[VITS] Fix nightly tests

tests/models/vits/test_modeling_vits.py

@@ -27,6 +27,7 @@
     is_flaky,
     is_torch_available,
     require_torch,
+    require_torch_multi_gpu,
     slow,
     torch_device,
 )
@@ -177,6 +178,30 @@ def test_model_forward(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model_forward(*config_and_inputs)
 
+    @require_torch_multi_gpu
+    # override to force all elements of the batch to have the same sequence length across GPUs
+    def test_multi_gpu_data_parallel_forward(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.use_stochastic_duration_prediction = False
+
+        # move input tensors to cuda:O
+        for key, value in inputs_dict.items():
+            if torch.is_tensor(value):
+                # make all elements of the batch the same -> ensures the output seq lengths are the same for DP
+                value[1:] = value[0]
+                inputs_dict[key] = value.to(0)
+
+        for model_class in self.all_model_classes:
+            model = model_class(config=config)
+            model.to(0)
+            model.eval()
+
+            # Wrap model in nn.DataParallel
+            model = torch.nn.DataParallel(model)
+            set_seed(555)
+            with torch.no_grad():
+                _ = model(**self._prepare_for_class(inputs_dict, model_class)).waveform
+
     @unittest.skip("VITS is not deterministic")
     def test_determinism(self):
         pass

tests/models/vits/test_tokenization_vits.py

@@ -174,7 +174,8 @@ def test_tokenizer_integration(self):
         for tokenizer_class in tokenizer_classes:
             tokenizer = tokenizer_class.from_pretrained(
                 "facebook/mms-tts-eng",
-                revision="089bbb15da46b2ab2b282145941399aae353d917",  # to pin the tokenizer version
+                revision="28cedf176aa99de5023a4344fd8a2cc477126fb8",  # to pin the tokenizer version
+                pad_token="<pad>",
             )
 
             encoding = tokenizer(sequences, padding=True, normalize=True)