├── tests ├── __init__.py ├── speech_recognition │ ├── __init__.py │ ├── test_cross_entropy.py │ └── test_collaters.py ├── test_iterators.py ├── test_file_io.py ├── test_character_token_embedder.py ├── test_convtbc.py ├── test_dictionary.py ├── test_multihead_attention.py ├── test_concat_dataset.py ├── test_memory_efficient_fp16.py ├── test_utils.py └── test_sparse_multihead_attention.py ├── fairseq_cli └── __init__.py ├── scripts ├── __init__.py ├── spm_train.py ├── compound_split_bleu.sh ├── sacrebleu_pregen.sh ├── convert_dictionary.lua ├── compare_namespaces.py ├── read_binarized.py ├── spm_decode.py ├── shard_docs.py ├── count_docs.py └── wav2vec_manifest.py ├── fairseq ├── data │ ├── audio │ │ └── __init__.py │ ├── num_samples_dataset.py │ ├── id_dataset.py │ ├── legacy │ │ ├── __init__.py │ │ └── masked_lm_dictionary.py │ ├── offset_tokens_dataset.py │ ├── roll_dataset.py │ ├── strip_token_dataset.py │ ├── encoders │ │ ├── space_tokenizer.py │ │ ├── nltk_tokenizer.py │ │ ├── __init__.py │ │ ├── utils.py │ │ ├── fastbpe.py │ │ ├── sentencepiece_bpe.py │ │ ├── gpt2_bpe.py │ │ ├── subword_nmt_bpe.py │ │ ├── hf_bert_bpe.py │ │ └── moses_tokenizer.py │ ├── raw_label_dataset.py │ ├── lru_cache_dataset.py │ ├── sort_dataset.py │ ├── list_dataset.py │ ├── numel_dataset.py │ ├── colorize_dataset.py │ ├── pad_dataset.py │ ├── truncate_dataset.py │ ├── prepend_dataset.py │ ├── append_token_dataset.py │ ├── prepend_token_dataset.py │ ├── replace_dataset.py │ ├── base_wrapper_dataset.py │ ├── concat_sentences_dataset.py │ ├── sharded_dataset.py │ ├── data_utils_fast.pyx │ ├── subsample_dataset.py │ └── fairseq_dataset.py ├── modules │ ├── lightconv_layer │ │ ├── __init__.py │ │ ├── setup.py │ │ ├── lightconv_cuda.cpp │ │ └── lightconv_cuda.cuh │ ├── dynamicconv_layer │ │ ├── __init__.py │ │ ├── setup.py │ │ ├── dynamiconv_cpu.cpp │ │ ├── dynamicconv_cuda.cuh │ │ └── dynamicconv_cuda.cpp │ ├── grad_multiply.py │ ├── unfold.py │ ├── layer_norm.py │ ├── gelu.py │ ├── logsumexp_moe.py │ ├── scalar_bias.py │ ├── positional_embedding.py │ ├── conv_tbc.py │ ├── sparse_transformer_sentence_encoder_layer.py │ ├── highway.py │ ├── beamable_mm.py │ ├── learned_positional_embedding.py │ ├── mean_pool_gating_network.py │ ├── __init__.py │ └── adaptive_input.py ├── models │ ├── bart │ │ └── __init__.py │ ├── nat │ │ └── __init__.py │ ├── roberta │ │ ├── __init__.py │ │ ├── model_camembert.py │ │ └── model_xlmr.py │ ├── fairseq_encoder.py │ ├── composite_encoder.py │ ├── distributed_fairseq_model.py │ └── model_utils.py ├── tokenizer.py ├── __init__.py ├── clib │ ├── libnat_cuda │ │ ├── edit_dist.h │ │ └── binding.cpp │ └── libbleu │ │ └── module.cpp ├── criterions │ ├── __init__.py │ └── fairseq_criterion.py ├── optim │ ├── lr_scheduler │ │ ├── __init__.py │ │ ├── fairseq_lr_scheduler.py │ │ └── fixed_schedule.py │ ├── __init__.py │ ├── adagrad.py │ ├── sgd.py │ ├── adadelta.py │ └── fused_lamb.py ├── tasks │ ├── translation_from_pretrained_xlm.py │ ├── audio_pretraining.py │ └── __init__.py ├── pdb.py └── incremental_decoding_utils.py ├── examples ├── .gitignore ├── speech_recognition │ ├── __init__.py │ ├── tasks │ │ └── __init__.py │ ├── data │ │ ├── __init__.py │ │ └── replabels.py │ ├── models │ │ └── __init__.py │ └── criterions │ │ └── __init__.py ├── noisychannel │ ├── __init__.py │ └── rerank_score_lm.py ├── roberta │ ├── commonsense_qa │ │ ├── __init__.py │ │ └── download_cqa_data.sh │ ├── wsc │ │ └── __init__.py │ └── preprocess_RACE.sh ├── __init__.py ├── language_model │ ├── prepare-wikitext-103.sh │ ├── conv_lm │ │ └── README.md │ └── transformer_lm │ │ └── README.md ├── backtranslation │ └── README.md ├── conv_seq2seq │ └── README.md ├── camembert │ └── README.md └── wav2vec │ └── README.md ├── docs ├── docutils.conf ├── requirements.txt ├── _static │ └── theme_overrides.css ├── modules.rst ├── Makefile ├── criterions.rst ├── make.bat ├── optim.rst ├── lr_scheduler.rst ├── index.rst ├── data.rst ├── tasks.rst └── command_line_tools.rst ├── fairseq.gif ├── fairseq_logo.png ├── .github ├── ISSUE_TEMPLATE.md ├── ISSUE_TEMPLATE │ ├── documentation.md │ ├── feature_request.md │ ├── how-to-question.md │ └── bug_report.md ├── PULL_REQUEST_TEMPLATE.md └── workflows │ ├── build.yml │ └── build_windows.yml ├── score.py ├── train.py ├── eval_lm.py ├── generate.py ├── validate.py ├── interactive.py ├── preprocess.py ├── LICENSE ├── CONTRIBUTING.md ├── hubconf.py └── .gitignore /tests/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /fairseq_cli/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /scripts/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /fairseq/data/audio/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /tests/speech_recognition/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /examples/.gitignore: -------------------------------------------------------------------------------- 1 | !*/*.sh 2 | !*/*.md 3 | -------------------------------------------------------------------------------- /docs/docutils.conf: -------------------------------------------------------------------------------- 1 | [writers] 2 | option-limit=0 3 | -------------------------------------------------------------------------------- /docs/requirements.txt: -------------------------------------------------------------------------------- 1 | sphinx<2.0 2 | sphinx-argparse 3 | -------------------------------------------------------------------------------- /fairseq.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/urvashik/knnlm/HEAD/fairseq.gif -------------------------------------------------------------------------------- /fairseq_logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/urvashik/knnlm/HEAD/fairseq_logo.png -------------------------------------------------------------------------------- /examples/speech_recognition/__init__.py: -------------------------------------------------------------------------------- 1 | from . import tasks, criterions, models # noqa 2 | -------------------------------------------------------------------------------- /docs/_static/theme_overrides.css: -------------------------------------------------------------------------------- 1 | .wy-table-responsive table td kbd { 2 | white-space: nowrap; 3 | } 4 | .wy-table-responsive table td { 5 | white-space: normal !important; 6 | } 7 | .wy-table-responsive { 8 | overflow: visible !important; 9 | } 10 | -------------------------------------------------------------------------------- /examples/noisychannel/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from .rerank_options import * # noqa 7 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE.md: -------------------------------------------------------------------------------- 1 | ## 👉 [Please follow one of these issue templates](https://github.com/pytorch/fairseq/issues/new/choose) 👈 2 | 3 | Note: to keep the backlog clean and actionable, issues may be immediately closed if they do not follow one of the above issue templates. 4 | -------------------------------------------------------------------------------- /examples/roberta/commonsense_qa/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from . import commonsense_qa_task # noqa 7 | -------------------------------------------------------------------------------- /examples/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | __version__ = '0.9.0' 7 | 8 | import examples.noisychannel # noqa 9 | -------------------------------------------------------------------------------- /fairseq/modules/lightconv_layer/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from .lightconv_layer import LightconvLayer # noqa 7 | -------------------------------------------------------------------------------- /docs/modules.rst: -------------------------------------------------------------------------------- 1 | Modules 2 | ======= 3 | 4 | Fairseq provides several stand-alone :class:`torch.nn.Module` classes that may 5 | be helpful when implementing a new :class:`~fairseq.models.BaseFairseqModel`. 6 | 7 | .. automodule:: fairseq.modules 8 | :members: 9 | :undoc-members: 10 | -------------------------------------------------------------------------------- /fairseq/models/bart/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from .hub_interface import * # noqa 7 | from .model import * # noqa 8 | -------------------------------------------------------------------------------- /fairseq/modules/dynamicconv_layer/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from .dynamicconv_layer import DynamicconvLayer # noqa 7 | -------------------------------------------------------------------------------- /examples/roberta/wsc/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from . import wsc_criterion # noqa 7 | from . import wsc_task # noqa 8 | -------------------------------------------------------------------------------- /examples/speech_recognition/tasks/__init__.py: -------------------------------------------------------------------------------- 1 | import importlib 2 | import os 3 | 4 | for file in os.listdir(os.path.dirname(__file__)): 5 | if file.endswith('.py') and not file.startswith('_'): 6 | task_name = file[:file.find('.py')] 7 | importlib.import_module('examples.speech_recognition.tasks.' + task_name) 8 | -------------------------------------------------------------------------------- /fairseq/models/nat/__init__.py: -------------------------------------------------------------------------------- 1 | from .fairseq_nat_model import * 2 | from .nonautoregressive_transformer import * 3 | from .nat_crf_transformer import * 4 | from .iterative_nonautoregressive_transformer import * 5 | from .cmlm_transformer import * 6 | from .levenshtein_transformer import * 7 | from .insertion_transformer import * 8 | -------------------------------------------------------------------------------- /examples/speech_recognition/data/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from .asr_dataset import AsrDataset 7 | 8 | __all__ = [ 9 | 'AsrDataset', 10 | ] 11 | -------------------------------------------------------------------------------- /examples/speech_recognition/models/__init__.py: -------------------------------------------------------------------------------- 1 | import importlib 2 | import os 3 | 4 | for file in os.listdir(os.path.dirname(__file__)): 5 | if file.endswith('.py') and not file.startswith('_'): 6 | model_name = file[:file.find('.py')] 7 | importlib.import_module('examples.speech_recognition.models.' + model_name) 8 | -------------------------------------------------------------------------------- /score.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 -u 2 | # Copyright (c) Facebook, Inc. and its affiliates. 3 | # 4 | # This source code is licensed under the MIT license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | from fairseq_cli.score import cli_main 8 | 9 | 10 | if __name__ == '__main__': 11 | cli_main() 12 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 -u 2 | # Copyright (c) Facebook, Inc. and its affiliates. 3 | # 4 | # This source code is licensed under the MIT license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | from fairseq_cli.train import cli_main 8 | 9 | 10 | if __name__ == '__main__': 11 | cli_main() 12 | -------------------------------------------------------------------------------- /eval_lm.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 -u 2 | # Copyright (c) Facebook, Inc. and its affiliates. 3 | # 4 | # This source code is licensed under the MIT license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | from fairseq_cli.eval_lm import cli_main 8 | 9 | 10 | if __name__ == '__main__': 11 | cli_main() 12 | -------------------------------------------------------------------------------- /generate.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 -u 2 | # Copyright (c) Facebook, Inc. and its affiliates. 3 | # 4 | # This source code is licensed under the MIT license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | from fairseq_cli.generate import cli_main 8 | 9 | 10 | if __name__ == '__main__': 11 | cli_main() 12 | -------------------------------------------------------------------------------- /validate.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 -u 2 | # Copyright (c) Facebook, Inc. and its affiliates. 3 | # 4 | # This source code is licensed under the MIT license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | from fairseq_cli.validate import cli_main 8 | 9 | 10 | if __name__ == '__main__': 11 | cli_main() 12 | -------------------------------------------------------------------------------- /interactive.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 -u 2 | # Copyright (c) Facebook, Inc. and its affiliates. 3 | # 4 | # This source code is licensed under the MIT license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | from fairseq_cli.interactive import cli_main 8 | 9 | 10 | if __name__ == '__main__': 11 | cli_main() 12 | -------------------------------------------------------------------------------- /preprocess.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 -u 2 | # Copyright (c) Facebook, Inc. and its affiliates. 3 | # 4 | # This source code is licensed under the MIT license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | from fairseq_cli.preprocess import cli_main 8 | 9 | 10 | if __name__ == '__main__': 11 | cli_main() 12 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/documentation.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: 📚 Documentation/Typos 3 | about: Report an issue related to documentation or a typo 4 | labels: 'documentation, needs triage' 5 | --- 6 | 7 | ## 📚 Documentation 8 | 9 | For typos and doc fixes, please go ahead and: 10 | 11 | 1. Create an issue. 12 | 2. Fix the typo. 13 | 3. Submit a PR. 14 | 15 | Thanks! 16 | -------------------------------------------------------------------------------- /fairseq/models/roberta/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from .hub_interface import * # noqa 7 | from .model import * # noqa 8 | from .model_camembert import * # noqa 9 | from .model_xlmr import * # noqa 10 | -------------------------------------------------------------------------------- /fairseq/tokenizer.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import re 7 | 8 | SPACE_NORMALIZER = re.compile(r"\s+") 9 | 10 | 11 | def tokenize_line(line): 12 | line = SPACE_NORMALIZER.sub(" ", line) 13 | line = line.strip() 14 | return line.split() 15 | -------------------------------------------------------------------------------- /fairseq/data/num_samples_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from . import FairseqDataset 7 | 8 | 9 | class NumSamplesDataset(FairseqDataset): 10 | 11 | def __getitem__(self, index): 12 | return 1 13 | 14 | def __len__(self): 15 | return 0 16 | 17 | def collater(self, samples): 18 | return sum(samples) 19 | -------------------------------------------------------------------------------- /scripts/spm_train.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # Copyright (c) Facebook, Inc. and its affiliates. 3 | # All rights reserved. 4 | # 5 | # This source code is licensed under the license found in the 6 | # LICENSE file in the root directory of this source tree. 7 | 8 | from __future__ import absolute_import, division, print_function, unicode_literals 9 | 10 | import sys 11 | 12 | import sentencepiece as spm 13 | 14 | 15 | if __name__ == "__main__": 16 | spm.SentencePieceTrainer.Train(" ".join(sys.argv[1:])) 17 | -------------------------------------------------------------------------------- /fairseq/data/id_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch 7 | 8 | from . import FairseqDataset 9 | 10 | 11 | class IdDataset(FairseqDataset): 12 | 13 | def __getitem__(self, index): 14 | return index 15 | 16 | def __len__(self): 17 | return 0 18 | 19 | def collater(self, samples): 20 | return torch.tensor(samples) 21 | -------------------------------------------------------------------------------- /fairseq/data/legacy/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from .masked_lm_dictionary import BertDictionary, MaskedLMDictionary 7 | from .block_pair_dataset import BlockPairDataset 8 | from .masked_lm_dataset import MaskedLMDataset 9 | 10 | __all__ = [ 11 | 'BertDictionary', 12 | 'BlockPairDataset', 13 | 'MaskedLMDataset', 14 | 'MaskedLMDictionary', 15 | ] 16 | -------------------------------------------------------------------------------- /fairseq/data/offset_tokens_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from . import BaseWrapperDataset 7 | 8 | 9 | class OffsetTokensDataset(BaseWrapperDataset): 10 | 11 | def __init__(self, dataset, offset): 12 | super().__init__(dataset) 13 | self.offset = offset 14 | 15 | def __getitem__(self, idx): 16 | return self.dataset[idx] + self.offset 17 | -------------------------------------------------------------------------------- /fairseq/modules/grad_multiply.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch 7 | 8 | 9 | class GradMultiply(torch.autograd.Function): 10 | @staticmethod 11 | def forward(ctx, x, scale): 12 | ctx.scale = scale 13 | res = x.new(x) 14 | return res 15 | 16 | @staticmethod 17 | def backward(ctx, grad): 18 | return grad * ctx.scale, None 19 | -------------------------------------------------------------------------------- /scripts/compound_split_bleu.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | if [ $# -ne 1 ]; then 4 | echo "usage: $0 GENERATE_PY_OUTPUT" 5 | exit 1 6 | fi 7 | 8 | GEN=$1 9 | 10 | SYS=$GEN.sys 11 | REF=$GEN.ref 12 | 13 | if [ $(tail -n 1 $GEN | grep BLEU | wc -l) -ne 1 ]; then 14 | echo "not done generating" 15 | exit 16 | fi 17 | 18 | grep ^H $GEN | awk -F '\t' '{print $NF}' | perl -ple 's{(\S)-(\S)}{$1 ##AT##-##AT## $2}g' > $SYS 19 | grep ^T $GEN | cut -f2- | perl -ple 's{(\S)-(\S)}{$1 ##AT##-##AT## $2}g' > $REF 20 | fairseq-score --sys $SYS --ref $REF 21 | -------------------------------------------------------------------------------- /fairseq/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | __all__ = ['pdb'] 7 | __version__ = '0.9.0' 8 | 9 | import fairseq.criterions # noqa 10 | import fairseq.models # noqa 11 | import fairseq.modules # noqa 12 | import fairseq.optim # noqa 13 | import fairseq.optim.lr_scheduler # noqa 14 | import fairseq.pdb # noqa 15 | import fairseq.tasks # noqa 16 | 17 | import fairseq.benchmark # noqa 18 | -------------------------------------------------------------------------------- /examples/speech_recognition/criterions/__init__.py: -------------------------------------------------------------------------------- 1 | import importlib 2 | import os 3 | 4 | 5 | # ASG loss requires wav2letter 6 | blacklist = set() 7 | try: 8 | import wav2letter 9 | except ImportError: 10 | blacklist.add("ASG_loss.py") 11 | 12 | for file in os.listdir(os.path.dirname(__file__)): 13 | if file.endswith(".py") and not file.startswith("_") and file not in blacklist: 14 | criterion_name = file[: file.find(".py")] 15 | importlib.import_module( 16 | "examples.speech_recognition.criterions." + criterion_name 17 | ) 18 | -------------------------------------------------------------------------------- /fairseq/data/roll_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch 7 | 8 | from . import BaseWrapperDataset 9 | 10 | 11 | class RollDataset(BaseWrapperDataset): 12 | 13 | def __init__(self, dataset, shifts): 14 | super().__init__(dataset) 15 | self.shifts = shifts 16 | 17 | def __getitem__(self, index): 18 | item = self.dataset[index] 19 | return torch.roll(item, self.shifts) 20 | -------------------------------------------------------------------------------- /fairseq/data/strip_token_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from . import BaseWrapperDataset 7 | 8 | 9 | class StripTokenDataset(BaseWrapperDataset): 10 | 11 | def __init__(self, dataset, id_to_strip): 12 | super().__init__(dataset) 13 | self.id_to_strip = id_to_strip 14 | 15 | def __getitem__(self, index): 16 | item = self.dataset[index] 17 | return item[item.ne(self.id_to_strip)] 18 | -------------------------------------------------------------------------------- /fairseq/modules/unfold.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch.nn.functional as F 7 | 8 | 9 | def unfold1d(x, kernel_size, padding_l, pad_value=0): 10 | '''unfold T x B x C to T x B x C x K''' 11 | if kernel_size > 1: 12 | T, B, C = x.size() 13 | x = F.pad(x, (0, 0, 0, 0, padding_l, kernel_size - 1 - padding_l), value=pad_value) 14 | x = x.as_strided((T, B, C, kernel_size), (B*C, C, 1, B*C)) 15 | else: 16 | x = x.unsqueeze(3) 17 | return x 18 | -------------------------------------------------------------------------------- /fairseq/modules/lightconv_layer/setup.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # Copyright (c) Facebook, Inc. and its affiliates. 3 | # 4 | # This source code is licensed under the MIT license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | from setuptools import setup 8 | from torch.utils.cpp_extension import CUDAExtension, BuildExtension 9 | 10 | setup( 11 | name='lightconv_layer', 12 | ext_modules=[ 13 | CUDAExtension('lightconv_cuda', [ 14 | 'lightconv_cuda.cpp', 15 | 'lightconv_cuda_kernel.cu', 16 | ]), 17 | ], 18 | cmdclass={ 19 | 'build_ext': BuildExtension 20 | }) 21 | -------------------------------------------------------------------------------- /fairseq/data/encoders/space_tokenizer.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import re 7 | 8 | from fairseq.data.encoders import register_tokenizer 9 | 10 | 11 | @register_tokenizer('space') 12 | class SpaceTokenizer(object): 13 | 14 | def __init__(self, source_lang=None, target_lang=None): 15 | self.space_tok = re.compile(r"\s+") 16 | 17 | def encode(self, x: str) -> str: 18 | return self.space_tok.sub(' ', x) 19 | 20 | def decode(self, x: str) -> str: 21 | return x 22 | -------------------------------------------------------------------------------- /fairseq/data/raw_label_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch 7 | 8 | from . import FairseqDataset 9 | 10 | 11 | class RawLabelDataset(FairseqDataset): 12 | 13 | def __init__(self, labels): 14 | super().__init__() 15 | self.labels = labels 16 | 17 | def __getitem__(self, index): 18 | return self.labels[index] 19 | 20 | def __len__(self): 21 | return len(self.labels) 22 | 23 | def collater(self, samples): 24 | return torch.tensor(samples) 25 | -------------------------------------------------------------------------------- /fairseq/modules/layer_norm.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch 7 | 8 | 9 | def LayerNorm(normalized_shape, eps=1e-5, elementwise_affine=True, export=False): 10 | if not export and torch.cuda.is_available(): 11 | try: 12 | from apex.normalization import FusedLayerNorm 13 | return FusedLayerNorm(normalized_shape, eps, elementwise_affine) 14 | except ImportError: 15 | pass 16 | return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine) 17 | -------------------------------------------------------------------------------- /docs/Makefile: -------------------------------------------------------------------------------- 1 | # Minimal makefile for Sphinx documentation 2 | # 3 | 4 | # You can set these variables from the command line. 5 | SPHINXOPTS = 6 | SPHINXBUILD = python -msphinx 7 | SPHINXPROJ = fairseq 8 | SOURCEDIR = . 9 | BUILDDIR = _build 10 | 11 | # Put it first so that "make" without argument is like "make help". 12 | help: 13 | @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 14 | 15 | .PHONY: help Makefile 16 | 17 | # Catch-all target: route all unknown targets to Sphinx using the new 18 | # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). 19 | %: Makefile 20 | @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) -------------------------------------------------------------------------------- /examples/roberta/commonsense_qa/download_cqa_data.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # Copyright (c) Facebook, Inc. and its affiliates. 3 | # 4 | # This source code is licensed under the MIT license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | OUTDIR=data/CommonsenseQA 8 | 9 | mkdir -p $OUTDIR 10 | 11 | wget -O $OUTDIR/train.jsonl https://s3.amazonaws.com/commensenseqa/train_rand_split.jsonl 12 | wget -O $OUTDIR/valid.jsonl https://s3.amazonaws.com/commensenseqa/dev_rand_split.jsonl 13 | wget -O $OUTDIR/test.jsonl https://s3.amazonaws.com/commensenseqa/test_rand_split_no_answers.jsonl 14 | wget -O $OUTDIR/dict.txt https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt 15 | -------------------------------------------------------------------------------- /fairseq/data/lru_cache_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from functools import lru_cache 7 | 8 | from . import BaseWrapperDataset 9 | 10 | 11 | class LRUCacheDataset(BaseWrapperDataset): 12 | 13 | def __init__(self, dataset, token=None): 14 | super().