GitLab

Summary: Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/835

Differential Revision: D16904038

Pulled By: myleott

fbshipit-source-id: 2c9d0b913f8d688297ac80fcabd905bd1397f66a

Summary:
CUDA code for light/dynamicconv kernels, including pytorch modules. Modules can be built by running setup.py in each respective folder, and can then be imported and used like any other module.
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/547

Reviewed By: myleott, shubho

Differential Revision: D15703660

Pulled By: nng555

fbshipit-source-id: e9c913753be3a1cd571965f7200df6678b644520

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/pytorch/fairseq/pull/720

Differential Revision: D15259091

Pulled By: myleott

fbshipit-source-id: 06a35996c06ccddb49fdc9e01e348ff3c9da334e

Summary:
The unfold1d.py has the same name as the function `unfold1d` function, which will cause an error when using DynamicConv1dTBC with `unfold=True`.
This doesn't affect the NMT models which don't use the unfolding mode though.

I rename `unfold1d.py` as `unfold.py` to fix this bug.

Originally we would get `TypeError` when running this code:
```
import torch
from fairseq.modules import LightweightConv1dTBC, DynamicConv1dTBC

x = torch.rand(4, 10, 8)
m = LightweightConv1dTBC(8, 4, 3)
o = m(x, unfold=True)

m = DynamicConv1dTBC(8, 4, 3)
o = m(x, unfold=True)
```
Pull Request resolved: https://github.com/pytorch/fairseq/pull/593

Differential Revision: D14597117

Pulled By: myleott

fbshipit-source-id: 59752fd7ff62c53a4aba8b56b83155291e5f5792

Summary:
Changelog:
- `e330f56`: Add code for the "Pay Less Attention with Lightweight and Dynamic Convolutions" paper
- `5e3b98c`: Add scripts for computing tokenized BLEU with compound splitting and sacrebleu
- update READMEs
- misc fixes
Pull Request resolved: https://github.com/pytorch/fairseq/pull/473

Differential Revision: D13819717

Pulled By: myleott

fbshipit-source-id: f2dc12ea89a436b950cafec3593ed1b04af808e9