Up tókànItọsọna atẹle
Iṣiro iranti GPU ti o nilo fun awọn LLM
Imọ-ẹrọ
Imọ Itọsọna
GPU Memory Management ati Fragmentation
Bii awọn ilana AI ṣe pin, tun lo, ati tun gba iranti to lopin lori GPU kan, ati idi ti awọn ela ajẹkù (fifọ) le fa awọn aṣiṣe iranti-jade paapaa nigbati ọpọlọpọ iranti ni imọ-ẹrọ wa.
Gbọ si itọsọna yiiAI-ṣe afihan · ~2 min
Lori iwe yi2 min ka
Akopọ
Loye rẹ jẹ bọtini lati ni ibamu awọn awoṣe nla ati yago fun awọn ipadanu aramada.
Jin Dive
Iranti GPU jẹ ti o wa titi ati iyebíye: kaadi le ni 24, 80, tabi 192 GB lapapọ, pinpin nipasẹ awọn iwọn awoṣe, awọn iṣẹ ṣiṣe, awọn gradients, awọn ipinlẹ iṣapeye, ati awọn ifipamọ igba diẹ. Pipe awakọ lati pin iranti lori gbogbo iṣẹ ṣiṣe yoo lọra, nitorinaa awọn ilana bii PyTorch lo olupilẹṣẹ caching ti o mu awọn bulọọki nla ni iwaju ati fi ọwọ si awọn ege kekere, lẹhinna tọju awọn ege ominira ni adagun-odo fun atunlo. Apeja naa jẹ pipin: bi awọn tenors ti awọn titobi oriṣiriṣi ti pin ati ominira, aaye ọfẹ n pin si awọn ege tuka. O le ni 5 GB ni ọfẹ ni apapọ sibẹsibẹ kuna lati pin ipin tensor 2 GB ti o ni itara nitori ko si aafo kan ti o tobi to. Eyi ni idi ti ikẹkọ le jamba pẹlu awọn aṣiṣe iranti-jade laibikita yara ori ti o dabi ẹnipe o wa.
Real-World imuse
A training team examines allocator statistics before testing expandable segments on a variable-batch workload.
A developer uses memory_summary and a memory snapshot to investigate active tensors and inactive split blocks.
An inference team uses vLLM’s paged KV-cache management to reduce cache-allocation waste.
A training team lowers batch size or checkpoints activations after confirming live tensor memory is the limiting factor.
Ojo iwaju ti GPU Memory Management ati Fragmentation
Isakoso iranti n di ijafafa ati oju-iwe diẹ sii, atilẹyin nipasẹ awọn ọna ṣiṣe. Awọn ilana bii awọn alafo ara-iranti-iranti ati akiyesi oju-iwe (ti a lo lati ṣakoso kaṣe KV lakoko itọkasi) dinku egbin ati pipin ni iyalẹnu. Reti awọn ilana si aiyipada si faagun, awọn olupilẹṣẹ defragmenting, hihan ti o dara julọ nipasẹ awọn profaili ti a ṣe sinu, ati isọpọ pọpọ pẹlu piparẹ ati iṣipopada ki eto naa ju GPU, Sipiyu, ati iranti disk laifọwọyi lati tọju iṣamulo ga ati awọn ipadanu toje.
Fifi o sinu iwa
Kini idi ti o ṣe pataki
- Iye owo ati isuna. Awọn ipinnu faaji ṣe awakọ iṣẹ ati idiyele iṣẹ fun awọn ọdun.
- Awọn ipinnu diẹ sii. Ẹkọ imọ-ẹrọ ṣe iranlọwọ fun awọn ẹgbẹ lati yan akopọ to tọ, kii ṣe ọkan tuntun nikan.
- Iṣakoso didara. Awọn yiyan imọ-ẹrọ to dara julọ dinku awọn iṣẹlẹ igbẹkẹle ni iṣelọpọ.
