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Summary:
1. add call to binarization to complete preprocessing pipeline
2. add ability to specify task to select the dictionary, and add a bert task
3. Get rid of function calls that are no longer needed after moving functions from fairseq here

Reviewed By: jingfeidu

Differential Revision: D13977842

fbshipit-source-id: ec9bbb4e98e62e12c20ba68bb52b8bcc94aee91d

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/489

Differential Revision: D13956810

Pulled By: myleott

fbshipit-source-id: 61ace179d1d3790226c38b3f3e47f5452b5ec514

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/482

With this change, we can use different dictionary classes when calling build_dictionary and build_and_save_dictionary

Reviewed By: liaimi

Differential Revision: D13855100

fbshipit-source-id: 62e6db310b5f078e05c547d2671252233be7b7f0

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:
Although both are supported by Python 3.6, I think it would be better to unify the usage of string format function.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/467

Differential Revision: D13802506

Pulled By: myleott

fbshipit-source-id: 5c4877547b1c4ca806ab54c80ae483cfbaa7827a

Summary:
On a multi-gpu training scenario, the `train.py` script spawns new processes with `torch.multiprocessing.spawn`. Unfortunately those child processes don't inherit the modules imported with `--user-dir`.

This pull request fixes this problem: custom module import in now explicit on every `main()` function.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/449

Differential Revision: D13676922

Pulled By: myleott

fbshipit-source-id: 520358d66155697885b878a37e7d0484bddbc1c6

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:
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

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/372

Differential Revision: D13114426

Pulled By: myleott

fbshipit-source-id: 6c24b96a3556a0ecd3d1f350642a884254a40bd3

Summary:
step 2 of pipeline for LM training
assumes tokenized text data as input. Splits it into train/validation/test, and runs binarization
(step a_ii in https://fb.quip.com/kazzAxvZHBj9)

Reviewed By: borguz

Differential Revision: D10454705

fbshipit-source-id: 74e8679041f5507c4e404c1b719547c2ae9ed983

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'

* Allow more flexible pre-processing and generation

* Addressing CR comments

* small fix

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

* Add <eos> for unk replacement
* Add IndexedRawTextDataset to load raw text files
* Replace unk with original string
* Add load_raw_text_dataset() and --output-format
* Move has_binary_files to data.py