From 02e0b5f9eabcc183f4c05a139270a49fbe725852 Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Fri, 1 Dec 2017 15:00:31 +0800 Subject: [PATCH] follow comments, unify picture fonts, color and sizes --- doc/design/mkldnn/README.MD | 67 ++++++++++++++------------ doc/design/mkldnn/image/engine.png | Bin 36180 -> 17102 bytes doc/design/mkldnn/image/gradients.png | Bin 57433 -> 31247 bytes doc/design/mkldnn/image/layers.png | Bin 57028 -> 14414 bytes doc/design/mkldnn/image/matrix.png | Bin 19755 -> 22085 bytes doc/design/mkldnn/image/overview.png | Bin 9884 -> 16329 bytes 6 files changed, 37 insertions(+), 30 deletions(-) diff --git a/doc/design/mkldnn/README.MD b/doc/design/mkldnn/README.MD index 7c863197e7b..287ee620e12 100644 --- a/doc/design/mkldnn/README.MD +++ b/doc/design/mkldnn/README.MD @@ -5,7 +5,7 @@ 充分展现英特尔平台的优势,有效提升PaddlePaddle在英特尔架构上的性能。
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Figure 1. PaddlePaddle on IA
@@ -28,9 +28,7 @@ Figure 1. PaddlePaddle on IA - [Parameters](#parameters) - [Gradients](#gradients) - [Unit Tests](#unit-tests) - - [Protobuf Messages](#protobuf-messages) - [Python API](#python-api) - - [Demos](#demos) - [Benchmarking](#benchmarking) - [Others](#others) - [Design Concerns](#design-concerns) @@ -41,10 +39,19 @@ Figure 1. PaddlePaddle on IA 同时,为了进一步提升PaddlePaddle在基本数学运算的计算速度,我们也将MKLML即(MKL small library\[[1](#references)\]) 作为另一个第三方库集成进PaddlePaddle,它只会包括生成好的动态库和头文件。 + +MKL,MKLML以及MKL-DNN三者关系如下表: + +| Name | Open Source | License | Descriptions | +|------------|----------------| ------------| --------------| +| MKL | No | Proprietary | Accelerate math processing routines | +| MKLML | No | Proprietary | Small package of MKL, especially for Machine Learning | +| MKL-DNN | Yes | Apache 2.0 | Accelerate primitives processing routines especially for Deep Neural Networks | + MKLML可以与MKL-DNN共同使用,以此达到最好的性能。
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+
Figure 2. PaddlePaddle with MKL Engines
@@ -84,15 +91,19 @@ PaddlePaddle/Paddle - `WITH_MKLML` 控制是否使用MKLML库。 当打开`WITH_MKL`时,会自动使用MKLML库作为PaddlePaddle的CBLAS和LAPACK库,同时会开启Intel OpenMP用于提高MKLML的性能。 +编译时会把对应的头文件和库放在`build/third_party/install/mklml/*`目录下对应的地方。 +MKLML的库目前都是动态库,主要包括`libiomp5.so`和`libmklml_intel.so`。 - `WITH_MKLDNN` 控制是否使用MKL-DNN。 当开启`WITH_MKL`时,会自动根据硬件配置[[2](#references)]选择是否编译MKL-DNN。 +编译时会把对应的头文件和库放在`build/third_party/install/mkldnn/*`目录下对应的地方。 +MKL-DNN的库目前只有动态库`libmkldnn.so`。 ### Matrix -目前在PaddlePaddle中数据都是以`nchw`的格式存储,但是在MKL-DNN中的排列方式不止这一种。 +目前在PaddlePaddle中数据都是以`NCHW`的格式存储,但是在MKL-DNN中的排列方式不止这一种。 所以我们定义了一个`MKLDNNMatrix`用于管理MKL-DNN数据的不同格式以及相互之间的转换。
