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/spec_decode/test_extract_hidden_states.py

@@ -27,6 +27,7 @@ from vllm.v1.spec_decode.extract_hidden_states import ExtractHiddenStatesPropose
 from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
 
 model_dir = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+DEVICE_TYPE = current_platform.device_type
 
 
 def _create_proposer(
@@ -51,7 +52,7 @@ def _create_proposer(
         },
     )
 
-    device = current_platform.device_type
+    device = DEVICE_TYPE
     vllm_config = VllmConfig(
         model_config=model_config,
         cache_config=CacheConfig(),
@@ -101,7 +102,7 @@ def test_proposer_initialization_missing_layer_ids():
         },
     )
 
-    device = current_platform.device_type
+    device = DEVICE_TYPE
     vllm_config = VllmConfig(
         model_config=model_config,
         cache_config=CacheConfig(),
@@ -130,7 +131,7 @@ def test_prepare_next_token_ids_padded():
     For each request we either use the sampled token (if valid and not discarded)
     or a backup token from the request state.
     """
-    device = torch.device(current_platform.device_type)
+    device = torch.device(DEVICE_TYPE)
 
     num_requests = 4
     req_ids = [f"req_{i + 1}" for i in range(num_requests)]
@@ -197,7 +198,7 @@ def test_propose():
     2. Return the sampled tokens as "draft" tokens (shape [batch_size, 1])
     3. Cache the hidden states in the model's KV cache
     """
-    device = torch.device(current_platform.device_type)
+    device = torch.device(DEVICE_TYPE)
 
     # Setup test parameters
     batch_size = 2
@@ -273,7 +274,7 @@ def test_propose():
 @pytest.mark.parametrize("num_hidden_layers", [1, 4, 8])
 def test_propose_different_layer_counts(num_hidden_layers):
     """Test that propose works correctly with different numbers of hidden layers."""
-    device = torch.device(current_platform.device_type)
+    device = torch.device(DEVICE_TYPE)
 
     batch_size = 2
     num_tokens = 5