Summary:
This unit test guards the bmuf code.
change:
1. distributed_init assumes we are always using cuda device which is not the case if you are using "gloo" backend on CPU machine.
Reviewed By: jay-mahadeokar
Differential Revision: D17821391
fbshipit-source-id: 28e1bb39f7a4889b1dc6bd636b7c499e55bfc69a
Summary:
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/877
This PR implements guided alignment training described in "Jointly Learning to Align and Translate with Transformer Models (https://arxiv.org/abs/1909.02074)".
In summary, it allows for training selected heads of the Transformer Model with external alignments computed by Statistical Alignment Toolkits. During inference, attention probabilities from the trained heads can be used to extract reliable alignments. In our work, we did not see any regressions in the translation performance because of guided alignment training.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/1095
Differential Revision: D17170337
Pulled By: myleott
fbshipit-source-id: daa418bef70324d7088dbb30aa2adf9f95774859
Summary:
This PR implements a new attention module which combines cross-attention (encoder-decoder attention) and the decoder self-attention. This work was accepted as an abstract at WeCNLP 2019 (https://www.wecnlp.ai/wecnlp-2019).
Cross+Self-Attention reduces the amount of parameter and increases the inference speed without any degradation in translation quality.
More details can be found in the attached [abstract](https://github.com/pytorch/fairseq/files/3561282/paper.pdf)
Pull Request resolved: https://github.com/pytorch/fairseq/pull/1097
Differential Revision: D17653168
Pulled By: myleott
fbshipit-source-id: deb834c2c78a229d7418ffbfea20ba3ce252991c
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:
As discussed with Naman earlier today. Weighted sampling with
replacement can be done on a per-epoch basis using `set_epoch()`
functionality, which generates the samples as a function of random seed
and epoch.
Additionally, `FairseqTask` needs to set the starting epoch for the
dataset at the very beginning of iterator construction.
Not yet implemented is the per-epoch iterator construction, which
is necessary to actually regenerate the batches for each epoch.
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/861
Differential Revision: D17460687
Pulled By: jma127
fbshipit-source-id: 1c2a54f04ac96b3561c100a6fd66a9fccbe3c658
Summary:
Initial code for speech recognition task.
Right now only one ASR model added - https://arxiv.org/abs/1904.11660
unit test testing:
python -m unittest discover tests
also run model training with this code and obtained
5.0 test_clean | 13.4 test_other
on librispeech with pytorch/audio features
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/810
Reviewed By: cpuhrsch
Differential Revision: D16706659
Pulled By: okhonko
fbshipit-source-id: 89a5f9883e50bc0e548234287aa0ea73f7402514
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:
Pull Request resolved: https://github.com/facebookresearch/pytext/pull/804
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/746
Pull Request resolved: https://github.com/pytorch/fairseq/pull/894
Adding an implementation of the sparse transformer to multi-head attention using the fixed attention pattern specified https://arxiv.org/pdf/1904.10509.pdf. The sparse_mask masks out words using -inf; after softmax, -inf becomes 0. Thus, a mask does not need to be re-calculated and re-applied when multiplying attn_weights and values.
Four inputs are added to the config: sparse, is_bidirectional, stride, expressivity. If we are using the sparse transformer, is_bidirectional, stride, and expressivity must be specified (there are defaults). If is_bidirectional is False, the mask values using the fixed attention pattern described in the paper. If is_bidirectional is True, subset one includes all values in the current stride window and a summary from every stride window--all other values are masked. Stride (L in the paper) controls the window size and expressivity (c in the paper) controls the size of the summary.
Reviewed By: borguz
Differential Revision: D16042988
fbshipit-source-id: c59166dc7cfe89187a256e4076000c2458842fd5
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
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
Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/747
In https://github.com/pytorch/fairseq/pull/647, checkpoint averaging
is not Implemented correctly when it comes to shared parameters. This diff
has the right Implementation and a test case to guard future change.
Reviewed By: myleott
Differential Revision: D15402943
fbshipit-source-id: 8004836d5c2571814ea54844650618008a9ee522
Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/730
Pull Request resolved: https://github.com/pytorch/translate/pull/528
Add/modify necessary functions for ConcatDataset to work in PytorchTranslateTask and replace MultiCorpusSampledDataset which doesn't support mixed batch.
Any idea on how to implement collater here for mixed batch? Now I'm just using the collater of the first dataset.
Reviewed By: liezl200
Differential Revision: D15260872
fbshipit-source-id: 14b148c506e9f8ebf4fe60a49f95444d4123d76f
Summary:
Move `load_checkpoint`, `save_checkpoint` and `reload_train` from train.py to checkpoint_utils.py
Move `get_perplexity` from train.py to utils.py.
This will make train.py lighter and allow us to reuse all this utils functionality when fairseq is used as external library.
