Ukuqeqeshwa Okunembayo Okuxubile
Ukuqeqeshwa kokunemba okuxubile kusheshisa ukuqeqeshwa kwenethiwekhi ye-neural futhi kunqamule ukusetshenziswa kwememori ngokwenza izibalo eziningi endaweni eyi-16-bit endaweni ye-32-bit.
Uhlolojikelele
It lets the same GPU train bigger models faster with almost no loss in accuracy.
I-Deep Dive
Ukuqeqeshwa kwendabuko kugcina izisindo futhi kusebenzisa izibalo endaweni engu-32-bit elintantayo (FP32). Ukunemba okuxubile kusebenzisa amafomethi anonemba aphansi we-16-bit (FP16 noma i-bfloat16) ekuphindaphindeni kwe-matrix esindayo, kuyilapho kugcinwa 'ikhophi eyinhloko' engu-32-bit yezisindo ukuze kube nezibuyekezo ezizinzile. Ngenxa yokuthi izinombolo ezingu-16-bit ziwuhhafu wosayizi, zilingana kakhudlwana kumemori ye-GPU futhi i-Tensor Cores zicubungula cishe ngo-2-8x ngokushesha. Ukubamba ububanzi be-FP16: ama-gradient amancane angageleza aze afike ku-zero. Ukulungiswa okujwayelekile isikali sokulahlekelwa, esiphindaphinda ukulahlekelwa ngento enkulu ngaphambi kokusabalalisa i-backpropagation ukuze ama-gradients amancane ahlale emelele, bese kukuhlukanisela emuva ngaphambi kokubuyekezwa kwesisindo. I-Apex ye-NVIDIA kanye ne-AMP eyakhelwe ngaphakathi (Automatic Mixed Precision) ku-PyTorch kanye ne-TensorFlow zenza lokhu ngokuzenzakalelayo.
I-Technical Insight
I-FP16 inama-exponent bits angu-5 kuphela, enikeza ububanzi obuncane obuguquguqukayo obubangela ukugeleza okuphansi kwe-gradient. I-Bfloat16 igcina amabhithi e-eksponenti angu-8 (okufana nobubanzi be-FP32) kodwa amabhithi e-mantissa ambalwa, ngakho ayivamisile ukudinga ukukala ukulahlekelwa - isizathu esiyinhloko Google ama-TPU nama-GPU esimanje athanda yona. I-Tensor Cores isheshisa umsebenzi ngokuphindaphinda ama-operand angu-16-bit kodwa iqongelela isamba semali esiyingxenye ku-FP32, igcina ukunemba lapho amaphutha esifinyezo ebengahlangana khona.
I-Strategic Impact
Izindleko kanye nesabelomali
Izinqumo zezakhiwo ziqhuba ukusebenza kanye nezindleko zokusebenza iminyaka.
Izinqumo ezicacile
Imfundo yobuchwepheshe isiza amaqembu ukuthi akhethe isitaki esifanele, hhayi nje esisha.
Ukulawulwa kwekhwalithi
Izinketho ezingcono zobunjiniyela zinciphisa izehlakalo ezinokwethenjelwa ekukhiqizeni.
Ikusasa Lokuqeqeshwa Okunembayo Okuxubile
Ukunemba kulokhu kwehla. Ukuqeqeshwa kwe-FP8, okusekelwa ku-NVIDIA Hopper kanye ne-Blackwell GPUs, kuba indinganiso kumamodeli asemngceleni, futhi ucwaningo lwe-FP4 namafomethi we-microscaling (MXFP) luqhubekela phambili. Lindela izinhlaka zokukhetha ngokuzenzakalelayo ukunemba kwesendlalelo ngasinye, izingxenyekazi zekhompuyutha ukuze ziphathe amafomethi ahlala encipha, kanye nokuqeqeshwa kokwazi ukulinganisa ukuze kufiphazwe umugqa phakathi kokuqeqeshwa okunemba okuphansi nokuchazwayo, kunciphe izindleko zokuqeqesha amamodeli wepharamitha eyizigidigidi.
