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Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/317

When upgrading `state_dict` variable, `upgrade_state_dict` function in TransformerEncoder/TransformerDecoder doesn't handle multiple encoders/decoders, however, D10052908 will be the case.

Before the change, we will hit error message [1] when loading checkpoint for multilingual_transformer model in D10052908. This diff will fix it.

Reviewed By: myleott, liezl200

Differential Revision: D10375418

fbshipit-source-id: 7104c1a463e78f3fa33d8479a37c51608be50610

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/316

This code should actually be keeping the padded positions as `padding_idx` (though note that this is on the ONNX export path, and it has no effect in the most common case when using the exported network to do un-batched inference).

Reviewed By: myleott

Differential Revision: D10431872

fbshipit-source-id: 79fe4ac27cafcd4701e0f2a90e29d1b7362dc6f8

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:
Pull Request resolved: https://github.com/pytorch/translate/pull/232

Though transpose operations are essentially free during PyTorch execution, they can result in costly operations when exported to Caffe2 inference nets via ONNX tracing, especially when applied repeatedly to large tensors.

For this reason, we update `MultiheadAttention` to store its incremental state with shape (bsz, num_heads, seq_len, head_dim), that is after transposing the projected input. This should result in non-trivially faster exported models without changing the semantics or speed of PyTorch execution.

Reviewed By: myleott

Differential Revision: D10186506

fbshipit-source-id: 8a42712423ee767ea49ed88d2a4653f900d14fba

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: Pull Request resolved: https://github.com/pytorch/fairseq/pull/300

Differential Revision: D10154711

Pulled By: edunov

fbshipit-source-id: 859d1ac59923b67c1547b6f7acb94f801b0c3318

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

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/296

Differential Revision: D10121830

Pulled By: alexeib

fbshipit-source-id: 1b73430bdfdcb20a9a6123abfca3472a0d307b3b

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

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

- no more FP16Trainer, we just have an FP16Optimizer wrapper
- most of the distributed code is moved to a new wrapper class called DistributedFairseqModel, which behaves like DistributedDataParallel and a FairseqModel at the same time
- Trainer now requires an extra dummy_batch argument at initialization, which we do fwd/bwd on when there's an uneven number of batches per worker. We hide the gradients from these dummy batches by multiplying the loss by 0
- Trainer.train_step now takes a list of samples, which will allow cleaner --update-freq

Update readme with WMT'18 model (#433)

Oss master