Reviewed By: myleott
Differential Revision: D15289607
fbshipit-source-id: 4b7c95225ac22e402bcda3497811361809110df1
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
Summary:
Following discussion in https://github.com/pytorch/fairseq/issues/574:
- Implemented MMapIndexedDataset and MMapIndexedDatasetBuilder compatible with IndexedDataset/IndexedDatasetBuilder
- Update scripts/read_binarized.py to support new MMapIndexedDataset
- Option '--raw-text' and '--lazy-load' replaced with '--dataset-impl' and moved the option definition custom task args to more high-level options.add_dataset_args() (more appropriate)
- Implemented also utils functions in indexed_dataset: make_dataset(), dataset_exists()
Pull Request resolved: https://github.com/pytorch/fairseq/pull/589
Differential Revision: D14597128
Pulled By: myleott
fbshipit-source-id: 4e92d99920cbaa52cfe5a0f1f5d9ae5c92d4268e
Summary:
Co-authored-by: myleott <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
Summary:
Pull Request resolved: https://github.com/pytorch/translate/pull/508
The previous version applied the temperature after the softmax. Fix that, and
also generalize so it works with other search approaches.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/694
Differential Revision: D15175160
Pulled By: myleott
fbshipit-source-id: cc87ff0e97a8a1dd37f9983163f58a8641155ab0
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/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: 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
Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/541
Just a combo of a stacked pair D14057943 & D14176011,
Made this as a separete diff cause there seems to be some issue with porting a stacked change into github repo
Differential Revision: D14251048
fbshipit-source-id: 0a47f534a69d6ab2ebe035fba40fd51748cccfb8
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
Summary:
Changelog:
- `4889802`: can now remove detokenize sentencepiece output with `--remove-bpe=sentencepiece` (fixes#331). Also added `--sacrebleu` for computing detokenized BLEU.
- `0d76427`: fix assertion error when training language model with dataset containing empty sentences
- minor bug and style fixes
Pull Request resolved: https://github.com/pytorch/fairseq/pull/483
Differential Revision: D13867899
Pulled By: myleott
fbshipit-source-id: 25c940b847fe270262ac8f5ac838407b3977fdda
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
Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/340
This allows us to do a lot less copy paste when adding new word shuffle function tests
Reviewed By: xianxl
Differential Revision: D12810304
fbshipit-source-id: a56b5df093d17be2b73837897c526978cab92b70
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
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:
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: 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:
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
A Task defines the data format, stores shared state (e.g., dictionaries) and provides helpers for building the model/criterion and calculating the loss.
Changes:
- Add TranslationTask and LanguageModelingTask. New tasks can be registered with @register_task decorator.
- Add EpochBatchIterator to encapsulate batching and saving/restoring dataloader position
- Remove LEFT_PAD_* constants and make them configurable per task
This implements convolutional language model from https://arxiv.org/pdf/1612.08083.pdf
There are 3 modes for constructing batches:
- token block: fill each sample with a specified number of tokens without regard for sentence delimiters - this is what was used for training in the paper
- complete: fill each sample with a specified number of tokens but make sure it contains only complete sentences (i.e. if next sentence goes over token block limit, move it to the next sample) - this was used for evaluation in the paper
- eos: one sentence per sample (skip blank lines)
some results:
GCNN-13 - GBW - 37.46
GCNN-14B - GBW - 33.88
GCNN-8 - Wiki103 - 43.76
GCNN-14 - Wiki103 - 35.66
train:
python train.py /private/home/abaevski/data/wiki103 --save-dir /tmp --fp16 --max-epoch 35 --save-interval 1 --save-interval-updates 1000 --keep-interval-updates 25 --arch fconv_lm --optimizer nag --lr 1.0 --lr-scheduler reduce_lr_on_plateau --lr-shrink 0.5 --decoder-embed-dim 280 --decoder-layers '[(850, 6)] * 3 + [(850,1)] + [(850,5)] * 4 + [(850,1)] + [(850,4)] * 3 + [(1024,4)] + [(2048, 4)]' --clip-norm 0.1 --dropout 0.2 --weight-decay 5e-06 --criterion cross_entropy --max-tokens 1024 --max-target-positions 1024 --seed 1 --log-format json --log-interval 500
eval:
python eval_lm.py ~abaevski/data/wiki103 --path '/checkpoint02/abaevski/2018-04-27/lm_wiki.fp16.mxup300000.fconv.adam.lrs=reduce_lr_on_plateau.emb280.layers(850,6)*3+(850,1)+(850,5)*4+(850,1)+(850,4)*3+(1024,1)+(2048,4).lr0.0005.clp0.1.drp0.3.wd0.0.crt=cross_entropy.mxtk2048.smptk256.seed1.ngpu8/checkpoint_last.pt'
Changes:
- 7d19e36: Add `--sampling` flag to generate.py to sample instead of doing beam search
- c777340: Add `scripts/average_checkpoints.py` to average multiple checkpoints into a combined model
- 3ea882c: Add `--max-update` option to train.py to stop training after a given number of updates
- small bugfixes for distributed training, LSTM, inverse square root LR scheduler
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
* 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