__init__(dataset) 15 | 16 | @lru_cache(maxsize=8) 17 | def __getitem__(self, index): 18 | return self.dataset[index] 19 | 20 | @lru_cache(maxsize=8) 21 | def collater(self, samples): 22 | return self.dataset.collater(samples) 23 | -------------------------------------------------------------------------------- /.github/PULL_REQUEST_TEMPLATE.md: -------------------------------------------------------------------------------- 1 | # Before submitting 2 | 3 | - [ ] Was this discussed/approved via a Github issue? (no need for typos, doc improvements) 4 | - [ ] Did you read the [contributor guideline](https://github.com/pytorch/fairseq/blob/master/CONTRIBUTING.md)? 5 | - [ ] Did you make sure to update the docs? 6 | - [ ] Did you write any new necessary tests? 7 | 8 | ## What does this PR do? 9 | Fixes # (issue). 10 | 11 | ## PR review 12 | Anyone in the community is free to review the PR once the tests have passed. 13 | If we didn't discuss your PR in Github issues there's a high chance it will not be merged. 14 | 15 | ## Did you have fun? 16 | Make sure you had fun coding 🙃 17 | -------------------------------------------------------------------------------- /scripts/sacrebleu_pregen.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | if [ $# -ne 4 ]; then 4 | echo "usage: $0 TESTSET SRCLANG TGTLANG GEN" 5 | exit 1 6 | fi 7 | 8 | TESTSET=$1 9 | SRCLANG=$2 10 | TGTLANG=$3 11 | 12 | GEN=$4 13 | 14 | echo 'Cloning Moses github repository (for tokenization scripts)...' 15 | git clone https://github.com/moses-smt/mosesdecoder.git 16 | 17 | SCRIPTS=mosesdecoder/scripts 18 | DETOKENIZER=$SCRIPTS/tokenizer/detokenizer.perl 19 | 20 | grep ^H $GEN \ 21 | | sed 's/^H\-//' \ 22 | | sort -n -k 1 \ 23 | | cut -f 3 \ 24 | | perl $DETOKENIZER -l $TGTLANG \ 25 | | sed "s/ - /-/g" \ 26 | > $GEN.sorted.detok 27 | 28 | sacrebleu --test-set $TESTSET --language-pair "${SRCLANG}-${TGTLANG}" < $GEN.sorted.detok 29 | -------------------------------------------------------------------------------- /fairseq/data/sort_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import numpy as np 7 | 8 | from . import BaseWrapperDataset 9 | 10 | 11 | class SortDataset(BaseWrapperDataset): 12 | 13 | def __init__(self, dataset, sort_order): 14 | super().__init__(dataset) 15 | if not isinstance(sort_order, (list, tuple)): 16 | sort_order = [sort_order] 17 | self.sort_order = sort_order 18 | 19 | assert all(len(so) == len(dataset) for so in sort_order) 20 | 21 | def ordered_indices(self): 22 | return np.lexsort(self.sort_order) 23 | -------------------------------------------------------------------------------- /fairseq/modules/dynamicconv_layer/setup.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # Copyright (c) Facebook, Inc. and its affiliates. 3 | # 4 | # This source code is licensed under the MIT license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | from setuptools import setup 8 | from torch.utils.cpp_extension import CUDAExtension, BuildExtension 9 | 10 | setup( 11 | name='dynamicconv_layer', 12 | ext_modules=[ 13 | CUDAExtension( 14 | name='dynamicconv_cuda', 15 | sources=[ 16 | 'dynamicconv_cuda.cpp', 17 | 'dynamicconv_cuda_kernel.cu', 18 | ], 19 | ), 20 | ], 21 | cmdclass={ 22 | 'build_ext': BuildExtension 23 | }) 24 | -------------------------------------------------------------------------------- /fairseq/clib/libnat_cuda/edit_dist.h: -------------------------------------------------------------------------------- 1 | /** 2 | * Copyright 2017-present, Facebook, Inc. 3 | * All rights reserved. 4 | * 5 | * This source code is licensed under the license found in the 6 | * LICENSE file in the root directory of this source tree. 7 | */ 8 | 9 | #pragma once 10 | 11 | #include 12 | 13 | torch::Tensor LevenshteinDistanceCuda( 14 | torch::Tensor source, 15 | torch::Tensor target, 16 | torch::Tensor source_length, 17 | torch::Tensor target_length); 18 | 19 | torch::Tensor GenerateDeletionLabelCuda( 20 | torch::Tensor source, 21 | torch::Tensor operations); 22 | 23 | std::pair GenerateInsertionLabelCuda( 24 | torch::Tensor source, 25 | torch::Tensor operations); 26 | -------------------------------------------------------------------------------- /fairseq/data/encoders/nltk_tokenizer.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from fairseq.data.encoders import register_tokenizer 7 | 8 | 9 | @register_tokenizer('nltk') 10 | class NLTKTokenizer(object): 11 | 12 | def __init__(self, source_lang=None, target_lang=None): 13 | try: 14 | from nltk.tokenize import word_tokenize 15 | self.word_tokenize = word_tokenize 16 | except ImportError: 17 | raise ImportError('Please install nltk with: pip install nltk') 18 | 19 | def encode(self, x: str) -> str: 20 | return ' '.join(self.word_tokenize(x)) 21 | 22 | def decode(self, x: str) -> str: 23 | return x 24 | -------------------------------------------------------------------------------- /tests/test_iterators.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import unittest 7 | 8 | from fairseq.data import iterators 9 | 10 | 11 | class TestIterators(unittest.TestCase): 12 | 13 | def test_counting_iterator(self): 14 | x = list(range(10)) 15 | itr = iterators.CountingIterator(x) 16 | self.assertTrue(itr.has_next()) 17 | self.assertEqual(next(itr), 0) 18 | self.assertEqual(next(itr), 1) 19 | itr.skip(3) 20 | self.assertEqual(next(itr), 5) 21 | itr.skip(3) 22 | self.assertEqual(next(itr), 9) 23 | self.assertFalse(itr.has_next()) 24 | 25 | 26 | if __name__ == '__main__': 27 | unittest.main() 28 | -------------------------------------------------------------------------------- /fairseq/criterions/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import importlib 7 | import os 8 | 9 | from fairseq import registry 10 | from fairseq.criterions.fairseq_criterion import FairseqCriterion 11 | 12 | 13 | build_criterion, register_criterion, CRITERION_REGISTRY = registry.setup_registry( 14 | '--criterion', 15 | base_class=FairseqCriterion, 16 | default='cross_entropy', 17 | ) 18 | 19 | 20 | # automatically import any Python files in the criterions/ directory 21 | for file in os.listdir(os.path.dirname(__file__)): 22 | if file.endswith('.py') and not file.startswith('_'): 23 | module = file[:file.find('.py')] 24 | importlib.import_module('fairseq.criterions.' + module) 25 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/feature_request.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: 🚀 Feature Request 3 | about: Submit a proposal/request for a new feature 4 | labels: 'enhancement, help wanted, needs triage' 5 | --- 6 | 7 | ## 🚀 Feature Request 8 | 9 | 10 | ### Motivation 11 | 12 | 13 | 14 | ### Pitch 15 | 16 | 17 | 18 | ### Alternatives 19 | 20 | 21 | 22 | ### Additional context 23 | 24 | 25 | -------------------------------------------------------------------------------- /fairseq/data/encoders/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | 7 | import importlib 8 | import os 9 | 10 | from fairseq import registry 11 | 12 | 13 | build_tokenizer, register_tokenizer, TOKENIZER_REGISTRY = registry.setup_registry( 14 | '--tokenizer', 15 | default=None, 16 | ) 17 | 18 | 19 | build_bpe, register_bpe, BPE_REGISTRY = registry.setup_registry( 20 | '--bpe', 21 | default=None, 22 | ) 23 | 24 | 25 | # automatically import any Python files in the encoders/ directory 26 | for file in os.listdir(os.path.dirname(__file__)): 27 | if file.endswith('.py') and not file.startswith('_'): 28 | module = file[:file.find('.py')] 29 | importlib.import_module('fairseq.data.encoders.' + module) 30 | -------------------------------------------------------------------------------- /fairseq/data/list_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from . import BaseWrapperDataset 7 | 8 | 9 | class ListDataset(BaseWrapperDataset): 10 | 11 | def __init__(self, dataset, sizes=None): 12 | super().__init__(dataset) 13 | self._sizes = sizes 14 | 15 | def __iter__(self): 16 | for x in self.dataset: 17 | yield x 18 | 19 | def collater(self, samples): 20 | return samples 21 | 22 | @property 23 | def sizes(self): 24 | return self._sizes 25 | 26 | def num_tokens(self, index): 27 | return self.sizes[index] 28 | 29 | def size(self, index): 30 | return self.sizes[index] 31 | 32 | def set_epoch(self, epoch): 33 | pass 34 | -------------------------------------------------------------------------------- /docs/criterions.rst: -------------------------------------------------------------------------------- 1 | .. role:: hidden 2 | :class: hidden-section 3 | 4 | .. _Criterions: 5 | 6 | Criterions 7 | ========== 8 | 9 | Criterions compute the loss function given the model and batch, roughly:: 10 | 11 | loss = criterion(model, batch) 12 | 13 | .. automodule:: fairseq.criterions 14 | :members: 15 | 16 | .. autoclass:: fairseq.criterions.FairseqCriterion 17 | :members: 18 | :undoc-members: 19 | 20 | .. autoclass:: fairseq.criterions.adaptive_loss.AdaptiveLoss 21 | :members: 22 | :undoc-members: 23 | .. autoclass:: fairseq.criterions.composite_loss.CompositeLoss 24 | :members: 25 | :undoc-members: 26 | .. autoclass:: fairseq.criterions.cross_entropy.CrossEntropyCriterion 27 | :members: 28 | :undoc-members: 29 | .. autoclass:: fairseq.criterions.label_smoothed_cross_entropy.LabelSmoothedCrossEntropyCriterion 30 | :members: 31 | :undoc-members: 32 | -------------------------------------------------------------------------------- /fairseq/modules/gelu.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | """ 6 | See "Gaussian Error Linear Units (GELUs)" by Dan Hendrycks and Kevin Gimpel with 7 | the corresponding GitHub repo: https://github.com/hendrycks/GELUs 8 | """ 9 | 10 | import math 11 | 12 | import torch 13 | 14 | 15 | def gelu_accurate(x): 16 | if not hasattr(gelu_accurate, "_a"): 17 | gelu_accurate._a = math.sqrt(2 / math.pi) 18 | return 0.5 * x * (1 + torch.tanh(gelu_accurate._a * (x + 0.044715 * torch.pow(x, 3)))) 19 | 20 | 21 | def gelu(x: torch.Tensor) -> torch.Tensor: 22 | if hasattr(torch.nn.functional, 'gelu'): 23 | return torch.nn.functional.gelu(x.float()).type_as(x) 24 | else: 25 | return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0))) 26 | -------------------------------------------------------------------------------- /fairseq/optim/lr_scheduler/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import importlib 7 | import os 8 | 9 | from fairseq import registry 10 | from fairseq.optim.lr_scheduler.fairseq_lr_scheduler import FairseqLRScheduler 11 | 12 | 13 | build_lr_scheduler, register_lr_scheduler, LR_SCHEDULER_REGISTRY = registry.setup_registry( 14 | '--lr-scheduler', 15 | base_class=FairseqLRScheduler, 16 | default='fixed', 17 | ) 18 | 19 | # automatically import any Python files in the optim/lr_scheduler/ directory 20 | for file in os.listdir(os.path.dirname(__file__)): 21 | if file.endswith('.py') and not file.startswith('_'): 22 | module = file[:file.find('.py')] 23 | importlib.import_module('fairseq.optim.lr_scheduler.' + module) 24 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/how-to-question.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: ❓ Questions/Help 3 | about: If you have questions, please first search existing issues and docs 4 | labels: 'question, needs triage' 5 | --- 6 | 7 | ## ❓ Questions and Help 8 | 9 | ### Before asking: 10 | 1. search the issues. 11 | 2. search the docs. 12 | 13 | 14 | 15 | #### What is your question? 16 | 17 | #### Code 18 | 19 | 20 | 21 | #### What have you tried? 22 | 23 | #### What's your environment? 24 | 25 | - fairseq Version (e.g., 1.0 or master): 26 | - PyTorch Version (e.g., 1.0) 27 | - OS (e.g., Linux): 28 | - How you installed fairseq (`pip`, source): 29 | - Build command you used (if compiling from source): 30 | - Python version: 31 | - CUDA/cuDNN version: 32 | - GPU models and configuration: 33 | - Any other relevant information: 34 | -------------------------------------------------------------------------------- /fairseq/data/numel_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import numpy as np 7 | import torch 8 | 9 | from . import BaseWrapperDataset 10 | 11 | 12 | class NumelDataset(BaseWrapperDataset): 13 | 14 | def __init__(self, dataset, reduce=False): 15 | super().__init__(dataset) 16 | self.reduce = reduce 17 | 18 | def __getitem__(self, index): 19 | item = self.dataset[index] 20 | if torch.is_tensor(item): 21 | return torch.numel(item) 22 | else: 23 | return np.size(item) 24 | 25 | def __len__(self): 26 | return len(self.dataset) 27 | 28 | def collater(self, samples): 29 | if self.reduce: 30 | return sum(samples) 31 | else: 32 | return torch.tensor(samples) 33 | -------------------------------------------------------------------------------- /scripts/convert_dictionary.lua: -------------------------------------------------------------------------------- 1 | -- Copyright (c) Facebook, Inc. and its affiliates. 2 | -- 3 | -- This source code is licensed under the MIT license found in the 4 | -- LICENSE file in the root directory of this source tree. 5 | -- 6 | -- Usage: convert_dictionary.lua 7 | require 'fairseq' 8 | require 'torch' 9 | require 'paths' 10 | 11 | if #arg < 1 then 12 | print('usage: convert_dictionary.lua ') 13 | os.exit(1) 14 | end 15 | if not paths.filep(arg[1]) then 16 | print('error: file does not exit: ' .. arg[1]) 17 | os.exit(1) 18 | end 19 | 20 | dict = torch.load(arg[1]) 21 | dst = paths.basename(arg[1]):gsub('.th7', '.txt') 22 | assert(dst:match('.txt$')) 23 | 24 | f = io.open(dst, 'w') 25 | for idx, symbol in ipairs(dict.index_to_symbol) do 26 | if idx > dict.cutoff then 27 | break 28 | end 29 | f:write(symbol) 30 | f:write(' ') 31 | f:write(dict.index_to_freq[idx]) 32 | f:write('\n') 33 | end 34 | f:close() 35 | -------------------------------------------------------------------------------- /fairseq/data/colorize_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch 7 | 8 | from . import BaseWrapperDataset 9 | 10 | 11 | class ColorizeDataset(BaseWrapperDataset): 12 | """ Adds 'colors' property to net input that is obtained from the provided color getter for use by models """ 13 | def __init__(self, dataset, color_getter): 14 | super().__init__(dataset) 15 | self.color_getter = color_getter 16 | 17 | def collater(self, samples): 18 | base_collate = super().collater(samples) 19 | if len(base_collate) > 0: 20 | base_collate["net_input"]["colors"] = torch.tensor( 21 | list(self.color_getter(self.dataset, s["id"]) for s in samples), 22 | dtype=torch.long, 23 | ) 24 | return base_collate 25 | -------------------------------------------------------------------------------- /fairseq/modules/logsumexp_moe.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch 7 | 8 | 9 | class LogSumExpMoE(torch.autograd.Function): 10 | """Standard LogSumExp forward pass, but use *posterior* for the backward. 11 | 12 | See `"Mixture Models for Diverse Machine Translation: Tricks of the Trade" 13 | (Shen et al., 2019) `_. 14 | """ 15 | 16 | @staticmethod 17 | def forward(ctx, logp, posterior, dim=-1): 18 | ctx.save_for_backward(posterior) 19 | ctx.dim = dim 20 | return torch.logsumexp(logp, dim=dim) 21 | 22 | @staticmethod 23 | def backward(ctx, grad_output): 24 | posterior, = ctx.saved_tensors 25 | grad_logp = grad_output.unsqueeze(ctx.dim) * posterior 26 | return grad_logp, None, None 27 | -------------------------------------------------------------------------------- /docs/make.bat: -------------------------------------------------------------------------------- 1 | @ECHO OFF 2 | 3 | pushd %~dp0 4 | 5 | REM Command file for Sphinx documentation 6 | 7 | if "%SPHINXBUILD%" == "" ( 8 | set SPHINXBUILD=python -msphinx 9 | ) 10 | set SOURCEDIR=. 11 | set BUILDDIR=_build 12 | set SPHINXPROJ=fairseq 13 | 14 | if "%1" == "" goto help 15 | 16 | %SPHINXBUILD% >NUL 2>NUL 17 | if errorlevel 9009 ( 18 | echo. 19 | echo.The Sphinx module was not found. Make sure you have Sphinx installed, 20 | echo.then set the SPHINXBUILD environment variable to point to the full 21 | echo.path of the 'sphinx-build' executable. Alternatively you may add the 22 | echo.Sphinx directory to PATH. 23 | echo. 24 | echo.If you don't have Sphinx installed, grab it from 25 | echo.http://sphinx-doc.org/ 26 | exit /b 1 27 | ) 28 | 29 | %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% 30 | goto end 31 | 32 | :help 33 | %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% 34 | 35 | :end 36 | popd 37 | -------------------------------------------------------------------------------- /fairseq/clib/libbleu/module.cpp: -------------------------------------------------------------------------------- 1 | /** 2 | * Copyright 2017-present, Facebook, Inc. 3 | * All rights reserved. 4 | * 5 | * This source code is licensed under the license found in the 6 | * LICENSE file in the root directory of this source tree. 7 | */ 8 | 9 | #include 10 | 11 | 12 | static PyMethodDef method_def[] = { 13 | {NULL, NULL, 0, NULL} 14 | }; 15 | 16 | static struct PyModuleDef module_def = { 17 | PyModuleDef_HEAD_INIT, 18 | "libbleu", /* name of module */ 19 | NULL, /* module documentation, may be NULL */ 20 | -1, /* size of per-interpreter state of the module, 21 | or -1 if the module keeps state in global variables. */ 22 | method_def 23 | }; 24 | 25 | 26 | #if PY_MAJOR_VERSION == 2 27 | PyMODINIT_FUNC init_libbleu() 28 | #else 29 | PyMODINIT_FUNC PyInit_libbleu() 30 | #endif 31 | { 32 | PyObject *m = PyModule_Create(&module_def); 33 | if (!m) { 34 | return NULL; 35 | } 36 | return m; 37 | } 38 | -------------------------------------------------------------------------------- /fairseq/data/pad_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from fairseq.data import data_utils 7 | 8 | from . import BaseWrapperDataset 9 | 10 | 11 | class PadDataset(BaseWrapperDataset): 12 | 13 | def __init__(self, dataset, pad_idx, left_pad): 14 | super().__init__(dataset) 15 | self.pad_idx = pad_idx 16 | self.left_pad = left_pad 17 | 18 | def collater(self, samples): 19 | return data_utils.collate_tokens(samples, self.pad_idx, left_pad=self.left_pad) 20 | 21 | 22 | class LeftPadDataset(PadDataset): 23 | 24 | def __init__(self, dataset, pad_idx): 25 | super().__init__(dataset, pad_idx, left_pad=True) 26 | 27 | 28 | class RightPadDataset(PadDataset): 29 | 30 | def __init__(self, dataset, pad_idx): 31 | super().__init__(dataset, pad_idx, left_pad=False) 32 | -------------------------------------------------------------------------------- /examples/language_model/prepare-wikitext-103.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # Adapted from https://github.com/facebookresearch/MIXER/blob/master/prepareData.sh 3 | 4 | URLS=( 5 | "https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-v1.zip" 6 | ) 7 | FILES=( 8 | "wikitext-103-v1.zip" 9 | ) 10 | 11 | for ((i=0;i<${#URLS[@]};++i)); do 12 | file=${FILES[i]} 13 | if [ -f $file ]; then 14 | echo "$file already exists, skipping download" 15 | else 16 | url=${URLS[i]} 17 | wget "$url" 18 | if [ -f $file ]; then 19 | echo "$url successfully downloaded." 20 | else 21 | echo "$url not successfully downloaded." 22 | exit -1 23 | fi 24 | if [ ${file: -4} == ".tgz" ]; then 25 | tar zxvf $file 26 | elif [ ${file: -4} == ".tar" ]; then 27 | tar xvf $file 28 | elif [ ${file: -4} == ".zip" ]; then 29 | unzip $file 30 | fi 31 | fi 32 | done 33 | cd .. 34 | -------------------------------------------------------------------------------- /docs/optim.rst: -------------------------------------------------------------------------------- 1 | .. role:: hidden 2 | :class: hidden-section 3 | 4 | .. _optimizers: 5 | 6 | Optimizers 7 | ========== 8 | 9 | Optimizers update the Model parameters based on the gradients. 10 | 11 | .. automodule:: fairseq.optim 12 | :members: 13 | 14 | .. autoclass:: fairseq.optim.FairseqOptimizer 15 | :members: 16 | :undoc-members: 17 | 18 | .. autoclass:: fairseq.optim.adadelta.Adadelta 19 | :members: 20 | :undoc-members: 21 | .. autoclass:: fairseq.optim.adagrad.Adagrad 22 | :members: 23 | :undoc-members: 24 | .. autoclass:: fairseq.optim.adafactor.FairseqAdafactor 25 | :members: 26 | :undoc-members: 27 | .. autoclass:: fairseq.optim.adam.FairseqAdam 28 | :members: 29 | :undoc-members: 30 | .. autoclass:: fairseq.optim.fp16_optimizer.FP16Optimizer 31 | :members: 32 | :undoc-members: 33 | .. autoclass:: fairseq.optim.nag.FairseqNAG 34 | :members: 35 | :undoc-members: 36 | .. autoclass:: fairseq.optim.sgd.SGD 37 | :members: 38 | :undoc-members: 39 | -------------------------------------------------------------------------------- /fairseq/data/truncate_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import numpy as np 7 | 8 | from . import BaseWrapperDataset 9 | 10 | 11 | class TruncateDataset(BaseWrapperDataset): 12 | 13 | def __init__(self, dataset, truncation_length): 14 | super().__init__(dataset) 15 | assert truncation_length is not None 16 | self.truncation_length = truncation_length 17 | self.dataset = dataset 18 | 19 | def __getitem__(self, index): 20 | item = self.dataset[index] 21 | item_len = item.size(0) 22 | if item_len > self.truncation_length: 23 | item = item[:self.truncation_length] 24 | return item 25 | 26 | @property 27 | def sizes(self): 28 | return np.minimum(self.dataset.sizes, self.truncation_length) 29 | 30 | def __len__(self): 31 | return len(self.dataset) 32 | -------------------------------------------------------------------------------- /fairseq/modules/dynamicconv_layer/dynamiconv_cpu.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | 4 | std::vector dynamicconv_cpu_forward( 5 | float* input, 6 | float* filters, 7 | int padding_l); 8 | 9 | std::vector dynamicconv_cpu_backward( 10 | float* gradOutput, 11 | int padding_l, 12 | float* input, 13 | float* filters); 14 | 15 | std::vector dynamicconv_forward( 16 | float* input, 17 | float* filters, 18 | int padding_l) { 19 | 20 | return dynamicconv_cpu_forward(input, filters, padding_l); 21 | } 22 | 23 | std::vector dynamicconv_backward( 24 | float* gradOutput, 25 | int padding_l, 26 | float* input, 27 | float* filters) { 28 | 29 | return dynamicconv_cpu_backward(gradOutput, padding_l, input, filters); 30 | } 31 | 32 | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { 33 | m.def("forward", &dynamicconv_forward, "dynamicconv forward (CPU)"); 34 | m.def("backward", &dynamicconv_backward, "dynamicconv backward (CPU)"); 35 | } 36 | -------------------------------------------------------------------------------- /fairseq/data/encoders/utils.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch 7 | from fairseq.data import encoders 8 | 9 | 10 | def get_whole_word_mask(args, dictionary): 11 | bpe = encoders.build_bpe(args) 12 | if bpe is not None: 13 | def is_beginning_of_word(i): 14 | if i < dictionary.nspecial: 15 | # special elements are always considered beginnings 16 | return True 17 | tok = dictionary[i] 18 | if tok.startswith('madeupword'): 19 | return True 20 | try: 21 | return bpe.is_beginning_of_word(tok) 22 | except ValueError: 23 | return True 24 | mask_whole_words = torch.ByteTensor(list( 25 | map(is_beginning_of_word, range(len(dictionary))) 26 | )) 27 | return mask_whole_words 28 | return None 29 | -------------------------------------------------------------------------------- /fairseq/modules/scalar_bias.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | # 6 | 7 | import torch 8 | 9 | 10 | class ScalarBias(torch.autograd.Function): 11 | """ 12 | Adds a vector of scalars, used in self-attention mechanism to allow 13 | the model to optionally attend to this vector instead of the past 14 | """ 15 | 16 | @staticmethod 17 | def forward(ctx, input, dim, bias_init): 18 | size = list(input.size()) 19 | size[dim] += 1 20 | output = input.new(*size).fill_(bias_init) 21 | output.narrow(dim, 1, size[dim] - 1).copy_(input) 22 | ctx.dim = dim 23 | return output 24 | 25 | @staticmethod 26 | def backward(ctx, grad): 27 | return grad.narrow(ctx.dim, 1, grad.size(ctx.dim) - 1), None, None 28 | 29 | 30 | def scalar_bias(input, dim, bias_init=0): 31 | return ScalarBias.apply(input, dim, bias_init) 32 | -------------------------------------------------------------------------------- /fairseq/data/prepend_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import numpy as np 7 | import torch 8 | 9 | from . import BaseWrapperDataset 10 | 11 | 12 | class PrependDataset(BaseWrapperDataset): 13 | def __init__(self, dataset, prepend_getter, ensure_first_token_is=None): 14 | super().