Résumé
Taggat yaram bu jaxaso dafay gaawal taggat yaram ci reso neuronal bi ba noppi wàññi jëfandikoo mémoire ndax dafay def math yu bari ci 16-bit floating point ci barabu 32-bit. Dafay may benn GPU bi mu tàggat model yu gëna mag yi ci anam wu gëna gaaw te daanaka du ñàkk benn njub.
Taggat ci jaar-jaar bu jaxaso, jumtukaay la buy tabax xarala yu am njeexital ci kalite model bi, njëgu infrastructure bi, yeexal bi, ak wóor ci eskaal bi.
Plongeur bu xóot
Taggat yaram bu yàgg dafay denc poid yi ak def math ci point flottant 32-bit (FP32). Precision mixed dafay jëfandikoo formaa 16-bit yu gëna ndaw (FP16 wala bfloat16) ngir matrix yu diis yi, boole ci denc 'kopi master' bu 32-bit ci poids yi ngir yeesali stabil. Ndax nimero 16-bit yi genn-wàll lañu ci dayo bi, moo gëna méngoo ak mémoire GPU bi, Tensor Cores yi ñoo leen di gëna gaaw ci lu tollu ci 2-8x. Li ñuy jàpp mooy FP16 ci diggante bu sew bi: gradient yu ndaw yi mën nañu wàcci ba amul dara. Fix standard bi mooy scaling perte, luy yokk perte bi ak facteur bu mag balaa backpropagation suko defee gradient yu ndaw yi des ci representable, ba noppi xaaj ko balaa yeesal diisaay bi. Apex bu NVIDIA ak AMP biñ tabax ci biir (Njàngale mu jaxaso ci boppam) ci PyTorch ak TensorFlow ñoo koy def ci boppam.
Gis-gis xarala
FP16 amul lenn ludul 5 bit exponent, loolu mooy joxe ab rang dynamique bu ndaw buy waral gradient bi di wàcci. Bfloat16 dafay denc 8 bit exponent (mënngoo ak FP32) waaye bit mantissa yu néew, moo tax daawu soxla eskalaasioŋ bu ñàkk - sabab bu mag bi TPUs ak GPUs yu bees yi bëgg ko. Tensor Cores dafay gaawal liggéey bi ci yokk ay operand 16-bit waaye di dajale ay somme yu néew ci FP32, ba noppi denc njubte gi ci barab yi njuumti somme yi di gëna yokk.
Mastering tàggat yaram bu jaxaso
Taggat yaram bu jaxaso dafay gaawal taggat yaram ci reso neuronal bi ba noppi wàññi jëfandikoo mémoire ndax dafay def math yu bari ci 16-bit floating point ci barabu 32-bit. Dafay may benn GPU bi mu tàggat model yu gëna mag yi ci anam wu gëna gaaw te daanaka du ñàkk benn njub. Taggat ci jaar-jaar bu jaxaso, jumtukaay la buy tabax xarala yu am njeexital ci kalite model bi, njëgu infrastructure bi, yeexal bi, ak wóor ci eskaal bi. Ngir tabax xam-xam bu xóot, jàppal Mixed Precision Training ni xeetu liggéey, du benn man-man: leeral njariñ yi nga bëgg, leeral xalaat yi, ak tàqale li sistem bi mëna def ci anam wu wóor ak li ba leegi soxla àtteb kàngam.
Ci jëf, ekip yu am doole yiy jëfandikoo Tàggat bu Jaxasoo, dañuy gëna baaxal architecture, done, ak tànneefi infrastructure ci wàllu wóor ak njëg. Dañuy bind kritër yu leer ngir am ndam, natt leen ci done yu dëggu ak def liggéey, ba noppi ñu baamtu ci anamu ñàkka mëna seetlu, du ci benn yoon benchmark wins. Mooy barab bi xam-xam theorie bi di soppiku nekk kàttan buy yàgg ci produit yi, ci politik yi ak ci liggéey yi.
Dogal yi architecture di jël dañuy indi njariñ ak njëgu liggéey bi ay at ci ginaaw. Ci jamano jooju, Optimisation benn benchmark mën na nëbb ñakk kattan yu gëna yaatu ci sistem bi. Xeetu jëf bi gëna dëgër mooy boole gaawaayu jàngat ak disipline nguur: doxal pilote, jàpp firnde, siiwal dogal yi, ak wéy di yeesal kaaraange gi ci anam wi ñuy doxalee, li jëfandikukat bi di xaar, ak sàrti sàrt yi di jëm kanam.
njeextalu pexe
Dogal yi architecture di jël dañuy indi njariñ ak njëgu liggéey bi ay at ci ginaaw.
Dogal yi architecture di jël dañuy indi njariñ ak njëgu liggéey bi ay at ci ginaaw. Ci jëfandikoo yu am kalite bu kawe, loolu dañu koy tekki ci sàrti liggéey yuñ mëna natt, ay peggu boroom, ak ay xew-xewu xoolaat yu bari suko defee ekip yi mëna yokk wóolu seen bopp ci barabu yokk lu jaxasoo.
