InoteveraGaidhi rinotevera
Kusiyanisa Kuvanzika
Tekinoroji
Nhungamiro yehunyanzvi
PagedAttention uye vLLM
PagedAttention manages transformer key/value caches using fixed-size blocks mapped from logical token positions.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
Pa peji ino2 min verenga
Pfupiso
This reduces cache-allocation waste and supports sharing, while vLLM’s actual throughput depends on the workload and system.
Kudzika Kwakadzika
Kana modhi yemutauro yaburitsa mavara, inochengeta 'KV cache' (kiyi uye kukosha mavheta) pachiratidzo chega chega chayakaona kuitira kuti chiratidzo chinotevera chigone kuenderana nemamiriro akazara. Sechivanhu chikumbiro chega chega chaichengeta imwe hombe inobatika yeGPU ndangariro yakaenzana nekureba kwayo, kutambisa huwandu hukuru apo kutevedzana kwaive kupfupi kana kusiana pakureba. PagedAttention, yakaunzwa mu2023 vLLM bepa kubva kuUC Berkeley, inokwereta iyo pfungwa yekurangarira ndangariro paging kubva kune anoshanda masisitimu: inotsemura iyo KV cache kuita yakagadziriswa-saizi mabhuroko anogona kugara chero kupi mundangariro uye kugovaniswa painodiwa. Tafura yekutarisa inoburitsa logic token positions kumabhuroko emuviri. Izvi zvinenge zvapedza kupatsanurwa kwendangariro uye zvinoita kuti zvivharo zvigoverwe, semuenzaniso pane zvakawanda zvinobuda kubva pakukurumidza kumwechete.
Kuitwa muNyika yeChokwadi
Kubata yakavhurika-sosi LLM API uko vLLM inoshandira vazhinji vanobatana vashandisi vekutaura kubva kune imwe GPU pakakwirira kuburikidza.
Kugovera hurongwa hurefu hwekukurumidza muzviuru zvezvikumbiro kuburikidza ne prefix caching saka inogadziriswa kamwe chete, kwete kudzokororwa.
Kumhanya kutsvaga danda kana akawanda masampuli ekupedzisira anogovera KV zvidhinha zveyakajairwa kukurumidza kuburikidza nekukopa-pa-kunyora.
Kucheka marara echiyeuchidzo cheGPU kubva mukutsemuka kuitira kuti mupi agone kurongedza mamwe masesheni panguva imwe chete pane imwechete hardware.
Ramangwana rePagedAttention uye vLLM
vLLM yave yakasarudzika yakavhurika-sosi inference musana, uye pfungwa dzePagedAttention zvino dzava kuoneka pane akawanda anoshumira stacks. Tarisira zvakadzika prefix caching (kushandisazve cached system zvinokurudzira vashandisi vese), prefill yakapatsanurwa uye decode pamichina yakaparadzana, mitemo yekudzinga yakangwara, uye kubatana kwakasimba nehuwandu uye kufungidzira decoding. Sezvo mahwindo emukati achikura kuita mamirioni ezviratidzo, inoshanda peji yeKV manejimendi inova yakanyanya pakati pekuchengetedza kushumira kuchikwanisika.
Kuzvishandisa
Nei zvichikosha
- Mutengo uye bhajeti. Zvisarudzo zvezvivakwa zvinotyaira kuita uye mutengo wekushandisa kwemakore.
- Sarudzo dzakajeka. Dzidzo yehunyanzvi inobatsira zvikwata kusarudza murwi wakakodzera, kwete iwo mutsva chete.
- Kudzora kwemhando yepamusoro. Sarudzo dzeinjiniya dziri nani dzinoderedza zviitiko zvekuvimbika mukugadzira.
Chenjera
- Kugadzirisa imwe bhenji kunogona kuvanza yakafara system kushaya simba.
- Infrastructure uye mari yekugadzirisa inowanzotarisirwa pasi.
- Chengetedzo uye kucherechedzwa mapundu anogona kukura sezvo masisitimu anowedzera kuoma.
Matanho anotevera
- Tsanangura latency, mhando, uye mutengo zvinangwa usati waitwa.
- Benchmark pasi pechokwadi mutoro uye data mamiriro.
- Chishandiso chekutarisa zvikanganiso, kudonha, uye mushandisi maitiro.
- Gadzirira nzira dzekudzosera kumashure uye dzezviitiko usati wawedzera.
Ramba Uchiongorora
Tsamba yemahara
Tora iyo yemazuva ese AI pfupiso
Nhatu dzakasimbiswa dzeAI nyaya mangwanani ega ega pakati pevhiki, dzakanyorwa neChirungu chakajeka. Yemahara zvachose, hapana ads.
Imwe email zuva rega rega revhiki. Bvisa kunyoresa kamwe chete. Hatimbotengese kana kugovera kero yako.
Zviedze iwe pachako
Take the PagedAttention and vLLM quiz
Mhinduro yepakarepo pamhinduro yega yega, uye chitupa chinogovaniswa chine ID inobatika kana wapedza kosi.
Tsigira dzidzo yemahara yeAI. AI Understanding ndeye 501(c)(3) isingabatsiri - hapana ads, hapana paywall, zvachose. Ita chipo
Mibvunzo inowanzo bvunzwa
Chii chinonzi PagedAttention uye vLLM?
PagedAttention manages transformer key/value caches using fixed-size blocks mapped from logical token positions. This reduces cache-allocation waste and supports sharing, while vLLM’s actual throughput depends on the workload and system.
Chii chinochengeterwa 'KV cache' panguva yekugadzira zvinyorwa?
Iyo KV cache inobata kiyi uye inokoshesa mavheji kune yega yega chiratidzo, ichirega modhi ichipinda kune yakazara mamiriro pasina kuidzokorora nhanho imwe neimwe.
Ndeipi inoshanda-system pfungwa yakafuridzira PagedAttention?
PagedAttention inokwereta chaiyo yekurangarira paging: iyo KV cache yakakamurwa kuita yakagadziriswa-saizi mapeji anogona kugara chero kupi, akamepurwa nebhuroka tafura.
Ndeipi dambudziko neyakagoverwa yeKV cache inogadziriswa PagedAttention?
Kuchengeta imwe hombe contiguous slab pachikumbiro, saizi yehurefu hwemax, yakapambadza huwandu hukuru hweGPU ndangariro. Paging inogovera zvidhinha zvidiki pane zvinodiwa, kucheka marara.
Sei PagedAttention inorega akawanda anobuda achigovera ndangariro kune yakajairwa kukurumidza?
Zvakafanana prefixes (kukurudzira kwakagovaniswa kana matavi ekutsvagisa danda) anonongedza iwo mapeji eKV chaiwo, achikopa chete kana bazi rasiyana.
VLLM nePagedAttention zvakagadziridzwa kupi?
vLLM uye iyo PagedAttention algorithm yakabuda muUC Berkeley mubepa ra2023 uye nekukasira ikava injini inoshandiswa zvakanyanya yakavhurika sosi.
Ramba uchidzidza
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