__init__(dataset) 15 | self.prepend_getter = prepend_getter 16 | self.ensure_first_token = ensure_first_token_is 17 | 18 | def __getitem__(self, idx): 19 | item = self.dataset[idx] 20 | is_tuple = isinstance(item, tuple) 21 | src = item[0] if is_tuple else item 22 | 23 | assert self.ensure_first_token is None or src[0] == self.ensure_first_token 24 | prepend_idx = self.prepend_getter(self.dataset, idx) 25 | assert isinstance(prepend_idx, int) 26 | src[0] = prepend_idx 27 | item = tuple((src,) + item[1:]) if is_tuple else src 28 | return item 29 | -------------------------------------------------------------------------------- /fairseq/optim/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import importlib 7 | import os 8 | 9 | from fairseq import registry 10 | from fairseq.optim.fairseq_optimizer import FairseqOptimizer 11 | from fairseq.optim.fp16_optimizer import FP16Optimizer, MemoryEfficientFP16Optimizer 12 | from fairseq.optim.bmuf import FairseqBMUF # noqa 13 | 14 | 15 | __all__ = [ 16 | 'FairseqOptimizer', 17 | 'FP16Optimizer', 18 | 'MemoryEfficientFP16Optimizer', 19 | ] 20 | 21 | 22 | build_optimizer, register_optimizer, OPTIMIZER_REGISTRY = registry.setup_registry( 23 | '--optimizer', 24 | base_class=FairseqOptimizer, 25 | default='nag', 26 | ) 27 | 28 | 29 | # automatically import any Python files in the optim/ directory 30 | for file in os.listdir(os.path.dirname(__file__)): 31 | if file.endswith('.py') and not file.startswith('_'): 32 | module = file[:file.find('.py')] 33 | importlib.import_module('fairseq.optim.' + module) 34 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) Facebook, Inc. and its affiliates. 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /docs/lr_scheduler.rst: -------------------------------------------------------------------------------- 1 | .. role:: hidden 2 | :class: hidden-section 3 | 4 | .. _Learning Rate Schedulers: 5 | 6 | Learning Rate Schedulers 7 | ======================== 8 | 9 | Learning Rate Schedulers update the learning rate over the course of training. 10 | Learning rates can be updated after each update via :func:`step_update` or at 11 | epoch boundaries via :func:`step`. 12 | 13 | .. automodule:: fairseq.optim.lr_scheduler 14 | :members: 15 | 16 | .. autoclass:: fairseq.optim.lr_scheduler.FairseqLRScheduler 17 | :members: 18 | :undoc-members: 19 | 20 | .. autoclass:: fairseq.optim.lr_scheduler.cosine_lr_scheduler.CosineSchedule 21 | :members: 22 | :undoc-members: 23 | .. autoclass:: fairseq.optim.lr_scheduler.fixed_schedule.FixedSchedule 24 | :members: 25 | :undoc-members: 26 | .. autoclass:: fairseq.optim.lr_scheduler.inverse_square_root_schedule.InverseSquareRootSchedule 27 | :members: 28 | :undoc-members: 29 | .. autoclass:: fairseq.optim.lr_scheduler.reduce_lr_on_plateau.ReduceLROnPlateau 30 | :members: 31 | :undoc-members: 32 | .. autoclass:: fairseq.optim.lr_scheduler.triangular_lr_scheduler.TriangularSchedule 33 | :members: 34 | :undoc-members: 35 | -------------------------------------------------------------------------------- /docs/index.rst: -------------------------------------------------------------------------------- 1 | .. fairseq documentation master file, created by 2 | sphinx-quickstart on Fri Aug 17 21:45:30 2018. 3 | You can adapt this file completely to your liking, but it should at least 4 | contain the root `toctree` directive. 5 | 6 | :github_url: https://github.com/pytorch/fairseq 7 | 8 | 9 | fairseq documentation 10 | ===================== 11 | 12 | Fairseq is a sequence modeling toolkit written in `PyTorch 13 | `_ that allows researchers and developers to 14 | train custom models for translation, summarization, language modeling and other 15 | text generation tasks. 16 | 17 | .. toctree:: 18 | :maxdepth: 1 19 | :caption: Getting Started 20 | 21 | getting_started 22 | command_line_tools 23 | 24 | .. toctree:: 25 | :maxdepth: 1 26 | :caption: Extending Fairseq 27 | 28 | overview 29 | tutorial_simple_lstm 30 | tutorial_classifying_names 31 | 32 | .. toctree:: 33 | :maxdepth: 2 34 | :caption: Library Reference 35 | 36 | tasks 37 | models 38 | criterions 39 | optim 40 | lr_scheduler 41 | data 42 | modules 43 | 44 | 45 | Indices and tables 46 | ================== 47 | 48 | * :ref:`genindex` 49 | * :ref:`search` 50 | -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contributing to Facebook AI Research Sequence-to-Sequence Toolkit (fairseq) 2 | We want to make contributing to this project as easy and transparent as 3 | possible. 4 | 5 | ## Pull Requests 6 | We actively welcome your pull requests. 7 | 8 | 1. Fork the repo and create your branch from `master`. 9 | 2. If you've added code that should be tested, add tests. 10 | 3. If you've changed APIs, update the documentation. 11 | 4. Ensure the test suite passes. 12 | 5. Make sure your code lints. 13 | 6. If you haven't already, complete the Contributor License Agreement ("CLA"). 14 | 15 | ## Contributor License Agreement ("CLA") 16 | In order to accept your pull request, we need you to submit a CLA. You only need 17 | to do this once to work on any of Facebook's open source projects. 18 | 19 | Complete your CLA here: 20 | 21 | ## Issues 22 | We use GitHub issues to track public bugs. Please ensure your description is 23 | clear and has sufficient instructions to be able to reproduce the issue. 24 | 25 | ## License 26 | By contributing to Facebook AI Research Sequence-to-Sequence Toolkit (fairseq), 27 | you agree that your contributions will be licensed under the LICENSE file in 28 | the root directory of this source tree. 29 | -------------------------------------------------------------------------------- /scripts/compare_namespaces.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | """Helper script to compare two argparse.Namespace objects.""" 3 | 4 | from argparse import Namespace # noqa 5 | 6 | 7 | def main(): 8 | 9 | ns1 = eval(input('Namespace 1: ')) 10 | ns2 = eval(input('Namespace 2: ')) 11 | 12 | def keys(ns): 13 | ks = set() 14 | for k in dir(ns): 15 | if not k.startswith('_'): 16 | ks.add(k) 17 | return ks 18 | 19 | k1 = keys(ns1) 20 | k2 = keys(ns2) 21 | 22 | def print_keys(ks, ns1, ns2=None): 23 | for k in ks: 24 | if ns2 is None: 25 | print('{}\t{}'.format(k, getattr(ns1, k, None))) 26 | else: 27 | print('{}\t{}\t{}'.format(k, getattr(ns1, k, None), getattr(ns2, k, None))) 28 | 29 | print('Keys unique to namespace 1:') 30 | print_keys(k1 - k2, ns1) 31 | print() 32 | 33 | print('Keys unique to namespace 2:') 34 | print_keys(k2 - k1, ns2) 35 | print() 36 | 37 | print('Overlapping keys with different values:') 38 | ks = [k for k in k1 & k2 if getattr(ns1, k, 'None') != getattr(ns2, k, 'None')] 39 | print_keys(ks, ns1, ns2) 40 | print() 41 | 42 | 43 | if __name__ == '__main__': 44 | main() 45 | -------------------------------------------------------------------------------- /fairseq/data/append_token_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import numpy as np 7 | import torch 8 | 9 | from . import BaseWrapperDataset 10 | 11 | 12 | class AppendTokenDataset(BaseWrapperDataset): 13 | 14 | def __init__(self, dataset, token=None): 15 | super().__init__(dataset) 16 | self.token = token 17 | if token is not None: 18 | self._sizes = np.array(dataset.sizes) + 1 19 | else: 20 | self._sizes = dataset.sizes 21 | 22 | def __getitem__(self, idx): 23 | item = self.dataset[idx] 24 | if self.token is not None: 25 | item = torch.cat([item, item.new([self.token])]) 26 | return item 27 | 28 | @property 29 | def sizes(self): 30 | return self._sizes 31 | 32 | def num_tokens(self, index): 33 | n = self.dataset.num_tokens(index) 34 | if self.token is not None: 35 | n += 1 36 | return n 37 | 38 | def size(self, index): 39 | n = self.dataset.size(index) 40 | if self.token is not None: 41 | n += 1 42 | return n 43 | -------------------------------------------------------------------------------- /fairseq/data/encoders/fastbpe.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from fairseq import file_utils 7 | from fairseq.data.encoders import register_bpe 8 | 9 | 10 | @register_bpe('fastbpe') 11 | class fastBPE(object): 12 | 13 | @staticmethod 14 | def add_args(parser): 15 | # fmt: off 16 | parser.add_argument('--bpe-codes', type=str, 17 | help='path to fastBPE BPE') 18 | # fmt: on 19 | 20 | def __init__(self, args): 21 | if args.bpe_codes is None: 22 | raise ValueError('--bpe-codes is required for --bpe=subword_nmt') 23 | codes = file_utils.cached_path(args.bpe_codes) 24 | try: 25 | import fastBPE 26 | self.bpe = fastBPE.fastBPE(codes) 27 | self.bpe_symbol = "@@ " 28 | except ImportError: 29 | raise ImportError('Please install fastBPE with: pip install fastBPE') 30 | 31 | def encode(self, x: str) -> str: 32 | return self.bpe.apply([x])[0] 33 | 34 | def decode(self, x: str) -> str: 35 | return (x + ' ').replace(self.bpe_symbol, '').rstrip() 36 | -------------------------------------------------------------------------------- /fairseq/tasks/translation_from_pretrained_xlm.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from fairseq.data.legacy.masked_lm_dictionary import MaskedLMDictionary 7 | from fairseq.tasks.translation import TranslationTask 8 | 9 | from . import register_task 10 | 11 | 12 | @register_task("translation_from_pretrained_xlm") 13 | class TranslationFromPretrainedXLMTask(TranslationTask): 14 | """ 15 | Same as TranslationTask except use the MaskedLMDictionary class so that 16 | we can load data that was binarized with the MaskedLMDictionary class. 17 | 18 | This task should be used for the entire training pipeline when we want to 19 | train an NMT model from a pretrained XLM checkpoint: binarizing NMT data, 20 | training NMT with the pretrained XLM checkpoint, and subsequent evaluation 21 | of that trained model. 22 | """ 23 | 24 | @classmethod 25 | def load_dictionary(cls, filename): 26 | """Load the masked LM dictionary from the filename 27 | 28 | Args: 29 | filename (str): the filename 30 | """ 31 | return MaskedLMDictionary.load(filename) 32 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/bug_report.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: 🐛 Bug Report 3 | about: Submit a bug report to help us improve 4 | labels: 'bug, needs triage' 5 | --- 6 | 7 | ## 🐛 Bug 8 | 9 | 10 | 11 | ### To Reproduce 12 | 13 | Steps to reproduce the behavior (**always include the command you ran**): 14 | 15 | 1. Run cmd '....' 16 | 2. See error 17 | 18 | 19 | 20 | 21 | #### Code sample 22 | 24 | 25 | ### Expected behavior 26 | 27 | 28 | 29 | ### Environment 30 | 31 | - fairseq Version (e.g., 1.0 or master): 32 | - PyTorch Version (e.g., 1.0) 33 | - OS (e.g., Linux): 34 | - How you installed fairseq (`pip`, source): 35 | - Build command you used (if compiling from source): 36 | - Python version: 37 | - CUDA/cuDNN version: 38 | - GPU models and configuration: 39 | - Any other relevant information: 40 | 41 | ### Additional context 42 | 43 | 44 | -------------------------------------------------------------------------------- /fairseq/data/prepend_token_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import numpy as np 7 | import torch 8 | 9 | from . import BaseWrapperDataset 10 | 11 | 12 | class PrependTokenDataset(BaseWrapperDataset): 13 | 14 | def __init__(self, dataset, token=None): 15 | super().__init__(dataset) 16 | self.token = token 17 | if token is not None: 18 | self._sizes = np.array(dataset.sizes) + 1 19 | else: 20 | self._sizes = dataset.sizes 21 | 22 | def __getitem__(self, idx): 23 | item = self.dataset[idx] 24 | if self.token is not None: 25 | item = torch.cat([item.new([self.token]), item]) 26 | return item 27 | 28 | @property 29 | def sizes(self): 30 | return self._sizes 31 | 32 | def num_tokens(self, index): 33 | n = self.dataset.num_tokens(index) 34 | if self.token is not None: 35 | n += 1 36 | return n 37 | 38 | def size(self, index): 39 | n = self.dataset.size(index) 40 | if self.token is not None: 41 | n += 1 42 | return n 43 | -------------------------------------------------------------------------------- /fairseq/models/roberta/model_camembert.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | """ 6 | CamemBERT: a Tasty French Language Model 7 | """ 8 | 9 | from fairseq.models import register_model 10 | 11 | from .hub_interface import RobertaHubInterface 12 | from .model import RobertaModel 13 | 14 | 15 | @register_model('camembert') 16 | class CamembertModel(RobertaModel): 17 | 18 | @classmethod 19 | def hub_models(cls): 20 | return { 21 | 'camembert.v0': 'http://dl.fbaipublicfiles.com/fairseq/models/camembert.v0.tar.gz', 22 | } 23 | 24 | @classmethod 25 | def from_pretrained(cls, model_name_or_path, checkpoint_file='model.pt', data_name_or_path='.', bpe='sentencepiece', **kwargs): 26 | from fairseq import hub_utils 27 | x = hub_utils.from_pretrained( 28 | model_name_or_path, 29 | checkpoint_file, 30 | data_name_or_path, 31 | archive_map=cls.hub_models(), 32 | bpe=bpe, 33 | load_checkpoint_heads=True, 34 | **kwargs, 35 | ) 36 | return RobertaHubInterface(x['args'], x['task'], x['models'][0]) 37 | -------------------------------------------------------------------------------- /fairseq/pdb.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import multiprocessing 7 | import os 8 | import pdb 9 | import sys 10 | 11 | 12 | __all__ = ['set_trace'] 13 | 14 | 15 | _stdin = [None] 16 | _stdin_lock = multiprocessing.Lock() 17 | try: 18 | _stdin_fd = sys.stdin.fileno() 19 | except Exception: 20 | _stdin_fd = None 21 | 22 | 23 | class MultiprocessingPdb(pdb.Pdb): 24 | """A Pdb wrapper that works in a multiprocessing environment. 25 | 26 | Usage: `from fairseq import pdb; pdb.set_trace()` 27 | """ 28 | 29 | def __init__(self): 30 | pdb.Pdb.__init__(self, nosigint=True) 31 | 32 | def _cmdloop(self): 33 | stdin_bak = sys.stdin 34 | with _stdin_lock: 35 | try: 36 | if _stdin_fd is not None: 37 | if not _stdin[0]: 38 | _stdin[0] = os.fdopen(_stdin_fd) 39 | sys.stdin = _stdin[0] 40 | self.cmdloop() 41 | finally: 42 | sys.stdin = stdin_bak 43 | 44 | 45 | def set_trace(): 46 | pdb = MultiprocessingPdb() 47 | pdb.set_trace(sys._getframe().f_back) 48 | -------------------------------------------------------------------------------- /fairseq/models/roberta/model_xlmr.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | """ 6 | Unsupervised Cross-lingual Representation Learning at Scale 7 | """ 8 | 9 | from fairseq.models import register_model 10 | 11 | from .hub_interface import RobertaHubInterface 12 | from .model import RobertaModel 13 | 14 | 15 | @register_model('xlmr') 16 | class XLMRModel(RobertaModel): 17 | 18 | @classmethod 19 | def hub_models(cls): 20 | return { 21 | 'xlmr.base': 'http://dl.fbaipublicfiles.com/fairseq/models/xlmr.base.tar.gz', 22 | 'xlmr.large': 'http://dl.fbaipublicfiles.com/fairseq/models/xlmr.large.tar.gz', 23 | } 24 | 25 | @classmethod 26 | def from_pretrained(cls, model_name_or_path, checkpoint_file='model.pt', data_name_or_path='.', bpe='sentencepiece', **kwargs): 27 | from fairseq import hub_utils 28 | x = hub_utils.from_pretrained( 29 | model_name_or_path, 30 | checkpoint_file, 31 | data_name_or_path, 32 | archive_map=cls.hub_models(), 33 | bpe=bpe, 34 | load_checkpoint_heads=True, 35 | **kwargs, 36 | ) 37 | return RobertaHubInterface(x['args'], x['task'], x['models'][0]) 38 | -------------------------------------------------------------------------------- /examples/language_model/conv_lm/README.md: -------------------------------------------------------------------------------- 1 | # Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017) 2 | 3 | ## Example usage 4 | 5 | First download and preprocess the data following the main [language modeling 6 | README](../README.md). 7 | 8 | Then to train a convolutional LM using the `fconv_lm_dauphin_wikitext103` 9 | architecture: 10 | ```bash 11 | fairseq-train --task language_modeling \ 12 | data-bin/wikitext-103 \ 13 | --save-dir checkpoints/fconv_wikitext-103 \ 14 | --arch fconv_lm_dauphin_wikitext103 \ 15 | --max-epoch 35 \ --optimizer nag \ 16 | --lr 1.0 --lr-scheduler reduce_lr_on_plateau --lr-shrink 0.5 \ 17 | --clip-norm 0.1 --dropout 0.2 --weight-decay 5e-06 --criterion adaptive_loss \ 18 | --adaptive-softmax-cutoff 10000,20000,200000 --max-tokens 1024 --tokens-per-sample 1024 \ 19 | --ddp-backend=no_c10d 20 | ``` 21 | 22 | And evaluate with: 23 | ```bash 24 | fairseq-eval-lm data-bin/wikitext-103 --path checkpoints/fconv_wiki103/checkpoint_best.pt 25 | ``` 26 | 27 | ## Citation 28 | 29 | ```bibtex 30 | @inproceedings{dauphin2017language, 31 | title={Language Modeling with Gated Convolutional Networks}, 32 | author={Dauphin, Yann N and Fan, Angela and Auli, Michael and Grangier, David}, 33 | booktitle={Proceedings of the 34th International Conference on Machine Learning-Volume 70}, 34 | pages={933--941}, 35 | year={2017}, 36 | organization={JMLR} 37 | } 38 | ``` 39 | -------------------------------------------------------------------------------- /fairseq/modules/positional_embedding.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch.nn as nn 7 | from .learned_positional_embedding import LearnedPositionalEmbedding 8 | from .sinusoidal_positional_embedding import SinusoidalPositionalEmbedding 9 | 10 | 11 | def PositionalEmbedding( 12 | num_embeddings: int, 13 | embedding_dim: int, 14 | padding_idx: int, 15 | learned: bool = False, 16 | ): 17 | if learned: 18 | # if padding_idx is specified then offset the embedding ids by 19 | # this index and adjust num_embeddings appropriately 20 | # TODO: The right place for this offset would be inside 21 | # LearnedPositionalEmbedding. Move this there for a cleaner implementation. 22 | if padding_idx is not None: 23 | num_embeddings = num_embeddings + padding_idx + 1 24 | m = LearnedPositionalEmbedding(num_embeddings, embedding_dim, padding_idx) 25 | nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5) 26 | if padding_idx is not None: 27 | nn.init.constant_(m.weight[padding_idx], 0) 28 | else: 29 | m = SinusoidalPositionalEmbedding( 30 | embedding_dim, padding_idx, init_size=num_embeddings + padding_idx + 1, 31 | ) 32 | return m 33 | -------------------------------------------------------------------------------- /fairseq/optim/adagrad.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch.optim 7 | 8 | from . import FairseqOptimizer, register_optimizer 9 | 10 | 11 | @register_optimizer('adagrad') 12 | class Adagrad(FairseqOptimizer): 13 | def __init__(self, args, params): 14 | super().__init__(args) 15 | self._optimizer = torch.optim.Adagrad(params, **self.optimizer_config) 16 | 17 | @staticmethod 18 | def add_args(parser): 19 | """Add optimizer-specific arguments to the parser.""" 20 | # fmt: off 21 | parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', 22 | help='weight decay') 23 | # fmt: on 24 | 25 | @property 26 | def optimizer_config(self): 27 | """ 28 | Return a kwarg dictionary that will be used to override optimizer 29 | args stored in checkpoints. This allows us to load a checkpoint and 30 | resume training using a different set of optimizer args, e.g., with a 31 | different learning rate. 32 | """ 33 | return { 34 | 'lr': self.args.lr[0], 35 | 'weight_decay': self.args.weight_decay, 36 | } 37 | 38 | @property 39 | def supports_flat_params(self): 40 | return True 41 | -------------------------------------------------------------------------------- /docs/data.rst: -------------------------------------------------------------------------------- 1 | .. role:: hidden 2 | :class: hidden-section 3 | 4 | .. module:: fairseq.data 5 | 6 | Data Loading and Utilities 7 | ========================== 8 | 9 | .. _datasets: 10 | 11 | Datasets 12 | -------- 13 | 14 | **Datasets** define the data format and provide helpers for creating 15 | mini-batches. 16 | 17 | .. autoclass:: fairseq.data.FairseqDataset 18 | :members: 19 | .. autoclass:: fairseq.data.LanguagePairDataset 20 | :members: 21 | .. autoclass:: fairseq.data.MonolingualDataset 22 | :members: 23 | 24 | **Helper Datasets** 25 | 26 | These datasets wrap other :class:`fairseq.data.FairseqDataset` instances and 27 | provide additional functionality: 28 | 29 | .. autoclass:: fairseq.data.BacktranslationDataset 30 | :members: 31 | .. autoclass:: fairseq.data.ConcatDataset 32 | :members: 33 | .. autoclass:: fairseq.data.ResamplingDataset 34 | :members: 35 | .. autoclass:: fairseq.data.RoundRobinZipDatasets 36 | :members: 37 | .. autoclass:: fairseq.data.TransformEosDataset 38 | :members: 39 | 40 | 41 | Dictionary 42 | ---------- 43 | 44 | .. autoclass:: fairseq.data.Dictionary 45 | :members: 46 | 47 | 48 | Iterators 49 | --------- 50 | 51 | .. autoclass:: fairseq.data.CountingIterator 52 | :members: 53 | .. autoclass:: fairseq.data.EpochBatchIterator 54 | :members: 55 | .. autoclass:: fairseq.data.GroupedIterator 56 | :members: 57 | .. autoclass:: fairseq.data.ShardedIterator 58 | :members: 59 | -------------------------------------------------------------------------------- /fairseq/modules/conv_tbc.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch 7 | from torch.nn.modules.utils import _single 8 | 9 | 10 | class ConvTBC(torch.nn.Module): 11 | """1D convolution over an input of shape (time x batch x channel) 12 | 13 | The implementation uses gemm to perform the convolution. This implementation 14 | is faster than cuDNN for small kernel sizes. 15 | """ 16 | def __init__(self, in_channels, out_channels, kernel_size, padding=0): 17 | super(ConvTBC, self).__init__() 18 | self.in_channels = in_channels 19 | self.out_channels = out_channels 20 | self.kernel_size = _single(kernel_size) 21 | self.padding = _single(padding) 22 | 23 | self.weight = torch.nn.Parameter(torch.Tensor( 24 | self.kernel_size[0], in_channels, out_channels)) 25 | self.bias = torch.nn.Parameter(torch.Tensor(out_channels)) 26 | 27 | def forward(self, input): 28 | return torch.conv_tbc(input.contiguous(), self.weight, self.bias, self.padding[0]) 29 | 30 | def __repr__(self): 31 | s = ('{name}({in_channels}, {out_channels}, kernel_size={kernel_size}' 32 | ', padding={padding}') 33 | if self.bias is None: 34 | s += ', bias=False' 35 | s += ')' 36 | return s.format(name=self.__class__.__name__, **self.__dict__) 37 | -------------------------------------------------------------------------------- /.github/workflows/build.yml: -------------------------------------------------------------------------------- 1 | name: build 2 | 3 | on: 4 | # Trigger the workflow on push to master or any pull request 5 | push: 6 | branches: 7 | - master 8 | pull_request: 9 | 10 | jobs: 11 | build: 12 | 13 | strategy: 14 | max-parallel: 4 15 | matrix: 16 | platform: [ubuntu-latest, macos-latest] 17 | python-version: [3.6, 3.7] 18 | 19 | runs-on: ${{ matrix.platform }} 20 | 21 | steps: 22 | - uses: actions/checkout@v1 23 | - name: Set up Python ${{ matrix.python-version }} 24 | uses: actions/setup-python@v1 25 | with: 26 | python-version: ${{ matrix.python-version }} 27 | - name: Conditionally install pytorch 28 | if: matrix.platform == 'windows-latest' 29 | run: pip3 install torch -f https://download.pytorch.org/whl/torch_stable.html 30 | - name: Install locally 31 | run: | 32 | python -m pip install --upgrade pip 33 | python setup.py build_ext --inplace 34 | python -m pip install --editable . 35 | - name: Lint with flake8 36 | run: | 37 | pip install flake8 38 | # stop the build if there are Python syntax errors or undefined names 39 | flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics 40 | # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide 41 | flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics 42 | - name: Run tests 43 | run: | 44 | python setup.py test 45 | -------------------------------------------------------------------------------- /.github/workflows/build_windows.yml: -------------------------------------------------------------------------------- 1 | name: build_windows 2 | 3 | on: 4 | # Trigger the workflow on push to master or any pull request 5 | push: 6 | branches: 7 | - master 8 | pull_request: 9 | 10 | jobs: 11 | build: 12 | 13 | strategy: 14 | max-parallel: 4 15 | matrix: 16 | platform: [windows-latest] 17 | python-version: [3.6, 3.7] 18 | 19 | runs-on: ${{ matrix.platform }} 20 | 21 | steps: 22 | - uses: actions/checkout@v1 23 | - name: Set up Python ${{ matrix.python-version }} 24 | uses: actions/setup-python@v1 25 | with: 26 | python-version: ${{ matrix.python-version }} 27 | - name: Conditionally install pytorch 28 | if: matrix.platform == 'windows-latest' 29 | run: pip3 install torch -f https://download.pytorch.org/whl/torch_stable.html 30 | - name: Install locally 31 | run: | 32 | python -m pip install --upgrade pip 33 | python setup.py build_ext --inplace 34 | python -m pip install --editable . 