Lint

Summary: Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/817

Differential Revision: D16762905

Pulled By: myleott

fbshipit-source-id: d920595bec44ed26b72dfc6fbc15c0aa107b4e56

fairseq/__init__.py

@@ -6,10 +6,10 @@
 __all__ = ['pdb']
 __version__ = '0.7.2'
 
-import fairseq.criterions
-import fairseq.models
-import fairseq.modules
-import fairseq.optim
-import fairseq.optim.lr_scheduler
-import fairseq.pdb
-import fairseq.tasks
+import fairseq.criterions  # noqa
+import fairseq.models  # noqa
+import fairseq.modules  # noqa
+import fairseq.optim  # noqa
+import fairseq.optim.lr_scheduler  # noqa
+import fairseq.pdb  # noqa
+import fairseq.tasks  # noqa

fairseq/checkpoint_utils.py

@@ -3,7 +3,6 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-import argparse
 from collections import OrderedDict
 from typing import Union
 import collections

fairseq/criterions/binary_cross_entropy.py

@@ -68,4 +68,4 @@ class BinaryCrossEntropyCriterion(FairseqCriterion):
         }
         if sample_size != ntokens:
             agg_output['nll_loss'] = loss_sum / ntokens / math.log(2)
-        return agg_output
+        return agg_output
\ No newline at end of file

fairseq/data/data_utils.py

@@ -149,8 +149,9 @@ def filter_by_size(indices, size_fn, max_positions, raise_exception=False):
         else:
             # Hacky as heck, for the specific case of multilingual training with RoundRobin.
             if isinstance(size_fn(idx), dict) and isinstance(max_positions, tuple):
-                return all(a is None or b is None or a <= b
-                           for a, b in zip(size_fn(idx).values(), max_positions)
+                return all(
+                    a is None or b is None or a <= b
+                    for a, b in zip(size_fn(idx).values(), max_positions)
                 )
             # For MultiCorpusSampledDataset, will generalize it later
             if not isinstance(size_fn(idx), Iterable):

fairseq/data/encoders/fastbpe.py

@@ -6,6 +6,7 @@
 from fairseq import file_utils
 from fairseq.data.encoders import register_bpe
 
+
 @register_bpe('fastbpe')
 class fastBPE(object):
 

fairseq/data/encoders/gpt2_bpe_utils.py

@@ -7,7 +7,6 @@ Original license: MIT
 
 from functools import lru_cache
 import json
-import os
 
 
 @lru_cache()

fairseq/distributed_utils.py

@@ -3,7 +3,6 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-from collections import namedtuple
 import os
 import pickle
 import socket
@@ -12,7 +11,6 @@ import warnings
 
 import torch
 import torch.distributed as dist
-from torch import nn
 
 from fairseq import utils
 

fairseq/hub_utils.py

@@ -7,7 +7,6 @@
 import argparse
 import copy
 import os
-from typing import List
 
 import torch
 from torch import nn

fairseq/models/__init__.py

@@ -3,11 +3,6 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-MODEL_REGISTRY = {}
-ARCH_MODEL_REGISTRY = {}
-ARCH_MODEL_INV_REGISTRY = {}
-ARCH_CONFIG_REGISTRY = {}
-
 import argparse
 import importlib
 import os
@@ -28,6 +23,12 @@ from .composite_encoder import CompositeEncoder
 from .distributed_fairseq_model import DistributedFairseqModel
 
 
+MODEL_REGISTRY = {}
+ARCH_MODEL_REGISTRY = {}
+ARCH_MODEL_INV_REGISTRY = {}
+ARCH_CONFIG_REGISTRY = {}
+
+
 __all__ = [
     'BaseFairseqModel',
     'CompositeEncoder',
@@ -43,7 +44,6 @@ __all__ = [
 ]
 
 
-
 def build_model(args, task):
     return ARCH_MODEL_REGISTRY[args.arch].build_model(args, task)
 

fairseq/models/fairseq_model.py

@@ -6,7 +6,6 @@
 Base classes for various fairseq models.
 """
 
-import os
 from typing import Dict, List, Optional
 
 import torch
@@ -259,6 +258,7 @@ class FairseqModel(FairseqEncoderDecoderModel):
             stacklevel=4,
         )
 
+
 class FairseqMultiModel(BaseFairseqModel):
     """Base class for combining multiple encoder-decoder models."""
 

