HyperMixing is a token-mixing techniques to be used as linear-time alternative to attention, for example in Transformer-like architecture like HyperMixer.
This repository serves as the unified PyTorch implementation for both single-head hypermixing and multi-head-hypermixing.
Code was tested with:
- Python 3.10
- PyTorch 2.0
You can create an environment with the required dependencies by running
conda env create -f environment.ymlcd hypermixing
pip install .import torch
from hypermixing import HyperMixing
input_dim = 128
hypernet_size = 512
tied = False
num_heads = 2
max_length = 3000
token_mixer = HyperMixing(input_output_dim=input_dim,
hypernet_size=hypernet_size,
tied=tied,
num_heads=num_heads,
max_length=max_length)
queries = torch.randn((64, 50, 128)) # [bsize, num_queries, emb_dim]
keys = torch.randn((64, 25, 128)) # [bsize, num_keys, emb_dim]
values = torch.randn((64, 25, 128)) # [bsize, num_keys, emb_dim]
out = token_mixer(queries, keys, values) # [bsize, num_queries, emb_dim]
assert out.size() == queries.size()If you use or build on HyperMixer, please cite the following papers:
@inproceedings{mai2023hypermixer,
author = {Mai, F. and Pannatier, A. and Fehr, F. and Chen, H. and Marelli, F. and Fleuret, F. and Henderson, J.},
title = {HyperMixer: An MLP-based Low Cost Alternative to Transformers},
booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
year = {2023}
}
@article{mai2023multihead-hypermixer,
author={Mai, F. and Zuluaga-Gomez, J. and Parcollet, T. and Motlicek, P.},
title={HyperConformer: Multi-head HyperMixer for Efficient Speech Recognition},
booktitle = {Proc. Interspeech 2023},
year={2023}
}
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