35 | - name: Lint with flake8 36 | run: | 37 | pip install flake8 38 | # stop the build if there are Python syntax errors or undefined names 39 | flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics 40 | # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide 41 | flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics 42 | - name: Run tests 43 | run: | 44 | python setup.py test 45 | -------------------------------------------------------------------------------- /fairseq/optim/lr_scheduler/fairseq_lr_scheduler.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from .. import FairseqOptimizer 7 | 8 | 9 | class FairseqLRScheduler(object): 10 | 11 | def __init__(self, args, optimizer): 12 | super().__init__() 13 | if not isinstance(optimizer, FairseqOptimizer): 14 | raise ValueError('optimizer must be an instance of FairseqOptimizer') 15 | self.args = args 16 | self.optimizer = optimizer 17 | self.best = None 18 | 19 | @staticmethod 20 | def add_args(parser): 21 | """Add arguments to the parser for this LR scheduler.""" 22 | pass 23 | 24 | def state_dict(self): 25 | """Return the LR scheduler state dict.""" 26 | return {'best': self.best} 27 | 28 | def load_state_dict(self, state_dict): 29 | """Load an LR scheduler state dict.""" 30 | self.best = state_dict['best'] 31 | 32 | def step(self, epoch, val_loss=None): 33 | """Update the learning rate at the end of the given epoch.""" 34 | if val_loss is not None: 35 | if self.best is None: 36 | self.best = val_loss 37 | else: 38 | self.best = min(self.best, val_loss) 39 | 40 | def step_update(self, num_updates): 41 | """Update the learning rate after each update.""" 42 | return self.optimizer.get_lr() 43 | -------------------------------------------------------------------------------- /fairseq/data/replace_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from . import BaseWrapperDataset 7 | 8 | 9 | class ReplaceDataset(BaseWrapperDataset): 10 | """Replaces tokens found in the dataset by a specified replacement token 11 | 12 | Args: 13 | dataset (~torch.utils.data.Dataset): dataset to replace tokens in 14 | replace_map(Dictionary[int,int]): map of token to replace -> replacement token 15 | offsets (List[int]): do not replace tokens before (from left if pos, right if neg) this offset. should be 16 | as many as the number of objects returned by the underlying dataset __getitem__ method. 17 | """ 18 | 19 | def __init__(self, dataset, replace_map, offsets): 20 | super().__init__(dataset) 21 | assert len(replace_map) > 0 22 | self.replace_map = replace_map 23 | self.offsets = offsets 24 | 25 | def __getitem__(self, index): 26 | item = self.dataset[index] 27 | is_tuple = isinstance(item, tuple) 28 | srcs = item if is_tuple else [item] 29 | 30 | for offset, src in zip(self.offsets, srcs): 31 | for k, v in self.replace_map.items(): 32 | src_off = src[offset:] if offset >= 0 else src[:offset] 33 | src_off.masked_fill_(src_off == k, v) 34 | 35 | item = srcs if is_tuple else srcs[0] 36 | return item 37 | -------------------------------------------------------------------------------- /scripts/read_binarized.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # Copyright (c) Facebook, Inc. and its affiliates. 3 | # 4 | # This source code is licensed under the MIT license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | import argparse 8 | 9 | from fairseq.data import data_utils, Dictionary, indexed_dataset 10 | 11 | 12 | def get_parser(): 13 | parser = argparse.ArgumentParser( 14 | description='writes text from binarized file to stdout') 15 | # fmt: off 16 | parser.add_argument('--dataset-impl', help='dataset implementation', 17 | choices=indexed_dataset.get_available_dataset_impl()) 18 | parser.add_argument('--dict', metavar='FP', help='dictionary containing known words', default=None) 19 | parser.add_argument('--input', metavar='FP', required=True, help='binarized file to read') 20 | # fmt: on 21 | 22 | return parser 23 | 24 | 25 | def main(): 26 | parser = get_parser() 27 | args = parser.parse_args() 28 | 29 | dictionary = Dictionary.load(args.dict) if args.dict is not None else None 30 | dataset = data_utils.load_indexed_dataset( 31 | args.input, 32 | dictionary, 33 | dataset_impl=args.dataset_impl, 34 | default='lazy', 35 | ) 36 | 37 | for tensor_line in dataset: 38 | if dictionary is None: 39 | line = ' '.join([str(int(x)) for x in tensor_line]) 40 | else: 41 | line = dictionary.string(tensor_line) 42 | 43 | print(line) 44 | 45 | 46 | if __name__ == '__main__': 47 | main() 48 | -------------------------------------------------------------------------------- /tests/speech_recognition/test_cross_entropy.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # Copyright (c) Facebook, Inc. and its affiliates. 3 | # 4 | # This source code is licensed under the MIT license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | from examples.speech_recognition.criterions.cross_entropy_acc import CrossEntropyWithAccCriterion 8 | from .asr_test_base import CrossEntropyCriterionTestBase 9 | 10 | 11 | class CrossEntropyWithAccCriterionTest(CrossEntropyCriterionTestBase): 12 | def setUp(self): 13 | self.criterion_cls = CrossEntropyWithAccCriterion 14 | super().setUp() 15 | 16 | def test_cross_entropy_all_correct(self): 17 | sample = self.get_test_sample(correct=True, soft_target=False, aggregate=False) 18 | loss, sample_size, logging_output = self.criterion( 19 | self.model, sample, "sum", log_probs=True 20 | ) 21 | assert logging_output["correct"] == 20 22 | assert logging_output["total"] == 20 23 | assert logging_output["sample_size"] == 20 24 | assert logging_output["ntokens"] == 20 25 | 26 | def test_cross_entropy_all_wrong(self): 27 | sample = self.get_test_sample(correct=False, soft_target=False, aggregate=False) 28 | loss, sample_size, logging_output = self.criterion( 29 | self.model, sample, "sum", log_probs=True 30 | ) 31 | assert logging_output["correct"] == 0 32 | assert logging_output["total"] == 20 33 | assert logging_output["sample_size"] == 20 34 | assert logging_output["ntokens"] == 20 35 | -------------------------------------------------------------------------------- /fairseq/data/base_wrapper_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from torch.utils.data.dataloader import default_collate 7 | 8 | from . import FairseqDataset 9 | 10 | 11 | class BaseWrapperDataset(FairseqDataset): 12 | 13 | def __init__(self, dataset): 14 | super().__init__() 15 | self.dataset = dataset 16 | 17 | def __getitem__(self, index): 18 | return self.dataset[index] 19 | 20 | def __len__(self): 21 | return len(self.dataset) 22 | 23 | def collater(self, samples): 24 | if hasattr(self.dataset, 'collater'): 25 | return self.dataset.collater(samples) 26 | else: 27 | return default_collate(samples) 28 | 29 | @property 30 | def sizes(self): 31 | return self.dataset.sizes 32 | 33 | def num_tokens(self, index): 34 | return self.dataset.num_tokens(index) 35 | 36 | def size(self, index): 37 | return self.dataset.size(index) 38 | 39 | def ordered_indices(self): 40 | return self.dataset.ordered_indices() 41 | 42 | @property 43 | def supports_prefetch(self): 44 | return getattr(self.dataset, 'supports_prefetch', False) 45 | 46 | def prefetch(self, indices): 47 | self.dataset.prefetch(indices) 48 | 49 | def set_epoch(self, epoch): 50 | super().set_epoch(epoch) 51 | if hasattr(self.dataset, 'set_epoch'): 52 | self.dataset.set_epoch(epoch) 53 | -------------------------------------------------------------------------------- /fairseq/models/fairseq_encoder.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch.nn as nn 7 | 8 | 9 | class FairseqEncoder(nn.Module): 10 | """Base class for encoders.""" 11 | 12 | def __init__(self, dictionary): 13 | super().__init__() 14 | self.dictionary = dictionary 15 | 16 | def forward(self, src_tokens, src_lengths=None, **kwargs): 17 | """ 18 | Args: 19 | src_tokens (LongTensor): tokens in the source language of shape 20 | `(batch, src_len)` 21 | src_lengths (LongTensor): lengths of each source sentence of shape 22 | `(batch)` 23 | """ 24 | raise NotImplementedError 25 | 26 | def reorder_encoder_out(self, encoder_out, new_order): 27 | """ 28 | Reorder encoder output according to `new_order`. 29 | 30 | Args: 31 | encoder_out: output from the ``forward()`` method 32 | new_order (LongTensor): desired order 33 | 34 | Returns: 35 | `encoder_out` rearranged according to `new_order` 36 | """ 37 | raise NotImplementedError 38 | 39 | def max_positions(self): 40 | """Maximum input length supported by the encoder.""" 41 | return 1e6 # an arbitrary large number 42 | 43 | def upgrade_state_dict(self, state_dict): 44 | """Upgrade a (possibly old) state dict for new versions of fairseq.""" 45 | return state_dict 46 | -------------------------------------------------------------------------------- /fairseq/optim/sgd.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch.optim 7 | 8 | from . import FairseqOptimizer, register_optimizer 9 | 10 | 11 | @register_optimizer('sgd') 12 | class SGD(FairseqOptimizer): 13 | def __init__(self, args, params): 14 | super().__init__(args) 15 | self._optimizer = torch.optim.SGD(params, **self.optimizer_config) 16 | 17 | @staticmethod 18 | def add_args(parser): 19 | """Add optimizer-specific arguments to the parser.""" 20 | # fmt: off 21 | parser.add_argument('--momentum', default=0.0, type=float, metavar='M', 22 | help='momentum factor') 23 | parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', 24 | help='weight decay') 25 | # fmt: on 26 | 27 | @property 28 | def optimizer_config(self): 29 | """ 30 | Return a kwarg dictionary that will be used to override optimizer 31 | args stored in checkpoints. This allows us to load a checkpoint and 32 | resume training using a different set of optimizer args, e.g., with a 33 | different learning rate. 34 | """ 35 | return { 36 | 'lr': self.args.lr[0], 37 | 'momentum': self.args.momentum, 38 | 'weight_decay': self.args.weight_decay, 39 | } 40 | 41 | @property 42 | def supports_flat_params(self): 43 | return True 44 | -------------------------------------------------------------------------------- /tests/test_file_io.py: -------------------------------------------------------------------------------- 1 | # This source code is licensed under the MIT license found in the 2 | # LICENSE file in the root directory of this source tree. 3 | 4 | import sys 5 | import tempfile 6 | import os 7 | import shutil 8 | 9 | from typing import Optional 10 | 11 | import unittest 12 | from unittest.mock import MagicMock 13 | 14 | 15 | class TestFileIO(unittest.TestCase): 16 | 17 | _tmpdir: Optional[str] = None 18 | _tmpfile: Optional[str] = None 19 | _tmpfile_contents = "Hello, World" 20 | 21 | @classmethod 22 | def setUpClass(cls) -> None: 23 | cls._tmpdir = tempfile.mkdtemp() 24 | with open(os.path.join(cls._tmpdir, "test.txt"), "w") as f: 25 | cls._tmpfile = f.name 26 | f.write(cls._tmpfile_contents) 27 | f.flush() 28 | 29 | @classmethod 30 | def tearDownClass(cls) -> None: 31 | # Cleanup temp working dir. 32 | if cls._tmpdir is not None: 33 | shutil.rmtree(cls._tmpdir) # type: ignore 34 | 35 | def test_file_io(self): 36 | from fairseq.file_io import PathManager 37 | with PathManager.open(os.path.join(self._tmpdir, "test.txt"), "r") as f: 38 | s = f.read() 39 | self.assertEqual(s, self._tmpfile_contents) 40 | 41 | def test_file_io_oss(self): 42 | # Mock fvcore to simulate oss environment. 43 | sys.modules['fvcore'] = MagicMock() 44 | from fairseq.file_io import PathManager 45 | with PathManager.open(os.path.join(self._tmpdir, "test.txt"), "r") as f: 46 | s = f.read() 47 | self.assertEqual(s, self._tmpfile_contents) 48 | -------------------------------------------------------------------------------- /hubconf.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import functools 7 | 8 | from fairseq.hub_utils import BPEHubInterface as bpe # noqa 9 | from fairseq.hub_utils import TokenizerHubInterface as tokenizer # noqa 10 | from fairseq.models import MODEL_REGISTRY 11 | 12 | 13 | dependencies = [ 14 | 'numpy', 15 | 'regex', 16 | 'requests', 17 | 'torch', 18 | ] 19 | 20 | 21 | # torch.hub doesn't build Cython components, so if they are not found then try 22 | # to build them here 23 | try: 24 | import fairseq.data.token_block_utils_fast 25 | except (ImportError, ModuleNotFoundError): 26 | try: 27 | import cython 28 | import os 29 | from setuptools import sandbox 30 | sandbox.run_setup( 31 | os.path.join(os.path.dirname(__file__), 'setup.py'), 32 | ['build_ext', '--inplace'], 33 | ) 34 | except (ImportError, ModuleNotFoundError): 35 | print( 36 | 'Unable to build Cython components. Please make sure Cython is ' 37 | 'installed if the torch.hub model you are loading depends on it.' 38 | ) 39 | 40 | 41 | for _model_type, _cls in MODEL_REGISTRY.items(): 42 | for model_name in _cls.hub_models().keys(): 43 | globals()[model_name] = functools.partial( 44 | _cls.from_pretrained, 45 | model_name, 46 | ) 47 | # to simplify the interface we only expose named models 48 | # globals()[_model_type] = _cls.from_pretrained 49 | -------------------------------------------------------------------------------- /docs/tasks.rst: -------------------------------------------------------------------------------- 1 | .. role:: hidden 2 | :class: hidden-section 3 | 4 | .. module:: fairseq.tasks 5 | 6 | .. _Tasks: 7 | 8 | Tasks 9 | ===== 10 | 11 | Tasks store dictionaries and provide helpers for loading/iterating over 12 | Datasets, initializing the Model/Criterion and calculating the loss. 13 | 14 | Tasks can be selected via the ``--task`` command-line argument. Once selected, a 15 | task may expose additional command-line arguments for further configuration. 16 | 17 | Example usage:: 18 | 19 | # setup the task (e.g., load dictionaries) 20 | task = fairseq.tasks.setup_task(args) 21 | 22 | # build model and criterion 23 | model = task.build_model(args) 24 | criterion = task.build_criterion(args) 25 | 26 | # load datasets 27 | task.load_dataset('train') 28 | task.load_dataset('valid') 29 | 30 | # iterate over mini-batches of data 31 | batch_itr = task.get_batch_iterator( 32 | task.dataset('train'), max_tokens=4096, 33 | ) 34 | for batch in batch_itr: 35 | # compute the loss 36 | loss, sample_size, logging_output = task.get_loss( 37 | model, criterion, batch, 38 | ) 39 | loss.backward() 40 | 41 | 42 | Translation 43 | ----------- 44 | 45 | .. autoclass:: fairseq.tasks.translation.TranslationTask 46 | 47 | .. _language modeling: 48 | 49 | Language Modeling 50 | ----------------- 51 | 52 | .. autoclass:: fairseq.tasks.language_modeling.LanguageModelingTask 53 | 54 | 55 | Adding new tasks 56 | ---------------- 57 | 58 | .. autofunction:: fairseq.tasks.register_task 59 | .. autoclass:: fairseq.tasks.FairseqTask 60 | :members: 61 | :undoc-members: 62 | -------------------------------------------------------------------------------- /fairseq/modules/dynamicconv_layer/dynamicconv_cuda.cuh: -------------------------------------------------------------------------------- 1 | /** 2 | * Copyright (c) Facebook, Inc. and its affiliates. 3 | * 4 | * This source code is licensed under the MIT license found in the 5 | * LICENSE file in the root directory of this source tree. 6 | */ 7 | 8 | #include 9 | #include 10 | 11 | #include 12 | #include 13 | #include 14 | 15 | #include 16 | #include 17 | #include 18 | #include 19 | #include 20 | #include 21 | 22 | #include 23 | #include 24 | #include 25 | 26 | #define SHFL_MASK 0xffffffff 27 | 28 | template 29 | __global__ 30 | void dynamicconv_forward_kernel(const scalar_t* input, 31 | const scalar_t* weight, 32 | int minibatch, 33 | int sequenceLength, 34 | int numFeatures, 35 | int numFiltersInBlock, 36 | int numHeads, 37 | scalar_t* output); 38 | 39 | template 40 | __global__ 41 | void dynamicconv_backward_kernel( 42 | const scalar_t* gradOutput, // B * C * T 43 | const scalar_t* input, // B * C * T 44 | const scalar_t* weight, 45 | int minibatch, 46 | int sequenceLength, 47 | int numFeatures, 48 | int numFiltersInBlock, 49 | int numHeads, 50 | scalar_t* gradWeight, 51 | scalar_t* gradInput); // B * H * k * T 52 | -------------------------------------------------------------------------------- /fairseq/modules/lightconv_layer/lightconv_cuda.cpp: -------------------------------------------------------------------------------- 1 | /** 2 | * Copyright (c) Facebook, Inc. and its affiliates. 3 | * 4 | * This source code is licensed under the MIT license found in the 5 | * LICENSE file in the root directory of this source tree. 6 | */ 7 | 8 | #include 9 | #include 10 | 11 | std::vector lightconv_cuda_forward( 12 | at::Tensor input, 13 | at::Tensor filters, 14 | int padding_l); 15 | 16 | std::vector lightconv_cuda_backward( 17 | at::Tensor gradOutput, 18 | int padding_l, 19 | at::Tensor input, 20 | at::Tensor filters); 21 | 22 | 23 | #define CHECK_CUDA(x) AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor") 24 | #define CHECK_CONTIGUOUS(x) AT_ASSERTM(x.is_contiguous(), #x " must be contiguous") 25 | #define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) 26 | 27 | std::vector lightconv_forward( 28 | at::Tensor input, 29 | at::Tensor filters, 30 | int padding_l) { 31 | 32 | CHECK_INPUT(input); 33 | CHECK_INPUT(filters); 34 | 35 | return lightconv_cuda_forward(input, filters, padding_l); 36 | } 37 | 38 | std::vector lightconv_backward( 39 | at::Tensor gradOutput, 40 | int padding_l, 41 | at::Tensor input, 42 | at::Tensor filters) { 43 | 44 | CHECK_INPUT(gradOutput); 45 | CHECK_INPUT(input); 46 | CHECK_INPUT(filters); 47 | 48 | return lightconv_cuda_backward(gradOutput, padding_l, input, filters); 49 | } 50 | 51 | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { 52 | m.def("forward", &lightconv_forward, "lighconv forward (CUDA)"); 53 | m.def("backward", &lightconv_backward, "lighconv backward (CUDA)"); 54 | } 55 | -------------------------------------------------------------------------------- /scripts/spm_decode.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # Copyright (c) Facebook, Inc. and its affiliates. 3 | # All rights reserved. 4 | # 5 | # This source code is licensed under the license found in the 6 | # LICENSE file in the root directory of this source tree. 7 | 8 | from __future__ import absolute_import, division, print_function, unicode_literals 9 | 10 | import argparse 11 | 12 | import sentencepiece as spm 13 | 14 | 15 | def main(): 16 | parser = argparse.ArgumentParser() 17 | parser.add_argument("--model", required=True, 18 | help="sentencepiece model to use for decoding") 19 | parser.add_argument("--input", required=True, help="input file to decode") 20 | parser.add_argument("--input_format", choices=["piece", "id"], default="piece") 21 | args = parser.parse_args() 22 | 23 | sp = spm.SentencePieceProcessor() 24 | sp.Load(args.model) 25 | 26 | if args.input_format == "piece": 27 | def decode(l): 28 | return "".join(sp.DecodePieces(l)) 29 | elif args.input_format == "id": 30 | def decode(l): 31 | return "".join(sp.DecodeIds(l)) 32 | else: 33 | raise NotImplementedError 34 | 35 | def tok2int(tok): 36 | # remap reference-side (represented as <>) to 0 37 | return int(tok) if tok != "<>" else 0 38 | 39 | with open(args.input, "r", encoding="utf-8") as h: 40 | for line in h: 41 | if args.input_format == "id": 42 | print(decode(list(map(tok2int, line.rstrip().split())))) 43 | elif args.input_format == "piece": 44 | print(decode(line.rstrip().split())) 45 | 46 | if __name__ == "__main__": 47 | main() 48 | -------------------------------------------------------------------------------- /fairseq/modules/dynamicconv_layer/dynamicconv_cuda.cpp: -------------------------------------------------------------------------------- 1 | /** 2 | * Copyright (c) Facebook, Inc. and its affiliates. 3 | * 4 | * This source code is licensed under the MIT license found in the 5 | * LICENSE file in the root directory of this source tree. 6 | */ 7 | 8 | #include 9 | #include 10 | 11 | std::vector dynamicconv_cuda_forward( 12 | at::Tensor input, 13 | at::Tensor filters, 14 | int padding_l); 15 | 16 | std::vector dynamicconv_cuda_backward( 17 | at::Tensor gradOutput, 18 | int padding_l, 19 | at::Tensor input, 20 | at::Tensor filters); 21 | 22 | 23 | #define CHECK_CUDA(x) AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor") 24 | #define CHECK_CONTIGUOUS(x) AT_ASSERTM(x.is_contiguous(), #x " must be contiguous") 25 | #define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) 26 | 27 | std::vector dynamicconv_forward( 28 | at::Tensor input, 29 | at::Tensor filters, 30 | int padding_l) { 31 | 32 | CHECK_INPUT(input); 33 | CHECK_INPUT(filters); 34 | 35 | return dynamicconv_cuda_forward(input, filters, 36 | padding_l); 37 | } 38 | 39 | std::vector dynamicconv_backward( 40 | at::Tensor gradOutput, 41 | int padding_l, 42 | at::Tensor input, 43 | at::Tensor filters) { 44 | 45 | CHECK_INPUT(gradOutput); 46 | CHECK_INPUT(input); 47 | CHECK_INPUT(filters); 48 | 49 | return dynamicconv_cuda_backward(gradOutput, padding_l, 50 | input, filters); 51 | } 52 | 53 | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { 54 | m.def("forward", &dynamicconv_forward, "dynamicconv forward (CUDA)"); 55 | m.def("backward", &dynamicconv_backward, "dynamicconv backward (CUDA)"); 56 | } 57 | -------------------------------------------------------------------------------- /scripts/shard_docs.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # Copyright (c) Facebook, Inc. and its affiliates. 3 | # 4 | # This source code is licensed under the MIT license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | """ 7 | Split a large file into shards while respecting document boundaries. Documents 8 | should be separated by a single empty line. 9 | """ 10 | 11 | import argparse 12 | import contextlib 13 | 14 | 15 | def main(): 16 | parser = argparse.ArgumentParser() 17 | parser.add_argument('input') 18 | parser.add_argument('--num-shards', type=int) 19 | args = parser.parse_args() 20 | 21 | assert args.num_shards is not None and args.num_shards > 1 22 | 23 | with open(args.input, 'r', encoding='utf-8') as h: 24 | with contextlib.ExitStack() as stack: 25 | outputs = [ 26 | stack.enter_context(open(args.input + ".shard" + str(i), "w", encoding="utf-8")) 27 | for i in range(args.num_shards) 28 | ] 29 | 30 | doc = [] 31 | first_doc = [True]*args.num_shards 32 | 33 | def output_doc(i): 34 | if not first_doc[i]: 35 | outputs[i].write("\n") 36 | first_doc[i] = False 37 | for line in doc: 38 | outputs[i].write(line) 39 | doc.clear() 40 | 41 | num_docs = 0 42 | for line in h: 43 | if line.strip() == "": # empty line indicates new document 44 | output_doc(num_docs % args.num_shards) 45 | num_docs += 1 46 | else: 47 | doc.append(line) 48 | output_doc(num_docs % args.num_shards) 49 | 50 | 51 | if __name__ == '__main__': 52 | main() 53 | -------------------------------------------------------------------------------- /fairseq/data/encoders/sentencepiece_bpe.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from fairseq import file_utils 7 | from fairseq.data.encoders import register_bpe 8 | 9 | 10 | @register_bpe('sentencepiece') 11 | class SentencepieceBPE(object): 12 | 13 | @staticmethod 14 | def add_args(parser): 15 | # fmt: off 16 | parser.add_argument('--sentencepiece-vocab', type=str, 17 | help='path to sentencepiece vocab') 18 | # fmt: on 19 | 20 | def __init__(self, args): 21 | vocab = file_utils.cached_path(args.sentencepiece_vocab) 22 | try: 23 | import sentencepiece as spm 24 | self.sp = spm.SentencePieceProcessor() 25 | self.sp.Load(vocab) 26 | except ImportError: 27 | raise ImportError('Please install sentencepiece with: pip install sentencepiece') 28 | 29 | def encode(self, x: str) -> str: 30 | return ' '.join(self.sp.EncodeAsPieces(x)) 31 | 32 | def decode(self, x: str) -> str: 33 | return x.replace(' ', '').replace('\u2581', ' ').strip() 34 | 35 | def is_beginning_of_word(self, x: str) -> bool: 36 | if x in ['', '', '', '']: 37 | # special elements are always considered beginnings 38 | # HACK: this logic is already present in fairseq/tasks/masked_lm.py 39 | # but these special tokens are also contained in the sentencepiece 40 | # vocabulary which causes duplicate special tokens. This hack makes 41 | # sure that they are all taken into account. 