Funda Ubunjiniyela Bokwenza I-LLM Inference Ngokusekelwe Ezimisweni Eziyisisekelo

Iyini i-Inference Engineering Zero to Hero?

I-Inference Engineering Zero to Hero iyinqolobane yezifundo ezisebenzayo yokuqonda amasistimu esikhathi sokusebenza ngemuva kokunikezwa kwama-large language model. Esikhundleni sokugxila ekuqeqesheni noma ekuthuthukiseni ikhwalithi yemodeli, ihlola okwenzeka ngemva kokuba isicelo sifinyelele ku-LLM.

Iphrojekthi iqala ngemisebenzi emincane ewusizo ye-inference, bese kancane kancane yakha i-inference runtime ehlangene. Ukuqhubeka okuhlosiwe kungokulandelayo:

tokenization → prefill → decode → KV cache → sampling → request state → scheduling → continuous batching → memory management → serving-engine internals

Indlela egxile ekusetshenzisweni igcizelela ukuhlola ngokuqondile ama-tensor, imiphumela ekhiqiziwe, isimo se-cache, nokusetshenziswa kwememori. Lokhu kusiza ukuxhumanisa ukusebenza kwemodeli nezinto ezisebenzayo zokunikezwa kwayo, njenge-latency, throughput, ukusetshenziswa kwe-GPU, nokusetshenziswa kwememori.

Ozokufunda

Lesi sifundo senzelwe ukwakha indlela yokucabanga egxile kumasistimu mayelana ne-LLM inference. Izihloko ezisemqoka zihlanganisa:

• Tokenization: indlela i-prompt eba ngayo ama-token ID asetshenziswa yimodeli.
• Prefill: indlela imodeli ecubungula ngayo i-prompt yokuqala futhi idale isimo esidingekayo ekukhiqizeni.
• Decode: indlela ama-token alandelayo akhiqizwa ngayo isinyathelo esisodwa ngesikhathi.
• KV caching: indlela isimo se-attention esikhula ngayo futhi kugwenywe ukubalwa kabusha okungadingekile.
• Sampling: indlela i-token ekhethwa ngayo kumaphuzu okukhipha emodelini.
• Request state: indlela i-runtime elandelela ngayo ama-prompt, ama-token akhiqiziwe, nesimo sokuqedwa.
• Scheduling: indlela i-engine enquma ngayo ukuthi iziphi izicelo okufanele zicutshungulwe.
• Continuous batching: indlela izicelo ezisebenzayo ezihlanganiswa ngayo kahle njengoba zingena futhi ziphuma ohlelweni.
• Memory management: lapho kusetshenziswa khona imemori ye-GPU nokuthi ukuhlelwa kwe-cache kuthinta kanjani umthamo.
• Serving-engine internals: indlela amasistimu azungeza ukusetshenziswa kwemodeli ngayo athuthukisa ukusebenza kahle.

Isimo Nesakhiwo Sesifundo

Inqolobane ihlukaniswe yaba amazinga e-Beginner, Intermediate, kanye ne-Advanced. Okwamanje, ithrekhi yeBeginner isaqhubeka. Okuqukethwe kwe-Intermediate ne-Advanced akukatholakali.

Isahluko Esitholakalayo

• Isahluko 01: Manual LLM Inference From First Principles — siyatholakala ku-beginner/01_manual_llm_inference/.

Izahluko Ze-Beginner Ezihleliwe

1. Sampling
2. Request State
3. Simple Scheduler
4. Continuous Batching
5. KV Cache Management
6. Paged or Block KV Cache
7. Prefix Caching
8. Benchmarking Inference

Uhlelo lwezinyathelo luguquguquka ngamabomu. Izahluko zingangezwa, zisuswe, zihlukaniswe, noma zihlelwe kabusha njengoba umsebenzi wokusebenzisa uthola izinkinga ezintsha.

Ukufaka Nokulungiselela

I-README yenqolobane ayinikezi umyalo ogxilile wokufaka okuncikile noma incazelo yendawo yokusebenza. Qala ngokulanda noma ngokwenza i-clone yenqolobane, bese uhlola amafayela nemiyalelo efakwe neSahluko 1 ngaphambi kokufaka noma ukusebenzisa noma yini.

