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Summary:
In https://github.com/pytorch/fairseq/issues/656, people are often confused about how to set multilingual translation parameters at inference time.

This diff add more checks to ensure the arguments (`--lang-pairs`, `--encoder-langtok`, `--decoder-langtok`) load from checkpoint are consistent with arguments specified in generate/interactive command line.
We also add a section in example page to explain how to set the arguments

Reviewed By: myleott

Differential Revision: D15682169

fbshipit-source-id: 64e6db94cd72ea7ce2d0aa1067c9c2dcd3b8a2ac

Summary:
It's so much faster to extract (3 minutes instead of 20).
Pull Request resolved: https://github.com/pytorch/fairseq/pull/803

Differential Revision: D15795810

Pulled By: myleott

fbshipit-source-id: 3b2ae8bd7924a77ac8e795f5e1a7da0c4ae27374

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/793

Differential Revision: D15758755

Pulled By: myleott

fbshipit-source-id: b93e4ac11bde36a0b59b4d6d1c84d31c3124d767

Summary:
Add `\` to fix for the shell command.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/561

Differential Revision: D14460091

Pulled By: myleott

fbshipit-source-id: 3658ca41e69bcd00d4ad8ec2d79ddcc6a8de586e

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:
Code for the paper: [Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)](https://arxiv.org/abs/1902.07816).
Pull Request resolved: https://github.com/pytorch/fairseq/pull/521

Differential Revision: D14188021

Pulled By: myleott

fbshipit-source-id: ed5b1ed5ad9a582359bd5215fa2ea26dc76c673e

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:
- `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:
Changelog:
- `90f52a1`: Support loading subsets of the data on each worker with the `--fix-batches-to-gpus` flag. This should fix #217 and #266.
- `6eda0a9`: Update README for replicating the "Scaling Neural Machine Translation" paper
- `b14c7cf`: Fallback to no_c10d backend for pytorch 0.4.1 (fixes #294)
Pull Request resolved: https://github.com/pytorch/fairseq/pull/295

Differential Revision: D10121559

Pulled By: myleott

fbshipit-source-id: 41c84d0ee4cdd113544b5d3aa38ae8b23acc2c27

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

specify a single GPU setup for IWSLT14

Update training commands in data/README to match the latest version of this project according to #132.

Continue from 3c072958: add omitted "\".

Update training commands in data/README to match the latest version of this project according to #132.

- Motivation: in the previous data/README, the commands are obsolete and will cause the error "unrecognized arguments: --label-smoothing 0.1 --force-anneal 50". 
- What's changed: add arguments "--criterion label_smoothed_cross_entropy" and "--lr-scheduler fixed" to the training commands of all 3 datasets.
- Result: the new commands run without error on all 3 datasets.

Change "awailable" to "available".

Change "awailable" to "available".