Adalu konge Training
Ikẹkọ konge adapọ ṣe iyara ikẹkọ nẹtiwọọki nkankikan ati gige lilo iranti nipasẹ ṣiṣe iṣiro pupọ julọ ni aaye lilefoofo 16-bit dipo 32-bit.
Akopọ
It lets the same GPU train bigger models faster with almost no loss in accuracy.
Jin Dive
Ikẹkọ aṣa tọju awọn iwuwo ati ṣiṣe iṣiro ni aaye lilefoofo 32-bit (FP32). Isọye ti o dapọ nlo awọn ọna kika 16-bit ti o kere ju (FP16 tabi bfloat16) fun awọn isodipupo matrix wuwo, lakoko ti o tọju 32-bit 'daakọ tuntun' ti awọn iwuwo fun awọn imudojuiwọn iduroṣinṣin. Nitori awọn nọmba 16-bit jẹ idaji iwọn, ibamu diẹ sii ni iranti GPU ati Awọn Cores Tensor ṣe ilana wọn ni aijọju 2-8x yiyara. Apeja naa jẹ sakani dín FP16: awọn gradients kekere le wa labẹ sisan si odo. Atunṣe boṣewa jẹ igbelowọn pipadanu, eyiti o ṣe isodipupo pipadanu nipasẹ ifosiwewe nla ṣaaju isọdọtun nitorinaa awọn gradients kekere duro ni aṣoju, lẹhinna pin pin sẹhin ṣaaju imudojuiwọn iwuwo. NVIDIA's Apex ati AMP ti a ṣe sinu (Konge Adalu Aifọwọyi) ni PyTorch ati TensorFlow ṣe adaṣe eyi.
Imọ-imọ-ẹrọ
FP16 ni awọn ege olupilẹṣẹ 5 nikan, fifun ni iwọn ti o ni agbara kekere ti o fa ki iṣan omi kekere. Bfloat16 ntọju awọn iwọn olutayo 8 (ibaramu iwọn FP32) ṣugbọn awọn die-die mantissa diẹ, nitorinaa o ṣọwọn nilo iwọn pipadanu - idi pataki kan Google TPUs ati awọn GPU ode oni ṣe ojurere rẹ. Awọn Cores Tensor mu iṣẹ naa pọ si nipa isodipupo awọn iṣẹ ṣiṣe 16-bit ṣugbọn ikojọpọ awọn akopọ apa kan ni FP32, titoju deede nibiti awọn aṣiṣe akopọ yoo bibẹẹkọ pọ.
Ipa Ilana
Iye owo ati isuna
Awọn ipinnu faaji ṣe awakọ iṣẹ ati idiyele iṣẹ fun awọn ọdun.
Awọn ipinnu diẹ sii
Ẹkọ imọ-ẹrọ ṣe iranlọwọ fun awọn ẹgbẹ lati yan akopọ to tọ, kii ṣe ọkan tuntun nikan.
Iṣakoso didara
Awọn yiyan imọ-ẹrọ to dara julọ dinku awọn iṣẹlẹ igbẹkẹle ni iṣelọpọ.
Ọjọ iwaju ti Ikẹkọ Itọka Ajọpọ
Itọkasi n tẹsiwaju silẹ. Ikẹkọ FP8, ni atilẹyin lori NVIDIA Hopper ati Blackwell GPUs, n di idiwọn fun awọn awoṣe aala, ati iwadii sinu FP4 ati awọn ọna kika microscaling (MXFP) titari siwaju. Reti awọn ilana lati yan adaṣe-itọkasi-Layer kọọkan, ohun elo ohun elo lati mu ni abinibi mu awọn ọna kika ti o dín nigbagbogbo, ati ikẹkọ ti o mọye lati di laini laini laarin ikẹkọ konge kekere ati itọkasi, idinku idiyele ti awọn awoṣe paramita aimọye-imọye.
Real-World imuse
PyTorch's torch.cuda.amp.autocast n murasilẹ lupu ikẹkọ si aijọju iranti idaji ati ilọpo meji lori GPU kan
Ikẹkọ awọn awoṣe ede nla bii awọn oluyipada ara-GPT ni bfloat16 lori awọn TPU lati yago fun yiyi iwọn-pipadanu
Ni ibamu iwọn ipele ti o tobi julọ lori olumulo RTX GPU nipa yiyipada ikẹkọ aworan ResNet lati FP32 si FP16
FP8 dapọ konge lori NVIDIA H100 GPUs lati ge idiyele ti iṣaju iṣaju awọn awoṣe iwọn-aala
Awọn ewu & Awọn ọna iṣọ
Ṣiṣepe ala-ilẹ kan le tọju awọn ailagbara eto ti o gbooro.
Awọn ohun elo amayederun ati awọn idiyele itọju nigbagbogbo ni aibikita.
Aabo ati awọn ela akiyesi le dagba bi awọn eto ṣe di eka sii.
Ilana Ilana imuse
Ṣetumo lairi, didara, ati awọn ibi-afẹde idiyele ṣaaju imuse.
Aṣepari labẹ ẹru ojulowo ati awọn ipo data.
Abojuto ohun elo fun awọn aṣiṣe, fiseete, ati ipa olumulo.
Mura ipadasẹhin pada ati awọn ipa ọna esi iṣẹlẹ ṣaaju iwọn.
Tesiwaju Ṣiṣawari
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Itọsọna atẹle
Afarape-Labeling ati Ara-Ikẹkọ
Awọn ibeere ti a beere nigbagbogbo
What is Mixed Precision Training?
Ikẹkọ konge adapọ ṣe iyara ikẹkọ nẹtiwọọki nkankikan ati gige lilo iranti nipasẹ ṣiṣe iṣiro pupọ julọ ni aaye lilefoofo 16-bit dipo 32-bit. O jẹ ki GPU kanna ṣe ikẹkọ awọn awoṣe nla ni iyara pẹlu fere ko si pipadanu ni deede.
Kini idi ti ikẹkọ konge idapọmọra tọju ‘ẹda titunto si’ 32-bit ti awọn iwuwo?
Awọn imudojuiwọn iwuwo nigbagbogbo kere pupọ; ikojọpọ wọn ni 16-bit yoo padanu konge, nitorinaa ẹda titunto si pipe jẹ ki awọn imudojuiwọn jẹ deede.
Iṣoro wo ni igbelowọn pipadanu yanju ni ikẹkọ FP16?
FP16 ni iwọn agbara to lopin, nitorinaa awọn gradients kekere le yika si odo; isodipupo isonu ṣaaju ki o to backprop ntọju wọn aṣoju.
Kini anfani ni bfloat16 lori FP16?
Bfloat16 ṣe itọju awọn iwọn 8 olupilẹṣẹ bii FP32, nitorinaa iwọn agbara rẹ tobi ati ṣiṣan aladiẹ jẹ toje.
Bawo ni Awọn Cores Tensor ṣe tọju deede lakoko lilo awọn igbewọle 16-bit?
Tensor Cores isodipupo 16-bit operands sugbon kojọpọ ni 32-bit, idilọwọ awọn aṣiṣe akopọ lati compounding.
Kini anfani akọkọ ti lilo 16-bit dipo awọn iye 32-bit lakoko ikẹkọ?
Awọn nọmba iwọn-idaji baamu data diẹ sii ni iranti GPU ati jẹ ki matrix ilana ohun elo amọja ni ọpọlọpọ igba yiyara.