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Sparsifying the Transformer has garnered considerable interest, as trainingthe Transformer is very computationally demanding. Prior efforts to sparsifythe Transformer have either used a fixed pattern or data-driven approach toreduce the number of operations involving the computation of multi-headattention, which is the main bottleneck of the Transformer. However, existingmethods suffer from inevitable problems, such as the potential loss ofessential sequence features due to the uniform fixed pattern applied across alllayers, and an increase in the model size resulting from the use of additionalparameters to learn sparsity patterns in attention operations. In this paper,we propose a novel sparsification scheme for the Transformer that integratesconvolution filters and the flood filling method to efficiently capture thelayer-wise sparse pattern in attention operations. Our sparsification approachreduces the computational complexity and memory footprint of the Transformerduring training. Efficient implementations of the layer-wise sparsifiedattention algorithm on GPUs are developed, demonstrating a new SPION thatachieves up to 3.08X speedup over existing state-of-the-art sparse Transformermodels, with better evaluation quality.

SPION: Layer-Wise Sparse Training of Transformer via Convolutional Flood Filling