From 8d73752c737d29eb00d5b3ea6bcd754c4c7f93c8 Mon Sep 17 00:00:00 2001 From: Yancey1989 Date: Mon, 16 Apr 2018 12:00:09 +0800 Subject: [PATCH] add async update design doc --- doc/fluid/design/dist_train/async_update.md | 52 ++++++++++++++++++ .../dist_train/src/async_pserver.graffle | Bin 0 -> 10621 bytes .../design/dist_train/src/async_pserver.png | Bin 0 -> 164416 bytes .../dist_train/src/async_update.graffle | Bin 0 -> 8121 bytes .../design/dist_train/src/async_update.png | Bin 0 -> 192539 bytes 5 files changed, 52 insertions(+) create mode 100644 doc/fluid/design/dist_train/async_update.md create mode 100644 doc/fluid/design/dist_train/src/async_pserver.graffle create mode 100644 doc/fluid/design/dist_train/src/async_pserver.png create mode 100644 doc/fluid/design/dist_train/src/async_update.graffle create mode 100644 doc/fluid/design/dist_train/src/async_update.png diff --git a/doc/fluid/design/dist_train/async_update.md b/doc/fluid/design/dist_train/async_update.md new file mode 100644 index 00000000000..05175596f7a --- /dev/null +++ b/doc/fluid/design/dist_train/async_update.md @@ -0,0 +1,52 @@ +# Design Doc: Asynchronous Update With Distributed Training + +## Background + +For the typical synchronous distributed training, some significant steps are as follows: + +1. A Trainer will compute the gradients and SEND them to the Parameter +Server(PServer) nodes. +1. After the PServer node received gradients came from all the Trainers, it would apply the gradient to the respective variables, and using an optimize algorithms(SGD, + Momentment...) to update the parameters. +1. The Trainer would wait for the PServers finished the optimize stage, and GET the parameters from PServer, so all the Trainers would get the same parameters. + +In the synchronously distributed training, there should be a `Barrier` to synchronise the +parameters after the optimizing stage. The performance of a distributed training job +depends on the lowest node, if there were hundreds or thousand training nodes in a Job, +the performance of synchronously distributed training might be very slow because of +the slow node. So this design doc would introduce an approach to implement +*asynchronously* distributed training in PaddlePaddle Fluid. + +## Design + + + +As the figure above, we describe a global view of asynchronously update process and use +the parameter `w1` as an example to introduce the steps: +1. For each gradient variables, they may distribute on different GPU card and aggregate +them while they are all calculated. +1. Split the gradient variable into multiple blocks according to the number of PServer +instances and sent them. +1. PServer would run an `Optimize Block` to use a specified optimize algorithm to update +the specified parameter, such as `w1`. +1. The trainer will fetch the latest parameter