Summary:
Typically `torch.hub.load(...)` doesn't call `pip install`, so our Cython components never get built. We have a hack in our hubconf that builds these components by running the equivalent of `python setup.py build_ext --inplace` using the setuptools sandbox: f6677b6755/hubconf.py (L52-L55).
Unfortunately, this sandbox gets mad if you modify the filesystem, which is what this recent change does: f6677b6755/setup.py (L203-L205). Combined this breaks torch.hub.
The solution is that when we're doing `build_ext`, don't setup the symlinks. This is fine, since `build_ext` doesn't actually build a package, so we don't care about including config or examples.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/2762
Reviewed By: alexeib
Differential Revision: D24430228
Pulled By: myleott
fbshipit-source-id: e05d075a003ddfde196cb8a86b32882d73808015
Summary:
Imported from https://github.com/fairinternal/fairseq-py/pull/1284. Updated according to PR comments.
Main changes:
* New task: `fairseq.tasks.speech_to_text`
* Multilingual support: multiple train sub-splits, temperature-based sampling, language ID tokens
* New dataset: `fairseq.data.audio.speech_to_text_dataset`
* Added accuracy metrics and BOS prefix removal to label smoothed cross entropy
* New models: Transformer (`fairseq.models.speech_to_text.s2t_transformer`) and BLSTM (`fairseq.models.speech_to_text.berard`)
* Extended scorers:
* Added a base scorer class: `fairseq.scorers.BaseScorer` (the parent class for all scorers except the BLEU scorer in CPP)
* Added an evaluation tokenizer: `fairseq.scorers.eval_tokenizer` which leverages sacreBLEU's built-in tokenizers and allows character-level tokenization as well as punctuation removal (for WER scoring).
* Added chrF scorer: `fairseq.scorers.chrf`
* Online Mel-filter bank speech feature extraction (via CPP-based pyKaldi or Python-based TorchAudio): `fairseq.data.audio.audio_utils`
* Online speech feature transforms: `fairseq.data.audio.feature_transforms.*`
* Fixed the subsampled sequence lengths in VGGTransformer (`examples.speech_recognition.models.vggtransformer`)
* Examples under `examples/speech_to_text`:
* LibriSpeech (ASR): better results than VGGTransformer with smaller Transformer-based models
* MuST-C (ST): comparable to [SOTA results](https://arxiv.org/pdf/2004.10234.pdf) but with less tricks
Reviewed By: jmp84
Differential Revision: D24065273
fbshipit-source-id: 5f842ca9c826f92d4af660705611885fe440a9ab
Summary:
# Before submitting
- [ ] Was this discussed/approved via a Github issue? (no need for typos, doc improvements)
- [x] Did you read the [contributor guideline](https://github.com/pytorch/fairseq/blob/master/CONTRIBUTING.md)?
- [x] Did you make sure to update the docs?
- [x] Did you write any new necessary tests?
## What does this PR do?
Runs CI for `fairseq` on all major platforms provided by GitHub actions.
## PR review
Anyone in the community is free to review the PR once the tests have passed.
If we didn't discuss your PR in Github issues there's a high chance it will not be merged.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/1595
Differential Revision: D19438282
Pulled By: myleott
fbshipit-source-id: a64db46d7785e6f583848f27699f6463c4dc3170
Summary:
## What does this PR do?
CUDA implementation for Levenshtein distance for NAT and other potential application.
It will make training Levenshtein Transformer slightly faster and clean the functions.
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/960
Test Plan: Imported from GitHub. Tested locally.
Reviewed By: cndn
Differential Revision: D19207096
Pulled By: MultiPath
fbshipit-source-id: 4890bbaa851ffd302648c0d949173158dc3167e2
Summary:
Code for our NeurIPS paper [Levenshtein Transformer](https://arxiv.org/abs/1905.11006)
* Added Levenshtein Transformer model, task and criterion class
* Added iterative NAT Transformer, insertion Transformer and CMLM Transformer model class for baselines
* Add an option for prepending BOS to dictionary class and translation task class
Reviewed By: myleott
Differential Revision: D17297372
fbshipit-source-id: 54eca60831ae95dc721c2c34e882e1810ee575c7
Summary:
Cythonized token block dataset code, it's `> 100x` faster. Token block for entire `bookwiki+CC+stories+openweb` is just ~`39.9` seconds.
