Title: On-the-fly Operation Batching in Dynamic Computation Graphs,
Abstract: Dynamic neural network toolkits such as PyTorch, DyNet, and Chainer offer
more flexibility for implementing models that cope with data of varying
dimensions and structure, relative to toolkits that operate on statically
declared computations (e.g., TensorFlow, CNTK, and Theano). However, existing
toolkits - both static and dynamic - require that the developer organize the
computations into the batches necessary for exploiting high-performance
algorithms and hardware. This batching task is generally difficult, but it
becomes a major hurdle as architectures become complex. In this paper, we
present an algorithm, and its implementation in the DyNet toolkit, for
automatically batching operations. Developers simply write minibatch
computations as aggregations of single instance computations, and the batching
algorithm seamlessly executes them, on the fly, using computationally efficient
batched operations. On a variety of tasks, we obtain throughput similar to that
obtained with manual batches, as well as comparable speedups over
single-instance learning on architectures that are impractical to batch
manually.