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README
Apache-2.0

ATT

Ascend Training Tools,昇腾训练工具链。针对训练&大模型场景,提供端到端命令行&可视化调试调优工具,帮助用户快速提高模型开发效率。

模型训练迁移全流程

输入图片说明

使用说明

分析迁移工具

  1. 脚本分析工具

    脚本分析工具提供分析脚本,帮助用户在执行迁移操作前,分析基于GPU平台的PyTorch训练脚本中算子、三方库套件、亲和API分析以及动态shape的支持情况。

  2. (推荐)自动迁移工具

    自动迁移只需在训练脚本中导入库代码即可完成模型脚本迁移,使用方式较简单,且修改内容最少。

  3. 脚本迁移工具

    脚本迁移工具提供后端命令行用于将GPU上训练的PyTorch脚本迁移至NPU上,得到新的训练脚本用于训练。

精度工具

  1. api_accuracy_checker(Ascend模型精度预检工具)

    在昇腾NPU上扫描用户训练模型中所有API,进行API复现,给出精度情况的诊断和分析。

  2. ptdbg_ascend(PyTorch精度工具)

    进行PyTorch整网API粒度的数据dump、精度比对和溢出检测,从而定位PyTorch训练场景下的精度问题。

性能工具

  1. compare_tools(性能比对工具)

    提供NPU与GPU性能拆解功能以及算子、通信、内存性能的比对功能。

  2. cluster_analyse(集群分析工具)

    提供多机多卡的集群分析能力(基于通信域的通信分析和迭代耗时分析), 当前需要配合Ascend Insight的集群分析功能使用。

  3. merge_profiling_timeline(合并大json工具)

    融合多个Profiling的timeline在一个json文件中的功能。

Tensorboard

Tensorboard支持NPU性能数据可视化插件PyTorch Profiler TensorBoard NPU Plugin。

支持将Ascend平台采集、解析的Pytorch Profiling数据可视化呈现,也兼容GPU数据采集、解析可视化。

分支维护策略

ATT工具版本分支的维护阶段如下:

状态 时间 说明
计划 1—3 个月 计划特性
开发 3个月 开发特性
维护 6—12个月 合入所有已解决的问题并发布版本
无维护 0—3 个月 合入所有已解决的问题,无专职维护人员,无版本发布
生命周期终止(EOL) N/A 分支不再接受任何修改

现有分支的维护状态

ATT分支名称格式为:版本号-ATT,而版本号命名规则如下:

  1. ATT仓每年发布4个版本,每个版本都将对应一个分支;以v6.0为例,其将对应v6.0.RC1、v6.0.RC2、v6.0.RC3以及v6.0.0四个版本,在仓库中将存在与之对应的分支(增加后缀ATT,例如v6.0.0-ATT);
  2. 同时每个版本都将对应一个标签,例如v6.0.RC1-ATT,后续在对分支进行维护过程中将定期进行对应版本标签的更新,例如对v6.0.RC1-ATT标签再次更新之后将打标签v6.0.RC1.1,后续更新末尾数字依次增加。
分支 状态 发布日期 后续状态 EOL日期
v6.0.0 维护 2023/12/12 预计2024/12/12起无维护

参与贡献

  1. Fork 本仓库
  2. 新建 xxx 分支
  3. 提交代码
  4. 新建 Pull Request
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针对训练&大模型场景,提供端到端命令行&可视化调试调优工具,帮助用户快速提高模型开发效率 展开 收起
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