GUIDE teknik
RMSNorm ak yamale bu njëkk
RMSNorm ab couche normalisation bu woyof la buy reescale activation yi ci seen root moyenne carré, ak couche normalisation pre-couche biy jéego balaa subcouche bu nekk moo gën ginaaw.
Déglul tegtal biiIA-nettali · ~2 min
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Résumé
Together they make deep transformers train stably without warmup tricks.
Plongeur bu xóot
Standard LayerNorm dafay dindi moyenne bi ba noppi xaaj ko ak jaar-jaar bi ci vecteur bi, ba noppi jëfandikoo echel biñ jàng ak coppite. RMSNorm, bi Zhang ak Sennrich dugal ci 2019, dafay daaneel moyenne-centring ak biais bi yépp: dafay xaaj vecteur bu nekk ak root moyenne carré ci élément yi, ba noppi yokk ko ci benefiis bu nekk ci man-man yi. Loolu dafay dindi benn lim ak yenn jëf, dagg xayma ci lu tollu ci 10-50% ci couche norme bi boole ci njub. Ci beneen wàll, plasement 'Pre-LN' (norm balaa bàyyi xel / MLP, ak yoon wu sell wi ko wër) dafay tëye magnitude gradient yi ci ndoorte li, kon xeetu GPT-3, LLaMA, ak PaLM di tàggat te duñu am hacks yuy jàng-taux warmup bi soxla Trans-LN transformer.
Doxal ci àdduna dëgg
LLaMA, Mistral, ak Qwen ñoom ñépp ñu ngi wecci LayerNorm ak RMSNorm ngir dindi njuumte ci bepp jeton
Pre-LN dafay may model yu nuroo ak GPT ñu tàggat te duñu am tàngoor buy jàng bi transformatëru Post-LN 2017 soxla
QK-normalisation dafay jëfandikoo RMSNorm ci laajte ak caabi ngir dakkal logits ñu baña kalaate ci model yu mag
Transformatër mobile ak boor yi dañuy jëfandikoo RMSNorm ndax wàññi moyenne ak biais dafay wàññi dem bi ak dikk bi ci mémoire bi
Ëlëgu RMSNorm ak Normalisasioŋ bu njëkk
RMSNorm mooy li ñuy jagleel LLM yu bari yi (LLAMA, Mistral, Qwen, Gemma), kon xaarandil mu wéy di nekk standard. Gëstu dafay setal rëset bi: QK-norm dafay jëfandikoo RMSNorm ci laaj ak caabi ngir dakkal màgg logit, ba noppi yenn laboratuwaar yi dañu boole pre- ak post-norm ('sandwich' wala 'peri-LN') ngir gëna dëgër ci escale trillion-paramètre. kernel hardware yi dañuy wéy di boole liggéey bi ngir gaaw.
Teg ko ci jëf
Lu tax mu am solo
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Moytul
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Weyal di banneexu
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Nattal sa bopp
Take the RMSNorm and Pre-Layer Normalization quiz
Feedback ci saasi ci bépp tontu, ak sertifikaa buñ mëna séddoo ak ID buñ mëna saytu soo jàllee ci njàng mi.
Jàppale njàngum IA te doo fay. AI Understanding mbootaay la buy def te Yàlla tax — amul yëgle, amul paywall, musul am. Defal ab sarax
Laaj yi ñuy faral di laaj
What is RMSNorm and Pre-Layer Normalization?
RMSNorm ab couche normalisation bu woyof la buy reescale activation yi ci seen root moyenne carré, ak couche normalisation pre-couche biy jéego balaa subcouche bu nekk moo gën ginaaw. Ñu bokk defar ay transformatër yu xóot yuy tàggat bu baax te duñu am benn pexe tàngoor.
Lan la RMSNorm bàyyi buñu ko méngale ak LayerNorm buñ miin?
RMSNorm dafay sànni ordinatër bi ak dindi moyenne bi, normalise ko ci root moyenne carré bi ci aktivasioŋ yi.
Ak ban limu RMSNorm di xaaj vecteur bu nekk ci aktivasioŋ?
RMSNorm dafay xaaj ak sqrt ci moyenne élément carré, maanaam root moyenne carré, ba noppi jëfandikoo benefiis buñ jàng.
What gradient behavior does Pre-LN support at initialization in deep Transformers?
Xiong et al. analyze gradient behavior and show Pre-LN gradients are better behaved at initialization; this does not guarantee warmup-free training for every setup.
Ci benn bloku Pre-LN, ban yoon la couche normalisation bi di bàyyi te kenn laalu ko?
Pre-LN dafay normalise duggal gi ci benn sublayer waaye gaawaayu residu bi daf koy romb, ba noppi denc otoroute bu sell.
Ban famiy xeetu poid ouvert bu bees bi mooy jëfandikoo RMSNorm ci default?
RMSNorm moo nekkoon normalisasioŋ buñ miin ci LLama, Mistral, Qwen, Gemma ak LLM yu bees yi.
Weyal di jàng
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