Njàngalem xarala yi dafay jàppale ekip yi ñu tànn li gën, te baña yam ci li gëna bees daal.
Njàngalem xarala yi dafay jàppale ekip yi ñu tànn li gën, te baña yam ci li gëna bees daal. Ci jëfandikoo yu am kalite bu kawe, loolu dañu koy tekki ci sàrti liggéey yuñ mëna natt, ay peggu boroom, ak ay xew-xewu xoolaat yu bari suko defee ekip yi mëna yokk wóolu seen bopp ci barabu yokk lu jaxasoo.
Tanneef yu gëna baax ci wàllu ingeñër dina wàññi jafe-jafe yi ci wàllu wóor ci liggéey bi.
Tanneef yu gëna baax ci wàllu ingeñër dina wàññi jafe-jafe yi ci wàllu wóor ci liggéey bi. Ci jëfandikoo yu am kalite bu kawe, loolu dañu koy tekki ci sàrti liggéey yuñ mëna natt, ay peggu boroom, ak ay xew-xewu xoolaat yu bari suko defee ekip yi mëna yokk wóolu seen bopp ci barabu yokk lu jaxasoo.
Doxal ci àdduna dëgg
torch.cuda.amp.autocast bu PyTorch dafay boole ab boucle de formation ngir xaaj mémoire bi ak ñaari yoon limuy def ci benn GPU
Taggat xeetu làkk yu mag yu melni transformatër yu nuroo ak GPT ci bfloat16 ci kaw TPU yi ngir moytu tuning buy ñàkk
Defar ab dayo bu gëna mag ci GPU RTX konsomatër ci soppi ResNet nataal tàggat ci FP32 dem FP16
FP8 dafa jaxasoo ci NVIDIA H100 GPUs ngir wàññi njëgu tàggat model yu mag yi
Modèlu jëfandikoo
Taggat yaram bu jaxaso ci jëf
Torch.cuda.amp.autocast bu PyTorch dafay boole ab boucle de formation ngir xaaj mémoire bi ak ñaari yoon limuy def ci benn GPU.
PyTorch's torch.cuda.amp.autocast dafay laxas benn bouclage de formation ngir xaaj memory bi ak ñaari produit ci benn GPU.
Taggat yaram bu jaxaso ci jëf
Taggat xeetu làkk yu mag yu melni transformatër yu nuroo ak GPT ci bfloat16 ci kaw TPU ngir moytu tuning buy ñàkk.
Taggat xeetu làkk yu mag yu melni GPT-style transformers ci bfloat16 ci TPUs ngir moytu tuning-scaling perte.
Taggat yaram bu jaxaso ci jëf
Defar ab dayo bu gëna mag ci ab GPU RTX konsomatër ci soppi ResNet nataal tàggat ci FP32 dem FP16.
Fitting benn batch bu gëna mag ci GPU RTX konsomatër ci soppi ResNet image training ci FP32 ba FP16 Teams yi dañuy faral di am njariñ yu gëna baax suñu joxee thresholds yu baax ci kanam, tëye yoonu escalation nit ngir jafe-jafe yi, ba noppi topp njariñu produit ak njëgu njuumte ci diir bi.
Taggat yaram bu jaxaso ci jëf
FP8 dafa jaxasoo ci NVIDIA H100 GPUs ngir wàññi njëgu tàggat model yu mag yi.
FP8 jaxase njubte ci NVIDIA H100 GPUs ngir wàññi njëgu modelu frontier-scale Teams yi dañuy faral di am njariñ yu gëna baax suñu joxee threshold yu baax ci kanam, tëye yoonu escalation nit ngir jafe-jafe yi, ba noppi topp njariñu produit ak njëgu njuumte ci diir bi.
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.
Mandargal latency, kalite, ak njëg yi laata ngay jëfandikoo. Japp jéego bu nekk ni buntu firnde: sudee mattul kritër yi, noppali génne gi, tëj bërëb bi, ba noppi nga yaatal jëfandikoo gi.
Benchmark ci biir sargal ak done yu dëggu.
Benchmark ci biir sargal ak done yu dëggu. Japp jéego bu nekk ni buntu firnde: sudee mattul kritër yi, noppali génne gi, tëj bërëb bi, ba noppi nga yaatal jëfandikoo gi.
Jumtukaay bi di saytu njuumte yi, derive bi ak njeextalu jëfandikukat bi.
Jumtukaay bi di saytu njuumte yi, derive bi ak njeextalu jëfandikukat bi. Japp jéego bu nekk ni buntu firnde: sudee mattul kritër yi, noppali génne gi, tëj bërëb bi, ba noppi nga yaatal jëfandikoo gi.
Waajal rollback ak yooni tontu ci jafe-jafe yi laata ngay eskale.
Waajal rollback ak yooni tontu ci jafe-jafe yi laata ngay eskale. Japp jéego bu nekk ni buntu firnde: sudee mattul kritër yi, noppali génne gi, tëj bërëb bi, ba noppi nga yaatal jëfandikoo gi.