Geometric Vector Perceptron

Implementation of Geometric Vector Perceptron, a simple circuit with 3d rotation equivariance for learning over large biomolecules, in Pytorch. The repository may also contain experimentation to see if this could be easily extended to self-attention.

Install

$ pip install geometric-vector-perceptron

Functionality

  • GVP: Implementing the basic geometric vector perceptron.
  • GVPDropout: Adapted dropout for GVP in MPNN context
  • GVPLayerNorm: Adapted LayerNorm for GVP in MPNN context
  • GVP_MPNN: Adapted instance of Message Passing class from torch-geometric package. Still not tested.

Usage

import torch
from geometric_vector_perceptron import GVP

model = GVP(
    dim_vectors_in = 1024,
    dim_feats_in = 512,
    dim_vectors_out = 256,
    dim_feats_out = 512
)

feats, vectors = (torch.randn(1, 512), torch.randn(1, 1024, 3))

feats_out, vectors_out = model( (feats, vectors) ) # (1, 256), (1, 512, 3)

With the specialized dropout and layernorm as described in the paper

import torch
from torch import nn
from geometric_vector_perceptron import GVP, GVPDropout, GVPLayerNorm

model = GVP(
    dim_vectors_in = 1024,
    dim_feats_in = 512,
    dim_vectors_out = 256,
    dim_feats_out = 512
)

dropout = GVPDropout(0.2)
norm = GVPLayerNorm(512)

feats, vectors = (torch.randn(1, 512), torch.randn(1, 1024, 3))

feats, vectors = model( (feats, vectors) )
feats, vectors = dropout(feats, vectors)
feats, vectors = norm(feats, vectors)  # (1, 256), (1, 512, 3)

TF implementation:

The original implementation in TF by the paper authors can be found here: https://github.com/drorlab/gvp/

Citations

@inproceedings{
    anonymous2021learning,
    title={Learning from Protein Structure with Geometric Vector Perceptrons},
    author={Anonymous},
    booktitle={Submitted to International Conference on Learning Representations},
    year={2021},
    url={https://openreview.net/forum?id=1YLJDvSx6J4},
    note={under review}
}

GitHub

https://github.com/lucidrains/geometric-vector-perceptron