[tests] make 2 tests device-agnostic (#30008)

add torch device

tests/models/blip_2/test_modeling_blip_2.py

@@ -992,7 +992,7 @@ class Blip2ModelIntegrationTest(unittest.TestCase):
 
         # prepare image
         image = prepare_img()
-        inputs = processor(images=image, return_tensors="pt").to(0, dtype=torch.float16)
+        inputs = processor(images=image, return_tensors="pt").to(f"{torch_device}:0", dtype=torch.float16)
 
         predictions = model.generate(**inputs)
         generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()
@@ -1003,7 +1003,7 @@ class Blip2ModelIntegrationTest(unittest.TestCase):
 
         # image and context
         prompt = "Question: which city is this? Answer:"
-        inputs = processor(images=image, text=prompt, return_tensors="pt").to(0, dtype=torch.float16)
+        inputs = processor(images=image, text=prompt, return_tensors="pt").to(f"{torch_device}:0", dtype=torch.float16)
 
         predictions = model.generate(**inputs)
         generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()

tests/test_modeling_utils.py

@@ -776,7 +776,7 @@ class ModelUtilsTest(TestCasePlus):
 
         tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
         inputs = tokenizer("Hello, my name is", return_tensors="pt")
-        output = model.generate(inputs["input_ids"].to(0))
+        output = model.generate(inputs["input_ids"].to(f"{torch_device}:0"))
 
         text_output = tokenizer.decode(output[0].tolist())
         self.assertEqual(text_output, "Hello, my name is John. I'm a writer, and I'm a writer. I'm")