42 | return True 43 | return x.startswith('\u2581') 44 | -------------------------------------------------------------------------------- /fairseq/data/legacy/masked_lm_dictionary.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from fairseq.data import Dictionary 7 | 8 | 9 | class MaskedLMDictionary(Dictionary): 10 | """ 11 | Dictionary for Masked Language Modelling tasks. This extends Dictionary by 12 | adding the mask symbol. 13 | """ 14 | def __init__( 15 | self, 16 | pad='', 17 | eos='', 18 | unk='', 19 | mask='', 20 | ): 21 | super().__init__(pad, eos, unk) 22 | self.mask_word = mask 23 | self.mask_index = self.add_symbol(mask) 24 | self.nspecial = len(self.symbols) 25 | 26 | def mask(self): 27 | """Helper to get index of mask symbol""" 28 | return self.mask_index 29 | 30 | 31 | class BertDictionary(MaskedLMDictionary): 32 | """ 33 | Dictionary for BERT task. This extends MaskedLMDictionary by adding support 34 | for cls and sep symbols. 35 | """ 36 | def __init__( 37 | self, 38 | pad='', 39 | eos='', 40 | unk='', 41 | mask='', 42 | cls='', 43 | sep='' 44 | ): 45 | super().__init__(pad, eos, unk, mask) 46 | self.cls_word = cls 47 | self.sep_word = sep 48 | self.cls_index = self.add_symbol(cls) 49 | self.sep_index = self.add_symbol(sep) 50 | self.nspecial = len(self.symbols) 51 | 52 | def cls(self): 53 | """Helper to get index of cls symbol""" 54 | return self.cls_index 55 | 56 | def sep(self): 57 | """Helper to get index of sep symbol""" 58 | return self.sep_index 59 | -------------------------------------------------------------------------------- /fairseq/data/concat_sentences_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch 7 | 8 | from . import FairseqDataset 9 | 10 | 11 | class ConcatSentencesDataset(FairseqDataset): 12 | 13 | def __init__(self, *datasets): 14 | super().__init__() 15 | self.datasets = datasets 16 | assert all(len(ds) == len(datasets[0]) for ds in datasets), \ 17 | 'datasets must have the same length' 18 | 19 | def __getitem__(self, index): 20 | return torch.cat([ds[index] for ds in self.datasets]) 21 | 22 | def __len__(self): 23 | return len(self.datasets[0]) 24 | 25 | def collater(self, samples): 26 | return self.datasets[0].collater(samples) 27 | 28 | @property 29 | def sizes(self): 30 | return sum(ds.sizes for ds in self.datasets) 31 | 32 | def num_tokens(self, index): 33 | return sum(ds.num_tokens(index) for ds in self.datasets) 34 | 35 | def size(self, index): 36 | return sum(ds.size(index) for ds in self.datasets) 37 | 38 | def ordered_indices(self): 39 | return self.datasets[0].ordered_indices() 40 | 41 | @property 42 | def supports_prefetch(self): 43 | return any( 44 | getattr(ds, 'supports_prefetch', False) for ds in self.datasets 45 | ) 46 | 47 | def prefetch(self, indices): 48 | for ds in self.datasets: 49 | if getattr(ds, 'supports_prefetch', False): 50 | ds.prefetch(indices) 51 | 52 | def set_epoch(self, epoch): 53 | super().set_epoch(epoch) 54 | for ds in self.datasets: 55 | if hasattr(ds, 'set_epoch'): 56 | ds.set_epoch(epoch) 57 | -------------------------------------------------------------------------------- /fairseq/data/encoders/gpt2_bpe.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from fairseq import file_utils 7 | from fairseq.data.encoders import register_bpe 8 | 9 | from .gpt2_bpe_utils import get_encoder 10 | 11 | 12 | DEFAULT_ENCODER_JSON = 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json' 13 | DEFAULT_VOCAB_BPE = 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe' 14 | 15 | 16 | @register_bpe('gpt2') 17 | class GPT2BPE(object): 18 | 19 | @staticmethod 20 | def add_args(parser): 21 | # fmt: off 22 | parser.add_argument('--gpt2-encoder-json', type=str, 23 | default=DEFAULT_ENCODER_JSON, 24 | help='path to encoder.json') 25 | parser.add_argument('--gpt2-vocab-bpe', type=str, 26 | default=DEFAULT_VOCAB_BPE, 27 | help='path to vocab.bpe') 28 | # fmt: on 29 | 30 | def __init__(self, args): 31 | encoder_json = file_utils.cached_path( 32 | getattr(args, 'gpt2_encoder_json', DEFAULT_ENCODER_JSON) 33 | ) 34 | vocab_bpe = file_utils.cached_path( 35 | getattr(args, 'gpt2_vocab_bpe', DEFAULT_VOCAB_BPE) 36 | ) 37 | self.bpe = get_encoder(encoder_json, vocab_bpe) 38 | 39 | def encode(self, x: str) -> str: 40 | return ' '.join(map(str, self.bpe.encode(x))) 41 | 42 | def decode(self, x: str) -> str: 43 | return self.bpe.decode([ 44 | int(tok) if tok not in {'', ''} else tok 45 | for tok in x.split() 46 | ]) 47 | 48 | def is_beginning_of_word(self, x: str) -> bool: 49 | return self.decode(x).startswith(' ') 50 | -------------------------------------------------------------------------------- /fairseq/modules/sparse_transformer_sentence_encoder_layer.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from fairseq.modules import TransformerSentenceEncoderLayer 7 | from fairseq.modules.sparse_multihead_attention import SparseMultiheadAttention 8 | 9 | 10 | class SparseTransformerSentenceEncoderLayer(TransformerSentenceEncoderLayer): 11 | """ 12 | Implements a Sprase Transformer Encoder Layer (see SparseMultiheadAttention) 13 | """ 14 | 15 | def __init__( 16 | self, 17 | embedding_dim: int = 768, 18 | ffn_embedding_dim: int = 3072, 19 | num_attention_heads: int = 8, 20 | dropout: float = 0.1, 21 | attention_dropout: float = 0.1, 22 | activation_dropout: float = 0.1, 23 | activation_fn: str = 'relu', 24 | add_bias_kv: bool = False, 25 | add_zero_attn: bool = False, 26 | export: bool = False, 27 | is_bidirectional: bool = True, 28 | stride: int = 32, 29 | expressivity: int = 8, 30 | ) -> None: 31 | 32 | super().__init__( 33 | embedding_dim, ffn_embedding_dim, num_attention_heads, dropout, 34 | attention_dropout, activation_dropout, activation_fn, add_bias_kv, 35 | add_zero_attn, export 36 | ) 37 | 38 | self.self_attn = SparseMultiheadAttention( 39 | self.embedding_dim, 40 | num_attention_heads, 41 | dropout=attention_dropout, 42 | add_bias_kv=add_bias_kv, 43 | add_zero_attn=add_zero_attn, 44 | self_attention=True, 45 | is_bidirectional=is_bidirectional, 46 | stride=stride, 47 | expressivity=expressivity, 48 | ) 49 | -------------------------------------------------------------------------------- /fairseq/data/encoders/subword_nmt_bpe.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from fairseq import file_utils 7 | from fairseq.data.encoders import register_bpe 8 | 9 | 10 | @register_bpe('subword_nmt') 11 | class SubwordNMTBPE(object): 12 | 13 | @staticmethod 14 | def add_args(parser): 15 | # fmt: off 16 | parser.add_argument('--bpe-codes', type=str, 17 | help='path to subword NMT BPE') 18 | parser.add_argument('--bpe-separator', default='@@', 19 | help='BPE separator') 20 | # fmt: on 21 | 22 | def __init__(self, args): 23 | if args.bpe_codes is None: 24 | raise ValueError('--bpe-codes is required for --bpe=subword_nmt') 25 | codes = file_utils.cached_path(args.bpe_codes) 26 | try: 27 | from subword_nmt import apply_bpe 28 | bpe_parser = apply_bpe.create_parser() 29 | bpe_args = bpe_parser.parse_args([ 30 | '--codes', codes, 31 | '--separator', args.bpe_separator, 32 | ]) 33 | self.bpe = apply_bpe.BPE( 34 | bpe_args.codes, 35 | bpe_args.merges, 36 | bpe_args.separator, 37 | None, 38 | bpe_args.glossaries, 39 | ) 40 | self.bpe_symbol = bpe_args.separator + ' ' 41 | except ImportError: 42 | raise ImportError('Please install subword_nmt with: pip install subword-nmt') 43 | 44 | def encode(self, x: str) -> str: 45 | return self.bpe.process_line(x) 46 | 47 | def decode(self, x: str) -> str: 48 | return (x + ' ').replace(self.bpe_symbol, '').rstrip() 49 | -------------------------------------------------------------------------------- /tests/test_character_token_embedder.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch 7 | import unittest 8 | 9 | from fairseq.data import Dictionary 10 | from fairseq.modules import CharacterTokenEmbedder 11 | 12 | 13 | class TestCharacterTokenEmbedder(unittest.TestCase): 14 | def test_character_token_embedder(self): 15 | vocab = Dictionary() 16 | vocab.add_symbol('hello') 17 | vocab.add_symbol('there') 18 | 19 | embedder = CharacterTokenEmbedder(vocab, [(2, 16), (4, 32), (8, 64), (16, 2)], 64, 5, 2) 20 | 21 | test_sents = [['hello', 'unk', 'there'], ['there'], ['hello', 'there']] 22 | max_len = max(len(s) for s in test_sents) 23 | input = torch.LongTensor(len(test_sents), max_len + 2).fill_(vocab.pad()) 24 | for i in range(len(test_sents)): 25 | input[i][0] = vocab.eos() 26 | for j in range(len(test_sents[i])): 27 | input[i][j + 1] = vocab.index(test_sents[i][j]) 28 | input[i][j + 2] = vocab.eos() 29 | embs = embedder(input) 30 | 31 | assert embs.size() == (len(test_sents), max_len + 2, 5) 32 | self.assertAlmostEqual(embs[0][0], embs[1][0]) 33 | self.assertAlmostEqual(embs[0][0], embs[0][-1]) 34 | self.assertAlmostEqual(embs[0][1], embs[2][1]) 35 | self.assertAlmostEqual(embs[0][3], embs[1][1]) 36 | 37 | embs.sum().backward() 38 | assert embedder.char_embeddings.weight.grad is not None 39 | 40 | def assertAlmostEqual(self, t1, t2): 41 | self.assertEqual(t1.size(), t2.size(), "size mismatch") 42 | self.assertLess((t1 - t2).abs().max(), 1e-6) 43 | 44 | 45 | if __name__ == '__main__': 46 | unittest.main() 47 | -------------------------------------------------------------------------------- /examples/backtranslation/README.md: -------------------------------------------------------------------------------- 1 | # Understanding Back-Translation at Scale (Edunov et al., 2018) 2 | 3 | This page includes pre-trained models from the paper [Understanding Back-Translation at Scale (Edunov et al., 2018)](https://arxiv.org/abs/1808.09381). 4 | 5 | ## Pre-trained models 6 | 7 | Model | Description | Dataset | Download 8 | ---|---|---|--- 9 | `transformer.wmt18.en-de` | Transformer
([Edunov et al., 2018](https://arxiv.org/abs/1808.09381))
WMT'18 winner | [WMT'18 English-German](http://www.statmt.org/wmt18/translation-task.html) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt18.en-de.ensemble.tar.gz)
See NOTE in the archive 10 | 11 | ## Example usage (torch.hub) 12 | 13 | We require a few additional Python dependencies for preprocessing: 14 | ```bash 15 | pip install subword_nmt sacremoses 16 | ``` 17 | 18 | Then to generate translations from the full model ensemble: 19 | ```python 20 | import torch 21 | 22 | # List available models 23 | torch.hub.list('pytorch/fairseq') # [..., 'transformer.wmt18.en-de', ... ] 24 | 25 | # Load the WMT'18 En-De ensemble 26 | en2de_ensemble = torch.hub.load( 27 | 'pytorch/fairseq', 'transformer.wmt18.en-de', 28 | checkpoint_file='wmt18.model1.pt:wmt18.model2.pt:wmt18.model3.pt:wmt18.model4.pt:wmt18.model5.pt', 29 | tokenizer='moses', bpe='subword_nmt') 30 | 31 | # The ensemble contains 5 models 32 | len(en2de_ensemble.models) 33 | # 5 34 | 35 | # Translate 36 | en2de_ensemble.translate('Hello world!') 37 | # 'Hallo Welt!' 38 | ``` 39 | 40 | ## Citation 41 | ```bibtex 42 | @inproceedings{edunov2018backtranslation, 43 | title = {Understanding Back-Translation at Scale}, 44 | author = {Edunov, Sergey and Ott, Myle and Auli, Michael and Grangier, David}, 45 | booktitle = {Conference of the Association for Computational Linguistics (ACL)}, 46 | year = 2018, 47 | } 48 | ``` 49 | -------------------------------------------------------------------------------- /tests/test_convtbc.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch 7 | import unittest 8 | from fairseq.modules import ConvTBC 9 | import torch.nn as nn 10 | 11 | 12 | class TestConvTBC(unittest.TestCase): 13 | 14 | def test_convtbc(self): 15 | # ksz, in_channels, out_channels 16 | conv_tbc = ConvTBC(4, 5, kernel_size=3, padding=1) 17 | # out_channels, in_channels, ksz 18 | conv1d = nn.Conv1d(4, 5, kernel_size=3, padding=1) 19 | 20 | conv_tbc.weight.data.copy_(conv1d.weight.data.transpose(0, 2)) 21 | conv_tbc.bias.data.copy_(conv1d.bias.data) 22 | 23 | input_tbc = torch.randn(7, 2, 4, requires_grad=True) 24 | input1d = input_tbc.data.transpose(0, 1).transpose(1, 2) 25 | input1d.requires_grad = True 26 | 27 | output_tbc = conv_tbc(input_tbc) 28 | output1d = conv1d(input1d) 29 | 30 | self.assertAlmostEqual(output_tbc.data.transpose(0, 1).transpose(1, 2), output1d.data) 31 | 32 | grad_tbc = torch.randn(output_tbc.size()) 33 | grad1d = grad_tbc.transpose(0, 1).transpose(1, 2).contiguous() 34 | 35 | output_tbc.backward(grad_tbc) 36 | output1d.backward(grad1d) 37 | 38 | self.assertAlmostEqual(conv_tbc.weight.grad.data.transpose(0, 2), conv1d.weight.grad.data) 39 | self.assertAlmostEqual(conv_tbc.bias.grad.data, conv1d.bias.grad.data) 40 | self.assertAlmostEqual(input_tbc.grad.data.transpose(0, 1).transpose(1, 2), input1d.grad.data) 41 | 42 | def assertAlmostEqual(self, t1, t2): 43 | self.assertEqual(t1.size(), t2.size(), "size mismatch") 44 | self.assertLess((t1 - t2).abs().max(), 1e-4) 45 | 46 | 47 | if __name__ == '__main__': 48 | unittest.main() 49 | -------------------------------------------------------------------------------- /fairseq/modules/highway.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch 7 | 8 | from torch import nn 9 | 10 | 11 | class Highway(torch.nn.Module): 12 | """ 13 | A `Highway layer `_. 14 | Adopted from the AllenNLP implementation. 15 | """ 16 | 17 | def __init__( 18 | self, 19 | input_dim: int, 20 | num_layers: int = 1 21 | ): 22 | super(Highway, self).__init__() 23 | self.input_dim = input_dim 24 | self.layers = nn.ModuleList([nn.Linear(input_dim, input_dim * 2) 25 | for _ in range(num_layers)]) 26 | self.activation = nn.ReLU() 27 | 28 | self.reset_parameters() 29 | 30 | def reset_parameters(self): 31 | for layer in self.layers: 32 | # As per comment in AllenNLP: 33 | # We should bias the highway layer to just carry its input forward. We do that by 34 | # setting the bias on `B(x)` to be positive, because that means `g` will be biased to 35 | # be high, so we will carry the input forward. The bias on `B(x)` is the second half 36 | # of the bias vector in each Linear layer. 37 | nn.init.constant_(layer.bias[self.input_dim:], 1) 38 | 39 | nn.init.constant_(layer.bias[:self.input_dim], 0) 40 | nn.init.xavier_normal_(layer.weight) 41 | 42 | def forward( 43 | self, 44 | x: torch.Tensor 45 | ): 46 | for layer in self.layers: 47 | projection = layer(x) 48 | proj_x, gate = projection.chunk(2, dim=-1) 49 | proj_x = self.activation(proj_x) 50 | gate = torch.sigmoid(gate) 51 | x = gate * x + (gate.new_tensor([1]) - gate) * proj_x 52 | return x 53 | -------------------------------------------------------------------------------- /fairseq/incremental_decoding_utils.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from typing import Dict, Optional 7 | import uuid 8 | 9 | from torch import Tensor 10 | 11 | 12 | class FairseqIncrementalState(object): 13 | 14 | def __init__(self, *args, **kwargs): 15 | super().__init__(*args, **kwargs) 16 | self.init_incremental_state() 17 | 18 | def init_incremental_state(self): 19 | self._incremental_state_id = str(uuid.uuid4()) 20 | 21 | def _get_full_incremental_state_key(self, key: str) -> str: 22 | return "{}.{}".format(self._incremental_state_id, key) 23 | 24 | def get_incremental_state( 25 | self, 26 | incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], 27 | key: str, 28 | ) -> Optional[Dict[str, Optional[Tensor]]]: 29 | """Helper for getting incremental state for an nn.Module.""" 30 | full_key = self._get_full_incremental_state_key(key) 31 | if incremental_state is None or full_key not in incremental_state: 32 | return None 33 | return incremental_state[full_key] 34 | 35 | def set_incremental_state( 36 | self, 37 | incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], 38 | key: str, 39 | value: Dict[str, Optional[Tensor]], 40 | ) -> Optional[Dict[str, Dict[str, Optional[Tensor]]]]: 41 | """Helper for setting incremental state for an nn.Module.""" 42 | if incremental_state is not None: 43 | full_key = self._get_full_incremental_state_key(key) 44 | incremental_state[full_key] = value 45 | return incremental_state 46 | 47 | 48 | def with_incremental_state(cls): 49 | cls.__bases__ = (FairseqIncrementalState,) + tuple(b for b in cls.__bases__ if b != FairseqIncrementalState) 50 | return cls 51 | -------------------------------------------------------------------------------- /fairseq/modules/beamable_mm.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch 7 | import torch.nn as nn 8 | 9 | 10 | class BeamableMM(nn.Module): 11 | """This module provides an optimized MM for beam decoding with attention. 12 | 13 | It leverage the fact that the source-side of the input is replicated beam 14 | times and the target-side of the input is of width one. This layer speeds up 15 | inference by replacing the inputs {(bsz x 1 x nhu), (bsz x sz2 x nhu)} 16 | with smaller inputs {(bsz/beam x beam x nhu), (bsz/beam x sz2 x nhu)}. 17 | """ 18 | def __init__(self, beam_size=None): 19 | super(BeamableMM, self).__init__() 20 | self.beam_size = beam_size 21 | 22 | def forward(self, input1, input2): 23 | if ( 24 | not self.training and # test mode 25 | self.beam_size is not None and # beam size is set 26 | input1.dim() == 3 and # only support batched input 27 | input1.size(1) == 1 # single time step update 28 | ): 29 | bsz, beam = input1.size(0), self.beam_size 30 | 31 | # bsz x 1 x nhu --> bsz/beam x beam x nhu 32 | input1 = input1[:, 0, :].unfold(0, beam, beam).transpose(2, 1) 33 | 34 | # bsz x sz2 x nhu --> bsz/beam x sz2 x nhu 35 | input2 = input2.unfold(0, beam, beam)[:, :, :, 0] 36 | 37 | # use non batched operation if bsz = beam 38 | if input1.size(0) == 1: 39 | output = torch.mm(input1[0, :, :], input2[0, :, :]) 40 | else: 41 | output = input1.bmm(input2) 42 | return output.view(bsz, 1, -1) 43 | else: 44 | return input1.bmm(input2) 45 | 46 | def set_beam_size(self, beam_size): 47 | self.beam_size = beam_size 48 | -------------------------------------------------------------------------------- /examples/conv_seq2seq/README.md: -------------------------------------------------------------------------------- 1 | # Convolutional Sequence to Sequence Learning (Gehring et al., 2017) 2 | 3 | ## Pre-trained models 4 | 5 | Description | Dataset | Model | Test set(s) 6 | ---|---|---|--- 7 | Convolutional
([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.v2.en-fr.fconv-py.tar.bz2) | newstest2014:
[download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.v2.en-fr.newstest2014.tar.bz2)
newstest2012/2013:
[download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.v2.en-fr.ntst1213.tar.bz2) 8 | Convolutional
([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT14 English-German](http://statmt.org/wmt14/translation-task.html#Download) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-de.fconv-py.tar.bz2) | newstest2014:
[download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.en-de.newstest2014.tar.bz2) 9 | Convolutional
([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT17 English-German](http://statmt.org/wmt17/translation-task.html#Download) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt17.v2.en-de.fconv-py.tar.bz2) | newstest2014:
[download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt17.v2.en-de.newstest2014.tar.bz2) 10 | 11 | ## Example usage 12 | 13 | See the [translation README](../translation/README.md) for instructions on reproducing results for WMT'14 En-De and 14 | WMT'14 En-Fr using the `fconv_wmt_en_de` and `fconv_wmt_en_fr` model architectures. 15 | 16 | ## Citation 17 | 18 | ```bibtex 19 | @inproceedings{gehring2017convs2s, 20 | title = {Convolutional Sequence to Sequence Learning}, 21 | author = {Gehring, Jonas, and Auli, Michael and Grangier, David and Yarats, Denis and Dauphin, Yann N}, 22 | booktitle = {Proc. of ICML}, 23 | year = 2017, 24 | } 25 | ``` 26 | -------------------------------------------------------------------------------- /fairseq/optim/adadelta.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch.optim 7 | 8 | from . import FairseqOptimizer, register_optimizer 9 | 10 | 11 | @register_optimizer('adadelta') 12 | class Adadelta(FairseqOptimizer): 13 | def __init__(self, args, params): 14 | super().__init__(args) 15 | self._optimizer = torch.optim.Adadelta(params, **self.optimizer_config) 16 | 17 | @staticmethod 18 | def add_args(parser): 19 | """Add optimizer-specific arguments to the parser.""" 20 | # fmt: off 21 | parser.add_argument('--adadelta-rho', type=float, default=0.9, metavar='RHO', 22 | help='coefficient used for computing a running average of squared gradients') 23 | parser.add_argument('--adadelta-eps', type=float, default=1e-6, metavar='EPS', 24 | help='term added to the denominator to improve numerical stability') 25 | parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', 26 | help='weight decay') 27 | parser.add_argument('--anneal-eps', action='store_true', help='flag to anneal eps') 28 | # fmt: on 29 | 30 | @property 31 | def optimizer_config(self): 32 | """ 33 | Return a kwarg dictionary that will be used to override optimizer 34 | args stored in checkpoints. This allows us to load a checkpoint and 35 | resume training using a different set of optimizer args, e.g., with a 36 | different learning rate. 37 | """ 38 | return { 39 | 'lr': self.args.lr[0], 40 | 'rho': self.args.adadelta_rho, 41 | 'eps': self.args.adadelta_eps, 42 | 'weight_decay': self.args.weight_decay, 43 | } 44 | 45 | @property 46 | def supports_flat_params(self): 47 | return True 48 | -------------------------------------------------------------------------------- /fairseq/data/encoders/hf_bert_bpe.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from fairseq.data.encoders import register_bpe 7 | 8 | 9 | @register_bpe('bert') 10 | class BertBPE(object): 11 | 12 | @staticmethod 13 | def add_args(parser): 14 | # fmt: off 15 | parser.add_argument('--bpe-cased', action='store_true', 16 | help='set for cased BPE', 17 | default=False) 18 | parser.add_argument('--bpe-vocab-file', type=str, 19 | help='bpe vocab file.') 20 | # fmt: on 21 | 22 | def __init__(self, args): 23 | try: 24 | from pytorch_transformers import BertTokenizer 25 | from pytorch_transformers.tokenization_utils import clean_up_tokenization 26 | except ImportError: 27 | raise ImportError( 28 | 'Please install 1.0.0 version of pytorch_transformers' 29 | 'with: pip install pytorch-transformers' 30 | ) 31 | 32 | if 'bpe_vocab_file' in args: 33 | self.bert_tokenizer = BertTokenizer( 34 | args.bpe_vocab_file, 35 | do_lower_case=not args.bpe_cased 36 | ) 37 | else: 38 | vocab_file_name = 'bert-base-cased' if args.bpe_cased else 'bert-base-uncased' 39 | self.bert_tokenizer = BertTokenizer.from_pretrained(vocab_file_name) 40 | self.clean_up_tokenization = clean_up_tokenization 41 | 42 | def encode(self, x: str) -> str: 43 | return ' '.join(self.bert_tokenizer.tokenize(x)) 44 | 45 | def decode(self, x: str) -> str: 46 | return self.clean_up_tokenization( 47 | self.bert_tokenizer.convert_tokens_to_string(x.split(' ')) 48 | ) 49 | 50 | def is_beginning_of_word(self, x: str) -> bool: 51 | return not x.startswith('##') 52 | -------------------------------------------------------------------------------- /scripts/count_docs.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # Copyright (c) Facebook, Inc. and its affiliates. 3 | # 4 | # This source code is licensed under the MIT license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | """ 7 | Count the number of documents and average number of lines and tokens per 8 | document in a large file. Documents should be separated by a single empty line. 9 | """ 10 | 11 | import argparse 12 | import gzip 13 | import sys 14 | 15 | import numpy as np 16 | 17 | 18 | def main(): 19 | parser = argparse.ArgumentParser() 20 | parser.add_argument('input') 21 | parser.add_argument('--gzip', action='store_true') 22 | args = parser.parse_args() 23 | 24 | def gopen(): 25 | if args.gzip: 26 | return gzip.open(args.input, 'r') 27 | else: 28 | return open(args.input, 'r', encoding='utf-8') 29 | 30 | num_lines = [] 31 | num_toks = [] 32 | with gopen() as h: 33 | num_docs = 1 34 | num_lines_in_doc = 0 35 | num_toks_in_doc = 0 36 | for i, line in enumerate(h): 37 | if len(line.strip()) == 0: # empty line indicates new document 38 | num_docs += 1 39 | num_lines.append(num_lines_in_doc) 40 | num_toks.append(num_toks_in_doc) 41 | num_lines_in_doc = 0 42 | num_toks_in_doc = 0 43 | else: 44 | num_lines_in_doc += 1 45 | num_toks_in_doc += len(line.rstrip().split()) 46 | if i % 1000000 == 0: 47 | print(i, file=sys.stderr, end="", flush=True) 48 | elif i % 100000 == 0: 49 | print(".", file=sys.stderr, end="", flush=True) 50 | print(file=sys.stderr, flush=True) 51 | 52 | print("found {} docs".format(num_docs)) 53 | print("average num lines per doc: {}".format(np.mean(num_lines))) 54 | print("average num toks per doc: {}".format(np.mean(num_toks))) 55 | 56 | 57 | if __name__ == '__main__': 58 | main() 59 | -------------------------------------------------------------------------------- /fairseq/optim/fused_lamb.