pytorch-adaptive-computation-time

This library implements PyTorch modules for recurrent neural networks that can learn to execute variable-time algorithms, as presented in Adaptive Computation Time for Recurrent Neural Networks (Graves 2016). These models can learn patterns requiring varying amounts of computation for a fixed-size input, which is difficult or impossible for traditional neural networks. The library aims to be clean, idiomatic, and extensible, offering a similar interface to PyTorch’s builtin recurrent modules.

The main features are:

• A nearly drop-in replacement for torch.nn.RNN- and torch.nn.RNNCell-style RNNs, but with the power of variable computation time.
• A wrapper which adds adaptive computation time to any RNNCell.
• Data generators, configs, and training scripts to reproduce experiments from the paper.

Example

Vanilla PyTorch GRU:

rnn = torch.nn.GRU(64, 128, num_layers=2)
output, hidden = rnn(inputs, initial_hidden)

GRU with adaptive computation time:

rnn = models.AdaptiveGRU(64, 128, num_layers=2, time_penalty=1e-3)
output, hidden, ponder_cost = rnn(inputs, initial_hidden)

Documentation

Documentation is hosted on Read the Docs.

BibTeX

You don’t need to cite this code, but if it helps you in your research and you’d like to:

@misc{swope2020ACT,
  title   = "pytorch-adaptive-computation-time",
  author  = "Swope, Aidan",
  journal = "GitHub",
  year    = "2020",
  url     = "https://github.com/maxwells-daemons/pytorch-adaptive-computation-time"
}

If you use the experiment code, please also consider citing PyTorch Lightning.