Overview
Kuchekerera kwakamisikidzwa kunobvisa zvikamu zvese zveneural network, senge misoro yekutarisisa, neurons, kana yese layer, saka iyo slimmer modhi inomhanya nekukurumidza pane zvakajairika hardware. Layer kudonhedza ndiyo yakanyanya hutsinye vhezheni, ichidzima yakazara transformer mabhuroki kuti idzikame kudzika.
Structured Pruning and Layer Drop isimbi yekuvaka inobata mhando yemhando, mutengo wezvivakwa, latency, uye kuvimbika pachiyero.
Deep Dive
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).
Technical Insight
Recipe yakajairwa kudzika-kuchekerera inokwevera bhuroko rega rega nemafanana mapindiro ayo uye zvinobuda zvakavanzwa nyika: kana dhizaini rikasachinja rwizi rwasara (yakakwira cosine kufanana), iri kubatsira zvishoma uye inogona kudonhedzwa. Misoro inogona kuverengerwa nekunzwa, kuwedzera kwekurasikirwa kana yakavharwa. Mushure mekubvisa zvibodzwa zvakaderera-zvibodzwa, nhanho pfupi yedistillation inoita kuti huremu huripo hutorezve basa rezvakachekererwa uye kudzoreredza kunaka.
Mastering 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. Layer kudonhedza ndiyo yakanyanya hutsinye vhezheni, ichidzima yakazara transformer mabhuroki kuti idzikame kudzika. Structured Pruning and Layer Drop isimbi yekuvaka inobata mhando yemhando, mutengo wezvivakwa, latency, uye kuvimbika pachiyero. Kuti uvake kunzwisisa kwakadzama, tora Structured Pruning uye Layer Drop semuenzaniso wekushandisa, kwete chinhu chimwe chete: tsanangura zvinodiwa, kujekesa fungidziro, uye patsanura zvinogona kuitwa nehurongwa hwakavimbika kubva kune zvichiri kuda kutonga kwenyanzvi.
Mukuita, zvikwata zvakasimba zvinoshandisa Structured Pruning uye Layer Kudonhedza zvinogonesa zvivakwa, data, uye sarudzo dzezvivakwa zvinopesana nekuvimbika uye mutengo. Ivo vanonyora zvakajeka maitiro ebudiriro, bvunzo vachipokana ne data rechokwadi uye mafambiro ebasa, uye iterate zvichibva pane zvakacherechedzwa maitiro ekutadza kwete kuhwina-nguva imwe chete yebhenji. Apa ndipo apo kunzwisisa kwe theoretical kunoshanduka kuve kugona kwakasimba pane chigadzirwa, mutemo, uye mashandiro.
Zvisarudzo zvezvivakwa zvinotyaira kuita uye mutengo wekushandisa kwemakore. Panguva imwecheteyo, Kukwirisa imwe bhenji kunogona kuvanza yakafara system kushaya simba. Nzira yakatsiga ndeyekubatanidza kukurumidza kuyedza nekutonga: mhanyisa vatyairi vendege, tora humbowo, buritsa matanda esarudzo, uye urambe uchivandudza chengetedzo semaitiro emuenzaniso, zvinotarisirwa nemushandisi, uye zvinodikanwa zvekutonga.
Strategic Impact
Zvisarudzo zvezvivakwa zvinotyaira kuita uye mutengo wekushandisa kwemakore.
Zvisarudzo zvezvivakwa zvinotyaira kuita uye mutengo wekushandisa kwemakore. Mukutumirwa kwemhando yepamusoro, izvi zvinoshandurirwa kuita mitemo inoyerwa yekushanda, miganhu yevaridzi, uye tsika dzekudzokorora dzinodzokororwa kuitira kuti zvikwata zvikwire kuvimba pane kukwidza kusajeka.
Dzidzo yehunyanzvi inobatsira zvikwata kusarudza murwi wakakodzera, kwete iwo mutsva chete.
