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/trainer/test_trainer.py

@@ -3224,7 +3224,7 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
         # For more than 1 GPUs, since the randomness is introduced in the model and with DataParallel (which is used
         # in this test for more than 2 GPUs), the calls to the torch RNG will happen in a random order (sometimes
         # GPU 0 will call first and sometimes GPU 1).
-        random_torch = not torch.cuda.is_available() or torch.cuda.device_count() <= 1
+        random_torch = not torch.cuda.is_available() or backend_device_count(torch_device) <= 1
 
         if torch.cuda.is_available():
             torch.backends.cudnn.deterministic = True