Uhlolojikelele
Indlela yokuhlukanisa izibalo ngaphakathi kwesendlalelo esisodwa se-neural-network kuma-GPU amaningi ukuze imodeli enkulu kakhulu kudivayisi eyodwa isakwazi ukusebenza. Kubalulekile ngoba amamodeli asemngceleni anamakhulu ezigidigidi zamapharamitha okungekho i-GPU eyodwa engakwazi ukuyibamba noma ukubala ngokushesha ngokwanele iyodwa.
I-Tensor Parallelism Yamamodeli Amakhulu iyibhulokhi yokwakha yobuchwepheshe ethinta ikhwalithi yamamodeli, izindleko zengqalasizinda, ukubambezeleka, nokuthembeka esikalini.
I-Deep Dive
I-Tensor parallelism (ebuye ibizwe ngokuthi i-intra-layer model parallelism) ihlukanisa u-matric wesisindo somuntu ngamunye kuwo wonke ama-GPU esikhundleni sokubeka izendlalelo eziphelele kumadivayisi ahlukene. Ku-transformer, ukuphindaphinda kwe-matrix enkulu—ukuqagela kokunakwa kanye ne-feed-forward MLP—kuyahlukaniswa: isibonelo, i-matrix yesisindo sokuqala ye-MLP ihlukaniswa ngamakholomu futhi eyesibili ngemigqa, ngakho-ke i-GPU ngayinye ibala ucezu futhi ukunciphisa konke okukodwa kuhlanganisa imiphumela. Ukunaka kuhlukaniswa ngamakhanda, i-GPU ngayinye iphethe isethi engaphansi. Ngoba yonke i-GPU yenza ingxenye yazo zonke izendlalelo ngasikhathi sinye, i-tensor parallelism kunciphisa inkumbulo ye-GPU ngayinye futhi isheshisa ikhompuyutha, kodwa idinga ukuxhumana okuvamile, okunomkhawulokudonsa ophezulu phakathi kwe-GPU isendlalelo ngasinye. Kungakho ivamise ukuvalelwa ngaphakathi kwendawo exhunywe yi-NVLink, futhi ihlanganiswe nepayipi kanye nokufana kwedatha yokuqeqeshwa okukhulu kakhulu nemisebenzi yokuhlinzeka.
I-Technical Insight
Iqhinga, elidume yi-Megatron-LM, likhetha ubukhulu bokuhlukanisa ukuze ukuxhumana kube kuncane. Ukuhlukanisa ikholomu ye-matrix yokuqala ye-MLP kuvumela i-GPU ngayinye ukuthi isebenzise ukungaqondile endaweni ngaphandle kokuvumelanisa; ukuhlukanisa umugqa wesibili ngokuhlakanipha kusho ukuthi okuphumayo kudinga nje ukunciphisa okukodwa ukuze kube nesamba semiphumela engaphelele. Ngakho-ke ungqimba ngalunye lufaka cishe ukuncishiswa okubili konke (kuya phambili) nokubili (emuva). Ngoba lezi ziqoqwana zenzeka kuzo zonke izendlalelo, ukubambezeleka kuyabusa-ngakho ukufana kwe-tensor kuhlala ngemuva kwezixhumanisi ezisheshayo ze-intra-node njenge-NVLink kunokuba amanethiwekhi ahamba kancane phakathi kwama-node.
I-Mastering Tensor Parallelism yamamodeli amakhulu
Indlela yokuhlukanisa izibalo ngaphakathi kwesendlalelo esisodwa se-neural-network kuma-GPU amaningi ukuze imodeli enkulu kakhulu kudivayisi eyodwa isakwazi ukusebenza. Kubalulekile ngoba amamodeli asemngceleni anamakhulu ezigidigidi zamapharamitha okungekho i-GPU eyodwa engakwazi ukuyibamba noma ukubala ngokushesha ngokwanele iyodwa. I-Tensor Parallelism Yamamodeli Amakhulu iyibhulokhi yokwakha yobuchwepheshe ethinta ikhwalithi yamamodeli, izindleko zengqalasizinda, ukubambezeleka, nokuthembeka esikalini. Ukuze wakhe ukuqonda okujulile, phatha i-Tensor Parallelism Yamamodeli Amakhulu njengemodeli yokusebenza, hhayi isici esisodwa: chaza imiphumela efiselekayo, ucacise ukucabanga, futhi uhlukanise lokho uhlelo olungakwenza ngokwethembeka kulokho okusadinga ukwahlulela kochwepheshe.
