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Model pruning inodzikisira neural network nekubvisa uremu kana zvimiro zvese zvinopa zvishoma pakubuda kwayo.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
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Pfupiso
It cuts size, memory, and compute cost while aiming to keep accuracy nearly intact.
Kudzika Kwakadzika
Akadzidziswa neural network anowanzo over-parameterized: akawanda anongedzo anotakura huremu hudiki husina kukanganisa kufanotaura. Kuchekerera kunoratidza uye kubvisa izvi, zvichisiya muenzaniso wakaonda. Kuchekerera zvisina kurongeka kunobvisa huremu hwemunhu, kugadzira matrices mashoma anogona kudzvanywa zvakanyanya asi anoda maware akakosha kana maraibhurari kuti akurumidze. Kuchekerera kwakamisikidzwa kunobvisa mayuniti akazara - neurons, misoro yekutarisisa, chiteshi, kana maseru - ichipa diki dense modhi inomhanya nekukurumidza pane zvakajairika hardware. Recipe yakajairika ndeye iterative loop: chitima, chekerera zvisinganyanyi kukosha neimwe chirevo (kazhinji huremu huremu), wozonyatso gadzirisa kuti udzore yakarasika chokwadi, uchidzokorora kusvika saizi kana kukurumidza chinangwa chasangana. Kuchekerera mapairi zvakajairika ne quantization uye distillation mumapaipi ekutumirwa.
Kuitwa muNyika yeChokwadi
Kudzvanya modhi yemutauro muhombe kuti umhanye pane mutengi mumwechete GPU pachinzvimbo chesevha cluster.
Kurerutsa modhi yechiratidzo kuitira kuti ikwane mukati mendangariro ye smartphone kana yakamisikidzwa kamera.
Kubvisa misoro yekutarisisa yakawandisa kubva kuTransformer ine kudonha kudiki kunoyerwa mumhando.
Kuderedza inference simba uye latency kune yakakwira-traffic masevhisi kudzikisa mutengo wegore.
Ramangwana reMuenzaniso Kuchekerera
Pruning is increasingly applied to large language models, where structured methods can remove supported attention heads, neurons, or layers to fit models onto smaller GPUs and edge devices. Some hardware and kernels accelerate specific supported sparsity patterns, such as NVIDIA 2:4 structured sparsity; arbitrary unstructured zeros do not automatically run faster. Expect pruning to be combined with quantization and distillation in compression pipelines measured against concrete latency, energy, memory, and quality targets.
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.
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Mibvunzo inowanzo bvunzwa
What is Model Pruning?
Model pruning inodzikisira neural network nekubvisa uremu kana zvimiro zvese zvinopa zvishoma pakubuda kwayo. Iyo inocheka saizi, ndangariro, uye compute mutengo uchivavarira kuchengetedza huchokwadi hunenge hwakasimba.
What does model pruning remove from a trained network?
Kuchekerera kunoshandisa pamusoro-parameterization nekucheka uremu-mupiro wakaderera kana mayuniti kudzikisa modhi uchichengetedza yakawanda yekururama kwayo.
Chii chinosiyanisa kuchekerera kwakarongwa kubva kune zvisina kurongwa?
Kuchekerera kwakamisikidzwa kunobvisa zvikamu zvese, kuburitsa madiki madiki modhi anomhanya nekukurumidza pane yakajairwa Hardware, nepo isina kurongeka ichichekerera mazero huremu hwemunhu, ichigadzira sparsity.
Nei kuchekerera kusina kurongeka kuchiwanzotadza kumhanyisa modhi pane zvakajairika hardware?
Mazero akapararira haashandure otomatiki kumakombuta mashoma; dense hardware ichiri kuagadzirisa kunze kwekunge akashandiswa sparse kernels kana accelerators.
Chii chinowanzoitwa iterative pruning resipi?
Iterative pruning inochinjana pakati pekubvisa-yakaderera-kukosha paramita uye kunyatso-tuning kuti udzore huroyi, zvishoma nezvishoma kusvika pakukura kana kumhanya kwainotarirwa.
Chii chinorehwa neLottery Tikiti Hypothesis?
Mafungiro akacherekedza kuti yakanyatsosarudzwa sparse subnetwork ('kuhwina tikiti'), yakadzidziswa kubva pakutanga kwayo, inogona kusvika kune chokwadi cheiyo yakazara dense network.
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