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

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

* Allow more flexible pre-processing and generation

* Addressing CR comments

* small fix

* Add prefix

* Fixes

* Keep original scores with prefix

* Improve prefix code

* Replace 'repeat' with 'expand'

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

* Split generate.py to generate.py and interactive.py and refactor code

The main motivation behind these changes is to try to decorrelate use
cases in order to implement future improvements such as unk replacement
with original string during evaluation on test and writing predictions
to output file.
The previous implementation worked well but I found it difficult to
integrate these future improvements.

* Add --replace-unk arg to be used without align dict

Replacing <unk> tokens can be beneficial even without an alignment
dictionary.

* 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