From c94b2b24665a685c8d1efb5c31e72060f951f67d Mon Sep 17 00:00:00 2001 From: looop5 Date: Thu, 13 Aug 2020 14:39:12 +0800 Subject: [PATCH] README add Chinese version --- README.md | 7 +++--- README_CN.md | 63 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 67 insertions(+), 3 deletions(-) create mode 100644 README_CN.md diff --git a/README.md b/README.md index c75da4a..72a4093 100644 --- a/README.md +++ b/README.md @@ -8,12 +8,14 @@ - [Release Notes](#release-notes) - [License](#license) +[查看中文](./README_CN.md) + ## What Is AKG AKG(Auto Kernel Generator) is an optimizer for operators in Deep Learning Networks. It provides the ability to automatically fuse ops with specific patterns. AKG works with MindSpore-GraphKernel to improve the performance of networks running on different hardware backends. AKG composes with four basic optimization module, normalization, auto schedule, instruction emit and backend optimization. -- **normalization.** The mainly optimization of normalization includes three address transform, common subexpression elimination, copy propagation and so on. -- **auto schedule.** The auto schedule module mainly have vectorization, loop tiling, mem promotion and loop distribution. +- **normalization.** In order to solve the limitation in expression ability of polyhedral(which can only process static linear programs), the computation IR needs to be normalized first. The mainly optimization of normalization module includes auto-inline, loop partition, common subexpression elimination and so on. +- **auto schedule.** Base on polyhedral technology, the auto schedule module mainly have auto-vectorization, auto-tiling, dependency analysis and memory promotion. - **instruction emit.** The instruction emitting module has the optimization about loop normalization, auto pragma and emit instruction. - **backend optimization.** The backend optimization module consists of double buffer optimization, storage rewrite optimization and inject sync optimization. @@ -31,7 +33,6 @@ See [MindSpore README.md](https://gitee.com/mindspore/mindspore/blob/master/READ We suggest you build and run akg together with MindSpore. And we also provide a way to run case in standalone mode for convenience sake. Ascend platform is needed to build this mode. Refer to [MindSpore Installation](https://www.mindspore.cn/install/en) for more information about compilation dependencies. ``` - git submodule update --init bash build.sh ``` ## Run Standalone diff --git a/README_CN.md b/README_CN.md new file mode 100644 index 0000000..bdd63fe --- /dev/null +++ b/README_CN.md @@ -0,0 +1,63 @@ +- [AKG简述](#AKG简述) +- [硬件后端支持](#硬件后端支持) +- [构建](#构建) + - [从MindSpore侧构建](#从MindSpore侧构建) + - [独立构建](#独立构建) +- [运行](#运行) +- [贡献](#贡献) +- [版本说明](#版本说明) +- [许可证](#许可证) + +[View English](./README.md) + +## AKG简述 +AKG(Auto Kernel Generator)对深度神经网络中的算子进行优化，并提供特定模式下的算子自动融合功能。AKG与MindSpore的图算融合功能协同工作，可提升在不同硬件后端上运行网络的性能。 + +AKG由四个基本的优化模块组成：规范化、自动调度、指令发射和后端优化。 +- **规范化：** 为了解决polyhedral表达能力的局限性（只能处理静态的线性程序），需要首先对计算公式IR进行规范化。规范化模块中的优化主要包括自动运算符inline、循环拆分和公共子表达式优化等。 +- **自动调度：** 自动调度模块基于polyhedral技术，主要包括自动向量化、自动切分、依赖分析和数据搬移等。 +- **指令发射：** 指令发射模块的优化主要包括循环规范化、标签自动生成和指令发射等。 +- **后端优化：** 后端优化模块的优化主要包括双缓冲区、存储重写和同步指令插入等。 + + + +## 硬件后端支持 +当前仅支持`Ascend910`，更多硬件后端支持待开发。 + +## 构建 + +### 从MindSpore侧构建 +详细细节请参考[MindSpore README.md](https://gitee.com/mindspore/mindspore/blob/master/README.md)。 + +### 独立构建 +我们建议您从MindSpore侧构建运行AKG代码，但同时为了方便开发，我们提供了独立编译运行AKG的方式。 +独立构建模式下需要Ascend平台的支持，详细的编译依赖请参考[MindSpore安装指南](https://www.mindspore.cn/install)。 + ``` + bash build.sh + ``` +## 运行 +1. 设置环境变量 + ``` + cd tests + source ./test_env.sh amd64 + export RUNTIME_MODE='air_cloud' + export PATH=${PATH}:${YOUR_CCEC_COMPILER_PATH} + ``` + +2. 运行测试用例 + ``` + cd tests/operators/vector + pytest -s test_abs_001.py -m "level0" # 运行level0测试用例 + ``` + +## 贡献 + +欢迎您的贡献，具体细节请参考[MindSpore贡献者Wiki](https://gitee.com/mindspore/mindspore/blob/master/CONTRIBUTING.md)。 + +## 版本说明 + +版本说明详见[RELEASE](RELEASE.md). + +## 许可证 + +[Apache License 2.0](LICENSE) -- GitLab