pyg-team/pytorch_geometric

Local multi-headed self-attention

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#8,972 opened on Feb 26, 2024

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Description

🚀 The feature, motivation and pitch

I am unable to find the clean implementation of local multi-headed self-attention in pytorch geometric. I found three types of multi-head attention, one TransformerConv (https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.conv.TransformerConv.html#torch_geometric.nn.conv.TransformerConv). But this one calculates a linear combination of all features with different attention weights as opposed to dividing features into multiple heads and taking their linear combination: another RGATConv in the similar direction (https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.conv.RGATConv.html). And finally GPSConv (https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.conv.GPSConv.html) that does multi-head attention but is global.

Alternatives

I think it is nice to have the implementation of local self-attention with multiple heads where each head looks into a part of the feature dimension.

Additional context

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Contributor guide

Research direction
Implement a local multi headed self attention mechanism that splits feature dimensions across heads, similar to TransformerConv but with per head feature subspaces. Study existing implementations like TransformerConv, RGATConv, and GPSConv in PyTorch Geometric to understand patterns and extend them.
Tech stack
pythonpytorch
Domain
machine learning
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-2 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Mostly clear
Prerequisites
PythonPyTorchGraph Neural Networks basics
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
30