diff --git a/tests/conftest.py b/tests/conftest.py
index 25e70319e2c..000da74c2db 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -272,7 +272,8 @@ class HfRunner:
     def get_default_device(self):
         from vllm.platforms import current_platform
 
-        return ("cpu" if current_platform.is_cpu() else "cuda")
+        return ("cpu"
+                if current_platform.is_cpu() else current_platform.device_type)
 
     def wrap_device(self, x: _T, device: Optional[str] = None) -> _T:
         if x is None or isinstance(x, (bool, )):
diff --git a/tests/v1/sample/test_sampler.py b/tests/v1/sample/test_sampler.py
index 5f041b44893..24b759bc1fa 100644
--- a/tests/v1/sample/test_sampler.py
+++ b/tests/v1/sample/test_sampler.py
@@ -6,6 +6,7 @@
 import pytest
 import torch
 
+from vllm.platforms import current_platform
 from vllm.utils import make_tensor_with_pad
 from vllm.v1.sample.metadata import SamplingMetadata
 from vllm.v1.sample.sampler import Sampler
@@ -13,7 +14,8 @@
 VOCAB_SIZE = 1024
 NUM_OUTPUT_TOKENS = 20
 CUDA_DEVICES = [
-    f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2)
+    f"{current_platform.device_type}:{i}"
+    for i in range(1 if current_platform.device_count() == 1 else 2)
 ]
 MAX_NUM_PROMPT_TOKENS = 64
 
diff --git a/vllm/worker/multi_step_model_runner.py b/vllm/worker/multi_step_model_runner.py
index 7ddf382079c..d044e18c257 100644
--- a/vllm/worker/multi_step_model_runner.py
+++ b/vllm/worker/multi_step_model_runner.py
@@ -14,6 +14,7 @@
                                                 SamplerOutput,
                                                 SamplingMetadata, get_logprobs,
                                                 get_pythonized_sample_results)
+from vllm.platforms import current_platform
 from vllm.sequence import (CompletionSequenceGroupOutput, IntermediateTensors,
                            Logprob, SequenceGroupMetadata, SequenceOutput)
 from vllm.utils import PyObjectCache, async_tensor_h2d, current_stream
@@ -158,8 +159,8 @@ class StatefulModelInput(BroadcastableModelInput):
     is_first_multi_step: bool = False
     base_output_proc_callback: Optional[Callable] = None
     # ping-pong data structures for multi-step to wait on the previous step
-    step_cuda_events: List[torch.cuda.Event] = field(
-        default_factory=lambda: [torch.cuda.Event(blocking=True)] * 2)
+    step_cuda_events: List[current_platform.Event] = field(
+        default_factory=lambda: [current_platform.Event(blocking=True)] * 2)
     num_seqs: int = -1
     num_queries: int = -1
     num_single_step_prefills: int = 0