diff --git a/test/test_network_ops/test_upsample_linear1d_backward.py b/test/test_network_ops/test_upsample_linear1d_backward.py
index 4cbfa26a31fd57e62824e18504af0527a0ec92d6..ff7531669ab1b14161eeccd5dc04544598e95b73 100644
--- a/test/test_network_ops/test_upsample_linear1d_backward.py
+++ b/test/test_network_ops/test_upsample_linear1d_backward.py
@@ -51,8 +51,8 @@ class TestUpsampleLinear1DBackward(TestCase):
         output = output.to("cpu")
         return output.detach().numpy(), gradnpu.detach().numpy()
 
-    def test_upsample_linear1d_backward(self, device="npu"):
-        for item in self.creat_shape_format(device):
+    def test_upsample_linear1d_backward(self):
+        for item in self.creat_shape_format():
             cpu_input, npu_input = create_common_tensor(item, 0, 100)
 
             size = list(item[2])
diff --git a/test/test_network_ops/test_var.py b/test/test_network_ops/test_var.py
index 6ef4ffd19384601ae3f89a280c23b830d11ae7a7..12482c7dc92d68210b8185996e308f335d949d22 100644
--- a/test/test_network_ops/test_var.py
+++ b/test/test_network_ops/test_var.py
@@ -143,7 +143,7 @@ class TestVar(TestCase):
             npu_input = npu_input.npu_format_cast(npuformat)
         return cpu_input, npu_input
         
-    def test_var_shape_format_fp16(self, device="npu"):
+    def test_var_shape_format_fp16(self):
         format_list = [-1]
         shape_list = [[32, 24], [32, 8, 24]]
         unbiased_list = [True, False]
@@ -158,7 +158,7 @@ class TestVar(TestCase):
             npu_output = self.npu_op_exec(npu_input, item[3])
             self.assertRtolEqual(cpu_output, npu_output)
 
-    def test_var_shape_format_fp32(self, device="npu"):
+    def test_var_shape_format_fp32(self):
         format_list = [-1]
         shape_list = [[32, 24], [32, 8, 24]]
         unbiased_list = [True, False]
@@ -171,7 +171,7 @@ class TestVar(TestCase):
             npu_output = self.npu_op_exec(npu_input, item[3])
             self.assertRtolEqual(cpu_output, npu_output)
 
-    def test_var_dim_shape_format_fp16(self, device="npu"):
+    def test_var_dim_shape_format_fp16(self):
         format_list = [-1]
         shape_list = [[32, 24], [32, 8, 24]]
         dim_list = [0]
@@ -189,7 +189,7 @@ class TestVar(TestCase):
             npu_output = self.npu_op_dim_exec(npu_input, item[3], item[4], item[5])
             self.assertRtolEqual(cpu_output, npu_output)
 
-    def test_var_dim_shape_format_fp32(self, device="npu"):
+    def test_var_dim_shape_format_fp32(self):
         format_list = [-1]
         shape_list = [[32, 24], [32, 8, 24]]
         dim_list = [0]
@@ -205,7 +205,7 @@ class TestVar(TestCase):
             npu_output = self.npu_op_dim_exec(npu_input, item[3], item[4], item[5])
             self.assertRtolEqual(cpu_output, npu_output)
     
-    def test_var_names_dim_shape_format_fp16(self, device="npu"):
+    def test_var_names_dim_shape_format_fp16(self):
         format_list = [-1]
         shape_list1 = [[32, 24], [32, 8, 24]]
         dim_list = [0]
@@ -223,7 +223,7 @@ class TestVar(TestCase):
             npu_output = self.npu_op_names_dim_exec(npu_input, item[3], item[4], item[5])       
             self.assertRtolEqual(cpu_output, npu_output)
 
-    def test_var_names_dim_shape_format_fp32(self, device="npu"):
+    def test_var_names_dim_shape_format_fp32(self):
         format_list = [-1]
         shape_list1 = [[32, 24], [32, 8, 24]]
         dim_list = [0]
@@ -240,7 +240,7 @@ class TestVar(TestCase):
             self.assertRtolEqual(cpu_output, npu_output)
 
