From d5ae49706aff988f65dec3bf29f9a948555e3cd0 Mon Sep 17 00:00:00 2001 From: chengyanfu Date: Wed, 25 Oct 2023 14:57:49 +0800 Subject: [PATCH] [WeeklyReport] kevincheng2 2023.10.10~2023.10.24 --- .../[WeeklyReport]2023.10.1~2023.10.20.md | 78 ++++++++++++++++++ .../[WeeklyReport]2023.10.10~2023.10.24.md | 51 ++++++++++++ ...3f4ddfa095019702db95ba-1698204944186-2.png | Bin 0 -> 204695 bytes .../48afe88f743f4ddfa095019702db95ba.png | Bin 0 -> 204695 bytes WeeklyReports/18_ranchongzhi/README.md | 4 + .../[WeeklyReport]2023.10.10~2023.10.24.md | 40 +++++++++ 6 files changed, 173 insertions(+) create mode 100644 WeeklyReports/03_zhangyuqin1998/[WeeklyReport]2023.10.1~2023.10.20.md create mode 100644 WeeklyReports/04_kevincheng2/[WeeklyReport]2023.10.10~2023.10.24.md create mode 100644 WeeklyReports/04_kevincheng2/assets/48afe88f743f4ddfa095019702db95ba-1698204944186-2.png create mode 100644 WeeklyReports/04_kevincheng2/assets/48afe88f743f4ddfa095019702db95ba.png create mode 100644 WeeklyReports/18_ranchongzhi/README.md create mode 100644 WeeklyReports/18_ranchongzhi/[WeeklyReport]2023.10.10~2023.10.24.md diff --git a/WeeklyReports/03_zhangyuqin1998/[WeeklyReport]2023.10.1~2023.10.20.md b/WeeklyReports/03_zhangyuqin1998/[WeeklyReport]2023.10.1~2023.10.20.md new file mode 100644 index 00000000..f0e23f03 --- /dev/null +++ b/WeeklyReports/03_zhangyuqin1998/[WeeklyReport]2023.10.1~2023.10.20.md @@ -0,0 +1,78 @@ +### 姓名 +张钰钦 + +### 实习项目 +新IR Pass建设 + +### 本周工作 + +详见 https://github.com/yuanlehome/Hackathon/wiki/%E6%96%B0IR%E5%B8%B8%E9%87%8F%E6%8A%98%E5%8F%A0#%E6%8E%A8%E7%90%86%E6%B5%81%E7%A8%8B%E6%A2%B3%E7%90%86 + +1. **梳理飞桨目前执行器的流程** + +#### 0. 训练阶段参数初始化与 scope 构建 +1. paddle.nn 中的函数会将参数初始化的 op 追加到 start program 中 +2. 执行器执行 start program +3. 在 NewIRInterpreter 阶段 build scope,把 op 用到的输入输出 var 都构造出来并放到 global scope 里 +4. NewIRInterpreter 按序执行 op kernel。之后,初始化后的参数就在 global scope 里了 +5. 执行器执行 main program,可以从 global scope 里拿到参数 +6. ... + +#### 1. 执行器python前端 +网络和参数都就绪之后,调用 Executor 的 run() 方法执行,这里以新执行器为例。新执行器会: +1. 创建 StandaloneExecutor实例 +2. feed data +3. 调用 StandaloneExecutor::run +#### 2. 执行器c++后端 +StandaloneExecutor 的构造函数依次做了几件重要的事情: +1. TranslateLegacyProgramToProgram,旧 IR 被翻译成新 IR +2. 常规 pass,如常量折叠、算子融合、死代码消除等 +3. PdOpLowerToKernelPass,新 IR 绑定到 kernel +4. inplace pass +5. 创建 InterpreterCore,将 impl 构造为 NewIRInterpreter **(直到这一步才用到 scope_,之前都没用到)** + +其中真正负责执行的是 NewIRInterpreter。 + +2. **新IR常量折叠问题分析** +#### 1. 新 IR 常量折叠的主要改写逻辑 +1. 根据 op 创建 temp_program +2. PdOpLowerToKernelPass,根据 temp_program 构造 kernel_program +3. 创建 InterpreterCore 并执行 +4. 根据 fetch_list 取出 out_tensor +5. 根据 out_tensor 构造 pir::Parameter +6. 在 scope_ 中创建新 param_var,并把 out_tensor 赋值给 param_var +7. 在原 program 中 SetParameter +8. 插入 get_parameter_op 并替换子图 + +#### 2. 问题分析 +可以看到,新IR常量折叠里主要存在两个问题: + +问题一:需要从原 program 中获取 Parameter,但我们在 TranslateLegacyProgramToProgram 时,只记录了 param_name,并没有创建 Parameter 对象(见上面流程梳理3.1)。目前我们仍然是把 param 以 var 的形式存储在 scope 里了。而在 pass 阶段,并没有传入 scope,也就没法获得原 program 的参数。 + +问题二:需要单独构造出 temp program,并构造 InterpreterCore。其实可以直接调用 op 的 kernel。 + +3. **梳理 codegen 流程** + +一个很自然的想法是,能不能通过 codegen 的方式为每个 op 生成 fold 方法?为此,我们梳理一下 op codegen 的大概流程 +在 gen.py 的 OpGenerator 里: + +1. 