Implementation of E(n)-Transformer, which extends the ideas of Welling's E(n)-Equivariant Graph Neural Network to attention

Related tags

Deep Learningdeep-learningartificial-intelligencetransformerattention-mechanismequivariance
Overview

E(n)-Equivariant Transformer (wip)

Implementation of E(n)-Equivariant Transformer, which extends the ideas from Welling's E(n)-Equivariant Graph Neural Network with attention.

Install

$ pip install En-transformer

Usage

import torch
from en_transformer import EnTransformer

model = EnTransformer(
    dim = 512,
    depth = 4,
    dim_head = 64,
    heads = 8,
    edge_dim = 4,
    fourier_features = 2
)

feats = torch.randn(1, 16, 512)
coors = torch.randn(1, 16, 3)
edges = torch.randn(1, 16, 16, 4)

feats, coors = model(feats, coors, edges)  # (1, 16, 512), (1, 16, 3)

Todo

  • masking
  • neighborhoods by radius

Citations

@misc{satorras2021en,
    title 	= {E(n) Equivariant Graph Neural Networks}, 
    author 	= {Victor Garcia Satorras and Emiel Hoogeboom and Max Welling},
    year 	= {2021},
    eprint 	= {2102.09844},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG}
}
Comments
  • Checkpoint sequential segments should equal number of layers instead of 1?

    Checkpoint sequential segments should equal number of layers instead of 1?

    https://github.com/lucidrains/En-transformer/blob/a37e635d93a322cafdaaf829397c601350b23e5b/en_transformer/en_transformer.py#L527

    Looking at the source code here: https://pytorch.org/docs/stable/_modules/torch/utils/checkpoint.html#checkpoint_sequential

    opened by aced125 2
  • On rotary embeddings

    On rotary embeddings

    Hi @lucidrains, thank you for your amazing work; big fan! I had a quick question on the usage of this repository.

    Based on my understanding, rotary embeddings are a drop-in replacement for the original sinusoidal or learnt PEs in Transformers for sequential data, as in NLP or other temporal applications. If my application is not on sequential data, is there a reason why I should still use rotary embeddings?

    E.g. for molecular datasets such as QM9 (from the En-GNNs paper), would it make sense to have rotary embeddings?

    opened by chaitjo 1
  • Is this line required?

    Is this line required?

    https://github.com/lucidrains/En-transformer/blob/7247e258fab953b2a8b5a73b8dfdfb72910711f8/en_transformer/en_transformer.py#L159

    Is this line required? Does line 157, two lines above, make this line redundant?

    opened by aced125 1
  • Performance drop with checkpointing update

    Performance drop with checkpointing update

    I see a drop in performance (higher loss) when I update checkpointing from checkpoint_sequential(self.layers, 1, inp) to checkpoint_sequential(self.layers, len(self.layers), inp). Is this expected?

    opened by heiidii 0
  • varying number of nodes

    varying number of nodes

    @lucidrains Thank you for your efficient implementation. I was wondering how to use this implementation for the dataset when the number of nodes in each graph is not the same? For example, the datasets of small molecules.

    opened by mohaiminul2810 1
  • Edge model/rep

    Edge model/rep

    Hi,

    Thank you for providing this version of the EnGNN model. This is not really an issue just a query. The original model as implemented here (https://github.com/vgsatorras/egnn) has 3 main steps per layer: edge_feat = self.edge_model(h[row], h[col], radial, edge_attr) coord = self.coord_model(coord, edge_index, coord_diff, edge_feat) h, agg = self.node_model(h, edge_index, edge_feat, node_attr) I am interested in the edge_feat and was wondering what would be an equivalent edge representation in your implementation. Line 335 in EnTransformer.py: qk = self.edge_mlp(qk) seems like the best candidate. Thanks, Pooja

    opened by heiidii 1
  • efficient implementation

    efficient implementation

    Hi, I wonder if relative distances and coordinates can be handled more efficiently using memory efficient attention as in " Self-attention Does Not Need O(n^2) Memory". It is straightforward for the scalar part.

    opened by amrhamedp 2
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Phil Wang
Working with Attention. It's all we need.
Phil Wang
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