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Summary:
We'd like to resue the noising functions and DenoisingDataset in
adversarial training. However, current noising functions assume the input are
subword tokens. The goal of this diff is to extend it so the noising can be
applied to word tokens. Since we're mostly interested in the word shuffle
noising, so I only modified the WordShuffle class.

Reviewed By: liezl200

Differential Revision: D10523177

fbshipit-source-id: 1e5d27362850675010e73cd38850c890d42652ab

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/325

RoundRobinZipDataset requires size(index) method implemented in every dataset used. Also added missing return statements in a few methods.

Reviewed By: liezl200

Differential Revision: D10457159

fbshipit-source-id: 01856eb455f2f3a21e7fb723129ff35fbe29e0ae

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/306

This uses a source dataset to generate a batch of {source: noisy source, target: original clean source} which allows us to train a denoising autoencoding component as part of a seq2seq model.

Reviewed By: xianxl

Differential Revision: D10078981

fbshipit-source-id: 026225984d4a97062ac05dc3a36e79b5c841fe9c

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/305

Previously, noising code assumed that every sentence had an EOS which had to be excluded from noising operations (since we shouldn't drop, blank, or shuffle EOS). This logic allows the noising module to handle sentences with EOS and without EOS

Reviewed By: xianxl

Differential Revision: D10114425

fbshipit-source-id: 04ec8547343eb94266bda1ac7fca3d8a1991c9f4

Summary:
If we want our parallel data to have EOS at the end of source, we keep the EOS at the end of the generated source dialect backtranslation.
If we don't want our parallel data to have EOS at the end of source, we **remove** the EOS at the end of the generated source dialect backtranslation.

Note: we always want EOS at the end of our target / reference in parallel data so our model can learn to generate a sentence at any arbitrary length. So we make sure that the original target has an EOS before returning a batch of {generated src, original target}. If our original targets in tgt dataset doesn't have an EOS, we append EOS to each tgt sample before collating.
We only do this for the purpose of collating a {generated src, original tgt} batch AFTER generating the backtranslations. We don't enforce any EOS before passing tgt to the tgt->src model for generating the backtranslation. The users of this dataset is expected to format tgt dataset examples in the correct format that the tgt->src model expects.

Reviewed By: jmp84

Differential Revision: D10157725

fbshipit-source-id: eb6a15f13c651f7c435b8db28103c9a8189845fb

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

Differential Revision: D10174608

Pulled By: myleott

fbshipit-source-id: 4e2dfc76eae97afc5488f29b47e74f9897a643ff

Summary: This generalizes BacktranslationDataset to allow us to use any SequenceGenerator class. For example, if we want to use this model in PyTorch Translate, we can pass the following to BacktraanslationDataset init: (1) a PyTorch Translate SequenceGenerator class as generator_class and (2) the appropriate args for initializing that class as kwargs.

Reviewed By: xianxl

Differential Revision: D10156552

fbshipit-source-id: 0495d825bf4727da96d0d9a40dc434135ff3486c

Summary:
Using argparse Namespace hides the actual args that are expected and makes code harder to read.

Note the difference in style for the args list

    def __init__(
        self,
        tgt_dataset,
        tgt_dict,
        backtranslation_model,
        unkpen,
        sampling,
        beam,
        max_len_a,
        max_len_b,
    ):

instead of

    def __init__(
        self, tgt_dataset, tgt_dict, backtranslation_model, unkpen, sampling,
        beam,  max_len_a, max_len_b,
    ):

Reviewed By: dpacgopinath

Differential Revision: D10152331

fbshipit-source-id: 6539ccba09d48acf23759996b7e32fb329b3e3f6

Co-authored-by: liezl200 <lie@fb.com>

adds -reset-optimizer, --reset-lr-scheduler, and --optimizer-overrides flags

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