fairseq/models/roberta/hub_interface.py

@@ -3,8 +3,6 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-from typing import List
-
 import numpy as np
 import torch
 import torch.nn as nn

fairseq/models/transformer.py

@@ -20,7 +20,6 @@ from fairseq.models import (
 from fairseq.modules import (
     AdaptiveSoftmax,
     LayerNorm,
-    MultiheadAttention,
     PositionalEmbedding,
     SinusoidalPositionalEmbedding,
     TransformerDecoderLayer,
@@ -51,6 +50,7 @@ class TransformerModel(FairseqEncoderDecoderModel):
 
     @classmethod
     def hub_models(cls):
+        # fmt: off
         return {
             'transformer.wmt14.en-fr': 'https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-fr.joined-dict.transformer.tar.bz2',
             'transformer.wmt16.en-de': 'https://dl.fbaipublicfiles.com/fairseq/models/wmt16.en-de.joined-dict.transformer.tar.bz2',
@@ -64,6 +64,7 @@ class TransformerModel(FairseqEncoderDecoderModel):
             'transformer.wmt19.de-en.single_model': 'https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.single_model.tar.gz',
             'transformer.wmt19.ru-en.single_model': 'https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.single_model.tar.gz',
         }
+        # fmt: on
 
     def __init__(self, encoder, decoder):
         super().__init__(encoder, decoder)

fairseq/models/wav2vec.py

@@ -351,7 +351,7 @@ class ConvAggegator(nn.Module):
             residual = x
             x = conv(x)
             if self.skip_connections:
-                if rproj != None:
+                if rproj is not None:
                     residual = rproj(residual)
                 x = (x + residual) * self.residual_scale
         return x

fairseq/optim/__init__.py

@@ -9,7 +9,7 @@ import os
 from fairseq import registry
 from fairseq.optim.fairseq_optimizer import FairseqOptimizer
 from fairseq.optim.fp16_optimizer import FP16Optimizer, MemoryEfficientFP16Optimizer
-from fairseq.optim.bmuf import FairseqBMUF
+from fairseq.optim.bmuf import FairseqBMUF  # noqa
 
 
 __all__ = [

fairseq/optim/adam.py

@@ -19,7 +19,7 @@ class FairseqAdam(FairseqOptimizer):
         super().__init__(args, params)
         if torch.cuda.is_available():
             try:
-                from apex.optimizers import FusedAdam as _FusedAdam
+                from apex.optimizers import FusedAdam as _FusedAdam  # noqa
                 self._optimizer = FusedAdam(params, **self.optimizer_config)
             except ImportError:
                 self._optimizer = Adam(params, **self.optimizer_config)

fairseq/optim/bmuf.py

@@ -3,10 +3,6 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-
-import sys
-import time
-
 import torch
 import torch.distributed as dist
 

fairseq/options.py

@@ -350,7 +350,7 @@ def add_optimization_args(parser):
     group.add_argument('--min-lr', default=-1, type=float, metavar='LR',
                        help='stop training when the learning rate reaches this minimum')
     group.add_argument('--use-bmuf', default=False, action='store_true',
-                        help="specify global optimizer for syncing models on different GPUs/Shards")
+                       help='specify global optimizer for syncing models on different GPUs/shards')
     # fmt: on
     return group
 

fairseq/progress_bar.py

@@ -11,7 +11,6 @@ from collections import OrderedDict
 import json
 from numbers import Number
 import os
-import re
 import sys
 
 from fairseq import distributed_utils
@@ -19,6 +18,7 @@ from fairseq.meters import AverageMeter, StopwatchMeter, TimeMeter
 
 g_tbmf_wrapper = None
 
+
 def build_progress_bar(args, iterator, epoch=None, prefix=None, default='tqdm', no_progress_bar='none'):
     if args.log_format is None:
         args.log_format = no_progress_bar if args.no_progress_bar else default

fairseq/sequence_generator.py

@@ -7,7 +7,7 @@ import math
 
 import torch
 
-from fairseq import search, utils
+from fairseq import search
 from fairseq.models import FairseqIncrementalDecoder
 
 

fairseq/tasks/audio_pretraining.py

@@ -47,12 +47,12 @@ class AudioPretrainingTask(FairseqTask):
 
         manifest = os.path.join(self.args.data, '{}.tsv'.format(split))
         self.datasets[split] = RawAudioDataset(manifest,
-                                                 sample_rate=self.args.sample_rate,
-                                                 max_sample_size=self.args.max_sample_size,
-                                                 min_sample_size=self.args.min_sample_size)
+                                               sample_rate=self.args.sample_rate,
+                                               max_sample_size=self.args.max_sample_size,
+                                               min_sample_size=self.args.min_sample_size)
 
     @property
     def target_dictionary(self):
         """Return the :class:`~fairseq.data.Dictionary` for the language
         model."""
-        return None
+        return None
\ No newline at end of file