From 1462687d310d61ca4a3fd1787f9442e1b2b83cb1 Mon Sep 17 00:00:00 2001 From: Chen Weihang Date: Tue, 10 May 2022 04:03:56 +0000 Subject: [PATCH 1/3] add phi design doc --- docs/design/phi/design.md | 1749 ++++++++++++++++++++++ docs/design/phi/images/kernel-design.png | Bin 0 -> 102777 bytes docs/design/phi/images/tensor-design.png | Bin 0 -> 134604 bytes 3 files changed, 1749 insertions(+) create mode 100644 docs/design/phi/design.md create mode 100644 docs/design/phi/images/kernel-design.png create mode 100644 docs/design/phi/images/tensor-design.png diff --git a/docs/design/phi/design.md b/docs/design/phi/design.md new file mode 100644 index 00000000000..efb363f8ce2 --- /dev/null +++ b/docs/design/phi/design.md @@ -0,0 +1,1749 @@ + +飞桨高可复用算子库 PHI (Paddle HIgh reusability operator library)，或者我们也成为函数式算子库，支持组合式算子功能复用、Primitive算子内核复用、插件式硬件加速库复用。针对飞桨框架原算子库存在的算子接口不清晰、算子复用成本较高、调用性能不够快的问题，我们重构了飞桨框架的算子库，设计了灵活、高效的函数式算子库 Phi，可以通过对函数式算子接口组合调用的方式实现新算子。新算子库提供了 200 余个跟 python 开发接口保持一致的 C++ 运算类 API，以及近500个可供组合调用的前、反向函数式算子内核 Kernel，可大幅降低框架原生算子和自定义算子的开发成本。新算子库支持Primitive API方式开发算子内核，可支持不同硬件（比如GPU和XPU）的算子内核复用。新算子库支持以插件方式接入硬件（比如NPU）的加速库，实现低成本复用硬件加速库。 + +> 本文档撰写于phi架构基本成型之时（2022年2月），仅代表该时间点的基本设计形态，可能和最新形态有细微差别；此外，在2.3版本发布的phi算子库仍然处于初期形态，后续仍然需要持续建设并完善，设计上也有可能调整。 + +# 一、背景与目标 + +> 介绍设计并建设 phi 要解决的问题 + + +最初 phi 项目的启动仅是为了优化飞桨动态图调度开销、并提升Kernel开发的复用能力而提出来的，但后续决定借此机会，建立能够同时在训练和推理场景（包括服务器端和移动端场景）中使用的“训推一体”算子库，长远上降低 paddle 生态中各基础设施开发及维护算子的成本，逐渐扩充了项目的目标范围，目前 phi 已经承载了多维度的意义。 + +> 关于算子库的命名，开发过程中有过迭代：初期算子库目录名为 pten ，意为paddle Tensor运算库 (Paddle Tensor Operation Library)，因此一些历史 PR 以PTen为前缀，后期认为该名称表述范围不够准确，因此更名为 phi + +## 1.1 背景问题 + +具体地，phi 算子库项目，承载着解决 Paddle 以下问题的期望： + +### 1.1.1 Op&OpKernel之间可复用性差，冗余代码较多 + +2.3版本之前，Paddle中的Operator（后续简称Op）之间的可复用性比较差，仅在少数的反向Op中，通过在GradOpMaker实现中调用SetType复用了一些简单的运算，大部分本身可以复用已有Op实现的情况，代码都是copy重写的。 + +可复用性差的根本原因还是原先Op体系设计导致的： + +1. 当一个Op去复用另一个Op的`Opkernel::Compute`方法，都需要先构造一个`ExecutionContext`，复用上是比较繁琐的 + + - 如果能直接调用一个函数形式的Kernel，就会方便很多 + +2. 由于额外的数据结构构造及独立Op调度引入了开销，从计算性能的角度考虑，复用Op不如直接把计算代码copy过来，导致我们逐渐抛弃了早期反向Op复用前向Op的原则，开始为每个反向Op单独实现Kernel + + - 只有Op之前复用的开销足够小，复用已有Op实现新Op才有可能被大范围推广 + +### 1.1.2 执行调度的简洁性与细粒度化 + +#### 1.1.2.1 动态图 + +Paddle 2.0发布之后，多次收到内外部用户反馈动态图在小模型CPU执行场景下与竞品在性能上有数倍的差距。 + +这个问题的主要原因是：Padddle动态图C++端的执行路径比较冗长，调度开销比较重，这和动态图早期设计兼容静态图，继承了静态图Op的许多对象构造过程有关 + +- 问题issue：https://github.com/PaddlePaddle/Paddle/issues/28774 + +因此，动态图需要升级为基于函数的调度架构，抛开原先复杂的Op体系，才能解决这个问题，这依赖于OpKernel改为函数式的写法。 + +#### 1.1.2.2 静态图 + IR + +我们目前的静态图还不够“静态”，目前静态图仍然有许多运行时动态选择的逻辑，例如，运行时选择OpKernel，运行时判断是否要进行跨设备数据拷贝等等，但这些其实可以在静态图模型组网编译期间就确定下来，将执行过程确定为一系列OpKernel的执行，不再做动态的判断选择，从而进一步提升执行效率。 + +而这些依赖于OpKernel本身的细粒度化，将现有复杂的大OpKernel解耦成具体场景、具体设备的小Kernel，才能支持这样的调度。 + +### 1.1.3 自定义算子的易用性提升需求 + +2021年初上线的新自定义C++外部算子体系，在接口与函数编写的层面上，用法已经比较直观了，但是因为我们缺少基本运算的C++ API体系，事实上，在实现具体的自定义Op运算逻辑时，一些基础的加减乘除及矩阵运算都仍然需要重新实现一遍，不能复用Paddle已有的、经过优化的基础运算，因此一些复杂运算的外部开发成本仍然是比较高的。而要想复用Paddle内部的基础运算，有赖于的Op体系升级为函数式，并整理对应的C++ API体系才能解决。 + +### 1.1.4 共建训推一体算子库，降低推理算子维护成本 + +长久以来，由于paddle主框架和paddle-lite的算子是分开维护的，paddle新增的算子，lite需要的话，就要手动在lite中重新实现一遍，而且当主框架算子升级，lite又没有及时感知到，会直接导致推理模型在lite执行时出现bug，这维护成本是很高的，只有统一算子库，仅维护一份代码，才能长久解决这个问题。 + +因此，本次函数式算子库会由训练和推理共同建设，计算库整理完成后，作为独立的编译组件和底层基础设施（目前还没有独立拆分出来），能够同时服务于训练、预测以及Lite等执行体系。 + +### 1.1.5 推理新Runtime设计infrt的适配 + +推理设计了新的runtime infrt，预计要统一paddle-inference和paddle-lite的执行，将来需要直接调用本次共建的phi算子库中的算子，因此在设计时需要考虑对infrt的适配。 + +### 1.1.6 Op及Kernel参数规范化 + +python 2.0 API项目规范了Paddle Python端API的参数列表，使其变得简洁、易用，但是限于当时的情况，Op层面的参数列表并没有规范化，因此会有不少早期开发的算子和Python API参数相差较多，例如conv op这种，python API仅有7个参数，但C++ Op却有30+参数的分裂情况，而API和Op本质上是同一层概念，都是对一个运算的描述，参数应该是一致的。推理为了解决此问题，推动了算子定义增强项目，为部分不需要的参数添加了AsExtra以及AsQuant的声明，但并未从根本上解决问题，这也是phi算子库构建希望重点去解决的。 + +我们希望能做到，Python API -> Op(C++ API) -> Kernel API三层参数一致，使整体架构清晰，每一层复用也清晰，一套python官方文档，基本能够满足三层API的共同参考需求，不再着重维护额外的文档体系，降低维护成本。 + +## 1.2 目标及范围 + +- 总体目标：飞桨核心框架复用同一函数式算子库，基础数据结构Tensor具备良好的可扩展性，从根本上做到训练推理协同一致、基础组件稳定可靠、增量开发体验良好。 + +- 目标范围： + + - phi算子库初期构建更关注Kernel“迁移”，人力因素，原Kernel逻辑迁移时暂不强制升级为“组合式”写法，前反向Kernel均如此 + - phi算子库初期提供的"组合式Kernel二次开发"能力面向后续增量的新算子使用，已有算子仍然保持其原先的编码实现，降低迁移成本 + - phi算子库初期提供的“新硬件扩展能力”仅在新硬件自身范围内提供，比如XPU已经实现了50个Kernel，后续其可以基于50个Kernel去组合新的Kernel，但这仅限于XPU范围内，其实现不和CPU、CUDA等实现通用 + - phi算子库项目重点关注“Kernel函数化&Op规范化”的工作，Kernel改为函数式，C++API与Op命名及参数列表在尽可能确保兼容性的前提下与逐渐规范化为与Python API一致 + + +# 二、设计概览 + +## 2.1 命名及位置 + +飞桨高可复用算子库 (Paddle HIgh reusability operator library)，简称 PHI(phi)，phi代码目录在paddle目录下，和fluid平级，而不是放在fluid目录下，这样放置的原因是：phi是一个由fluid，lite，infrt等多种上层runtime共同调用的基础组件，后续会作为单独的编译的动态库存在，不适合作为fluid的子模块。 + +## 2.2 目录结构 + +### 2.2.1 目录结构设计需满足的需求 + +训练和推理对算子库目录的清晰度也有诸多诉求： + +- 在目录设计上支持算子库的各种拆分编译需求，包括 + + - 按运算设备拆分编译 + - 例如：仅编译cpu的，或者仅编译cuda的 + - 按训练和推理场景拆分编译 + - 例如：推理不编译反向相关kernel，也不编译带有Intermediate输出的前向kernel + - 按移动端设备实际使用算子精准裁剪编译（目前尚未支持） + - 例如：一个模型只用了add和mul，极致情况下应该能裁到仅剩2个kernel +- 长线上支持良好的kernel复用实现需求 + - 解释：kernel复用实现时，能否通过简单的include引入对应函数，不会因为目录过于复杂而找不到复用的kernel + +- 长线上支持跨设备kernel的写法统一需求，并且直观易用，不引入不必要的模板参数 + - 解释：算子库下层还有Kernel Primitive API模块，其长线愿景是每个运算，只要一个kernel，能够适应多种设备，真正区分设备的代码，仅在Kernel Primitive API实现中；不希望未来的kernel在复用时从传入较复杂的模板参数，需要尽可能限制地简洁一些 + +- 易用性上，开发者能精准理解自己新增Kernel应该放到什么位置，无歧义 + - 解释：开发者新增一个API，不会困惑自己应该将对应kernel放在那个目录，也不会出现不同的人对于同一个kernel应该放在什么位置出现二义性的理解 + +- 不引入大量的重复目录设计 + - 解释：概念拆分是需要的，但也要有边界，避免在多个目录下有命名相同的子目录，容易混乱，比如不能cpu下面有eigen, funcs, math等，gpu下面也有。新算子库的目录设计以根据设备拆分为主，其他层次的目录拆分尽可能弱化，比如尽量不根据功能拆分，尽量不根据领域拆分等 + +- 不造成迁移时的文件数目膨胀 + - 解释：不能因为kernel设备拆分，导致kernel实现文件大规模增多 + +- 不引入层级过深的目录设计 + - 解释：目录层级不应过深，理解和维护成本都较高 + +- 不引入过高的迁移成本 + - 解释：迁移kernel时，不能要求对kernel本身做太多改动和拆分，否则迁移成本太高 + +### 2.2.2 具体目录设计 + +#### 2.2.2.1 一级目录 + +``` +paddle/phi +./api (对外暴露的高层API及其实现) + ./include（对外暴露的高层API头文件） + ./lib（对外暴露API的实现） +./common (内外部均会使用到的基础数据结构) +./core (基础组件，比如基础Tensor相关接口，kernel注册接口，管理单元等) +./backends (各设备及后端的基础组件，下设cpu，gpu等后端目录) +./infermeta (shape、dtype、layout等meta信息的推导函数) +./kernels (各设备及后端的kernel实现) +./ops (各op的定义，后续采取自动生成的方式完成大部分工作，目前仅有兼容用的代码) +./tests (单元测试) +``` + +部分目录结构说明： + +- `api`：API模块，面向外部用户 + - 直接使用类python的C++ Tensor计算 API，和Python端形式高度一致 + - 该部分可能反向依赖框架的DeviceContextPool等实现，所以单独管理 + - 在该类API上，训练和预测也可能是不同的 +- `common`：phi内部及phi api目录均要使用的数据结构，这些数据结构既不属于phi core，也不属于api目录 +- `core`：phi内部会有一些自己需要的，公用的模块实现，比如基础DenseTensor、，kernel注册及管理模块 +- `backends`：backends中组织后续需要为各个后端的新增的数据结构，比如CPUContext、GPUContext等 + - core中放置对于算子库来讲通用的基础数据结构，而特定后端的专用数据结构不放在core中，且依赖关系严格保证backends依赖core，但core不能依赖backends + - 例1：Context如果有基类，则在core中，而继承的CPUContext在backends/cpu中，GPUContext在baackends/gpu中 + - 例2：TensorBase在core中，DenseTensor给多数设备使用，也在core中，如果有MKLDNNTensor的话，因为它只给mkldnn用，应该在backends/dnnl中 +- `infermeta`: infermeta函数的整理位置，infermeta函数相当于infershape+inferdtype+inferlayout等 +- `kernels`：各设备相关kernels + - `cpu, gpu, ...` +- `ops`: ops中组织新形式的Op定义、以及兼容原有op的一些组件 + + +#### 2.2.2.2 Kernels目录 + +``` +paddle/phi/kernels +./ (放置设备无关的kernel声明和实现) +./cpu（仅放置cpu后端的kernel实现） +./gpu +./xpu +./dnnl +./gpudnn +./impl (考虑到现状，放置原先Kernel在CPU和GPU或其他设备一致的实现，便于复用) +./funcs（放置原fluid operators下一些支持多设备的functor和funcs） +./primitive（放置Kernel Primitive API的基础实现） +... +``` + +目录结构说明如下： + +- kernels下主目录，放置设备无关的kernel.h和kernel.cc，原则上每个kernel一个.h和.cc + - 例如一个kernel是使用Primitive api实现的，或者是复用其他基础kernel实现的，那么不论在什么设备上，应该都只有一种实现，所以它的声明和实现均直接放置到kernels目录下即可（这是将来的理想状态） + - 目前我们大部分kernel都不具备跨设备实现统一的特征，但是kernel的输入参数返回值除了DeviceContext之外，应该是一致的，所以kernel参数声明头文件还放到主目录下（和原先的设计保持一致，DeviceContext和T作为模板参数），各设备的函数实现在相应的设备文件夹中 + - 注意，这里跨设备实现统一，并不是指一个kernel的CPU和GPU实现就算统一了，而是在所有设备的实现都一样，目前至少包括CPU，GPU，XPU，MKLDNN，GPUDNN等 + - 反向kernel如果不需要支持裁剪，可以做适当归并（但如果要为支持端侧训练留可能性，反向kernel可能也是裁剪的潜在目标） +- kernels下一级子目录，原则上按照backend分类按需新建，仅保留两个特殊的目录: + - funcs：为了兼容原先fluid operators中functor和function设计保留的目录，放置支持多种后端的function和functor，还按照原先的一个头文件，多个.cc(u)的方式组织（这部分代码在将来可能被移除，因为会逐渐被Kernel Primirive API及Kernel间复用替代，这里不做过度设计） + - 例1：一个公共函数XXXFunction在reduce CPU和reduce CUDA的kernel实现中都被调用，并且reduce CPU和reduce GPU的kernel实现是不一样的，那么这个XXXFunction应该在funcs目录中 + - primitive：Kernel Primitive API，多设备统一kernel实现的一些基础工具 + - impl：paddle目前的op kernel实现，有很多仍然是CPU和GPU复用同一份代码的，在大量的xx_op.h，这部分代码，不适合放在cpu或者gpu目录中，也不适合放在funcs目录中（会导致funcs目录中最终放置了相当一部分kernel实现，过于臃肿且混乱，funcs目录的定位是放置原先operators/math目录下那样的工具functor和function），也不适合放到kernels根目录下（并不是真正设备无关的实现，仅是cpu和gpu共用的实现），因此为了使这部分代码迁移时不需要做过多考虑，并且放置的位置也相对符合其实现性质，创建了impl这个目录 + - impl目录下，仅放置跨部分设备实现一致的kernel函数，均为头文件，命名均以xxx_kernel_impl.h为后缀 + - 例如：scale，fill_constant，fill_any_like这些kernel均属于此类情况 +- kernel迁移过来之后，首先创建对应kenrel头文件直接放置到kernels的根目录中，各后端的kernel实现放在相应的设备文件夹中 + - 可参考原先op的归并程度，如matmul原先是单独的.h/.cc，那移过来之后保持，但activation相关的基本写在一个.h/.cc，移过来也仍然保持归并（后续有必要再进一步拆分） + - 例1：原先cast op的Kernel在cast_op.h中，迁移过来之后在根目录创建cast_kernel.h，cast_kernel.cc/cu根据使用的后端放到对应的目录，即cast_kernel.cc放置到cpu中，cast_kernel.cu放置到gpu中 + - 例2：原先scale op的kernel使用eigen实现，CPU和GPU实现一致，迁移过来之后，公共实现应该在impl中的scale_kernel_impl.h中，公共头文件在kernels根目录下的scale_kernel.h中，scale_kernel.cc在cpu中，scale_kernel.cu在gpu中 +- 迁移时，只有本kernel用到的辅助函数，一律和kernel实现放到同一个backend文件中，创建.h管理代码，不再单独在别处整理代码，除非这些辅助的函数实现是有多处使用的 + - 即使有多处调用，如果仍然限于同一设备，直接建头文件放到同一个目录下 +- 反向kernel与前向kernel实现放置在不同的文件中，文件后缀采用``*_grad_kernel.*``，便于cmake分离编译 + - 不再为反向kernel单独创建目录，否则反向kernel目录下还要创建cpu/gpu等目录 + - 二阶导、三阶导的实现统一也放到grad kernel实现文件中 + +- 为什么目录名叫`gpu`而不是`cuda`和`hip`? + - cuda和hip代码重复度非常高，统一实现维护成本较低 + + +## 2.3 核心组件 + +### 2.3.1 公共基础数据结构 + +#### 2.3.1.1 Backend + +``` +/** + * [ Why need Backend? ] + * + * Backend not only means place. Backend is a superset of place. + * + * Place cannot indicate the difference in calculation methods on the device, + * but in order to make the boundary of the kernel clearer and the function + * more specific, we need to distinguish the calculation method. + * + * Such as the kernel for CPU device, it can be a native CPU kernel, + * or a kernel implemented by MKLDNN library. + * + * Note(chenweihang): HIP is not needed now, we can added it if needed + * in the future + */ +enum class Backend : uint8_t { + UNDEFINED = 0, + + // basic kernel backend + CPU, + + // various acceleration devices' backends + GPU, + XPU, // XPU currently does not exist at the same time as CUDA + NPU, // NPU currently does not exist at the same time as CUDA + + // the third library backend + MKLDNN, + GPUDNN, + + // end of backend types + NUM_BACKENDS, + + /** + * [ Why we need ALL in baisc kernel key member? ] + * + * For Tensor, ALL represents an illegal Backend, but for Kernel, some + * kernels may be device-independent by nature, such as reshape; and when + * and some kernels are also device-independent when implemented based on + * primitive API. + * + * In this case, we need to provide a more concise registration method, + * instead of registering the kernels for each device with almost + * repetitive code, we need one registration covers all situations, + * so if we provide the ALL field with Register the kernel in this statement. + * + * Of course, we have also considered solving this problem through different + * named macros, for example, if we define + * + * PT_REGISTER_KERNEL_FOR_ALL_BACKEND + * + * Based on this design pattern, the dtype and layout also have the same + * requirements, this cause we need to define a series of macros + * + * PT_REGISTER_KERNEL_FOR_ALL_DTYPE + * PT_REGISTER_KERNEL_FOR_ALL_LAYOUT + * PT_REGISTER_KERNEL_FOR_ALL_BACKEND_AND_LAYOUT + * PT_REGISTER_KERNEL_FOR_ALL_BACKEND_AND_DTYPE + * PT_REGISTER_KERNEL_FOR_ALL_LAYOUT_AND_DTYPE + * PT_REGISTER_KERNEL_FOR_ALL_BACKEND_AND_LAYOUT_AND_DTYPE + * + * It makes the system of registering macros more complicated, we think + * this is not a simple design, so we still adopt the design of providing + * the ALL field. + * + * Note: ALL_BACKEND only used for Kernel registration and selection + */ + ALL_BACKEND = UNDEFINED, +}; +``` + +#### 2.3.1.2 DataLayout + +``` +// Note: Here the DataLayout is public api for external users, the prefix `k` +// maybe confuse users, so we use all uppercase names +enum class DataLayout { + UNDEFINED = 0, + // TODO(chenweihang): keep ANY for compatibility, remove it later + ANY = UNDEFINED, + NHWC, + NCHW, + MKLDNN, + NUM_DATA_LAYOUTS, + // See Note [ Why we need ALL in basic kernel key member? ] + ALL_LAYOUT = UNDEFINED, + // Note: Unify phi DataLayout and fluid::framework::DataLayout, + // for compatible with fluid DataLayout, here need prefix `k` + // Note: The original `kAnyLayout (enum value 2)` is a strange design. + // `kAnyLayout` originally cannot represent any kind of Layout, + // at the same time, it can also represent any Layout. + // Strictly, it means "default" or "undefined" layout, + // and should not be mixed with other meaningful layouts. + kAnyLayout = ANY, + kNHWC = NHWC, + kNCHW = NCHW, + kMKLDNN = MKLDNN, // all layouts supported by MKLDNN internally +}; +``` + +#### 2.3.1.3 DataType + +``` +enum class DataType { + UNDEFINED = 0, + BOOL, + INT8, // Char + UINT8, // BYte + INT16, + INT32, + UINT32, + INT64, + UINT64, + BFLOAT16, + FLOAT16, + UINT16, + FLOAT32, + FLOAT64, + COMPLEX64, + COMPLEX128, + NUM_DATA_TYPES, + // See Note [ Why we need ALL in baisc kernel key member? ] + ALL_DTYPE = UNDEFINED, +}; +``` + +- 这里什么不使用原先fluid的VarType？ + - 理由1：原先fluid的DataType和VarType是同级概念，设计是比较混乱的，例如LoDTensor和FLOAT32是同级概念，但这两者显然不是的，我们不希望继承原先有明显缺陷的设计 + - 理由2：和fluid解耦依赖，便于后续phi可以独立编译 + +#### 2.3.1.4 Scalar + +Scalar (标量)用来统一表示具有不同基础数据类型(float, double, int, bool等)的变量。（目前也支持表示元素数量为1的Tensor标量，但后续可能会放弃该功能的支持） + +以`ScaleKernel`为例，其中的`scale`参数可以传入int，float，double等普通数据类型。如果不使用`Scalar`来表示的话，需要为每种数据类型单独创建一个函数接口，这样会大大增加开发Kernel的代码量，因此`Scalar`主要应用在具有不同数据类型的同一参数上，可以避免该场景下需要编写多个重载函数的问题。 + +``` +template +void ScaleKernel(const Context& dev_ctx, + const DenseTensor& x, + const Scalar& scale, + float bias, + bool bias_after_scale, + DenseTensor* out); +``` + +#### 2.3.1.5 IntArray + +IntArray 是一个整数类型数组，可以由`vector`,`Tensor`以及`vector`进行构造，目前主要用来表示shape，index以及aixs等维度索引变量。 + +以FullKernel为例，其中的shape参数用来表示返回Tensor的维度信息（如[2，8，8]），在调用FullKernel时该项参数传入`vector`,`Tensor`和`vector`类型的变量兼可完成调用。使用IntArray避免了每种shape类型单独编写一个重载函数的问题。 + +``` +template +void FullKernel(const Context& dev_ctx, + const IntArray& shape, + const Scalar& val, + DenseTensor* out); +``` + +### 2.3.2 Tensor体系 + +整体设计类图如下 + +![tensor-design.png](./images/tensor-design.png) + + +以下依次进行介绍。 + +#### 2.3.2.1 API Tensor接口 + +- 最上层是API级别的Tensor接口封装，里面包含两个指针成员，TensorBase和AbstractAutogradMeta。 + - 两个成员均使用了Interface设计，不会依赖于真实的Tensor和Autograd实现 + - AutogradMeta仅在动态图API级别的Tensor中有意义，在具体的kernel计算中，不会被使用到，所以将其放到最上层的Tensor接口中 + - 另外，这样设计也是为了方便数据共享，并且减少拷贝开销 + - 当一个Tensor赋值给另一个Tensor，或者Tensor作为函数返回值时，实际上只会拷贝指针，不会产生真实的数据拷贝 + +- 最上层C++ Tensor与Python端Tensor扮演类似的角色，在接口设计上尽可能与Python端保持一致 + - 包含基础的Tensor属性访问及数据访问方法 + - shape, place, dtype, data + - 包含动态图Tensor需要的autograd方法 + - gradient, backward + - 包含Tensor间的转换方法 + - cpu, gpu, xpu等 + - 包含tensor相关的计算方法（暂未添加） + - `paddle.tensor` 模块下所有方法 + +- 编译解耦： + + - 这里带有的autograd信息，只是一个指针索引，默认为空 + - `std::unique_ptr autograd_meta_ = nullptr;` + - 而这里的AbstractAutogradMeta是一个抽象类接口，不会依赖autograd的任何模块，因此不会影响 phi 的独立编译，同时又兼顾了动态图Tensor需要持有反向信息的需求 + +- 这里的AutogradMeta仅在动态图场景中才会设置，不需要的场景，比如静态图内就仅仅是个空指针而已 + +Tensor设备的判断及转换 + +- Tensor的设备及类型判断 + +``` +bool is_cpu() const; +bool is_gpu() const; +bool is_xpu() const; +bool is_dense_tensor() const; +bool is_selected_rows() const; +bool is_opencl() const; // 待添加 +bool is_metal() const; // 待添加 +``` + +- Tensor间类型转换，通过与Python端一致的API实现（待添加） + +``` +Tensor cpu() const; // 转换为cpu tensor +Tensor gpu() const; // 转换为gpu tensor +Tensor xpu() const; +Tensor mkldnn() const; +``` + +- 这个转换的过程可能是cast，也可能是copy + - 如果不需要进行数据拷贝，就是cast + - 如果需要进行数据拷贝，就是copy + - 转换通过函数式kernel去实现 + +- 在API场景中的使用 + - 用户在完整训练场景中，使用API的时候，最初读入的数据一般是从磁盘读入，先放入CPU，然后再转换到具体执行设备上，比如DataLoader + +#### 2.3.2.2 TensorBase + +- Tensor实现的接口类，接口中仅包含必要的纯虚Tensor方法，不包含有实际含义的成员，这里的方法在开发过程中也要严格控制 + +- 为什么要在这一层用抽象类设计？ + - 一方面是为了隔离Tensor API与Tensor具体实现，不产生过多依赖，如果将来Tensor API需要重新设计，或者说需要放弃掉autograd信息，只需要重新设计一个Tensor API即可，对于底层Tensor的实现几乎没有影响 + - 另一方面是为了给异构化的Tensor保留充足的扩展空间，框架API层仅需要一个Tensor数据结构即可，不需要再暴露多种数据结构设计，这里其实做了一个大范围定义，框架内所有数据结构均是Tensor + - 对于内存布局基本一致，或者说Tensor描述基本一致的实现，可以基于一种DenseTensor的实现去继承 + - 如果是异构化程度高的Tensor，可以直接从Interface继承去实现新的Tensor分支，比如只有一个Object的Tensor，确保在Tensor扩展灵活性上不会出现瓶颈 + +#### 2.3.3.3 DenseTensor、SparseTensor + +- 对应原fluid内的LoDTensor类，是Tensor的基类实现，Allocation就是现有Allocation，包含现有Tensor的基础成员 +- SparseCsrTensor、SparseCooTensor为新设计的稀疏tensor类型，详见代码实现 + +> 为了兼容原先框架调度及算子，SelectedRows我们也迁移过来作为一种基础Tensor类型，后续如果能够被新的稀疏Tensor替代，长期会移除 + +#### 2.3.3.4 其他异构Tensor + +- 如果现有Allocation的描述无法满足一些第三方库对于Tensor内存的描述需求，可以继承TensorBase之后，使用新的Allocation实现 +- 而这种Tensor本质上没有脱离通用Tensor的范畴，只是访存方式有所区别，其他的TensorMeta信息，它仍然是需要的 +- 可以自行定义特殊的TensorAllocation描述类，去构建自定义的Tensor，例如MetalTensor + +``` +template +class SpatialTensor : public TensorBase { + public: + SpatialTensor(std::shared_ptr allocation, + std::unique_ptr meta) + : allocation_(std::move(allocation)), + meta_(std::move(meta)) {} + + private: + std::shared_ptr allocation_; + std::unique_ptr meta_; +}; + +template +class MetalTensor : public SpatialTensor {}; + +template +class OpenCLTensor : public SpatialTensor {}; +``` + +- 通过这种方式，无论Tensor的需求如何特殊，均可以在对外API保持一致的前提下进行内部适配 + +其他高自由度Tensor继承：直接继承TensorBase + +- TensorBase是抽象类，为具体Tensor的描述留了较大的空间，如果传统Tensor的描述无法满足需求，可以设计特异化的Tensor实现 + + +### 2.3.3 C++ API + +#### 2.3.3.1 C++ API形式 + +> 本节要点： +> 1. C++ API与Python 2.0 API对应，函数名、参数名、参数顺序、返回值均一致 + +经过调研，我们发现只有框架产品在设计时考虑了C++ API易用性层面的问题的。出于长期考虑，我们若想要吸引更多的开发者共建飞桨生态，提供规范易用的C++ API体系也是十分重要的。同时，Python 2.0 API项目为C++ API奠定了良好的参考基础，我们可以直接继承其成果。 + +因此，目前我们期望Tensor计算库的C++ API声明形式如下： + +``` +Tensor mean(const Tensor& x); + +Tensor scale(const Tensor& x, + const Scalar& scale, + float bias, + bool bias_after_scale); +``` + +说明如下： + +- 尽可能与Python API属性保持一致，函数名，参数列表，返回值均保持一致，使用户在Python与C++的切换中，几乎没有新增的学习成本（如果必须不一致，可以增加新的C++ API，Python已有的运算类API与C++ API一一对应） + +**这个新建的C++ API体系目前主要用于什么场景？** + +1. 作为自定义算子开发时可调用的C++ API，提升易用性 + - 例如现在用户在自定义算子中初始化一个Tensor需要循环遍历Tensor数据并赋值，有API之后可以直接调用`paddle::ones`，`paddle::fill`这些API +2. 作为新动态图的基础调用单元 + - 新动态图会以API作为调度计算单元，不会再调用Op体系，以提升调度性能 +3. 作为反向Op复用前向Op进行开发的基础 + - 现在反向op kernel需要单独实现，在API体系成型后，希望可以通过复用前向API完成反向Op实现 + +#### 2.3.3.2 C++ API自动生成 + +**为什么要自动生成C++ API？** + + - C++ API的实现代码在形式上相对固定，理论上可以采用自动生成的方式来实现 + - 使用代码自动生成可以有效降低C++ API的开发成本，且方便修改和维护 + +**如何自动生成C++ API？** + + C++ API的自动生成是通过解析Yaml配置文件来进行生成的，Yaml配置文件分为： + + - 前向API配置文件(`python/paddle/utils/code_gen/api.yaml`，解析后生成代码文件为`paddle/phi/api/include/api.h`和`paddle/phi/api/lib/api.cc`) + - 反向API配置文件(`python/paddle/utils/code_gen/backward.yaml`，解析后生成的代码文件为`paddle/phi/api/backward/backward_api.h`和`paddle/phi/api/lib/backward_api.cc`)。 + +C++ API生成的关键在于Yaml文件的配置，以matmul为例，其前向和反向的配置文件如下： + +``` +# 前向API配置 +- api : matmul + args : (Tensor x, Tensor y, bool transpose_x=false, bool transpose_y=false) + output : Tensor + infer_meta : + func : MatmulInferMeta + kernel : + func : matmul + backward : matmul_grad + +# 反向API配置 +- backward_api : matmul_grad + forward : matmul (Tensor x, Tensor y, bool transpose_x, bool transpose_y) -> Tensor(out) + args : (Tensor x, Tensor y, Tensor out_grad, bool transpose_x=false, bool transpose_y=false) + output : Tensor(x_grad), Tensor(y_grad) + infer_meta : + func : MatmulGradInferMeta + kernel : + func : matmul_grad +``` + +其中各项配置参数含义： + +- api：函数名称，需与Phi Kernel注册的函数名相同 +- args：函数参数，顺序和数据类型必须与Phi Kernel同名函数完全一致，Attributes类型必须排在Tensor类型之后。 +- output：输出类型，如果有多个输出间用逗号(“,”) 分隔开。可以在类型后用"()"选择性标记每个输入的名字(如`Tensor(out)`)，如果没有标记则默认处理为out0, out1, … +- infer_meta：计算返回Tensor的维度与类型（详见InferMeta函数介绍） + - func为调用的InferMeta函数，默认输入为args项的所有参数和output参数，其中的Tensor类型变量会自动替换为MetaTensor。 +- kernel：API调用的具体Kernel函数 + - func：kernel函数的注册名（REGISTER使用的name，非函数名），默认输入为args项的所有参数和output参数 +- backward：（可选）对应的反向函数名称，没有则生成纯前向API。 + +Yaml解析脚本将根据上述配置项自动生成对应的C++ API，生成的代码中会完成包括Kernel自动选择、Tensor转换、Data Transform、InferMeta以及Kernel调用等相关处理逻辑，具体可参考生成的`api.cc`内代码。 + +由于C++ API数量较多，且有着各种各样的形式与功能，为此在Yaml配置机制上也提供了很多更为灵活的配置项，如`invoke`等。 + +### 2.3.4 Kernel形式、注册及管理 + +#### 2.3.4.1 Kernel形式 + +> 本节要点： +> 1. Kernel函数形式要点： +> （1）数据类型T，与DeviceContext（简写为Context）作为模板参数； +> （2）Context作为Kernel第一个参数； +> （3）返回值Tensor以指针形式作为输入参数，Kernel本身返回值为void + +这一层是具体的Kernel层，这一层实现的函数，会作为Kernel注册到框架中，供框架统一查找和调度。 + +目前我们期望这一层的形式如下，以`scale`为例： + +``` +template +void Scale(const Context& dev_ctx, + const DenseTensor& x, + float scale, + float bias, + bool bias_after_scale, + DenseTensor* out) { + ... +} +``` + +说明如下： + +- 不同设备的kernel要有不同的函数实现，函数名采用**驼峰式命名**，除了首字母大写之外，命名尽可能和API函数名保持一致，同一个计算的函数命名保持一致，通过不同文件或者目录管理不同设备的函数 +- 一般有两个模板参数，T和Context（尽可能），用于运行时决定数据类型和设备类型 + - 按照我们目前的体系，绝大多数的Kernel都是按照**特化DeviceContext和数据类型**这种方式缩减代码的，这与原先OpKernel的形式一致性比较强 + - 形式要统一，将来如果Kernel层也作为细粒度API暴露的话，易用性有保障 +- 函数输入参数规定： + - 以具体的DeviceContext作为第一个输入参数，如CPUContext，CUDAContext，用于满足运行时需要特定上下文信息的需求，如多stream需要传stream进来 + - 暂不支持一个Kernel传入多个DeviceContext参数，目前认为这样的需求不太合理 + - 参数列表和API保持一致，如果有其他的特殊信息需要传入Kernel，通过Context传递 + - 随后是所有的输入Tensor与输入Attribute，均以const &方式传入，POD类型直接以值传入 + - 输入的Tensor是具体的Tensor类型，如DenseTensor或SelectedRows，不是对外接口API那个Tensor + - 最后是函数的返回值Tensor，以指针形式传入 + - 为了满足灵活性，让kernel可以适配更多的场景，后续会允许声明灵活类型的输入、输出和参数，参考tfrt的Argument（输入）, Attribute,（属性） Return（输出）等模板，以适配非Tensor的输入输出，以及Tensor类的Attribute，让机制更加灵活 +- 函数内部实现按需决定： + - 短期： + - 将现有OpKernel内实现，迁移到具体的设备Kernel内 + - 将存在设备公用的OpKernel实现抽离为函数，由多个设备Kernel共同调用 + - 长期： + - 复杂Kernel直接调用基础Kernel完成计算，鼓励Kernel复用，简化代码 + +> FAQ： + +>- 为什么需要使用模板参数？为什么不和torch一样，没有模板参数？ + - 运行时数据类型T和设备Device的选择是在kernel选择时必要的操作，各个框架都是一样的 + - torch在写法上避免了用模板实现kernel选择，但实际上采用了全局kernel map查找的选择方式，这种方式的开销是比较重的，一个kernel的执行过程中，可能存在多次kernel map的查找 + - 基本流程如下图： + - ![图片](http://bos.bj.bce-internal.sdns.baidu.com/agroup-bos-bj/bj-2aafdb051eaea7120bdf9604eb738029dcd3162a) + - 这种方式存在的性能问题已经被torch自身认识到，所以torch也在做算子库重构，但是积重难返，他们重构也并未对此问题从根本上解决，只是减少了一些redispatch的层数，我们不能一味模仿竞品自身都认为有问题的设计 +- 为什么第一个参数需要是DeviceContext？为什么不能不传？ + - phi kernel要求是纯函数形式，即函数内使用的变量均通过参数传入，或者在函数内部创建，不允许在函数内部使用全局单例，为了适配多样的kernel需求，像DeviceContext这种存储上下文信息的参数是必要的 +- 为什么需要两个模板参数？ + - 为了方便设备无关kernel的复用，假如我们要实现一个傅里叶变换fft kernel，假设这个kernel能够使用基础kernel组合得出， + +#### 2.3.4.3 Kernel实现 + +> 本节要点： +> 1. Kernel专注表达数学算法，不掺杂调度逻辑 +> 2. Kernel足够细粒度，边界清晰，没有可选参数，便于复用 + +现有Kernel因为Op参数过于复杂，引入了调度逻辑，例如 + +- 通过`use_cudnn`判断是否执行cudnn分支，在新的Tensor计算库中，使用cudnn计算是单独的Kernel + +为了降低成本，Phi Kernel实现会尽可能继承原先的OpKernel实现，大部分Kernel的实现仅需要将原先OpKernel中取Input，Output的逻辑移除，并且修改一些关键方法即可，以sign为例： + +原先sign OpKernel： + +``` +template +class SignKernel : public framework::OpKernel { + public: + virtual void Compute(const framework::ExecutionContext& context) const { + auto* out = context.Output("Out"); + auto* in = context.Input("X"); + out->mutable_data(in->place()); + + auto eigen_out = framework::EigenVector::Flatten(*out); + auto eigen_in = framework::EigenVector::Flatten(*in); + auto& place = + *context.template device_context().eigen_device(); + EigenSign, T>::Eval(place, eigen_out, + eigen_in); + } +}; +``` + +迁移后的phi sign kernel： + +``` +template +void SignKernel(const Context& dev_ctx, + const DenseTensor& x, + DenseTensor* out) { + dev_ctx.template Alloc(out); + auto eigen_out = phi::EigenVector::Flatten(*out); + auto eigen_x = phi::EigenVector::Flatten(x); + + auto& dev = *dev_ctx.eigen_device(); + paddle::operators::EigenSign, T>::Eval( + dev, eigen_out, eigen_x); +} +``` + +除了kernel形式从结构体变为函数式之外，还有两处主要变化： + +1. 由于参数都是具体的输入，所以不需要再到context里取输入输出，相关代码移除 +2. phi kernel中要求输出tensor的内存申请统一使用`ctx.Alloc`或者`ctx.HostAlloc`方法，不能再使用原先的`mutable_data`申请内存 + +> FAQ +> 1. 为什么mutable_data要替换成ctx.Alloc？ +> 答：因为原先的mutable_data方法中调用的全局方法memory::AllocShared内部使用了全局单例进行内存分配，这不符合前面说过的纯函数设计原则，从业务需求上来讲，kernel里面如果使用单例确定显存分配的方式，在推理的多线程环境中，不能线程不能指定不同的存储分配方式。 + + +#### 2.3.4.4 Kernel注册 + +> 本节要点： +> 1. Kernel将自身全部关键信息暴露给框架，记录其输入、输出和属性的信息，否则将导致框架调度与 Kernel 计算之间界限不清 + +现有 fluid Kernel 注册时仅记录了 Kernel 的 place，layout，dtype，输入输出等统一由 ExecutionContext管理，没有相应的信息记录，现在kernel要改成函数式，每一个函数的输入输出和属性都是明确的，我们希望在这里记录每一个输入输出的信息，也是为了兼容paddle-lite的调度。 + +同时，我们需要简化Kernel注册的写法，现有的写法都不够简洁： + +1. fluid的Kernel注册写法，有不少冗余信息，以scale为例，可以看到每个kernel除了最后的data type，前面函数名和DeviceContext特化的信息都是冗余的 + + ``` + REGISTER_OP_CPU_KERNEL( + scale, ops::ScaleKernel, + ops::ScaleKernel, + ops::ScaleKernel, + ops::ScaleKernel, + ops::ScaleKernel, + ops::ScaleKernel, + ops::ScaleKernel, + ops::ScaleKernel); + ``` + +2. Paddle-Lite的kernel注册写法，为每一个Kernel都声明了输入输出信息，但由于每个数据类型的kernel都是不同的，也会造成写法上的冗余，如下代码可以看到，除了data type，其他的信息也基本是冗余的 + + ``` + #ifdef LITE_BUILD_EXTRA + using scale_int32_f = + paddle::lite::kernels::arm::ScaleCompute; + REGISTER_LITE_KERNEL(scale, kARM, kFloat, kNCHW, scale_int32_f, int32) + .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt32))}) + .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt32))}) + .Finalize(); + + using scale_int64_f = + paddle::lite::kernels::arm::ScaleCompute; + REGISTER_LITE_KERNEL(scale, kARM, kFloat, kNCHW, scale_int64_f, int64) + .