Dlvm: a modern compiler framework for neural network dsls

Many current approaches to deep learning make use of high-level toolkits such as TensorFlow, Torch, or Caffe. Toolkits such as Caffe have a layer-based programming framework with hard-coded gradients specified for each layer type, making research using novel layer types problematic. Toolkits such as Torch and TensorFlow define a computation graph in a host language such as Python, where each node represents a linear algebra operation parallelized as a compute kernel on GPU and stores the result of evaluation; some of these toolkits subsequently perform runtime interpretation over that graph, storing the results of forward calculations and reverse-accumulated gradients at each node. This approach is more flexible, but these toolkits take a very limited and ad-hoc approach to performing optimization. Also problematic are the facts that most toolkits lack type safety, and target only a single (usually GPU) architecture, limiting users’ abilities to make use of heterogeneous and emerging hardware architectures. We introduce a novel framework for high-level programming that addresses all of the above shortcomings.