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  1. 06 Jun, 2019 1 commit
  3. 04 Jun, 2019 1 commit
    • Matt Le's avatar
      Fix loading XLM pretraining · 5408bc08
      Matt Le authored 
      Summary: We never actually load the model parameters from an XLM model when using tranformer_from_pretrained_xlm.  Also, change encoder_learned_pos from True -> False

Reviewed By: liezl200

Differential Revision: D15629061

fbshipit-source-id: 759eadc88041eae94505477960de57dd78a99dcb
      5408bc08
  5. 30 May, 2019 1 commit
  7. 24 May, 2019 1 commit
  9. 20 May, 2019 1 commit
  11. 17 May, 2019 1 commit
  13. 15 May, 2019 1 commit
    • Myle Ott's avatar
      Updates to model API (#561) · dffb1674
      Myle Ott authored 
      Summary:
- `FairseqModel` -> `FairseqEncoderDecoderModel`
- add `FairseqDecoder.extract_features` and `FairseqDecoder.output_layer`
- `encoder_out_dict` -> `encoder_out`
- rm unused `remove_head` functions
- update docs
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/561

Differential Revision: D15271142

Pulled By: myleott

fbshipit-source-id: 8e8864e399336020f0271c780598e968ff51a264
      dffb1674
  15. 14 May, 2019 2 commits
  17. 09 May, 2019 1 commit
    • Jingfei Du's avatar
      expose arguments for bias_kv and zero_attn for masked_lm · 93ec8d0b
      Jingfei Du authored 
      Summary: the old no_bias_kv argument for masked_lm models are not used. Split it into 2 arguments and expose them.

Reviewed By: myleott

Differential Revision: D15266154

fbshipit-source-id: 60b041f8370ca1d8869ed3402fb9a67d1cd8e0e8
      93ec8d0b
  19. 07 May, 2019 2 commits
  21. 06 May, 2019 1 commit
    • Naman Goyal's avatar
      allowing sharded dataset (#696) · 0add50c2
      Naman Goyal authored 
      

Summary:
Co-authored-by: default avatarmyleott <myleott@fb.com>

Changing `data` to be `str` with colon separated list for loading sharded datasets. This change is useful for loading large datasets that cannot fit into, memory. The large dataset can be sharded and then each shard is loaded in one epoch in roudrobin manner.

For example, if there are `5` shards of data and `10` epochs then the shards will be iterated upon `[0, 1, 2, 3, 4, 0, 1, 2, 3, 4]`.

myleott We need to look into `translation.py` as it currently already expects a list and then concats the datasets.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/696

Differential Revision: D15214049

fbshipit-source-id: 03e43a7b69c7aefada2ca668abf1eac1969fe013
      0add50c2
  23. 04 May, 2019 1 commit
  25. 30 Apr, 2019 1 commit
  27. 25 Apr, 2019 3 commits
  29. 17 Apr, 2019 1 commit
  31. 15 Apr, 2019 1 commit
  33. 10 Apr, 2019 1 commit
  35. 12 Mar, 2019 2 commits
    • Dmytro Okhonko's avatar
      Handle 3+ dimensional input in sequence_generator + nits · 860010e9
      Dmytro Okhonko authored 
      Summary: sequence_generator assumes that model input is 2d tensor of longs. But it can be something like 3d tensor of floats and we should be able to handle this as long as first dimension is batch size followed by source lengths.

Reviewed By: myleott

Differential Revision: D14420044

fbshipit-source-id: bf8b1e42ad1873f7b803c1a377b0af21648db015
      860010e9
    • Dmytro Okhonko's avatar
      Adadelta optimizer · d17fa851
      Dmytro Okhonko authored 
      Summary: Adding Adadelta optimizer to fairseq as wrapper around torch.optim.Adadelta

Reviewed By: myleott

Differential Revision: D14418635

fbshipit-source-id: 6bf5ec008e905a4a2cbf7415e9492f5eea3ff07f
      d17fa851
  37. 28 Feb, 2019 2 commits
  39. 26 Feb, 2019 1 commit
  41. 22 Feb, 2019 1 commit
  43. 01 Feb, 2019 1 commit
    • Davide Caroselli's avatar
      Support custom Dictionary implementations in 'preprocess.py' (#448) · bbb4120b
      Davide Caroselli authored 
      Summary:
The `preprocess.py` script has been refactored in order to:

1. Use the `options` module for command line arguments  parsing. This will give to `preprocess.py` the ability to load custom modules with `--user-dir` flag (already implemented to all other binaries)
2. Dictionary loading and building code has moved to Task implementation. This allows custom Dictionary classes to be used during the data generation step.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/448

Differential Revision: D13674819

Pulled By: myleott

fbshipit-source-id: b40648a98ed6c08284577e5ec25876e018d8c822
      bbb4120b
  45. 30 Jan, 2019 2 commits
  47. 25 Jan, 2019 1 commit
  49. 05 Jan, 2019 1 commit
  51. 26 Nov, 2018 1 commit
    • Myle Ott's avatar
      Refactor BacktranslationDataset to be more reusable (#354) · 3c19878f
      Myle Ott authored 
      Summary:
- generalize AppendEosDataset -> TransformEosDataset
- remove EOS logic from BacktranslationDataset (use TransformEosDataset instead)
- BacktranslationDataset takes a backtranslation_fn instead of building the SequenceGenerator itself
Pull Request resolved: https://github.com/pytorch/fairseq/pull/354

Reviewed By: liezl200

Differential Revision: D12970233

Pulled By: myleott

fbshipit-source-id: d5c5b0e0a75eca1bd3a50382ac24621f35c32f36
      3c19878f
  53. 18 Nov, 2018 1 commit
  55. 07 Nov, 2018 1 commit
    • Liezl Puzon's avatar
      Support BPE end of word marker suffix in fairseq noising module · 2b13f3c0
      Liezl Puzon authored 
      Summary:
There are 2 ways to implement BPE:
1. use a continuation marker suffix to indicate that there is at least one more subtoken left in the word
2. use a end of word marker suffix to indicate that there is no more subtokens left in the word

This adds some logic to account for either kind of BPE marker suffix. This diff adds a corresponding test. I also refactored the test setup to reduce the number of boolean args when setting up test data.

Reviewed By: xianxl

Differential Revision: D12919428

fbshipit-source-id: 405e9f346dce6e736c1305288721dfc7b63e872a
      2b13f3c0
  57. 02 Nov, 2018 2 commits
  59. 01 Nov, 2018 1 commit
  61. 27 Oct, 2018 1 commit
    • Xian Li's avatar
      Extend WordShuffle noising function to apply to non-bpe tokens · 90c01b3a
      Xian Li authored 
      Summary:
We'd like to resue the noising functions and DenoisingDataset in
adversarial training. However, current noising functions assume the input are
subword tokens. The goal of this diff is to extend it so the noising can be
applied to word tokens. Since we're mostly interested in the word shuffle
noising, so I only modified the WordShuffle class.

Reviewed By: liezl200

Differential Revision: D10523177

fbshipit-source-id: 1e5d27362850675010e73cd38850c890d42652ab
      90c01b3a
  63. 23 Oct, 2018 1 commit