Edge-aware attention cuts graph-generation GPU memory about 65%
Original title: Efficient Edge-aware Attention Network for Graph Generation
A FlexAttention implementation kept generation quality near the baseline while improving training and sampling throughput.
Researchers propose an edge-aware attention layer for graph generation that injects structure through head-wise score modulation and updates edges with a lightweight residual mechanism. The design can use PyTorch FlexAttention while respecting graph masks. They report similar generation quality to their baseline, about 65% lower peak GPU memory, and higher training and sampling throughput.
- Dynamic edge updates are described as a major source of quadratic memory use.
- Edge updates remain dependent on attention scores through a residual mechanism.
- The authors frame the layer as a step toward scalable graph foundation models.
From the original article
1 min read at arxiv.orgGraph generative models commonly use graph-specific Transformer architectures to jointly update node and edge features. However, these architectures do not directly benefit from recent hardware-aware attention implementations, limiting their scalability.
By Vincent Jung · arxiv.org
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