Poly Attention
Implementation of Poly-Attention, a general scheme for higher-order self-attention
Install
$ pip install poly-attention
Usage
import torch
from poly_attention import PolyAttention
attn = PolyAttention(
dim = 512,
heads = 8,
dim_head = 64,
causal = False
)
tokens = torch.randn(1, 1024, 512)
out = attn(tokens) # (1, 1024, 512)
A Vision Transformer based on Poly-Attention
import torch
from poly_attention import PolyViT
vit = PolyViT(
image_size = 256,
patch_size = 32,
num_classes = 1000,
dim = 1024,
depth = 6,
heads = 16,
mlp_dim = 2048,
order = 2 # standard poly attention order 2
)
images = torch.randn(1, 3, 256, 256)
preds = vit(images) # (1, 1000)
Quick test
python train_function_composition.py --poly_layers=1 --base_layers=2
Appreciation
-
@dillfrescott for submitting a stability fix
-
@pranoyr for more efficient caching of the GQA heads!
Citations
@inproceedings{chakrabarti2026poly,
title = {Poly-attention: a general scheme for higher-order self-attention},
author = {Chakrabarti, Sayak and Pitassi, Toniann and Alman, Josh},
booktitle = {International Conference on Learning Representations (ICLR)},
year = {2026}
}
@misc{kayyam2026transformersneedprojectionssystematic,
title = {Do Transformers Need Three Projections? Systematic Study of QKV Variants},
author = {Ali Kayyam and Anusha Madan Gopal and M Anthony Lewis},
year = {2026},
eprint = {2606.04032},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2606.04032},
}
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