From a0fefc27d7d3df2553e49229343b94ad59ed1b3e Mon Sep 17 00:00:00 2001 From: Wu Yi Date: Wed, 4 Jul 2018 21:01:43 +0800 Subject: [PATCH] Add NCCL2 dist train design doc (#11885) * add_nccl2_dist_design * update * update by comments --- .../design/dist_train/dist_train_nccl2.md | 35 ++++++++++++++++++ .../design/dist_train/src/ncc2_design.graffle | Bin 0 -> 6823 bytes .../design/dist_train/src/ncc2_design.png | Bin 0 -> 93754 bytes 3 files changed, 35 insertions(+) create mode 100644 doc/fluid/design/dist_train/dist_train_nccl2.md create mode 100644 doc/fluid/design/dist_train/src/ncc2_design.graffle create mode 100644 doc/fluid/design/dist_train/src/ncc2_design.png diff --git a/doc/fluid/design/dist_train/dist_train_nccl2.md b/doc/fluid/design/dist_train/dist_train_nccl2.md new file mode 100644 index 000000000..aa7455ec5 --- /dev/null +++ b/doc/fluid/design/dist_train/dist_train_nccl2.md @@ -0,0 +1,35 @@ +# Distributed Training with NCCL2 + +We design a pattern that can enable training with `ParallelExecutor` and +using [NCCL2](https://developer.nvidia.com/nccl) as it's collective +communication library. + +In `ParallelExecutor` we can use `AllReduce` or `Reduce` and `Broadcast` +to do multi GPU training. And if we initialize NCCL2 communicators as +ranks in a distributed environment, we can simply run the `ParallelExecutor` +as a distributed program! The only thing that may be different than in +the single node version is that we need to broadcast the NCCL unique ID +to all the nodes, and initialize communicators using that ID, so NCCL2 +will know each other as ranks. + +To achieve this feature, we introduce a new operator: `gen_nccl_id` op, +so we are ***not*** "bind to" running NCCL2 with MPI, we can run it in +what ever platform you like. + +It have two running modes: + +1. Generate and broadcast mode, which should be used on trainer 0; +1. Listen and fetch mode, which should be used on trainers other than 0. + +In both two modes, this op can save the NCCL ID into current scope as a +persistable variable, Then we can insert this op at the end of +"startup program" of fluid, so that all workers can get the same ID to +initialize NCCL communicator objects. + + + +The above figure indicates the general process when training with NCCL2 +distributed. Each trainer have the number of communicators equal to the +number of GPUs, but the ranks should match the global ranks number: here +we have total 8 GPUs, so `nranks==8`, for each trainer, the ranks should +be from 0 ~ 3 on trainer 0 and 4 ~ 7 on trainer 1. diff --git a/doc/fluid/design/dist_train/src/ncc2_design.graffle b/doc/fluid/design/dist_train/src/ncc2_design.graffle new file mode 100644 index 0000000000000000000000000000000000000000..7d2753bbb03bc28c7a0054bb0aa424deb072ffbf GIT binary patch literal 6823 zcmV;Y8d&8YiwFP!000030PUS=m!e3z$6rr<3U9x;GdH_ZR+paYz9-le7Zh0(Po6V} zO+*kBP}Dj7-EV-Hsi~Qjl{HnjW{T|7CCfja2qK>67ZDK3r~ln8{mRykVmAza{{_8A zeyP}j8Cq`O{Qip~a~~VO{O=zhKK&Onpk-~yR#v_nCzYW>^?17S%g1UJtX95VtuiuG z8TNQdu0R{rDl7a_`DLCYtKX{CG)?dIOm%OD%S;$ohf%n)qh#NM#y-LX_f}&40<$?< zzbFnoYPn|e$A`as^6dQ|S$3|eC-A&_lzpmZ$Kh!`iu7#bFQ4Khg601MW_KSh1J{Yd z&FX#tw_1@toB4MAQ?-zssnG^`kHMng2!_}3GsUOs$#Px-IZ<6r27GC9Q z&V=S>X$Q&kT5^NLcI@a6l=xIVZJ*L@T+5Doq3PMy^L&KCRW7+&bz|4?uZpFy(sMn( z)E%1+H!#oWx-?gX>A8XTlJ)Dt^mQk&lP}T7uo%+ZhKVj6sQM^@K`<*^?K5qSBEKQE z-$?v+TOSSCU(ne7aBF96*VY5ex8HnGLYUZZuYy73I&Sc0`4F}q1-;gb-=Kv%2_MYD zM`;nlML8uZOpOmW?+aKzXzkj`?d0stdoEKTKOj&j5L3(p!GWb=Y`)Fc4%LZZ<#4ZI?^0QV0=&O!B&rN z6z|F&&zu`OT_JXPu|=aL`=^fM@)D1Nc&=MvdZxIL95qa&r|0GW%WW^oPgJimNTSfQ zzexsX+vl6dR*vmy7%t!F6vw(}T-c#!1F`tn@17+-H9U&mldz?0_vB?mpILb^E%Ivx zc`?-mfS=OfILr>m8?U9yA^@`S#-(DnE;aNw%K-SUb1P!d7gI*rhD_`GBrtaUkA~;>Btdh#_m3(7&eOZOu$Jw;wEcp}pu6_6_PR{}rCGq

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