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We present a GPU-based Locality Sensitive Hashing (LSH) algorithm to speed upbeam search for sequence models. We utilize the winner-take-all (WTA) hash,which is based on relative ranking order of hidden dimensions and thusresilient to perturbations in numerical values. Our algorithm is designed byfully considering the underling architecture of CUDA-enabled GPUs(Algorithm/Architecture Co-design): 1) A parallel Cuckoo hash table is appliedfor LSH code lookup (guaranteed O(1) lookup time); 2) Candidate lists areshared across beams to maximize the parallelism; 3) Top frequent words aremerged into candidate lists to improve performance. Experiments on 4large-scale neural machine translation models demonstrate that our algorithmcan achieve up to 4x speedup on softmax module, and 2x overall speedup withouthurting BLEU on GPU.

Fast Locality Sensitive Hashing for Beam Search on GPU