GitLab

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/667

Use smaller models so that unittests won't timeout

Reviewed By: pipibjc

Differential Revision: D15056894

fbshipit-source-id: af9fbda6ea6e56cf82d52555620121b189e2f013

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/666

Option to load the XLM weights into only the encoder or the decoder

Reviewed By: pipibjc

Differential Revision: D14881004

fbshipit-source-id: 6d0d598ea9c445ec468f71b8e855712de89a5dac

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/629

Use GeLU as an alternate activation layer for ReLU.

Reviewed By: lematt1991

Differential Revision: D14689851

fbshipit-source-id: 7ec81fa34bc7bd0e1e43b337847ae932dcbf8b15

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/653

After this diff, you can train a transformer model with --activation-fn 'relu', 'gelu', or 'gelu_fast'

gelu_fast is the default implementation in https://github.com/hendrycks/GELUs/blob/master/mnist_fcn.py#L72-L77
gelu is the alternate implementation in https://github.com/hendrycks/GELUs/blob/master/mnist_fcn.py#L72-L77 and the default implementation in https://github.com/facebookresearch/XLM

Reviewed By: pipibjc

Differential Revision: D14966006

fbshipit-source-id: 94e95fb99bd548ba47cf23b4999265c7b6833fc1

Summary:
Added link to blog post about incremental decoder in the FairseqIncrementalDecoder class description.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/662

Differential Revision: D15077845

Pulled By: myleott

fbshipit-source-id: f23294721739600e14feb2cca4ece95f2b968f44

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/665

Differential Revision: D15077853

Pulled By: huihuifan

fbshipit-source-id: 2a0d3f6236ae002579f1ee72735d6d8000b8e6b6

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/661

Differential Revision: D15068312

Pulled By: myleott

fbshipit-source-id: 1216835fd4c7f83ea5e350bff83901c93ac57447

Summary:
Because the size of `unfinalized_scores` is equal to current `bsz` and not initial batch size, we need to index it by `unfin_idx` instead of `sent` in `is_finished`.
Fixes #588.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/627

Differential Revision: D15034641

Pulled By: myleott

fbshipit-source-id: 2638e68e877ae01256cac7d8e69b5b7fec8f7017

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/647

the current implementation of average_checkpoints requires loading all
the model parameters into memory and then do the averaging. To average large
models (e.g., transformer) over a large number of checkpoints (e.g., >50),
it may require over 100GB memory.

Loading all the parameters is not necessary, as we know the number of models in advance.

Reviewed By: skritika

Differential Revision: D15027513

fbshipit-source-id: 0afe37c9a031a9ab0f1e78844a37be49ec5f76f1

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/641

Fix breaking import

Reviewed By: pipibjc

Differential Revision: D14978454

fbshipit-source-id: 7b43152cb30100881e9991ead871531ee3f60e07

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/639

Add argument sampling_func in the constructor to enable custom sampling over a list of dataset keys. The default strategy is to sample uniformly as it did previously.

Reviewed By: liezl200

Differential Revision: D14965774

fbshipit-source-id: f3285688a9ae3729c0ba12c22254c1144d0eea9e

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/638

RT

Reviewed By: liezl200

Differential Revision: D14967268

fbshipit-source-id: 2da361497743d90a841fdbf2a50085136c70b468

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/635

Adding a task and relevant models, datasets and criteria needed for training Cross-lingual Language Models similar to Masked Language Model used in XLM (Lample and Conneau, 2019 - https://arxiv.org/abs/1901.07291).