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from fairseq.optim import FairseqOptimizer, register_optimizer 7 | 8 | 9 | @register_optimizer('lamb') 10 | class FairseqLAMB(FairseqOptimizer): 11 | """LAMB optimizer.""" 12 | 13 | def __init__(self, args, params): 14 | super().__init__(args) 15 | try: 16 | from apex.optimizers import FusedLAMB 17 | self._optimizer = FusedLAMB(params, **self.optimizer_config) 18 | except ImportError: 19 | raise ImportError('Please install apex to use LAMB optimizer') 20 | 21 | @staticmethod 22 | def add_args(parser): 23 | """Add optimizer-specific arguments to the parser.""" 24 | # fmt: off 25 | parser.add_argument('--lamb-betas', default='(0.9, 0.999)', metavar='B', 26 | help='betas for LAMB optimizer') 27 | parser.add_argument('--lamb-eps', type=float, default=1e-8, metavar='D', 28 | help='epsilon for LAMB optimizer') 29 | parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', 30 | help='weight decay') 31 | # fmt: on 32 | 33 | @property 34 | def optimizer_config(self): 35 | """ 36 | Return a kwarg dictionary that will be used to override optimizer 37 | args stored in checkpoints. This allows us to load a checkpoint and 38 | resume training using a different set of optimizer args, e.g., with a 39 | different learning rate. 40 | """ 41 | return { 42 | 'lr': self.args.lr[0], 43 | 'betas': eval(self.args.lamb_betas), 44 | 'eps': self.args.lamb_eps, 45 | 'weight_decay': self.args.weight_decay, 46 | } 47 | 48 | @property 49 | def supports_flat_params(self): 50 | return False 51 | -------------------------------------------------------------------------------- /fairseq/clib/libnat_cuda/binding.cpp: -------------------------------------------------------------------------------- 1 | /** 2 | * Copyright 2017-present, Facebook, Inc. 3 | * All rights reserved. 4 | * 5 | * This source code is licensed under the license found in the 6 | * LICENSE file in the root directory of this source tree. 7 | */ 8 | 9 | /* 10 | This code is partially adpoted from https://github.com/1ytic/pytorch-edit-distance 11 | */ 12 | 13 | #include "edit_dist.h" 14 | #include 15 | 16 | #ifndef TORCH_CHECK 17 | #define TORCH_CHECK AT_CHECK 18 | #endif 19 | 20 | #define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") 21 | #define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") 22 | #define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) 23 | 24 | 25 | torch::Tensor LevenshteinDistance( 26 | torch::Tensor source, 27 | torch::Tensor target, 28 | torch::Tensor source_length, 29 | torch::Tensor target_length) { 30 | 31 | CHECK_INPUT(source); 32 | CHECK_INPUT(target); 33 | CHECK_INPUT(source_length); 34 | CHECK_INPUT(target_length); 35 | return LevenshteinDistanceCuda(source, target, source_length, target_length); 36 | } 37 | 38 | torch::Tensor GenerateDeletionLabel( 39 | torch::Tensor source, 40 | torch::Tensor operations) { 41 | 42 | CHECK_INPUT(source); 43 | CHECK_INPUT(operations); 44 | return GenerateDeletionLabelCuda(source, operations); 45 | } 46 | 47 | std::pair GenerateInsertionLabel( 48 | torch::Tensor target, 49 | torch::Tensor operations) { 50 | 51 | CHECK_INPUT(target); 52 | CHECK_INPUT(operations); 53 | return GenerateInsertionLabelCuda(target, operations); 54 | } 55 | 56 | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { 57 | m.def("levenshtein_distance", &LevenshteinDistance, "Levenshtein distance"); 58 | m.def("generate_deletion_labels", &GenerateDeletionLabel, "Generate Deletion Label"); 59 | m.def("generate_insertion_labels", &GenerateInsertionLabel, "Generate Insertion Label"); 60 | } 61 | -------------------------------------------------------------------------------- /fairseq/modules/learned_positional_embedding.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch.nn as nn 7 | 8 | from fairseq import utils 9 | 10 | 11 | class LearnedPositionalEmbedding(nn.Embedding): 12 | """ 13 | This module learns positional embeddings up to a fixed maximum size. 14 | Padding ids are ignored by either offsetting based on padding_idx 15 | or by setting padding_idx to None and ensuring that the appropriate 16 | position ids are passed to the forward function. 17 | """ 18 | 19 | def __init__( 20 | self, 21 | num_embeddings: int, 22 | embedding_dim: int, 23 | padding_idx: int, 24 | ): 25 | super().__init__(num_embeddings, embedding_dim, padding_idx) 26 | self.onnx_trace = False 27 | if self.padding_idx is not None: 28 | self.max_positions = self.num_embeddings - self.padding_idx - 1 29 | else: 30 | self.max_positions = self.num_embeddings 31 | 32 | def forward(self, input, incremental_state=None, positions=None): 33 | """Input is expected to be of size [bsz x seqlen].""" 34 | assert ( 35 | (positions is None) or (self.padding_idx is None) 36 | ), "If positions is pre-computed then padding_idx should not be set." 37 | 38 | if positions is None: 39 | if incremental_state is not None: 40 | # positions is the same for every token when decoding a single step 41 | # Without the int() cast, it doesn't work in some cases when exporting to ONNX 42 | positions = input.data.new(1, 1).fill_(int(self.padding_idx + input.size(1))) 43 | else: 44 | positions = utils.make_positions( 45 | input, self.padding_idx, onnx_trace=self.onnx_trace, 46 | ) 47 | return super().forward(positions) 48 | -------------------------------------------------------------------------------- /fairseq/data/sharded_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import itertools 7 | import os 8 | import random 9 | 10 | from . import BaseWrapperDataset 11 | from fairseq.data import data_utils 12 | 13 | 14 | class ShardedDataset(BaseWrapperDataset): 15 | """A :class:`~fairseq.data.FairseqDataset` wrapper that appends/prepends/strips EOS. 16 | 17 | Loads a dataset which has been sharded into multiple files. each shard is only loaded for each specific epoch 18 | 19 | """ 20 | 21 | def __init__( 22 | self, 23 | dictionary, 24 | dataset_impl: str, 25 | path: str, 26 | split: str, 27 | epoch: int, 28 | name: str = None, 29 | combine: bool = False, 30 | seed: int = 0, 31 | ): 32 | self._name = name if name is not None else os.path.basename(path) 33 | num_shards = 0 34 | for i in itertools.count(): 35 | if not os.path.exists(os.path.join(path, "shard" + str(i))): 36 | break 37 | num_shards += 1 38 | 39 | if num_shards > 0 and split == "train": 40 | random.seed(seed ^ epoch) 41 | shard = random.randint(0, num_shards - 1) 42 | split_path = os.path.join(path, "shard" + str(shard), split) 43 | else: 44 | split_path = os.path.join(path, split) 45 | if os.path.isdir(split_path): 46 | split_path = os.path.join(split_path, split) 47 | 48 | dataset = data_utils.load_indexed_dataset( 49 | split_path, dictionary, dataset_impl, combine=combine 50 | ) 51 | if dataset is None: 52 | raise FileNotFoundError( 53 | "Dataset not found: {} ({})".format(split, split_path) 54 | ) 55 | 56 | super().__init__(dataset) 57 | 58 | @property 59 | def name(self): 60 | return self._name 61 | -------------------------------------------------------------------------------- /fairseq/models/composite_encoder.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from fairseq.models import FairseqEncoder 7 | 8 | 9 | class CompositeEncoder(FairseqEncoder): 10 | """ 11 | A wrapper around a dictionary of :class:`FairseqEncoder` objects. 12 | 13 | We run forward on each encoder and return a dictionary of outputs. The first 14 | encoder's dictionary is used for initialization. 15 | 16 | Args: 17 | encoders (dict): a dictionary of :class:`FairseqEncoder` objects. 18 | """ 19 | 20 | def __init__(self, encoders): 21 | super().__init__(next(iter(encoders.values())).dictionary) 22 | self.encoders = encoders 23 | for key in self.encoders: 24 | self.add_module(key, self.encoders[key]) 25 | 26 | def forward(self, src_tokens, src_lengths): 27 | """ 28 | Args: 29 | src_tokens (LongTensor): tokens in the source language of shape 30 | `(batch, src_len)` 31 | src_lengths (LongTensor): lengths of each source sentence of shape 32 | `(batch)` 33 | 34 | Returns: 35 | dict: 36 | the outputs from each Encoder 37 | """ 38 | encoder_out = {} 39 | for key in self.encoders: 40 | encoder_out[key] = self.encoders[key](src_tokens, src_lengths) 41 | return encoder_out 42 | 43 | def reorder_encoder_out(self, encoder_out, new_order): 44 | """Reorder encoder output according to new_order.""" 45 | for key in self.encoders: 46 | encoder_out[key] = self.encoders[key].reorder_encoder_out(encoder_out[key], new_order) 47 | return encoder_out 48 | 49 | def max_positions(self): 50 | return min(self.encoders[key].max_positions() for key in self.encoders) 51 | 52 | def upgrade_state_dict(self, state_dict): 53 | for key in self.encoders: 54 | self.encoders[key].upgrade_state_dict(state_dict) 55 | return state_dict 56 | -------------------------------------------------------------------------------- /fairseq/data/encoders/moses_tokenizer.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from fairseq.data.encoders import register_tokenizer 7 | 8 | 9 | @register_tokenizer('moses') 10 | class MosesTokenizer(object): 11 | 12 | @staticmethod 13 | def add_args(parser): 14 | # fmt: off 15 | parser.add_argument('--moses-source-lang', metavar='SRC', 16 | help='source language') 17 | parser.add_argument('--moses-target-lang', metavar='TARGET', 18 | help='target language') 19 | parser.add_argument('--moses-no-dash-splits', action='store_true', default=False, 20 | help='don\'t apply dash split rules') 21 | parser.add_argument('--moses-no-escape', action='store_true', default=False, 22 | help='don\'t perform HTML escaping on apostrophy, quotes, etc.') 23 | # fmt: on 24 | 25 | def __init__(self, args): 26 | self.args = args 27 | 28 | if getattr(args, 'moses_source_lang', None) is None: 29 | args.moses_source_lang = getattr(args, 'source_lang', 'en') 30 | if getattr(args, 'moses_target_lang', None) is None: 31 | args.moses_target_lang = getattr(args, 'target_lang', 'en') 32 | 33 | try: 34 | from sacremoses import MosesTokenizer, MosesDetokenizer 35 | self.tok = MosesTokenizer(args.moses_source_lang) 36 | self.detok = MosesDetokenizer(args.moses_target_lang) 37 | except ImportError: 38 | raise ImportError('Please install Moses tokenizer with: pip install sacremoses') 39 | 40 | def encode(self, x: str) -> str: 41 | return self.tok.tokenize( 42 | x, 43 | aggressive_dash_splits=(not self.args.moses_no_dash_splits), 44 | return_str=True, 45 | escape=(not self.args.moses_no_escape), 46 | ) 47 | 48 | def decode(self, x: str) -> str: 49 | return self.detok.detokenize(x.split()) 50 | -------------------------------------------------------------------------------- /examples/language_model/transformer_lm/README.md: -------------------------------------------------------------------------------- 1 | # Adaptive Input Representations for Neural Language Modeling (Baevski and Auli, 2018) 2 | 3 | ## Pre-trained models 4 | 5 | Description | Parameters | Dataset | Model and Test set(s) 6 | ---|---:|---|--- 7 | Adaptive Inputs
([Baevski and Auli, 2018](https://arxiv.org/abs/1809.10853)) | 1026M | [Google Billion Words](https://github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_gbw_huge.bz2) 8 | Adaptive Inputs
([Baevski and Auli, 2018](https://arxiv.org/abs/1809.10853)) | 247M | [WikiText-103](https://einstein.ai/research/the-wikitext-long-term-dependency-language-modeling-dataset) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_wiki103.bz2) 9 | 10 | ## Training an LM with adaptive inputs 11 | 12 | First, see the general [language modeling README](../README.md) for instructions 13 | on preprocessing the WikiText-103 data. 14 | 15 | Then use the following training command to train a model with adaptive inputs 16 | using the `transformer_lm_wiki103` model architecture: 17 | ```bash 18 | fairseq-train --task language_modeling \ 19 | data-bin/wikitext-103 \ 20 | --save-dir checkpoints/transformer_wikitext-103 \ 21 | --arch transformer_lm_wiki103 \ 22 | --max-update 286000 --max-lr 1.0 --t-mult 2 --lr-period-updates 270000 --lr-scheduler cosine --lr-shrink 0.75 \ 23 | --warmup-updates 16000 --warmup-init-lr 1e-07 --min-lr 1e-09 --optimizer nag --lr 0.0001 --clip-norm 0.1 \ 24 | --criterion adaptive_loss --max-tokens 3072 --update-freq 3 --tokens-per-sample 3072 --seed 1 \ 25 | --sample-break-mode none --skip-invalid-size-inputs-valid-test --ddp-backend=no_c10d 26 | ``` 27 | 28 | ## Citation 29 | 30 | ```bibtex 31 | @inproceedings{ 32 | baevski2018adaptive, 33 | title={Adaptive Input Representations for Neural Language Modeling}, 34 | author={Alexei Baevski and Michael Auli}, 35 | booktitle={International Conference on Learning Representations}, 36 | year={2019}, 37 | url={https://openreview.net/forum?id=ByxZX20qFQ}, 38 | } 39 | ``` 40 | -------------------------------------------------------------------------------- /tests/test_dictionary.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import tempfile 7 | import unittest 8 | 9 | import torch 10 | 11 | from fairseq.data import Dictionary 12 | 13 | 14 | class TestDictionary(unittest.TestCase): 15 | 16 | def test_finalize(self): 17 | txt = [ 18 | 'A B C D', 19 | 'B C D', 20 | 'C D', 21 | 'D', 22 | ] 23 | ref_ids1 = list(map(torch.IntTensor, [ 24 | [4, 5, 6, 7, 2], 25 | [5, 6, 7, 2], 26 | [6, 7, 2], 27 | [7, 2], 28 | ])) 29 | ref_ids2 = list(map(torch.IntTensor, [ 30 | [7, 6, 5, 4, 2], 31 | [6, 5, 4, 2], 32 | [5, 4, 2], 33 | [4, 2], 34 | ])) 35 | 36 | # build dictionary 37 | d = Dictionary() 38 | for line in txt: 39 | d.encode_line(line, add_if_not_exist=True) 40 | 41 | def get_ids(dictionary): 42 | ids = [] 43 | for line in txt: 44 | ids.append(dictionary.encode_line(line, add_if_not_exist=False)) 45 | return ids 46 | 47 | def assertMatch(ids, ref_ids): 48 | for toks, ref_toks in zip(ids, ref_ids): 49 | self.assertEqual(toks.size(), ref_toks.size()) 50 | self.assertEqual(0, (toks != ref_toks).sum().item()) 51 | 52 | ids = get_ids(d) 53 | assertMatch(ids, ref_ids1) 54 | 55 | # check finalized dictionary 56 | d.finalize() 57 | finalized_ids = get_ids(d) 58 | assertMatch(finalized_ids, ref_ids2) 59 | 60 | # write to disk and reload 61 | with tempfile.NamedTemporaryFile(mode='w') as tmp_dict: 62 | d.save(tmp_dict.name) 63 | d = Dictionary.load(tmp_dict.name) 64 | reload_ids = get_ids(d) 65 | assertMatch(reload_ids, ref_ids2) 66 | assertMatch(finalized_ids, reload_ids) 67 | 68 | 69 | if __name__ == '__main__': 70 | unittest.main() 71 | -------------------------------------------------------------------------------- /tests/test_multihead_attention.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch 7 | import unittest 8 | from fairseq.modules.multihead_attention import MultiheadAttention 9 | 10 | 11 | class TestMultiheadAttention(unittest.TestCase): 12 | def test_append_prev_key_padding_mask(self): 13 | bsz = 1 14 | src_len = 4 15 | 16 | cases = [ 17 | # no padding mask 18 | (None, None, None), 19 | # current padding mask only 20 | ( 21 | torch.tensor([[1]]).bool(), 22 | None, 23 | torch.tensor([[0, 0, 0, 1]]).bool(), 24 | ), 25 | # previous padding mask only 26 | ( 27 | None, 28 | torch.tensor([[0, 1, 0]]).bool(), 29 | torch.tensor([[0, 1, 0, 0]]).bool(), 30 | ), 31 | # both padding masks 32 | ( 33 | torch.tensor([[1]]).bool(), 34 | torch.tensor([[0, 1, 0]]).bool(), 35 | torch.tensor([[0, 1, 0, 1]]).bool(), 36 | ), 37 | ] 38 | for c in cases: 39 | key_padding_mask = MultiheadAttention._append_prev_key_padding_mask( 40 | c[0], 41 | c[1], 42 | batch_size=bsz, 43 | src_len=src_len, 44 | static_kv=False, 45 | ) 46 | 47 | if key_padding_mask is not None: 48 | self.assertTrue( 49 | torch.all(torch.eq(key_padding_mask, c[2])), 50 | f'Unexpected resultant key padding mask: {key_padding_mask}' 51 | f' given current: {c[0]} and previous: {c[1]}', 52 | ) 53 | self.assertEqual(key_padding_mask.size(0), bsz) 54 | self.assertEqual(key_padding_mask.size(1), src_len) 55 | else: 56 | self.assertIsNone(c[2]) 57 | 58 | 59 | if __name__ == '__main__': 60 | unittest.main() 61 | -------------------------------------------------------------------------------- /examples/camembert/README.md: -------------------------------------------------------------------------------- 1 | # CamemBERT: a French BERT 2 | 3 | ## Introduction 4 | 5 | CamemBERT is a pretrained language model trained on 138GB of French text based on RoBERTa. 6 | 7 | Also available in [github.com/huggingface/transformers](https://github.com/huggingface/transformers/). 8 | 9 | ## Pre-trained models 10 | 11 | Model | #params | vocab size | Download 12 | ---|---|---|--- 13 | `CamemBERT` | 110M | 32k | [camembert.v0.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/camembert.v0.tar.gz) 14 | 15 | 16 | ## Example usage 17 | 18 | ##### Load CamemBERT from torch.hub (PyTorch >= 1.1): 19 | ```python 20 | import torch 21 | camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0') 22 | camembert.eval() # disable dropout (or leave in train mode to finetune) 23 | ``` 24 | 25 | ##### Load CamemBERT (for PyTorch 1.0 or custom models): 26 | ```python 27 | # Download camembert model 28 | wget https://dl.fbaipublicfiles.com/fairseq/models/camembert.v0.tar.gz 29 | tar -xzvf camembert.v0.tar.gz 30 | 31 | # Load the model in fairseq 32 | from fairseq.models.roberta import CamembertModel 33 | camembert = CamembertModel.from_pretrained('/path/to/camembert.v0') 34 | camembert.eval() # disable dropout (or leave in train mode to finetune) 35 | ``` 36 | 37 | ##### Filling masks: 38 | ```python 39 | masked_line = 'Le camembert est :)' 40 | camembert.fill_mask(masked_line, topk=3) 41 | # [('Le camembert est délicieux :)', 0.4909118115901947, ' délicieux'), 42 | # ('Le camembert est excellent :)', 0.10556942224502563, ' excellent'), 43 | # ('Le camembert est succulent :)', 0.03453322499990463, ' succulent')] 44 | ``` 45 | 46 | ##### Extract features from Camembert: 47 | ```python 48 | # Extract the last layer's features 49 | line = "J'aime le camembert !" 50 | tokens = camembert.encode(line) 51 | last_layer_features = camembert.extract_features(tokens) 52 | assert last_layer_features.size() == torch.Size([1, 10, 768]) 53 | 54 | # Extract all layer's features (layer 0 is the embedding layer) 55 | all_layers = camembert.extract_features(tokens, return_all_hiddens=True) 56 | assert len(all_layers) == 13 57 | assert torch.all(all_layers[-1] == last_layer_features) 58 | ``` 59 | -------------------------------------------------------------------------------- /docs/command_line_tools.rst: -------------------------------------------------------------------------------- 1 | .. _Command-line Tools: 2 | 3 | Command-line Tools 4 | ================== 5 | 6 | Fairseq provides several command-line tools for training and evaluating models: 7 | 8 | - :ref:`fairseq-preprocess`: Data pre-processing: build vocabularies and binarize training data 9 | - :ref:`fairseq-train`: Train a new model on one or multiple GPUs 10 | - :ref:`fairseq-generate`: Translate pre-processed data with a trained model 11 | - :ref:`fairseq-interactive`: Translate raw text with a trained model 12 | - :ref:`fairseq-score`: BLEU scoring of generated translations against reference translations 13 | - :ref:`fairseq-eval-lm`: Language model evaluation 14 | 15 | 16 | .. _fairseq-preprocess: 17 | 18 | fairseq-preprocess 19 | ~~~~~~~~~~~~~~~~~~ 20 | .. automodule:: preprocess 21 | 22 | .. argparse:: 23 | :module: fairseq.options 24 | :func: get_preprocessing_parser 25 | :prog: fairseq-preprocess 26 | 27 | 28 | .. _fairseq-train: 29 | 30 | fairseq-train 31 | ~~~~~~~~~~~~~ 32 | .. automodule:: train 33 | 34 | .. argparse:: 35 | :module: fairseq.options 36 | :func: get_training_parser 37 | :prog: fairseq-train 38 | 39 | 40 | .. _fairseq-generate: 41 | 42 | fairseq-generate 43 | ~~~~~~~~~~~~~~~~ 44 | .. automodule:: generate 45 | 46 | .. argparse:: 47 | :module: fairseq.options 48 | :func: get_generation_parser 49 | :prog: fairseq-generate 50 | 51 | 52 | .. _fairseq-interactive: 53 | 54 | fairseq-interactive 55 | ~~~~~~~~~~~~~~~~~~~ 56 | .. automodule:: interactive 57 | 58 | .. argparse:: 59 | :module: fairseq.options 60 | :func: get_interactive_generation_parser 61 | :prog: fairseq-interactive 62 | 63 | 64 | .. _fairseq-score: 65 | 66 | fairseq-score 67 | ~~~~~~~~~~~~~ 68 | .. automodule:: score 69 | 70 | .. argparse:: 71 | :module: fairseq_cli.score 72 | :func: get_parser 73 | :prog: fairseq-score 74 | 75 | 76 | .. _fairseq-eval-lm: 77 | 78 | fairseq-eval-lm 79 | ~~~~~~~~~~~~~~~ 80 | .. automodule:: eval_lm 81 | 82 | .. argparse:: 83 | :module: fairseq.options 84 | :func: get_eval_lm_parser 85 | :prog: fairseq-eval-lm 86 | -------------------------------------------------------------------------------- /tests/test_concat_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import unittest 7 | 8 | import torch 9 | from fairseq.data import LanguagePairDataset, TokenBlockDataset 10 | from fairseq.data.concat_dataset import ConcatDataset 11 | from tests.test_train import mock_dict 12 | 13 | 14 | class TestConcatDataset(unittest.TestCase): 15 | def setUp(self): 16 | d = mock_dict() 17 | tokens_1 = torch.LongTensor([1]).view(1, -1) 18 | tokens_ds1 = TokenBlockDataset( 19 | tokens_1, 20 | sizes=[tokens_1.size(-1)], 21 | block_size=1, 22 | pad=0, 23 | eos=1, 24 | include_targets=False, 25 | ) 26 | self.dataset_1 = LanguagePairDataset( 27 | tokens_ds1, tokens_ds1.sizes, d, shuffle=False 28 | ) 29 | tokens_2 = torch.LongTensor([2]).view(1, -1) 30 | tokens_ds2 = TokenBlockDataset( 31 | tokens_2, 32 | sizes=[tokens_2.size(-1)], 33 | block_size=1, 34 | pad=0, 35 | eos=1, 36 | include_targets=False, 37 | ) 38 | self.dataset_2 = LanguagePairDataset( 39 | tokens_ds2, tokens_ds2.sizes, d, shuffle=False 40 | ) 41 | 42 | def test_concat_dataset_basics(self): 43 | d = ConcatDataset( 44 | [self.dataset_1, self.dataset_2] 45 | ) 46 | assert(len(d) == 2) 47 | assert(d[0]['source'][0] == 1) 48 | assert(d[1]['source'][0] == 2) 49 | 50 | d = ConcatDataset( 51 | [self.dataset_1, self.dataset_2], sample_ratios=[1, 2] 52 | ) 53 | assert(len(d) == 3) 54 | assert(d[0]['source'][0] == 1) 55 | assert(d[1]['source'][0] == 2) 56 | assert(d[2]['source'][0] == 2) 57 | 58 | d = ConcatDataset( 59 | [self.dataset_1, self.dataset_2], sample_ratios=[2, 1] 60 | ) 61 | assert(len(d) == 3) 62 | assert(d[0]['source'][0] == 1) 63 | assert(d[1]['source'][0] == 1) 64 | assert(d[2]['source'][0] == 2) 65 | -------------------------------------------------------------------------------- /fairseq/modules/mean_pool_gating_network.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch 7 | import torch.nn.functional as F 8 | 9 | 10 | class MeanPoolGatingNetwork(torch.nn.Module): 11 | """A simple mean-pooling gating network for selecting experts. 12 | 13 | This module applies mean pooling over an encoder's output and returns 14 | reponsibilities for each expert. The encoder format is expected to match 15 | :class:`fairseq.models.transformer.TransformerEncoder`. 16 | """ 17 | 18 | def __init__(self, embed_dim, num_experts, dropout=None): 19 | super().__init__() 20 | self.embed_dim = embed_dim 21 | self.num_experts = num_experts 22 | 23 | self.fc1 = torch.nn.Linear(embed_dim, embed_dim) 24 | self.dropout = torch.nn.Dropout(dropout) if dropout is not None else None 25 | self.fc2 = torch.nn.Linear(embed_dim, num_experts) 26 | 27 | def forward(self, encoder_out): 28 | if not ( 29 | hasattr(encoder_out, 'encoder_out') 30 | and hasattr(encoder_out, 'encoder_padding_mask') 31 | and encoder_out.encoder_out.size(2) == self.embed_dim 32 | ): 33 | raise ValueError('Unexpected format for encoder_out') 34 | 35 | # mean pooling over time 36 | encoder_padding_mask = encoder_out.encoder_padding_mask # B x T 37 | encoder_out = encoder_out.encoder_out.transpose(0, 1) # B x T x C 38 | if encoder_padding_mask is not None: 39 | encoder_out = encoder_out.clone() # required because of transpose above 40 | encoder_out[encoder_padding_mask] = 0 41 | ntokens = torch.sum(~encoder_padding_mask, dim=1, keepdim=True) 42 | x = torch.sum(encoder_out, dim=1) / ntokens.type_as(encoder_out) 43 | else: 44 | x = torch.mean(encoder_out, dim=1) 45 | 46 | x = torch.tanh(self.fc1(x)) 47 | if self.dropout is not None: 48 | x = self.dropout(x) 49 | x = self.fc2(x) 50 | return F.log_softmax(x, dim=-1, dtype=torch.float32).type_as(x) 51 | -------------------------------------------------------------------------------- /fairseq/data/data_utils_fast.pyx: -------------------------------------------------------------------------------- 1 | # cython: language_level=3 2 | # Copyright (c) Facebook, Inc. and its affiliates. 