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt64))}) + .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt64))}) + .Finalize(); + #endif // LITE_BUILD_EXTRA + + #ifdef ENABLE_ARM_FP16 + using scale_float16 = + paddle::lite::kernels::arm::ScaleCompute; + REGISTER_LITE_KERNEL(scale, kARM, kFP16, kNCHW, scale_float16, def) + .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kFP16))}) + .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kFP16))}) + .Finalize(); + + #endif // ENABLE_ARM_FP16 + + using scale_float = + paddle::lite::kernels::arm::ScaleCompute; + REGISTER_LITE_KERNEL(scale, kARM, kFloat, kNCHW, scale_float, def) + .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kFloat))}) + .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kFloat))}) + .Finalize(); + + using scale_int32 = + paddle::lite::kernels::arm::ScaleCompute; + REGISTER_LITE_KERNEL(scale, kARM, kInt32, kNCHW, scale_int32, def) + .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt32))}) + .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt32))}) + .Finalize(); + + using scale_int64 = + paddle::lite::kernels::arm::ScaleCompute; + REGISTER_LITE_KERNEL(scale, kARM, kInt64, kNCHW, scale_int64, def) + .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt64))}) + .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt64))}) + .Finalize(); + ``` + +因此，本次设计，不希望继续保持目前这种冗余的写法，希望kernel注册方法足够简洁，同时还能够灵活地满足Kernel输入输出信息配置的需求。 + +对于这个问题，关键点在于kernel需要指定自己的device，layout和dtype作为它自己的key信息，而大部分kernel输入输出Tensor的device，layout和dtype和kernel自身是一致的，对于这类kernel，我们可以按照kernel的信息自动生成填充每个输入输出的信息，不需要通过BindInput，BindOutput声明；我们只需要针对与kernel信息不一致的输入输出去配置特殊信息即可。 + +新实现的kernel注册形式如下： + +``` +PT_REGISTER_KERNEL("sign", CPU, NCHW, pt::Sign, float, double) {} + +PT_REGISTER_KERNEL("mean", CPU, NCHW, pt::Mean, float, double) {} + +PT_REGISTER_KERNEL("scale", CPU, NCHW, pt::Scale, float, double, bfloat16, + uint8_t, int8_t, int16_t, int, int64_t) {} + +PT_REGISTER_KERNEL("scale.host", CPU, NCHW, pt::ScaleHost, float, double, bfloat16, + uint8_t, int8_t, int16_t, int, int64_t) { + kernel->InputAt(1).SetBackend(pt::Backend::kCPU); +} +``` + +说明如下： + +- 去除了之前注册方法中大量的冗余信息，可以一行代码完成8个数据类型的scale kernel注册，同时根据kernel信息默认记录每个输入输出的信息 +- 对于有`ScaleTensor`这种动态attr输入的kernel，可以在函数体重配置具体参数的Backend，Layout和Dtype信息；没有此类需求的，函数体为空即可 + +此外，在`PT_REGISTER_KERNEL`宏内，通过模板推导，对Kernel函数的函数形式了归一化处理。 + +输入参数列表各异的kernel统一被归一化为如下形式，从而能够以统一的函数指针存储到下文中的Kernel数据结构中： + +``` +using KernelFn = void (*)(KernelContext* ctx); +``` + +通过在Kernel函数外包裹`PT_KERNEL`进行自动推导 + +``` +#define PT_KERNEL(...) \ + ::pt::KernelImpl::Compute +``` + +此外，目前仅实现了基本的模板适配，后续我们会根据需求添加，以让在整体机制更加灵活，适用范围更广。 + +#### 2.3.4.4 Kernel管理 + +> 本节要点： +> 1. 介绍目前Kernel管理组件的设计 + +对于新形式Kernel的管理，目前设计类图如下： + +![kernel-design.png](./images/kernel-design.png) + +说明如下： + +- `KernelFactory`作为管理Kernel的全局单例数据结构，和fluid的OpKernelMap类似，两级map，第一层根据name找到Kernel集合，第二层根据KernelKey找到具体的Kernel +- `KernelKey`和原先的OpKernelType类似，但将palce和library_type字段合二为一称之为Backend，因为原先的LibraryType是一个有局限的枚举类，原本就和place是强相关的，拆分反而增加了理解成本 +- `Kernel`相比原先的OpKernel持有了更多信息，除了执行时的Function，还持有了具体参数的信息，即`KernelArgsDef`，对于Tensor类输入输出，保存了Tensor类型信息、Device，数据类型、数据布局，对于Attribute类输入输出，保存了类型信息 + + +### 2.3.5 Kernel自动化编译及依赖分析 + +> 本节要点： +> 1. 介绍kernel的自动化编译设计 +> 2. 介绍kernel的自动化依赖分析设计 + +原OpKernel迁移至phi之后，在编译上需要创建新的编译target，目前phi也设计了相应的自动化编译方式，使大家在迁移之后，尽可能不需要关注编译相关的内容。 + +#### 2.3.5.1 Kernel自动化编译 + +目前按照相应的规范迁移kernel之后，重新执行cmake，cmake会自动根据新增kernel的文件名，创建相应的编译对象，相关的逻辑在`paddle/phi/kernels/CMakeLists.txt` + +``` +set(COMMON_KERNEL_DEPS dense_tensor sparse_coo_tensor sparse_csr_tensor kernel_context kernel_factory arg_map_context convert_utils lod_utils) +set(COMMON_KERNEL_DEPS ${COMMON_KERNEL_DEPS} eigen_function blas math_function) +# remove this dep after removing fluid deps on tensor creation +set(COMMON_KERNEL_DEPS ${COMMON_KERNEL_DEPS} phi_api_utils) +set(COMMON_KERNEL_DEPS ${COMMON_KERNEL_DEPS} infermeta) + +# auto build kernel targets by cmake +register_kernels(EXCLUDES math_kernel DEPS ${COMMON_KERNEL_DEPS}) + +set(MATH_KERNEL_DEPS ${COMMON_KERNEL_DEPS} cast_kernel copy_kernel phi_transpose_cpu) +if(WITH_GPU OR WITH_ROCM) + set(MATH_KERNEL_DEPS ${MATH_KERNEL_DEPS} phi_transpose_gpu) +endif() +kernel_library(math_kernel DEPS ${MATH_KERNEL_DEPS}) +``` + +1. 首先，定义kernel的公共依赖集合`COMMON_KERNEL_DEPS`，有较多kernel依赖的组件均可以放置到该集合中 +2. 通过函数`register_kernels`，自动解析kernels目录下的`***_kernel.h`文件，自动创建对应的target +3. 如果某个kernel有自己独特的依赖，可以将其标记在`register_kernels`的EXCLUDES集合中，跳过对其的自动生成，后面再使用`kernel_library`函数，生成对应的kernel target，`kernel_library`也是根据文件名自动生成编译target的 + +具体`register_kernels`和`kernel_library`如果扫描文件并生成编译对象，可以参考`camke/phi.cmake`中的函数实现，此处不展开介绍了 + +#### 2.3.5.2 Kernel依赖自动化分析 + +phi kernel整体改为了函数式，本意就是让kernel之间可以更加方便地复用，但是复用kernel会引入kernel之间的编译依赖关系，比如A Kernel调用了B Kernel，那么在编译上，A Kernel需要DEPS B Kernel，这样的编译依赖声明对于开发者来讲同样是非常繁琐的，因此我们也设计了对应的自动化解析方式，具体如下： + +在编译A Kernel时，我们会分析A Kernel相关的`.h`和`.cc/cu`文件中include声明，如果A Kernel include了 B Kernel的头文件声明，我们会自动为A Kernel添加B Kernel target的依赖，例如： + +dot_kernel.h有`#include "paddle/phi/kernels/empty_kernel.h"`，那么在编译时，dot_kernel会自动依赖empty_kernel，这一过程也是在`register_kernels`和`kernel_library`函数中实现的，可以参考`camke/phi.cmake`中的函数实现。 + +因此，开发时如果需要进行Kernel复用，正确include相应头文件即可。 + +> 注意：这里只有kernel间的复用是会自动解析的，如果某个kernel依赖了某个function或者functor，仍然是需要手动声明依赖的，phi的设计鼓励kernel之间的复用，因为kernel本身也成为function了，因此像之前那种调用function的方式长期来讲是基本可以被淘汰掉的，只需要尽可能将function实现为kernel即可 + +### 2.3.6 InferMeta(Shape)抽象整合 + +原先fluid Op的InferShape和OpKernel一样，存在重复开发的问题，因为不同Op的InferShape函数无法复用，因此即使不同Op的InferShape逻辑一样或者类似，也都是重写一遍，本次phi的重构也需要解决此问题。 + +我们将InferShape同样改写为函数式，支持不同的Op可以调用同一个InferShape函数，提升易用性，降低维护成本。 + +> FAQ： +> 1. 为什么要叫InferMeta，而不是继续叫InferShape？ +> 答：InferMeta的Meta来源于DenseTensor中的meta成员，在phi中，一个op有两大组件，InferMeta和Kernel。这里InferMeta覆盖了InferShape的功能，但又不限于InferShape，除了对dims和lod的推断，InferMeta中也会承担dtype和layout的推断，这一点和原先是不一样的。 + +#### 2.3.6.1 InferMeta相关设计 + +首先InferMeta也为函数式，几个示例如下： + +``` +void UnchangedInferMeta(const MetaTensor& x, MetaTensor* out) { + out->share_meta(x); +} + +void CastInferMeta(const MetaTensor& x, DataType out_dtype, MetaTensor* out) { + out->set_dims(x.dims()); + out->set_dtype(out_dtype); + out->set_layout(x.layout()); +} + +void CreateLikeInferMeta(const MetaTensor& x, + DataType dtype, + DataLayout layout, + MetaTensor* out) { + out->set_dims(x.dims()); + out->set_dtype(dtype == DataType::UNDEFINED ? x.dtype() : dtype); + out->set_layout(layout == DataLayout::UNDEFINED ? x.layout() : layout); +} + +void ConcatInferMeta(const std::vector& x, + const Scalar& axis_scalar, + MetaTensor* out, + MetaConfig config = MetaConfig()); +``` + +特征介绍如下： + +1. 函数命名为`[FunctionDesc|OpName]InferMeta` +2. 函数形式与Kernel类似，函数参数依次为MetaTensor输入，Attribute，MetaTensor输出，返回值为空，原则上InferMeta函数与其对应Kernel函数的参数列表是一一对应的，差别仅为Tensor参数类型，InferMeta函数的Tensor参数为MetaTensor，Kernel函数的Tensor参数为DenseTensor，SparseTensor等 +3. 对于一些需要区分编译期与执行期的InferMeta函数，在末尾添加MetaConfig参数，config中有is_runtime的flag成员，之所以用结构体，是为了便于后续扩展其他flag成员。 + +这里使用MetaTensor是为了屏蔽多种Tensor类型，以及兼容原先fluid的VarDesc及Variable，一个op对应一个InferMeta函数即可，如果不对类型进行屏蔽，本身InferMeta函数就会因为输入类型不同而重复开发多份。 + +其中MetaTensor的基础设计如下： + +``` +class MetaTensor { + public: + explicit MetaTensor(TensorBase* tensor) : tensor_(tensor) {} + + MetaTensor() = default; + MetaTensor(const MetaTensor&) = default; + MetaTensor(MetaTensor&&) = default; + MetaTensor& operator=(const MetaTensor&) = delete; + MetaTensor& operator=(MetaTensor&&) = delete; + + virtual ~MetaTensor() = default; + + virtual int64_t numel() const; + virtual DDim dims() const; + virtual DataType dtype() const; + virtual DataLayout layout() const; + virtual void set_dims(const DDim& dims); + virtual void set_dtype(DataType dtype); + virtual void set_layout(DataLayout layout); + virtual void share_lod(const MetaTensor& meta_tensor); + + private: + const LoD& lod() const; + TensorBase* tensor_; +}; +``` + +基类的MetaTensor中有一个TensorBase的指针成员，因此在phi中可以兼容DenseTensor，SelectedRows，SparseTensor等多种类型。 + +#### 2.3.6.2 InferMeta注册管理 + +为了支持InferMeta函数的统一调用，InferMeta函数也进行了统一的注册管理。 + +首先也需要类似前述Kernel形式归一化的`PT_KERTNEL`工具宏，命名为`PT_INFER_META`，并实现类似KernelContext的InferMetaContext（实现不展开了，仅放置部分片段，详见`phi/core/infermeta_utils.h`） + +``` +class InferMetaContext { + public: + InferMetaContext() = default; + ... +}; + +#define PT_INFER_META(...) \ + ::phi::InferMetaFnImpl::Call + +template +struct InferMetaFnImpl; + +template +struct InferMetaFnImpl { + static void Call(InferMetaContext* ctx) { + InferMetaFnCallHelper>::template Call<0, 0, 0>(ctx); + } + + private: + template + struct InferMetaFnCallHelper; + + ... +}; +``` + +然后设计对应的单例类用来存储MetaFn + +``` +class MetaFnFactory { + public: + static MetaFnFactory& Instance(); + + bool Contains(const std::string& kernel_name_prefix) const { + return meta_fn_map_.count(kernel_name_prefix) > 0; + } + + void Insert(std::string kernel_name_prefix, InferMetaFn infer_meta_fn) { + PADDLE_ENFORCE_NE( + Contains(kernel_name_prefix), + true, + phi::errors::AlreadyExists( + "`%s`'s Series Kernel's InferMetaFn has been registered.", + kernel_name_prefix)); + meta_fn_map_.insert( + {std::move(kernel_name_prefix), std::move(infer_meta_fn)}); + } + + const InferMetaFn& Get(const std::string& kernel_name_prefix) const { + auto it = meta_fn_map_.find(kernel_name_prefix); + PADDLE_ENFORCE_NE( + it, + meta_fn_map_.end(), + phi::errors::NotFound( + "`%s`'s Series Kernel's InferMetaFn is not registered.", + kernel_name_prefix)); + return it->second; + } + + private: + MetaFnFactory() = default; + + /** + * [ Why use kernel name prefix? ] + * + * one op -> a matrix of kernels + * + * such as, scale op, it may correspond to the following kernels: + * + * - scale, scale_sr, scale_dnnl + * - scale_raw, scale_raw_sr, scale_raw_dnnl + * + * All the kernels in each row correspond to the same infershape function, + * the number of kernel arguments in the same row is the same, and only + * the tensor types in the arguments are different. + */ + paddle::flat_hash_map meta_fn_map_; + + DISABLE_COPY_AND_ASSIGN(MetaFnFactory); +}; +``` + +封装对应的注册宏，用于InferMeta的注册，注册写法示例如下： + +``` +PT_REGISTER_INFER_META_FN(sign, phi::UnchangedInferMeta); +``` + +对于InferMeta的注册，一般不需要开发者手写，我们通过yaml中api name和InferMeta的映射关系，自动生成对应的注册条目。 + +#### 2.3.6.3 InferMeta兼容fluid InferShape + +在fluid中，继承MetaTensor实现CompatMetaTensor，重写对应的成员方法，以使InferMeta函数兼容VarDesc和Variable的输入，以dims为例，CompatMetaTensor的dims实现为： + +``` +class CompatMetaTensor : public phi::MetaTensor { + public: + CompatMetaTensor(InferShapeVarPtr var, bool is_runtime) + : var_(std::move(var)), is_runtime_(is_runtime) {} + + CompatMetaTensor() = default; + CompatMetaTensor(const CompatMetaTensor&) = default; + CompatMetaTensor(CompatMetaTensor&&) = default; + CompatMetaTensor& operator=(const CompatMetaTensor&) = delete; + CompatMetaTensor& operator=(CompatMetaTensor&&) = delete; + + ... + + DDim dims() const override { + if (is_runtime_) { + auto* var = BOOST_GET_CONST(Variable*, var_); + if (var->IsType()) { + return var->Get().dims(); + } else if (var->IsType()) { + return var->Get().dims(); + } else { + PADDLE_THROW(platform::errors::Unimplemented( + "Currently, only can get dims from DenseTensor or SelectedRows.")); + } + } else { + auto* var = BOOST_GET_CONST(VarDesc*, var_); + return make_ddim(var->GetShape()); + } + } + ... +}; +``` + +然后，为了将函数式的InferMeta嫁接回fluid的Op体系上，需要将函数式的InferMeta归一化为functor形式。 + +通过前面介绍的PT_INFER_META宏归一化函数形式，然后将`PT_INFER_META(***InferMeta)`包装到一个functor中，functor中先将InferShapeContext转换为InferMetaContext，再调用相应InferMeta函数，通过一个宏统一管理代码 + +``` +#define DELCARE_INFER_SHAPE_FUNCTOR(op_type, functor_name, fn) \ + struct functor_name : public paddle::framework::InferShapeBase { \ + void operator()( \ + paddle::framework::InferShapeContext* ctx) const override { \ + auto infer_meta_context = \ + paddle::framework::BuildInferMetaContext(ctx, #op_type); \ + fn(&infer_meta_context); \ + } \ + } +``` + +这其中的关键函数是`BuildInferMetaContext`，这个函数会从InferShapeContext中，将InferMeta函数需要的参数取出，统一放到InferMetaContext中并返回，InferMeta需要的参数列表通过ArgumentMapping函数获取（详细在2.4 动静态图执行兼容适配中介绍）。 + +然后将该functor在Op注册时维护到相应OpInfo中即可，同时删除原先Op的InferShape实现，示例如下 + +``` +// 原先实现 +class SignOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + void InferShape(framework::InferShapeContext *ctx) const override { + OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "sign"); + OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "sign"); + + ctx->SetOutputDim("Out", ctx->GetInputDim("X")); + ctx->ShareLoD("X", /*->*/ "Out"); + } +}; + +namespace ops = paddle::operators; + +REGISTER_OPERATOR(sign, ops::SignOp, ops::SignOpMaker, + ops::SignGradMaker, + ops::SignGradMaker); + +// 升级后实现 +class SignOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; +}; + +DELCARE_INFER_SHAPE_FUNCTOR( + sign, SignInferShapeFunctor, PT_INFER_META(phi::UnchangedInferMetaNew)); +REGISTER_OPERATOR(sign, ops::SignOp, ops::SignOpMaker, + ops::SignGradMaker, + ops::SignGradMaker, + SignInferShapeFunctor); + +``` + +至此，实现原Op的InferShape函数迁移至phi InferMeta之后，可以重新注册回fluid中被调用，从而实现InferShape的函数化复用与全局统一。 + +## 2.4 动静态图执行兼容适配 + +> 本节要点： +> 1. 新形式Kernel如何在现有静态图和动态图体系中调用，难点在于解决多参数Op到少参数Kernel的匹配问题 + +### 2.4.1 ArgumentMapping体系设计 + +由于新形式Kernel参数列表与Python API对齐，和原先的OpMaker中注册的参数列表存在差异，导致新形式Kernel在原先fluid体系中调用时会很难匹配 + +例如conv2d op，它的OpMaker中注册了4个Input，1个Output，26个Attribute，而conv2d的Python API一共只有8个参数（不算name，3个Tensor输入，5个Attribute输入） + +运行时，调用新Kernel之前，需要将Kernel需要的参数从OpMaker注册的参数中选出来，再传给新Kernel使用。 + +对于一些原先就编写规范的算子，它的OpMaker参数和Python api参数本就是对应的，这种标准的情况，不存在需要选参数的需求，对于这部分算子，根据OpProto中输入输出属性的注册顺序，跳过标记为Extra和Quant的成员，可以解决一部分Op和Kernel的参数匹配问题；然而对于一些不太规范，或者说是fluid时代遗留的算子，比如像conv，就需要这样的映射函数，且这个映射函数根据op不同，可能存在非常复杂的判断逻辑，因此现阶段没有办法可以自动化处理。 + +为此，目前设计了ArgumentMapping函数映射的体系，在phi/ops/compat目录下，实现相应的映射函数并注册，然后在phi kernel执行适配时，会调用对应的ArgumentMapping函数，得到phi kernel需要的参数，例如scale op的映射函数如下： + +``` +/** + * Note [ Why does the ArgumentMapping function need to be so complicated? ] + * + * In order to meet the requirements of infrt, the function used to match Op + * and Kernel parameters, need to be placed in phi as a compatible component, + * and does not depend on fluid. + * + * Because infrt not only needs to dynamically call this argument mapping + * function at runtime, but also needs to statically declare all possible + * results of the function before running without any information. + * + * The infrt declare like: + * + * def PDKEL_Reshape_to_CPU : Pat< + * (PD_ReshapeOp $x, $shape_tensor， $shape_attr), // OpMaker arguements + * (PDKEL_ReshapeKernelAttr $x, fn($shape_attr)>; // Kernel arguments + * def PDKEL_Reshape_to_CPU : Pat< + * (PD_ReshapeOp $x, $shape_tensor， $shape_attr), + * (PDKEL_ReshapeKernelAttr $x, fn($shape_tensor)>; + * + * Therefore, we need to write out each result of the argument mapping function, + * like `KernelSignature("full", {}, {"ShapeTensor", "value"}, {"Out"})`, it + * cannot contains variable, only can contains const char* string. + * + * Infrt will parse all results before running for the generation of the above + * static declare, which leads to some functions being written in a long way, + * and the complicated ones may have hundreds of lines, which has certain side + * effects on the programming experience. + */ +KernelSignature ScaleOpArgumentMapping(const ArgumentMappingContext& ctx) { + if (ctx.IsDenseTensorInput("X")) { + if (ctx.HasInput("ScaleTensor")) { + return KernelSignature( + "scale", {"X"}, {"ScaleTensor", "bias", "bias_after_scale"}, {"Out"}); + } else { + return KernelSignature( + "scale", {"X"}, {"scale", "bias", "bias_after_scale"}, {"Out"}); + } + } else if (ctx.IsSelectedRowsInput("X")) { + if (ctx.HasInput("ScaleTensor")) { + return KernelSignature("scale_sr", + {"X"}, + {"ScaleTensor", "bias", "bias_after_scale"}, + {"Out"}); + } else { + return KernelSignature( + "scale_sr", {"X"}, {"scale", "bias", "bias_after_scale"}, {"Out"}); + } + } else { + return KernelSignature("unregistered", {}, {}, {}); + } +} +``` + +其中的ArgumentMappingContext基本接口设计如下： + +``` +// TODO(chenweihang): Add more methods if needed in future +class ArgumentMappingContext { + public: + virtual ~ArgumentMappingContext() = default; + + virtual bool HasInput(const std::string& name) const = 0; + virtual bool HasOutput(const std::string& name) const = 0; + virtual bool HasAttr(const std::string& name) const = 0; + + // now we can't use Attribute here, it will cause phi relay on + // boost::variant and BlockDesc + virtual paddle::any Attr(const std::string& name) const = 0; + + virtual size_t InputSize(const std::string& name) const = 0; + virtual size_t OutputSize(const std::string& name) const = 0; + + virtual bool IsDenseTensorInput(const std::string& name) const = 0; + virtual bool IsSelectedRowsInput(const std::string& name) const = 0; + + virtual bool IsDenseTensorOutput(const std::string& name) const = 0; + virtual bool IsSelectedRowsOutput(const std::string& name) const = 0; +}; +``` + +无论ScaleOpArgumentMapping是在fluid中使用，还是在infrt中使用，只要能够构造出特定框架的ArgumentMappingContext，即可获得对应的参数映射关系。 + +**1）对fluid的适配** + +在fluid中，该函数需要同时在静态图和动态图中使用，比较直接的思路是，直接通过ExecutionContext构造ArgumentMappingContext，然后在op执行时调用，例如 + +``` +// TODO(chenweihang): split impl based OpProto or Dygraph if needed +class ExecutionArgumentMappingContext : public phi::ArgumentMappingContext { + public: + ExecutionArgumentMappingContext(const ExecutionContext& ctx) : ctx_(ctx) {} + + bool HasInput(const std::string& name) const override { + return ctx_.HasInput(name); + } + + bool HasOutput(const std::string& name) const override { + return ctx_.HasOutput(name); + } + + bool HasAttr(const std::string& name) const override { + return ctx_.HasAttr(name); + } + + size_t InputSize(const std::string& name) const override { + return ctx_.InputSize(name); + } + + size_t OutputSize(const std::string& name) const override { + return ctx_.OutputSize(name); + } + + bool IsDenseTensorInput(const std::string& name) const override { + return ctx_.InputVar(name)->IsType() || + ctx_.InputVar(name)->IsType(); + } + + bool IsSelectedRowsInput(const std::string& name) const override { + return ctx_.InputVar(name)->IsType(); + } + + private: + const ExecutionContext& ctx_; +}; +``` + +**2）对infrt的适配** + +若在infrt中，infrt只有训练存储的推理program，也就是只有Proto这一层的信息，那么可以通过Proto信息去构造对应的Context使用，**proto中的信息目前在支持参数匹配上是完备的**，例如 + +``` +class ProtoArgumentMappingContext : public phi::ArgumentMappingContext { + public: + ProtoArgumentMappingContext(proto::OpProto* op, proto::BlockDesc* block) : op_(op), block_(block) {} + + bool HasInput(const std::string& name) const override { + // simple search + for (int i = 0; i < proto_->input_size(); ++i) { + auto& in = proto_->inputs()[i]; + if (in.name() == name) { + return true; + } + } + return false; + } + + bool HasOutput(const std::string& name) const override { + // simple search + for (int i = 0; i < proto_->output_size(); ++i) { + auto& out = proto_->outputs()[i]; + if (out.name() == name) { + return true; + } + } + return false; + } + + bool HasAttr(const std::string& name) const override { + // simple search + for (int i = 0; i < proto_->attrs_size(); ++i) { + auto& attr = proto_->attrs()[i]; + if (attr.name() == name) { + return true; + } + } + return false; + } + + size_t InputSize(const std::string& name) const override { + return proto_->input_size(); + } + + size_t OutputSize(const std::string& name) const override { + return proto_->output_size(); + } + + bool IsDenseTensorInput(const std::string& name) const override { + for (int i = 0; i < block_.vars_size(); ++i) { + auto& var = block_.vars()[i]; + if (var.name() == name) { + if (var.type() == proto::VarType::LOD_TENSOR) { + return true; + } + } + } + // TODO(chenweihang): throw error when cannot found + return false; + } + + bool IsSelectedRowsInput(const std::string& name) const override { + for (int i = 0; i < block_.vars_size(); ++i) { + auto& var = block_.vars()[i]; + if (var.name() == name) { + if (var.type() == proto::VarType::SELECTED_ROWS) { + return true; + } + } + } + // TODO(chenweihang): throw error when cannot found + return false; + } + + private: + proto::OpProto op_*; + proto::BlockDesc block_*; +}; +``` + +### 2.4.2 phi Kernel兼容调度执行 + +目前phi kernel可以兼容地在老Executor，ParallelExecutor，动态图的Tracer，Engine，推理的Predictor，以及新执行器InterpreterCore等在执行体系中被调度执行。 + +具体地，在动静态图调用OpKernel之前，判断对于当前计算，比如`scale`是否有新形式的Kernel已经注册，如果已经注册了，则调用新形式的Kernel去执行，如果没找到合适的Kernel，仍然执行之前已有的OpKernel。 + +``` + if (phi::KernelFactory::Instance().HasCompatiblePhiKernel(type_)) { + if (pt_kernel_signature_ == nullptr || pt_kernel_ == nullptr) { + pt_kernel_signature_.reset(new KernelSignature( + std::move(GetExpectedPhiKernelArgs(exe_ctx)))); + VLOG(6) << *pt_kernel_signature_.get(); + + kernel_type_.reset( + new OpKernelType(std::move(InnerGetExpectedKernelType(exe_ctx)))); + dev_ctx = pool.Get(kernel_type_->place_); + + pt_kernel_name = pt_kernel_signature_->name; + pt_kernel_key = TransOpKernelTypeToPhiKernelKey(*kernel_type_.get()); + pt_kernel_.reset( + new phi::Kernel(phi::KernelFactory::Instance().SelectKernel( + pt_kernel_name, pt_kernel_key))); + + if (pt_kernel_->IsValid()) { + VLOG(6) << "Static mode ChoosePhiKernel - kernel name: " + << pt_kernel_name << " | kernel key: " << pt_kernel_key + << " | kernel: " << *pt_kernel_; + } else { + VLOG(6) << "Static mode ChoosePhiKernel - kernel `" << pt_kernel_name + << "` not found."; + } + } + if (pt_kernel_->IsValid()) { + run_phi_kernel_ = true; + } else { + auto& all_op_kernels = AllOpKernels(); + auto kernels_iter = all_op_kernels.find(type_); + if (kernels_iter == all_op_kernels.end() || + kernels_iter->second.find(*kernel_type_.get()) == + kernels_iter->second.end() +#ifdef PADDLE_WITH_XPU + || + paddle::platform::is_xpu_place(kernel_type_->place_) && // NOLINT + !paddle::platform::is_xpu_support_op( + type_, *kernel_type_.get()) // NOLINT + || paddle::platform::is_in_xpu_black_list(type_) +#endif + ) { + auto pt_cpu_kernel_key = + FallBackToCpu(*kernel_type_.get(), pt_kernel_key, *this); + pt_kernel_.reset( + new phi::Kernel(phi::KernelFactory::Instance().SelectKernel( + pt_kernel_name, pt_cpu_kernel_key))); + + dev_ctx = pool.Get(platform::CPUPlace()); + if (pt_kernel_->IsValid()) { + VLOG(6) << "Static mode PrepareImpl - kernel name: " << pt_kernel_name + << " | kernel key: " << pt_cpu_kernel_key + << " | kernel: " << *pt_kernel_; + run_phi_kernel_ = true; + } + } + } + } + if (!run_phi_kernel_) { + if (kernel_type_.get() == nullptr || kernel_func_.get() == nullptr) { + ChooseKernel(exe_ctx); + dev_ctx = pool.Get(kernel_type_->place_); + } + } + +... + + if (run_phi_kernel_) { + phi::KernelContext pt_kernel_context; + // Do data transform before building KernelContext + // TODO(zhiqiu): support TransferInplaceVarsBack + PreparePhiData(exec_scope, *pt_kernel_, *pt_kernel_signature_, + runtime_ctx); + BuildPhiKernelContext(*runtime_ctx, dev_ctx, &pt_kernel_context); + (*pt_kernel_)(&pt_kernel_context); + } else { + (*kernel_func_)( + ExecutionContext(*this, exec_scope, *dev_ctx, *runtime_ctx)); + } +``` + +对于phi kernel的执行，有两个关键函数 + +**GetExpectedPhiKernelArgs** + +- 在调用phi kernel时，要完成多属性到少属性的匹配，这里就需要调用前述的ArgumentMapping函数，从而得到phi kernel的参数列表，GetExpectedPhiKernelArgs实现如下： + +``` +KernelSignature OperatorWithKernel::GetExpectedPhiKernelArgs( + const ExecutionContext& ctx) const { + ExecutionArgumentMappingContext arg_mapping_ctx(ctx); + return phi::OpUtilsMap::Instance().GetArgumentMappingFn(Type())( + arg_mapping_ctx); +} +``` + +**BuildPhiKernelContext** + +- 要调用phi kernel，需要准备phi kernel需要的Context，PhiKernelContext和原先的RuntimeContext及ExecutionContext不同之处在于，PhiKernelContext中是以SmallVector存储输入输出及属性，访问效率上要比原先的map高一些 +- PhiKernelContext中不存储输入输出及属性的name，要求这几项顺次存储，和kernel的参数列表顺序一致 + +Phi KernelContext的基本设计如下： + +``` +/** + * Note: KernelContext doesn't manage the life of DeviceContext and Tensor + * + * Note: KernelContext does not couple the concept of framework, + * its constructor can only take the members it needs as parameters, + * not Scope, RuntimeContext, etc. as parameters + */ +class KernelContext { + public: + KernelContext() = default; + explicit KernelContext(DeviceContext* dev_ctx) : dev_ctx_(dev_ctx) {} + + void SetDeviceContext(DeviceContext* dev_ctx) { dev_ctx_ = dev_ctx; } + + template + const CtxType& GetDeviceContext() const { + return static_cast(*dev_ctx_); + } + + void EmplaceBackInput(const TensorBase* input); + + void EmplaceBackInputWithoutSetRange(const TensorBase* input); + + void EmplaceBackInputs(paddle::small_vector