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Summary:
Added link to blog post about incremental decoder in the FairseqIncrementalDecoder class description.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/662

Differential Revision: D15077845

Pulled By: myleott

fbshipit-source-id: f23294721739600e14feb2cca4ece95f2b968f44

Fix some recursive functions (e.g., reorder_incremental_state) to only touch each module once (#379)

Summary:
This can happen if a module is registered in more than one place in the network.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/379

Differential Revision: D13154498

Pulled By: myleott

fbshipit-source-id: a35575d1956a46cd35ac8b16a719ad20ac3e380a

remove completed sentences from batch and allow batching uneven lengths (with fixes to make padded sequences work correctly in all models)

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

* Move some functionality out of FConvModel into FairseqModel base class
* Move incremental decoding functionality into FairseqIncrementalDecoder module
* Refactor positional embeddings to be more specific to FConvModel