Efficient implementation of structured sparsity inducing attention mechanisms: fusedmax, oscarmax and sparsemax.
Note: If you are just looking for sparsemax, I recommend the implementation in this OpenNMT-py module
Currently available for pytorch >= 0.4.1. (For older versions, use a previous release of this package.) Requires python >= 2.7, cython, numpy, scipy.
Usage example:
In [1]: import torch
In [2]: import torchsparseattn
In [3]: a = torch.tensor([1, 2.1, 1.9], dtype=torch.double)
In [4]: lengths = torch.tensor([3])
In [5]: fusedmax = torchsparseattn.Fusedmax(alpha=.1)
In [6]: fusedmax(a, lengths)
Out[6]: tensor([0.0000, 0.5000, 0.5000], dtype=torch.float64)For details, check out our paper:
Vlad Niculae and Mathieu Blondel A Regularized Framework for Sparse and Structured Neural Attention In: Proceedings of NIPS, 2017. https://arxiv.org/abs/1705.07704
See also:
André F. T. Martins and Ramón Fernandez Astudillo From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification In: Proceedings of ICML, 2016 https://arxiv.org/abs/1602.02068
X. Zeng and M. Figueiredo, The ordered weighted L1 norm: Atomic formulation, dual norm, and projections. eprint http://arxiv.org/abs/1409.4271