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Dajale degrade
Accumulation gradient daf lay may nga simule ab lot bu rëy ci memory GPU bu gàtt soo boole gradient yi ci ay mini-lot yu ndaw balaa ngay yeesal poid yi.
Déglul tegtal biiIA-nettali · ~2 min
Ci xët wii2 simili jàng
Résumé
Mooy saafara biñ gëna xam ngir tàggat ay model yu mag sudee fàttaliku bi mooy bottleneck bi.
Plongeur bu xóot
Daanaka jéego tàggat yaram dafay liggéey ci benn lote, xayma gradient yi, ba noppi yeesal ci saasi ay parametre. Ak dajale degrade, dangay def ay paas yu bari ci kanam ak ci ginaaw ci micro-lote yu gëna ndaw, nga yokk seeni degrade ci tampon parametre yi, nga woo jéego optimisatër bi (ak nul degrade yi) ginaaw N micro-lote. Dayo lote bi am solo dafay nekk dayo micro-lote yoon N, doonte mémoire bi gëna kawe du musa tëye benn micro-lote ci aktivasioŋ yi. Loolu dafa am solo ndax rëset yu bari ci tàggat yaram dañuy laaj ay lots yu bari ngir am lim bu dëgër, te model yu melni transformateur yu mag mënu ñu mëna ànd ak lots target bu mat ci benn aparey. Japp bi: lim-normalisation lots ñu ngi koy xayma ci micro-lot bu nekk, kon norm layer wala norm groupe ñoo gëna méngoo ak accumulation, te danga wara scale perte bi ci anam wu jaar yoon ngir mëna wéy di jàng bu baax.
Doxal ci àdduna dëgg
Defar bu baax xeetu làkk bu yaatu ci benn GPU konsomatër ci dajale lu ëpp 8 wala 16 micro-lote ngir yegg ci lote bu am téemeeri.
Taggat gis-gis bu am dayo bu kawe wala xeetu segmentation fu benn lot bu 2 mëna méngoo, waaye rëset bi soxla na lot bu am doole bu 32.
Taggatkat kanam buy laxasu ak PyTorch Lightning dañuy wane ab jekkal degrade_accumulation_jéego yu ñuy jëfandikoo ci jekkal VRAM yu yam.
Defar resultaa yi ci lots yu bari ci këyit ci hardware bu gëna ndaw ci méngale dayo lots bi jaaraleko ci accumulation.
Ëlëgu dajale gradient
Dajale gradient bi dina des ci levier biñ jagleel ndax dayo model bi dafa ëpp benn mémoire bu benn aparey. Dafay gëna boole ak njubte bu jaxaso, saytu poñ yi, sharding ZeRO, ak paralelism pipeline ci kaadar yu melni DeepSpeed ak FSDP. Xaarandi otomatisation bu gëna dëgër fu bibliotek yi di auto-tune jéego yi ci budget memory, ak wéy di am solo ngir fine-tuning model yu mag ci hardware yu woyof, boole ci GPUs consommateurs fu ñuy ubbi tàggat yaram bu mënul am.
Teg ko ci jëf
Lu tax mu am solo
- 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.
Moytul
- 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.
Jéego yi ci topp
- 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.
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Nattal sa bopp
Take the Gradient Accumulation quiz
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Laaj yi ñuy faral di laaj
Luy dajale gradient?
Accumulation gradient daf lay may nga simule ab lot bu rëy ci memory GPU bu gàtt soo boole gradient yi ci ay mini-lot yu ndaw balaa ngay yeesal poid yi. Mooy saafara biñ gëna xam ngir tàggat ay model yu mag sudee fàttaliku bi mooy bottleneck bi.
Lan lay njëkka may nga def dajale gradient?
Soo boole gradient yi ci kaw ay micro-batch yu bari balaa ngay yeesal, dangay toppandoo batch bu mag te doo tëye lépp ci sa memory benn yoon.
With an accumulation factor N, when should optimizer.step() and zero_grad() occur?
Accumulate gradients across N micro-batches, call optimizer.step() once for the effective batch, then clear gradients before the next window; begin training with cleared buffers.
Ban couche de normalisation moo gëna set ak dajalu gradient bi?
Norme-lote dafay xayma lim yi ci micro-lote bu nekk, suko defee norme couche wala groupe moytu ñu baña méngoo ak micro-lote yu ndaw yi.
Why may four micro-batches with BatchNorm behave differently from one physical batch of the same total size?
Batch-dependent forward computations break exact equivalence.
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