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[housekeeping] Upgrade # type Python 2 syntax

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cc @sshleifer
This commit is contained in:
Julien Chaumond 2020-04-23 10:39:24 -04:00
parent cb3c2212c7
commit a946b6b51b
2 changed files with 7 additions and 7 deletions
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4
src/transformers/configuration_utils.py
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@ -52,8 +52,8 @@ class PretrainedConfig(object):
torchscript (:obj:`bool`, `optional`, defaults to :obj:`False`): torchscript (:obj:`bool`, `optional`, defaults to :obj:`False`):
Is the model used with Torchscript (for PyTorch models). Is the model used with Torchscript (for PyTorch models).
""" """
pretrained_config_archive_map = {} # type: Dict[str, str] pretrained_config_archive_map: Dict[str, str] = {}
model_type = "" # type: str model_type: str = ""
def __init__(self, **kwargs): def __init__(self, **kwargs):
# Attributes with defaults # Attributes with defaults

10
src/transformers/modeling_bart.py
View File

@ -570,7 +570,7 @@ class SelfAttention(nn.Module):
need_weights=False, need_weights=False,
) -> Tuple[Tensor, Optional[Tensor]]: ) -> Tuple[Tensor, Optional[Tensor]]:
"""Input shape: Time(SeqLen) x Batch x Channel""" """Input shape: Time(SeqLen) x Batch x Channel"""
static_kv = self.encoder_decoder_attention # type: bool static_kv: bool = self.encoder_decoder_attention
tgt_len, bsz, embed_dim = query.size() tgt_len, bsz, embed_dim = query.size()
assert embed_dim == self.embed_dim assert embed_dim == self.embed_dim
assert list(query.size()) == [tgt_len, bsz, embed_dim] assert list(query.size()) == [tgt_len, bsz, embed_dim]
@ -666,7 +666,7 @@ class SelfAttention(nn.Module):
assert v is not None assert v is not None
v = torch.cat([prev_value, v], dim=1) v = torch.cat([prev_value, v], dim=1)
assert k is not None and v is not None assert k is not None and v is not None
prev_key_padding_mask = saved_state.get("prev_key_padding_mask", None) # type: Optional[Tensor] prev_key_padding_mask: Optional[Tensor] = saved_state.get("prev_key_padding_mask", None)
key_padding_mask = self._cat_prev_key_padding_mask( key_padding_mask = self._cat_prev_key_padding_mask(
key_padding_mask, prev_key_padding_mask, bsz, k.size(1), static_kv key_padding_mask, prev_key_padding_mask, bsz, k.size(1), static_kv
) )
@ -798,7 +798,7 @@ class BartModel(PretrainedBartModel):
input_ids, input_ids,
attention_mask=None, attention_mask=None,
decoder_input_ids=None, decoder_input_ids=None,
encoder_outputs=None, # type: Tuple encoder_outputs: Optional[Tuple] = None,
decoder_attention_mask=None, decoder_attention_mask=None,
decoder_cached_states=None, decoder_cached_states=None,
use_cache=False, use_cache=False,
@ -831,9 +831,9 @@ class BartModel(PretrainedBartModel):
use_cache=use_cache, use_cache=use_cache,
) )
# Attention and hidden_states will be [] or None if they aren't needed # Attention and hidden_states will be [] or None if they aren't needed
decoder_outputs = _filter_out_falsey_values(decoder_outputs) # type: tuple decoder_outputs: Tuple = _filter_out_falsey_values(decoder_outputs)
assert isinstance(decoder_outputs[0], torch.Tensor) assert isinstance(decoder_outputs[0], torch.Tensor)
encoder_outputs = _filter_out_falsey_values(encoder_outputs) # type: tuple encoder_outputs: Tuple = _filter_out_falsey_values(encoder_outputs)
return decoder_outputs + encoder_outputs return decoder_outputs + encoder_outputs
def get_input_embeddings(self): def get_input_embeddings(self):