Commit Graph

8 Commits

Author SHA1 Message Date
Myle Ott
b41c74dc5b Add code for "Pay Less Attention with Lightweight and Dynamic Convolutions" (#473)
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
2019-01-25 15:40:26 -08:00
Davide Caroselli
ebaf8c5030 '--user-dir' documentation (correct) (#447)
Summary:
Command line option --user-dir documented in docs/overview.rst
Pull Request resolved: https://github.com/pytorch/fairseq/pull/447

Differential Revision: D13674744

Pulled By: myleott

fbshipit-source-id: 17049ee5c9f692f5298ef9fa7381ee583f269cde
2019-01-15 11:54:17 -08:00
Myle Ott
14bd9c62a3 Update docs for --lazy-load and torch.distributed.launch
Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/433

Differential Revision: D13588032

Pulled By: myleott

fbshipit-source-id: 0e5ff361e27b206c4490264f0f51863367499e81
2019-01-07 15:28:09 -08:00
Myle Ott
7633129ba8 Merge internal changes (#283)
Summary:
Pull Request resolved: https://github.com/pytorch/translate/pull/283

Pull Request resolved: https://github.com/pytorch/fairseq/pull/428

Differential Revision: D13564190

Pulled By: myleott

fbshipit-source-id: 3b62282d7069c288f5bdd1dd2c120788cee4abb5
2019-01-04 20:03:19 -08:00
Sergey Edunov
1082ba352c Switch to DistributedDataParallelC10d and bump version 0.5.0 -> 0.6.0
- no more FP16Trainer, we just have an FP16Optimizer wrapper
- most of the distributed code is moved to a new wrapper class called DistributedFairseqModel, which behaves like DistributedDataParallel and a FairseqModel at the same time
- Trainer now requires an extra dummy_batch argument at initialization, which we do fwd/bwd on when there's an uneven number of batches per worker. We hide the gradients from these dummy batches by multiplying the loss by 0
- Trainer.train_step now takes a list of samples, which will allow cleaner --update-freq
2018-09-25 17:36:43 -04:00
Sergey Edunov
fe2d1581a4 Fix docs 2018-09-17 22:34:17 -07:00
Myle Ott
4a47b88992 Update documentation 2018-09-03 20:03:37 -04:00
Myle Ott
6381cc977f Add documentation 2018-09-03 19:15:23 -04:00