switch to device agnostic device calling for test cases (#38247)

* use device agnostic APIs in test cases

Signed-off-by: Matrix Yao <matrix.yao@intel.com>

* fix style

Signed-off-by: Matrix Yao <matrix.yao@intel.com>

* add one more

Signed-off-by: YAO Matrix <matrix.yao@intel.com>

* xpu now supports integer device id, aligning to CUDA behaviors

Signed-off-by: Matrix Yao <matrix.yao@intel.com>

* update to use device_properties

Signed-off-by: Matrix Yao <matrix.yao@intel.com>

* fix style

Signed-off-by: Matrix Yao <matrix.yao@intel.com>

* update comment

Signed-off-by: Matrix Yao <matrix.yao@intel.com>

* fix comments

Signed-off-by: Matrix Yao <matrix.yao@intel.com>

* fix style

Signed-off-by: Matrix Yao <matrix.yao@intel.com>

---------

Signed-off-by: Matrix Yao <matrix.yao@intel.com>
Signed-off-by: YAO Matrix <matrix.yao@intel.com>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

tests/models/helium/test_modeling_helium.py

@@ -79,15 +79,6 @@ class HeliumModelTest(GemmaModelTest, unittest.TestCase):
 # @require_torch_gpu
 class HeliumIntegrationTest(unittest.TestCase):
     input_text = ["Hello, today is a great day to"]
-    # This variable is used to determine which CUDA device are we using for our runners (A10 or T4)
-    # Depending on the hardware we get different logits / generations
-    cuda_compute_capability_major_version = None
-
-    @classmethod
-    def setUpClass(cls):
-        if is_torch_available() and torch.cuda.is_available():
-            # 8 is for A100 / A10 and 7 for T4
-            cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0]
 
     @require_read_token
     def test_model_2b(self):