Massimo Pavone

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Pavone
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Massimo
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    Item type:Publication,
    Reducing the Transformer Architecture to a Minimum
    (SciTePress, 2024-11)
    Bernhard Bermeitinger  
    ;
    Tomas Hrycej  
    ;
    Massimo Pavone  
    ;
    Julianus Kath
    ;
    Siegfried Handschuh  
    Transformers are a widespread and successful model architecture, particularly in Natural Language Processing (NLP) and Computer Vision (CV). The essential innovation of this architecture is the Attention Mechanism, which solves the problem of extracting relevant context information from long sequences in NLP and realistic scenes in CV. A classical neural network component, a Multi-Layer Perceptron (MLP), complements the attention mechanism. Its necessity is frequently justified by its capability of modeling nonlinear relationships. However, the attention mechanism itself is nonlinear through its internal use of similarity measures. A possible hypothesis is that this nonlinearity is sufficient for modeling typical application problems. As the MLPs usually contain the most trainable parameters of the whole model, their omission would substantially reduce the parameter set size. Further components can also be reorganized to reduce the number of parameters. Under some conditions, query and key matrices can be collapsed into a single matrix of the same size. The same is true about value and projection matrices, which can also be omitted without eliminating the substance of the attention mechanism. Initially, the similarity measure was defined asymmetrically, with peculiar properties such as that a token is possibly dissimilar to itself. A possible symmetric definition requires only half of the parameters. All these parameter savings make sense only if the representational performance of the architecture is not significantly reduced. A comprehensive empirical proof for all important domains would be a huge task. We have laid the groundwork by testing widespread CV benchmarks: MNIST, CIFAR-10, and, with restrictions, ImageNet. The tests have shown that simplified transformer architectures (a) without MLP, (b) with collapsed matrices, and (c) symmetric similarity matrices exhibit similar performance as the original architecture, saving up to 90 % of parameters, without hurting the classification performance.
    Type:conference paper
    Journal:Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
    DOI:10.5220/0012891000003838
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/123207
    Scopus© Citations 2