1. Thola ikhophi yendawo yenqolobane ye-GitHub.
2. Vula ifolda yenqolobane ku-terminal, ku-editor, noma endaweni ye-notebook.
3. Yiya ku-beginner/01_manual_llm_inference/.
4. Funda noma yimiphi imiyalelo eqondene nesahluko bese uthola i-notebook ekulowo mkhombandlela.
5. Faka kuphela okuncikile okucelwe yizinto zesahluko.
6. Vula i-notebook bese usebenzisa amaseli ayo ngokulandelana.

beginner/01_manual_llm_inference/

Ngenxa yokuthi i-README ayisho izinguqulo eziqondile zamaphakheji, izidingo ze-hardware, noma imiyalo yokufaka, sebenzisa i-notebook yesahluko njengomthombo onegunya wokulungiselela indawo yayo yokusebenza.

Ukusetshenziswa Okuyisisekelo: Sebenza Nge-Manual Inference

Isahluko 1 siyindawo enconyiwe yokuqala. Inhloso yaso ukuhlola i-LLM inference ngokwenza kusukela ezimisweni zokuqala, esikhundleni sokufihla ukukhiqizwa ngemuva kwe-API ephelele yokunikezela.

Isinyathelo 1: Landela I-Notebook Ngokulandelana

Qalisa i-notebook kusukela ekuqaleni kuze kube sekugcineni. Gwema ukweqa amaseli okulungiselela noma okuhlola, ngoba imisebenzi yakamuva ingancika kuma-model object, ama-input asevele enziwe i-tokenization, noma isimo se-cache esidalwe ngaphambili.

Isinyathelo 2: Landelela I-Prompt

Bheka indlela i-input prompt eshintsha ngayo njengoba idlula enqubweni ye-inference. Ukulandelela komqondo okuwusizo kungokulandelayo:

prompt text
→ token IDs
→ prompt processing or prefill
→ next-token scores
→ selected token
→ updated sequence
→ repeated decoding

Qopha ubukhulu bama-tensor kanye nenani lama-token noma nini lapho i-notebook ikuveza. Lokhu kwenza kube lula ukubona umehluko phakathi kokucubungula i-prompt yonke nokukhiqiza i-token eyengeziwe eyodwa.

Isinyathelo 3: Hlukanisa I-Prefill Ne-Decode

Prefill icubungula i-prompt futhi isungule isimo esidingekayo ekukhiqizeni. Decode ikhiqiza ama-token angeziwe kancane kancane. Ukuphatha lezi zigaba njengezihlukile kubalulekile ngoba zinezici ezihlukene zokubala nezememori.

Ngesikhathi usebenzisa i-notebook, buza:

• Mangaki ama-input token aphathwa ngesikhathi se-prefill?
• Iyiphi i-input edluliswa esinyathelweni ngasinye se-decode?
• Yimaphi ama-tensor ashintshayo ngemva kokukhiqizwa kwe-token entsha?
• Yisiphi isimo esingaphinde sisetshenziswe esikhundleni sokubalwa kabusha?

Isinyathelo 4: Hlola Imiphumela Ngokuqondile

Qhathanisa ama-token ID nombhalo ohlaziyiweyo, futhi uhlole imiphumela ephakathi lapho i-notebook ikuvumela khona. Inhloso akukhona ukukhiqiza umbhalo kuphela, kodwa ukuqonda ukulandelana kwemisebenzi okubangela lowo mbhalo.

Ukuqonda Imodeli Ye-Runtime

Indlela ewusizo yokusondela kule nqolobane ukuphatha ukukhiqizwa njengeluphu enesimo esigcinwayo esikhundleni socingo olulodwa lwemodeli:

lungisa isicelo
lungisa umyalo
gcina isimo esingasetshenziswa futhi
khetha ithokheni elandelayo
buyekeza isimo sesicelo
phinda kuze kuqedwe

Lo mbono ukulungiselela izahluko ezilandelayo. I-sampling inquma ukuthi yiliphi ithokheni elikhethwayo, isimo sesicelo silandelela ukukhiqizwa ngakunye, ukuhlela kukhetha umsebenzi, kanti i-continuous batching ihlanganisa umsebenzi ovela ezicelweni eziningi ezisebenzayo.

Amathiphu Okufunda Athuthukile

Landela Izinhlobo, Isimo, Nenkumbulo Ndawonye

Ungahloli ama-tensor ngokwehlukana. Kumsebenzi ngamunye obalulekile, qaphela uhlobo lokufaka, uhlobo lokukhipha, ubude bokulandelana, isimo se-cache, nokukhula kwenkumbulo okulindelekile. Lokhu kuhlanganisa ukwenziwa kwemodeli kwezibalo nokuziphatha kwezinhlelo.

Qhathanisa Ubude Bomyalelo Nobude Bokukhipha

Lapho isahluko sikuvumela ukuthi ushintshe okokufaka, qhathanisa imiyalelo emifushane nemide kanye nokukhiqizwa okufushane nokude. Bheka ukuthi yiziphi izindleko ezenzeka ngesikhathi se-prefill nokuthi yiziphi eziqoqekayo ngesikhathi se-decoding. Ungacabangi ukuthi iziphetho zama-benchmark zisebenza ngaphezu kwalokho okuboniswa yizilinganiso zakho.

Yakha Ithebula Lesimo Sesicelo

Ngaphambi kokuba isahluko esizinikele esimweni sesicelo sitholakale, ungahlela lokho okubonile usebenzisa izinkambu ezifana nalezi:

• Omazisi bamathokheni okufaka
• Omazisi bamathokheni akhiqiziwe
• Ubude bamanje bokulandelana
• Isimo se-cache
• Isimo sokukhiqiza

Le yindlela yokufunda, hhayi isici esithenjiswa yireposithri yamanje. Isiza ukuveza ukuthi kungani izinjini ze-inference zidinga izinhlelo ezizungeze imodeli uqobo.

Linganisa Ngaphambi Kokwenza Ukuthuthukisa

Uhlelo lomgwaqo luhlanganisa isahluko esihleliwe mayelana nokulinganisa ukusebenza kwe-inference. Kuze kube sitholakala, gcina izivivinyo zilawulwa: shintsha ukufaka okukodwa noma umbandela wokukhiqiza owodwa ngesikhathi, bhala phansi lokho okubonile, futhi uhlukanise ukuziphatha okulinganisiwe kokucatshangwayo.

Buyela Ezahlukweni Zangaphambilini

Njengoba kunezelwa izahluko ze-sampling, ukuhlela, i-batching, nokuphathwa kwe-cache, buyela ku-manual inference. Hlobanisa isakhiwo esisha ngasinye nomjikelezo wokuqala wokukhiqiza amathokheni ukuze ukuthuthukiswa kuhlale kusekelwe ekwenziweni kwemodeli kwangempela.

Imikhawulo Yamanje

Okwamanje kubhalwe kuphela isahluko sokuqala se-Beginner njengotholakalayo. I-sampling, ama-scheduler, i-continuous batching, amasu okuphatha i-cache, i-benchmarking, kanye namathrekhi e-Intermediate ne-Advanced kusahleliwe. Ngakho-ke, ireposithri kufanele ithathwe njengesifundo esisathuthuka, hhayi njengohlaka oluphelele lwenjini ye-inference.

Isiphetho

I-Inference Engineering Zero to Hero inikeza indlela esebenzayo yokungena ohlangothini lwe-runtime lwama-LLM. Qala nge-beginner/01_manual_llm_inference/, sebenzisa i-notebook ngokulandelana, futhi ugxile ekutheni imiyalelo, amathokheni, ama-tensor, nesimo esingasetshenziswa futhi kuhamba kanjani phakathi nokukhiqiza. Lesi sisekelo sizokwenza kube lula ukuqonda izifundo ezihleliwe zokuhlela, i-batching, inkumbulo, kanye nengaphakathi lezinjini zokusevisa.