Ukuqaliswa Komhlaba Wangempela
I-PyTorch's torch.cuda.amp.autocast isonga iluphu yokuqeqesha ukuze icishe ihhafu inkumbulo kanye nokuphuma kabili kwe-GPU eyodwa
Ukuqeqesha amamodeli amakhulu olimi afana neziguquli zesitayela se-GPT ku-bfloat16 kuma-TPU ukugwema ukushuna kokulahlekelwa
Ukufaka usayizi weqoqo elikhudlwana kumthengi we-RTX GPU ngokushintsha ukuqeqeshwa kwesithombe se-ResNet kusuka ku-FP32 kuye ku-FP16
Ukunemba okuxubile kwe-FP8 kuma-NVIDIA H100 GPUs ukuze kwehliswe izindleko zamamodeli esikali somngcele
Izingozi & Guardrails
Ukuthuthukisa ibhentshimakhi eyodwa kungafihla ubuthakathaka obubanzi besistimu.
Izindleko zengqalasizinda nezokulungisa zivame ukubukelwa phansi.
Izikhala zokuphepha nokubonakala zingakhula njengoba izinhlelo ziba nzima kakhulu.
Ukuqalisa Umhlahlandlela
Chaza ukubambezeleka, ikhwalithi, nezindleko ezihlosiwe ngaphambi kokuqaliswa.
Ibhentshimakhi ngaphansi komthwalo wangempela nezimo zedatha.
Ukuqapha amathuluzi amaphutha, ukukhukhuleka, nomthelela wabasebenzisi.
Lungiselela izindlela zokuhlehlisa nezigameko ngaphambi kokukala.
Qhubeka Uhlole
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Umhlahlandlela olandelayo
I-Pseudo-Labeling kanye nokuziqeqesha ngokwakho
Imibuzo evame ukubuzwa
What is Mixed Precision Training?
Ukuqeqeshwa kokunemba okuxubile kusheshisa ukuqeqeshwa kwenethiwekhi ye-neural futhi kunqamule ukusetshenziswa kwememori ngokwenza izibalo eziningi endaweni eyi-16-bit endaweni ye-32-bit. Ivumela amamodeli afanayo e-GPU aqeqeshe amamodeli amakhulu ngokushesha cishe akukho ukulahlekelwa ngokunemba.
Kungani ukuqeqeshwa kokunemba okuxubile kugcina 'ikhophi eyinhloko' ye-32-bit yezisindo?
Ukubuyekezwa kwesisindo ngokuvamile kuncane kakhulu; ukuwaqoqa ku-16-bit kuzolahlekelwa ukunemba, ngakho-ke ikhophi eyinhloko enembe ngokugcwele igcina izibuyekezo zinembile.
Iyiphi inkinga isikali sokulahlekelwa esiyixazululayo ekuqeqeshweni kwe-FP16?
I-FP16 inobubanzi obuguquguqukayo obunomkhawulo, ngakho-ke ama-gradient amancane angazungeza aze afike kuqanda; ukuphindaphinda ukulahlekelwa ngaphambi kwe-backprop kuzigcina zimeleka.
Iyiphi inzuzo i-bfloat16 enayo ngaphezu kwe-FP16?
I-Bfloat16 igcina ama-exponent bits angu-8 njenge-FP32, ngakho ububanzi bayo obuguquguqukayo bukhulu futhi ukugeleza okungaphansi kwe-gradient akuvamile.
I-Tensor Cores ikugcina kanjani ukunemba ngenkathi usebenzisa okokufaka kwe-16-bit?
Ama-Tensor Cores aphindaphinda ama-operands angu-16-bit kodwa anqwabelana ngamabhithi angu-32, avimbele amaphutha e-summary ukuthi ahlanganiswe.
Iyiphi inzuzo eyinhloko yokusebenzisa i-16-bit esikhundleni samavelu angu-32-bit phakathi nokuqeqeshwa?
Izinombolo zosayizi ohhafu zilingana idatha eyengeziwe kumemori ye-GPU futhi zivumela i-Hardware inqubo yezibalo ze-matrix izikhathi ezimbalwa ngokushesha.