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Figure 3. MKLDNNMatrix
@@ -102,29 +113,30 @@ Figure 3. MKLDNNMatrix 子类只需要使用定义好的接口,实现具体的函数功能即可。
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Figure 4. MKLDNNLayer
-每个`MKLDNNlayer`都会有`inVal_`,`inGrad_`,`outVal_`和`outGrad_`的`MKLDNNMatrix`, -分别代表input value, input gradient,output value和output gradient。 -它们会存放MKL-DNN用到的internal memory,同时还会定义以*ext*开头的`MKLDNNMatrix`(表示external的memory)。 -他们主要是当数据格式与PaddlePaddle默认的`nchw`格式不匹配时,用于转换内存的工作。 +每个MKLDNNLayer都包含用于内部存储和外部存储的一系列MKLDNNMatrix: -必要的转换函数也会在`MKLDNNLayer`中提前定义好(具体包括reset input、output的value和grad), -这些函数会根据输入参数重新设置internal和external的memory(当然这两者也可以相等,即表示不需要转换), -每个`MKLDNNlayer`的子类只需要使用internal的memory就可以了,所有external的转换工作都会在reset函数中都准备好。 +- 内部存储(internel memory):`inVal_`,`inGrad_`,`outVal_`和`outGrad_`,分别代表输入数据,输入梯度,输出数据和输出梯度。 +- 外部存储(external memory):都是以ext开头,比如`extInVal_`和`extInGrad_`,它们主要是用于, +当数据格式与PaddlePaddle默认的`NCHW`格式不匹配时,转换内存的工作。 +需要注意的是,PaddlePaddle的activation会直接使用`output_.value`和`output_.grad`, +所以`extOutVal_`和`extOutGrad_`必须分别与`output_.value`和`output_.grad`共享内存, +如果不需要外部存储用于转换,那么对应的内部存储也会与它们共享内存。 +- 转换函数(resetXXX): 包括`resetInValue`,`resetInGrad`,`resetOutValue`和`resetOutGrad`, +表示对输入数据,输入梯度,输出数据和输出梯度的转换。 +这些函数会根据输入参数重新设置内部和外部存储,当然这两者也可以相等,即表示不需要转换。 -一般来说,每个`MKLDNNLayer`中的`extOutVal_`和`extOutGrad_`必须分别与`output_.value`和`output_.grad`共享内存, -因为PaddlePaddle的activation会直接使用`output_.value`和`output_.grad`, -如果不需要external的buffer用于转换,那么internal的buffer也会与它们共享内存。 +注意:每个`MKLDNNlayer`的子类只需要使用内部存储就可以了,所有外部的转换工作都会在reset系列函数中都准备好。 ### Activations -在重构前的PaddlePaddle中,激活函数是独立于`Layer`的概念,并且输入输出都是公用一块内存, +在重构前的PaddlePaddle中,激活函数是独立于`Layer`的概念,并且输入输出都是共用一块内存, 所以添加了对应的`MKLDNNActivation`来实现,方式类似于`MKLDNNLayer`。 ### Parameters -对于有参数的层,我们会保证`MKLDNNLayer`使用的参数与PaddlePaddle申请的buffer公用一块内存。 +对于有参数的层,我们会保证`MKLDNNLayer`使用的参数与PaddlePaddle申请的buffer共用一块内存。 如果存在数据排列格式不一样的情况时,我们会在网络训练之前把格式转换为MKL-DNN希望的格式, 在训练结束的时候再保存为PaddlePaddle的格式,但是整个训练过程中不需要任何转换。 这样既使得最终保存的参数格式与PaddlePaddle一致,又可以避免不必要的转换。 @@ -138,18 +150,15 @@ Figure 4. MKLDNNLayer 所以整体上,在实现每个子类的时候就不需要关心分支的事情了。
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Figure 5. Merge Gradients
