refactor hard coded device string in test files under tests/v1 and tests/lora (#37566)

Signed-off-by: Liao, Wei <wei.liao@intel.com>

tests/v1/determinism/test_rms_norm_batch_invariant.py

@@ -13,6 +13,9 @@ from utils import skip_unsupported
 
 from vllm.model_executor.layers.batch_invariant import rms_norm as triton_rms_norm
 from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.platforms import current_platform
+
+DEVICE_TYPE = current_platform.device_type
 
 
 @skip_unsupported
@@ -34,7 +37,7 @@ def test_rms_norm_batch_invariant_vs_standard(
     equivalent results to the standard CUDA implementation across various
     configurations.
     """
-    device = torch.device("cuda")
+    device = torch.device(DEVICE_TYPE)
 
     # Create test input and weight
     torch.manual_seed(42)
@@ -81,7 +84,7 @@ def test_rms_norm_3d_input(
     Ensures that the batch-invariant RMS norm correctly handles multi-dimensional
     inputs that are common in transformer models.
     """
-    device = torch.device("cuda")
+    device = torch.device(DEVICE_TYPE)
     dtype = torch.bfloat16
     eps = 1e-6
 
@@ -120,7 +123,7 @@ def test_rms_norm_numerical_stability(default_vllm_config):
     Ensures that both implementations handle edge cases like very small or large
     values without producing NaN or Inf.
     """
-    device = torch.device("cuda")
+    device = torch.device(DEVICE_TYPE)
     dtype = torch.float16
     eps = 1e-6
     hidden_size = 2048
@@ -179,7 +182,7 @@ def test_rms_norm_formula(default_vllm_config):
 
     Verifies: output = input / sqrt(mean(input^2) + eps) * weight
     """
-    device = torch.device("cuda")
+    device = torch.device(DEVICE_TYPE)
     dtype = torch.float32  # Use float32 for higher precision in formula check
     eps = 1e-6
     hidden_size = 1024
@@ -214,7 +217,7 @@ def test_rms_norm_different_hidden_sizes(default_vllm_config, hidden_size: int):
     The Triton kernel uses a fixed BLOCK_SIZE=1024, so this tests that it
     correctly handles hidden sizes both smaller and larger than the block size.
     """
-    device = torch.device("cuda")
+    device = torch.device(DEVICE_TYPE)
     dtype = torch.bfloat16
     eps = 1e-6
     batch_size = 16
@@ -251,7 +254,7 @@ def test_rms_norm_determinism(default_vllm_config):
     Runs the same input through the kernel multiple times and verifies
     identical outputs.
     """
-    device = torch.device("cuda")
+    device = torch.device(DEVICE_TYPE)
     dtype = torch.bfloat16
     eps = 1e-6
     hidden_size = 4096
@@ -283,7 +286,7 @@ if __name__ == "__main__":
     # Run a quick smoke test
     print("Running quick smoke test of RMS norm implementations...")
 
-    device = torch.device("cuda")
+    device = torch.device(DEVICE_TYPE)
     batch_size = 8
     hidden_size = 4096
     dtype = torch.bfloat16