       if(!no_gamma) {
         DType ele_gamma = gamma[l];
         data_grad[bid * nchannel + l] +=
-          ele_out_grad * ele_gamma * invstd_eps - sum_val0 - (ele_x - mean) * invstd_eps * sum_val1
+          ele_out_grad * ele_gamma * invstd_eps - sum_val0 - (ele_x - mean) * invstd_eps * sum_val1;
       } else {
         data_grad[bid * nchannel + l] +=
-          ele_out_grad * invstd_eps - sum_val0 - (ele_x - mean) * invstd_eps * sum_val1
+          ele_out_grad * invstd_eps - sum_val0 - (ele_x - mean) * invstd_eps * sum_val1;
       }
     }
   }

    // Try to use the accelerated CUDA kernels
    return LayerNormGradGPUContig(param, ctx, inputs, req, outputs);
  }
  return LayerNormGradComputeGeneral<gpu>(attrs, ctx, inputs, req, outputs);
}


    // Write to destination
    for(int i = 0; i < LOAD_UNROLL; ++i) {
      if(!no_gamma) {
        gamma_grad[l + i] += local_gamma_grad[i];
      }
      if(!no_beta) {
        beta_grad[l + i] += local_beta_grad[i];
      }
    }
  }
  // Handle the rest elements that cannot be divided by LOAD_UNROLL
  for(; l < nchannel; ++l) {
    local_mean[0] = mean_data[l];
    local_std[0] = std_data[l];