inputs); + + void EmplaceBackOutput(TensorBase* output); + + void EmplaceBackOutputWithoutSetRange(TensorBase* output); + + void EmplaceBackOutputs(paddle::small_vector outputs); + + void EmplaceBackAttr(paddle::any attr); + + const std::pair& InputRangeAt(size_t idx) const; + + const std::pair& OutputRangeAt(size_t idx) const; + + void AssignInputRange(std::pair&& range, size_t idx); + + void AssignOutputRange(std::pair&& range, size_t idx); + + template + const TensorType& InputAt(size_t idx) const { + return static_cast(*(inputs_.at(idx))); + } + + template + paddle::optional OptionalInputAt(size_t idx) const { + const auto& input = inputs_.at(idx); + return input ? paddle::optional{static_cast< + const TensorType&>(*input)} + : paddle::optional{paddle::none}; + } + + template + std::vector MoveInputsBetween(size_t start, size_t end) { + std::vector v; + for (size_t i = start; i < end; ++i) { + auto t = static_cast(inputs_.at(i)); + v.emplace_back(*t); + inputs_[i] = nullptr; + } + return v; + } + + template + TensorType* MutableOutputAt(size_t idx) { + return static_cast(outputs_.at(idx)); + } + + template + std::vector MutableOutputBetween(size_t start, size_t end) { + std::vector v; + for (size_t i = start; i < end; ++i) { + v.emplace_back(static_cast(outputs_.at(i))); + } + return v; + } + + template + AttrType AttrAt(size_t idx) const { + try { + return paddle::any_cast(attrs_.at(idx)); + } catch (paddle::bad_any_cast&) { + PADDLE_THROW(phi::errors::InvalidArgument( + "Attribute cast error in Op Kernel Context.")); + } + } + + size_t InputsSize() const { return inputs_.size(); } + size_t OutputsSize() const { return outputs_.size(); } + size_t AttrsSize() const { return attrs_.size(); } + + private: + DeviceContext* dev_ctx_; + + paddle::small_vector inputs_; + paddle::small_vector outputs_; + paddle::small_vector attrs_; + + paddle::small_vector> input_range_; + paddle::small_vector> output_range_; +}; +``` + +## 2.5 产品思考及后续规划 + +目前，phi算子库仍然处在Kernel体系的建设阶段，Kernel尚未完全迁移，且仍然存在诸多完善点，但将来phi算子库会更好地将“算子”的概念纳入进来，这还需要比较长的时间和比较大的人力投入。最后，从“产品”的角度介绍一下phi后续对于算子开发范式的规划，也能够让开发者更容易理解 “为什么要做算子库重构？” 这件事。 + +### 2.5.2 原算子开发范式 + +我们应该如何描述“框架算子”这个概念？ + +万变不离其宗： + +- 我们如何描述一个人？1. 他叫什么，长什么样；2. 他的工作、兴趣、爱好、特长、品质等 +- 我们如何描述一个物品？1. 它叫什么，长什么样；2. 它的用途和功能是什么 + - 比如一个杯子：1. 它叫水杯，长这样；2. 它用来盛水的 + +简单说，我们描述一个对象，可以采用两段式结构： + +1. 它的名字，样子或者说形态 +2. 它的功能，特征，及细节 + +算子同样可以按照这个原则去类比： + +1. 这个算子叫什么，有哪些参数，返回值是什么（即Op） +2. 这个算子在不同场景，不同设备中，怎么执行，怎么计算（即Kernel） + +如果我们**能分得清楚1和2的边界，并守住这个边界**，我们设计就能够趋于简练。 + +这是什么意思？就是说如果我们一定要用两段式来介绍一个对象，那么哪部分应该在第一段？哪部分应该在第二段？得有个逻辑清晰的认知。例如，我们用两段式介绍一个人： + +- 方式1：1. 他叫张三；2. 他在百度工作，他喜欢唱歌、爬山、骑行，他待人真诚，认真负责 +- 方式2：1. 他叫张三，他喜欢唱歌；2. 他在百度工作，他喜欢爬山、骑行，他待人真诚，认真负责 + +哪种分段方式更好一些呢？答案是显然的，方式2的两段中有同样形式的内容，逻辑不清。 + +为什么用这种方式来类比？因为我们的算子开发面临的场景我觉得是一样的，市面上现有的框架，对于算子的定义，都围绕着**“1. 算子描述，2. 算子执行”**的两段式进行设计。 + +顺着这个思路，我从“语文、逻辑和信息认知”的角度介绍一下我对fluid算子开发现状的理解，如果把现在的算子体系当做一篇以**算子**为题目的“小学作文”来看的话，拿高分有点困难。 + +**（1）"生僻词"比较多** + +fluid的Op开发概念对于新人来讲，可能是一种看“文言文”的感觉，似懂非懂。 + +如果我要描述一个“运算”，我需要讲清楚它叫什么，输入输出有哪些，这就够了，例如一个乘法运算，`叫multiply，输入x，y，得到out`，在这一点上，Python API是足够简练的。 + +那么现在我们的内部算子要怎么描述呢？要实现以下类和函数，可以参考 [mul_op](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/operators/mul_op.cc) ： + +``` +# 前向op +OperatorWithKernel +- InferShape +- GetExpectedKernelType +- GetKernelTypeForVar +OpMaker +# 反向op +GradOp +GradOpMaker +# 类型推导 +VarTypeInference +# 显存优化 +InplaceOpInference +NoNeedBufferVarsInference +# Op注册 +REGISTER_OPERATOR +# Op版本管理 +REGISTER_OP_VERSION +``` + +直观看说实话会有点困惑，新人可能会有什么疑问呢？ + +- Operator可以理解，是算子，OpMaker也可以理解，是告诉这个算子怎么生成，但为什么要两个呢？OpMaker都已经告诉你要怎么生成这个Op了，为什么还需要再写一个Operator？Maker不应该把Operator make出来吗？ +- 除了这俩，剩下的都看不懂。。。这些都什么意思？什么时候用？我新开发这个算子哪个需要写，哪个不需要写？ +- 算了，我短时间内搞不懂，算子也着急，找一个类似的算子，copy一份，它写了什么我照着挪过来，能跑对就行。。。（这可能是大部分新人开发算子的心态） + +**（2）重复的“修饰”比较多** + +其实每个算子真正不同的信息，就那么几个词，剩余的东西都是模板一样的存在，人脑处理信息是有成本的，去区分差异是需要思考的，从产品的角度来讲，直接把不同的地方告诉用户，让用户只关注这些差异是最高效的。 + +以 [MulOpMaker](https://github.com/PaddlePaddle/Paddle/blob/c6f49f0b9f189e043b458348d7fd1468e2645621/paddle/fluid/operators/mul_op.cc#L72) 和 [DotOpMaker](https://github.com/PaddlePaddle/Paddle/blob/c6f49f0b9f189e043b458348d7fd1468e2645621/paddle/fluid/operators/dot_op.cc#L35) 的实现为例，我们可以发现以下几点： + +1. 除了Op名字，输入、输出和参数命名，两段结构极其类似？为什么我们不能把这几个空抠出来让开发者直接填空？ +2. 输入、输出和参数后面的大段描述属于**重复建设**，并且现阶段没有用处，因为Python端已经写过一遍了，并且写得更规范，更清楚，C++端这里的参数注释没有人把关，质量参差不齐。 + +再看Operator的GetExpectedKernelType（[mul](https://github.com/PaddlePaddle/Paddle/blob/c6f49f0b9f189e043b458348d7fd1468e2645621/paddle/fluid/operators/mul_op.cc#L41)和[dot](https://github.com/PaddlePaddle/Paddle/blob/c6f49f0b9f189e043b458348d7fd1468e2645621/paddle/fluid/operators/dot_op.cc#L28)）：，也一样，都是根据x选择kernel，那为什么还要让开发者写其他的内容呢？直接做个填空，填x是不是就行了。 + +我们开发Op的时候，这些组件多少都存在这样的问题，这增加了大家工作量和理解成本。 + +**（3）相同的“段落”写了好多遍** + +这里主要指OpKernel的开发，我们现在的OpKernel之间可复用性较差，比如已经有了mul和add的Kernel，我们现在要新增一个fc算子（由mul和add）组成，我们得去mul和add的kernel中把代码拷贝一份过来使用，而不能直接调用mul和add的kernel。 + +这是我们建设phi初期要解决的问题，并且我从周围新人的口中已经听到过多次这样的反馈： + +- 我开发新算子，需要一个broadcast操作，我得去另一个算子里copy过来，还得先调通，copy的时候可能没copy全，或者应用场景稍有不同，这都需要额外的时间 +- 实现gumbol-softmax算子，因为softmax是其中的子运算，我得先把softmax的kernel实现copy过来 + +**（4）“描述”本身有二义性分段** + +说回开始的两段式结构，”1.算子描述；2.算子执行“，分这两段是必要的，也是业界普遍的做法，我们不需要再分第三段了，但paddle目前存在第三段，算子描述分了两段进行，并且这两段还不一致，即PythonaAPI和Op。 + +API和Op都是对算子运行行为的概要描述，本质上只是同一段内容的不同展现形式，比如[python dot API](https://github.com/PaddlePaddle/Paddle/blob/c6f49f0b9f189e043b458348d7fd1468e2645621/python/paddle/tensor/linalg.py#L993)和[DotOpMaker](https://github.com/PaddlePaddle/Paddle/blob/c6f49f0b9f189e043b458348d7fd1468e2645621/paddle/fluid/operators/dot_op.cc#L35)，就是告诉别人“它叫什么，参数都是什么”。 + + +咱们对同一个东西的描述，分两个地方写，还写得不一样，这是很令人费解的。就好像你介绍一个人，在学校你说他叫”张三“，在公司你说他叫”张三丰“，有相像之处，但又不是一个意思。 + +对于一个算子，它的输入、输出应该在各个场景下都是一致的，如果不一致，那本质上就不是一个算子。 + +比如，conv2d的api和op，[Python conv2d API](https://github.com/PaddlePaddle/Paddle/blob/c6f49f0b9f189e043b458348d7fd1468e2645621/python/paddle/nn/functional/conv.py#L416)，很简单，8个输入参数；但是对应的[conv2d op](https://github.com/PaddlePaddle/Paddle/blob/c6f49f0b9f189e043b458348d7fd1468e2645621/paddle/fluid/operators/conv_op.cc#L259)，有**32个**输入参数，让人摸不着头脑。 + +开发者也会很困惑，我开发op的时候，API和Op不是一个东西吗，我应该写得一样呢？还是不一样？ + +推理之前为什么要做**算子增强推全**，就是op的参数太多了，但API的参数很少，这两者本来是介绍一个东西，却差别如此之大，所以需要发动全员，在op的某些参数上标记AsExtra，就声明这个参数可能是多余的。 + +当然我们演变到如此田地，有一定历史原因： + +1. Op输入输出参数规范限制差，留的口子太大，可以天马行空地写； +2. 2.0 API对外层Python API的形态做了大范围规整，但是Op层保持不变，是导致目前同一段描述差异变大的一个主要原因。 + +对于这个问题的解决，我们的方向是很明确的，就是**Op层描述向API层靠拢，因为API层的定义是经过2.0 API项目仔细设计过的**。 + +### 2.5.1 新算子开发范式：完形填空 + 拼积木 + +phi期望的Op开发方式：**“完形填空”式算子描述实现 + “堆积木”式算子执行实现** + +**Op实现：** + +需要写的内容如下： + +``` +# 配置文件 api.yaml +- api : add + args : (const Tensor& x, const Tensor& y) + output : Tensor + infer_meta : + func : ElementwiseInferMeta + param : [x, y, -1] + kernel : + func : add + param : [x, y, -1] +``` + +以填空为主要方式，名字，输入、输出、输出的增强推断，用什么Kernel。 + +原先需要写得大段重复代码，全部通过”代码自动生成“的手段去实现，开发者不用再关注。 + +主要思想：仅让开发者关注最小的差异化信息集合，填空指定信息。 + +这里Op配置时，要求和Python端参数命名等完全一致，做到上下层描述一致，不给开发者留空间在op层自由发挥，导致想加什么加什么的随意行为。如果需要给op加参数，API也要一起更新，这首先需要通过不兼容升级评审。 + +**Kernel实现：** + +``` +template +Fc(const Context& dev_ctx, const Tensor& x, const Tensor& w, const Tensor& b, Tensor* out) { + phi::add(phi::mul(x, w), b, out); +} + +PT_REGISTE_KERNEL("fc", Fc, ...) +``` + +mul和add操作的拼接，代码量很少，再加一个注册声明。 + 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zkq}>-wL$;wUIvr6{lY7nDfJtlSi)36syEu^t)k+8Lge`07n4wrO(a|ylLm~NC*YYQ zjL-@;5G+u!>s9;fB9Ze=?UlhoAvqVIeS35LC_2IVU{JtWWsuw1+MxS?E7bws(NH3B z#D>i7YMes9a&>cf=%ZCg{3gY;55Q**vt_#-`xM9SO$QNF*W*VHa()D!`z z+;tzTLgtpTWVCGSWadal{$Cfl-SvBIN9BC-ftXl{4@II|=R4Es9%{aG#zv?9FJ(Tz`g42Ou?qzu zL+wOp|7QmdQCRDy3J37DvKSMz!lp!Ce*MUw%?+3tv}n`zKWk9eKk&m7@tuv2tJ2aC z9_5BWFxgpnWuUdMD_fU)-w_Kv{=i72I#$l;ultt5C9(`YLT|f0ie8s>tSbg^)i%(P zX)ix5lOT{^4@l5G(%$xD@kUpctn5M(Nyc^E+Xz6GOzGm5rwasGwVc*@^Z&|`G?fyk zG+ZuTGKzdQN;{eJo6PDxhn8^|?@JQB+ZQ9t z9~p3=Y76b+JO3xdm2m^Yxf*(9F?ar2P5-;2Q~y6>kNf}c=L>nA;$EjrLxrl_;c^J{H+2>{eb@iQJN^C literal 0 HcmV?d00001 From 2f5ba6937caa0226e4cfd6a6cb1f91e3c19aa529 Mon Sep 17 00:00:00 2001 From: Chen Weihang Date: Tue, 10 May 2022 07:41:37 +0000 Subject: [PATCH 2/3] unify python style --- docs/design/phi/design.