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refactor output tracing in dpr #44030

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refactor output tracing in dpr #44030

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69ddc32
refactor output tracing in `dpr`
omkar-334 Feb 16, 2026
127d918
fixes
omkar-334 Feb 17, 2026
aa68746
Merge branch 'main' into refactor-dpr
omkar-334 Feb 17, 2026
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69 changes: 11 additions & 58 deletions src/transformers/models/dpr/modeling_dpr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,6 +23,7 @@
from ...utils import (
ModelOutput,
auto_docstring,
can_return_tuple,
logging,
)
from ..bert.modeling_bert import BertModel
Expand Down Expand Up @@ -118,35 +119,28 @@ def __init__(self, config: DPRConfig):
# Initialize weights and apply final processing
self.post_init()

@can_return_tuple
def forward(
self,
input_ids: Tensor,
attention_mask: Tensor | None = None,
token_type_ids: Tensor | None = None,
inputs_embeds: Tensor | None = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = False,
**kwargs,
) -> BaseModelOutputWithPooling | tuple[Tensor, ...]:
outputs = self.bert_model(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs,
)
sequence_output = outputs[0]
pooled_output = sequence_output[:, 0, :]

if self.projection_dim > 0:
pooled_output = self.encode_proj(pooled_output)

if not return_dict:
return (sequence_output, pooled_output) + outputs[2:]

return BaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
Expand All @@ -172,14 +166,12 @@ def __init__(self, config: DPRConfig):
# Initialize weights and apply final processing
self.post_init()

@can_return_tuple
def forward(
self,
input_ids: Tensor,
attention_mask: Tensor,
inputs_embeds: Tensor | None = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = False,
**kwargs,
) -> DPRReaderOutput | tuple[Tensor, ...]:
# notations: N - number of questions in a batch, M - number of passages per questions, L - sequence length
Expand All @@ -189,9 +181,7 @@ def forward(
input_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs,
)
sequence_output = outputs[0]

Expand All @@ -207,9 +197,6 @@ def forward(
end_logits = end_logits.view(n_passages, sequence_length)
relevance_logits = relevance_logits.view(n_passages)

if not return_dict:
return (start_logits, end_logits, relevance_logits) + outputs[2:]

return DPRReaderOutput(
start_logits=start_logits,
end_logits=end_logits,
Expand Down Expand Up @@ -272,16 +259,14 @@ def __init__(self, config: DPRConfig):
# Initialize weights and apply final processing
self.post_init()

@can_return_tuple
@auto_docstring
def forward(
self,
input_ids: Tensor | None = None,
attention_mask: Tensor | None = None,
token_type_ids: Tensor | None = None,
inputs_embeds: Tensor | None = None,
output_attentions: bool | None = None,
output_hidden_states: bool | None = None,
return_dict: bool | None = None,
**kwargs,
) -> DPRContextEncoderOutput | tuple[Tensor, ...]:
r"""
Expand Down Expand Up @@ -322,12 +307,6 @@ def forward(
>>> embeddings = model(input_ids).pooler_output
```"""

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")
elif input_ids is not None:
Expand All @@ -353,13 +332,9 @@ def forward(
attention_mask=attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs,
)

if not return_dict:
return outputs[1:]
return DPRContextEncoderOutput(
pooler_output=outputs.pooler_output, hidden_states=outputs.hidden_states, attentions=outputs.attentions
)
Expand All @@ -378,16 +353,14 @@ def __init__(self, config: DPRConfig):
# Initialize weights and apply final processing
self.post_init()

@can_return_tuple
@auto_docstring
def forward(
self,
input_ids: Tensor | None = None,
attention_mask: Tensor | None = None,
token_type_ids: Tensor | None = None,
inputs_embeds: Tensor | None = None,
output_attentions: bool | None = None,
output_hidden_states: bool | None = None,
return_dict: bool | None = None,
**kwargs,
) -> DPRQuestionEncoderOutput | tuple[Tensor, ...]:
r"""
Expand Down Expand Up @@ -428,12 +401,6 @@ def forward(
>>> embeddings = model(input_ids).pooler_output
```
"""
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")
elif input_ids is not None:
Expand All @@ -460,13 +427,9 @@ def forward(
attention_mask=attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs,
)

if not return_dict:
return outputs[1:]
return DPRQuestionEncoderOutput(
pooler_output=outputs.pooler_output, hidden_states=outputs.hidden_states, attentions=outputs.attentions
)
Expand All @@ -485,15 +448,13 @@ def __init__(self, config: DPRConfig):
# Initialize weights and apply final processing
self.post_init()

@can_return_tuple
@auto_docstring
def forward(
self,
input_ids: Tensor | None = None,
attention_mask: Tensor | None = None,
inputs_embeds: Tensor | None = None,
output_attentions: bool | None = None,
output_hidden_states: bool | None = None,
return_dict: bool | None = None,
**kwargs,
) -> DPRReaderOutput | tuple[Tensor, ...]:
r"""
Expand Down Expand Up @@ -534,12 +495,6 @@ def forward(
>>> relevance_logits = outputs.relevance_logits
```
"""
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")
elif input_ids is not None:
Expand All @@ -559,9 +514,7 @@ def forward(
input_ids,
attention_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs,
)


Expand Down