Guide-yakabatana mibvunzo · Zvakaoma Nhanho

Block-Sparse uye Native Sparse Attention Quiz

Inotarisa block-sparse kutarisisa, NSA's compression-selection-window mapazi, hardware alignment uye kuyera nekutevedzana kwakareba.

Nzira dzekutungamira dzinoenderanaVimba Sparse Uye Native Sparse Attention
Mubvunzo 1 ye 8

Sei kuzvitarisira kwakajairika kuchidhura kune dzakatevedzana dzakareba?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  1. Sei kuzvitarisira kwakajairika kuchidhura kune dzakatevedzana dzakareba?
    • Mutengo wayo unokura quadratically nekutevedzana kureba
    • Inoshandisa mashoma parameters
    • Haikwanise kumhanya paGPUs
    • Inongoteerera kune yekutanga chiratidzo
  2. Ndeipi pfungwa huru ye block-sparse attention?
    • Kubvisa mafeed-forward layers
    • Kuverengera kutarisa chete pakati pezvivharo zvakasarudzwa zvetokens
    • Kushandisa musoro mumwe chete
    • Kupeta kaviri dimension yekumisikidza
  3. Chii chinoita kuti Native Sparse Attention (NSA) ive mutsauko kubva kune dzakawanda dzakambopfuura nzira shoma?
    • Inobvisa kutarisa zvachose
    • Inoshanda chete panguva yekufungidzira
    • Iyo inodzidziswa kuguma-kusvika-kumagumo uye hardware-yakarongeka
    • Izvo hazvidi GPU
  4. Ndeapi mapazi matatu anosanganisirwa neNSA makiyi nehunhu hwayo?
    • Encoder, decoder, uye embedding
    • Dropout, normalization, uye pooling
    • Quantization, kuchekerera, uye distillation
    • Kudzvanya, kusarudzwa, uye hwindo rinotsvedza
  5. Nei NSA ichishandisa block-level mashandiro pane zvekupokana token-level sparsity?
    • Kuenderana neGPU ndangariro yekuwana uye tensor-core throughput yekumhanyisa chaiko
    • Kuita kuti modhi ive yakakura
    • Kudzivisa kushandisa pfungwa zvachose
    • Kuderedza kuwanda kwezvikamu
  6. Munguva ye autoregressive decoding, kutarisisa kushoma kunonyanya kubatsira nekuti danho iro rinowanzo:
    • Kombuta-yakasungwa pamatrix inowanda
    • Memory-yakasungwa, saka kudzikisira kune ma tokeni kunomhanyisa
    • Yakazvimirira yekutevedzana kureba
    • Mhanya chete paCPUs
  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