Suppress no-adapter warning when adapter_names is given

src/transformers/adapters/model_mixin.py

@@ -440,13 +440,14 @@ def freeze_model(self, freeze=True):
             param.requires_grad = not freeze
         self.model_freezed = freeze
 
-    def pre_transformer_forward(self):
+    def pre_transformer_forward(self, **kwargs):
         """
         This method should be called by every adapter-implementing model at the very beginning of the forward() method.
         """
         # some warnings if we don't use available adapters
-        if not self.active_adapters and self.has_adapters():
-            logger.warning("There are adapters available but none are passed to model.forward")
+        active_adapters = self.active_adapters or kwargs.get("adapter_names", None)
+        if not active_adapters and self.has_adapters():
+            logger.warning("There are adapters available but none are activated for the forward pass.")
 
         self.config.adapters.is_parallelized = False
 

src/transformers/models/bart/modeling_bart.py

@@ -1174,7 +1174,7 @@ def forward(
         )
         use_cache = use_cache if use_cache is not None else self.config.use_cache
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        self.pre_transformer_forward()
+        self.pre_transformer_forward(**kwargs)
 
         if encoder_outputs is None:
             encoder_outputs = self.encoder(
@@ -1714,7 +1714,7 @@ def __init__(self, config):
         self._init_adapter_modules()
 
     def forward(self, *args, **kwargs):
-        self.pre_transformer_forward()
+        self.pre_transformer_forward(**kwargs)
 
         return self.decoder(*args, **kwargs)
 

src/transformers/models/bert/modeling_bert.py

@@ -939,7 +939,7 @@ def forward(
             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
-        self.pre_transformer_forward()
+        self.pre_transformer_forward(**kwargs)
 
         if self.config.is_decoder:
             use_cache = use_cache if use_cache is not None else self.config.use_cache

src/transformers/models/distilbert/modeling_distilbert.py

@@ -483,7 +483,7 @@ def forward(
             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
-        self.pre_transformer_forward()
+        self.pre_transformer_forward(**kwargs)
 
         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")

src/transformers/models/gpt2/modeling_gpt2.py

@@ -627,7 +627,7 @@ def forward(
         return_dict=None,
         **kwargs
     ):
-        self.pre_transformer_forward()
+        self.pre_transformer_forward(**kwargs)
 
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (

src/transformers/models/mbart/modeling_mbart.py

@@ -1182,7 +1182,7 @@ def forward(
         )
         use_cache = use_cache if use_cache is not None else self.config.use_cache
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        self.pre_transformer_forward()
+        self.pre_transformer_forward(**kwargs)
 
         # different to other models, MBart automatically creates decoder_input_ids from
         # input_ids if no decoder_input_ids are provided
@@ -1726,7 +1726,7 @@ def __init__(self, config):
         self._init_adapter_modules()
 
     def forward(self, *args, **kwargs):
-        self.pre_transformer_forward()
+        self.pre_transformer_forward(**kwargs)
 
         return self.decoder(*args, **kwargs)
 

src/transformers/models/roberta/modeling_roberta.py

@@ -782,7 +782,7 @@ def forward(
             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
-        self.pre_transformer_forward()
+        self.pre_transformer_forward(**kwargs)
 
         if self.config.is_decoder:
             use_cache = use_cache if use_cache is not None else self.config.use_cache