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README
Apache-2.0

MindSpore Golden Stick

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概述

MindSpore Golden Stick是华为诺亚团队和华为MindSpore团队联合设计开发的一个模型压缩算法集。MindSpore Golden Stick的架构图如下图所示,分为五个部分:

  1. 底层的MindSpore Rewrite模块提供修改前端网络的能力,基于此模块提供的接口,算法开发者可以按照特定的规则对MindSpore的前端网络做节点和拓扑关系的增删查改;

  2. 基于MindSpore Rewite这个基础能力,MindSpore Golden Stick会提供各种类型的算法,比如SimQAT算法、SLB量化算法、SCOP剪枝算法等;

  3. 在算法的更上层,MindSpore Golden Stick还规划了如AMC(自动模型压缩技术)、NAS(网络结构搜索)、HAQ(硬件感知的自动量化)等高阶技术;

  4. 为了方便开发者分析调试算法,MindSpore Golden Stick提供了一些工具,如Visualization工具(可视化工具)、Profiler工具(逐层分析工具)、Summary工具(算法压缩效果分析工具)等;

  5. 在最外层,MindSpore Golden Stick封装了一套简洁的用户接口。

金箍棒架构图

架构图是MindSpore Golden Stick的全貌,其中包含了当前已经实现的功能以及规划在RoadMap中能力。具体开放的功能可以参考对应版本的ReleaseNotes。

设计思路

MindSpore Golden Stick除了提供丰富的模型压缩算法外,一个重要的设计理念是针对业界种类繁多的模型压缩算法,提供给用户一个尽可能统一且简洁的体验,降低用户的算法应用成本。MindSpore Golden Stick通过两个举措来实现该理念:

  1. 统一的算法接口设计,降低用户应用成本

    模型压缩算法种类繁多,有如量化感知训练算法、剪枝算法、矩阵分解算法、知识蒸馏算法等;在每类压缩算法中,还有会各种具体的算法,比如LSQ、PACT都是量化感知训练算法。不同算法的应用方式往往各不相同,这增加了用户应用算法的学习成本。MindSpore Golden Stick对算法应用流程做了梳理和抽象,提供了一套统一的算法应用接口,最大程度缩减算法应用的学习成本。同时这也方便了后续在算法生态的基础上,做一些AMC、NAS、HAQ等高阶技术的探索。

  2. 提供前端网络修改能力,降低算法接入成本

    模型压缩算法往往会针对特定的网络结构做设计或者优化,如感知量化算法往往在网络中的Conv2d、Conv2d + BatchNorm2d或者Conv2d + BatchNorm2d + Relu结构上插入伪量化节点。MindSpore Golden Stick提供了通过接口修改前端网络的能力,算法开发者可以基于此能力制定通用的改图规则去实现算法逻辑,而不需要对每个特定的网络都实现一遍算法逻辑算法。此外MindSpore Golden Stick还会提供了一些调测能力,包括网络dump、逐层profiling、算法效果分析、可视化等能力,旨在帮助算法开发者提升开发和研究效率,帮助用户寻找契合于自己需求的算法。

应用MindSpore Golden Stick算法的一般流程

金箍棒流程图

  1. 压缩阶段

    压缩阶段是指使用MindSpore Golden Stick算法对网络进行压缩的过程,以量化算法为例,压缩阶段主要包含改造网络为伪量化网络、量化重训或者校正、量化参数统计、量化权重、改造网络为真实量化网络。

  2. 部署阶段

    部署阶段是将压缩后的网络在部署环境进行推理的过程,由于MindSpore暂不支持将前端网络进行序列化,所以部署同样需要调用对应的算法接口对网络进行改造,以加载压缩后的checkpoint文件。加载完压缩的checkpoint文件以后的流程和一般的推理流程无异。

  • 应用MindSpore Golden Stick算法的细节,可以在每个算法章节中找到详细说明和示例代码。
  • 流程中的"ms.export"步骤可以参考导出mindir格式文件章节。
  • 流程中的"昇思推理优化工具和运行时"步骤可以参考昇思推理章节。

文档

安装

请参考MindSpore Golden Stick安装教程

快速入门

以一个简单的算法Simulated Quantization (SimQAT) 作为例子,演示如何在训练中应用金箍棒中的算法。

压缩算法

概览
架构
流程
APIs 样例
自动化压缩(TBD)
训练后量化
PTQ RoundToNearest
量化感知训练
SimQAT SLB
剪枝
SCOP uni_pruning(demo) LRP(demo)
其他
Ghost

模型部署

请参考MindSpore Golden Stick部署教程

社区

治理

查看MindSpore如何进行开放治理

交流

贡献

欢迎参与贡献。

许可证

Apache License 2.0

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简介

MindSpore Golden Stick is a open source deep learning model compression algorithom framework. 展开 收起
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