Overview
GPTQ neAWQ inzira mbiri dzinotungamira dzekudzikisa mamodheru emitauro yakatodzidziswa kuenda ku4-bit chaiyo saka inomhanya pane yakachipa, diki hardware. Ndosaka iwe uchigona kumhanyisa modhi inokwanisa pane imwechete mutengi GPU panzvimbo yedatacenter rack.
GPTQ uye AWQ Post-Training Quantization inyanzvi yekuvaka inobata mhando yemhando, mutengo wezvivakwa, latency, uye kuvimbika pachiyero.
Deep Dive
Post-training quantization (PTQ) inodzvanya modhi yakapedzwa isina kuidzidzirazve, kugadzira maremu epamusoro-soro kudzika kusvika ku4 bits kusvika kukota yendangariro. Dambudziko nderekuita izvi pasina kukanganisa chokwadi. GPTQ (kunatsa kweOBQ) inoyera huremu dhizaini nedhizari, uchishandisa yechipiri-odha ruzivo kubva kudiki rekodhi dataset kugadzirisa huremu hwasara uye kubhadhara kukanganisa kwega kwega kutenderedza. AWQ (Activation-aware Weight Quantization) inotora imwe kona: inoona kuti chikamu chidiki chehuremu machanera chakakosha zvisingaenzaniswi, chinoonekwa nekutarisa activation magnitudes, uye inodzivirira idzo nzira dzakasimba nekuyera pane kudziisa zvine hukasha. Ose ari maviri anorega mamodheru akaita seLlama achimhanya mu4-bit, uye zvishandiso zvakaita sevLLM, llama.cpp, uye AutoGPTQ zvaita kuti ive huru kune yemuno uye inodhura-inoshanda inference.
Technical Insight
GPTQ inoshandisa fungidziro yeHessian (curvature yekurasikirwa) kusarudza kuti kutenderedza huremu humwe hunofanira kukwenya vamwe sei, kuderedza chikanganiso chakaunzwa. AWQ inosvetukira maHessians zvachose: inoverengera imwe-chiteshi kuyera chinhu kuitira kuti huremu hwakakosha huchengetedze huremu hwahwo hunoshanda, hwobva hwawedzera zvakafanana. Ose ari maviri anochengeta ma activation ari muhuremu hwepamusoro uye anongomanikidza uremu, sezvo uremu huchitonga ndangariro nepo activation quantization inokuvadza kurongeka zvakanyanya.
Mastering GPTQ uye AWQ Post-Kudzidzisa Quantization
GPTQ neAWQ inzira mbiri dzinotungamira dzekudzikisa mamodheru emitauro yakatodzidziswa kuenda ku4-bit chaiyo saka inomhanya pane yakachipa, diki hardware. Ndosaka iwe uchigona kumhanyisa modhi inokwanisa pane imwechete mutengi GPU panzvimbo yedatacenter rack. GPTQ uye AWQ Post-Training Quantization inyanzvi yekuvaka inobata mhando yemhando, mutengo wezvivakwa, latency, uye kuvimbika pachiyero. Kuti uvake kunzwisisa kwakadzama, bata GPTQ uye AWQ Post-Training Quantization semuenzaniso wekushandisa, kwete chinhu chimwe chete: tsanangura zvinodikanwa, kujekesa fungidziro, uye patsanura zvinogona kuitwa nehurongwa hwakavimbika kubva kune zvichiri kuda kutonga kwenyanzvi.
Mukuita, zvikwata zvakasimba zvinoshandisa GPTQ uye AWQ Post-Training Quantization inokwidziridza 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 zviitiko zvekuvimbika mukugadzira.
Sarudzo dzeinjiniya dziri nani dzinoderedza zviitiko zvekuvimbika 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
Kumhanyisa 70-bhiriyoni-parameter Llama modhi pane imwechete 24 GB mutengi GPU uchishandisa 4-bit GPTQ huremu.
AWQ-yakaenzana modhi inoshandirwa pakubuda kwepamusoro muvLLM kune inodhura-inoshanda kugadzira APIs.
llama.cpp uchishandisa maremu eGGUF akaverengerwa kumhanyisa mamodheru emitauro munharaunda palaptop yeCPU.
Hugging Face's AutoGPTQ uye AutoAWQ maraibhurari achibvumira vanogadzira kuyera modhi yakatorwa mumitsetse mishoma yekodhi.
Maitiro Ekuita
GPTQ uye AWQ Post-Training Quantization mukuita
Kumhanyisa 70-bhiriyoni-parameter Llama modhi pane imwechete 24 GB mutengi GPU uchishandisa 4-bit GPTQ huremu.
Kumhanyisa 70-bhiriyoni-parameta Llama modhi pane imwechete 24 GB mutengi GPU uchishandisa 4-bit GPTQ uremu Matimu anowanzo kuwana mhedzisiro iri nani kana achinge atsanangura emhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi emupendero, uye kuteedzera zvese zvakawanikwa zvekubereka uye kukanganisa mutengo nekufamba kwenguva.
GPTQ uye AWQ Post-Training Quantization mukuita
AWQ-yakaenzana modhi inoshandirwa pakubuda kwepamusoro muvLLM kune inodhura-inoshanda kugadzira APIs.
AWQ-yakaenzana modhi inoshandirwa nepamusoro-soro muvLLM yekugadzira-inodhura-inoshanda APIs ekugadzira Matimu anowanzo kuwana mhedzisiro iri nani kana achinge atsanangura emhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi emupendero, uye kuteedzera zvese zvakawanikwa zvechigadzirwa uye mutengo wekukanganisa nekufamba kwenguva.
GPTQ uye AWQ Post-Training Quantization mukuita
llama.cpp uchishandisa maremu eGGUF akaverengerwa kumhanyisa mamodheru emitauro munharaunda palaptop yeCPU.
llama.cpp uchishandisa huremu hweGGUF kumhanyisa mamodheru emitauro munharaunda palaptop CPU Matimu anowanzo kuwana mibairo iri nani kana achinge atsanangura hunhu hwepamberi, chengetedza nzira yekukwira kwevanhu yemakesi ekumucheto, uye kuteedzera zvese zvakawanikwa zvechigadzirwa nemitengo yekukanganisa nekufamba kwenguva.
GPTQ uye AWQ Post-Training Quantization mukuita
Hugging Face's AutoGPTQ uye AutoAWQ maraibhurari achibvumira vanogadzira kuyera modhi yakatorwa mumitsetse mishoma yekodhi.
Hugging Face's AutoGPTQ uye AutoAWQ maraibhurari achirega vanogadzira kuyera modhi yakadhindwa mumitsara mishoma yekodhi Matimu anowanzo kuwana mhedzisiro iri nani kana vachinge vatsanangura mhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi emupendero, 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.