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qubvel wants to merge 6 commits into from
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Remove return_dict kwarg from all the models #38991

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d35359d
albert, align, altclip, audio_spec_tr, autoformer, bark
qubvel Jun 23, 2025
51ba6f9
aria, autoformer, aya_vision
qubvel Jun 23, 2025
335ab62
bart
qubvel Jun 23, 2025
2e46938
beit, bert, bert_generation, big_bird
qubvel Jun 23, 2025
e467d51
Merge branch 'main' into apply-can-return-tuple
qubvel Jun 24, 2025
a189c08
Merge branch 'main' into apply-can-return-tuple
qubvel Jun 24, 2025
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79 changes: 17 additions & 62 deletions src/transformers/models/albert/modeling_albert.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,7 +41,7 @@
is_torch_greater_or_equal_than_2_2,
prune_linear_layer,
)
from ...utils import ModelOutput, auto_docstring, logging
from ...utils import ModelOutput, auto_docstring, can_return_tuple, logging
from .configuration_albert import AlbertConfig


Expand Down Expand Up @@ -498,15 +498,15 @@ def __init__(self, config: AlbertConfig):
self.embedding_hidden_mapping_in = nn.Linear(config.embedding_size, config.hidden_size)
self.albert_layer_groups = nn.ModuleList([AlbertLayerGroup(config) for _ in range(config.num_hidden_groups)])

@can_return_tuple
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
) -> Union[BaseModelOutput, tuple]:
) -> BaseModelOutput:
hidden_states = self.embedding_hidden_mapping_in(hidden_states)

all_hidden_states = (hidden_states,) if output_hidden_states else None
Expand Down Expand Up @@ -535,9 +535,6 @@ def forward(

if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)

if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
)
Expand Down Expand Up @@ -645,6 +642,7 @@ def _prune_heads(self, heads_to_prune: dict[int, list[int]]) -> None:
inner_group_idx = int(layer - group_idx * self.config.inner_group_num)
self.encoder.albert_layer_groups[group_idx].albert_layers[inner_group_idx].attention.prune_heads(heads)

@can_return_tuple
@auto_docstring
def forward(
self,
Expand All @@ -656,13 +654,11 @@ def forward(
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[BaseModelOutputWithPooling, tuple]:
) -> BaseModelOutputWithPooling:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict

if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
Expand Down Expand Up @@ -715,16 +711,12 @@ def forward(
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)

sequence_output = encoder_outputs[0]

pooled_output = self.pooler_activation(self.pooler(sequence_output[:, 0])) if self.pooler is not None else None

if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]

return BaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
Expand Down Expand Up @@ -761,6 +753,7 @@ def set_output_embeddings(self, new_embeddings: nn.Linear) -> None:
def get_input_embeddings(self) -> nn.Embedding:
return self.albert.embeddings.word_embeddings

@can_return_tuple
@auto_docstring
def forward(
self,
Expand All @@ -774,8 +767,7 @@ def forward(
sentence_order_label: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[AlbertForPreTrainingOutput, tuple]:
) -> AlbertForPreTrainingOutput:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
Expand All @@ -802,7 +794,6 @@ def forward(
>>> prediction_logits = outputs.prediction_logits
>>> sop_logits = outputs.sop_logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict

outputs = self.albert(
input_ids,
Expand All @@ -813,7 +804,6 @@ def forward(
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)

sequence_output, pooled_output = outputs[:2]
Expand All @@ -828,10 +818,6 @@ def forward(
sentence_order_loss = loss_fct(sop_scores.view(-1, 2), sentence_order_label.view(-1))
total_loss = masked_lm_loss + sentence_order_loss

if not return_dict:
output = (prediction_scores, sop_scores) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output

return AlbertForPreTrainingOutput(
loss=total_loss,
prediction_logits=prediction_scores,
Expand Down Expand Up @@ -907,6 +893,7 @@ def set_output_embeddings(self, new_embeddings: nn.Linear) -> None:
def get_input_embeddings(self) -> nn.Embedding:
return self.albert.embeddings.word_embeddings

@can_return_tuple
@auto_docstring
def forward(
self,
Expand All @@ -919,8 +906,7 @@ def forward(
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[MaskedLMOutput, tuple]:
) -> MaskedLMOutput:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
Expand Down Expand Up @@ -956,7 +942,6 @@ def forward(
0.81
```
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict

outputs = self.albert(
input_ids=input_ids,
Expand All @@ -967,7 +952,6 @@ def forward(
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_outputs = outputs[0]

Expand All @@ -978,10 +962,6 @@ def forward(
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))

if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output

return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
Expand Down Expand Up @@ -1009,6 +989,7 @@ def __init__(self, config: AlbertConfig):
# Initialize weights and apply final processing
self.post_init()

@can_return_tuple
@auto_docstring
def forward(
self,
Expand All @@ -1021,15 +1002,13 @@ def forward(
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[SequenceClassifierOutput, tuple]:
) -> SequenceClassifierOutput:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict

outputs = self.albert(
input_ids=input_ids,
Expand All @@ -1040,7 +1019,6 @@ def forward(
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)

pooled_output = outputs[1]
Expand Down Expand Up @@ -1071,10 +1049,6 @@ def forward(
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)

if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output

return SequenceClassifierOutput(
loss=loss,
logits=logits,
Expand All @@ -1101,6 +1075,7 @@ def __init__(self, config: AlbertConfig):
# Initialize weights and apply final processing
self.post_init()

@can_return_tuple
@auto_docstring
def forward(
self,
Expand All @@ -1113,13 +1088,11 @@ def forward(
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[TokenClassifierOutput, tuple]:
) -> TokenClassifierOutput:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict

outputs = self.albert(
input_ids,
Expand All @@ -1130,7 +1103,6 @@ def forward(
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)

sequence_output = outputs[0]
Expand All @@ -1143,10 +1115,6 @@ def forward(
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output

return TokenClassifierOutput(
loss=loss,
logits=logits,
Expand All @@ -1167,6 +1135,7 @@ def __init__(self, config: AlbertConfig):
# Initialize weights and apply final processing
self.post_init()

@can_return_tuple
@auto_docstring
def forward(
self,
Expand All @@ -1180,10 +1149,7 @@ def forward(
end_positions: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[AlbertForPreTrainingOutput, tuple]:
return_dict = return_dict if return_dict is not None else self.config.use_return_dict

) -> AlbertForPreTrainingOutput:
outputs = self.albert(
input_ids=input_ids,
attention_mask=attention_mask,
Expand All @@ -1193,7 +1159,6 @@ def forward(
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)

sequence_output = outputs[0]
Expand All @@ -1220,10 +1185,6 @@ def forward(
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2

if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output

return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
Expand All @@ -1245,6 +1206,7 @@ def __init__(self, config: AlbertConfig):
# Initialize weights and apply final processing
self.post_init()

@can_return_tuple
@auto_docstring
def forward(
self,
Expand All @@ -1257,8 +1219,7 @@ def forward(
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[AlbertForPreTrainingOutput, tuple]:
) -> AlbertForPreTrainingOutput:
r"""
input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Expand Down Expand Up @@ -1289,7 +1250,6 @@ def forward(
num_choices-1]` where *num_choices* is the size of the second dimension of the input tensors. (see
*input_ids* above)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]

input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
Expand All @@ -1310,7 +1270,6 @@ def forward(
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)

pooled_output = outputs[1]
Expand All @@ -1324,10 +1283,6 @@ def forward(
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)

if not return_dict:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output

return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
Expand Down
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