3 | # 4 | # This source code is licensed under the MIT license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | import numpy as np 8 | 9 | cimport cython 10 | cimport numpy as np 11 | 12 | DTYPE = np.int64 13 | ctypedef np.int64_t DTYPE_t 14 | 15 | 16 | cdef _is_batch_full(list batch, long num_tokens, long max_tokens, long max_sentences): 17 | if len(batch) == 0: 18 | return 0 19 | if max_sentences > 0 and len(batch) == max_sentences: 20 | return 1 21 | if max_tokens > 0 and num_tokens > max_tokens: 22 | return 1 23 | return 0 24 | 25 | 26 | @cython.cdivision(True) 27 | cpdef list batch_by_size_fast( 28 | np.ndarray[DTYPE_t, ndim=1] indices, 29 | num_tokens_fn, 30 | long max_tokens, 31 | long max_sentences, 32 | int bsz_mult, 33 | ): 34 | cdef long sample_len = 0 35 | cdef list sample_lens = [] 36 | cdef list batch = [] 37 | cdef list batches = [] 38 | cdef long mod_len 39 | cdef long i 40 | cdef long idx 41 | cdef long num_tokens 42 | cdef DTYPE_t[:] indices_view = indices 43 | 44 | for i in range(len(indices_view)): 45 | idx = indices_view[i] 46 | num_tokens = num_tokens_fn(idx) 47 | sample_lens.append(num_tokens) 48 | sample_len = max(sample_len, num_tokens) 49 | 50 | assert max_tokens <= 0 or sample_len <= max_tokens, ( 51 | "sentence at index {} of size {} exceeds max_tokens " 52 | "limit of {}!".format(idx, sample_len, max_tokens) 53 | ) 54 | num_tokens = (len(batch) + 1) * sample_len 55 | 56 | if _is_batch_full(batch, num_tokens, max_tokens, max_sentences): 57 | mod_len = max( 58 | bsz_mult * (len(batch) // bsz_mult), 59 | len(batch) % bsz_mult, 60 | ) 61 | batches.append(batch[:mod_len]) 62 | batch = batch[mod_len:] 63 | sample_lens = sample_lens[mod_len:] 64 | sample_len = max(sample_lens) if len(sample_lens) > 0 else 0 65 | batch.append(idx) 66 | if len(batch) > 0: 67 | batches.append(batch) 68 | return batches 69 | -------------------------------------------------------------------------------- /examples/speech_recognition/data/replabels.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | 3 | # Copyright (c) Facebook, Inc. and its affiliates. 4 | # 5 | # This source code is licensed under the MIT license found in the 6 | # LICENSE file in the root directory of this source tree. 7 | 8 | """ 9 | Replabel transforms for use with wav2letter's ASG criterion. 10 | """ 11 | 12 | 13 | def replabel_symbol(i): 14 | """ 15 | Replabel symbols used in wav2letter, currently just "1", "2", ... 16 | This prevents training with numeral tokens, so this might change in the future 17 | """ 18 | return str(i) 19 | 20 | 21 | def pack_replabels(tokens, dictionary, max_reps): 22 | """ 23 | Pack a token sequence so that repeated symbols are replaced by replabels 24 | """ 25 | if len(tokens) == 0 or max_reps <= 0: 26 | return tokens 27 | 28 | replabel_value_to_idx = [0] * (max_reps + 1) 29 | for i in range(1, max_reps + 1): 30 | replabel_value_to_idx[i] = dictionary.index(replabel_symbol(i)) 31 | 32 | result = [] 33 | prev_token = -1 34 | num_reps = 0 35 | for token in tokens: 36 | if token == prev_token and num_reps < max_reps: 37 | num_reps += 1 38 | else: 39 | if num_reps > 0: 40 | result.append(replabel_value_to_idx[num_reps]) 41 | num_reps = 0 42 | result.append(token) 43 | prev_token = token 44 | if num_reps > 0: 45 | result.append(replabel_value_to_idx[num_reps]) 46 | return result 47 | 48 | 49 | def unpack_replabels(tokens, dictionary, max_reps): 50 | """ 51 | Unpack a token sequence so that replabels are replaced by repeated symbols 52 | """ 53 | if len(tokens) == 0 or max_reps <= 0: 54 | return tokens 55 | 56 | replabel_idx_to_value = {} 57 | for i in range(1, max_reps + 1): 58 | replabel_idx_to_value[dictionary.index(replabel_symbol(i))] = i 59 | 60 | result = [] 61 | prev_token = -1 62 | for token in tokens: 63 | try: 64 | for _ in range(replabel_idx_to_value[token]): 65 | result.append(prev_token) 66 | prev_token = -1 67 | except KeyError: 68 | result.append(token) 69 | prev_token = token 70 | return result 71 | -------------------------------------------------------------------------------- /tests/test_memory_efficient_fp16.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import argparse 7 | import logging 8 | import unittest 9 | 10 | import torch 11 | 12 | from fairseq.optim.adam import FairseqAdam 13 | from fairseq.optim.fp16_optimizer import MemoryEfficientFP16Optimizer 14 | 15 | 16 | @unittest.skipIf(not torch.cuda.is_available(), 'test requires a GPU') 17 | class TestMemoryEfficientFP16(unittest.TestCase): 18 | 19 | def setUp(self): 20 | logging.disable(logging.CRITICAL) 21 | 22 | def tearDown(self): 23 | logging.disable(logging.NOTSET) 24 | 25 | def test_load_state_dict(self): 26 | # define simple FP16 model 27 | model = torch.nn.Linear(5, 5).cuda().half() 28 | params = list(model.parameters()) 29 | 30 | # initialize memory efficient FP16 optimizer 31 | optimizer = FairseqAdam( 32 | argparse.Namespace( 33 | lr=[0.00001], 34 | adam_betas='(0.9, 0.999)', 35 | adam_eps=1e-8, 36 | weight_decay=0.0, 37 | ), 38 | params, 39 | ) 40 | me_optimizer = MemoryEfficientFP16Optimizer( 41 | argparse.Namespace( 42 | fp16_init_scale=1, 43 | fp16_scale_window=1, 44 | fp16_scale_tolerance=1, 45 | threshold_loss_scale=1, 46 | min_loss_scale=1e-4, 47 | ), 48 | params, 49 | optimizer, 50 | ) 51 | 52 | # optimizer state is created in the first step 53 | loss = model(torch.rand(5).cuda().half()).sum() 54 | me_optimizer.backward(loss) 55 | me_optimizer.step() 56 | 57 | # reload state 58 | state = me_optimizer.state_dict() 59 | me_optimizer.load_state_dict(state) 60 | for k, v in me_optimizer.optimizer.state.items(): 61 | self.assertTrue(k.dtype == torch.float16) 62 | for v_i in v.values(): 63 | if torch.is_tensor(v_i): 64 | self.assertTrue(v_i.dtype == torch.float32) 65 | 66 | 67 | if __name__ == '__main__': 68 | unittest.main() 69 | -------------------------------------------------------------------------------- /tests/speech_recognition/test_collaters.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # Copyright (c) Facebook, Inc. and its affiliates. 3 | # 4 | # This source code is licensed under the MIT license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | import unittest 8 | 9 | import numpy as np 10 | import torch 11 | from examples.speech_recognition.data.collaters import Seq2SeqCollater 12 | 13 | 14 | class TestSeq2SeqCollator(unittest.TestCase): 15 | def test_collate(self): 16 | 17 | eos_idx = 1 18 | pad_idx = 0 19 | collater = Seq2SeqCollater( 20 | feature_index=0, label_index=1, pad_index=pad_idx, eos_index=eos_idx 21 | ) 22 | 23 | # 2 frames in the first sample and 3 frames in the second one 24 | frames1 = np.array([[7, 8], [9, 10]]) 25 | frames2 = np.array([[1, 2], [3, 4], [5, 6]]) 26 | target1 = np.array([4, 2, 3, eos_idx]) 27 | target2 = np.array([3, 2, eos_idx]) 28 | sample1 = {"id": 0, "data": [frames1, target1]} 29 | sample2 = {"id": 1, "data": [frames2, target2]} 30 | batch = collater.collate([sample1, sample2]) 31 | 32 | # collate sort inputs by frame's length before creating the batch 33 | self.assertTensorEqual(batch["id"], torch.tensor([1, 0])) 34 | self.assertEqual(batch["ntokens"], 7) 35 | self.assertTensorEqual( 36 | batch["net_input"]["src_tokens"], 37 | torch.tensor( 38 | [[[1, 2], [3, 4], [5, 6]], [[7, 8], [9, 10], [pad_idx, pad_idx]]] 39 | ), 40 | ) 41 | self.assertTensorEqual( 42 | batch["net_input"]["prev_output_tokens"], 43 | torch.tensor([[eos_idx, 3, 2, pad_idx], [eos_idx, 4, 2, 3]]), 44 | ) 45 | self.assertTensorEqual(batch["net_input"]["src_lengths"], torch.tensor([3, 2])) 46 | self.assertTensorEqual( 47 | batch["target"], 48 | torch.tensor([[3, 2, eos_idx, pad_idx], [4, 2, 3, eos_idx]]), 49 | ) 50 | self.assertEqual(batch["nsentences"], 2) 51 | 52 | def assertTensorEqual(self, t1, t2): 53 | self.assertEqual(t1.size(), t2.size(), "size mismatch") 54 | self.assertEqual(t1.ne(t2).long().sum(), 0) 55 | 56 | 57 | if __name__ == "__main__": 58 | unittest.main() 59 | -------------------------------------------------------------------------------- /examples/roberta/preprocess_RACE.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # Copyright (c) Facebook, Inc. and its affiliates. 3 | # 4 | # This source code is licensed under the MIT license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | 7 | 8 | # data should be downloaded and processed with reprocess_RACE.py 9 | if [[ $# -ne 2 ]]; then 10 | echo "Run as following:" 11 | echo "./examples/roberta/preprocess_RACE.sh " 12 | exit 1 13 | fi 14 | 15 | RACE_DATA_FOLDER=$1 16 | OUT_DATA_FOLDER=$2 17 | 18 | # download bpe encoder.json, vocabulary and fairseq dictionary 19 | wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json' 20 | wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe' 21 | wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt' 22 | 23 | SPLITS="train dev test-middle test-high" 24 | INPUT_TYPES="input0 input1 input2 input3 input4" 25 | for INPUT_TYPE in $INPUT_TYPES 26 | do 27 | for SPLIT in $SPLITS 28 | do 29 | echo "BPE encoding $SPLIT/$INPUT_TYPE" 30 | python -m examples.roberta.multiprocessing_bpe_encoder \ 31 | --encoder-json encoder.json \ 32 | --vocab-bpe vocab.bpe \ 33 | --inputs "$RACE_DATA_FOLDER/$SPLIT.$INPUT_TYPE" \ 34 | --outputs "$RACE_DATA_FOLDER/$SPLIT.$INPUT_TYPE.bpe" \ 35 | --workers 10 \ 36 | --keep-empty; 37 | 38 | done 39 | done 40 | 41 | for INPUT_TYPE in $INPUT_TYPES 42 | do 43 | LANG="input$INPUT_TYPE" 44 | fairseq-preprocess \ 45 | --only-source \ 46 | --trainpref "$RACE_DATA_FOLDER/train.$INPUT_TYPE.bpe" \ 47 | --validpref "$RACE_DATA_FOLDER/dev.$INPUT_TYPE.bpe" \ 48 | --testpref "$RACE_DATA_FOLDER/test-middle.$INPUT_TYPE.bpe,$RACE_DATA_FOLDER/test-high.$INPUT_TYPE.bpe" \ 49 | --destdir "$OUT_DATA_FOLDER/$INPUT_TYPE" \ 50 | --workers 10 \ 51 | --srcdict dict.txt; 52 | done 53 | 54 | rm -rf "$OUT_DATA_FOLDER/label" 55 | mkdir -p "$OUT_DATA_FOLDER/label" 56 | cp "$RACE_DATA_FOLDER/train.label" "$OUT_DATA_FOLDER/label/" 57 | cp "$RACE_DATA_FOLDER/dev.label" "$OUT_DATA_FOLDER/label/valid.label" 58 | cp "$RACE_DATA_FOLDER/test-middle.label" "$OUT_DATA_FOLDER/label/test.label" 59 | cp "$RACE_DATA_FOLDER/test-high.label" "$OUT_DATA_FOLDER/label/test1.label" 60 | -------------------------------------------------------------------------------- /examples/noisychannel/rerank_score_lm.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import os 7 | 8 | from fairseq import options 9 | 10 | from . import rerank_options, rerank_utils 11 | 12 | 13 | def score_lm(args): 14 | using_nbest = args.nbest_list is not None 15 | pre_gen, left_to_right_preprocessed_dir, right_to_left_preprocessed_dir, \ 16 | backwards_preprocessed_dir, lm_preprocessed_dir = \ 17 | rerank_utils.get_directories(args.data_dir_name, args.num_rescore, args.gen_subset, 18 | args.gen_model_name, args.shard_id, args.num_shards, 19 | args.sampling, args.prefix_len, args.target_prefix_frac, 20 | args.source_prefix_frac) 21 | 22 | predictions_bpe_file = pre_gen+"/generate_output_bpe.txt" 23 | if using_nbest: 24 | print("Using predefined n-best list from interactive.py") 25 | predictions_bpe_file = args.nbest_list 26 | 27 | gen_output = rerank_utils.BitextOutputFromGen(predictions_bpe_file, bpe_symbol=args.remove_bpe, nbest=using_nbest) 28 | 29 | if args.language_model is not None: 30 | lm_score_file = rerank_utils.rescore_file_name(pre_gen, args.prefix_len, args.lm_name, lm_file=True) 31 | 32 | if args.language_model is not None and not os.path.isfile(lm_score_file): 33 | print("STEP 4.5: language modeling for P(T)") 34 | if args.lm_bpe_code is None: 35 | bpe_status = "no bpe" 36 | elif args.lm_bpe_code == "shared": 37 | bpe_status = "shared" 38 | else: 39 | bpe_status = "different" 40 | 41 | rerank_utils.lm_scoring(lm_preprocessed_dir, bpe_status, gen_output, pre_gen, 42 | args.lm_dict, args.lm_name, args.language_model, 43 | args.lm_bpe_code, 128, lm_score_file, args.target_lang, 44 | args.source_lang, prefix_len=args.prefix_len) 45 | 46 | 47 | def cli_main(): 48 | parser = rerank_options.get_reranking_parser() 49 | args = options.parse_args_and_arch(parser) 50 | score_lm(args) 51 | 52 | 53 | if __name__ == '__main__': 54 | cli_main() 55 | -------------------------------------------------------------------------------- /fairseq/tasks/audio_pretraining.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import os 7 | 8 | from fairseq.data import FileAudioDataset 9 | from . import FairseqTask, register_task 10 | 11 | 12 | @register_task('audio_pretraining') 13 | class AudioPretrainingTask(FairseqTask): 14 | """ 15 | 16 | """ 17 | 18 | @staticmethod 19 | def add_args(parser): 20 | """Add task-specific arguments to the parser.""" 21 | parser.add_argument('data', help='path to data directory') 22 | parser.add_argument('--sample-rate', default=16000, type=int, 23 | help='target sample rate. audio files will be up/down sampled to this rate') 24 | parser.add_argument('--max-sample-size', default=None, type=int, 25 | help='max sample size to crop to for batching. default = min sample length') 26 | parser.add_argument('--min-sample-size', default=None, type=int, 27 | help='min sample size to crop to for batching. default = same as --max-sample-size') 28 | 29 | def __init__(self, args): 30 | super().__init__(args) 31 | 32 | @classmethod 33 | def setup_task(cls, args, **kwargs): 34 | """Setup the task (e.g., load dictionaries). 35 | 36 | Args: 37 | args (argparse.Namespace): parsed command-line arguments 38 | """ 39 | return cls(args) 40 | 41 | def load_dataset(self, split, **kwargs): 42 | """Load a given dataset split. 43 | 44 | Args: 45 | split (str): name of the split (e.g., train, valid, test) 46 | """ 47 | 48 | manifest = os.path.join(self.args.data, '{}.tsv'.format(split)) 49 | self.datasets[split] = FileAudioDataset(manifest, 50 | sample_rate=self.args.sample_rate, 51 | max_sample_size=self.args.max_sample_size, 52 | min_sample_size=self.args.min_sample_size) 53 | 54 | @property 55 | def target_dictionary(self): 56 | """Return the :class:`~fairseq.data.Dictionary` for the language 57 | model.""" 58 | return None 59 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # JetBrains PyCharm IDE 2 | .idea/ 3 | 4 | # Byte-compiled / optimized / DLL files 5 | __pycache__/ 6 | *.py[cod] 7 | *$py.class 8 | 9 | # C extensions 10 | *.so 11 | 12 | # macOS dir files 13 | .DS_Store 14 | 15 | # Distribution / packaging 16 | .Python 17 | env/ 18 | build/ 19 | develop-eggs/ 20 | dist/ 21 | downloads/ 22 | eggs/ 23 | .eggs/ 24 | lib/ 25 | lib64/ 26 | parts/ 27 | sdist/ 28 | var/ 29 | wheels/ 30 | *.egg-info/ 31 | .installed.cfg 32 | *.egg 33 | 34 | # Checkpoints 35 | checkpoints 36 | 37 | # PyInstaller 38 | # Usually these files are written by a python script from a template 39 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 40 | *.manifest 41 | *.spec 42 | 43 | # Installer logs 44 | pip-log.txt 45 | pip-delete-this-directory.txt 46 | 47 | # Unit test / coverage reports 48 | htmlcov/ 49 | .tox/ 50 | .coverage 51 | .coverage.* 52 | .cache 53 | nosetests.xml 54 | coverage.xml 55 | *.cover 56 | .hypothesis/ 57 | 58 | # Translations 59 | *.mo 60 | *.pot 61 | 62 | # Django stuff: 63 | *.log 64 | local_settings.py 65 | 66 | # Flask stuff: 67 | instance/ 68 | .webassets-cache 69 | 70 | # Scrapy stuff: 71 | .scrapy 72 | 73 | # Sphinx documentation 74 | docs/_build/ 75 | 76 | # PyBuilder 77 | target/ 78 | 79 | # Jupyter Notebook 80 | .ipynb_checkpoints 81 | 82 | # pyenv 83 | .python-version 84 | 85 | # celery beat schedule file 86 | celerybeat-schedule 87 | 88 | # SageMath parsed files 89 | *.sage.py 90 | 91 | # dotenv 92 | .env 93 | 94 | # virtualenv 95 | .venv 96 | venv/ 97 | ENV/ 98 | 99 | # Spyder project settings 100 | .spyderproject 101 | .spyproject 102 | 103 | # Rope project settings 104 | .ropeproject 105 | 106 | # mkdocs documentation 107 | /site 108 | 109 | # mypy 110 | .mypy_cache/ 111 | 112 | # Generated files 113 | /fairseq/temporal_convolution_tbc 114 | /fairseq/modules/*_layer/*_forward.cu 115 | /fairseq/modules/*_layer/*_backward.cu 116 | 117 | # data 118 | data-bin/ 119 | 120 | # reranking 121 | /examples/reranking/rerank_data 122 | 123 | # Cython-generated C++ source files 124 | /fairseq/data/data_utils_fast.cpp 125 | /fairseq/data/token_block_utils_fast.cpp 126 | 127 | # VSCODE 128 | .vscode/ftp-sync.json 129 | .vscode/settings.json 130 | 131 | # Experimental Folder 132 | experimental/* 133 | -------------------------------------------------------------------------------- /scripts/wav2vec_manifest.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # Copyright (c) Facebook, Inc. and its affiliates. 3 | # 4 | # This source code is licensed under the MIT license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | """ 7 | Data pre-processing: build vocabularies and binarize training data. 8 | """ 9 | 10 | import argparse 11 | import glob 12 | import os 13 | import soundfile 14 | import random 15 | 16 | 17 | def get_parser(): 18 | parser = argparse.ArgumentParser() 19 | parser.add_argument('root', metavar='DIR', help='root directory containing flac files to index') 20 | parser.add_argument('--valid-percent', default=0.01, type=float, metavar='D', 21 | help='percentage of data to use as validation set (between 0 and 1)') 22 | parser.add_argument('--dest', default='.', type=str, metavar='DIR', help='output directory') 23 | parser.add_argument('--ext', default='flac', type=str, metavar='EXT', help='extension to look for') 24 | parser.add_argument('--seed', default=42, type=int, metavar='N', help='random seed') 25 | parser.add_argument('--path-must-contain', default=None, type=str, metavar='FRAG', 26 | help='if set, path must contain this substring for a file to be included in the manifest') 27 | return parser 28 | 29 | 30 | def main(args): 31 | assert args.valid_percent >= 0 and args.valid_percent <= 1. 32 | 33 | dir_path = os.path.realpath(args.root) 34 | search_path = os.path.join(dir_path, '**/*.' + args.ext) 35 | rand = random.Random(args.seed) 36 | 37 | with open(os.path.join(args.dest, 'train.tsv'), 'w') as train_f, open( 38 | os.path.join(args.dest, 'valid.tsv'), 'w') as valid_f: 39 | print(dir_path, file=train_f) 40 | print(dir_path, file=valid_f) 41 | 42 | for fname in glob.iglob(search_path, recursive=True): 43 | file_path = os.path.realpath(fname) 44 | 45 | if args.path_must_contain and args.path_must_contain not in file_path: 46 | continue 47 | 48 | frames = soundfile.info(fname).frames 49 | dest = train_f if rand.random() > args.valid_percent else valid_f 50 | print('{}\t{}'.format(os.path.relpath(file_path, dir_path), frames), file=dest) 51 | 52 | 53 | if __name__ == '__main__': 54 | parser = get_parser() 55 | args = parser.parse_args() 56 | main(args) 57 | -------------------------------------------------------------------------------- /fairseq/data/subsample_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import logging 7 | 8 | import numpy as np 9 | 10 | from . import BaseWrapperDataset 11 | 12 | 13 | logger = logging.getLogger(__name__) 14 | 15 | 16 | class SubsampleDataset(BaseWrapperDataset): 17 | """Subsamples a given dataset by a specified ratio. Subsampling is done on the number of examples 18 | 19 | Args: 20 | dataset (~torch.utils.data.Dataset): dataset to subsample 21 | size_ratio(float): the ratio to subsample to. must be between 0 and 1 (exclusive) 22 | """ 23 | 24 | def __init__(self, dataset, size_ratio): 25 | super().__init__(dataset) 26 | assert size_ratio < 1 27 | self.actual_size = np.ceil(len(dataset) * size_ratio).astype(int) 28 | self.indices = np.random.choice( 29 | list(range(len(self.dataset))), self.actual_size, replace=False 30 | ) 31 | logger.info( 32 | "subsampled dataset from {} to {} (ratio={})".format( 33 | len(self.dataset), self.actual_size, size_ratio 34 | ) 35 | ) 36 | 37 | def __getitem__(self, index): 38 | return self.dataset[self.indices[index]] 39 | 40 | def __len__(self): 41 | return self.actual_size 42 | 43 | def collater(self, samples): 44 | return self.dataset.collater(samples) 45 | 46 | @property 47 | def sizes(self): 48 | return self.dataset.sizes[self.indices] 49 | 50 | @property 51 | def name(self): 52 | return self.dataset.name 53 | 54 | def num_tokens(self, index): 55 | return self.dataset.num_tokens(self.indices[index]) 56 | 57 | def size(self, index): 58 | return self.dataset.size(self.indices[index]) 59 | 60 | def ordered_indices(self): 61 | """Return an ordered list of indices. Batches will be constructed based 62 | on this order.""" 63 | if self.shuffle: 64 | order = [np.random.permutation(len(self))] 65 | else: 66 | order = [np.arange(len(self))] 67 | order.append(self.sizes) 68 | return np.lexsort(order) 69 | 70 | def prefetch(self, indices): 71 | self.dataset.prefetch(self.indices[indices]) 72 | -------------------------------------------------------------------------------- /examples/wav2vec/README.md: -------------------------------------------------------------------------------- 1 | # wav2vec 2 | 3 | Example to train a wav2vec model as described in [wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)](https://arxiv.org/abs/1904.05862). 4 | 5 | ## Pre-trained models 6 | 7 | Description | Parameters | Dataset | Model 8 | ---|---:|---|--- 9 | Wav2Vec large
