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Fix return_dict=False giving errors in a few VLM models (#38519)

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update

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar 2025-06-05 21:19:07 +02:00 committed by GitHub
parent 88912b8e95
commit 481b953170
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GPG Key ID: B5690EEEBB952194
6 changed files with 5 additions and 26 deletions
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4
src/transformers/models/chameleon/modeling_chameleon.py
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@ -1243,7 +1243,6 @@ class ChameleonForConditionalGeneration(ChameleonPreTrainedModel, GenerationMixi
use_cache: Optional[bool] = None, use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None, output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None, cache_position: Optional[torch.LongTensor] = None,
**kwargs: Unpack[KwargsForCausalLM], **kwargs: Unpack[KwargsForCausalLM],
) -> Union[Tuple, CausalLMOutputWithPast]: ) -> Union[Tuple, CausalLMOutputWithPast]:
@ -1277,7 +1276,6 @@ class ChameleonForConditionalGeneration(ChameleonPreTrainedModel, GenerationMixi
output_hidden_states = ( output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.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
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model( outputs = self.model(
@ -1290,7 +1288,7 @@ class ChameleonForConditionalGeneration(ChameleonPreTrainedModel, GenerationMixi
use_cache=use_cache, use_cache=use_cache,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states, output_hidden_states=output_hidden_states,
return_dict=return_dict, return_dict=True,
cache_position=cache_position, cache_position=cache_position,
**kwargs, **kwargs,
) )

3
src/transformers/models/kosmos2/modeling_kosmos2.py
View File

@ -1809,7 +1809,6 @@ class Kosmos2ForConditionalGeneration(Kosmos2PreTrainedModel, GenerationMixin):
use_cache: Optional[bool] = None, use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None, output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs: Unpack[KwargsForCausalLM], **kwargs: Unpack[KwargsForCausalLM],
) -> Union[Tuple, Kosmos2ForConditionalGenerationModelOutput]: ) -> Union[Tuple, Kosmos2ForConditionalGenerationModelOutput]:
r""" r"""
@ -1868,7 +1867,6 @@ class Kosmos2ForConditionalGeneration(Kosmos2PreTrainedModel, GenerationMixin):
output_hidden_states = ( output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.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
vision_model_output = None vision_model_output = None
projection_attentions = None projection_attentions = None
@ -1880,7 +1878,6 @@ class Kosmos2ForConditionalGeneration(Kosmos2PreTrainedModel, GenerationMixin):
pixel_values=pixel_values, pixel_values=pixel_values,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states, output_hidden_states=output_hidden_states,
return_dict=return_dict,
) )
# The whole `last_hidden_state` through `post_layernorm` instead of just `pooled_output`. # The whole `last_hidden_state` through `post_layernorm` instead of just `pooled_output`.
image_embeds = self.vision_model.model.post_layernorm(vision_model_output[0]) image_embeds = self.vision_model.model.post_layernorm(vision_model_output[0])

4
src/transformers/models/llava_next/modeling_llava_next.py
View File

@ -604,7 +604,6 @@ class LlavaNextForConditionalGeneration(LlavaNextPreTrainedModel, GenerationMixi
use_cache: Optional[bool] = None, use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None, output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None, cache_position: Optional[torch.LongTensor] = None,
logits_to_keep: Union[int, torch.Tensor] = 0, logits_to_keep: Union[int, torch.Tensor] = 0,
**kwargs: Unpack[KwargsForCausalLM], **kwargs: Unpack[KwargsForCausalLM],
@ -645,7 +644,6 @@ class LlavaNextForConditionalGeneration(LlavaNextPreTrainedModel, GenerationMixi
output_hidden_states = ( output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.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
vision_feature_layer = ( vision_feature_layer = (
vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
) )
@ -668,7 +666,7 @@ class LlavaNextForConditionalGeneration(LlavaNextPreTrainedModel, GenerationMixi
use_cache=use_cache, use_cache=use_cache,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states, output_hidden_states=output_hidden_states,
return_dict=return_dict, return_dict=True,
cache_position=cache_position, cache_position=cache_position,
**kwargs, **kwargs,
) )

8
src/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py
View File

@ -1525,7 +1525,6 @@ class Qwen2_5_VLForConditionalGeneration(Qwen2_5_VLPreTrainedModel, GenerationMi
use_cache: Optional[bool] = None, use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None, output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
pixel_values: Optional[torch.Tensor] = None, pixel_values: Optional[torch.Tensor] = None,
pixel_values_videos: Optional[torch.FloatTensor] = None, pixel_values_videos: Optional[torch.FloatTensor] = None,
image_grid_thw: Optional[torch.LongTensor] = None, image_grid_thw: Optional[torch.LongTensor] = None,
@ -1588,7 +1587,6 @@ class Qwen2_5_VLForConditionalGeneration(Qwen2_5_VLPreTrainedModel, GenerationMi
output_hidden_states = ( output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.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
outputs = self.model( outputs = self.model(
input_ids=input_ids, input_ids=input_ids,
@ -1604,7 +1602,7 @@ class Qwen2_5_VLForConditionalGeneration(Qwen2_5_VLPreTrainedModel, GenerationMi
use_cache=use_cache, use_cache=use_cache,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states, output_hidden_states=output_hidden_states,
return_dict=return_dict, return_dict=True,
cache_position=cache_position, cache_position=cache_position,
**kwargs, **kwargs,
) )
@ -1616,10 +1614,6 @@ class Qwen2_5_VLForConditionalGeneration(Qwen2_5_VLPreTrainedModel, GenerationMi
if labels is not None: if labels is not None:
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size) loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return Qwen2_5_VLCausalLMOutputWithPast( return Qwen2_5_VLCausalLMOutputWithPast(
loss=loss, loss=loss,
logits=logits, logits=logits,

8
src/transformers/models/qwen2_5_vl/modular_qwen2_5_vl.py
View File

@ -770,7 +770,6 @@ class Qwen2_5_VLForConditionalGeneration(Qwen2VLForConditionalGeneration):
use_cache: Optional[bool] = None, use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None, output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
pixel_values: Optional[torch.Tensor] = None, pixel_values: Optional[torch.Tensor] = None,
pixel_values_videos: Optional[torch.FloatTensor] = None, pixel_values_videos: Optional[torch.FloatTensor] = None,
image_grid_thw: Optional[torch.LongTensor] = None, image_grid_thw: Optional[torch.LongTensor] = None,
@ -833,7 +832,6 @@ class Qwen2_5_VLForConditionalGeneration(Qwen2VLForConditionalGeneration):
output_hidden_states = ( output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.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
outputs = self.model( outputs = self.model(
input_ids=input_ids, input_ids=input_ids,
@ -849,7 +847,7 @@ class Qwen2_5_VLForConditionalGeneration(Qwen2VLForConditionalGeneration):
use_cache=use_cache, use_cache=use_cache,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states, output_hidden_states=output_hidden_states,
return_dict=return_dict, return_dict=True,
cache_position=cache_position, cache_position=cache_position,
**kwargs, **kwargs,
) )
@ -861,10 +859,6 @@ class Qwen2_5_VLForConditionalGeneration(Qwen2VLForConditionalGeneration):
if labels is not None: if labels is not None:
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size) loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return Qwen2_5_VLCausalLMOutputWithPast( return Qwen2_5_VLCausalLMOutputWithPast(
loss=loss, loss=loss,
logits=logits, logits=logits,

4
src/transformers/models/qwen2_vl/modeling_qwen2_vl.py
View File

@ -1409,7 +1409,6 @@ class Qwen2VLForConditionalGeneration(Qwen2VLPreTrainedModel, GenerationMixin):
use_cache: Optional[bool] = None, use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None, output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
pixel_values: Optional[torch.Tensor] = None, pixel_values: Optional[torch.Tensor] = None,
pixel_values_videos: Optional[torch.FloatTensor] = None, pixel_values_videos: Optional[torch.FloatTensor] = None,
image_grid_thw: Optional[torch.LongTensor] = None, image_grid_thw: Optional[torch.LongTensor] = None,
@ -1469,7 +1468,6 @@ class Qwen2VLForConditionalGeneration(Qwen2VLPreTrainedModel, GenerationMixin):
output_hidden_states = ( output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.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
outputs = self.model( outputs = self.model(
input_ids=input_ids, input_ids=input_ids,
@ -1484,7 +1482,7 @@ class Qwen2VLForConditionalGeneration(Qwen2VLPreTrainedModel, GenerationMixin):
use_cache=use_cache, use_cache=use_cache,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states, output_hidden_states=output_hidden_states,
return_dict=return_dict, return_dict=True,
cache_position=cache_position, cache_position=cache_position,
**kwargs, **kwargs,
) )