Skip to content

refactor output tracing in vision_text_dual_encoder #44000

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
omkar-334 wants to merge 3 commits into from
Closed

refactor output tracing in vision_text_dual_encoder #44000

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
b3176c5
refactor output tracing in `vision_text_dual_encoder`
omkar-334 Feb 14, 2026
774a997
fixes
omkar-334 Feb 17, 2026
9dc83bc
Merge branch 'main' into refactor-vtde
omkar-334 Feb 17, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
29 changes: 8 additions & 21 deletions src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -168,6 +168,7 @@ def get_image_features(

return vision_outputs

@can_return_tuple
@auto_docstring
def forward(
self,
Expand All @@ -177,11 +178,8 @@ def forward(
position_ids: torch.LongTensor | None = None,
return_loss: bool | None = None,
token_type_ids: torch.LongTensor | None = None,
output_attentions: bool | None = None,
output_hidden_states: bool | None = None,
return_dict: bool | None = None,
**kwargs,
) -> tuple[torch.Tensor] | CLIPOutput:
**kwargs: Unpack[TransformersKwargs],
) -> CLIPOutput:
r"""
return_loss (`bool`, *optional*):
Whether or not to return the contrastive loss.
Expand Down Expand Up @@ -239,29 +237,23 @@ def forward(
>>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score
>>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
```"""
return_dict = return_dict if return_dict is not None else self.config.return_dict

vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs,
)

text_outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs,
)

image_embeds = vision_outputs[1] # pooler_output
image_embeds = vision_outputs.pooler_output
image_embeds = self.visual_projection(image_embeds)

text_embeds = text_outputs[1] # pooler_output
text_embeds = text_outputs.pooler_output
text_embeds = self.text_projection(text_embeds)

# normalized features
Expand All @@ -277,10 +269,6 @@ def forward(
if return_loss:
loss = clip_loss(logits_per_text)

if not return_dict:
output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)
return ((loss,) + output) if loss is not None else output

return CLIPOutput(
loss=loss,
logits_per_image=logits_per_image,
Expand Down Expand Up @@ -323,8 +311,7 @@ def from_vision_text_pretrained(
All remaining positional arguments will be passed to the underlying model's `__init__` method.

kwargs (remaining dictionary of keyword arguments, *optional*):
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
`output_attentions=True`).
Can be used to update the configuration object (after it being loaded) and initiate the model.

- To update the text configuration, use the prefix *text_* for each configuration parameter.
- To update the vision configuration, use the prefix *vision_* for each configuration parameter.
Expand Down