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refactor output tracing in univnet #44001

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refactor output tracing in univnet #44001

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12 changes: 3 additions & 9 deletions src/transformers/models/univnet/modeling_univnet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,7 @@

from ...modeling_outputs import ModelOutput
from ...modeling_utils import PreTrainedModel
from ...utils import auto_docstring, logging
from ...utils import auto_docstring, can_return_tuple, logging
from .configuration_univnet import UnivNetConfig


Expand Down Expand Up @@ -467,16 +467,16 @@ def __init__(self, config: UnivNetConfig):
# Initialize weights and apply final processing
self.post_init()

@can_return_tuple
@auto_docstring
def forward(
self,
input_features: torch.FloatTensor,
noise_sequence: torch.FloatTensor | None = None,
padding_mask: torch.FloatTensor | None = None,
generator: torch.Generator | None = None,
return_dict: bool | None = None,
**kwargs,
) -> tuple[torch.FloatTensor] | UnivNetModelOutput:
) -> UnivNetModelOutput:
r"""
noise_sequence (`torch.FloatTensor`, *optional*):
Tensor containing a noise sequence of standard Gaussian noise. Can be batched and of shape `(batch_size,
Expand Down Expand Up @@ -516,8 +516,6 @@ def forward(
[1, 140288]
```
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict

# Resolve batch sizes for noise_sequence and spectrogram
spectrogram_batched = input_features.dim() == 3
if not spectrogram_batched:
Expand Down Expand Up @@ -582,10 +580,6 @@ def forward(
# Padding is always contiguous and added on the right
waveform_lengths = torch.sum(padding_mask, dim=1)

if not return_dict:
outputs = (waveform, waveform_lengths)
return outputs

return UnivNetModelOutput(
waveforms=waveform,
waveform_lengths=waveform_lengths,
Expand Down