Up tókànItọsọna atẹle
Ifarabalẹ Yipada ati Ori Pruning
Imọ-ẹrọ
Imọ Itọsọna
Ifarabalẹ Flash
FlashAttention jẹ́ ọ̀nà kan tí ó mọ̀ nípa IO láti ṣírò àfiyèsí (attention) gangan láìnílò láti kó gbogbo àtòjọ àfiyèsí náà sínú ìrántí (memory) tí ó ní agbára gíga.
Gbọ si itọsọna yiiAI-ṣe afihan · ~2 min
Lori iwe yi2 min ka
Akopọ
It uses tiling and online softmax to reduce memory traffic; speed and memory gains depend on sequence length, hardware and implementation.
Jin Dive
Standard attention compares every token to every other token, producing an N-by-N score matrix that grows quadratically with sequence length. Naively, that matrix is written to and read back from GPU high-bandwidth memory (HBM), and that shuttling — not the multiplications — is the real bottleneck. Flash Attention, introduced by Tri Dao and colleagues in 2022, reorganizes the computation so the matrix is never fully stored. It processes queries, keys, and values in small tiles that fit in fast on-chip SRAM, computes partial results, and stitches them together using an online running-softmax trick. The algorithm computes the same attention operation while reducing high-bandwidth-memory traffic and intermediate storage. It can be faster, especially on long sequences, but measured gains depend on GPU, sequence length, dtype and implementation.
Real-World imuse
Ikẹkọ awọn awoṣe ede nla bii Llama ati awọn ọna ṣiṣe GPT pẹlu awọn window ọrọ to gun ni idiyele iranti kekere.
Ṣiṣẹ awọn oluranlọwọ iwiregbe ni iyara nipasẹ iyara soke ipele iṣaju nibiti a ti ka itọsi gigun kan ni akọkọ.
Ṣiṣe awọn irinṣẹ-itupalẹ iwe-ipamọ ti o nwọle gbogbo awọn iwe tabi awọn koodu koodu nipa ṣiṣe akiyesi ọkọọkan gigun ni o ṣeeṣe lori GPU kan.
Iran agbara ati Awọn Ayirapada ohun nibiti awọn igbewọle ti o ga-giga ṣẹda awọn ilana ami gigun pupọ.
Ojo iwaju ti akiyesi Flash
FlashAttention-2 and FlashAttention-3 research explores improved work partitioning, asynchronous execution, and hardware-specific low-precision paths. Results depend on the GPU, sequence length, dtype and implementation. These algorithms can reduce memory traffic for dense attention, but they do not remove the underlying query-key pair computations. Benchmark the exact training or serving workload before assuming a speedup.
Fifi o sinu iwa
Kini idi ti o ṣe pataki
- Iye owo ati isuna. Awọn ipinnu faaji ṣe awakọ iṣẹ ati idiyele iṣẹ fun awọn ọdun.
- Awọn ipinnu diẹ sii. Ẹkọ imọ-ẹrọ ṣe iranlọwọ fun awọn ẹgbẹ lati yan akopọ to tọ, kii ṣe ọkan tuntun nikan.
- Iṣakoso didara. Awọn yiyan imọ-ẹrọ to dara julọ dinku awọn iṣẹlẹ igbẹkẹle ni iṣelọpọ.
Ṣọra fun
- Ṣiṣepe ala-ilẹ kan le tọju awọn ailagbara eto ti o gbooro.
- Awọn ohun elo amayederun ati awọn idiyele itọju nigbagbogbo ni aibikita.
- Aabo ati awọn ela akiyesi le dagba bi awọn eto ṣe di eka sii.
Next awọn igbesẹ
- Ṣetumo lairi, didara, ati awọn ibi-afẹde idiyele ṣaaju imuse.
- Aṣepari labẹ ẹru ojulowo ati awọn ipo data.
- Abojuto ohun elo fun awọn aṣiṣe, fiseete, ati ipa olumulo.
- Mura ipadasẹhin pada ati awọn ipa ọna esi iṣẹlẹ ṣaaju iwọn.
Tesiwaju Ṣiṣawari
Ìwé ìròyìn ọfẹ
Gba ìkànìyàn AI ojoojúmọ́
Ìtàn AI mẹta tó jẹ́rìí ní gbogbo owurọ́ ọjọ́ iṣẹ́, kọ́wé ní Gẹ̀ẹ́sì tó rọrùn. Ọfẹ́ fún gbogbo àkókò, kò sí ìpolówó.
Ìmeèlì kan ní gbogbo ọjọ́ iṣẹ́. Yọkúrò ní ìkànìyàn ní ìkànìyàn kan. A kò ta tàbí pín àdírẹ́sì rẹ.
Ṣe idanwo fun ara rẹ
Take the Flash Attention quiz
Ìdáhùn lẹ́sẹ̀kẹsẹ̀ lórí gbogbo ìdáhùn, àti iwe-ẹ̀rí tó lè pin pẹ̀lú ID tó lè jẹ́rìí lẹ́yìn tí o bá kọ́ ẹ̀kọ́.
Ṣe àtìlẹ́yìn ẹ̀kọ́ AI ọfẹ́. AI Understanding jẹ́ àjọ àìní èrè 501(c)(3) — kò sí ìpolówó, kò sí àkúnya owó, ní gbogbo àkókò. Ṣe ẹbun kan
Awọn ibeere ti a beere nigbagbogbo
Kini akiyesi Flash?
FlashAttention is an IO-aware way to compute exact attention without materializing the full attention matrix in high-bandwidth memory. It uses tiling and online softmax to reduce memory traffic; speed and memory gains depend on sequence length, hardware and implementation.
Kini igo akọkọ ti Ifarabalẹ Flash fojusi?
Ifarabalẹ Filaṣi jẹ IO-mọ: o dinku data ti o pa laarin SRAM on-chip yiyara ati iranti bandiwidi giga ti o lọra, eyiti o jẹ igo gidi kuku ju iṣiro funrararẹ.
Bawo ni Ifarabalẹ Filaṣi ṣe yago fun titoju matrix akiyesi N-by-N ni kikun?
O ṣe iṣiro iṣiro naa nitorinaa bulọọki kọọkan baamu ni SRAM iyara, ṣiṣe iṣiro ati ikojọpọ awọn abajade apa kan laisi ohun elo gbogbo matrix ni HBM.
Ilana wo ni o jẹ ki Ifarabalẹ Flash ṣe iṣiro softmax ni deede laisi ri gbogbo ila ni ẹẹkan?
softmax ori ayelujara n tọju iwọn ṣiṣe ti o pọju ati iyeida ṣiṣiṣẹ lakoko awọn alẹmọ ṣiṣanwọle, tun ṣe awọn abajade apa kan ni iṣaaju ki isọdọtun ipari jẹ deede.
Kini idi ti Ifarabalẹ Filaṣi ṣe iranlọwọ pupọ julọ pẹlu awọn ilana gigun?
Awọn iwọn matrix akiyesi aifọwọyi bi N-squared ni iranti, nitorinaa yago fun ibi ipamọ ni kikun n jẹ ki awọn ifowopamọ ti o tobi julọ ni deede nigbati awọn ilana ba gun.
Kini apẹrẹ 'IO-mọ' ti akiyesi Flash ṣe pataki idinku?
IO-mọ tumọ si pe algorithm jẹ apẹrẹ ni ayika idiyele gbigbe data ni awọn ipo iranti, idinku ijabọ HBM kuku ju awọn iṣẹ ṣiṣe iṣiro.
Tesiwaju kikọ
Jẹmọ awọn itọsọna
Awọn itọsọna diẹ sii ti a yan fun koko yii