Dzidzo yehunyanzvi inobatsira zvikwata kusarudza murwi wakakodzera, kwete iwo mutsva chete. Mukutumirwa kwemhando yepamusoro, izvi zvinoshandurirwa kuita mitemo inoyerwa yekushanda, miganhu yevaridzi, uye tsika dzekudzokorora dzinodzokororwa kuitira kuti zvikwata zvikwire kuvimba pane kukwidza kusajeka.
Sarudzo dzeinjiniya dziri nani dzinoderedza kuvimbika zviitiko mukugadzira.
Sarudzo dzeinjiniya dziri nani dzinoderedza kuvimbika zviitiko mukugadzira. Mukutumirwa kwemhando yepamusoro, izvi zvinoshandurirwa kuita mitemo inoyerwa yekushanda, miganhu yevaridzi, uye tsika dzekudzokorora dzinodzokororwa kuitira kuti zvikwata zvikwire kuvimba pane kukwidza kusajeka.
Real-World Implementation
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
Maitiro Ekuita
Yakagadziriswa Pruning uye Layer Kudonha mukuita
Kubvisa modhi diki, inokurumidza yemudzidzi kubva kumudzidzisi mukuru nekuchekerera maseru uye kugadzirisa zvakanaka kuti udzore humbowo.
Kudhiraivha diki, inokurumidza mudzidzi modhi kubva kumudzidzisi mukuru nekuchekerera maseru uye kugadzirisa zvakanaka kuti udzore huroi Matimu anowanzo kuwana mibairo iri nani kana achinge atsanangura emhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi emupendero, uye kuteedzera zvese zvakawanikwa zvechigadzirwa nemitengo yekukanganisa nekufamba kwenguva.
Yakagadziriswa Pruning uye Layer Kudonha mukuita
Kubvisa misoro yekutarisisa mumhando yeshanduro yekucheka latency pamidziyo yemupendero.
Kubvisa misoro yekutarisisa mumhando yeshanduro yekucheka latency pamidziyo yemupendero Matimu anowanzo kuwana mibairo iri nani kana achinge atsanangura mhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi ekumucheto, uye kuteedzera zvese zvakawanikwa zvechigadzirwa nemitengo yekukanganisa nekufamba kwenguva.
Yakagadziriswa Pruning uye Layer Kudonha mukuita
Kudonhedza epamusoro transformer mabhuroki eLLM kurova yakasimba mobile inference latency target.
Kudonhedza mabhuroko epamusoro etransformer eLLM kurova yakasimba nhare inference latency tarisiro Matimu anowanzo kuwana mhedzisiro iri nani kana achinge atsanangura mhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi ekumucheto, uye kuteedzera zvese zvakawanikwa zvechigadzirwa nemitengo yekukanganisa nekufamba kwenguva.
Yakagadziriswa Pruning uye Layer Kudonha mukuita
Kugadzira mhuri yemasaizi emhando kubva kune imwe yakafanodzidziswa cheki nekuchekerera kwakasiyana kudzika uye upamhi.
Kugadzira mhuri yemasaizi emhando kubva kune imwe yakafanodzidziswa yekutarisa nekuchekerera kwakasiyana kwakadzama nehupamhi Matimu anowanzo kuwana mhedzisiro iri nani kana achinge atsanangura emhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi ekumucheto, uye kuteedzera zvese zvakawanikwa zvechigadzirwa nemitengo yekukanganisa nekufamba kwenguva.
Njodzi & Guardrails
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.
Implementation Roadmap
Tsanangura latency, mhando, uye mutengo zvinangwa usati waitwa.
Tsanangura latency, mhando, uye mutengo zvinangwa usati waitwa. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.
Benchmark pasi pechokwadi mutoro uye data mamiriro.
Benchmark pasi pechokwadi mutoro uye data mamiriro. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.
Chishandiso chekutarisa zvikanganiso, kudonha, uye mushandisi maitiro.
Chishandiso chekutarisa zvikanganiso, kudonha, uye mushandisi maitiro. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.
Gadzirira nzira dzekudzosera kumashure uye dzezviitiko usati wawedzera.
Gadzirira nzira dzekudzosera kumashure uye dzezviitiko usati wawedzera. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.