[Whisper test] Fix some failing tests (#33450)

* Fix failing tensor placement in Whisper

* fix long form generation tests

* more return_timestamps=True

* make fixup

* [run_slow] whisper

* [run_slow] whisper

tests/models/whisper/test_modeling_whisper.py

@@ -1683,9 +1683,9 @@ def test_labels_sequence_max_length_correct(self):
             input_features = input_dict["input_features"]
 
             labels_length = config.max_target_positions
-            labels = torch.ones(1, labels_length, dtype=torch.int64)
+            labels = torch.ones(1, labels_length, dtype=torch.int64).to(torch_device)
 
-            model = model_class(config)
+            model = model_class(config).to(torch_device)
             model(input_features=input_features, labels=labels)
 
     def test_labels_sequence_max_length_correct_after_changing_config(self):
@@ -1697,9 +1697,9 @@ def test_labels_sequence_max_length_correct_after_changing_config(self):
             config.max_target_positions += 100
 
             labels_length = config.max_target_positions
-            labels = torch.ones(1, labels_length, dtype=torch.int64)
+            labels = torch.ones(1, labels_length, dtype=torch.int64).to(torch_device)
 
-            model = model_class(config)
+            model = model_class(config).to(torch_device)
             model(input_features=input_features, labels=labels)
 
     def test_labels_sequence_max_length_error(self):
@@ -1709,21 +1709,21 @@ def test_labels_sequence_max_length_error(self):
             input_features = input_dict["input_features"]
 
             labels_length = config.max_target_positions + 1
-            labels = torch.ones(1, labels_length, dtype=torch.int64)
+            labels = torch.ones(1, labels_length, dtype=torch.int64).to(torch_device)
 
-            model = model_class(config)
+            model = model_class(config).to(torch_device)
             with self.assertRaises(ValueError):
                 model(input_features=input_features, labels=labels)
 
     def test_labels_sequence_max_length_error_after_changing_config(self):
         config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
 
         for model_class in self.all_generative_model_classes:
-            model = model_class(config)
+            model = model_class(config).to(torch_device)
             input_features = input_dict["input_features"]
 
             labels_length = config.max_target_positions + 1
-            labels = torch.ones(1, labels_length, dtype=torch.int64)
+            labels = torch.ones(1, labels_length, dtype=torch.int64).to(torch_device)
 
             new_max_length = config.max_target_positions + 100
             model.config.max_length = new_max_length
@@ -2385,7 +2385,9 @@ def test_tiny_token_timestamp_generation_longform(self):
         )
 
         inputs = inputs.to(torch_device)
-        generate_outputs = model.generate(**inputs, return_segments=True, return_token_timestamps=True)
+        generate_outputs = model.generate(
+            **inputs, return_segments=True, return_token_timestamps=True, return_timestamps=True
+        )
 
         token_timestamps_shape = [
             [segment["token_timestamps"].shape for segment in segment_list]
@@ -2550,14 +2552,14 @@ def test_default_multilingual_transcription_long_form(self):
         ).input_features.to(torch_device)
 
         # task defaults to transcribe
-        sequences = model.generate(input_features)
+        sequences = model.generate(input_features, return_timestamps=True)
 
         transcription = processor.batch_decode(sequences)[0]
 
         assert transcription == " मिर्ची में कितने विबिन्द प्रजातियां हैं? मिर्ची में कितने विबिन्द प्रजातियां हैं?"
 
         # set task to translate
-        sequences = model.generate(input_features, task="translate")
+        sequences = model.generate(input_features, task="translate", return_timestamps=True)
         transcription = processor.batch_decode(sequences)[0]
 
         assert (
@@ -3264,6 +3266,7 @@ def test_whisper_empty_longform(self):
             "num_beams": 5,
             "language": "fr",
             "task": "transcribe",
+            "return_timestamps": True,
         }
 
         torch.manual_seed(0)