Idanwo ti o sopọ mọ itọsọna · Lile Ipele

SwiGLU ati Idanwo Awọn iṣẹ ṣiṣe Gated

Tests the gated projection, SiLU gate, parameter-cost trade-offs and why hidden width changes when a SwiGLU replaces a standard MLP.

Jẹmọ itọsọna awọn ọnaSwiglu Ati awọn iṣẹ ṣiṣe Gated
Ibeere 1 ti 8

Iṣe mojuto wo ni asọye Ẹka Linear Gated kan?

Awotẹlẹ gbogbo ibeere8 ibeere

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

  1. Iṣe mojuto wo ni asọye Ẹka Linear Gated kan?
    • Ilọpo-ọlọgbọn eroja ti isọtẹlẹ kan nipasẹ isọtẹlẹ keji ti mu ṣiṣẹ
    • Fifi awọn asọtẹlẹ laini meji pọ
    • Concatenating akiyesi olori
    • Pipin awọn iṣẹ ṣiṣe nipasẹ iwuwasi wọn
  2. Iṣẹ imuṣiṣẹ wo ni SwiGLU lo fun ẹka ẹnu-ọna rẹ?
    • ReLU
    • Siwiṣi (SiLU)
    • Tanh
    • Softmax
  3. Awọn matiri iwuwo iwuwo melo ni idinaki ifunni siwaju SwiGLU lo?
    • Ọkan
    • Meji
    • Mẹta
    • Mẹrin
  4. Kini idi ti iwọn ti o farapamọ ṣe deede iwọn si bii ida meji ninu mẹta ni awọn fẹlẹfẹlẹ SwiGLU?
    • Lati mu imudara iye to
    • Lati ṣe awoṣe jinle
    • Lati yọ iwulo fun isọdọtun
    • Lati tọju awọn paramita lapapọ ati awọn FLOPs ni afiwe si MLP boṣewa laibikita matrix afikun
  5. Ohun-ini iyasọtọ ti Swish ni akawe si ReLU ni pe o jẹ:
    • Dan ati ti kii-monotonic, gbigba awọn odi kekere
    • Ti n pọ si ni ilodi si ati pe ni 1
    • Aami si iṣẹ sigmoid
    • Nikan ni asọye fun awọn igbewọle rere
  6. Tani o gbakiki SwiGLU fun awọn ipele ifunni-ilọsiwaju ni 2020?
    • Geoffrey Hinton
    • Noam Shazeer
    • Yan LeCun
    • Andrej Karpathy
  7. In the SwiGLU product Swish(xW) multiplied by xV, what happens when one gate value is near zero?
    • The entire Transformer layer is skipped
    • The output projection is replaced by a sigmoid
    • The matching value-branch dimension contributes little to the output
    • The value branch is permanently frozen
  8. Why can keeping a standard MLP’s hidden width unchanged increase SwiGLU cost?
    • The SiLU gate removes the output projection
    • SwiGLU computes a full Hessian
    • The value branch has no learned weights
    • SwiGLU uses an extra projection matrix, so the comparable-width design would have more parameters and FLOPs