Reviewed By: liezl200

Differential Revision: D14943776

fbshipit-source-id: 3e416a730303d1dd4f5b92550c78db989be27073

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/615

Differential Revision: D14933742

Pulled By: myleott

fbshipit-source-id: c2c20425875743c89bbc2ac564a2fbb6ff4958b2

Summary:
If arg.keep_interval_updates or args.keep_last_epochs > 0, `checkpoints` would refer to a list of checkpoint files to be removed, which can be empty. So moved the logging code to the right position.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/634

Differential Revision: D14933655

Pulled By: myleott

fbshipit-source-id: 68182ee99d9701e1536833d31e0a7c5d2eb2d679

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/625

Differential Revision: D14822123

Pulled By: myleott

fbshipit-source-id: 8a263d30020588577ee02fb8c6959ff918705103

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/633

Pull Request resolved: https://github.com/pytorch/translate/pull/456

This diff makes it easier to upgrade the state dict for components that use TransformerEncoderLayer

Reviewed By: jhcross

Differential Revision: D14916941

fbshipit-source-id: 6d0258c8a9492a720684dadce59c90fc87cbf5cf

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/630

sacrebleu scorer has stopped working in pytorch_translate (maybe
fairseq too) probably due to  a recent api change.

Reviewed By: jmp84

Differential Revision: D14792797

fbshipit-source-id: c2a00246e08bc913c41e60c5fbf8ab4ab5e80d18

Summary:
I added an upgrade_state_dict function so that loading old models will still work

layer_norms[0] --> self_attn_layer_norm
layer_norms[1] --> final_layer_norm

Reviewed By: pipibjc

Differential Revision: D14689849

fbshipit-source-id: b2809262c11fe9d083e571fa31044798aefd48ce

Summary: Used in fairspeq/train.py

Reviewed By: myleott, yqwangustc

Differential Revision: D14841512

fbshipit-source-id: 02fd7b58841c32e2797e3159e65f2bef36f02da1

Summary:
- Add language token to MultilingualTranslation task
- Add back translation and denoising loss to MultilingualTranslation task
Pull Request resolved: https://github.com/pytorch/fairseq/pull/620

Reviewed By: liezl200

Differential Revision: D14756873

Pulled By: pipibjc

fbshipit-source-id: 89d668db26848fd95f446edf5923bab2113636f7

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/628

Updating embedding layers in TransformerSentenceEncoder to be compatible with the transformer model.

Reviewed By: liezl200

Differential Revision: D14836883

fbshipit-source-id: 2240f61bf40b191d01b4efdaac4dd7562b4166c6

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/626

While training a model on multiple GPUs, the current fairseq train workflow fails while creating the directory from which to load a checkpoint. This seems to be happening because multiple nodes attempt to create the same directory thus causing some weird interaction with os.makedirs option "exist_ok=True". Fixing this by making sure only rank 0 creates this directory.

Reviewed By: myleott

Differential Revision: D14841304

fbshipit-source-id: c9b73ba804de97e2cb19a616189fefce476d8c74

Summary: There are constantly wait timeout issue for using multiple nodes, even setting copylocallytempdir:/ doesn't help, eg f105637629. It seems to be working after I moved distributed_init after get_batch_iterator, eg f106520580

Reviewed By: myleott

Differential Revision: D14817769

fbshipit-source-id: edbb101a28d8082241c7bdd8c5500c9dad27647c

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/605

Eval and log on a subset of directions for multimodel training

This reduces code duplication in PyTorch Translate's semi_supervised task and will enable clean multitask setups in the future.

Reviewed By: pipibjc, dpacgopinath

Differential Revision: D14672779

fbshipit-source-id: 1342c71781f0824cc56a38ad1c1822e34eaef337

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/622

Updating some defaults to more meaningful values

Reviewed By: rutyrinott

Differential Revision: D14761263

fbshipit-source-id: 7ac670aa370f315ddfb511c63273583a6062c569

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/621

In this commit, I add some modules to Fairseq needed to set up Bert/XLM style pretraining.

Reviewed By: borguz

Differential Revision: D14719663

fbshipit-source-id: 1c5c36b6b2cde1c9bcd3c9e9ac853d2b7ae64102

Summary:
This diff adds:

1. Aligned training task specifically for doing cross entropy criterion training using prod data and prod like models
2. Few changes to correctly register the task and criterions.
3. Changes to trainer code for propogating accuracy metrics which we care about for training.

Couple of things are hacky right now:
- The reporting is not modular (this needs to be thought about in general for fairseq).

- The get dummy batch could be specific to task instead of specific for dataset.