TODO:
1) I think, I can make it 2x more faster.
2) cleanup.
EDIT History:
~~First pass at parellelizing `token_block_dataset`. The code feels somewhat complicated and cluttered.
This is 2-3x faster though on my tests on `bookwiki` dataset with both `complete` and `complete_doc` modes.
myleott Can you take a look for correctness as I am still not 100% sure that I am not missing corner cases.~~
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/834
Test Plan:
Imported from GitHub, without a `Test Plan:` line.
Test workflow: f133816198
Reviewed By: myleott
Differential Revision: D16970257
Pulled By: myleott
fbshipit-source-id: ec45a308193c9e9f3e7075336c15df4723228d6f
Summary:
The previous BSD+PATENTS license was controversial. We have been
approved to relicense fairseq under the MIT license.
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/786
Differential Revision: D16560654
Pulled By: myleott
fbshipit-source-id: f78b1beb4f2895dd7b9bfc79f5f952a2bfb94034
Summary:
No major API changes since the last release. Cutting a new release since we'll be merging significant (possibly breaking) changes to logging, data loading and the masked LM implementation soon.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/891
Differential Revision: D16377132
Pulled By: myleott
fbshipit-source-id: f1cb88e671ccd510e53334d0f449fe18585268c7
Summary:
Fairseq wouldn't install on macOS.
A workaround was found here: https://github.com/pytorch/fairseq/issues/289
This is now automatic in setup.py, maybe be there's a cleaner way to do it.
I checked that it compiles fine on Linux and macOS.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/862
Differential Revision: D16142105
Pulled By: myleott
fbshipit-source-id: 998ac7781d7a1ac047f4f9239c1fe16eab4be0dd
Summary:
Notable (possibly breaking) changes:
- d45db80: Remove checkpoint utility functions from utils.py into checkpoint_utils.py
- f2563c2: Move LM definitions into separate files
- dffb167: Updates to model API:
- `FairseqModel` -> `FairseqEncoderDecoderModel`
- add `FairseqDecoder.extract_features` and `FairseqDecoder.output_layer`
- `encoder_out_dict` -> `encoder_out`
- rm unused `remove_head` functions
- 34726d5: Move `distributed_init` into `DistributedFairseqModel`
- cf17068: Simplify distributed launch by automatically launching multiprocessing on each node for all visible GPUs (allows launching just one job per node instead of one per GPU)
- d45db80: Change default LR scheduler from `reduce_lr_on_plateau` to `fixed`
- 96ac28d: Rename `--sampling-temperature` -> `--temperature`
- fc1a19a: Deprecate dummy batches
- a1c997b: Add memory mapped datasets
- 0add50c: Allow cycling over multiple datasets, where each one becomes an "epoch"
Plus many additional features and bugfixes
Pull Request resolved: https://github.com/pytorch/fairseq/pull/817
Differential Revision: D15913844
Pulled By: myleott
fbshipit-source-id: d5b5d678efdd9dd3e4d7ca848ddcf1ec2b21bf6b
Summary:
See #467. Ping myleott to review.
This is a work-related contribution. Ping lark to review.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/794
Differential Revision: D15756816
Pulled By: myleott
fbshipit-source-id: 6dce3ff3a713bf5f60e5782bc260b2ca9d2c0a9b
- 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
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
Release notes:
- 5c7f495: Added simple LSTM model with input feeding and attention
- 6e4b7e2: Refactored model definitions and incremental generation to be cleaner
- 7ae79c1: Split interactive generation out of generate.py and into a new binary: interactive.py
- 19a3865: 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.
- 97d7fcb: 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.
- f442f89: 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).
- c6d6256: Add `--log-format` option and JSON logger
