ZeRO ak Optimisatër yu xaaj
ZeRO (Zero Redondance Optimizer) dafay dindi ñaari yoon ci mémoire bu yàqu-yàqu ci paralelism done ci xaaj staadu optimisatër bi, degrade yi ak poid yi ci GPU yi.
Résumé
It lets you train enormous models with the simplicity of data parallelism but a fraction of the per-GPU memory.
Plongeur bu xóot
Ci parallelism done bu gëna neex, GPU bu nekk dafay denc benn kopi bu mat sëkk ci staadu optimiser bi, gradient yi, ak ay parametre, te loolu dafay yàq lu bari, rawatina ci Adam, fu staadu optimiser bi mën nekk lu bari yoon ci dayo model bi ci boppam. ZeRO, bi Microsoft dugal ci DeepSpeed, dindi na redondance bi ci xaaj tensor yooyu ci GPU yi suko defee aparey bu nekk am benn dagg. ZeRO dafa am ñatti pàcc yu jëm kanam: pàcc 1 dafay xaaj staadu optimiser, pàcc 2 dafay yokk xaaj gradient, ak pàcc 3 dafay xaaj parametre yi ci seen bopp. Soo ko soxlaa, GPU yi dañuy dajale daggite yi ñàkk ci jokkoo, xayma, ba noppi bàyyi leen. Lépp soo ko boolee mu am memory bu gëna néew ci GPU bu nekk, loolu mooy tax ñu mëna tàggat ay paramet yu tollu ci ay miliyaar ba ay triliyoŋ, boole ci tëye xeetu prograam bu yomb bi ci paralelism done.
Gis-gis xarala
ZeRO dafay jënd ay jokkoo yu gëna bari ngir sakkanal mémoire. Ci 3ème étape bi, balaa layer bi di jàll ci kanam, all-gather dafay dajale parametre layer bi yépp ci GPU bu nekk; Ginaaw loolu ñu sànni daggite yiñ moomul ngir am mémoire bi. Degrade yi dañuy wàññeeku-tasaaroo, kon GPU bu nekk du am lenn ludul daggitu degrade bi méngoo ak parametre yi mu am. FSDP bu PyTorch (Done yuñ xaaj bu mat sëkk) dafay jëfandikoo benn xalaat bi ci boppam, di laxas ay modle ngir xaaj ak xaajwaat ci saasi.
njeextalu pexe
Njëgg ak budget
Dogal yi architecture di jël dañuy indi njariñ ak njëgu liggéey bi ay at ci ginaaw.
dogal yu gëna leer
Njàngalem xarala yi dafay jàppale ekip yi ñu tànn li gën, te baña yam ci li gëna bees daal.
Xool kalite
Tanneef yu gëna baax ci wàllu ingeñër dina wàññi jafe-jafe yi ci wàllu wóor ci liggéey bi.
Ëlëgu ZeRO ak Optimiser yu Sharded
Sharding mingi nekk lu ñuy jagleel tàggat yaram bu yaatu, du tànneef bu wuute. Xaarandi mboolem bu gëna xóot ak yobbu (push ay daggite ci CPU wala NVMe jaaraleko ci ZeRO-Infinity), gëna jëmmal lépp-dajale ak wàññi-tasaaroo ak xayma ngir nëbb seen njëg, ak boole ak tensor ak parallelism pipeline. Ginaaw model yi dañuy gëna màgg, optimisatëru sharded yu am mémoire yi ñoo gëna am solo ngir méngale leen ak budget hardware yu dëggu yi.
Doxal ci àdduna dëgg
Jëfandikoo DeepSpeed ZeRO Stage 2 ngir gëna suqali xeetu làkk bu am ay miliyaar ciy parametre, luko moy dina fees ci mémoire GPU.
Taggat ak PyTorch FSDP, mooy xaaj parametre yi, degrade yi, ak nekkinu optimisatër bi ci GPU yi ba noppi dajale leen ci couche bu nekk ci laaj bi.
Jëfandikoo ZeRO-Offload ngir puus nekkinu optimisatër bi ci mémoire CPU bi, may benn GPU mu tàggat model bu gëna mag VRAM bi yoon yu bari.
Eskaleer ab xeetu parametre bu am ay bilioŋ ak ZeRO-Infinity ci di dajale ay paramet yu bawoo ci dencukaay NVMe su GPU ak CPU jeexee.
Risk yi ak balustrade yi
Optimize benn benchmark mën na nëbb ñakk kattan yu gëna yaatu ci sistem bi.
Njëg li ñuy fay ci infrastructure yi ak ci toppatoo dañuy faral di suufeel.
Bu sistem yi di gëna xawa jafee xam, jafe-jafe yi am ci wàllu kaaraange ak seetlu mën nañu gëna bari.
Roadmap ngir samp gi
Mandargal latency, kalite, ak njëg yi laata ngay jëfandikoo.
Benchmark ci biir sargal ak done yu dëggu.
Jumtukaay bi di saytu njuumte yi, derive bi ak njeextalu jëfandikukat bi.
Waajal rollback ak yooni tontu ci jafe-jafe yi laata ngay eskale.
Weyal di banneexu
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Gis bi ci topp
Lookahead ak gaynde yiy gëna xéewale
Laaj yi ñuy faral di laaj
What is ZeRO and Sharded Optimizers?
ZeRO (Zero Redondance Optimizer) dafay dindi ñaari yoon ci mémoire bu yàqu-yàqu ci paralelism done ci xaaj staadu optimisatër bi, degrade yi ak poid yi ci GPU yi. Daf lay may nga tàggat ay model yu rëy ci parallelism done yu yomb waaye ci memory bu GPU bu nekk.
Ban redondance la ZeRO di dindi buñu ko méngale ak parallelism done bu leer?
Parallelism done buñ miin dafay denc ab kopi bu mat sëkk ci tolluwaayu optimisatër bi, degrade yi ak poid yi ci GPU bu nekk; ZeRO dafay xaaj yii suko defee GPU bu nekk tëye benn dagg.
Lan moo waral optimiser nekk mbaam xuux bi gëna mag ci mémoire ak Adam?
Adam dafay tëye xayma yiy daw lu melni saa yu njëkk yi ak ñaareel yi ci parametre bu nekk, boole ci fp32 diisaay master mën na wàññi dayo model bi boppam.
Lan la ZeRO Stage 3 xaaj te Stage 1 ak 2 duñu def?
etape 1 dafay xaaj etaa optimisateur, etape 2 yokk gradient, ak etape 3 dem lu gëna sori ci sharding parametre model ci GPUs itam.
Ci ZeRO Stage 3, naka la GPU di def ba mëna am paramet yi mu soxla ngir mëna dem ca kanam?
Laata ñuy xayma benn couche, all-gather dafay boole ay parametram yépp ci GPU bu nekk; buñu ko defee, dañuy bàyyi daggitu yi amul moomeel ngir wutaat memory bi.
Ban man-manu PyTorch mooy jëfandikoo sharding bu nuroo ak ZeRO?
PyTorch dafay xaaj ay done yu paralel (FSDP) di xaaj ay parametre, degrade yi, ak nekkinu optimisatër bi, dajale leen ba noppi xaaj leen ci saasi, di wane ZeRO.