GitLab

Remove Python 3.6 format strings (fixes #55)

Release notes:
- 5c7f4954: Added simple LSTM model with input feeding and attention
- 6e4b7e22: Refactored model definitions and incremental generation to be cleaner
- 7ae79c12: Split interactive generation out of generate.py and into a new binary: interactive.py
- 19a3865d: Subtle correctness fix in beam search decoder. Previously, for a beam size of k, we might emit a hypotheses
           if the <eos> was among the top 2*k candidates. Now we only emit hypotheses for which the <eos> is among the
           top-k candidates. This may subtly change generation results, and in the case of k=1 we will now produce
           strictly greedy outputs.
- 97d7fcb9: Fixed bug in padding direction, where previously we right-padded the source and left-padded the target. We
           now left-pad the source and right-pad the target. This should not effect existing trained models, but may
           change (usually improves) the quality of new models.
- f442f896: Add support for batching based on the number of sentences (`--max-sentences`) in addition to the number of
           tokens (`--max-tokens`). When batching by the number of sentences, one can optionally normalize the gradients
           by the number of sentences with `--sentence-avg` (the default is to normalize by the number of tokens).
- c6d6256b: Add `--log-format` option and JSON logger

Release notes:
- 5c7f4954: Added simple LSTM model with input feeding and attention
- 6e4b7e22: Refactored model definitions and incremental generation to be cleaner
- 7ae79c12: Split interactive generation out of generate.py and into a new binary: interactive.py
- 19a3865d: Subtle correctness fix in beam search decoder. Previously, for a beam size of k, we might emit a hypotheses
           if the <eos> was among the top 2*k candidates. Now we only emit hypotheses for which the <eos> is among the
           top-k candidates. This may subtly change generation results, and in the case of k=1 we will now produce
           strictly greedy outputs.
- 97d7fcb9: Fixed bug in padding direction, where previously we right-padded the source and left-padded the target. We
           now left-pad the source and right-pad the target. This should not effect existing trained models, but may
           change (usually improves) the quality of new models.
- f442f896: Add support for batching based on the number of sentences (`--max-sentences`) in addition to the number of
           tokens (`--max-tokens`). When batching by the number of sentences, one can optionally normalize the gradients
           by the number of sentences with `--sentence-avg` (the default is to normalize by the number of tokens).
- c6d6256b: Add `--log-format` option and JSON logger

We previously assumed that once a model parameter's gradient buffer was allocated, it stayed fixed during training.
However, this assumption is violated in recent versions of PyTorch (i.e., the gradient buffer may be reallocated during
training), and it's no longer a safe assumption to make.

This is primarily relevant when we do the all-reduce, since we all-reduce a flattened (i.e., contiguous) copy of the
gradients. We can make this more robust by copying the result of the all-reduce back into the model parameter's gradient
buffers after each update. Intra-device copies are cheap, so this doesn't affect performance.

* 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