Batching si yi edzi tso Gɔmeɖose Gbãtɔwo me (2025)

Continuous batching nye dynamic inference scheduling technique si naa hardware ƒe dɔwɔwɔ dzina ɖe edzi to biabia yeyewo dede active processing batch me le ɣeyiɣi si me slot aɖe ɖe asi le eŋu, si ɖea akɔntabubu ƒe tsatsam siwo mewɔa dɔ o le dɔwo dome ɖa. Gɔmesese tso gɔmeɖose gbãtɔwo me ɖe nusita wòzu gɔmeɖoanyi xɔtuɖaŋu na AI subɔsubɔ ƒe ɖoɖo ɖesiaɖe si wɔa dɔ nyuie si wozã le agbɔsɔsɔ me le ƒe 2025 me.

Nuka tututue Nye Continuous Batching eye Nukatae Static Batching Do kpo nu?

Be nàkpɔ ŋudzedze ɖe batching si yia edzi ŋu la, ele be nàse nusi wòɖɔli la gɔme gbã. Dekɔnu static batching ƒoa biabia xexlẽme aɖe si woɖo ɖi nu ƒu ɖekae, wɔa wo ŋudɔ abe unit ɖeka ene, eye ne batch bliboa wu enu ko hafi wòxɔa biabia yeyewo. Vodada vevitɔ enye be gbegbɔgblɔ ƒe kpɔɖeŋu gãwo naa dzesi siwo ƒe didime trɔna — biabia ɖeka ateŋu awu enu le dzesi 20 megbe esime bubu si le hatsotso ma ke me aƒu du hena 2,000. GPU ɖesiaɖe si le ƒuƒoƒoa me nɔa anyi dɔ aɖeke mawɔmawɔ hele lalam be ɖoɖo diditɔ kekeake nawu enu hafi dɔ yeye aɖeke nadze egɔme.

Batching si yi edzi, si wodze le ƒe 2022 ƒe agbalẽ ɖedzesi si nye "Orca: A Distributed Serving System for Transformer-Based Generative Models" me, gblẽ mɔxenu sia me keŋkeŋ. Ewɔa dɔ le iteration level tsɔ wu be wòawɔ dɔ le biabia ƒe ɖoɖo nu. Ne ŋgɔyiyi ɖekaɖeka ɖesiaɖe to kpɔɖeŋua me vɔ la, ɖoɖowɔla la léa ŋku ɖe eŋu nenye be ɖoɖo aɖe ɖo eƒe nuwuwu ƒe dzesi gbɔ hã. Ne ewɔe la, wogbugbɔa slot ma enumake eye wodea asi na biabia si le fli me — lala aɖeke meli o, gbegblẽ aɖeke meli o. Batch ƒe wɔwɔme trɔna bɔbɔe le decode afɔɖeɖe ɖesiaɖe me, si wɔnɛ be hardware zazã te ɖe theoretical maximum ŋu ɣesiaɣi.

Aleke KV Cache wɔa Nu Kple Batching si Yia Edzi le System Level?

Key-value cache nye ŋkuɖodzinu ƒe wɔwɔme si na transformer inference tractable. Le dzesi ɖesiaɖe si ŋu wotrɔ asi le gome la, kpɔɖeŋua bua susu ƒe safuiwo kple asixɔxɔ siwo wòle be woalé ɖe asi ale be dzesi siwo kplɔe ɖo nagagbugbɔ akɔntabubu si mehiã o awɔ o. Le static batching system me la, KV cache ƒe mama le tẽ: dzra ŋkuɖodzinu ɖo si sɔ kple ɖoɖo ƒe didime si sɔ gbɔ wu na biabia ɖesiaɖe le batch la me.

