Kediska Feejignaanta Flash

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Waa maxay cilladda ugu weyn ee Flash Feejignaanta bartilmaameedsanayo?
   • Akhrinta iyo qorista matrixka dareenka weyn si loo yareeyo xusuusta GPU-da
   • Tirada isku dhufashada-dhibcaha sabaynta ee dareenka
   • Qiimaha raritaanka miisaanka model ka diskka
   • Latency network ee u dhexeeya GPU-yo badan
2. Sidee Fiishka Feejignaanta uga fogaanayaa kaydinta fiirsashada N-by-N buuxda?
   • Waxay ku qiyaastaa fiiro gaar ah oo leh qiyaaso aan kala sooc lahayn
   • Waxay ku socodsiisaa su'aalaha, furayaasha, iyo qiyamka taayirada yaryar waxayna isku daraysaa natiijooyinka qayb ahaan
   • Waxay ka boodaa inta badan lamaanayaasha calaamada gabi ahaanba
   • Waxay ku cadaadisaa matrixka iyadoo la tixgalinayo
3. Farsamo noocee ah ayaa u oggolaanaysa Flash Attention inay si sax ah u xisaabiso softmax iyada oo aan la arkin safka oo dhan hal mar?
   • Shaxda tilmaame ee hore loo xisaabiyey
   • Hoos u dhigista softmax iyo adeegsiga qiyaas toosan
   • A orodka (online) softmax kaas oo la socda heerka ugu sarreeya iyo wadarta
   • Kala soocida buundooyinka ka hor inta aan caadi laga dhigin
4. Marka la barbar dhigo dareenka caadiga ah, wax soo saarka Flash Attention waa:
   • Qiyaasta qallafsan ee saxnaanta ka beddeleysa xawaaraha
   • Aad u saxsan sababtoo ah saxnaanta sare
   • Kaliya waxay ku ansaxaysaa taxanaha gaaban
   • Xisaab ahaan isku mid, si hufan ayaa loo xisaabiyaa
5. Waa maxay sababta Flash Attention ugu caawiso taxanaha dheer?
   • Sababtoo ah xusuusta feejignaanta waxay u koraan afar-geesood dhererka isku xigxiga
   • Sababtoo ah taxanaha dheer wuxuu u baahan yahay lakabyo yar
   • Sababtoo ah softmax waxay noqotaa mid aan loo baahnayn wax gelinta dheer
   • Sababtoo ah GPU-yadu si tartiib tartiib ah ayey u socdaan taxane gaaban
6. Muxuu nashqada 'IO-aware' ee feejignaanta Flash mudnaan siinayaa yaraynta?
   • Tirada qiyaasaha moodeelka
   • Xusuustu waxay akhridaa oo wax ka qortaa inta u dhaxaysa SRAM iyo HBM
   • Qoto dheer ee shabakada neerfaha
   • Cabbirka ereyada ee tokenizer
7. What remains computationally quadratic for dense exact self-attention even when FlashAttention reduces memory traffic?
   • The number of model layers must double
   • Every token is compared with only one other token
   • The number of query-key pair interactions grows with the square of sequence length
   • The number of model parameters becomes zero
8. Why can FlashAttention’s results differ slightly from a reference implementation despite computing the same mathematical attention?
   • It intentionally drops all long-distance tokens
   • It changes the model weights during inference
   • It replaces softmax with a random sample
   • Floating-point operations can occur in a different order during tiled accumulation