https://github.com/kyegomez/gated-slot-attention

An implementation of the paper: "Gated Slot Attention for Efficient Linear-Time Sequence Modeling" in PyTorch
https://github.com/kyegomez/gated-slot-attention

ai attentions deep-neural-networks deeplearning machinelearnin ml pytorch tensorflow torch torchvision

Last synced: 3 months ago
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An implementation of the paper: "Gated Slot Attention for Efficient Linear-Time Sequence Modeling" in PyTorch

• Host: GitHub
• URL: https://github.com/kyegomez/gated-slot-attention
• Owner: kyegomez
• License: mit
• Created: 2024-09-13T17:59:42.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-09-23T12:00:19.000Z (3 months ago)
• Last Synced: 2024-09-25T01:03:50.311Z (3 months ago)
• Topics: ai, attentions, deep-neural-networks, deeplearning, machinelearnin, ml, pytorch, tensorflow, torch, torchvision
• Language: Python
• Homepage: https://discord.com/servers/agora-999382051935506503
• Size: 2.17 MB
• Stars: 3
• Watchers: 2
• Forks: 0
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

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README

        


# Gated Slot Attention

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Implementation of Gated Slot Attention in Pytorch from scratch in one file from the paper [Gated Slot Attention for Efficient Linear-Time Sequence Modeling](https://arxiv.org/pdf/2409.07146)

## Install

```bash

pip3 install -U gated-slot-attention

```

## Usage

For full usage, use your own tokenizer and vocab size.

```python

import torch

from gated_slot_attention.model import GSATransformer

model = GSATransformer(

    dim=512,

    heads=8,

    m=64,

    tau=0.1,

    depth=1,

    vocab_size=10000,

    max_seq_len=1024,

)

x = torch.randint(0, 10000, (1, 1024))

out = model(x)

print(out.shape)

```

# License

MIT

# Citation

```Bibtex

@misc{zhang2024gatedslotattentionefficient,

    title={Gated Slot Attention for Efficient Linear-Time Sequence Modeling}, 

    author={Yu Zhang and Songlin Yang and Ruijie Zhu and Yue Zhang and Leyang Cui and Yiqiao Wang and Bolun Wang and Freda Shi and Bailin Wang and Wei Bi and Peng Zhou and Guohong Fu},

    year={2024},

    eprint={2409.07146},

    archivePrefix={arXiv},

    primaryClass={cs.CL},

    url={https://arxiv.org/abs/2409.07146}, 

}

```