Batching atraɖii na esia sesẽna ɖe edzi le atsyã me. Esi wònye be biabiawo gena ɖe batch la me hedoa go le ɣeyiɣi siwo womate ŋu agblɔ ɖi o ta la, ɖoɖoa mate ŋu ama ŋkuɖodzinu siwo woɖo ɖi siwo tsiã ɖe enu do ŋgɔ o. Esia tututu tae vLLM ƒe PagedAttention — si woto vɛ le ƒe 2023 me — va zu nusi womate ŋu ama tso batching si yia edzi le ewɔwɔ ƒe dɔwɔwɔwo me o. PagedAttention doa virtual memory paging model tso dɔwɔɖoɖowo gbɔ, eye wòmaa KV cache ɖe block siwo metsi tre ɖe wo nɔewo ŋu o siwo ƒe lolome sɔ me. Woateŋu akaka sequence ƒe cache axawo ɖe GPU memory dzi abe alesi virtual memory axawo kaka ɖe physical RAM dzi ene. Nusi dona tso emee nye ŋkuɖodzinu gbegblẽ si gogo zero tso mama me, si gɔmeɖeɖe tẽe nye batch ƒe lolome siwo lolo wu kple dɔwɔwɔ si lolo wu xɔtunuwo ƒe gadede bubu aɖeke manɔmee.

Nukae Nye Ðoɖowɔɖi ƒe Mɔnu Vevi Siwo Naa Batching si Yia Edzi Nawɔa Dɔ?

Ɣeyiɣiɖoɖo ŋuti nyametsotso etɔ̃ siwo nɔa te ɖe wo nɔewo dzi kpɔa ŋusẽ ɖe batching ɖoɖo ɖesiaɖe si yia edzi dzi:

• Do ŋgɔ ƒe ɖoɖo: Ne ŋkuɖodzinu ƒe nyaƒoɖeamenu lolo eye biabia yeye si le vevie ŋutɔ va ɖo la, ele be ɖoɖowɔla natso nya me nenye be yeado ŋgɔ na ɖoɖo si me nu vevitɔ mele o si le du dzi, atrɔ eƒe KV cache ɖe CPU RAM me, alo agbugbɔ akɔ akɔnta tso gɔmedzedzea me emegbe. Swap-based preemption kpɔa akɔntabubu ta gake ezãa PCIe bandwidth; akɔntabubu gbugbɔgawɔ gblẽa GPU ƒe tsatsam gake enaa ŋkuɖodzinu nɔa dzadzɛ.
• Amexɔxɔ dzikpɔkpɔ: Ele be ɖoɖowɔla nagblɔe ɖi nenye be biabia yeye ƒe KV cache asɔ ɖe ŋkuɖodzinu si li me le eƒe dzidzime bliboa ƒe agbenɔɣi katã. Numabumabu nu tsɛe hea nusiwo me susu mele o ƒe gbagbã vɛ le ɖoɖo si nu wole titina; akɔntabubu si gbɔ eme nana dɔ wua fli me nɔlawo madzemadzee. Egbegbe ɖoɖowo zãa profiled length distributions kple reservation buffers tsɔ da sɔ le afɔku siawo me.
• Chunked prefill: Prefill ƒe akpa — si wɔa dɔ tso zãla ƒe nyawo tsɔtsɔ yi eme ƒe nyabiase ŋu — nye akɔntabubu-si bla eye ateŋu axɔ GPU la ɖeka, si ahe decode afɔɖeɖewo ɖe megbe na ɖoɖo siwo le dɔ wɔm xoxo. Chunked prefill ma nyabiase didiwo ɖe chunks siwo ƒe lolome woɖo ɖi siwo wotsɔ decode iterations tsaka me, si ɖea ɣeyiɣi-ɖe-first-token latency dzi kpɔtɔna na concurrent users le ga si nye raw prefill throughput si bɔbɔ vie.
• Filiɖoɖo gbãtɔ: Dɔwɔƒewo ƒe dɔwɔwɔwo ƒe akpa ƒe biabiawo to SLA ƒe hatsotso nu. API yɔyɔ siwo sea nu le ɣeyiɣi didi me doa ŋgɔ na agbagbadzedze nyuitɔ kekeake ƒe batch dɔwo. Ne ƒuƒoƒo sia meli o la, nuŋlɔɖi didi ɖeka ƒe nuƒoƒoƒu ƒe dɔ ateŋu agblẽ nu le zãla ƒe nuteƒekpɔkpɔ si le kadodo me ŋu na ɣeyiɣi alafa geɖe siwo wowɔna le ɣeyiɣi ɖeka me.