Empeleni, amaqembu aqinile asebenzisa i-Tensor Parallelism Yamamodeli Amakhulu alungiselela izakhiwo, idatha, nokukhetha kwengqalasizinda ngokumelene nokuthembeka nezindleko. Babhala imibandela yempumelelo ecacile, ukuhlola okuqhathaniswa nedatha engokoqobo nokugeleza komsebenzi, futhi baphindaphinde ngokusekelwe kumaphethini okuhluleka aqashiwe esikhundleni sokuwina kwebhentshimakhi yesikhathi esisodwa. Yilapho ukuqonda kwethiyori kuguquka kube amandla ahlala njalo kuwo wonke umkhiqizo, inqubomgomo, kanye nokusebenza.
Izinqumo zezakhiwo ziqhuba ukusebenza kanye nezindleko zokusebenza iminyaka. Ngesikhathi esifanayo, Ukuthuthukisa ibhentshimakhi eyodwa kungafihla ubuthakathaka obubanzi besistimu. Indlela eqine kakhulu iwukuhlanganisa isivinini sokuhlola nesiyalo sokuphatha: qhuba abashayeli bezindiza, bamba ubufakazi, ushicilele amalogi ezinqumo, futhi ubuyekeze izivikelo ngokuqhubekayo njengoba imodeli yokuziphatha, okulindelwe ngabasebenzisi, kanye nezimfuneko zokulawula zishintsha.
I-Strategic Impact
Izinqumo zezakhiwo ziqhuba ukusebenza kanye nezindleko zokusebenza iminyaka.
Izinqumo zezakhiwo ziqhuba ukusebenza kanye nezindleko zokusebenza iminyaka. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Imfundo yobuchwepheshe isiza amaqembu ukuthi akhethe isitaki esifanele, hhayi nje esisha.
Imfundo yobuchwepheshe isiza amaqembu ukuthi akhethe isitaki esifanele, hhayi nje esisha. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Izinketho ezingcono zobunjiniyela zinciphisa izehlakalo ezinokwethenjelwa ekukhiqizeni.
Izinketho ezingcono zobunjiniyela zinciphisa izehlakalo ezinokwethenjelwa ekukhiqizeni. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Ukuqaliswa Komhlaba Wangempela
Ukuqeqesha imodeli yepharamitha engu-175B ngokuhlukanisa u-matric wesisindo sesendlalelo ngasinye kuma-GPU angu-8 endaweni eyodwa exhunywe ku-NVLink usebenzisa i-Megatron-LM.
Inikeza imodeli yengxoxo yepharamitha engu-70B ku-vLLM ene-tensor_parallel_size=4 ukuze izisindo zilingane kuma-GPU amane futhi ziphendule ngesikhathi sangempela.
Ukunaka kwe-transformer kugxila kuwo wonke ama-GPU ukuze idivayisi ngayinye ihlanganise isethi engaphansi, bese ihlanganisa okuphumayo kwesendlalelo esilandelayo.
Ukuhlanganisa i-tensor parallelism ngaphakathi kwama-node nokufana kwamapayipi kuwo wonke ama-node ukuqeqesha amamodeli wamapharamitha ayizigidigidi kumaqoqo amakhulu e-GPU.
Amaphethini Okusebenzisa
I-Tensor Parallelism Yamamodeli Amakhulu ngokusebenza
Ukuqeqesha imodeli yepharamitha engu-175B ngokuhlukanisa u-matric wesisindo sesendlalelo ngasinye kuma-GPU angu-8 endaweni eyodwa exhunywe ku-NVLink usebenzisa i-Megatron-LM.
