diff --git a/mmcv/ops/csrc/pytorch/npu/focal_loss_npu.cpp b/mmcv/ops/csrc/pytorch/npu/focal_loss_npu.cpp
index da7b347d303..b7c995a223e 100644
--- a/mmcv/ops/csrc/pytorch/npu/focal_loss_npu.cpp
+++ b/mmcv/ops/csrc/pytorch/npu/focal_loss_npu.cpp
@@ -14,8 +14,6 @@ void sigmoid_focal_loss_forward_npu(Tensor input, Tensor target, Tensor weight,
   } else {
     target_y = at::one_hot(target, n_class);
   }
-  // target_y =
-  //     at_npu::native::NPUNativeFunctions::npu_dtype_cast(target_y, at::kInt);
   target_y = target_y.to(at::kInt);
   int64_t weight_size = weight.size(0);
   at::Tensor weight_y = at::ones_like(input);
diff --git a/mmcv/ops/csrc/pytorch/npu/voxelization_npu.cpp b/mmcv/ops/csrc/pytorch/npu/voxelization_npu.cpp
index 2b22646b9eb..32b1a50cc1e 100644
--- a/mmcv/ops/csrc/pytorch/npu/voxelization_npu.cpp
+++ b/mmcv/ops/csrc/pytorch/npu/voxelization_npu.cpp
@@ -19,8 +19,7 @@ int hard_voxelize_forward_npu(const at::Tensor &points, at::Tensor &voxels,
                               const int max_points, const int max_voxels,
                               const int NDim = 3) {
   at::Tensor voxel_num_tmp = OpPreparation::ApplyTensor(points, {1});
-  at::Tensor voxel_num = at_npu::native::NPUNativeFunctions::npu_dtype_cast(
-      voxel_num_tmp, at::kInt);
+  at::Tensor voxel_num = voxel_num_tmp.to(at::kInt);
 
   at::Tensor voxel_size_cpu = at::from_blob(
       const_cast<float *>(voxel_size.data()), {3}, dtype(at::kFloat));