Enable granite speech 3.3 tests (#37560)

* Enable granite speech 3.3 tests

* skip sdpa test for granite speech

* Explicitly move model to device

* Use granite speech 2b in tests

---------

Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>

tests/models/granite_speech/test_modeling_granite_speech.py

@@ -33,6 +33,7 @@ from transformers.testing_utils import (
 )
 from transformers.utils import (
     is_datasets_available,
+    is_peft_available,
     is_torch_available,
 )
 
@@ -306,11 +307,17 @@ class GraniteSpeechForConditionalGenerationModelTest(ModelTesterMixin, Generatio
                     if "SdpaAttention" in class_name or "SdpaSelfAttention" in class_name:
                         raise ValueError("The eager model should not have SDPA attention layers")
 
+    @pytest.mark.generate
+    @require_torch_sdpa
+    @slow
+    @unittest.skip(reason="Granite Speech doesn't support SDPA for all backbones")
+    def test_eager_matches_sdpa_generate(self):
+        pass
+
 
 class GraniteSpeechForConditionalGenerationIntegrationTest(unittest.TestCase):
     def setUp(self):
-        # TODO - use the actual model path on HF hub after release.
+        self.model_path = "ibm-granite/granite-speech-3.3-2b"
-        self.model_path = "ibm-granite/granite-speech"
         self.processor = AutoProcessor.from_pretrained(self.model_path)
         self.prompt = self._get_prompt(self.processor.tokenizer)
 
@@ -338,7 +345,7 @@ class GraniteSpeechForConditionalGenerationIntegrationTest(unittest.TestCase):
         return [x["array"] for x in speech_samples]
 
     @slow
-    @pytest.mark.skip("Public models not yet available")
+    @pytest.mark.skipif(not is_peft_available(), reason="Outputs diverge without lora")
     def test_small_model_integration_test_single(self):
         model = GraniteSpeechForConditionalGeneration.from_pretrained(self.model_path).to(torch_device)
         input_speech = self._load_datasamples(1)
@@ -364,9 +371,9 @@ class GraniteSpeechForConditionalGenerationIntegrationTest(unittest.TestCase):
         )
 
     @slow
-    @pytest.mark.skip("Public models not yet available")
+    @pytest.mark.skipif(not is_peft_available(), reason="Outputs diverge without lora")
     def test_small_model_integration_test_batch(self):
-        model = GraniteSpeechForConditionalGeneration.from_pretrained(self.model_path)
+        model = GraniteSpeechForConditionalGeneration.from_pretrained(self.model_path).to(torch_device)
         input_speech = self._load_datasamples(2)
         prompts = [self.prompt, self.prompt]
 
@@ -384,7 +391,7 @@ class GraniteSpeechForConditionalGenerationIntegrationTest(unittest.TestCase):
 
         EXPECTED_DECODED_TEXT = [
             "systemKnowledge Cutoff Date: April 2024.\nToday's Date: December 19, 2024.\nYou are Granite, developed by IBM. You are a helpful AI assistant\nusercan you transcribe the speech into a written format?\nassistantmister quilter is the apostle of the middle classes and we are glad to welcome his gospel",
-            "systemKnowledge Cutoff Date: April 2024.\nToday's Date: December 19, 2024.\nYou are Granite, developed by IBM. You are a helpful AI assistant\nusercan you transcribe the speech into a written format?\nassistantnor is mister quilter's manner less interesting than his matter"
+            "systemKnowledge Cutoff Date: April 2024.\nToday's Date: December 19, 2024.\nYou are Granite, developed by IBM. You are a helpful AI assistant\nusercan you transcribe the speech into a written format?\nassistantnor is mister quilp's manner less interesting than his matter"
         ]  # fmt: skip
 
         self.assertEqual(

tests/models/granite_speech/test_processor_granite_speech.py

@@ -33,14 +33,12 @@ if is_torchaudio_available():
     from transformers import GraniteSpeechFeatureExtractor, GraniteSpeechProcessor
 
 
-@pytest.skip("Public models not yet available", allow_module_level=True)
 @require_torch
 @require_torchaudio
 class GraniteSpeechProcessorTest(unittest.TestCase):
     def setUp(self):
         self.tmpdirname = tempfile.mkdtemp()
-        # TODO - use the actual model path on HF hub after release.
+        self.checkpoint = "ibm-granite/granite-speech-3.3-8b"
-        self.checkpoint = "ibm-granite/granite-speech"
         processor = GraniteSpeechProcessor.from_pretrained(self.checkpoint)
         processor.save_pretrained(self.tmpdirname)