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Attention Rollout uye Head Pruning
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Flash Attention (Kutarisa Kwemheni)
FlashAttention inzira inoziva IO yekugadzirisa attention chaiyo pasina kuumba iyo yakazara attention matrix mune high-bandwidth memory.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
Pa peji ino2 min verenga
Pfupiso
It uses tiling and online softmax to reduce memory traffic; speed and memory gains depend on sequence length, hardware and implementation.
Kudzika Kwakadzika
Standard attention compares every token to every other token, producing an N-by-N score matrix that grows quadratically with sequence length. Naively, that matrix is written to and read back from GPU high-bandwidth memory (HBM), and that shuttling — not the multiplications — is the real bottleneck. Flash Attention, introduced by Tri Dao and colleagues in 2022, reorganizes the computation so the matrix is never fully stored. It processes queries, keys, and values in small tiles that fit in fast on-chip SRAM, computes partial results, and stitches them together using an online running-softmax trick. The algorithm computes the same attention operation while reducing high-bandwidth-memory traffic and intermediate storage. It can be faster, especially on long sequences, but measured gains depend on GPU, sequence length, dtype and implementation.
Kuitwa muNyika yeChokwadi
Kudzidzisa mhando dzemitauro mikuru seLlama uye GPT-kirasi masisitimu ane marefu mamiriro windows pamutengo wakaderera wekuyeuka.
Kushandira vabatsiri vekutaura nekukurumidza nekumhanyisa prefill nhanho uko kukurumidza kukurumidza kunotanga kuverengwa.
Kugonesa maturusi ekuongorora-gwaro anomedza mabhuku ese kana macodebase nekuita kutevedzana-kurebesa kutarisisa kugoneke paGPU imwechete.
Simba rekuona uye odhiyo Transformers uko yakakwirira-resolution yekuisa inogadzira yakareba kwazvo tokeni kutevedzana.
Ramangwana reFlash Attention
FlashAttention-2 and FlashAttention-3 research explores improved work partitioning, asynchronous execution, and hardware-specific low-precision paths. Results depend on the GPU, sequence length, dtype and implementation. These algorithms can reduce memory traffic for dense attention, but they do not remove the underlying query-key pair computations. Benchmark the exact training or serving workload before assuming a speedup.
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Matanho anotevera
- Tsanangura latency, mhando, uye mutengo zvinangwa usati waitwa.
- Benchmark pasi pechokwadi mutoro uye data mamiriro.
- Chishandiso chekutarisa zvikanganiso, kudonha, uye mushandisi maitiro.
- Gadzirira nzira dzekudzosera kumashure uye dzezviitiko usati wawedzera.
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Take the Flash Attention quiz
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Chii chinonzi Flash Attention?
FlashAttention is an IO-aware way to compute exact attention without materializing the full attention matrix in high-bandwidth memory. It uses tiling and online softmax to reduce memory traffic; speed and memory gains depend on sequence length, hardware and implementation.
Ndeipi bhodhoro guru rinotariswa neFlash Attention?
Flash Attention iIO-inoziva: inoderedza iyo data yakavharika pakati pekukurumidza pa-chip SRAM uye inononoka yakakwira-bandwidth ndangariro, inova ndiyo chaiyo bhodhoro pane arithmetic pachayo.
Flash Attention inodzivirira sei kuchengetedza iyo yakazara N-by-N yekutarisisa matrix?
Iyo inoisa iyo computation kuitira kuti bhuroka rega rega rikwane muSRAM inokurumidza, komputa uye kuunganidza zvikamu zvakabuda pasina kumbogadzira iyo yese matrix muHBM.
Ndeipi nzira inobvumira Flash Attention kuverengera softmax nenzira kwayo pasina kuona mutsara wese kamwechete?
Iyo yepamhepo softmax inochengeta yakakwira uye inomhanya dhinominator uku uchitenderera mataira, ichidzoreredza zvakamboitika zvishoma zvakabuda kuitira kuti kujairana kwekupedzisira kuve chaiko.
Nei Flash Attention ichibatsira zvakanyanya nemateedzana akareba?
Iyo naive yekutarisisa matrix zviyero seN-yakapetwa mundangariro, saka kudzivirira kwayo kuzere kuchengetedza kunopa iyo yakakura kuchengetedza chaizvo kana kutevedzana kwakareba.
Chii chinonzi 'IO-aware' dhizaini yeFlash Attention inotungamira kudzikisira?
IO-kuziva zvinoreva kuti algorithm yakagadzirirwa kutenderedza mutengo wekufambisa data mundangariro, ichideredza HBM traffic pane kuita arithmetic.
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