diff --git a/test/infinicore/framework/base.py b/test/infinicore/framework/base.py
index e57e8ec11..477eab2e0 100644
--- a/test/infinicore/framework/base.py
+++ b/test/infinicore/framework/base.py
@@ -3,21 +3,22 @@
 Contains TestConfig, TestRunner, and BaseOperatorTest classes.
 """
 
-import torch
-import infinicore
 import traceback
 from abc import ABC, abstractmethod
 
-from .results import CaseResult
-from .datatypes import to_torch_dtype, to_infinicore_dtype
+import torch
+
+import infinicore
+
+from .benchmark import BenchmarkUtils
 from .devices import InfiniDeviceNames, torch_device_map
-from .tensor import TensorSpec, TensorInitializer
+from .results import CaseResult
+from .tensor import TensorSpec
+from .utils.compare_utils import create_test_comparator
 from .utils.tensor_utils import (
     clone_torch_tensor,
     infinicore_tensor_from_torch,
 )
-from .utils.compare_utils import create_test_comparator
-from .benchmark import BenchmarkUtils
 
 
 class TestConfig:
@@ -96,7 +97,7 @@ def run_tests(self, devices, test_func, test_type="Test"):
                         self.passed_tests.append(
                             f"{test_case} - {InfiniDeviceNames[device]}"
                         )
-                        print(f"\033[92m✓\033[0m Passed")
+                        print("\033[92m✓\033[0m Passed")
                     elif test_result.return_code == -1:
                         # Test failed - use the actual error message from test_result
                         fail_msg = f"{test_case} - {InfiniDeviceNames[device]} - {test_result.error_message}"
@@ -153,7 +154,6 @@ def print_summary(self):
         """
         total_tests = len(self.test_cases)
         passed_count = len(self.passed_tests)
-        skipped_count = len(self.skipped_tests)
         partial_count = len(self.partial_tests)
         failed_count = len(self.failed_tests)
 
@@ -179,10 +179,10 @@ def print_summary(self):
             # If there are skipped or partial tests, show appropriate message
             if self.skipped_tests or self.partial_tests:
                 print(
-                    f"\n\033[93mTests completed with some implementations missing\033[0m"
+                    "\n\033[93mTests completed with some implementations missing\033[0m"
                 )
             else:
-                print(f"\n\033[92mAll tests passed!\033[0m")
+                print("\n\033[92mAll tests passed!\033[0m")
 
         # Print benchmark summary if benchmarking was enabled
         if self.config.bench and (
@@ -448,6 +448,7 @@ def run_test(self, device, test_case, config):
 
         try:
             infini_result = self.infinicore_operator(*infini_inputs, **infini_kwargs)
+            infinicore.sync_device()
             if infini_result is None:
                 infini_implemented = False
         except NotImplementedError as e:
@@ -621,7 +622,7 @@ def run_test(self, device, test_case, config):
 
             is_valid = compare_fn(infini_comparison, torch_comparison)
             if not is_valid:
-                raise AssertionError(f"Result comparison failed.")
+                raise AssertionError("Result comparison failed.")
 
         # ==========================================================================
         # UNIFIED BENCHMARKING LOGIC
diff --git a/test/infinicore/framework/utils/tensor_utils.py b/test/infinicore/framework/utils/tensor_utils.py
index 19467d238..e16896a21 100644
--- a/test/infinicore/framework/utils/tensor_utils.py
+++ b/test/infinicore/framework/utils/tensor_utils.py
@@ -1,5 +1,7 @@
 import torch
+
 import infinicore
+
 from ..datatypes import to_infinicore_dtype, to_torch_dtype
 
 # =================================================================
@@ -32,6 +34,7 @@ def clone_torch_tensor(torch_tensor):
 
 
 def infinicore_tensor_from_torch(torch_tensor):
+    synchronize_device(torch_tensor.device.type)
     infini_device = infinicore.device(torch_tensor.device.type, 0)
     if torch_tensor.is_contiguous():
         return infinicore.from_blob(
@@ -71,6 +74,8 @@ def convert_infinicore_to_torch(infini_result):
         )
     temp_tensor = infinicore_tensor_from_torch(torch_result_from_infini)
     temp_tensor.copy_(infini_result)
+    infinicore.sync_device()
+    synchronize_device(torch_result_from_infini.device.type)
     return torch_result_from_infini