Done yuñ xaaj lépp paralel
Fully Sharded Data Parallel (FSDP) pexem tàggat yaram la buy xaaj parametru model bi, degrade yi, ak staadu optimisatër yi ci GPU yu bari suko defee aparey bu nekk tëye benn wàll kese.
Résumé
It makes training huge models possible on hardware that could never fit the whole model in one GPU's memory.
Plongeur bu xóot
Parallelism done yi fi yàgg a nekk dañuy denc benn kopi bu mat sëkk ci model bi ci GPU bu nekk, te loolu dafay yàq mémoire bi ba noppi di tënk dayo model bi. FSDP, PyTorch bu AIU_PROTECTED_13 moo ko siiwal, ZeRO bu AIU_PROTECTED_5_ moo ko inspiré, lu moy loolu dafay xaaj ñetti mbir ci aparey yi: paramet yi, gradient yi, ak staadu optimisatër bi. Ci diir bi ñuy jaar ci kanam, GPU bu nekk dafay dajale ci diir bu gàtt diisaay bi yépp ngir layer bi muy ordinatër jaaraleko ci all-gather, dawal calcul bi, ba noppi bàyyi ci saasi kopi biñ dajale. Dellu ginaaw dafay dox noonu, topp ci wàññi-tasaaroo biy séddale daggitu gradient yi ci seeni GPU. Ndax aparey bu nekk du denc ludul benn wàll ci model bi, jëfandikoo memory bi dafay wàññeeku lu tollu ci limu GPU yi, loolu mooy tax ekip yi tàggat model yu am fukki wala téemeeri miliyaar ciy parametre.
Gis-gis xarala
FSDP dafay jëfandikoo jokkoo bu gëna bari ngir sakkanal mémoire. Diisaayu couche bu nekk dañu koy defaraat ci laaj ak all-gather balaa ñu koy jëfandikoo ba noppi sànni ko ginaaw ga, ci noonu lañuy boole gradient yi ñu xaaj ko ak reduce-scatter. Jokkoo mën nañu ko boole ak xayma suñu njëkkee jël parametru layer bi ci topp bi layer bi fi nekk di dox, di nëbb lu bari ci latency reso bi. Tuning granularite sharding (politigu wrapping) dafay ekilibre emprent mémoire bi ak njëgu jokkoo bi.
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 done yu mat sëkk
FSDP mingi nekk xeetu tàggat yaram bu ubbeeku, ak FSDP2 ci PyTorch di gëna yombal jëfandikoo gi ak sharding ci parametre bu nekk. Xaarandil lëkkaloo bu gëna dëgër ak tensor ak parallelism pipeline ngir model yu am trillion paramètre, jàppale bu gëna baax ngir jaxaso bu jaar yoon ak fp8, ak wrapping otomatik bu gëna xarañ bi lay tànnal ay peggu sharding. Ginaaw inter-GPU yi ñuy boole lu melni NVLink ak InfiniBand dañuy gëna gaaw, njëgu jokkoog sharding dafay wéy di wàññeeku, muy gëna yomba jëfandikoo ci anam yu gëna mag.
Doxal ci àdduna dëgg
Defar bu baax benn xeetu Llama bu am 70 milyaar ciy parametre ci 8 GPU yu benn-benn mënu tëye poid yi yépp.
Taggat xeetu lakk yu mag ci labo IA ci xaaj etaa optimizer (yi ëpp doole ci mémoire bi ak Adam) ci téemeeri gaawaaykat.
Gëstukat yi jëfandikoo nañu wrapper FSDP bu PyTorch ngir tàggat transformatëri gis-gis ci benn cluster universite te duñu jënd ay GPU yu 80GB.
Njaxas FSDP ak bfloat16 bu jaxaso ngir xaaj mémoire bi ak gaawlu tàggat yaram ci xeetu multimodal.
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
Paralelismu done
Laaj yi ñuy faral di laaj
What is Fully Sharded Data Parallel?
Fully Sharded Data Parallel (FSDP) pexem tàggat yaram la buy xaaj parametru model bi, degrade yi, ak staadu optimisatër yi ci GPU yu bari suko defee aparey bu nekk tëye benn wàll kese. Dafay tax ñu mëna tàggat model yu mag ci hardware bu mënul mëna tëye model bi yépp ci benn mémoire GPU.
Lan la FSDP xaaj ci GPU yi nga xamni parallelism done buñ miin deful?
FSDP dafay xaaj parametru model bi, degrade yi, ak stade optimisatër yi ci aparey yi, waaye parallelism done buñ miin dafay nuru model bi yépp ci GPU bu nekk.
Ban operation collective la FSDP di jëfandikoo ngir defaraat poid yu mat yi ci benn couche balaa ñu koy calculer?
Laata benn couche di dox, FSDP dafay def all-gather ngir dajale ci diir bu gàtt parametre yépp ci shards yépp, ba noppi bàyyi leen ci ganaw.
Lan moo waral FSDP di dindi poids yiñ dajale ci saasi ginaaw biñu xaymaa benn couche?
Teg benn shard rek ba fàww ba noppi dajale poids yu mat ci diir bu gàtt mooy tax jëfandikoo memory bi di wàññeeku ba noppi di méngoo ak benn fraction ci model bi.
Ban xeetu mémoire-optimisation moo gënoon inspiré FSDP?
FSDP dafay xaaj paarametre yi, degrade yi, ak anam yi gëna xéewale, ñu topp xalaat yiñ dugal ci __AIU_PROTECTED_5_'s ZeRO bu bawoo ci bibliotek DeepSpeed.
naka la FSDP di nëbbe lu bari ci latency reso bi ci dajale ay poid?
FSDP dafay prefetch parametru layer bi ci topp bi layer bi fi nekk di ordinatër, di jaxasoo jokkoog all-gather ak liggéey bu am njariñ.