ƒe nyawo

ƒe nyawo

"Batching si yia edzi menaa throughput nyona ɖe edzi ko o — egbugbɔ ɖoa ganyawo ƒe kpɔɖeŋu si nye AI inference. To GPUs léle ɖe iteration granularity me tsɔ wu be woabia granularity, dɔwɔlawo ɖoa 5–10× geɖe wu zazã nyuie tso hardware ɖeka tɔgbe, si nye lever gãtɔ ɖeka si li be woaɖe per-token subɔsubɔ ƒe gazazãwo dzi akpɔtɔ le ƒe 2025 me."

ƒe nyawo

Aleke Xexeame Ŋutɔŋutɔ ƒe Dɔwɔwɔwo Dzia Dɔwɔwɔ ƒe Viɖewo?

| Viɖeawo dzena wu ne biabia ƒe didime ƒe vovototo lolo — nɔnɔme siwo tututu dea dzesi nuwɔwɔ ƒe dzeɖoɖo AI dɔwɔwɔ ƒe agba siwo me zãla ƒe biabiawo tso nya etɔ̃ ƒe nyabiasewo dzi va ɖo axa geɖe ƒe nuŋlɔɖiwo ɖoɖoɖa dzi.

Latency gblɔa ŋutinya si me nu vovovowo le wu. Ɣeyiɣi-ɖe-first-token nyona ɖe edzi ŋutɔ elabena ɖoɖoa melalana be static batch blibo naƒo ƒu hafi adze prefill gɔme o. Inter-token latency gakpɔtɔ li ke le agba si sɔ me gake egblẽna le atsyɔ̃ɖoɖo me le saturation te tsɔ wu be wòadze anyi, elabena ɖoɖowɔla la yia edzi le ŋgɔ yim le active sequences katã dzi ne fli la tsi goglo gɔ̃ hã. Le asitsaha siwo le ɣeyiɣi ŋutɔŋutɔ me AI ƒe nɔnɔmewo tum gome la, zi geɖe la, nugbegblẽ ƒe ʋuʋudedi sia si le atsyɔ̃ɖoɖo me la le vevie wu le asitsatsa me wu xexlẽme siwo me wowɔa dɔ le ƒe kɔkɔƒe.

Aleke Asitsahawo Ate Ŋu Awɔ Batching Gɔmeɖose Siwo Yia Edzi Ŋudɔ Le AI ƒe Nutsotso Ŋu?

Xɔtuɖaŋu ƒe gɔmesese si le megbe na batching si yia edzi — gbugbɔ xɔ nunɔamesiwo le granularity nyuitɔ kekeake si woate ŋui me eye woagbugbɔ wo ade asi na wo enumake tsɔ wu be woalala be dɔwɔwɔ ƒe akpa si ƒe nukuwo le ƒuƒuie nawu enu — nye gɔmeɖose gbadzaa na ɖoɖo ɖesiaɖe si kpɔa dɔwɔwɔ ƒe agba siwo to vovo dzi. Asitsadɔwɔɖoɖowo dze ŋgɔ kuxi ma ke: dɔ siwo ƒe ɣeyiɣi didi to vovo kura siwo le ho ʋlim ɖe dɔwɔwɔ ƒe ŋutete si wozãna ɖekae ŋu le CRM dɔwɔwɔwo katã me, asitsatsa ƒe nuwo wɔwɔ le wo ɖokui si, numekuku ƒe mɔ̃wo, kple e-asitsatsa ƒe dɔwɔnawo.

