{"id":107177,"date":"2023-03-10T12:48:22","date_gmt":"2023-03-10T10:48:23","guid":{"rendered":"https:\/\/prohoster.info\/?p=107177"},"modified":"2023-03-11T11:43:26","modified_gmt":"2023-03-11T09:43:26","slug":"predstavlen-openxla-instrumentarij-dlya-optimizaczii-i-kompilyaczii-modelej-mashinnogo-obucheniya","status":"publish","type":"post","link":"https:\/\/prohoster.info\/et\/blog\/news\/predstavlen-openxla-instrumentarij-dlya-optimizaczii-i-kompilyaczii-modelej-mashinnogo-obucheniya","title":{"rendered":"Esitatud on OpenXLA, t\u00f6\u00f6riistakomplekt masin\u00f5ppemudelite optimeerimiseks ja kompileerimiseks.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Suured masin\u00f5ppe arendamisega tegelevad ettev\u00f5tted on k\u00e4ivitanud projekti OpenXLA, mille eesm\u00e4rk on \u00fchendada j\u00f5ud t\u00f6\u00f6riistade arendamisel mudelite kompileerimise ja optimeerimise jaoks masin\u00f5ppe s\u00fcsteemides. Projekti all on v\u00e4lja t\u00f6\u00f6tatud t\u00f6\u00f6riistad, mis v\u00f5imaldavad \u00fchtlustada mudelite kompileerimist, mis on loodud TensorFlow, PyTorch ja JAX raamistikes, et t\u00f5husalt koolitada ja k\u00e4itada erinevates GPU-s ja spetsialiseeritud kiirendites. Projekti on kaasatud sellised ettev\u00f5tted nagu Google, NVIDIA, AMD, Intel, Meta, Apple, Arm, Alibaba ja Amazon.    <\/p>\n<p>Oodatakse, et juhtivate teadusmeeskondade ja kogukonna liikmete koost\u00f6\u00f6 tulemusena \u00f5nnestub edendada masin\u00f5ppes\u00fcsteemide arengut ja lahendada probleem, mis on seotud erinevate raamistike ja riistvara infrastruktuuri fragmentatsiooniga. OpenXLA v\u00f5imaldab rakendada t\u00f5husat tuge erinevale riistvarale, olenemata sellest, millises raamistikus on masin\u00f5ppe mudel loodud. Oodatakse, et OpenXLA abil \u00f5nnestub v\u00e4hendada mudelite koolitamise aega, suurendada l\u00e4bilaskev\u00f5imet, v\u00e4hendada viivitusi, alandada arvutusressursside kulusid ja l\u00fchendada toote turuletoomise aega.    <center><img decoding=\"async\" alt=\"Esitatud on OpenXLA, t\u00f6\u00f6riistakomplekt masin\u00f5ppemudelite optimeerimiseks ja kompileerimiseks.\" src=\"\/wp-content\/uploads\/2023\/03\/b684d2f57def6348f6a0037c36baf717.jpg\" style=\"display:block;margin: 0 auto;\" \/><\/center>      <\/p>\n<p>OpenXLA koosneb kolmest p\u00f5hikomponendist, mille kood on jagatud Apache 2.0 litsentsi all:  <\/p>\n<ul>\n<li class=\"l\"> XLA  (Accelerated Linear Algebra) &#8212; \u043a\u043e\u043c\u043f\u0438\u043b\u044f\u0442\u043e\u0440, \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u044e\u0449\u0438\u0439 \u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u043c\u043e\u0434\u0435\u043b\u0438 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0434\u043b\u044f \u0432\u044b\u0441\u043e\u043a\u043e\u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0433\u043e \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u044f \u043d\u0430 \u0440\u0430\u0437\u043d\u044b\u0445  \u0430\u043f\u043f\u0430\u0440\u0430\u0442\u043d\u044b\u0445 \u043f\u043b\u0430\u0442\u0444\u043e\u0440\u043c\u0430\u0445, \u0432\u043a\u043b\u044e\u0447\u0430\u044f GPU, CPU  \u0438 \u0441\u043f\u0435\u0446\u0438\u0430\u043b\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0435 \u0443\u0441\u043a\u043e\u0440\u0438\u0442\u0435\u043b\u0438 \u043e\u0442 \u0440\u0430\u0437\u043b\u0438\u0447\u043d\u044b\u0445 \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u0435\u0439.