根据 op_yaml_files 和 op_compat_yaml_file 解析生成 op_compat_item +2. 构造 OpInfoParser(op, op_compat_item) +3. 根据上面解析出的结果,格式化 OP_INFO_TEMPLATE,用于生成算子的 GetOpInfo() 方法。这东西很重要。**OpInfoTuple 把所有元信息和 op 打包在一起** +4. 根据模板生成头文件 +5. 根据模板 CC_FILE_TEMPLATE 生成源文件 + +理论上拿到 OpInfoTuple 就可以找到 kernel 并构建 InferMetaContext 和 KernelContext 了。只要把 KernelContext 传给 kernel 就能执行了。(需要先 BuildScope,为 kernel 的 output 在 scope 里申请出空间) +所以我们可以看出,并不需要额外的 codegen 了。想运行指定的 op,只要拿到它的 OpInfoTuple,然后传入 scope 就可以。 + +4. **调研** +调研 tvm、mlir、oneflow 中常量折叠的实现方案 + + + +### 下周工作 + +1. 搭建 新 ir 常量折叠的 demo 并跑动 + +### 导师点评 +对飞桨框架新IR的执行流程学习比较细致,本周工作按照预期推进,继续保持! diff --git a/WeeklyReports/04_kevincheng2/[WeeklyReport]2023.10.10~2023.10.24.md b/WeeklyReports/04_kevincheng2/[WeeklyReport]2023.10.10~2023.10.24.md new file mode 100644 index 00000000..e89efa47 --- /dev/null +++ b/WeeklyReports/04_kevincheng2/[WeeklyReport]2023.10.10~2023.10.24.md @@ -0,0 +1,51 @@ +### 姓名 +程延福 - [kevincheng2](https://github.com/kevincheng2) + +### 实习项目 +[项目四:组合机制前反向架构统一](https://github.com/PaddlePaddle/community/blob/master/hackathon/hackathon_5th/【PaddlePaddle Hackathon 5th】飞桨护航计划集训营项目合集.md#项目四组合机制前反向架构统一) + +### 本周工作 + +1. **熟悉静态图场景组合机制case** + * 理解了静态图下算子的组合机制代码调用方法 + * 熟悉 paddle 单元测试规则 + + +2. **完成新IR下Relu的反向迁移 [[Prim][PIR] Support for rule operator backward under new ir](https://github.com/PaddlePaddle/Paddle/pull/58210)** + + * 学习PR [[Prim][PIR] Support composite rules of Llama ops](https://github.com/PaddlePaddle/Paddle/pull/58018) 中的算子迁移方法 + + * 根据 [PR](https://github.com/PaddlePaddle/Paddle/pull/58018) 和 mentor 的指导,完成新IR下Relu的反向迁移,并提交 [Relu 反向迁移 PR](https://github.com/PaddlePaddle/Paddle/pull/58210) + + * 理解 call_vjp 调用规则 -- todo + + ![48afe88f743f4ddfa095019702db95ba](C:\Users\dell\Documents\vsCode\Camp\WeeklyReports\04_kevincheng2\assets\48afe88f743f4ddfa095019702db95ba-1698204944186-2.png) + +3. **熟悉新IR下的前向拆解规则** + + * 学习PR [[Prim][PIR] Sink Forward Prim](https://github.com/PaddlePaddle/Paddle/pull/58130) 中算子拆解下沉实现,理解 call_decomp 调用规则 -- todo + + ![image](https://user-images.githubusercontent.com/116002591/275771637-1c70c9ba-3fdf-4d41-9e63-ff6559cf9a21.png) + + * 参考PR中的mean算子,实现relu的前向拆解下沉 -- todo + +3. **问题疑惑与解答** + + + * 基础算子和组合算子怎么界定?有什么区别? + + 答:区分基础算子和组合算子需要考虑这么几个规则,原子性、可组合性、可优化性、高频率、算子库支持度。但是在实际应用实现的过程中,基础算子和组合算子的界定没有特别严格,设计时需要考虑算子实现的颗粒、算子的性能和算子功能性等具体的方面,针对不同的算子的不同特点去考虑。 + + * decomp_interface_gen_op_list.py 生成代码时,配置relu会同时生成 relu 和 relu_ 对应代码? + + 答:一个小bug,relu 和 relu_ 特性有区别,需要分开考虑,relu_ 不需要拆分,已提交pr [[Prim]fix interface decomp](https://github.com/PaddlePaddle/Paddle/pull/58322) + + +### 下周工作 + +1. 实现relu的前向拆解下沉 +2. 结合源码,理解 call_decomp 、call_vjp 的调用规则 +3. 其他算子的迁移和拆解工作 + +### 导师点评 +请联系导师填写 \ No newline at end of file diff --git a/WeeklyReports/04_kevincheng2/assets/48afe88f743f4ddfa095019702db95ba-1698204944186-2.png b/WeeklyReports/04_kevincheng2/assets/48afe88f743f4ddfa095019702db95ba-1698204944186-2.png new file mode 100644 index 0000000000000000000000000000000000000000..026c050ab5f2d2b52fe02d3c04844e24918aa3cc GIT binary patch literal 204695 zcmeFZWn7ip7c~k9q9B5#NQgnFG?EI4bazTgcXuc$B_%E09h(j*0cpuir*wDSwa+;o z&pChfcR$=u_kBO;Zv)TX&wAFHYtAvo9BcbaNeH2#+(UtbgF_R3DIfy}cU>F~4w3W5 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