Densifying Assumed-Sparse Tensors

Neural machine translation - using neural networks to translate human language - is an area of active research exploring new neuron types and network topologies with the goal of dramatically improving machine translation performance. Current state-of-the-art approaches, such as the multi-head attention-based transformer, require very large translation corpuses and many epochs to produce models of reasonable quality. Recent attempts to parallelize the official TensorFlow “Transformer” model across multiple nodes have hit roadblocks due to excessive memory use and resulting out of memory errors when performing MPI collectives.