([(Schneider et al., 2019)](https://arxiv.org/abs/1904.05862)) | 32.5M | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_large.pt) 10 | 11 | #### Example usage: 12 | ```python 13 | import torch 14 | from fairseq.models.wav2vec import Wav2VecModel 15 | 16 | cp = torch.load('/path/to/wav2vec.pt') 17 | model = Wav2VecModel.build_model(cp['args'], task=None) 18 | model.load_state_dict(cp['model']) 19 | model.eval() 20 | 21 | wav_input_16khz = torch.randn(1,10000) 22 | z = model.feature_extractor(wav_input_16khz) 23 | c = model.feature_aggregator(z) 24 | ``` 25 | 26 | ## Training a new model with the CLI tools 27 | 28 | Given a directory containing wav files to be used for pretraining (we recommend splitting each file into separate file 10 to 30 seconds in length) 29 | 30 | ### Prepare training data manifest: 31 | 32 | ``` 33 | $ python scripts/wav2vec_manifest.py /path/to/waves --dest /manifest/path --ext wav 34 | ``` 35 | 36 | ### Train a wav2vec model: 37 | 38 | ``` 39 | $ python train.py /manifest/path --save-dir /model/path --num-workers 6 --fp16 --max-update 400000 --save-interval 1 --no-epoch-checkpoints \ 40 | --arch wav2vec --task audio_pretraining --lr 1e-06 --min-lr 1e-09 --optimizer adam --max-lr 0.005 --lr-scheduler cosine \ 41 | --conv-feature-layers [(512, 10, 5), (512, 8, 4), (512, 4, 2), (512, 4, 2), (512, 4, 2), (512, 1, 1), (512, 1, 1)] \ 42 | --conv-aggregator-layers [(512, 2, 1), (512, 3, 1), (512, 4, 1), (512, 5, 1), (512, 6, 1), (512, 7, 1), (512, 8, 1), (512, 9, 1), (512, 10, 1), (512, 11, 1), (512, 12, 1), (512, 13, 1)] \ 43 | --skip-connections-agg --residual-scale 0.5 --log-compression --warmup-updates 500 --warmup-init-lr 1e-07 --criterion binary_cross_entropy --num-negatives 10 \ 44 | --max-sample-size 150000 --max-tokens 1500000 ---skip-invalid-size-inputs-valid-test 45 | ``` 46 | 47 | ### Extract embeddings from the downstream task data: 48 | 49 | ``` 50 | $ PYTHONPATH /path/to/fairseq python scripts/wav2vec_featurize.py --input /path/to/task/waves --output /path/to/output \ 51 | --model /model/path/checkpoint_best.pt --split train valid test 52 | ``` 53 | -------------------------------------------------------------------------------- /tests/test_utils.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import unittest 7 | 8 | import torch 9 | 10 | from fairseq import utils 11 | 12 | 13 | class TestUtils(unittest.TestCase): 14 | 15 | def test_convert_padding_direction(self): 16 | pad = 1 17 | left_pad = torch.LongTensor([ 18 | [2, 3, 4, 5, 6], 19 | [1, 7, 8, 9, 10], 20 | [1, 1, 1, 11, 12], 21 | ]) 22 | right_pad = torch.LongTensor([ 23 | [2, 3, 4, 5, 6], 24 | [7, 8, 9, 10, 1], 25 | [11, 12, 1, 1, 1], 26 | ]) 27 | 28 | self.assertAlmostEqual( 29 | right_pad, 30 | utils.convert_padding_direction( 31 | left_pad, 32 | pad, 33 | left_to_right=True, 34 | ), 35 | ) 36 | self.assertAlmostEqual( 37 | left_pad, 38 | utils.convert_padding_direction( 39 | right_pad, 40 | pad, 41 | right_to_left=True, 42 | ), 43 | ) 44 | 45 | def test_make_positions(self): 46 | pad = 1 47 | left_pad_input = torch.LongTensor([ 48 | [9, 9, 9, 9, 9], 49 | [1, 9, 9, 9, 9], 50 | [1, 1, 1, 9, 9], 51 | ]) 52 | left_pad_output = torch.LongTensor([ 53 | [2, 3, 4, 5, 6], 54 | [1, 2, 3, 4, 5], 55 | [1, 1, 1, 2, 3], 56 | ]) 57 | right_pad_input = torch.LongTensor([ 58 | [9, 9, 9, 9, 9], 59 | [9, 9, 9, 9, 1], 60 | [9, 9, 1, 1, 1], 61 | ]) 62 | right_pad_output = torch.LongTensor([ 63 | [2, 3, 4, 5, 6], 64 | [2, 3, 4, 5, 1], 65 | [2, 3, 1, 1, 1], 66 | ]) 67 | 68 | self.assertAlmostEqual( 69 | left_pad_output, 70 | utils.make_positions(left_pad_input, pad), 71 | ) 72 | self.assertAlmostEqual( 73 | right_pad_output, 74 | utils.make_positions(right_pad_input, pad), 75 | ) 76 | 77 | def assertAlmostEqual(self, t1, t2): 78 | self.assertEqual(t1.size(), t2.size(), "size mismatch") 79 | self.assertLess(utils.item((t1 - t2).abs().max()), 1e-4) 80 | 81 | 82 | if __name__ == '__main__': 83 | unittest.main() 84 | -------------------------------------------------------------------------------- /fairseq/data/fairseq_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import numpy as np 7 | import torch.utils.data 8 | 9 | 10 | class EpochListening: 11 | """Mixin for receiving updates whenever the epoch increments.""" 12 | def set_epoch(self, epoch): 13 | """Will receive the updated epoch number at the beginning of the epoch. 14 | """ 15 | pass 16 | 17 | 18 | class FairseqDataset(torch.utils.data.Dataset, EpochListening): 19 | """A dataset that provides helpers for batching.""" 20 | 21 | def __getitem__(self, index): 22 | raise NotImplementedError 23 | 24 | def __len__(self): 25 | raise NotImplementedError 26 | 27 | def collater(self, samples): 28 | """Merge a list of samples to form a mini-batch. 29 | 30 | Args: 31 | samples (List[dict]): samples to collate 32 | 33 | Returns: 34 | dict: a mini-batch suitable for forwarding with a Model 35 | """ 36 | raise NotImplementedError 37 | 38 | def num_tokens(self, index): 39 | """Return the number of tokens in a sample. This value is used to 40 | enforce ``--max-tokens`` during batching.""" 41 | raise NotImplementedError 42 | 43 | def size(self, index): 44 | """Return an example's size as a float or tuple. This value is used when 45 | filtering a dataset with ``--max-positions``.""" 46 | raise NotImplementedError 47 | 48 | def ordered_indices(self): 49 | """Return an ordered list of indices. Batches will be constructed based 50 | on this order.""" 51 | return np.arange(len(self)) 52 | 53 | @property 54 | def supports_prefetch(self): 55 | """Whether this dataset supports prefetching.""" 56 | return False 57 | 58 | def attr(self, attr: str, index: int): 59 | return getattr(self, attr, None) 60 | 61 | def prefetch(self, indices): 62 | """Prefetch the data required for this epoch.""" 63 | raise NotImplementedError 64 | 65 | 66 | class FairseqIterableDataset(torch.utils.data.IterableDataset, EpochListening): 67 | """For datasets that need to be read sequentially, usually because the data 68 | is being streamed or otherwise can't be manipulated on a single machine. 69 | """ 70 | 71 | def __iter__(self): 72 | raise NotImplementedError 73 | -------------------------------------------------------------------------------- /fairseq/modules/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from .adaptive_input import AdaptiveInput 7 | from .adaptive_softmax import AdaptiveSoftmax 8 | from .beamable_mm import BeamableMM 9 | from .character_token_embedder import CharacterTokenEmbedder 10 | from .conv_tbc import ConvTBC 11 | from .downsampled_multihead_attention import DownsampledMultiHeadAttention 12 | from .dynamic_convolution import DynamicConv, DynamicConv1dTBC 13 | from .dynamic_crf_layer import DynamicCRF 14 | from .gelu import gelu, gelu_accurate 15 | from .grad_multiply import GradMultiply 16 | from .highway import Highway 17 | from .layer_norm import LayerNorm 18 | from .learned_positional_embedding import LearnedPositionalEmbedding 19 | from .lightweight_convolution import LightweightConv, LightweightConv1dTBC 20 | from .linearized_convolution import LinearizedConvolution 21 | from .logsumexp_moe import LogSumExpMoE 22 | from .mean_pool_gating_network import MeanPoolGatingNetwork 23 | from .multihead_attention import MultiheadAttention 24 | from .positional_embedding import PositionalEmbedding 25 | from .scalar_bias import ScalarBias 26 | from .sinusoidal_positional_embedding import SinusoidalPositionalEmbedding 27 | from .transformer_sentence_encoder_layer import TransformerSentenceEncoderLayer 28 | from .transformer_sentence_encoder import TransformerSentenceEncoder 29 | from .unfold import unfold1d 30 | from .transformer_layer import TransformerDecoderLayer, TransformerEncoderLayer 31 | from .vggblock import VGGBlock 32 | 33 | __all__ = [ 34 | 'AdaptiveInput', 35 | 'AdaptiveSoftmax', 36 | 'BeamableMM', 37 | 'CharacterTokenEmbedder', 38 | 'ConvTBC', 39 | 'DownsampledMultiHeadAttention', 40 | 'DynamicConv1dTBC', 41 | 'DynamicConv', 42 | 'DynamicCRF', 43 | 'gelu', 44 | 'gelu_accurate', 45 | 'GradMultiply', 46 | 'Highway', 47 | 'LayerNorm', 48 | 'LearnedPositionalEmbedding', 49 | 'LightweightConv1dTBC', 50 | 'LightweightConv', 51 | 'LinearizedConvolution', 52 | 'LogSumExpMoE', 53 | 'MeanPoolGatingNetwork', 54 | 'MultiheadAttention', 55 | 'PositionalEmbedding', 56 | 'ScalarBias', 57 | 'SinusoidalPositionalEmbedding', 58 | 'TransformerSentenceEncoderLayer', 59 | 'TransformerSentenceEncoder', 60 | 'TransformerDecoderLayer', 61 | 'TransformerEncoderLayer', 62 | 'VGGBlock', 63 | 'unfold1d', 64 | ] 65 | -------------------------------------------------------------------------------- /fairseq/modules/lightconv_layer/lightconv_cuda.cuh: -------------------------------------------------------------------------------- 1 | /** 2 | * Copyright (c) Facebook, Inc. and its affiliates. 3 | * 4 | * This source code is licensed under the MIT license found in the 5 | * LICENSE file in the root directory of this source tree. 6 | */ 7 | 8 | #include 9 | #include 10 | 11 | #include 12 | #include 13 | 14 | #include 15 | #include 16 | #include 17 | #include 18 | #include 19 | #include 20 | 21 | #include 22 | #include 23 | 24 | #define SHFL_MASK 0xffffffff 25 | 26 | template 27 | __global__ 28 | void lightconv_forward_kernel(const scalar_t* input, 29 | const scalar_t* filters, 30 | int minibatch, int sequenceLength, 31 | int numFeatures, int numFiltersInBlock, 32 | scalar_t* output); 33 | 34 | template 35 | __global__ 36 | void lightconv_grad_wrt_input_kernel( 37 | const scalar_t* input, 38 | const scalar_t* filters, 39 | int minibatch, 40 | int sequenceLength, 41 | int numFeatures, 42 | int numFiltersInBlock, 43 | scalar_t* output); 44 | 45 | template 46 | __global__ 47 | void lightconv_grad_wrt_weights_firstpass_short_kernel( 48 | const scalar_t* input, 49 | const scalar_t* gradInput, 50 | int minibatch, 51 | int sequenceLength, 52 | int numFeatures, 53 | int numFiltersInBlock, 54 | int numHeads, 55 | float* output); 56 | 57 | template 58 | __global__ 59 | void lightconv_grad_wrt_weights_secondpass_short_kernel( 60 | const float* input, 61 | const int minibatch, 62 | const int numFiltersInBlock, 63 | scalar_t* output); 64 | 65 | template 66 | __global__ 67 | void lightconv_grad_wrt_weights_firstpass_kernel( 68 | const scalar_t* input, 69 | const scalar_t* gradInput, 70 | int minibatch, 71 | int sequenceLength, 72 | int numFeatures, 73 | int numFiltersInBlock, 74 | float* output); 75 | 76 | template 77 | __global__ 78 | void lightconv_grad_wrt_weights_secondpass_kernel( 79 | const float* input, 80 | const int minibatch, 81 | const int numFiltersInBlock, 82 | scalar_t* output); 83 | 84 | -------------------------------------------------------------------------------- /fairseq/optim/lr_scheduler/fixed_schedule.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from . import FairseqLRScheduler, register_lr_scheduler 7 | 8 | 9 | @register_lr_scheduler('fixed') 10 | class FixedSchedule(FairseqLRScheduler): 11 | """Decay the LR on a fixed schedule.""" 12 | 13 | def __init__(self, args, optimizer): 14 | super().__init__(args, optimizer) 15 | 16 | # set defaults 17 | args.warmup_updates = getattr(args, 'warmup_updates', 0) or 0 18 | 19 | self.lr = args.lr[0] 20 | if args.warmup_updates > 0: 21 | self.warmup_factor = 1. / args.warmup_updates 22 | else: 23 | self.warmup_factor = 1 24 | 25 | @staticmethod 26 | def add_args(parser): 27 | """Add arguments to the parser for this LR scheduler.""" 28 | # fmt: off 29 | parser.add_argument('--force-anneal', '--fa', type=int, metavar='N', 30 | help='force annealing at specified epoch') 31 | parser.add_argument('--lr-shrink', default=0.1, type=float, metavar='LS', 32 | help='shrink factor for annealing, lr_new = (lr * lr_shrink)') 33 | parser.add_argument('--warmup-updates', default=0, type=int, metavar='N', 34 | help='warmup the learning rate linearly for the first N updates') 35 | # fmt: on 36 | 37 | def get_next_lr(self, epoch): 38 | lrs = self.args.lr 39 | if self.args.force_anneal is None or epoch < self.args.force_anneal: 40 | # use fixed LR schedule 41 | next_lr = lrs[min(epoch, len(lrs) - 1)] 42 | else: 43 | # annneal based on lr_shrink 44 | next_lr = lrs[-1] * self.args.lr_shrink ** (epoch + 1 - self.args.force_anneal) 45 | return next_lr 46 | 47 | def step(self, epoch, val_loss=None): 48 | """Update the learning rate at the end of the given epoch.""" 49 | super().step(epoch, val_loss) 50 | self.lr = self.get_next_lr(epoch) 51 | self.optimizer.set_lr(self.warmup_factor * self.lr) 52 | return self.optimizer.get_lr() 53 | 54 | def step_update(self, num_updates): 55 | """Update the learning rate after each update.""" 56 | if self.args.warmup_updates > 0 and num_updates < self.args.warmup_updates: 57 | self.warmup_factor = (num_updates + 1) / float(self.args.warmup_updates) 58 | self.optimizer.set_lr(self.warmup_factor * self.lr) 59 | return self.optimizer.get_lr() 60 | -------------------------------------------------------------------------------- /fairseq/modules/adaptive_input.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | 7 | import torch 8 | from torch import nn 9 | 10 | from typing import List 11 | 12 | 13 | class AdaptiveInput(nn.Module): 14 | 15 | def __init__( 16 | self, 17 | vocab_size: int, 18 | padding_idx: int, 19 | initial_dim: int, 20 | factor: float, 21 | output_dim: int, 22 | cutoff: List[int], 23 | ): 24 | super().__init__() 25 | 26 | if vocab_size > cutoff[-1]: 27 | cutoff = cutoff + [vocab_size] 28 | else: 29 | assert vocab_size == cutoff[ 30 | -1], 'cannot specify cutoff larger than vocab size' 31 | 32 | self.cutoff = cutoff 33 | self.embedding_dim = output_dim 34 | self.padding_idx = padding_idx 35 | 36 | self.embeddings = nn.ModuleList() 37 | for i in range(len(self.cutoff)): 38 | prev = self.cutoff[i - 1] if i > 0 else 0 39 | size = self.cutoff[i] - prev 40 | dim = int(initial_dim // (factor ** i)) 41 | seq = nn.Sequential( 42 | nn.Embedding(size, dim, self.padding_idx), 43 | nn.Linear(dim, output_dim, bias=False) 44 | ) 45 | self.embeddings.append(seq) 46 | self.padding_idx = None 47 | self.padding_idx = padding_idx 48 | 49 | def init_weights(m): 50 | if isinstance(m, nn.Embedding): 51 | nn.init.normal_(m.weight, mean=0, std=m.weight.shape[1] ** -0.5) 52 | nn.init.constant_(m.weight[padding_idx], 0) 53 | elif hasattr(m, 'weight'): 54 | nn.init.xavier_uniform_(m.weight) 55 | 56 | self.apply(init_weights) 57 | 58 | self.register_buffer('_float_tensor', torch.FloatTensor(1)) 59 | 60 | def weights_for_band(self, band: int): 61 | return self.embeddings[band][0].weight, self.embeddings[band][1].weight 62 | 63 | def forward(self, input: torch.Tensor): 64 | result = self._float_tensor.new(input.shape + (self.embedding_dim,)) 65 | for i in range(len(self.cutoff)): 66 | mask = input.lt(self.cutoff[i]) 67 | if i > 0: 68 | mask.mul_(input.ge(self.cutoff[i - 1])) 69 | chunk_input = input[mask] - self.cutoff[i - 1] 70 | else: 71 | chunk_input = input[mask] 72 | if mask.any(): 73 | result[mask] = self.embeddings[i](chunk_input) 74 | return result 75 | -------------------------------------------------------------------------------- /fairseq/criterions/fairseq_criterion.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from typing import Any, Dict, List 7 | 8 | from torch.nn.modules.loss import _Loss 9 | 10 | from fairseq import metrics, utils 11 | 12 | 13 | class FairseqCriterion(_Loss): 14 | 15 | def __init__(self, args, task): 16 | super().__init__() 17 | self.args = args 18 | self.task = task 19 | self.padding_idx = task.target_dictionary.pad() if task.target_dictionary is not None else -100 20 | 21 | @staticmethod 22 | def add_args(parser): 23 | """Add criterion-specific arguments to the parser.""" 24 | pass 25 | 26 | @classmethod 27 | def build_criterion(cls, args, task): 28 | return cls(args, task) 29 | 30 | def forward(self, model, sample, reduce=True): 31 | """Compute the loss for the given sample. 32 | 33 | Returns a tuple with three elements: 34 | 1) the loss 35 | 2) the sample size, which is used as the denominator for the gradient 36 | 3) logging outputs to display while training 37 | """ 38 | raise NotImplementedError 39 | 40 | @staticmethod 41 | def aggregate_logging_outputs( 42 | logging_outputs: List[Dict[str, Any]], 43 | ) -> Dict[str, Any]: 44 | """Aggregate logging outputs from data parallel training.""" 45 | utils.deprecation_warning( 46 | 'The aggregate_logging_outputs API is deprecated. ' 47 | 'Please use the reduce_metrics API instead.' 48 | ) 49 | raise NotImplementedError 50 | 51 | @classmethod 52 | def reduce_metrics(cls, logging_outputs: List[Dict[str, Any]]) -> None: 53 | """Aggregate logging outputs from data parallel training.""" 54 | utils.deprecation_warning( 55 | 'Criterions should implement the reduce_metrics API. ' 56 | 'Falling back to deprecated aggregate_logging_outputs API.' 57 | ) 58 | agg_logging_outputs = cls.aggregate_logging_outputs(logging_outputs) 59 | for k, v in agg_logging_outputs.items(): 60 | if k in {'nsentences', 'ntokens', 'sample_size'}: 61 | continue 62 | metrics.log_scalar(k, v) 63 | 64 | @staticmethod 65 | def logging_outputs_can_be_summed() -> bool: 66 | """ 67 | Whether the logging outputs returned by `forward` can be summed 68 | across workers prior to calling `reduce_metrics`. Setting this 69 | to True will improves distributed training speed. 70 | """ 71 | return False 72 | -------------------------------------------------------------------------------- /fairseq/models/distributed_fairseq_model.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import inspect 7 | 8 | import torch.nn as nn 9 | 10 | from fairseq.legacy_distributed_data_parallel import LegacyDistributedDataParallel 11 | from fairseq.models import BaseFairseqModel 12 | 13 | 14 | def DistributedFairseqModel(args, model): 15 | """ 16 | Wrap a *model* to support distributed data parallel training. 17 | 18 | This is similar to the built-in DistributedDataParallel, but allows 19 | additional configuration of the DistributedDataParallel class to 20 | use, and also provides easier access to the wrapped model by 21 | forwarding requests for missing attributes to the wrapped model. 22 | 23 | Args: 24 | args (argparse.Namespace): fairseq args 25 | model (BaseFairseqModel): model to wrap 26 | """ 27 | # determine which DDP class to extend 28 | assert isinstance(model, nn.Module) 29 | if args.ddp_backend == 'c10d': 30 | ddp_class = nn.parallel.DistributedDataParallel 31 | init_kwargs = dict( 32 | module=model, 33 | device_ids=[args.device_id], 34 | output_device=args.device_id, 35 | broadcast_buffers=args.broadcast_buffers, 36 | bucket_cap_mb=args.bucket_cap_mb, 37 | ) 38 | # Maintain backward compatibility 39 | if 'check_reduction' in inspect.getargspec(ddp_class)[0]: 40 | init_kwargs['check_reduction'] = True 41 | if 'find_unused_parameters' in inspect.getargspec(ddp_class)[0]: 42 | init_kwargs['find_unused_parameters'] = args.find_unused_parameters 43 | elif args.ddp_backend == 'no_c10d': 44 | ddp_class = LegacyDistributedDataParallel 45 | init_kwargs = dict( 46 | module=model, 47 | world_size=args.distributed_world_size, 48 | buffer_size=2**28, 49 | ) 50 | else: 51 | raise ValueError('Unknown --ddp-backend: ' + args.ddp_backend) 52 | 53 | class _DistributedFairseqModel(ddp_class): 54 | """Extend DistributedDataParallel to check for missing 55 | attributes in the wrapped module.""" 56 | 57 | def __init__(self, *args, **kwargs): 58 | super().__init__(*args, **kwargs) 59 | 60 | def __getattr__(self, name): 61 | wrapped_module = super().__getattr__('module') 62 | if hasattr(wrapped_module, name): 63 | return getattr(wrapped_module, name) 64 | return super().__getattr__(name) 65 | 66 | return _DistributedFairseqModel(**init_kwargs) 67 | -------------------------------------------------------------------------------- /fairseq/models/model_utils.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | from typing import List, Optional 7 | 8 | import torch 9 | from torch import Tensor 10 | 11 | 12 | @torch.jit.script 13 | def script_skip_tensor_list(x: List[Tensor], mask): 14 | res = [xi[mask] if xi.size(0) == mask.size(0) else xi[:, mask] for xi in x] 15 | outputs = [] 16 | for i, t in enumerate(res): 17 | if t.numel() != 0: 18 | outputs.append(t) 19 | else: 20 | outputs.append(x[i]) 21 | return outputs 22 | 23 | 24 | @torch.jit.script 25 | def script_skip_tensor(x: Tensor, mask): 26 | # None case 27 | if x.size(0) == 0: 28 | return x 29 | res = x[mask] if x.size(0) == mask.size(0) else x[:, mask] 30 | if res.numel() == 0: 31 | return x 32 | else: 33 | return res 34 | 35 | 36 | @torch.jit.script 37 | def expand_2d_or_3d_tensor(x, trg_dim: int, padding_idx: int): 38 | """ 39 | Expand 2D/3D tensor on dim=1 40 | """ 41 | if x is None: 42 | return None 43 | 44 | assert x.dim() == 2 or x.dim() == 3 45 | assert trg_dim >= x.size(1), (trg_dim, x.size()) 46 | if trg_dim == x.size(1): 47 | return x 48 | 49 | dims = [x.size(0), trg_dim - x.size(1)] 50 | if x.dim() == 3: 51 | dims.append(x.size(2)) 52 | x = torch.cat([x, torch.zeros(dims).to(x).fill_(padding_idx)], 1) 53 | 54 | return x 55 | 56 | 57 | @torch.jit.script 58 | def coalesce(x: Optional[Tensor], y: Tensor) -> Tensor: 59 | return x if x is not None else y 60 | 61 | 62 | @torch.jit.script 63 | def fill_tensors(x: Optional[Tensor], mask, y: Optional[Tensor], padding_idx: int) -> Optional[Tensor]: 64 | """ 65 | Filling tensor x with y at masked positions (dim=0). 66 | """ 67 | if x is None or x.size()[0] == 0 or y is None: 68 | return x 69 | assert x.dim() == y.dim() and mask.size(0) == x.size(0) 70 | assert x.dim() == 2 or (x.dim() == 3 and x.size(2) == y.size(2)) 71 | 72 | n_selected = mask.sum() 73 | if n_selected == 0: 74 | return x 75 | assert n_selected == y.size(0) 76 | if n_selected == x.size(0): 77 | return y 78 | 79 | if x.size(1) < y.size(1): 80 | x = expand_2d_or_3d_tensor(x, y.size(1), padding_idx) 81 | x[mask] = y 82 | elif x.size(1) > y.size(1): 83 | x[mask] = torch.tensor(padding_idx).type_as(x) 84 | if x.dim() == 2: 85 | x[mask, :y.size(1)] = y 86 | else: 87 | x[mask, :y.size(1), :] = y 88 | else: 89 | x[mask] = y 90 | return x 91 | -------------------------------------------------------------------------------- /tests/test_sparse_multihead_attention.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch 7 | import unittest 8 | from fairseq.modules.sparse_multihead_attention import SparseMultiheadAttention 9 | 10 | 11 | class TestSparseMultiheadAttention(unittest.TestCase): 12 | def test_sparse_multihead_attention(self): 13 | attn_weights = torch.randn(1, 8, 8) 14 | bidirectional_sparse_mask = torch.tensor([ 15 | [0, 0, 0, 0, 0, float('-inf'), float('-inf'), 0], 16 | [0, 0, 0, 0, 0, float('-inf'), float('-inf'), 0], 17 | [0, 0, 0, 0, 0, float('-inf'), float('-inf'), 0], 18 | [0, 0, 0, 0, 0, float('-inf'), float('-inf'), 0], 19 | [float('-inf'), float('-inf'), float('-inf'), 0, 0, 0, 0, 0], 20 | [float('-inf'), float('-inf'), float('-inf'), 0, 0, 0, 0, 0], 21 | [float('-inf'), float('-inf'), float('-inf'), 0, 0, 0, 0, 0], 22 | [float('-inf'), float('-inf'), float('-inf'), 0, 0, 0, 0, 0] 23 | ]) 24 | 25 | bidirectional_attention = SparseMultiheadAttention(16, 1, stride=4, expressivity=1, is_bidirectional=True) 26 | bidirectional_attention_sparse_mask = bidirectional_attention.buffered_sparse_mask(attn_weights, 8, 8) 27 | torch.all(torch.eq(bidirectional_attention_sparse_mask, bidirectional_sparse_mask)) 28 | 29 | sparse_mask = torch.tensor([ 30 | [0, float('-inf'), float('-inf'), float('-inf'), float('-inf'), float('-inf'), 31 | float('-inf'), float('-inf')], 32 | [0, 0, float('-inf'), float('-inf'), float('-inf'), float('-inf'), float('-inf'), float('-inf')], 33 | [0, 0, 0, float('-inf'), float('-inf'), float('-inf'), float('-inf'), float('-inf')], 34 | [0, 0, 0, 0, float('-inf'), float('-inf'), float('-inf'), float('-inf')], 35 | [0, 0, 0, 0, 0, float('-inf'), float('-inf'), float('-inf')], 36 | [float('-inf'), float('-inf'), float('-inf'), 0, 0, 0, float('-inf'), float('-inf')], 37 | [float('-inf'), float('-inf'), float('-inf'), 0, 0, 0, 0, float('-inf')], 38 | [float('-inf'), float('-inf'), float('-inf'), 0, 0, 0, 0, 0], 39 | ]) 40 | 41 | attention = SparseMultiheadAttention(16, 1, stride=4, expressivity=1, is_bidirectional=False) 42 | attention_sparse_mask = attention.buffered_sparse_mask(attn_weights, 8, 8) 43 | 44 | torch.all(torch.eq(attention_sparse_mask, sparse_mask)) 45 | 46 | 47 | if __name__ == '__main__': 48 | unittest.main() 49 | -------------------------------------------------------------------------------- /fairseq/tasks/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import argparse 7 | import importlib 8 | import os 9 | 10 | from .fairseq_task import FairseqTask 11 | 12 | TASK_REGISTRY = {} 13 | TASK_CLASS_NAMES = set() 14 | 15 | 16 | def setup_task(args, **kwargs): 17 | return TASK_REGISTRY[args.task].setup_task(args, **kwargs) 18 | 19 | 20 | def register_task(name): 21 | """ 22 | New tasks can be added to fairseq with the 23 | :func:`~fairseq.tasks.register_task` function decorator. 24 | 25 | For example:: 26 | 27 | @register_task('classification') 28 | class ClassificationTask(FairseqTask): 29 | (...) 30 | 31 | .. note:: 32 | 33 | All Tasks must implement the :class:`~fairseq.tasks.FairseqTask` 34 | interface. 35 | 36 | Please see the 37 | 38 | Args: 39 | name (str): the name of the task 40 | """ 41 | 42 | def register_task_cls(cls): 43 | if name in TASK_REGISTRY: 44 | raise ValueError('Cannot register duplicate task ({})'.format(name)) 45 | if not issubclass(cls, FairseqTask): 46 | raise ValueError('Task ({}: {}) must extend FairseqTask'.format(name, cls.__name__)) 47 | if cls.__name__ in TASK_CLASS_NAMES: 48 | raise ValueError('Cannot register task with duplicate class name ({})'.format(cls.__name__)) 49 | TASK_REGISTRY[name] = cls 50 | TASK_CLASS_NAMES.add(cls.__name__) 51 | return cls 52 | 53 | return register_task_cls 54 | 55 | 56 | def get_task(name): 57 | return TASK_REGISTRY[name] 58 | 59 | 60 | # automatically import any Python files in the tasks/ directory 61 | tasks_dir = os.path.dirname(__file__) 62 | for file in os.listdir(tasks_dir): 63 | path = os.path.join(tasks_dir, file) 64 | if ( 65 | not file.startswith('_') 66 | and not file.startswith('.') 67 | and (file.endswith('.py') or os.path.isdir(path)) 68 | ): 69 | task_name = file[:file.find('.py')] if file.endswith('.py') else file 70 | importlib.import_module('fairseq.tasks.' + task_name) 71 | 72 | # expose `task_parser` for sphinx 73 | if task_name in TASK_REGISTRY: 74 | parser = argparse.ArgumentParser(add_help=False) 75 | group_task = parser.add_argument_group('Task name') 76 | # fmt: off 77 | group_task.add_argument('--task', metavar=task_name, 78 | help='Enable this task with: ``--task=' + task_name + '``') 79 | # fmt: on 80 | group_args = parser.add_argument_group('Additional command-line arguments') 81 | TASK_REGISTRY[task_name].add_args(group_args) 82 | globals()[task_name + '_parser'] = parser 83 | --------------------------------------------------------------------------------