Summary:
The previous BSD+PATENTS license was controversial. We have been
approved to relicense fairseq under the MIT license.
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/786
Differential Revision: D16560654
Pulled By: myleott
fbshipit-source-id: f78b1beb4f2895dd7b9bfc79f5f952a2bfb94034
Summary:
Pull Request resolved: https://github.com/facebookresearch/pytext/pull/804
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/746
Pull Request resolved: https://github.com/pytorch/fairseq/pull/894
Adding an implementation of the sparse transformer to multi-head attention using the fixed attention pattern specified https://arxiv.org/pdf/1904.10509.pdf. The sparse_mask masks out words using -inf; after softmax, -inf becomes 0. Thus, a mask does not need to be re-calculated and re-applied when multiplying attn_weights and values.
Four inputs are added to the config: sparse, is_bidirectional, stride, expressivity. If we are using the sparse transformer, is_bidirectional, stride, and expressivity must be specified (there are defaults). If is_bidirectional is False, the mask values using the fixed attention pattern described in the paper. If is_bidirectional is True, subset one includes all values in the current stride window and a summary from every stride window--all other values are masked. Stride (L in the paper) controls the window size and expressivity (c in the paper) controls the size of the summary.
Reviewed By: borguz
Differential Revision: D16042988
fbshipit-source-id: c59166dc7cfe89187a256e4076000c2458842fd5
