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Model ak paralelismu tuyo
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Benn anam buñu mëna xaajalee math bi ci biir benn reso neuronal ci GPU yu bari suko defee benn model bu rëy lool ci benn aparey mën dox ba leegi.
Déglul tegtal biiIA-nettali · ~2 min
It matters because frontier models have hundreds of billions of parameters that no single GPU can hold or compute fast enough alone.
Parallelism tensor (ñu koy woowe itam parallelism model intra-couche) dafay xaaj matrice yu diisaay yi ci GPU yi moo gën ñu def ay couche yépp ci ay aparey yu wuute. Ci biir transformateur, matrix bu mag bi dafay yokk - projection attention ak MLP feed-forward - dañuy xaaj: ci misaal, matrix poids bu njëkk bi ci MLP dañu ko xaaj ci kolon ak ñaareel bi ci ligne, kon GPU bu nekk dafay xayma benn dagg ak benn all-reduce boole ci resultaa yi. Dañu xaaj bàyyi xel ci bopp yi, GPU bu nekk di jëfandikoo benn subset. Ndax GPU bu nekk dafay bokk ci bépp couche ci benn yoon, tensor parallelism dafay wàññi memory bu GPU bu nekk ba noppi gaawal ordinatër bi, waaye dafay laaj jokkoo bu bari, bandwidth bu kawe diggante GPUs couche bu nekk. Loolu moo tax ñu koy faral di tëj ci biir benn node bu NVLink lëkkale, ba noppi boole ci pipeline ak parallelism ci done ngir tàggat yu yaatu lool ak liggéey.
Taggat ab xeetu paramet 175B ci xaaj matris bu diisaayu couche bu nekk ci 8 GPU ci benn node bu lëkkaloo ak NVLink di jëfandikoo Megatron-LM.
Defar ab xeetu waxtaan bu am 70B ci vLLM ak tensor_parallel_size = 4 suko defee diisaay yi mëna méngoo ak ñeenti GPU yi te tontu ci jamono dëgg.
Séddale boppu transformatër yi ci GPU yi suko defee aparey bu nekk xayma benn subset, ba noppi boole ay sorti ngir layer bi ci topp.
Njaxas paralelismu tensor ci biir node yi ak paralelismu pipeline ci biir node yi ngir tàggat model yu am bilioŋu paramet ci kaw cluster GPU yu mag.
Paralelism tensor mingi wéy di nekk lu am solo waaye mingi gëna jaxasoo ak 'paralelism 3D' (tensor + pipeline + done) ba noppi boole ci paralelism eksper ngir xeetu njaxasu-ekspert. Kadre yu melni Megatron-LM, DeepSpeed, ak vLLM ñooy otomatise xaaj bi. Lu GPU di lëkkaloo (NVLink, NVSwitch) ak tissu optik di gëna gaaw, yam ci node-frontière dafay féexal, may grupu tensor yu gëna yaatu. Xaarandil paralelisasioŋ otomatik bu gëna am xel buy tànn dimension shard ak dayo grupu ngir wàññi jokkoog topologie cluster buñ jox.
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Benn anam buñu mëna xaajalee math bi ci biir benn reso neuronal ci GPU yu bari suko defee benn model bu rëy lool ci benn aparey mën dox ba leegi. Dafa am solo ndax model frontier yi amnañu téemeeri milyaari parametre yu benn GPU mënul tëye wala xayma lu gaaw kese.
Parallelism tensor (ci biir couche) dafay xaaj matrice yu diis yi ci biir benn couche, kon GPU bu nekk dafay xayma benn wàll ci benn couche bi - wuute ak parallelism pipeline, biy def couche yu mat ci GPU yu wuute.
Séddale matrix bi njëkk ci kolon dafay tax GPU bu nekk jëfandikoo nonlinearite ci barab bi; xaaj ñaareel bi ci ligne yi dafay tekki ni benn-wàññi dafay boole génnug pàrtiel yi—di wàññi synchronisation.
Bépp couche dafay indi jokkoo bu mbooloo (ñépp-wàññi), kon trafik bu diis, latency-sensitif dafay laaj lëkkalekaay yu gaaw ci biir node yu melni NVLink moo gën reso inter-node yu yeex.
Bopp yiy bàyyi xel ci seen bopp, moo tax dañu leen séddale ci GPU yi—aparey bu nekk dafay xayma yenn bopp ba noppi boole li ñuy génne.
All-reduce dafay boole génnekaay yiñ xayma ci GPU bu nekk suko defee ñu déggoo ci njariñu couche bi; loolu dafay am yoon yu bari ci layer bu nekk ci paas yu jëm kanam ak yu ginaaw.
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