I-Multi-Head Latent Attention
I-Multi-Head Latent Attention (MLA) iyindlela yokunaka, eyethulwe ku-DeepSeek-V2, ecindezela inqolobane yenani lokhiye olambele inkumbulo ibe ivekhtha encane ecashile okwabelwana ngayo.
Uhlolojikelele
It lets large language models run with far less GPU memory while keeping quality close to standard attention.
I-Deep Dive
Uma i-transformer ikhiqiza umbhalo, igcina ukhiye kanye nevekhtha yenani yawo wonke amathokheni adlule 'kunqolobane ye-KV.' Leyo nqolobane ikhula ngobude bomongo futhi ibusa ukusetshenziswa kwememori ngesikhathi sokunquma. I-MLA ithatha indawo yamavektha kakhiye/inani amaningi anosayizi ogcwele ngethokheni eyodwa enezinga eliphansi elicashile ngethokheni, bese iphrojekthi elele emuva kokhiye bekhanda ngalinye kanye namanani lapho undiza. Ngenxa yokuthi i-compact latent kuphela egcinwe kunqolobane, i-DeepSeek-V2 ibike ukusika inkumbulo yenqolobane ye-KV ngaphezu kuka-90% uma kuqhathaniswa nokunaka okujwayelekile kwamakhanda amaningi, okuvumela izimo ezinde namasayizi amaqoqo amakhulu. Ngokudabukisayo, ama-matrices e-up-projection angagoqwa abe ezinye izisindo, ngakho-ke i-MLA ifinyelela lokhu kucindezelwa ngokulahleka okuncane noma okungekho okulinganisekayo kwikhwalithi yokumodela.
I-Technical Insight
I-MLA yenza ukuminyaniswa okuhlanganyelwe kwezinga eliphansi: isimo esifihliwe sethokheni ngayinye siboniswa phansi ku-vector encane ecashile, futhi ahlukanise ama-matrices akhuphukayo akha kabusha okhiye bekhanda ngalinye kanye namanani. Iqhinga elihlakaniphile 'ukumunca' izisindo ezikhuphukayo embuzweni nasekuqageleni okukhiphayo, ngakho imodeli ayilokothi isebenzise okhiye/amanani agcwele ngesikhathi sokuqagela. Ukushumeka kwendawo ejikelezayo kusingathwa ngokhiye onqanyuliwe, njengoba ukuzungezisa kungenakumuncwa ngendlela efanayo, kulondoloza ulwazi lwendawo.
I-Strategic Impact
Isivinini nesikali
Ukugeleza komsebenzi wolimi kungahamba ngokushesha ngaphandle kokudela ukuvumelana.
Finyelela futhi ufinyelele
Yandisa ukufinyelela kuzo zonke izilimi nezitayela zokuxhumana.
Izinqumo ezicacile
Amaqembu angachitha isikhathi esiningi ekwahluleleni kuyilapho i-automation isingatha impinda.
Ikusasa Le-Multi-Head Latent Attention
I-MLA isize ukwenza i-DeepSeek-V2 ne-V3 ibe ukonga ukuze isebenze ngezinga eliphezulu, futhi indlela iyasabalala njengoba amaqembu ejaha ukusho okushibhile kokuqukethwe okude. Lindela ukuminyanisa okufihlekile kwesitayela se-MLA ukuze kuhlanganiswe nezendlalelo Ezincane Zengxube-yezazi, izinqolobane ezibaliwe, nokukhishwa kwamakhodi okuqagelayo kumamodeli avulekile wesikhathi esizayo. Abacwaningi futhi bahlola ukuthi ubukhulu obufihlekile bungancipha kangakanani ngaphambi kokuba ikhwalithi yehle, nokuthi umbono ofanayo wezinga eliphansi ungacindezela ukunaka ngesikhathi sokuqeqeshwa, hhayi nje ukucabangela.
Ukuqaliswa Komhlaba Wangempela
Ukukhonza amamodeli engxoxo e-DeepSeek-V2/V3 anemigqa yenkumbulo emincane kakhulu ye-GPU ngesicelo ngasinye
Usebenzisa umbuzo wedokhumenti omude uphendula lapho inqolobane enkulu ye-KV ingaqeda i-VRAM
Ukwenyusa usayizi wenqwaba ye-inference ku-GPU engashintshi ngoba ukulandelana ngakunye kugcina ivekhtha encane ecashile
Inika amandla amafasitela womongo omude kuzingxenyekazi zekhompuyutha zempahla ukuze uthole abasizi abakhulisiwe
Izingozi & Guardrails
Amaqiniso akhonjiwe angafaka ngokuthula imibiko, ukugeleza kosekelo, noma imiphumela yocwaningo.
Ukuzwela okusheshayo kungadala imiphumela engahambisani kuzo zonke izicelo ezifanayo.
Idatha yombhalo ebucayi ingase idalulwe uma izilawuli zokufinyelela zibuthakathaka.
Ukuqalisa Umhlahlandlela
Chaza ifomethi yokuphumayo, ithoni, namazinga wekhwalithi ngaphambi kokukhishwa.
Izimpendulo eziyisisekelo ngemithombo ethembekile noma nini lapho ukunemba kubalulekile.
Gcina indawo yokuhlola isibuyekezo somuntu ukuze uthole imiphumela ephezulu.
Landela amaphethini okuhluleka futhi uqeqeshe kabusha imiyalo noma ukuhamba komsebenzi njalo.
Qhubeka Uhlole
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What is Multi-Head Latent Attention?
I-Multi-Head Latent Attention (MLA) iyindlela yokunaka, eyethulwe ku-DeepSeek-V2, ecindezela inqolobane yenani lokhiye olambele inkumbulo ibe ivekhtha encane ecashile okwabelwana ngayo. Ivumela amamodeli olimi amakhulu ukuthi asebenze ngememori ye-GPU encane kakhulu kuyilapho igcina ikhwalithi iseduze nokunakwa okujwayelekile.
Iyiphi inkinga eyinhloko Ukunakwa kwe-Multi-Head Latent yakhelwe ukunciphisa?
I-MLA iqondise kunqolobane ye-KV, ekhula ngobude bomongo futhi ilawule inkumbulo phakathi nokukhiqizwa kombhalo.
I-MLA iyinciphisa kanjani inqolobane ye-KV?
I-MLA igcina i-vector eyodwa ecashile eyodwa ngethokheni futhi yakhe kabusha okhiye namanani asuka kuyo ngokusebenzisa i-up-projection.
Iyiphi imodeli eyethula okokuqala i-Multi-Head Latent Attention?
I-MLA yethulwa yi-DeepSeek kumodeli yayo ye-DeepSeek-V2 futhi yafakwa ku-DeepSeek-V3.
Kungani i-MLA idinga indlela 'ehlukanisiwe' ehlukile yokushumeka kwesimo se-rotary?
Ukuguqulwa kwe-rotary akukwazi ukumuncwa kwamanye ama-matrices esisindo, ngakho-ke i-MLA igcina ingxenye yokhiye ehlukanisiwe ukuze iphathe ulwazi lwendawo.
Cishe ingakanani ukuncishiswa kwenkumbulo ye-KV-cache eyenziwe yi-DeepSeek-V2 evela ku-MLA ngokumelene nokunaka okujwayelekile kwamakhanda amaningi?
I-DeepSeek-V2 ibike ukusika inkumbulo yenqolobane ye-KV ngaphezu kuka-90%, ivumela izimo ezinde namaqoqo amakhulu.