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Reviewed By: pipibjc

Differential Revision: D15635402

fbshipit-source-id: e92fab914de40775d7bad851420355240d822bde

Summary: We never actually load the model parameters from an XLM model when using tranformer_from_pretrained_xlm.  Also, change encoder_learned_pos from True -> False

Reviewed By: liezl200

Differential Revision: D15629061

fbshipit-source-id: 759eadc88041eae94505477960de57dd78a99dcb

Summary: the old no_bias_kv argument for masked_lm models are not used. Split it into 2 arguments and expose them.

Reviewed By: myleott

Differential Revision: D15266154

fbshipit-source-id: 60b041f8370ca1d8869ed3402fb9a67d1cd8e0e8

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/translate/pull/508

The previous version applied the temperature after the softmax. Fix that, and
also generalize so it works with other search approaches.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/694

Differential Revision: D15175160

Pulled By: myleott

fbshipit-source-id: cc87ff0e97a8a1dd37f9983163f58a8641155ab0

Summary:
- Add --add-bos-token option to LM task
- Cleanup utils.py and options.py
Pull Request resolved: https://github.com/pytorch/fairseq/pull/654

Differential Revision: D15041794

Pulled By: myleott

fbshipit-source-id: 3ad00007769d5f48308052cfd40de39c5ffa1a6e

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/667

Use smaller models so that unittests won't timeout

Reviewed By: pipibjc

Differential Revision: D15056894

fbshipit-source-id: af9fbda6ea6e56cf82d52555620121b189e2f013

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/666

Option to load the XLM weights into only the encoder or the decoder

Reviewed By: pipibjc

Differential Revision: D14881004

fbshipit-source-id: 6d0d598ea9c445ec468f71b8e855712de89a5dac

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/629

Use GeLU as an alternate activation layer for ReLU.

Reviewed By: lematt1991

Differential Revision: D14689851

fbshipit-source-id: 7ec81fa34bc7bd0e1e43b337847ae932dcbf8b15

Summary: sequence_generator assumes that model input is 2d tensor of longs. But it can be something like 3d tensor of floats and we should be able to handle this as long as first dimension is batch size followed by source lengths.

Reviewed By: myleott

Differential Revision: D14420044

fbshipit-source-id: bf8b1e42ad1873f7b803c1a377b0af21648db015

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