\n<li class=\"l\"> StableHLO &#8212; \u0441\u043f\u0435\u0446\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u044f \u0438 \u0431\u0430\u0437\u043e\u0432\u0430\u044f \u0440\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f \u043d\u0430\u0431\u043e\u0440\u0430 \u0432\u044b\u0441\u043e\u043a\u043e\u0443\u0440\u043e\u0432\u043d\u0435\u0432\u044b\u0445 \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0439 (HLO, High-Level Operations) \u0434\u043b\u044f \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044f \u0432 \u043c\u043e\u0434\u0435\u043b\u044f\u0445 \u0441\u0438\u0441\u0442\u0435\u043c \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f. \u0412\u044b\u0441\u0442\u0443\u043f\u0430\u0435\u0442 \u043f\u0440\u043e\u0441\u043b\u043e\u0439\u043a\u043e\u0439 \u043c\u0435\u0436\u0434\u0443 \u0444\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a\u0430\u043c\u0438 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0438 \u043a\u043e\u043c\u043f\u0438\u043b\u044f\u0442\u043e\u0440\u0430\u043c\u0438, \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u0443\u044e\u0449\u0438\u043c\u0438 \u043c\u043e\u0434\u0435\u043b\u044c \u0434\u043b\u044f \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u044f \u043d\u0430 \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u043e\u043c \u043e\u0431\u043e\u0440\u0443\u0434\u043e\u0432\u0430\u043d\u0438\u0438.  \u041f\u0440\u043e\u0441\u043b\u043e\u0439\u043a\u0438 \u0434\u043b\u044f \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u0438 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0432 \u0444\u043e\u0440\u043c\u0430\u0442\u0435 StableHLO \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043b\u0435\u043d\u044b \u0434\u043b\u044f \u0444\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a\u043e\u0432 PyTorch, TensorFlow \u0438 JAX. \u0412 \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u0435 \u043e\u0441\u043d\u043e\u0432\u044b \u0434\u043b\u044f StableHLO \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d \u043d\u0430\u0431\u043e\u0440 MHLO, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0440\u0430\u0441\u0448\u0438\u0440\u0435\u043d \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u043e\u0439 \u0441\u0435\u0440\u0438\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0438 \u0438 \u0432\u0435\u0440\u0441\u0438\u043e\u043d\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f.\n<li class=\"l\"> IREE (Intermediate Representation Execution Environment) &#8212; \u043a\u043e\u043c\u043f\u0438\u043b\u044f\u0442\u043e\u0440 \u0438 runtime, \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u0443\u044e\u0449\u0438\u0439 \u043c\u043e\u0434\u0435\u043b\u0438 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0432 \u0443\u043d\u0438\u0432\u0435\u0440\u0441\u0430\u043b\u044c\u043d\u043e\u0435 \u043f\u0440\u043e\u043c\u0435\u0436\u0443\u0442\u043e\u0447\u043d\u043e\u0435 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u0438\u0435, \u043e\u0441\u043d\u043e\u0432\u0430\u043d\u043d\u043e\u0435 \u043d\u0430 \u0444\u043e\u0440\u043c\u0430\u0442\u0435 MLIR (Multi-Level Intermediate Representation) \u043e\u0442 \u043f\u0440\u043e\u0435\u043a\u0442\u0430 LLVM. \u0418\u0437 \u043e\u0441\u043e\u0431\u0435\u043d\u043d\u043e\u0441\u0442\u0435\u0439 \u043e\u0442\u043c\u0435\u0447\u0430\u0435\u0442\u0441\u044f \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0441\u0442\u044c \u043f\u0440\u0435\u0434\u0432\u0430\u0440\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0439 \u043a\u043e\u043c\u043f\u0438\u043b\u044f\u0446\u0438\u0438 (ahead-of-time), \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u0430 \u0443\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u044f \u043f\u043e\u0442\u043e\u043a\u043e\u043c, \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0441\u0442\u044c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044f  \u0434\u0438\u043d\u0430\u043c\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u044d\u043b\u0435\u043c\u0435\u043d\u0442\u043e\u0432 \u0432  \u043c\u043e\u0434\u0435\u043b\u044f\u0445, \u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u044f \u0434\u043b\u044f \u0440\u0430\u0437\u043d\u044b\u0445 CPU \u0438 GPU, \u043d\u0438\u0437\u043a\u0438\u0435 \u043d\u0430\u043a\u043b\u0430\u0434\u043d\u044b\u0435 \u0440\u0430\u0441\u0445\u043e\u0434\u044b.    <\/ul>\n<p>OpenXLA t\u00f6\u00f6riista peamised eelised:  <\/p>\n<ul>\n<li class=\"l\"> Optimaalse j\u00f5udluse saavutamine ilma vajaduseta s\u00fcveneda seadme-spetsiifilise koodi kirjutamisse. Valmis optimeerimiste pakkumine, mis h\u00f5lmab algebrailiste avaldiste lihtsustamist, efektiivset m\u00e4lukohta planeerimist, mille puhul arvestatakse m\u00e4lu tipptarbimise ja \u00fclekoormustega.\n<li class=\"l\"> M\u00f5\u00f5tmete ja arvutuste parendamine ja paralleelne teostamine. Arendajal on piisav, kui lisada annotatsioone teatud kriitiliste tensorite alamhulgas, mille p\u00f5hjal saab kompilaator automaatselt genereerida koodi paralleelseks arvutamiseks.\n<li class=\"l\"> Kandetransportimise tagamine, toetades erinevaid riistvaraplatvorme, nagu AMD ja NVIDIA GPU-d, x86 ja ARM arhitektuuril p\u00f5hinevad CPU-d, Google'i TPU ML-kiirendajad, AWS Trainium Inferentia, Graphcore ja Cerebras Wafer-Scale Engine.\n<li class=\"l\"> Toetatakse laienduste \u00fchendamist, mis rakendavad t\u00e4iendavaid v\u00f5imalusi, nagu s\u00fcgava masin\u00f5ppe primitiivide kirjutamise tugi, kasutades CUDA, HIP, SYCL, Triton ja muid paralleelsete arvutuste keeli. V\u00f5imalus manuaalselt timmida mudelite kitsaskohti.    <\/ul>\n<p>Allikas: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/www.opennet.ru\/opennews\/art.shtml?num=58773\">opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u041a\u0440\u0443\u043f\u043d\u0435\u0439\u0448\u0438\u0435 \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u0438, \u0437\u0430\u043d\u0438\u043c\u0430\u044e\u0449\u0438\u0435\u0441\u044f \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u043e\u0439 \u0432 \u043e\u0431\u043b\u0430\u0441\u0442\u0438 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f, \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u0438\u043b\u0438 \u043f\u0440\u043e\u0435\u043a\u0442 OpenXLA, \u043d\u0430\u0446\u0435\u043b\u0435\u043d\u043d\u044b\u0439 \u043d\u0430 \u0441\u043e\u0432\u043c\u0435\u0441\u0442\u043d\u043e\u0435 \u0440\u0430\u0437\u0432\u0438\u0442\u0438\u0435 \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u0430\u0440\u0438\u044f \u0434\u043b\u044f \u043a\u043e\u043c\u043f\u0438\u043b\u044f\u0446\u0438\u0438 \u0438 \u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u0438 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0434\u043b\u044f \u0441\u0438\u0441\u0442\u0435\u043c \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f. \u041f\u043e\u0434 \u043a\u0440\u044b\u043b\u043e \u043f\u0440\u043e\u0435\u043a\u0442\u0430 \u043f\u0435\u0440\u0435\u0448\u043b\u0430 \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u0430 \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u043e\u0432, \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u044e\u0449\u0438\u0445 \u0443\u043d\u0438\u0444\u0438\u0446\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u043a\u043e\u043c\u043f\u0438\u043b\u044f\u0446\u0438\u044e \u043c\u043e\u0434\u0435\u043b\u0435\u0439, \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043b\u0435\u043d\u043d\u044b\u0445 \u0432\u043e \u0444\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a\u0430\u0445 TensorFlow, PyTorch \u0438 JAX, \u0434\u043b\u044f \u044d\u0444\u0444\u0435\u043a\u0442\u0438\u0432\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0438 \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u044f \u043d\u0430 \u0440\u0430\u0437\u043d\u044b\u0445 GPU \u0438 \u0441\u043f\u0435\u0446\u0438\u0430\u043b\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0445 \u0443\u0441\u043a\u043e\u0440\u0438\u0442\u0435\u043b\u044f\u0445. \u041a \u0441\u043e\u0432\u043c\u0435\u0441\u0442\u043d\u043e\u0439 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