commplying with isort

examples/movement-pruning/bertarize.py

@@ -17,13 +17,13 @@ For instance, once the a model from the :class:`~emmental.MaskedBertForSequenceC
 as a standard :class:`~transformers.BertForSequenceClassification`.
 """
 
+import argparse
 import os
 import shutil
-import argparse
 
 import torch
 
-from emmental.modules import MagnitudeBinarizer, TopKBinarizer, ThresholdBinarizer
+from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
 
 
 def main(args):
@@ -40,13 +40,13 @@ def main(args):
     for name, tensor in model.items():
         if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
             pruned_model[name] = tensor
-            print(f"Pruned layer {name}")
+            print(f"Copied layer {name}")
         elif "classifier" in name or "qa_output" in name:
             pruned_model[name] = tensor
-            print(f"Pruned layer {name}")
+            print(f"Copied layer {name}")
         elif "bias" in name:
             pruned_model[name] = tensor
-            print(f"Pruned layer {name}")
+            print(f"Copied layer {name}")
         else:
             if pruning_method == "magnitude":
                 mask = MagnitudeBinarizer.apply(inputs=tensor, threshold=threshold)

examples/movement-pruning/counts_parameters.py

@@ -15,12 +15,12 @@
 Count remaining (non-zero) weights in the encoder (i.e. the transformer layers).
 Sparsity and remaining weights levels are equivalent: sparsity % = 100 - remaining weights %.
 """
-import os
 import argparse
+import os
 
 import torch
 
-from emmental.modules import TopKBinarizer, ThresholdBinarizer
+from emmental.modules import ThresholdBinarizer, TopKBinarizer
 
 
 def main(args):

examples/movement-pruning/emmental/__init__.py

-from .modules import *
-
 from .configuration_bert_masked import MaskedBertConfig
-
 from .modeling_bert_masked import (
-    MaskedBertModel,
+    MaskedBertForMultipleChoice,
     MaskedBertForQuestionAnswering,
     MaskedBertForSequenceClassification,
     MaskedBertForTokenClassification,
-    MaskedBertForMultipleChoice,
+    MaskedBertModel,
 )
+from .modules import *

examples/movement-pruning/emmental/configuration_bert_masked.py

@@ -19,8 +19,9 @@ and adapts it to the specificities of MaskedBert (`pruning_method`, `mask_init`
 
 import logging
 
-from transformers.configuration_utils import PretrainedConfig
 from transformers.configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP
+from transformers.configuration_utils import PretrainedConfig
+
 
 logger = logging.getLogger(__name__)
 

examples/movement-pruning/emmental/modeling_bert_masked.py

@@ -26,13 +26,16 @@ import torch
 from torch import nn
 from torch.nn import CrossEntropyLoss, MSELoss
 
+from emmental import MaskedBertConfig, MaskedLinear
 from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_callable
+from transformers.modeling_bert import (
+    ACT2FN,
+    BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
+    BertLayerNorm,
+    load_tf_weights_in_bert,
+)
 from transformers.modeling_utils import PreTrainedModel, prune_linear_layer
-from transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
-from transformers.modeling_bert import load_tf_weights_in_bert, ACT2FN, BertLayerNorm
 
-from emmental import MaskedLinear
-from emmental import MaskedBertConfig
 
 logger = logging.getLogger(__name__)
 

examples/movement-pruning/emmental/modules/__init__.py

-from .binarizer import ThresholdBinarizer, TopKBinarizer, MagnitudeBinarizer
+from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
 from .masked_nn import MaskedLinear

examples/movement-pruning/emmental/modules/masked_nn.py

@@ -19,14 +19,14 @@ the weight matrix to prune a portion of the weights.
 The pruned weight matrix is then multiplied against the inputs (and if necessary, the bias is added).
 """
 
+import math
+
 import torch
 from torch import nn
 from torch.nn import functional as F
 from torch.nn import init
 
-import math
-
-from .binarizer import ThresholdBinarizer, TopKBinarizer, MagnitudeBinarizer
+from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
 
 
 class MaskedLinear(nn.Linear):

examples/movement-pruning/masked_run_glue.py

@@ -24,12 +24,13 @@ import random
 
 import numpy as np
 import torch
-from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
-from torch.utils.data.distributed import DistributedSampler
 import torch.nn as nn
 import torch.nn.functional as F
+from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
+from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm, trange
 
+from emmental import MaskedBertConfig, MaskedBertForSequenceClassification
 from transformers import (
     WEIGHTS_NAME,
     AdamW,
@@ -43,7 +44,6 @@ from transformers import glue_convert_examples_to_features as convert_examples_t
 from transformers import glue_output_modes as output_modes
 from transformers import glue_processors as processors
 
-from emmental import MaskedBertConfig, MaskedBertForSequenceClassification
 
 try:
     from torch.utils.tensorboard import SummaryWriter

examples/movement-pruning/masked_run_squad.py

@@ -25,12 +25,13 @@ import timeit
 
 import numpy as np
 import torch
-from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
-from torch.utils.data.distributed import DistributedSampler
 import torch.nn as nn
 import torch.nn.functional as F
+from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
+from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm, trange
 
+from emmental import MaskedBertConfig, MaskedBertForQuestionAnswering
 from transformers import (
     WEIGHTS_NAME,
     AdamW,
@@ -48,8 +49,6 @@ from transformers.data.metrics.squad_metrics import (
 from transformers.data.processors.squad import SquadResult, SquadV1Processor, SquadV2Processor
 
 
-from emmental import MaskedBertConfig, MaskedBertForQuestionAnswering
-
 try:
     from torch.utils.tensorboard import SummaryWriter
 except ImportError: