Add processor and intergration test for qwen3vl

src/transformers/models/qwen3_vl/modeling_qwen3_vl.py

@@ -37,7 +37,7 @@
 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, can_return_tuple, is_torchdynamo_compiling
+from ...utils import TransformersKwargs, auto_docstring, is_torchdynamo_compiling
 from ...utils.deprecation import deprecate_kwarg
 from ...utils.generic import check_model_inputs
 from .configuration_qwen3_vl import Qwen3VLConfig, Qwen3VLTextConfig, Qwen3VLVisionConfig
@@ -1104,7 +1104,7 @@ def get_placeholder_mask(
         return special_image_mask, special_video_mask
 
     @auto_docstring
-    @can_return_tuple
+    @check_model_inputs
     def forward(
         self,
         input_ids: torch.LongTensor = None,
@@ -1235,8 +1235,6 @@ def forward(
         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,
         )
 
@@ -1313,8 +1311,7 @@ def language_model(self):
     def visual(self):
         return self.model.visual
 
-    @can_return_tuple
-    @auto_docstring
+    @check_model_inputs
     def forward(
         self,
         input_ids: torch.LongTensor = None,
@@ -1372,8 +1369,6 @@ 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,
         )
 

src/transformers/models/qwen3_vl/modular_qwen3_vl.py

@@ -33,7 +33,7 @@
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
 from ...processing_utils import ProcessingKwargs, Unpack, VideosKwargs
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
-from ...utils import auto_docstring, can_return_tuple, is_torchdynamo_compiling, logging
+from ...utils import auto_docstring, is_torchdynamo_compiling, logging
 from ...utils.generic import check_model_inputs
 from ...video_utils import VideoInput
 from ..qwen2_5_vl.modeling_qwen2_5_vl import (
@@ -1006,7 +1006,7 @@ def get_video_features(
         return self.get_image_features(pixel_values_videos, video_grid_thw)
 
     @auto_docstring
-    @can_return_tuple
+    @check_model_inputs
     def forward(
         self,
         input_ids: torch.LongTensor = None,
@@ -1137,8 +1137,6 @@ def forward(
         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,
         )
 
@@ -1151,6 +1149,7 @@ class Qwen3VLForConditionalGeneration(Qwen2_5_VLForConditionalGeneration):
     config: Qwen3VLConfig
     _checkpoint_conversion_mapping = {}
 
+    @check_model_inputs
     def forward(
         self,
         input_ids: torch.LongTensor = None,
@@ -1208,8 +1207,6 @@ 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,
         )
 

src/transformers/models/qwen3_vl_moe/configuration_qwen3_vl_moe.py

@@ -80,6 +80,8 @@ class Qwen3VLMoeTextConfig(PretrainedConfig):
             Number of routed experts.
         norm_topk_prob (`bool`, *optional*, defaults to `True`):
             Whether to normalize the topk probabilities.
+        router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
+            The aux loss factor for the total loss.
         mlp_only_layers (`List[int]`, *optional*, defaults to `[]`):
             Indicate which layers use Qwen3VLMoeMLP rather than Qwen3VLMoeSparseMoeBlock
             The list contains layer index, from 0 to num_layers-1 if we have num_layers layers
@@ -178,6 +180,7 @@ def __init__(
         num_experts_per_tok=4,
         num_experts=60,
         norm_topk_prob=True,
+        router_aux_loss_coef=0.001,
         mlp_only_layers=None,
         rope_scaling=None,
         head_dim=None,
@@ -213,6 +216,7 @@ def __init__(
         self.num_experts_per_tok = num_experts_per_tok
         self.num_experts = num_experts
         self.norm_topk_prob = norm_topk_prob
+        self.router_aux_loss_coef = router_aux_loss_coef
         self.mlp_only_layers = [] if mlp_only_layers is None else mlp_only_layers
 
         super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)