https://github.com/lucidrains/axial-positional-embedding

Axial Positional Embedding for Pytorch
https://github.com/lucidrains/axial-positional-embedding

artificial-intelligence deep-learning pytorch

Last synced: 16 days ago
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Axial Positional Embedding for Pytorch

• Host: GitHub
• URL: https://github.com/lucidrains/axial-positional-embedding
• Owner: lucidrains
• License: mit
• Created: 2020-06-08T03:11:23.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2021-04-30T14:58:56.000Z (over 3 years ago)
• Last Synced: 2024-10-29T16:41:55.056Z (19 days ago)
• Topics: artificial-intelligence, deep-learning, pytorch
• Language: Python
• Homepage:
• Size: 12.7 KB
• Stars: 61
• Watchers: 3
• Forks: 6
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
## Axial Positional Embedding

[![PyPI version](https://badge.fury.io/py/axial-positional-embedding.svg)](https://badge.fury.io/py/axial-positional-embedding)

A type of positional embedding that is very effective when working with attention networks on multi-dimensional data, or for language models in general.

## Install

```bash

$ pip install axial-positional-embedding

```

## Usage

```python

import torch

from axial_positional_embedding import AxialPositionalEmbedding

pos_emb = AxialPositionalEmbedding(

    dim = 512,

    axial_shape = (64, 64),          # axial shape will multiply up to the maximum sequence length allowed (64 * 64 = 4096)

    axial_dims = (256, 256)          # if not specified, dimensions will default to 'dim' for all axials and summed at the end. if specified, each axial will have the specified dimension and be concatted together. the concatted dimensions needs to sum up to the `dim` (256 + 256 = 512)

)

tokens = torch.randn(1, 1024, 512)  # assume are tokens

tokens = pos_emb(tokens) + tokens   # add positional embedding to token embeddings

```

## Citations

```bibtex

@inproceedings{kitaev2020reformer,

    title       = {Reformer: The Efficient Transformer},

    author      = {Nikita Kitaev and Lukasz Kaiser and Anselm Levskaya},

    booktitle   = {International Conference on Learning Representations},

    year        = {2020},

    url         = {https://openreview.net/forum?id=rkgNKkHtvB}

}

```

```bibtex

@misc{ho2019axial,

    title = {Axial Attention in Multidimensional Transformers},

    author = {Jonathan Ho and Nal Kalchbrenner and Dirk Weissenborn and Tim Salimans},

    year = {2019},

    archivePrefix = {arXiv}

}

```