Résumé
Noo ngi xaajalee, jëfandikoowaat, ak jëlaat memory bu néew bi ci GPU, ak lu tax ay bërëb yu des (fragmentation) mën na jur njuumte ci memory bi doonte memory bu bari des ci xarala. Xam ko mooy caabi ngir mëna ànd ak model yu mag yi ak moytu accident yu yéeme.
Doxal ak xaaj mémoire GPU ab bloku tabax la bu am njeexital ci kalite model bi, njëgu infrastructure bi, latency bi, ak wóor ci escale bi.
Plongeur bu xóot
Memoire GPU dafa yàgg te am solo: benn kart mën na am 24, 80, wala 192 GB ci lëmm, ñu bokk ko ci diisaayu model, aktivasioŋ, gradient, staadu optimisatër, ak tampon yu yàgg. Woo dawalkat bi ngir xaaj mémoire ci bépp liggéey dina yeex, kon kaadar yu melni PyTorch dañuy jëfandikoo ab xaaj cache buy jàpp blok yu rëy ci kanam ba noppi joxe ay piyees yu ndaw, ba noppi denc piyees yi ñu bàyyi ci piscine ngir jëfandikoowaat. Liy jàpp mooy fragmentation: ginaaw bi ñu xaajalee ay tensor yu bari te wuute, ba noppi ñu bàyyi leen, bayaal bi amul dara dafay xaajaloo nekk ay pàcc yu tasaaroo. Mën nga am 5 GB ci lëmm waaye doo mëna joxe 2 GB tensor bu laal ndax amul benn gap bu doy. Lii moo waral tàggat yaram mën na tass ak njuumte yu génn ci memory doonte daa melni am bayaal ci bopp.
Gis-gis xarala
PyTorch's CUDA cache allocator dafay xaaj mémoire bi ci ay bloc yu bari ba noppi jëfandikoowaat bloc yiñ bàyyi ñu méngoo ak dayo yiñ laaj, moytu woote cudaMalloc/cudaFree yu seer. Fragmentation dafay am sudee blok yuñ xaaj mënu ñu boolewaat. Jumtukaay yu melni torch.cuda.cache_bu amul dara, tànneef PYTORCH_CUDA_ALLOC_CONF, ak nataali mémoire yi dañuy jàppale. Xeetu jëfandikoo yu bees yi dañu leble xalaati mémoire virtuel, di boole xët physique yi nekkul ci benn rang virtuel bu jege, suko defee laaj yu bari mëna dem doonte dañu xaajaloo.
Xam ni ñuy yoree mémoire GPU ak xaaj
Noo ngi xaajalee, jëfandikoowaat, ak jëlaat memory bu néew bi ci GPU, ak lu tax ay bërëb yu des (fragmentation) mën na jur njuumte ci memory bi doonte memory bu bari des ci xarala. Xam ko mooy caabi ngir mëna ànd ak model yu mag yi ak moytu accident yu yéeme. Doxal ak xaaj mémoire GPU ab bloku tabax la bu am njeexital ci kalite model bi, njëgu infrastructure bi, latency bi, ak wóor ci escale bi. Ngir tabax xam-xam bu xóot, jàppal GPU Memory Management ak Fragmentation ni xeetu liggéey, du benn man-man: leeral njariñ yi nga bëgg, leeral xalaat yi, ak tàqale li sistem bi mëna def ci anam wu wóor ak li ba leegi soxla àtteb kàngam.
Ci jëf, ekip yu am doole yiy jëfandikoo GPU Mémoire Management ak Fragmentation dañuy gëna baaxal architecture, done, ak tànneefi infrastructure ci wàllu wóor ak njëg. Dañuy bind kritër yu leer ngir am ndam, natt leen ci done yu dëggu ak def liggéey, ba noppi ñu baamtu ci anamu ñàkka mëna seetlu, du ci benn yoon benchmark wins. Mooy barab bi xam-xam theorie bi di soppiku nekk kàttan buy yàgg ci produit yi, ci politik yi ak ci liggéey yi.
Dogal yi architecture di jël dañuy indi njariñ ak njëgu liggéey bi ay at ci ginaaw. Ci jamano jooju, Optimisation benn benchmark mën na nëbb ñakk kattan yu gëna yaatu ci sistem bi. Xeetu jëf bi gëna dëgër mooy boole gaawaayu jàngat ak disipline nguur: doxal pilote, jàpp firnde, siiwal dogal yi, ak wéy di yeesal kaaraange gi ci anam wi ñuy doxalee, li jëfandikukat bi di xaar, ak sàrti sàrt yi di jëm kanam.
njeextalu pexe
Dogal yi architecture di jël dañuy indi njariñ ak njëgu liggéey bi ay at ci ginaaw.
Dogal yi architecture di jël dañuy indi njariñ ak njëgu liggéey bi ay at ci ginaaw. Ci jëfandikoo yu am kalite bu kawe, loolu dañu koy tekki ci sàrti liggéey yuñ mëna natt, ay peggu boroom, ak ay xew-xewu xoolaat yu bari suko defee ekip yi mëna yokk wóolu seen bopp ci barabu yokk lu jaxasoo.
Njàngalem xarala yi dafay jàppale ekip yi ñu tànn li gën, te baña yam ci li gëna bees daal.
Njàngalem xarala yi dafay jàppale ekip yi ñu tànn li gën, te baña yam ci li gëna bees daal. Ci jëfandikoo yu am kalite bu kawe, loolu dañu koy tekki ci sàrti liggéey yuñ mëna natt, ay peggu boroom, ak ay xew-xewu xoolaat yu bari suko defee ekip yi mëna yokk wóolu seen bopp ci barabu yokk lu jaxasoo.
