CQS-Attention: Scaling Up the Standard Attention Computation for Infinitely Long Sequences
Transformer models suffer from unaffordable high memory consumption when the sequence is long and standard self-attention is utilized. We developed a sequence parallelism scheme called CQS-Attention that can break the limit of sequence length. A long sequence is divided into multiple overlapping sub...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10900388/ |
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