Recasting Self-Attention with Holographic Reduced Representations
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In recent years, self-attention has become the dominant paradigm for sequence modeling in a variety of domains. However, in domains with very long sequence lengths the 𝒪(T^2) memory and 𝒪(T^2 H) compute costs can make using transformers infeasible. Motivated by problems in malware detection, where sequence lengths of T ≥ 100,000 are a roadblock to deep learning, we re-cast self-attention using the neuro-symbolic approach of Holographic Reduced Representations (HRR). In doing so we perform the same high-level strategy of the standard self-attention: a set of queries matching against a set of keys, and returning a weighted response of the values for each key. Implemented as a “Hrrformer” we obtain several benefits including 𝒪(T H log H) time complexity, 𝒪(T H) space complexity, and convergence in 10× fewer epochs. Nevertheless, the Hrrformer achieves near state-of-the-art accuracy on LRA benchmarks and we are able to learn with just a single layer. Combined, these benefits make our Hrrformer the first viable Transformer for such long malware classification sequences and up to 280× faster to train on the Long Range Arena benchmark. Code is available at <https://github.com/NeuromorphicComputationResearchProgram/Hrrformer>
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