 
-    def test_var_out_shape_format_fp16(self, device="npu"):
+    def test_var_out_shape_format_fp16(self):
         format_list1 = [-1]
         shape_list = [[32, 24], [32, 8, 24]]
         dim_list = [0]
@@ -261,7 +261,7 @@ class TestVar(TestCase):
             cpu_output1 = cpu_output1.astype(np.float16)
             self.assertRtolEqual(cpu_output1, npu_output1)
 
-    def test_var_out_shape_format_fp32(self, device="npu"):
+    def test_var_out_shape_format_fp32(self):
         format_list1 = [-1]
         shape_list = [[32, 24], [32, 8, 24]]
         dim_list = [0]
@@ -279,7 +279,7 @@ class TestVar(TestCase):
             npu_output1 = self.npu_op_out_exec(npu_input1, item[3], npu_output, item[4], item[5])
             self.assertRtolEqual(cpu_output1, npu_output1)
 
-    def test__var_shape_format_fp16(self, device="npu"):
+    def test__var_shape_format_fp16(self):
         format_list = [-1]
         shape_list = [[32, 24], [32, 8, 24]]
         unbiased_list = [True, False]
@@ -295,7 +295,7 @@ class TestVar(TestCase):
             npu_output = self.npu_op_var_exec(npu_input, item[3])
             self.assertRtolEqual(cpu_output, npu_output)
 
-    def test__var_shape_format_fp32(self,device):
+    def test__var_shape_format_fp32(self):
         format_list = [-1]
         shape_list = [[32, 24], [32, 8, 24]]
         unbiased_list = [True, False]
@@ -309,7 +309,7 @@ class TestVar(TestCase):
             npu_output = self.npu_op_var_exec(npu_input, item[3])
             self.assertRtolEqual(cpu_output, npu_output)
 
-    def test_var_mean_shape_format_fp16(self, device="npu"):
+    def test_var_mean_shape_format_fp16(self):
         format_list = [-1]
         shape_list = [[32, 24], [32, 8, 24]]
         unbiased_list = [True, False]
@@ -327,7 +327,7 @@ class TestVar(TestCase):
             self.assertRtolEqual(cpu_output1, npu_output1)
             self.assertRtolEqual(cpu_output2, npu_output2)
 
-    def test_var_mean_shape_format_fp32(self, device="npu"):
+    def test_var_mean_shape_format_fp32(self):
         format_list = [-1]
         shape_list = [[32, 24], [32, 8, 24]]
         unbiased_list = [True, False]
@@ -342,7 +342,7 @@ class TestVar(TestCase):
             self.assertRtolEqual(cpu_output1, npu_output1)
             self.assertRtolEqual(cpu_output2, npu_output2)
 
-    def test_var_mean_dim_shape_format_fp16(self, device="npu"):
+    def test_var_mean_dim_shape_format_fp16(self):
         format_list1 = [-1]
         shape_list1 = [[32, 24], [32, 8, 24]]
         dim_list = [0]
@@ -362,7 +362,7 @@ class TestVar(TestCase):
             self.assertRtolEqual(cpu_output1, npu_output1)
             self.assertRtolEqual(cpu_output2, npu_output2)
 
-    def test_var_mean_dim_shape_format_fp32(self, device="npu"):
+    def test_var_mean_dim_shape_format_fp32(self):
         format_list = [-1]
         shape_list = [[32, 1024], [32, 8, 1024]]
         dim_list = [0]
@@ -379,7 +379,7 @@ class TestVar(TestCase):
             self.assertRtolEqual(cpu_output1, npu_output1)
             self.assertRtolEqual(cpu_output2, npu_output2)
 
