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Massive transformer-based models face several challenges, including slow andcomputationally intensive pre-training and over-parametrization. This paperaddresses these challenges by proposing a versatile method called GQKVA, whichgeneralizes query, key, and value grouping techniques. GQKVA is designed tospeed up transformer pre-training while reducing the model size. Ourexperiments with various GQKVA variants highlight a clear trade-off betweenperformance and model size, allowing for customized choices based on resourceand time limitations. Our findings also indicate that the conventionalmulti-head attention approach is not always the best choice, as there arelighter and faster alternatives available. We tested our method on ViT, whichachieved an approximate 0.3% increase in accuracy while reducing the model sizeby about 4% in the task of image classification. Additionally, our mostaggressive model reduction experiment resulted in a reduction of approximately15% in model size, with only around a 1% drop in accuracy.

GQKVA: Efficient Pre-training of Transformers by Grouping Queries, Keys, and Values