make style (#11442)

examples/research_projects/bert-loses-patience/run_glue_with_pabee.py

@@ -71,7 +71,7 @@ def set_seed(args):
 
 
 def train(args, train_dataset, model, tokenizer):
-    """ Train the model """
+    """Train the model"""
     if args.local_rank in [-1, 0]:
         tb_writer = SummaryWriter()
 

examples/research_projects/bertabs/modeling_bertabs.py

@@ -251,7 +251,7 @@ class TransformerDecoder(nn.Module):
         return output, state  # , state
 
     def init_decoder_state(self, src, memory_bank, with_cache=False):
-        """ Init decoder state """
+        """Init decoder state"""
         state = TransformerDecoderState(src)
         if with_cache:
             state._init_cache(memory_bank, self.num_layers)
@@ -479,11 +479,11 @@ class MultiHeadedAttention(nn.Module):
         head_count = self.head_count
 
         def shape(x):
-            """  projection """
+            """projection"""
             return x.view(batch_size, -1, head_count, dim_per_head).transpose(1, 2)
 
         def unshape(x):
-            """  compute context """
+            """compute context"""
             return x.transpose(1, 2).contiguous().view(batch_size, -1, head_count * dim_per_head)
 
         # 1) Project key, value, and query.
@@ -571,12 +571,12 @@ class DecoderState(object):
     """
 
     def detach(self):
-        """ Need to document this """
+        """Need to document this"""
         self.hidden = tuple([_.detach() for _ in self.hidden])
         self.input_feed = self.input_feed.detach()
 
     def beam_update(self, idx, positions, beam_size):
-        """ Need to document this """
+        """Need to document this"""
         for e in self._all:
             sizes = e.size()
             br = sizes[1]
@@ -592,7 +592,7 @@ class DecoderState(object):
 
 
 class TransformerDecoderState(DecoderState):
-    """ Transformer Decoder state base class """
+    """Transformer Decoder state base class"""
 
     def __init__(self, src):
         """
@@ -638,7 +638,7 @@ class TransformerDecoderState(DecoderState):
             self.cache["layer_{}".format(l)] = layer_cache
 
     def repeat_beam_size_times(self, beam_size):
-        """ Repeat beam_size times along batch dimension. """
+        """Repeat beam_size times along batch dimension."""
         self.src = self.src.data.repeat(1, beam_size, 1)
 
     def map_batch_fn(self, fn):

examples/research_projects/bertabs/test_utils_summarization.py

@@ -25,19 +25,19 @@ class SummarizationDataProcessingTest(unittest.TestCase):
         self.block_size = 10
 
     def test_fit_to_block_sequence_too_small(self):
-        """ Pad the sequence with 0 if the sequence is smaller than the block size."""
+        """Pad the sequence with 0 if the sequence is smaller than the block size."""
         sequence = [1, 2, 3, 4]
         expected_output = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
         self.assertEqual(truncate_or_pad(sequence, self.block_size, 0), expected_output)
 
     def test_fit_to_block_sequence_fit_exactly(self):
-        """ Do nothing if the sequence is the right size. """
+        """Do nothing if the sequence is the right size."""
         sequence = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
         expected_output = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
         self.assertEqual(truncate_or_pad(sequence, self.block_size, 0), expected_output)
 
     def test_fit_to_block_sequence_too_big(self):
-        """ Truncate the sequence if it is too long. """
+        """Truncate the sequence if it is too long."""
         sequence = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
         expected_output = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
         self.assertEqual(truncate_or_pad(sequence, self.block_size, 0), expected_output)

examples/research_projects/bertabs/utils_summarization.py

@@ -47,7 +47,7 @@ class CNNDMDataset(Dataset):
             self.documents.append(path_to_story)
 
     def __len__(self):
-        """ Returns the number of documents. """
+        """Returns the number of documents."""
         return len(self.documents)
 
     def __getitem__(self, idx):

examples/research_projects/bertology/run_bertology.py

@@ -49,14 +49,14 @@ logger = logging.getLogger(__name__)
 
 
 def entropy(p):
-    """ Compute the entropy of a probability distribution """
+    """Compute the entropy of a probability distribution"""
     plogp = p * torch.log(p)
     plogp[p == 0] = 0
     return -plogp.sum(dim=-1)
 
 
 def print_2d_tensor(tensor):
-    """ Print a 2D tensor """
+    """Print a 2D tensor"""
     logger.info("lv, h >\t" + "\t".join(f"{x + 1}" for x in range(len(tensor))))
     for row in range(len(tensor)):
         if tensor.dtype != torch.long:

examples/research_projects/bertology/run_prune_gpt.py

@@ -36,7 +36,7 @@ def save_model(model, dirpath):
 
 
 def entropy(p, unlogit=False):
-    """ Compute the entropy of a probability distribution """
+    """Compute the entropy of a probability distribution"""
     exponent = 2
     if unlogit:
         p = torch.pow(p, exponent)
@@ -46,7 +46,7 @@ def entropy(p, unlogit=False):
 
 
 def print_2d_tensor(tensor):
-    """ Print a 2D tensor """
+    """Print a 2D tensor"""
     logger.info("lv, h >\t" + "\t".join(f"{x + 1}" for x in range(len(tensor))))
     for row in range(len(tensor)):
         if tensor.dtype != torch.long:

examples/research_projects/deebert/run_glue_deebert.py

@@ -70,7 +70,7 @@ def get_wanted_result(result):
 
 
 def train(args, train_dataset, model, tokenizer, train_highway=False):
-    """ Train the model """
+    """Train the model"""
     if args.local_rank in [-1, 0]:
         tb_writer = SummaryWriter()
 

examples/research_projects/distillation/run_squad_w_distillation.py

@@ -92,7 +92,7 @@ def to_list(tensor):
 
 
 def train(args, train_dataset, model, tokenizer, teacher=None):
-    """ Train the model """
+    """Train the model"""
     if args.local_rank in [-1, 0]:
         tb_writer = SummaryWriter()
 

examples/research_projects/mm-imdb/run_mmimdb.py

@@ -64,7 +64,7 @@ def set_seed(args):
 
 
 def train(args, train_dataset, model, tokenizer, criterion):
-    """ Train the model """
+    """Train the model"""
     if args.local_rank in [-1, 0]:
         tb_writer = SummaryWriter()
 

examples/research_projects/movement-pruning/emmental/modeling_bert_masked.py

@@ -393,7 +393,7 @@ class MaskedBertPreTrainedModel(PreTrainedModel):
     base_model_prefix = "bert"
 
     def _init_weights(self, module):
-        """ Initialize the weights """
+        """Initialize the weights"""
         if isinstance(module, (nn.Linear, nn.Embedding)):
             # Slightly different from the TF version which uses truncated_normal for initialization
             # cf https://github.com/pytorch/pytorch/pull/5617

examples/research_projects/movement-pruning/masked_run_glue.py

@@ -105,7 +105,7 @@ def regularization(model: nn.Module, mode: str):
 
 
 def train(args, train_dataset, model, tokenizer, teacher=None):
-    """ Train the model """
+    """Train the model"""
     if args.local_rank in [-1, 0]:
         tb_writer = SummaryWriter(log_dir=args.output_dir)
 

examples/research_projects/movement-pruning/masked_run_squad.py

@@ -113,7 +113,7 @@ def to_list(tensor):
 
 
 def train(args, train_dataset, model, tokenizer, teacher=None):
-    """ Train the model """
+    """Train the model"""
     if args.local_rank in [-1, 0]:
         tb_writer = SummaryWriter(log_dir=args.output_dir)