Ṣọra fun
- Ṣiṣepe ala-ilẹ kan le tọju awọn ailagbara eto ti o gbooro.
- Awọn ohun elo amayederun ati awọn idiyele itọju nigbagbogbo ni aibikita.
- Aabo ati awọn ela akiyesi le dagba bi awọn eto ṣe di eka sii.
Next awọn igbesẹ
- Ṣetumo lairi, didara, ati awọn ibi-afẹde idiyele ṣaaju imuse.
- Aṣepari labẹ ẹru ojulowo ati awọn ipo data.
- Abojuto ohun elo fun awọn aṣiṣe, fiseete, ati ipa olumulo.
- Mura ipadasẹhin pada ati awọn ipa ọna esi iṣẹlẹ ṣaaju iwọn.
Tesiwaju Ṣiṣawari
Ìwé ìròyìn ọfẹ
Gba ìkànìyàn AI ojoojúmọ́
Ìtàn AI mẹta tó jẹ́rìí ní gbogbo owurọ́ ọjọ́ iṣẹ́, kọ́wé ní Gẹ̀ẹ́sì tó rọrùn. Ọfẹ́ fún gbogbo àkókò, kò sí ìpolówó.
Ìmeèlì kan ní gbogbo ọjọ́ iṣẹ́. Yọkúrò ní ìkànìyàn ní ìkànìyàn kan. A kò ta tàbí pín àdírẹ́sì rẹ.
Ṣe idanwo fun ara rẹ
Take the GPU Memory Management and Fragmentation quiz
Ìdáhùn lẹ́sẹ̀kẹsẹ̀ lórí gbogbo ìdáhùn, àti iwe-ẹ̀rí tó lè pin pẹ̀lú ID tó lè jẹ́rìí lẹ́yìn tí o bá kọ́ ẹ̀kọ́.
Ṣe àtìlẹ́yìn ẹ̀kọ́ AI ọfẹ́. AI Understanding jẹ́ àjọ àìní èrè 501(c)(3) — kò sí ìpolówó, kò sí àkúnya owó, ní gbogbo àkókò. Ṣe ẹbun kan
Awọn ibeere ti a beere nigbagbogbo
Kini iṣakoso iranti GPU ati Fragmentation?
Bii awọn ilana AI ṣe pin, tun lo, ati tun gba iranti to lopin lori GPU kan, ati idi ti awọn ela ajẹkù (fifọ) le fa awọn aṣiṣe iranti-jade paapaa nigbati ọpọlọpọ iranti ni imọ-ẹrọ wa. Loye rẹ jẹ bọtini lati ni ibamu awọn awoṣe nla ati yago fun awọn ipadanu aramada.
Kini pipin iranti GPU?
Fragmentation tumo si lapapọ free iranti to, sugbon o ti wa ni dà si ona, ki ko si nikan aafo ni o tobi to fun a tensor nla.
Kini idi ti awọn ilana bii PyTorch lo olupilẹṣẹ iranti caching?
Pipe cudaMalloc/cudaFree fun gbogbo op jẹ o lọra, nitorinaa olupilẹṣẹ caching gba awọn bulọọki nla ati tun lo awọn ti o ni ominira lati adagun-odo kan.
A CUDA allocation fails despite apparently unused reserved memory. What should a developer investigate?
Allocator statistics and snapshots help distinguish fragmentation from live tensors or other allocations. The error alone does not prove a single cause.
Which PyTorch allocator option can be tested for changing-size workloads that leave unused memory slivers?
Expandable segments can reduce waste for selected allocation patterns. Availability and benefit depend on allocator configuration and workload.
Kini vLLM's PagedAttention ṣakoso lati dinku egbin iranti lakoko itọkasi?
PagedAttention tọju kaṣe KV ni awọn oju-iwe iwọn ti o wa titi bii iranti foju OS, gige pipin ati ṣiṣe ọpọlọpọ awọn ibeere daradara.
Tesiwaju kikọ
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