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added type hints to xglm pytorch #16500

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Merged
Rocketknight1 merged 3 commits into from
Mar 31, 2022
Merged

added type hints to xglm pytorch #16500

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535ccce
added type hints to xglm pytorch
mowafess Mar 30, 2022
ac5bbbc
Update src/transformers/models/xglm/modeling_xglm.py
Rocketknight1 Mar 31, 2022
3d82c45
Update src/transformers/models/xglm/modeling_xglm.py
Rocketknight1 Mar 31, 2022
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56 changes: 28 additions & 28 deletions src/transformers/models/xglm/modeling_xglm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@

import math
import random
from typing import Optional, Tuple
from typing import List, Optional, Tuple, Union

import torch
import torch.utils.checkpoint
Expand Down Expand Up @@ -594,19 +594,19 @@ def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_em
)
def forward(
self,
input_ids=None,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
head_mask=None,
cross_attn_head_mask=None,
past_key_values=None,
inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Expand Down Expand Up @@ -852,20 +852,20 @@ def set_output_embeddings(self, new_embeddings):
)
def forward(
self,
input_ids=None,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
head_mask=None,
cross_attn_head_mask=None,
past_key_values=None,
inputs_embeds=None,
labels=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
Expand Down