Idanwo ti o sopọ mọ itọsọna · Lile Ipele

Dina-Sparse ati Native Sparse akiyesi adanwo

Checks block-sparse attention, NSA’s compression-selection-window branches, hardware alignment and scaling with long sequences.

Jẹmọ itọsọna awọn ọnaDina Sparse Ati Abinibi akiyesi fọnka
Ibeere 1 ti 8

Kini idi ti akiyesi ara ẹni boṣewa jẹ gbowolori fun awọn ọkọọkan gigun?

Awotẹlẹ gbogbo ibeere8 ibeere

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

  1. Kini idi ti akiyesi ara ẹni boṣewa jẹ gbowolori fun awọn ọkọọkan gigun?
    • Iye owo rẹ dagba ni ẹẹmẹrin pẹlu gigun ọkọọkan
    • O nlo ju diẹ sile
    • Ko le ṣiṣẹ lori awọn GPU
    • O ṣe deede si ami akọkọ nikan
  2. Kini ero pataki ti akiyesi-ainidiwọn?
    • Yiyọ awọn ifunni-siwaju awọn fẹlẹfẹlẹ
    • Ifojusi iṣiro nikan laarin awọn bulọọki ti a yan ti awọn ami
    • Lilo ọkan akiyesi ori nikan
    • Ilọpo meji iwọn ifibọ
  3. Kini o jẹ ki akiyesi Native Sparse akiyesi (NSA) yatọ si ọpọlọpọ awọn ọna fọnka ti iṣaaju?
    • O yọ akiyesi kuro patapata
    • O ṣiṣẹ nikan lakoko itọkasi
    • O jẹ ikẹkọ lati opin-si-opin ati ni ibamu pẹlu ohun elo
    • Ko nilo GPU
  4. Awọn ẹka mẹta wo ni NSA darapọ fun awọn bọtini ati iye rẹ?
    • Encoder, decoder, ati ifibọ
    • Dropout, normalization, ati pooling
    • Quantization, pruning, ati distillation
    • Funmorawon, yiyan, ati ferese sisun
  5. Kilode ti NSA nlo awọn iṣẹ ipele-dina kuku ju svarsity ipele-àmi lainidii?
    • Lati ṣe deede pẹlu iraye si iranti GPU ati ilosi tensor-core fun awọn iyara gidi
    • Lati jẹ ki awoṣe naa tobi
    • Lati yago fun lilo akiyesi ni gbogbo
    • Lati din awọn nọmba ti fẹlẹfẹlẹ
  6. Lakoko iyipada aifọwọyi, akiyesi ṣoki jẹ iranlọwọ paapaa nitori igbesẹ yẹn nigbagbogbo:
    • Iṣiro-owun lori matrix isodipupo
    • Iṣiro-iranti, nitorinaa idinku awọn ami ti o wa ni iyara rẹ
    • Ominira ti ipari ọkọọkan
    • Ṣiṣe nikan lori CPUs
  7. What information does NSA’s compression branch provide to the attention computation?
    • A full dense score for every token pair
    • Only the optimizer’s first moment
    • Coarse-grained summaries of blocks of tokens
    • A random subset of output labels
  8. Why can learned sparsity patterns be more useful than a fixed arbitrary mask?
    • They remove the need to compute attention scores
    • They guarantee zero memory use
    • They force every token to attend globally
    • They can select blocks relevant to the input while remaining aligned with efficient hardware operations