Ukuqeqesha imodeli yepharamitha engu-175B ngokuhlukanisa u-matrics wesisindo sengqimba ngayinye kuwo wonke ama-GPU angu-8 endaweni eyodwa exhunywe ku-NVLink kusetshenziswa Amaqembu e-Megatron-LM ngokuvamile athola imiphumela engcono lapho echaza imingcele yekhwalithi ngaphambili, egcina indlela yokukhuphuka yomuntu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
I-Tensor Parallelism Yamamodeli Amakhulu ngokusebenza
Inikeza imodeli yengxoxo yepharamitha engu-70B ku-vLLM ene-tensor_parallel_size=4 ukuze izisindo zilingane kuma-GPU amane futhi ziphendule ngesikhathi sangempela.
Ukukhonza imodeli yengxoxo yepharamitha engu-70B ku-vLLM ene-tensor_parallel_size=4 ukuze izisindo zilingane kuma-GPU amane futhi ziphendule ngesikhathi sangempela Amaqembu ngokuvamile athola imiphumela engcono uma echaza imingcele yekhwalithi ngaphambili, agcine indlela yokukhuphuka komuntu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
I-Tensor Parallelism Yamamodeli Amakhulu ngokusebenza
Ukunaka kwe-transformer kugxila kuwo wonke ama-GPU ukuze idivayisi ngayinye ihlanganise isethi engaphansi, bese ihlanganisa okuphumayo kwesendlalelo esilandelayo.
Ukuhlukanisa amakhanda okunaka kwe-transformer kuwo wonke ama-GPU ukuze idivayisi ngayinye ihlanganise isethi encane, bese ihlanganisa imiphumela yesendlalelo esilandelayo Amaqembu ngokuvamile athola imiphumela engcono uma echaza imikhawulo yekhwalithi ngaphambili, agcine indlela yokukhuphuka komuntu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
I-Tensor Parallelism Yamamodeli Amakhulu ngokusebenza
Ukuhlanganisa i-tensor parallelism ngaphakathi kwama-node nokufana kwamapayipi kuwo wonke ama-node ukuqeqesha amamodeli wamapharamitha ayizigidigidi kumaqoqo amakhulu e-GPU.
Ukuhlanganisa i-tensor parallelism ngaphakathi kwama-node nokufana kwamapayipi kuwo wonke ama-node ukuqeqesha amamodeli wepharamitha eyizigidigidi kumaqoqo amakhulu e-GPU Amaqembu ngokuvamile athola imiphumela engcono lapho echaza imingcele yekhwalithi ngaphambili, agcine indlela yokukhuphuka kwabantu yamacala asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
Izingozi & Guardrails
Ukuthuthukisa ibhentshimakhi eyodwa kungafihla ubuthakathaka obubanzi besistimu.
Izindleko zengqalasizinda nezokulungisa zivame ukubukelwa phansi.
Izikhala zokuphepha nokubonakala zingakhula njengoba izinhlelo ziba nzima kakhulu.
Ukuqalisa Umhlahlandlela
Chaza ukubambezeleka, ikhwalithi, nezindleko ezihlosiwe ngaphambi kokuqaliswa.
Chaza ukubambezeleka, ikhwalithi, nezindleko ezihlosiwe ngaphambi kokuqaliswa. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Ibhentshimakhi ngaphansi komthwalo wangempela nezimo zedatha.
Ibhentshimakhi ngaphansi komthwalo wangempela nezimo zedatha. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Ukuqapha amathuluzi amaphutha, ukukhukhuleka, nomthelela wabasebenzisi.
Ukuqapha amathuluzi amaphutha, ukukhukhuleka, nomthelela wabasebenzisi. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Lungiselela izindlela zokuhlehlisa nezigameko ngaphambi kokukala.
Lungiselela izindlela zokuhlehlisa nezigameko ngaphambi kokukala. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.