Output None as attention when layer is skipped (huggingface#30597)

* Output `None` as attention when layer is skipped

* Add test for output_attentions

src/transformers/models/wavlm/modeling_wavlm.py

@@ -727,7 +727,7 @@ def forward(
                 hidden_states, position_bias = layer_outputs[:2]
 
             if skip_the_layer:
-                layer_outputs = (None, None)
+                layer_outputs = (None, None, None)
 
             if output_attentions:
                 all_self_attentions = all_self_attentions + (layer_outputs[2],)
@@ -810,7 +810,7 @@ def forward(
                 hidden_states, position_bias = layer_outputs[:2]
 
             if skip_the_layer:
-                layer_outputs = (None, None)
+                layer_outputs = (None, None, None)
 
             if output_attentions:
                 all_self_attentions = all_self_attentions + (layer_outputs[2],)

tests/models/wavlm/test_modeling_wavlm.py

@@ -288,6 +288,15 @@ def check_seq_classifier_training(self, config, input_values, *args):
 
         loss.backward()
 
+    def check_output_attentions(self, config, input_values, attention_mask):
+        model = WavLMModel(config=config)
+        model.config.layerdrop = 1.0
+        model.to(torch_device)
+        model.train()
+
+        outputs = model(input_values, attention_mask=attention_mask, output_attentions=True)
+        self.parent.assertTrue(len(outputs.attentions) > 0)
+
     def check_labels_out_of_vocab(self, config, input_values, *args):
         model = WavLMForCTC(config)
         model.to(torch_device)
@@ -354,6 +363,10 @@ def test_seq_classifier_train(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_seq_classifier_training(*config_and_inputs)
 
+    def test_output_attentions(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.check_output_attentions(*config_and_inputs)
+
     def test_labels_out_of_vocab(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_labels_out_of_vocab(*config_and_inputs)