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Remove padding_masks from gpt_bigcode. (#27348)

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Update modeling_gpt_bigcode.py
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Susnato Dhar 2023-11-07 22:54:43 +05:30 committed by GitHub
parent 8c91f15ae5
commit cc9f27bb1e
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src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py
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@ -235,16 +235,10 @@ class GPTBigCodeAttention(nn.Module):
encoder_attention_mask: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = False, use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False, output_attentions: Optional[bool] = False,
**kwargs,
) -> Union[ ) -> Union[
Tuple[torch.Tensor, Optional[torch.Tensor]], Tuple[torch.Tensor, Optional[torch.Tensor]],
Tuple[torch.Tensor, Optional[torch.Tensor], Tuple[torch.Tensor, ...]], Tuple[torch.Tensor, Optional[torch.Tensor], Tuple[torch.Tensor, ...]],
]: ]:
if "padding_mask" in kwargs:
logger.warning_once(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
if encoder_hidden_states is not None: if encoder_hidden_states is not None:
if not hasattr(self, "q_attn") or not self.is_cross_attention: if not hasattr(self, "q_attn") or not self.is_cross_attention:
raise ValueError( raise ValueError(
@ -308,19 +302,10 @@ class GPTBigCodeFlashAttention2(GPTBigCodeAttention):
encoder_attention_mask: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = False, use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False, output_attentions: Optional[bool] = False,
**kwargs,
) -> Union[ ) -> Union[
Tuple[torch.Tensor, Optional[torch.Tensor]], Tuple[torch.Tensor, Optional[torch.Tensor]],
Tuple[torch.Tensor, Optional[torch.Tensor], Tuple[torch.Tensor, ...]], Tuple[torch.Tensor, Optional[torch.Tensor], Tuple[torch.Tensor, ...]],
]: ]:
if "padding_mask" in kwargs:
logger.warning_once(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
# overwrite attention_mask with padding_mask
attention_mask = kwargs.pop("padding_mask")
if encoder_hidden_states is not None: if encoder_hidden_states is not None:
if not hasattr(self, "q_attn") or not self.is_cross_attention: if not hasattr(self, "q_attn") or not self.is_cross_attention:
raise ValueError( raise ValueError(