### Unit Tests 我们会添加`test_MKLDNN.cpp`和`MKLDNNTester.*`用于MKL-DNN的测试。 -测试分为每个Layer(或Activation)的单元测试和简单网络的整体测试。 +测试分为每个Layer(或Activation)的单元测试和简单网络的整体测试。 每个测试会对比PaddlePaddle中CPU算出的结果与MKL-DNN的结果,小于某个比较小的阈值认为通过。 -### Protobuf Messages -根据具体layer的需求可能会在`proto/ModelConfig.proto`里面添加必要的选项。 - ### Python API 目前只考虑**v1 API**。 @@ -167,11 +176,9 @@ if use_mkldnn 同时,会在`paddle/utils.Flags`中添加一个`use_mkldnn`的flag,用于选择是否使用MKL-DNN的相关功能。 -### Demos -可能会在`v1_api_demo`目录下添加一个`mkldnn`的文件夹,里面放入一些用于MKL-DNN测试的demo脚本。 - ### Benchmarking -会添加`benchmark/paddle/image/run_mkldnn.sh`,用于测试和对比,在使用MKL-DNN前后的性能。 +会添加相应的脚本在[这里](https://github.com/PaddlePaddle/Paddle/tree/develop/benchmark/paddle/image),用于测试和对比在使用MKL-DNN前后的CNN网络性能。 +测试的性能对比结果会在[IntelOptimizedPaddle.md](https://github.com/PaddlePaddle/Paddle/blob/develop/benchmark/IntelOptimizedPaddle.md) ### Others 1. 如果在使用MKL-DNN的情况下,会把CPU的Buffer对齐为4096,具体可以参考MKL-DNN中的[memory](https://github.com/01org/mkl-dnn/blob/master/include/mkldnn.hpp#L673)。 @@ -189,8 +196,8 @@ if use_mkldnn 3. 创建`MKLDNNBase`,定义一些除了layer和memory相关的类和函数。 包括MKL-DNN会用到`MKLDNNStream`和`CPUEngine`,和未来可能还会用到`FPGAEngine`等。 4. 如果MKL-DNN layer的后面接有cpu device,那么就会使`output_.value`与`extOutVal_`共享内存, -同时数据格式就是`nchw`,这样下一个cpu device就能拿到正确的数据。 -在有cpu device的时候,external的memory的格式始终是`nchw`或者`nc`。 +同时数据格式就是`NCHW`,这样下一个cpu device就能拿到正确的数据。 +在有普通的CPU layer时, `extOutVal_`和`extOutGrad_`的格式始终是`NCHW`或者`NC`。 ## References 1. [MKL small library](https://github.com/01org/mkl-dnn#linking-your-application)是[Intel MKL](https://software.intel.com/en-us/mkl)的一个子集。 diff --git a/doc/design/mkldnn/image/engine.png b/doc/design/mkldnn/image/engine.png index 65bbb41fbb389ff5f7906b0284ada77ac2dc4ec9..a60b7ad5553bd6d7d5e255fabc14467ef8a57c88 100644 GIT binary patch literal 17102 zcmcJ11yEekwk0kBf`xSBnkEVEt|3T*1PSgC+zIXu!2`jaBtU@R(zrIR!QI_mo4Mq@ zKT?1G{P$;Sx~i*-=AO&3d-mFEtxebmc`2MHBu@|!5O8FqC6o{l5L1A6Fa{d%jW~f+ z0Pun6s3awh@OzMK8~6dmR7_3`0ii4!>&^fb_!-kqTEh_m;c3U?8}S>%Q&OOvk&J|x zimUE^x>p2wXL5xsOb1)0ih9ygj6uJJN%Na3s)1t%%;<^ zGY_Rd2ON%rvXg0lHt#43RJ?vQ|EmABs5h=P?nM$6jgLtki079?*d>lZErCbJ34Hj| zWaEot;KyefagxCC#Y1=Uva_jmpS;1#E_-uh@B0UXIxqw9gFn695D&;o>LwF% z9}8+~5}vH*YchD#P=zNR`sG;fVcnh0+S*#t!^4Bq2ku!DaWq`W2GUeDo)Hl@^PD6W zB9{1BQ^UjWuqNH`4QTRi^v@sVEtFxl(q)`oR%5vs5VxGw*8FfB+hsEF>t3UFlBVt{ zusa6(;fT7VSJ|>S#^aRWUVvwGrX%w64`9f#fWjU|pDiPYH{?Wxn@H~)5*f9x}^&N`Pxr&TWOnN0gG 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