Idanwo ti o sopọ mọ itọsọna · Lile Ipele

Iparapọ Amoye fun MoE Ṣiṣẹ adanwo

Test MoE expert placement, token dispatch and load imbalance, including how selected experts and communication affect distributed inference.

Jẹmọ itọsọna awọn ọnaIwé Parallelism Fun Moe sìn
Ibeere 1 ti 8

Kí ni iwé parallelism kaakiri kọja GPUs?

Awotẹlẹ gbogbo ibeere8 ibeere

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

  1. Kí ni iwé parallelism kaakiri kọja GPUs?
    • Awọn ẹda ti awoṣe kikun fun apọju
    • Awọn olori akiyesi ẹni kọọkan nikan
    • O yatọ si kikọ sii-siwaju amoye ti ẹya MoE Layer
    • Fẹlẹfẹlẹ ti tabili ifibọ
  2. Awoṣe ibaraẹnisọrọ wo ni o jẹ aringbungbun si ibaramu amoye MoE?
    • Paṣipaarọ gbogbo-si-gbogbo meji (firanṣẹ ati apapọ)
    • A nikan igbohunsafefe
    • Imuṣiṣẹpọ iwọn-si-ojuami
    • Ko si ibaraẹnisọrọ ti a nilo
  3. Kini idi ti MoE jẹ iṣiro-fun-aami ṣe iṣiro kekere laibikita awọn aye titobi lapapọ?
    • O compress awọn òṣuwọn to 1 bit
    • O foo awọn ipele akiyesi
    • Awọn amoye pin gbogbo awọn iwuwo wọn
    • Nikan awọn amoye ti o yan oke-k nṣiṣẹ fun ami kan
  4. Iṣoro wo ni o waye nitori ipa-ọna jẹ igbẹkẹle data?
    • bugbamu Gradient
    • Aiṣedeede fifuye, nibiti diẹ ninu awọn amoye gba awọn ami-ami pupọ diẹ sii
    • Awọn olulana ko le wa ni oṣiṣẹ
    • Àmi padanu ipo wọn ifibọ
  5. Why are capacity limits and padding used in some MoE implementations?
    • To control expert buffer sizes and make computation shapes more predictable
    • To require every expert to process every token
    • To eliminate all token routing
    • To guarantee that every MoE system drops tokens
  6. Bawo ni awọn eto ṣe tọju lairi ti gbogbo-si-gbogbo awọn igbesẹ?
    • Nipa nṣiṣẹ lori Sipiyu dipo
    • Nipa yiyọ igbese apapọ
    • Nipa jijẹ oṣuwọn ẹkọ
    • Nipa agbekọja ibaraẹnisọrọ pẹlu iširo iwé
  7. A routed token chooses two MoE experts on different devices. What must expert-parallel execution do?
    • Run every expert in the model regardless of routing
    • Dispatch its representation to the selected experts and combine their outputs
    • Replace both expert networks with an attention head
    • Drop the token solely because it selected two devices
  8. One MoE expert receives far more tokens than others. Why can the layer’s latency increase?
    • The total expert parameter count automatically doubles
    • The router’s outputs become attention positions
    • The overloaded expert becomes a straggler while other devices wait
    • All other experts are removed from memory