after PServer finished the optimize stage. +1. Broadcast the received variable into multiple GPU cards and continue to run the next +mini-batch. + +### Trainer + +- We need a new Operator named `RemoteOptimize` to send gradients to multiple PServer +instances and fetch the latest parameter. +- There could be a large number of gradient variables to be sent, so we need to use another +thread pool(IO Threadpool) which number of the schedulable threads is larger than the +computing thread pool to avoid competitive the thread resources with computing. + +### Parameter Server + + + +- There should be multiple trainer instances want to optimize the same parameter at +the same time, to avoid the pollution, we need one `BlockingQueue` for each gradient +variable to process them one by one. +- We need a `Map` structure to map a gradient variable name to the `OptimizeBlock` which +can optimize the respective parameter. diff --git a/doc/fluid/design/dist_train/src/async_pserver.graffle b/doc/fluid/design/dist_train/src/async_pserver.graffle new file mode 100644 index 0000000000000000000000000000000000000000..110eec7f9b3f26034cd90feb188b558c11f7ce02 GIT binary patch literal 10621 zcmaLc^H(Mgqb=}kYjRDtZA>-Ut|x1T@IT{t#;VLwuwP&=*)> zd-c_M{oH-~!_eY?@l;vyYRIWZlFeWR7yT&!Yz6>wOPXp)tiw?4PW=qNIVXO~<-Ar9 z@EroTKMqSr`9C(WzVT^jZ!a^O3O{6l9&G7>)kizb!q{d2(3i9RMmU4CxG?Z>9kt;# zVygOq?=ejHfj^1hmj{%8?=SU)8#JOgI~u<;1IiZs1nulYSLh|9b8LI8EvvUQ#3t*M zrIfNZ?Yw3wFfirfcwK(HL>FIU0-l3G2C2-!dzwCcf(CwAn3M{3&P6T9$Jmv5Qx!sN z1YHpYyUxnRzpR!RW6h}!4>#;xm$P}a@M5_{E0daog4G9>0f?1|z!a+XQLVabzeU2P z2kMGf0UiAZhD>XUs}iq`IgwbTB-dmfx2yq2T{}}}FDuS)-!C2EDX#n8ax1u*RD!mu z?z&8jvSz*6NNjbxOVue~1Dj`!>pSZs#7rHSrKOf`<3gxQ3dgl;?d)?!EyoE}4k6v= z?(ws-sj~#xey7cmwl*xX+*FjuB)Id&8lK8Q+aB^g6WAH6^<%&Jueq?{;#lpd?`}S` zioYgcTev7`G9x0Q;S$A8I5uZ`ina~>)7$;~LGX)frBusPD>{*k_;NNk_G|!~_htca zB~=xIR&*@EWZ4kuO#{l3#c%?UhIu7*IhWIs=tn8T@a!*I+uSH^&WA@vg+M|&emUiD zu8Pmi7sBEjS)+*V9^s!dZ$lTik&iz2DH9gkuPkQ77KoS`OwV0q8295^&^3-B_(Hi> zZ}ukSBf%X9zOa-Kn6vNYGTBX|lm=Lf8o);gDM#co^P=wPp@`(+9FO3n`oNDvvc`po#O#0+q<;ZxZ>V3r#v(&C)nR9FE)+LuyX@m-et(u+v~(Qq$h?UdSRi zs-|+tC|DKfFys_6OiYn@9ic8R<6gtwstBZ7GzT9DXg&1-rZI@_vi#o*feC?)WaKQ7 zBA3aw!U5%wC;?;vmO-i#VfDH9-+I!{U=|DGX$oLNWixqrhP|q7!UO?{;-ZTif9cm> zl@(K0*KjO}M=3?I8xgQ_3?g_PX23k}4FzAz4Ut#2U@F6EA(zUV}cC%p6`D_NL)m_Pl(5+u8zbC5O(CNY!lzFQ( za}SE{M~rKumj-1`TxN?{PD2AGq;}BLApa`Hc%q~&#NMeL_G{C zr_P}hfxp(q`KS=A`RUchEV(8b(MFR3bW zEr{^2oo=tGYDU7($WLlLN2#Yjta2u9g!zShK!-)gcqvoUYjq>pWNh@qqAasN*<_!k zmP5Wkyp^=205b;$Q9EG)PMcw4zv{vrd7IhE9p$91wzrtKB3Nx7*AsQVssvL8!a1_p zXn~Xcu(7aFtG_{?ApBl4ld#fRpY8#^aG;$W7pv3hUf(9hC~e8nD3bNFZ^!TsN-EW8 z@Z#MFXsYLHX}eFzMYsU7lJMP@z zqE2vFw#KR8wozW9KX6wAC`i4mATMH}FrlX!UZ=zEu1h93&AE{uGCN^-6)EMDL=|-0 z21&Wubd#Y;D~>6cV4Z~bGT~gilR9NaT;u%G56KC7g8h-Po(Cwpb1I%;5CRxb({)-oC@{c1%San78|gD)CO? 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