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arXiv · 2010.02164

A Streaming Approach For Efficient Batched Beam Search

Kevin Yang·Violet Yao·John DeNero·Dan Klein
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Abstract

We propose an efficient batching strategy for variable-length decoding on GPU architectures. During decoding, when candidates terminate or are pruned according to heuristics, our streaming approach periodically "refills" the batch before proceeding with a selected subset of candidates. We apply our method to variable-width beam search on a state-of-the-art machine translation model. Our method decreases runtime by up to 71% compared to a fixed-width beam search baseline and 17% compared to a variable-width baseline, while matching baselines' BLEU. Finally, experiments show that our method can speed up decoding in other domains, such as semantic and syntactic parsing.

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Kevin Yang, Violet Yao, John DeNero, Dan Klein. 2020-10-05. A Streaming Approach For Efficient Batched Beam Search. https://doi.org/10.18653/v1%2F2020.emnlp-main.366

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