GitLab

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/736

Differential Revision: D15314626

Pulled By: myleott

fbshipit-source-id: 1e0c32529afee57e43fe5d6c7991cd13eb8a52c4

Summary:
Following discussion in https://github.com/pytorch/fairseq/issues/574:

 - Implemented MMapIndexedDataset and MMapIndexedDatasetBuilder compatible with IndexedDataset/IndexedDatasetBuilder
- Update scripts/read_binarized.py to support new MMapIndexedDataset
- Option '--raw-text' and '--lazy-load' replaced with '--dataset-impl' and moved the option definition custom task args to more high-level options.add_dataset_args() (more appropriate)
- Implemented also utils functions in indexed_dataset: make_dataset(), dataset_exists()
Pull Request resolved: https://github.com/pytorch/fairseq/pull/589

Differential Revision: D14597128

Pulled By: myleott

fbshipit-source-id: 4e92d99920cbaa52cfe5a0f1f5d9ae5c92d4268e

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/681

Differential Revision: D15147107

fbshipit-source-id: 4452c98059586a4d748868a7659329285a76d5ef

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/647

the current implementation of average_checkpoints requires loading all
the model parameters into memory and then do the averaging. To average large
models (e.g., transformer) over a large number of checkpoints (e.g., >50),
it may require over 100GB memory.

Loading all the parameters is not necessary, as we know the number of models in advance.

Reviewed By: skritika

Differential Revision: D15027513

fbshipit-source-id: 0afe37c9a031a9ab0f1e78844a37be49ec5f76f1

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/586

Differential Revision: D14517550

Pulled By: myleott

fbshipit-source-id: fab68a8f597a98cf28d812d89eff845c5776b65b

Summary:
* Add example for multilingual translation on IWSLT'17
* Match dataset ordering for multilingual_translation and translation
* Fix bug with LegacyDistributedDataParallel when calling forward of sub-modules
Pull Request resolved: https://github.com/pytorch/fairseq/pull/527

Differential Revision: D14218372

Pulled By: myleott

fbshipit-source-id: 2e3fe24aa39476bcc5c9af68ef9a40192db34a3b

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/523

Differential Revision: D14200060

Pulled By: myleott

fbshipit-source-id: a2e3d6ec7c6b9cacc9f44565d2b91e65b580b084

Summary:
- fairseq can now be installed via pip: `pip install fairseq`
- command-line tools are globally accessible: `fairseq-preprocess`, `fairseq-train`, `fairseq-generate`, etc.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/495

Differential Revision: D14017761

Pulled By: myleott

fbshipit-source-id: 10c9f6634a3056074eac2f33324b4f1f404d4235

Summary:
Changelog:
- `4889802`: can now remove detokenize sentencepiece output with `--remove-bpe=sentencepiece` (fixes #331). Also added `--sacrebleu` for computing detokenized BLEU.
- `0d76427`: fix assertion error when training language model with dataset containing empty sentences
- minor bug and style fixes
Pull Request resolved: https://github.com/pytorch/fairseq/pull/483

Differential Revision: D13867899

Pulled By: myleott

fbshipit-source-id: 25c940b847fe270262ac8f5ac838407b3977fdda

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

Summary:
When opening text files without specifying the encoding (i.e. `open(path, "r")` or `open(path, "w")`), python3 will use the preferred locale encoding (`locale.getpreferredencoding()`) so the result is platform dependent and can change from one machine to another.

I believe fairseq should enforce its standard (UTF-8 seems like the best choice to me). This pull request explicity specify UTF-8 encoding when reading text files.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/460

Differential Revision: D13802525

Pulled By: myleott

fbshipit-source-id: 672fd55707ee559ab36d74bc1c24026166ea2367

Summary:
This is useful for averaging the last N checkpoints, ending at some "best" checkpoint.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/452

Differential Revision: D13695407

Pulled By: myleott

fbshipit-source-id: 5d9d2bff3706834f01501e9259834c77fb335817

Summary:
Not switching to Black formatting just yet, but adding fmt: off directives in case we decide to later.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/399

Differential Revision: D13364674

Pulled By: myleott

fbshipit-source-id: a20a11a18be3d583ee30eff770278fb4bd05b93c

Changes:
- 7d19e36: Add `--sampling` flag to generate.py to sample instead of doing beam search
- c777340: Add `scripts/average_checkpoints.py` to average multiple checkpoints into a combined model
- 3ea882c: Add `--max-update` option to train.py to stop training after a given number of updates
- small bugfixes for distributed training, LSTM, inverse square root LR scheduler