| Le esi teƒe be wòazi ƒuƒoƒowo dzi be woalala batch nyatakakawo nana ƒe tsatsamwo, fli siwo dzi woda asi ɖo ɖe wo nɔewo yome, alo siled dɔwɔnu ƒe asiɖeɖe le wo ŋu la, Mewayz wɔa asitsatsa ƒe nudzɔdzɔwo ŋudɔ ɣesiaɣi — etsɔa nuɖuɖu siwo wowu enu enumake yia anyime modules me abe alesi batching scheduler si yia edzi tsɔa GPU slots siwo woɖe asi le la naa nuɖuɖu wo trɔna yia biabia fli la gbɔ ene. Nusi dona tso emee nye ŋgɔyiyi si woate ŋu adzidze le dɔwɔwɔ me le asitsatsa ƒe dɔwɔna ŋutɔŋutɔwo me, ke menye dzidzenuwo ɖeɖeko o.

Nyabiase Siwo Wobiana Enuenu

Ðe batching si yia edzi la sɔ kple dynamic batching le TensorFlow Serving mea?

Ao. TensorFlow Serving ƒe dynamic batching ƒoa biabiawo nu ƒu ɖe batch siwo ƒe lolome trɔna me le ɣeyiɣi ƒe fesrewo kple fli ƒe goglome nu, gake egawɔa batch ɖesiaɖe ŋudɔ le atɔm nu tso gɔmedzedze vaseɖe nuwuwu. Batching si yia edzi wɔa dɔ le ame ɖekaɖekawo ƒe token dzidzi ƒe afɔɖeɖea me, si wɔnɛ be batch ƒe wɔwɔme trɔa ŋgɔgbe yiyi ɖesiaɖe. Granularity ƒe vovototoae nye nusitae batching si yia edzi la ɖoa throughput si lolo ŋutɔ gbɔ na autoregressive dzidzime dɔwɔwɔ ƒe agbawo koŋ.

Ðe batching si yia edzi hiã be woawɔ tɔtrɔ le model architecture ŋua?

Tɔtrɔmɔ̃ ƒe xɔtuɖaŋu siwo woɖo ɖi mehiã tɔtrɔ aɖeke o. Wowɔa batching si yia edzi la ŋudɔ bliboe le serving layer la me to tɔtrɔwo le inference scheduler, memory manager, kple attention kernel me. Ke hã, nyonyo aɖewo — vevietɔ PagedAttention — hiã CUDA kernels tɔxɛ siwo axɔ ɖe standard attention implementations teƒe, si tae production-grade continuous batching frameworks abe vLLM kple TensorRT-LLM menye drop-in ɖɔliɖɔli na general-purpose inference servers o.

Hardware ƒe mɔxenu kawoe xea mɔ na batching ƒe dɔwɔwɔ nyuie si yia edzi?

GPU HBM bandwidth kple VRAM ƒe ŋutete bliboa nye mɔxenu vevitɔwo. KV cache gãwo hiã ŋkuɖodzinu geɖe, si xea mɔ na ɣeyiɣi ɖeka me nɔnɔ si sɔ gbɔ wu. High-bandwidth interconnects (NVLink, Infiniband) va zua nu vevi aɖe na GPU geɖe ƒe dɔwɔwɔ afisi wòle be woama KV cache ɖe mɔ̃wo dzi. Le nɔnɔme siwo me ŋkuɖodzinu mele o me la, KV cache ƒe asixɔxɔwo ƒe agbɔsɔsɔmedzidzenu dzikutɔe (tso FP16 va ɖo INT8 alo INT4) gbugbɔa ŋutete gbugbɔa ŋutete le ga si woatsɔ agblẽ nu le nyateƒetoto ƒe gbegblẽ sue aɖe si dzi woda asi ɖo na asitsatsa ƒe dɔwɔwɔ akpa gãtɔ.

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