InoteveraGaidhi rinotevera
Kuchekwa kweModel
Tekinoroji
Nhungamiro yehunyanzvi
Yakamisikidzwa Kuchekerera uye Layer Kudonha
Kuchekerera kwakamisikidzwa kunobvisa zvikamu zvese zveneural network, senge misoro yekutarisisa, neurons, kana yese layer, saka iyo slimmer modhi inomhanya nekukurumidza pane zvakajairika hardware.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
Pa peji ino2 min verenga
Pfupiso
Layer kudonhedza ndiyo yakanyanya hutsinye vhezheni, ichidzima yakazara transformer mabhuroki kuti idzikame kudzika.
Kudzika Kwakadzika
Kusarongeka kuchekerera zero kunze kwehuremu hwemunhu, asi matrix izere nezero akapararira ichiri kumhanya nekumhanya kuzere paGPUs nekuti Hardware haiasvetuke. Kuchekerera kwakamisikidzwa pachinzvimbo kunobvisa zvivhariso zvakabatana, misoro yekutarisisa yese, feed-forward neurons, machani, kana maseru akazara, ayo anonyatso dhiza matensor uye achiburitsa chaiyo yekumhanyisa pasina yakakosha sparse kernels. Layer kudonhedza kunosundidzira uku zvakanyanya: kutsvagisa seLayerDrop uye gare gare kudzika-kuchekerera basa kunoratidza kuti akawanda ma transformer layer, kunyanya ari pakati nepamusoro stack, anoshamisa zvisingaite. Iwe unogona kazhinji kudzima 20 kusvika 40 muzana yezvikamu uye kudzoreredza yakawanda yakarasika yechokwadi nekupfupika kutenderera kwekugadzirisa zvakanaka kana ruzivo distillation. Kukosha kunotongwa nemametrics akadai seangular chinhambwe pakati pekupinza uye kubuda kwe layer (kuti inoshandura yakawanda sei chinomiririra).
Kuitwa muNyika yeChokwadi
Kubvisa diki, inokurumidza mudzidzi modhi kubva kumudzidzisi mukuru nekuchekerera maseru uye kugadzirisa zvakanaka kuti udzore humbowo.
Kubvisa misoro yekutarisisa mumhando yeshanduro yekucheka latency pamidziyo yemupendero
Kudonhedza epamusoro transformer mabhuroki eLLM kurova yakasimba mobile inference latency target
Kugadzira mhuri yemasaizi emhando kubva kune imwe yakafanodzidziswa cheki nekuchekerera kwakasiyana kudzika uye upamhi
Ramangwana Rokuchekerera Kwakagadzirwa uye Kudonhedza Layer
Kuchekerera kwakarongeka uye kwakadzama kwave kuve kwakajairwa kugadzira mhando dzakasiyana kubva kune imwe hombe yakadzidziswa network, sezvinoonekwa muhupamhi-uye-kudzika kuchekerera pamwe nemapaipi edistillation anotora mhando diki kubva kune hombe. Tarisira kubatanidzwa kwakasimba ne quantization uye routing, Hardware-aware pruning inonangana neaccelerator chaiyo, uye otomatiki kutsvaga inosarudza yega-yekutumirwa kudzika kana hupamhi hwekucheka kune yakapihwa latency bhajeti.
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 Structured Pruning and Layer Dropping 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 Structured Pruning uye Layer Kudonha?
Structured pruning inobvisa zvikamu zvese zveneural network, senge misoro yekutarisisa, neurons, kana maseru ese, saka iyo slimmer modhi inomhanya nekukurumidza pane zvakajairika hardware. Layer kudonhedza ndiyo yakanyanya hutsinye vhezheni, ichidzima yakazara transformer mabhuroki kuti idzikame kudzika.
Sei kuchekerera kwakarongeka kuchiburitsa kukurumidza kukuru asi kuchekerera kusina kurongeka kazhinji kusingaite?
Kubvisa misoro yese, ma neuron, kana maseru anodzoresa tension, saka yakajairwa dense Hardware inoita basa shoma, nepo mazero akapararira achiri kuverengerwa.
Chii chinonzi 'layer dropping' mumamiriro ekushandura?
Layer inodonha inobvisa yakazara transformer block, ichicheka kudzika kwetiweki, inova ndiyo yakanyanya hutsinye fomu yekuchekeresa yakarongeka.
Ndeipi chiratidzo chinowanzo ratidza kuti transformer layer inogona kudonhedzwa zvakachengeteka?
Kana dhizaini isingasandure rukova rwasara (yepamusoro pekuisa-kubuda kufanana), inopa zvishoma uye inomiririra kubviswa.
Ndeipi nhanho inowanzodzosa kurongeka mushure mekuchekerera kwakarongeka?
Pfupi-tuning kana distillation inoita kuti huremu hwasara hutorezve basa remayuniti akachekwa uye kudzoreredza mazhinji emhando yakarasika.
Misoro yetarisiro yega yega inowanzorongedzerwa sei pakuchekerera?
Kukosha kwemusoro kunofungidzirwa nekuwanda kwekurasikirwa kunokwira kana yavharwa; misoro isinganzwisisike inotemwa kutanga.
Ramba uchidzidza
Mazano anoenderana
Mamwe madhairekitori akasarudzirwa nyaya iyi