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Summary: Adding Adadelta optimizer to fairseq as wrapper around torch.optim.Adadelta

Reviewed By: myleott

Differential Revision: D14418635

fbshipit-source-id: 6bf5ec008e905a4a2cbf7415e9492f5eea3ff07f

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/543

Differential Revision: D14259481

Pulled By: myleott

fbshipit-source-id: fcb0a150b8e851cf86ea5ed1f083f56e1600588e

Summary:
The `preprocess.py` script has been refactored in order to:

1. Use the `options` module for command line arguments  parsing. This will give to `preprocess.py` the ability to load custom modules with `--user-dir` flag (already implemented to all other binaries)
2. Dictionary loading and building code has moved to Task implementation. This allows custom Dictionary classes to be used during the data generation step.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/448

Differential Revision: D13674819

Pulled By: myleott

fbshipit-source-id: b40648a98ed6c08284577e5ec25876e018d8c822

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/484

Differential Revision: D13880636

Pulled By: myleott

fbshipit-source-id: 984b2e1c3b281c28243102eb971ea45ec891d94e

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:
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

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/302

Differential Revision: D10174608

Pulled By: myleott

fbshipit-source-id: 4e2dfc76eae97afc5488f29b47e74f9897a643ff

A Task defines the data format, stores shared state (e.g., dictionaries) and provides helpers for building the model/criterion and calculating the loss.

Changes:
- Add TranslationTask and LanguageModelingTask. New tasks can be registered with @register_task decorator.
- Add EpochBatchIterator to encapsulate batching and saving/restoring dataloader position
- Remove LEFT_PAD_* constants and make them configurable per task

This implements convolutional language model from https://arxiv.org/pdf/1612.08083.pdf

There are 3 modes for constructing batches:

- token block: fill each sample with a specified number of tokens without regard for sentence delimiters - this is what was used for training in the paper
- complete: fill each sample with a specified number of tokens but make sure it contains only complete sentences (i.e. if next sentence goes over token block limit, move it to the next sample) - this was used for evaluation in the paper
- eos: one sentence per sample (skip blank lines)

some results:

GCNN-13 - GBW - 37.46
GCNN-14B - GBW - 33.88
GCNN-8 - Wiki103 - 43.76
GCNN-14 - Wiki103 - 35.66

train:

python train.py /private/home/abaevski/data/wiki103 --save-dir /tmp --fp16 --max-epoch 35 --save-interval 1 --save-interval-updates 1000 --keep-interval-updates 25 --arch fconv_lm --optimizer nag --lr 1.0 --lr-scheduler reduce_lr_on_plateau --lr-shrink 0.5 --decoder-embed-dim 280 --decoder-layers '[(850, 6)] * 3 + [(850,1)] + [(850,5)] * 4 + [(850,1)] + [(850,4)] * 3 + [(1024,4)] + [(2048, 4)]' --clip-norm 0.1 --dropout 0.2 --weight-decay 5e-06 --criterion cross_entropy --max-tokens 1024 --max-target-positions 1024 --seed 1 --log-format json --log-interval 500

eval:

python eval_lm.py ~abaevski/data/wiki103 --path '/checkpoint02/abaevski/2018-04-27/lm_wiki.fp16.mxup300000.fconv.adam.lrs=reduce_lr_on_plateau.emb280.layers(850,6)*3+(850,1)+(850,5)*4+(850,1)+(850,4)*3+(1024,1)+(2048,4).lr0.0005.clp0.1.drp0.3.wd0.0.crt=cross_entropy.mxtk2048.smptk256.seed1.ngpu8/checkpoint_last.pt'

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

This PR includes breaking API changes to modularize fairseq-py and adds support for distributed training across multiple nodes.

Changes:
- c7033ef: add support for distributed training! See updated README for usage.
- e016299: modularize fairseq-py, adding support for register_model, register_criterion, register_optimizer, etc.
- 154e440: update LSTM implementation to use PackedSequence objects in the encoder, better following best practices and improving perf
- 90c2973 and 1da6265: improve unit test coverage