Implementation of Lie Transformer, Equivariant Self-Attention, in Pytorch

Related tags

Deep Learningdeep-learningartificial-intelligencetransformerattention-mechanismse3equivariance
Overview

Lie Transformer - Pytorch (wip)

Implementation of Lie Transformer, Equivariant Self-Attention, in Pytorch. Only the SE3 version will be present in this repository, as it may be needed for Alphafold2 replication.

Install

$ pip install lie-transformer-pytorch

Usage

import torch
from lie_transformer_pytorch import LieTransformer

model = LieTransformer(
    dim = 512,
    depth = 2,
    heads = 8,
    dim_head = 64,
    liftsamples = 4
)

coors = torch.randn(1, 64, 3)
features = torch.randn(1, 64, 512)
mask = torch.ones(1, 64).bool()

out = model(features, coors, mask = mask) # (1, 256, 512) <- 256 = (seq len * liftsamples)

Todo

Credit

This repository is largely adapted from LieConv, cited below!

Citations

@misc{hutchinson2020lietransformer,
    title       = {LieTransformer: Equivariant self-attention for Lie Groups}, 
    author      = {Michael Hutchinson and Charline Le Lan and Sheheryar Zaidi and Emilien Dupont and Yee Whye Teh and Hyunjik Kim},
    year        = {2020},
    eprint      = {2012.10885},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG}
}
@misc{finzi2020generalizing,
    title   = {Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data}, 
    author  = {Marc Finzi and Samuel Stanton and Pavel Izmailov and Andrew Gordon Wilson},
    year    = {2020},
    eprint  = {2002.12880},
    archivePrefix = {arXiv},
    primaryClass = {stat.ML}
}
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Comments
  • Help needed

    Help needed

    It seems like we need equivariance (combined with attention) for alphafold2, so I am currently working on getting this and https://github.com/lucidrains/se3-transformer-pytorch ready. This is in preparation for a full replication for protein folding in silico as more details emerge.

    I could use some help, for anyone who is more of an expert in Lie Group theory out there, to get this library to a state where it is usable.

    • Figure out location based attention as described in section 3.2 I think it may be as simple as https://github.com/lucidrains/lie-transformer-pytorch/blob/main/lie_transformer_pytorch/lie_transformer_pytorch.py#L265-L269 .

    Any feedback would be appreciated!

    help wanted 
    opened by lucidrains 1
Owner
Phil Wang
Working with Attention. It's all we need.
Phil Wang
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