diff --git a/energon/nn/layer/parallel_1d/_operation.py b/energon/nn/layer/parallel_1d/_operation.py
index d6b851e..d5ff005 100644
--- a/energon/nn/layer/parallel_1d/_operation.py
+++ b/energon/nn/layer/parallel_1d/_operation.py
@@ -1,9 +1,12 @@
+import importlib
+
 import torch
 
 try:
-    import fused_mix_prec_layer_norm_cuda
+    # import fused_mix_prec_layer_norm_cuda
+    energon_layer_norm = importlib.import_module("energon_layer_norm")
 except:
-    fused_mix_prec_layer_norm_cuda = None
+    energon_layer_norm = None
 
 
 class FusedLayerNormAffineFunction1D(torch.autograd.Function):
@@ -27,7 +30,7 @@ def forward(ctx, input, weight, bias, normalized_shape, eps):
     input_ = input.contiguous()
     weight_ = weight.contiguous()
     bias_ = bias.contiguous()
-    output, mean, invvar = fused_mix_prec_layer_norm_cuda.forward_affine(
+    output, mean, invvar = energon_layer_norm.forward_affine(
         input_, ctx.normalized_shape, weight_, bias_, ctx.eps)
     ctx.save_for_backward(input_, weight_, bias_, mean, invvar)
     return output
@@ -38,9 +41,9 @@ def backward(ctx, grad_output):
     input_, weight_, bias_, mean, invvar = ctx.saved_tensors
     grad_input = grad_weight = grad_bias = None
     grad_input, grad_weight, grad_bias \
-      = fused_mix_prec_layer_norm_cuda.backward_affine(
+      = energon_layer_norm.backward_affine(
         grad_output.contiguous(), mean, invvar,
         input_, ctx.normalized_shape,
         weight_, bias_, ctx.eps)
 
-    return grad_input, grad_weight, grad_bias, None, None
\ No newline at end of file
+    return grad_input, grad_weight, grad_bias, None, None