Reviewed By: myleott

Differential Revision: D14670482

fbshipit-source-id: dc077247b2ae9d26a8e842a386ec5faa5771e836

Summary:
Pull Request resolved: https://github.com/pytorch/translate/pull/429

Pull Request resolved: https://github.com/pytorch/fairseq/pull/618

PyTorch export for transformer models was broken because as written, they used a placeholder `None` value during inference for the variable `key_padding_mask` to indicate no padding, but PyTorch is unable trace such values. This diff adds a minor hack to allow the use of an empty tensor for the same purpose.

Reviewed By: jmp84

Differential Revision: D14581730

fbshipit-source-id: 2ea4664c20ecab8478c578b2182a85319140036c

Summary: Sorts dictionaries lexicographically before creating counter. This makes distributed preprocessing deterministic

Reviewed By: myleott

Differential Revision: D14678214

fbshipit-source-id: 7a9e2f0cb367e8fb76da01e108dda4c6c5aab505

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/613

Differential Revision: D14712311

Pulled By: myleott

fbshipit-source-id: 3e7646629b539c10b6af89dece2c0c564f31125f

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/614

Differential Revision: D14712321

Pulled By: myleott

fbshipit-source-id: 8ef973c5d30ebccf0df0f1cabdddd590248a8f8d

Summary:
Correcting the syntax error in assert function cause of a character before error message.

Assertion and the code is working fine now, Tested with wmt-ende task.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/598

Differential Revision: D14712846

Pulled By: myleott

fbshipit-source-id: 3f708aa2362ceecba19174750f9ffc9238537512

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/606

Differential Revision: D14680968

Pulled By: myleott

fbshipit-source-id: 8044d828a8167199c10f2aee24f7e611feb91802

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/607

Differential Revision: D14681031

Pulled By: myleott

fbshipit-source-id: 466ee526a30543218e2b7138fb651db866ae5ab3

Summary:
Hi,

currently, the link to the language model readme is broken on the `examples/language_model/transformer_lm` page.

This PR fixes the link :)
Pull Request resolved: https://github.com/pytorch/fairseq/pull/600

Differential Revision: D14680985

Pulled By: myleott

fbshipit-source-id: 62291efbf4ece2af54fae45c408c2759863f9847

Summary:
This is pull request was created automatically because we noticed your project was missing a Code of Conduct file.

Code of Conduct files facilitate respectful and constructive communities by establishing expected behaviors for project contributors.

This PR was crafted with love by Facebook's Open Source Team.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/603

Differential Revision: D14680981

Pulled By: myleott

fbshipit-source-id: 653262641554735d89f96c392c72fb311e53a451

Summary:
The unfold1d.py has the same name as the function `unfold1d` function, which will cause an error when using DynamicConv1dTBC with `unfold=True`.
This doesn't affect the NMT models which don't use the unfolding mode though.

I rename `unfold1d.py` as `unfold.py` to fix this bug.

Originally we would get `TypeError` when running this code:
```
import torch
from fairseq.modules import LightweightConv1dTBC, DynamicConv1dTBC

x = torch.rand(4, 10, 8)
m = LightweightConv1dTBC(8, 4, 3)
o = m(x, unfold=True)

m = DynamicConv1dTBC(8, 4, 3)
o = m(x, unfold=True)
```
Pull Request resolved: https://github.com/pytorch/fairseq/pull/593

Differential Revision: D14597117

Pulled By: myleott

fbshipit-source-id: 59752fd7ff62c53a4aba8b56b83155291e5f5792

Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/597

Pull Request resolved: https://github.com/facebookresearch/pytext/pull/424

Fixes two issues:
1. the new Layernorm has issues in exporting
2. fix tensorboard writing by using the "RAW" operator_export_type

Differential Revision: D14610694

fbshipit-source-id: 1b859f54c571a90766128ab28539a9901375c3e6

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/587

Differential Revision: D14517597

Pulled By: myleott

fbshipit-source-id: 4831ea5a9da1c2e207529a4ab3c4d0b070f5f34e