memory-efficient-attention-pytorch
routing-transformer
| memory-efficient-attention-pytorch | routing-transformer | |
|---|---|---|
| 2 | 1 | |
| 227 | 284 | |
| - | - | |
| 6.1 | 0.0 | |
| over 1 year ago | about 3 years ago | |
| Python | Python | |
| MIT License | MIT License |
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memory-efficient-attention-pytorch
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[Discussion] Fine tune model for long context
Check these efficient attention mechanism which are almost a drop in replacement: efficient attention flash attention
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Will Transformers Take over Artificial Intelligence?
I would recommend Routing Transformer https://github.com/lucidrains/routing-transformer but the real truth is nothing beats full attention. Luckily, someone recently figured out how to get past the memory bottleneck. https://github.com/lucidrains/memory-efficient-attention-pyt...
routing-transformer
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Will Transformers Take over Artificial Intelligence?
I would recommend Routing Transformer https://github.com/lucidrains/routing-transformer but the real truth is nothing beats full attention. Luckily, someone recently figured out how to get past the memory bottleneck. https://github.com/lucidrains/memory-efficient-attention-pyt...
What are some alternatives?
flash-attention - Fast and memory-efficient exact attention
tab-transformer-pytorch - Implementation of TabTransformer, attention network for tabular data, in Pytorch
performer-pytorch - An implementation of Performer, a linear attention-based transformer, in Pytorch
conformer - Implementation of the convolutional module from the Conformer paper, for use in Transformers
x-transformers - A concise but complete full-attention transformer with a set of promising experimental features from various papers
enformer-pytorch - Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch
vit-pytorch - Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
memory-efficient-attention-pyt
Compact-Transformers - Escaping the Big Data Paradigm with Compact Transformers, 2021 (Train your Vision Transformers in 30 mins on CIFAR-10 with a single GPU!)