| Name | simplicial-attention JSON |
| Version |
0.1.5
JSON |
| download |
| home_page | None |
| Summary | (2) - Simplicial Attention |
| upload_time | 2025-07-16 19:50:40 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT License
Copyright (c) 2025 Phil Wang
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
| keywords |
artificial intelligence
attention mechanism
deep learning
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
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| coveralls test coverage |
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|
<img src="./fig2.png" width="400px"></img>
## Simplicial Attention
Implementation of [2-simplicial attention](https://arxiv.org/abs/1909.00668) proposed by Clift et al. (2019) and the recent attempt to make practical in [Fast and Simplex](https://arxiv.org/abs/2507.02754), Roy et al. (2025)
[Paper explanation by Gabriel Mongaras](https://www.youtube.com/watch?v=W-0LSbTnbVc)
## Install
```shell
$ pip install simplicial-attention
```
## Usage
```python
import torch
from simplicial_attention.triton_two_simplicial_attention import SlidingWindowTwoSimplicialMHA
higher_order_attn = SlidingWindowTwoSimplicialMHA(
dim = 512,
dim_head = 64,
heads = 8
).cuda()
tokens = torch.randn(2, 1024, 512).cuda()
assert higher_order_attn(tokens).shape == tokens.shape
```
## Example
Enwik8, every 2 layers
```shell
$ pip install '.[examples]' && python train.py
```
## Contributing
First install with `pytest`
```shell
$ pip install '.[test]'
```
Then add your code and make sure it passes
```shell
$ pytest tests
```
## Citations
```bibtex
@misc{roy2025fastsimplex2simplicialattention,
title = {Fast and Simplex: 2-Simplicial Attention in Triton},
author = {Aurko Roy and Timothy Chou and Sai Surya Duvvuri and Sijia Chen and Jiecao Yu and Xiaodong Wang and Manzil Zaheer and Rohan Anil},
year = {2025},
eprint = {2507.02754},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2507.02754},
}
```
```bibtex
@misc{clift2019logic2simplicialtransformer,
title = {Logic and the $2$-Simplicial Transformer},
author = {James Clift and Dmitry Doryn and Daniel Murfet and James Wallbridge},
year = {2019},
eprint = {1909.00668},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/1909.00668},
}
```
```bibtex
@article{Peng2024OnLO,
title = {On Limitations of the Transformer Architecture},
author = {Binghui Peng and Srini Narayanan and Christos Papadimitriou},
journal = {ArXiv},
year = {2024},
volume = {abs/2402.08164},
url = {https://api.semanticscholar.org/CorpusID:267636545}
}
```
Raw data
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"description": "<img src=\"./fig2.png\" width=\"400px\"></img>\n\n## Simplicial Attention\n\nImplementation of [2-simplicial attention](https://arxiv.org/abs/1909.00668) proposed by Clift et al. (2019) and the recent attempt to make practical in [Fast and Simplex](https://arxiv.org/abs/2507.02754), Roy et al. (2025)\n\n[Paper explanation by Gabriel Mongaras](https://www.youtube.com/watch?v=W-0LSbTnbVc)\n\n## Install\n\n```shell\n$ pip install simplicial-attention\n```\n\n## Usage\n\n```python\nimport torch\nfrom simplicial_attention.triton_two_simplicial_attention import SlidingWindowTwoSimplicialMHA\n\nhigher_order_attn = SlidingWindowTwoSimplicialMHA(\n dim = 512,\n dim_head = 64,\n heads = 8\n).cuda()\n\ntokens = torch.randn(2, 1024, 512).cuda()\n\nassert higher_order_attn(tokens).shape == tokens.shape\n```\n\n## Example\n\nEnwik8, every 2 layers\n\n```shell\n$ pip install '.[examples]' && python train.py\n```\n\n## Contributing\n\nFirst install with `pytest`\n\n```shell\n$ pip install '.[test]'\n```\n\nThen add your code and make sure it passes\n\n```shell\n$ pytest tests\n```\n\n## Citations\n\n```bibtex\n@misc{roy2025fastsimplex2simplicialattention,\n title = {Fast and Simplex: 2-Simplicial Attention in Triton}, \n author = {Aurko Roy and Timothy Chou and Sai Surya Duvvuri and Sijia Chen and Jiecao Yu and Xiaodong Wang and Manzil Zaheer and Rohan Anil},\n year = {2025},\n eprint = {2507.02754},\n archivePrefix = {arXiv},\n primaryClass = {cs.LG},\n url = {https://arxiv.org/abs/2507.02754}, \n}\n```\n\n```bibtex\n@misc{clift2019logic2simplicialtransformer,\n title = {Logic and the $2$-Simplicial Transformer}, \n author = {James Clift and Dmitry Doryn and Daniel Murfet and James Wallbridge},\n year = {2019},\n eprint = {1909.00668},\n archivePrefix = {arXiv},\n primaryClass = {cs.LG},\n url = {https://arxiv.org/abs/1909.00668}, \n}\n```\n\n```bibtex\n@article{Peng2024OnLO,\n title = {On Limitations of the Transformer Architecture},\n author = {Binghui Peng and Srini Narayanan and Christos Papadimitriou},\n journal = {ArXiv},\n year = {2024},\n volume = {abs/2402.08164},\n url = {https://api.semanticscholar.org/CorpusID:267636545}\n}\n```\n\n",
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