Repository: huggingface/transformers
Pull request: fix(Qwen3VLCausalLMOutputWithPast): missing `hidden_states` and `atte…

# What does this PR do?

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Fixes https://github.com/huggingface/transformers/issues/42594

As it was discussed in the issue, I've added the missing `hidden_states`  kwarg but also `attentions` to keep consistency with the other Qwen2 VL and 2.5 VL signatures and replaced the decorator based on what @zucchini-nlp said.

```diff
        return Qwen3VLModelOutputWithPast(
            last_hidden_state=outputs.last_hidden_state,
            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
            rope_deltas=self.rope_deltas,
        )
```

&nbsp;

If the PR is good, I will also update the signature for `modular_qwen3_vl_moe.py`? it seems to be missing there too.

## Before submitting
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## Who can review?

I'm tagging @Rocketknight1 and @zucchini-nlp  since you were discussing on the issue.

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Models:

- text models: @ArthurZucker @Cyrilvallez
- vision models: @yonigozlan @molbap
- audio models: @eustlb @ebezzam @vasqu
- multimodal models: @zucchini-nlp
- graph models: @clefourrier

Library:

- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
- tokenizers: @ArthurZucker and @itazap
- trainer: @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker
- CIs: @ydshieh

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- ray/raytune: @richardliaw, @amogkam
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- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo

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- AMD ROCm: @ivarflakstad
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Changed files:
filename: src/transformers/models/qwen3_vl/modeling_qwen3_vl.py
patch: @@ -38,7 +38,7 @@
 from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
 from ...processing_utils import Unpack
-from ...utils import TransformersKwargs, auto_docstring
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
 from ...utils.generic import check_model_inputs
 from .configuration_qwen3_vl import Qwen3VLConfig, Qwen3VLTextConfig, Qwen3VLVisionConfig
 
@@ -1297,7 +1297,7 @@ def get_video_features(
     def get_image_features(self, pixel_values: torch.FloatTensor, image_grid_thw: Optional[torch.LongTensor] = None):
         return self.model.get_image_features(pixel_values, image_grid_thw)
 
-    @check_model_inputs
+    @can_return_tuple
     def forward(
         self,
         input_ids: torch.LongTensor = None,
@@ -1389,6 +1389,8 @@ def forward(
             loss=loss,
             logits=logits,
             past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
             rope_deltas=outputs.rope_deltas,
         )
 

filename: src/transformers/models/qwen3_vl/modular_qwen3_vl.py
patch: @@ -34,7 +34,7 @@
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
 from ...processing_utils import ProcessingKwargs, Unpack
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
-from ...utils import auto_docstring, logging
+from ...utils import auto_docstring, can_return_tuple, logging
 from ...utils.generic import check_model_inputs
 from ...video_utils import VideoInput
 from ..llama.modeling_llama import LlamaRotaryEmbedding
@@ -1080,7 +1080,7 @@ class Qwen3VLForConditionalGeneration(Qwen2_5_VLForConditionalGeneration):
     config: Qwen3VLConfig
     _checkpoint_conversion_mapping = {}
 
-    @check_model_inputs
+    @can_return_tuple
     def forward(
         self,
         input_ids: torch.LongTensor = None,
@@ -1172,6 +1172,8 @@ def forward(
             loss=loss,
             logits=logits,
             past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
             rope_deltas=outputs.rope_deltas,
         )
 

filename: src/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py
patch: @@ -39,7 +39,7 @@
 from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
 from ...processing_utils import Unpack
-from ...utils import TransformersKwargs, auto_docstring
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
 from ...utils.generic import OutputRecorder, check_model_inputs
 from .configuration_qwen3_vl_moe import Qwen3VLMoeConfig, Qwen3VLMoeTextConfig, Qwen3VLMoeVisionConfig
 
@@ -1507,7 +1507,7 @@ def get_video_features(
     def get_image_features(self, pixel_values: torch.FloatTensor, image_grid_thw: Optional[torch.LongTensor] = None):
         return self.model.get_image_features(pixel_values, image_grid_thw)
 
-    @check_model_inputs
+    @can_return_tuple
     def forward(
         self,
         input_ids: torch.LongTensor = None,
@@ -1617,6 +1617,8 @@ def forward(
             aux_loss=aux_loss,
             logits=logits,
             past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
             rope_deltas=outputs.rope_deltas,
         )
 

filename: src/transformers/models/qwen3_vl_moe/modular_qwen3_vl_moe.py
patch: @@ -26,7 +26,7 @@
 from ...modeling_rope_utils import RopeParameters
 from ...modeling_utils import PreTrainedModel
 from ...processing_utils import Unpack
-from ...utils import TransformersKwargs, logging
+from ...utils import TransformersKwargs, can_return_tuple, logging
 from ..qwen3_moe.modeling_qwen3_moe import (
     Qwen3MoeDecoderLayer,
     Qwen3MoePreTrainedModel,
@@ -387,6 +387,7 @@ class Qwen3VLMoeModel(Qwen3VLModel):
 
 
 class Qwen3VLMoeForConditionalGeneration(Qwen3VLForConditionalGeneration):
+    @can_return_tuple
     def forward(
         self,
         input_ids: torch.LongTensor = None,
@@ -496,6 +497,8 @@ def forward(
             aux_loss=aux_loss,
             logits=logits,
             past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
             rope_deltas=outputs.rope_deltas,
         )