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/docs/design/phi/design.md b/docs/design/phi/design.md index efb363f8ce2..2e35d07a78d 100644 --- a/docs/design/phi/design.md +++ b/docs/design/phi/design.md @@ -1,5 +1,5 @@ -飞桨高可复用算子库 PHI (Paddle HIgh reusability operator library)，或者我们也成为函数式算子库，支持组合式算子功能复用、Primitive算子内核复用、插件式硬件加速库复用。针对飞桨框架原算子库存在的算子接口不清晰、算子复用成本较高、调用性能不够快的问题，我们重构了飞桨框架的算子库，设计了灵活、高效的函数式算子库 Phi，可以通过对函数式算子接口组合调用的方式实现新算子。新算子库提供了 200 余个跟 python 开发接口保持一致的 C++ 运算类 API，以及近500个可供组合调用的前、反向函数式算子内核 Kernel，可大幅降低框架原生算子和自定义算子的开发成本。新算子库支持Primitive API方式开发算子内核，可支持不同硬件（比如GPU和XPU）的算子内核复用。新算子库支持以插件方式接入硬件（比如NPU）的加速库，实现低成本复用硬件加速库。 +飞桨高可复用算子库 PHI (Paddle HIgh reusability operator library)，或者我们也成为函数式算子库，支持组合式算子功能复用、Primitive算子内核复用、插件式硬件加速库复用。针对飞桨框架原算子库存在的算子接口不清晰、算子复用成本较高、调用性能不够快的问题，我们重构了飞桨框架的算子库，设计了灵活、高效的函数式算子库 Phi，可以通过对函数式算子接口组合调用的方式实现新算子。新算子库提供了 200 余个跟 Python 开发接口保持一致的 C++ 运算类 API，以及近500个可供组合调用的前、反向函数式算子内核 Kernel，可大幅降低框架原生算子和自定义算子的开发成本。新算子库支持Primitive API方式开发算子内核，可支持不同硬件（比如GPU和XPU）的算子内核复用。新算子库支持以插件方式接入硬件（比如NPU）的加速库，实现低成本复用硬件加速库。 > 本文档撰写于phi架构基本成型之时（2022年2月），仅代表该时间点的基本设计形态，可能和最新形态有细微差别；此外，在2.3版本发布的phi算子库仍然处于初期形态，后续仍然需要持续建设并完善，设计上也有可能调整。 @@ -64,9 +64,9 @@ Paddle 2.0发布之后，多次收到内外部用户反馈动态图在小模型C ### 1.1.6 Op及Kernel参数规范化 -python 2.0 API项目规范了Paddle Python端API的参数列表，使其变得简洁、易用，但是限于当时的情况，Op层面的参数列表并没有规范化，因此会有不少早期开发的算子和Python API参数相差较多，例如conv op这种，python API仅有7个参数，但C++ Op却有30+参数的分裂情况，而API和Op本质上是同一层概念，都是对一个运算的描述，参数应该是一致的。推理为了解决此问题，推动了算子定义增强项目，为部分不需要的参数添加了AsExtra以及AsQuant的声明，但并未从根本上解决问题，这也是phi算子库构建希望重点去解决的。 +Python 2.0 API项目规范了Paddle Python端API的参数列表，使其变得简洁、易用，但是限于当时的情况，Op层面的参数列表并没有规范化，因此会有不少早期开发的算子和Python API参数相差较多，例如conv op这种，Python API仅有7个参数，但C++ Op却有30+参数的分裂情况，而API和Op本质上是同一层概念，都是对一个运算的描述，参数应该是一致的。推理为了解决此问题，推动了算子定义增强项目，为部分不需要的参数添加了AsExtra以及AsQuant的声明，但并未从根本上解决问题，这也是phi算子库构建希望重点去解决的。 -我们希望能做到，Python API -> Op(C++ API) -> Kernel API三层参数一致，使整体架构清晰，每一层复用也清晰，一套python官方文档，基本能够满足三层API的共同参考需求，不再着重维护额外的文档体系，降低维护成本。 +我们希望能做到，Python API -> Op(C++ API) -> Kernel API三层参数一致，使整体架构清晰，每一层复用也清晰，一套Python官方文档，基本能够满足三层API的共同参考需求，不再着重维护额外的文档体系，降低维护成本。 ## 1.2 目标及范围 @@ -142,7 +142,7 @@ paddle/phi 部分目录结构说明： - `api`：API模块，面向外部用户 - - 直接使用类python的C++ Tensor计算 API，和Python端形式高度一致 + - 直接使用类Python的C++ Tensor计算 API，和Python端形式高度一致 - 该部分可能反向依赖框架的DeviceContextPool等实现，所以单独管理 - 在该类API上，训练和预测也可能是不同的 - `common`：phi内部及phi api目录均要使用的数据结构，这些数据结构既不属于phi core，也不属于api目录 @@ -529,8 +529,8 @@ Tensor scale(const Tensor& x, C++ API的自动生成是通过解析Yaml配置文件来进行生成的，Yaml配置文件分为： - - 前向API配置文件(`python/paddle/utils/code_gen/api.yaml`，解析后生成代码文件为`paddle/phi/api/include/api.h`和`paddle/phi/api/lib/api.cc`) - - 反向API配置文件(`python/paddle/utils/code_gen/backward.yaml`，解析后生成的代码文件为`paddle/phi/api/backward/backward_api.h`和`paddle/phi/api/lib/backward_api.cc`)。 + - 前向API配置文件(`Python/paddle/utils/code_gen/api.yaml`，解析后生成代码文件为`paddle/phi/api/include/api.h`和`paddle/phi/api/lib/api.cc`) + - 反向API配置文件(`Python/paddle/utils/code_gen/backward.yaml`，解析后生成的代码文件为`paddle/phi/api/backward/backward_api.h`和`paddle/phi/api/lib/backward_api.cc`)。 C++ API生成的关键在于Yaml文件的配置，以matmul为例，其前向和反向的配置文件如下： @@ -1590,7 +1590,7 @@ class KernelContext { 目前，phi算子库仍然处在Kernel体系的建设阶段，Kernel尚未完全迁移，且仍然存在诸多完善点，但将来phi算子库会更好地将“算子”的概念纳入进来，这还需要比较长的时间和比较大的人力投入。最后，从“产品”的角度介绍一下phi后续对于算子开发范式的规划，也能够让开发者更容易理解 “为什么要做算子库重构？” 这件事。 -### 2.5.2 原算子开发范式 +### 2.5.1 原算子开发范式 我们应该如何描述“框架算子”这个概念？ @@ -1684,14 +1684,14 @@ REGISTER_OP_VERSION 说回开始的两段式结构，”1.算子描述；2.算子执行“，分这两段是必要的，也是业界普遍的做法，我们不需要再分第三段了，但paddle目前存在第三段，算子描述分了两段进行，并且这两段还不一致，即PythonaAPI和Op。 -API和Op都是对算子运行行为的概要描述，本质上只是同一段内容的不同展现形式，比如[python dot API](https://github.com/PaddlePaddle/Paddle/blob/c6f49f0b9f189e043b458348d7fd1468e2645621/python/paddle/tensor/linalg.py#L993)和[DotOpMaker](https://github.com/PaddlePaddle/Paddle/blob/c6f49f0b9f189e043b458348d7fd1468e2645621/paddle/fluid/operators/dot_op.cc#L35)，就是告诉别人“它叫什么，参数都是什么”。 +API和Op都是对算子运行行为的概要描述，本质上只是同一段内容的不同展现形式，比如[Python dot API](https://github.com/PaddlePaddle/Paddle/blob/c6f49f0b9f189e043b458348d7fd1468e2645621/Python/paddle/tensor/linalg.py#L993)和[DotOpMaker](https://github.com/PaddlePaddle/Paddle/blob/c6f49f0b9f189e043b458348d7fd1468e2645621/paddle/fluid/operators/dot_op.cc#L35)，就是告诉别人“它叫什么，参数都是什么”。 咱们对同一个东西的描述，分两个地方写，还写得不一样，这是很令人费解的。就好像你介绍一个人，在学校你说他叫”张三“，在公司你说他叫”张三丰“，有相像之处，但又不是一个意思。 对于一个算子，它的输入、输出应该在各个场景下都是一致的，如果不一致，那本质上就不是一个算子。 -比如，conv2d的api和op，[Python conv2d API](https://github.com/PaddlePaddle/Paddle/blob/c6f49f0b9f189e043b458348d7fd1468e2645621/python/paddle/nn/functional/conv.py#L416)，很简单，8个输入参数；但是对应的[conv2d op](https://github.com/PaddlePaddle/Paddle/blob/c6f49f0b9f189e043b458348d7fd1468e2645621/paddle/fluid/operators/conv_op.cc#L259)，有**32个**输入参数，让人摸不着头脑。 +比如，conv2d的api和op，[Python conv2d API](https://github.com/PaddlePaddle/Paddle/blob/c6f49f0b9f189e043b458348d7fd1468e2645621/Python/paddle/nn/functional/conv.py#L416)，很简单，8个输入参数；但是对应的[conv2d op](https://github.com/PaddlePaddle/Paddle/blob/c6f49f0b9f189e043b458348d7fd1468e2645621/paddle/fluid/operators/conv_op.cc#L259)，有**32个**输入参数，让人摸不着头脑。 开发者也会很困惑，我开发op的时候，API和Op不是一个东西吗，我应该写得一样呢？还是不一样？ @@ -1704,7 +1704,7 @@ API和Op都是对算子运行行为的概要描述，本质上只是同一段内 对于这个问题的解决，我们的方向是很明确的，就是**Op层描述向API层靠拢，因为API层的定义是经过2.0 API项目仔细设计过的**。 -### 2.5.1 新算子开发范式：完形填空 + 拼积木 +### 2.5.2 新算子开发范式：完形填空 + 拼积木 phi期望的Op开发方式：**“完形填空”式算子描述实现 + “堆积木”式算子执行实现** From fd94966dd724080c305840de0ae454c1ce16a293 Mon Sep 17 00:00:00 2001 From: Chen Long <1300851984@qq.com> Date: Tue, 10 May 2022 22:45:44 +0800 Subject: [PATCH 3/3] Update design.md --- docs/design/phi/design.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/design/phi/design.md b/docs/design/phi/design.md index 2e35d07a78d..c67d07f20df 100644 --- a/docs/design/phi/design.md +++ b/docs/design/phi/design.md @@ -1,5 +1,5 @@ -飞桨高可复用算子库 PHI (Paddle HIgh reusability operator library)，或者我们也成为函数式算子库，支持组合式算子功能复用、Primitive算子内核复用、插件式硬件加速库复用。针对飞桨框架原算子库存在的算子接口不清晰、算子复用成本较高、调用性能不够快的问题，我们重构了飞桨框架的算子库，设计了灵活、高效的函数式算子库 Phi，可以通过对函数式算子接口组合调用的方式实现新算子。新算子库提供了 200 余个跟 Python 开发接口保持一致的 C++ 运算类 API，以及近500个可供组合调用的前、反向函数式算子内核 Kernel，可大幅降低框架原生算子和自定义算子的开发成本。新算子库支持Primitive API方式开发算子内核，可支持不同硬件（比如GPU和XPU）的算子内核复用。新算子库支持以插件方式接入硬件（比如NPU）的加速库，实现低成本复用硬件加速库。 +飞桨高可复用算子库 PHI (Paddle HIgh reusability operator library)，或者我们也称为函数式算子库，支持组合式算子功能复用、Primitive算子内核复用、插件式硬件加速库复用。针对飞桨框架原算子库存在的算子接口不清晰、算子复用成本较高、调用性能不够快的问题，我们重构了飞桨框架的算子库，设计了灵活、高效的函数式算子库 Phi，可以通过对函数式算子接口组合调用的方式实现新算子。新算子库提供了 200 余个跟 Python 开发接口保持一致的 C++ 运算类 API，以及近500个可供组合调用的前、反向函数式算子内核 Kernel，可大幅降低框架原生算子和自定义算子的开发成本。新算子库支持Primitive API方式开发算子内核，可支持不同硬件（比如GPU和XPU）的算子内核复用。新算子库支持以插件方式接入硬件（比如NPU）的加速库，实现低成本复用硬件加速库。 > 本文档撰写于phi架构基本成型之时（2022年2月），仅代表该时间点的基本设计形态，可能和最新形态有细微差别；此外，在2.3版本发布的phi算子库仍然处于初期形态，后续仍然需要持续建设并完善，设计上也有可能调整。 @@ -50,7 +50,7 @@ Paddle 2.0发布之后，多次收到内外部用户反馈动态图在小模型C ### 1.1.3 自定义算子的易用性提升需求 -2021年初上线的新自定义C++外部算子体系，在接口与函数编写的层面上，用法已经比较直观了，但是因为我们缺少基本运算的C++ API体系，事实上，在实现具体的自定义Op运算逻辑时，一些基础的加减乘除及矩阵运算都仍然需要重新实现一遍，不能复用Paddle已有的、经过优化的基础运算，因此一些复杂运算的外部开发成本仍然是比较高的。而要想复用Paddle内部的基础运算，有赖于的Op体系升级为函数式，并整理对应的C++ API体系才能解决。 +2021年初上线的新自定义C++外部算子体系，在接口与函数编写的层面上，用法已经比较直观了，但是因为我们缺少基本运算的C++ API体系，事实上，在实现具体的自定义Op运算逻辑时，一些基础的加减乘除及矩阵运算都仍然需要重新实现一遍，不能复用Paddle已有的、经过优化的基础运算，因此一些复杂运算的外部开发成本仍然是比较高的。而要想复用Paddle内部的基础运算，依赖于的Op体系升级为函数式，并整理对应的C++ API体系才能解决。 ### 1.1.4 共建训推一体算子库，降低推理算子维护成本 @@ -146,7 +146,7 @@ paddle/phi - 该部分可能反向依赖框架的DeviceContextPool等实现，所以单独管理 - 在该类API上，训练和预测也可能是不同的 - `common`：phi内部及phi api目录均要使用的数据结构，这些数据结构既不属于phi core，也不属于api目录 -- `core`：phi内部会有一些自己需要的，公用的模块实现，比如基础DenseTensor、，kernel注册及管理模块 +- `core`：phi内部会有一些自己需要的，公用的模块实现，比如基础DenseTensor、kernel注册及管理模块 - `backends`：backends中组织后续需要为各个后端的新增的数据结构，比如CPUContext、GPUContext等 - core中放置对于算子库来讲通用的基础数据结构，而特定后端的专用数据结构不放在core中，且依赖关系严格保证backends依赖core，但core不能依赖backends - 例1：Context如果有基类，则在core中，而继承的CPUContext在backends/cpu中，GPUContext在baackends/gpu中 @@ -626,9 +626,9 @@ void Scale(const Context& dev_ctx, - 基本流程如下图： - ![图片](http://bos.bj.bce-internal.sdns.baidu.com/agroup-bos-bj/bj-2aafdb051eaea7120bdf9604eb738029dcd3162a) - 这种方式存在的性能问题已经被torch自身认识到，所以torch也在做算子库重构，但是积重难返，他们重构也并未对此问题从根本上解决，只是减少了一些redispatch的层数，我们不能一味模仿竞品自身都认为有问题的设计 -- 为什么第一个参数需要是DeviceContext？为什么不能不传？ +>- 为什么第一个参数需要是DeviceContext？为什么不能不传？ - phi kernel要求是纯函数形式，即函数内使用的变量均通过参数传入，或者在函数内部创建，不允许在函数内部使用全局单例，为了适配多样的kernel需求，像DeviceContext这种存储上下文信息的参数是必要的 -- 为什么需要两个模板参数？ +>- 为什么需要两个模板参数？ - 为了方便设备无关kernel的复用，假如我们要实现一个傅里叶变换fft kernel，假设这个kernel能够使用基础kernel组合得出， #### 2.3.4.3 Kernel实现