-    def test_var_mean_names_dim_shape_format_fp16(self, device="npu"):
+    def test_var_mean_names_dim_shape_format_fp16(self):
         shape = (1024, 8, 32)
         dimlist = ['N', 'C', 'H']
         cpu_input = torch.rand(shape, dtype=torch.float32)
@@ -395,7 +395,7 @@ class TestVar(TestCase):
         self.assertRtolEqual(cpu_output1, npu_output1)
         self.assertRtolEqual(cpu_output2, npu_output2)
 
-    def test_var_mean_names_dim_shape_format_fp32(self, device="npu"):
+    def test_var_mean_names_dim_shape_format_fp32(self):
         shape = (1024, 8, 32)
         dimlist = ['N', 'C', 'H']
         cpu_input = torch.rand(shape, dtype=torch.float32, names=('N', 'C', 'H'))
@@ -407,7 +407,7 @@ class TestVar(TestCase):
         self.assertRtolEqual(cpu_output1, npu_output1)
         self.assertRtolEqual(cpu_output2, npu_output2)
 
-    def test_var_dim_shape_format_5d_fp16(self, device="npu"):
+    def test_var_dim_shape_format_5d_fp16(self):
         format_list = [-1]
         shape_list = [[2, 94, 4, 52, 192]]
         dim_list = [0]
diff --git a/torch_npu/csrc/aten/ops/UpsampleLinear1dBackwardKernelNpu.cpp b/torch_npu/csrc/aten/ops/UpsampleLinear1dBackwardKernelNpu.cpp
index fdd5f5465dd29bcf5b167558b28fc376ab89402f..aed4a194d6c14bc77f94bd38c847e121cd1d2418 100644
--- a/torch_npu/csrc/aten/ops/UpsampleLinear1dBackwardKernelNpu.cpp
+++ b/torch_npu/csrc/aten/ops/UpsampleLinear1dBackwardKernelNpu.cpp
@@ -119,5 +119,6 @@ at::Tensor NPUNativeFunctions::upsample_linear1d_backward(
 
   return result;
 }
+
 } // namespace native
 } // namespace at_npu
\ No newline at end of file
diff --git a/torch_npu/csrc/aten/ops/UpsampleLinear1dKernelNpu.cpp b/torch_npu/csrc/aten/ops/UpsampleLinear1dKernelNpu.cpp
index e4d7aac4552fcdde8603ea1b35f8ff61a10fb531..bdd6d8adcd41420015786d39750289aa9e6336c7 100644
--- a/torch_npu/csrc/aten/ops/UpsampleLinear1dKernelNpu.cpp
+++ b/torch_npu/csrc/aten/ops/UpsampleLinear1dKernelNpu.cpp
@@ -105,7 +105,7 @@ at::Tensor& NPUNativeFunctions::upsample_linear1d_out(
 
   if (!NpuUtils::check_match(&result)) {
     at::Tensor contiguousResult = NpuUtils::format_contiguous(result);
-    at::Tensor newResult = upsample_linear1d_out_nocheck(self, output_size, align_corners, scales, result);
+    at::Tensor newResult = upsample_linear1d_out_nocheck(self, output_size, align_corners, scales, contiguousResult);
     NpuUtils::format_fresh_view(result, newResult);
   } else {
     upsample_linear1d_out_nocheck(self, output_size, align_corners, scales, result);
diff --git a/torch_npu/csrc/aten/ops/VarKernelNpu.cpp b/torch_npu/csrc/aten/ops/VarKernelNpu.cpp
index 2c3302aa7ae3e6bfc444d8cc31766482fb62a654..9c93fb023c87009e351f6ba0f56386b736d3eccb 100644
--- a/torch_npu/csrc/aten/ops/VarKernelNpu.cpp
+++ b/torch_npu/csrc/aten/ops/VarKernelNpu.cpp
@@ -221,5 +221,6 @@ tuple<at::Tensor, at::Tensor> NPUNativeFunctions::var_mean(const at::Tensor& sel
 
   return NPUNativeFunctions::var_mean(self, dim, unbiased, false);
 }
+
 } // namespace native
 } // namespace at_npu
\ No newline at end of file