效运行。 xlite公开了Transformer运行所需的模型构图以及算子，所有算子基于昇腾AscendC/CCE开发。

背景与动机

在大模型推理场景中，传统的单流串行执行模式存在以下问题：

核间负载不均：不同AICORE之间的任务分配不均衡，部分核心闲置
资源浪费多：计算资源和传输资源利用率低，存在显著浪费
执行时间长：Host CPU下发算子开销大，造成严重的host bond问题

xlite通过以下技术手段解决上述问题：

多流并行：充分利用卡内资源，将单流串行改为多流并行执行
核间负载均衡：核间负载均衡，提升资源利用率
CPU NPU协同：C++侧完全消除Python的GC、线程等干扰，简化Host tiling计算，去除小块内存申请释放及拷贝，消除Host bond

性能效果显著：

在GLM-4.7双机推理场景（40K输入、1K输出、prefix cache命中率约90%）下：

TPOT时延降低17%~30%
吞吐提升13%~41%

详细性能数据参考 PR #7935。

软件架构

xlite已适配vllm_ascend，可通过xlite_graph_config配置快速使能xlite加速效果，使用方法参考官方指导文档。

xlite支持模型见模型列表。

快速开始

安装

# 安装vllm_ascend, 可参考https://github.com/vllm-project/vllm-ascend/blob/main/README.md
# 安装xlite
pip install xlite --extra-index https://download.pytorch.org/whl/cpu/

离线推理示例

import os
from vllm import LLM

# xlite默认支持decode-only模式, 可通过设置 "full_mode": True 使能full模式
model = LLM(model="path/to/Qwen3-32B", tensor_parallel_size=8, additional_config={"xlite_graph_config": {"enabled": True, "full_mode": True}})
outputs = model.generate("Hello, how are you?")

在线服务示例

vllm serve path/to/Qwen3-32B --tensor-parallel-size 8 --additional-config='{"xlite_graph_config": {"enabled": true, "full_mode": true}}'

开发指南

编译构建、容器镜像、源码安装等开发相关内容，请参考开发指南

环境变量

xlite使用的环境变量及其配置方法，请参考环境变量

目录结构

.
├── csrc/    - 轻量化运行时的核心代码
├── xlite/   - python代码，包括tools等
├── doc/     - 相关文档介绍
├── docker/  - 容器镜像Dockerfile
└── tests/   - 测试用例

致谢

本项目的核心算子初始版本和思路来自于华为终端小艺AI Infra团队的贡献，相关优化实现可参考论文：

《XY-Serve: End-to-End Versatile Production Serving for Dynamic LLM Workloads》 [ASPLOS 2026]

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