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README
Apache-2.0

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图引擎模块(GE)是MindSpore的一个子模块,其代码由C++实现,位于前端模块ME和底层硬件之间,起到承接作用。图引擎模块以ME下发的图作为输入,然后进行一系列的深度图优化操作,最后输出一张可以在底层硬件上高效运行的图。GE针对昇腾AI处理器的硬件结构特点,做了特定的优化工作,以此来充分发挥出昇腾AI处理器的强大算力。在进行模型训练/推理时,GE会被自动调用而用户并不感知。GE主要由GE API和GE Core两部分组成,详细的架构图如下所示。

GE_schema

  • GE API

    GE API是连接前端模块ME和GE Core的接口,负责GE Core中初始化、Session管理模块的接口,支持运行环境初始化,Session创建、销毁,图添加执行。

  • GE Core

    GE Core是GE的核心模块,负责整个训练过程中的图管理。GE Core中的图处理可细分为六大步骤,分别是图准备、图拆分、图优化、图编译、图加载和图执行,对于ME下发的每一张图都会经过这六个步骤的操作,最终得到可以直接在底层硬件上高效执行的图。

    • 图准备 & 整图优化

      完成整图级别的数据准备和优化,涉及到IR库及算子库。使用IR库中算子的InferShape函数,完成整图的Shape推导,以便后续申请内存;同时根据算子的聚合属性,完成某些算子的聚合优化,如allreduce算子,会按照聚合参数,将若干各参数对应梯度的allreduce算子聚合为一个,以此减少通讯耗时。

    • 图拆分

      昇腾AI处理器是一种异构芯片,含有CPU(AICPU)和向量计算部件AICORE,图中每个算子会按照开销模型选择执行的核心,此阶段会对算子进行最优的核心分配,每种核心对应软件上的一个抽象引擎;按照之前对各算子的引擎分配,以引擎为边界,将整图拆分为若干子图,在图边界算子上插入相应的Placeholder算子以做标识,之后的优化、编译、加载操作均会以子图为单位进行,这样可以有效减少优化过程的耗时。

    • 子图优化

      根据子图所属引擎,调用不同的优化器接口执行优化。为了充分发挥昇腾AI处理器中AICORE模块的算力,在AICORE内CUBE单元进行计算的算子会采用一种5D的数据格式,图优化阶段会对相应算子进行4D/5D的类型转换;为了进一步发挥CUBE单元的算力,减少数据搬运次数,GE会对某种范式的算子连接进行融合操作,此步骤也在图优化阶段进行;对所有子图优化之后,需进行算子运行属性计算,以计算输入输出内存大小。

    • 图编译 & 图加载

      GE采用即时算子编译技术,即按照实际网络结构即时编译生成算子可执行程序,同时完成内存复用与内存分配、流分配、算子可执行程序加载等。每个算子执行任务绑定到特定的流上,同一个流的任务是串行执行的,不同流上的任务可以并行执行。图加载阶段按照引擎归属的runtime,将子图加载到硬件上准备执行。

    • 图执行

      最终在硬件上执行子图,并返回相应的输出值。为了提高运行效率,图执行阶段提供了一种下沉模式,可以在底层硬件上连续运行多轮再返回输出值,以此减少从底层硬件拷贝数据的次数。

在训练/推理过程中,上述过程会自动执行,通过上述图操作,GE可以将前端下发的图转换为一种可以在昇腾AI处理器上高效运行的图模式。

安装说明

安装GE

GE内嵌在MindSpore安装包中,MindSpore安装完毕后,GE以三个动态库的方式被调用。

源码安装

GE也支持由源码编译,请参考以下链接完成: 个人开发工具链

社区

贡献

欢迎参与贡献,更多信息详见Contributor Wiki

Release Notes

Release Notes请参考RELEASE.

License

Apache License 2.0

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A connecting link module between frontends and Ascend processors. 展开 收起
C 等 2 种语言
Apache-2.0
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