Tanneef yu gëna baax ci wàllu ingeñër dina wàññi jafe-jafe yi ci wàllu wóor ci liggéey bi.
Tanneef yu gëna baax ci wàllu ingeñër dina wàññi jafe-jafe yi ci wàllu wóor ci liggéey bi. Ci jëfandikoo yu am kalite bu kawe, loolu dañu koy tekki ci sàrti liggéey yuñ mëna natt, ay peggu boroom, ak ay xew-xewu xoolaat yu bari suko defee ekip yi mëna yokk wóolu seen bopp ci barabu yokk lu jaxasoo.
Doxal ci àdduna dëgg
Taggat yaram buy tas ak 'CUDA bi génn ci mémoire' doonte mémoire biñ denc dafay wane bayaal bu amul dara, ñu defar ko ci def PYTORCH_CUDA_ALLOC_CONF ngir mëna yokk segment yi.
Jëfandikool torch.cuda.memory_summary wala ab nataalu mémoire ngir xam ban tensor ak xaaj mooy lekk 80 GB ci GPU bi.
vLLM's PagedAttention dafay yor cache KV ci xët yu am dayo bu takku ngir mëna jàppale chat yu bari te baña yàq memory.
Wàññil dayo lots bi wala nga may poñ yiñy saytu ci gradient ngir dagg mémoire biy aktive ak moytu fragmentation biy bawoo ci paj mu génn ci mémoire bi.
Modèlu jëfandikoo
GPU jëfandikoo mémoire ak xaaj ci jëf
Taggat yaram buy tas ak 'CUDA bi génn ci mémoire' doonte mémoire biñ denc dafay wane bayaal bu amul dara, ñu defar ko ci def PYTORCH_CUDA_ALLOC_CONF ngir mëna yokk segment yi.
Taggat yaram buy daanu ak 'CUDA ci bitti mémoire' doonte mémoire biñ denc dafay wane bayaal bu amul dara, ñu defar ko ci PYTORCH_CUDA_ALLOC_CONF ngir may segment yuñ mëna yaatal.
GPU jëfandikoo mémoire ak xaaj ci jëf
Jëfandikool torch.cuda.memory_summary wala ab nataalu mémoire ngir xam ban tensor ak xaaj mooy lekk 80 GB ci GPU bi.
Jëfandikoo torch.cuda.memory_summary wala ab nataalu mémoire ngir seetlu ban tensor ak fragmentation ñooy lekk GPU's 80 GB Teams yi dañuy faral di am njariñ yu gëna baax suñu leeralee kalite ci kanam, tëye yoon wi nit ñi di yokk ngir jafe-jafe yi, ak topp gains yu njëkk yi.
GPU jëfandikoo mémoire ak xaaj ci jëf
vLLM's PagedAttention dafay yor cache KV ci xët yu am dayo bu takku ngir mëna jàppale chat yu bari te baña yàq memory.
vLLM's PagedAttention di yoriinu cache KV ci xët yu am dayo fixe ngir mëna liggéey ci laaj chat yu bari te du yàq memory. Ekip yi dañuy faral di am njariñ yu gëna baax suñu joxee thresholds yu baax ci kanam, tëye yoonu escalation nit ngir jafe-jafe yi, ak topp njuréefi produit yi ci diir bi ak e.
GPU jëfandikoo mémoire ak xaaj ci jëf
Wàññil dayo lots bi wala nga may poñ yiñy saytu ci gradient ngir dagg mémoire biy aktive ak moytu fragmentation biy bawoo ci paj mu génn ci mémoire bi.
Wàññil dayo batch bi wala may gradient checkpointing ngir dagg memory biy aktive ak moytu fragmentation-driven ci bitti memory bi ekip yi dañuy faral di am njariñ yu gëna baax suñu joxee thresholds yu baax ci kanam, tëye yoonu escalation nit ngir jafe-jafe yi, ak topp njuumte produit yi ci diir bi ak e.
Risk yi ak balustrade yi
Optimize benn benchmark mën na nëbb ñakk kattan yu gëna yaatu ci sistem bi.
Njëg li ñuy fay ci infrastructure yi ak ci toppatoo dañuy faral di suufeel.
Bu sistem yi di gëna xawa jafee xam, jafe-jafe yi am ci wàllu kaaraange ak seetlu mën nañu gëna bari.
Roadmap ngir samp gi
Mandargal latency, kalite, ak njëg yi laata ngay jëfandikoo.
Mandargal latency, kalite, ak njëg yi laata ngay jëfandikoo. Japp jéego bu nekk ni buntu firnde: sudee mattul kritër yi, noppali génne gi, tëj bërëb bi, ba noppi nga yaatal jëfandikoo gi.
Benchmark ci biir sargal ak done yu dëggu.
Benchmark ci biir sargal ak done yu dëggu. Japp jéego bu nekk ni buntu firnde: sudee mattul kritër yi, noppali génne gi, tëj bërëb bi, ba noppi nga yaatal jëfandikoo gi.
Jumtukaay bi di saytu njuumte yi, derive bi ak njeextalu jëfandikukat bi.
Jumtukaay bi di saytu njuumte yi, derive bi ak njeextalu jëfandikukat bi. Japp jéego bu nekk ni buntu firnde: sudee mattul kritër yi, noppali génne gi, tëj bërëb bi, ba noppi nga yaatal jëfandikoo gi.
Waajal rollback ak yooni tontu ci jafe-jafe yi laata ngay eskale.
Waajal rollback ak yooni tontu ci jafe-jafe yi laata ngay eskale. Japp jéego bu nekk ni buntu firnde: sudee mattul kritër yi, noppali génne gi, tëj bërëb bi, ba noppi nga yaatal jëfandikoo gi.