diff --git a/paddle/fluid/operators/layer_norm_op.cu b/paddle/fluid/operators/layer_norm_op.cu
old mode 100755
new mode 100644
index 25c722358c4e326897cef98be2b62e5071959cf9..0410e0511586070add58a1246ea4ebf20e379649
--- a/paddle/fluid/operators/layer_norm_op.cu
+++ b/paddle/fluid/operators/layer_norm_op.cu
@@ -399,9 +399,9 @@ __global__ void LayerNormBackwardComputeGradInput(
     const U *__restrict__ mean, const U *__restrict__ var, const float epsilon,
     const U *gamma, T *grad_input) {
 #ifdef __HIPCC__
-  for (auto i1 = hipBlockIdx_y; i1 < n1; i1 += hipGridDim_y) {
+  for (auto i1 = hipBlockIdx_x; i1 < n1; i1 += hipGridDim_x) {
 #else
-  for (auto i1 = blockIdx.y; i1 < n1; i1 += gridDim.y) {
+  for (auto i1 = blockIdx.x; i1 < n1; i1 += gridDim.x) {
 #endif
     U sum_loss1 = U(0);
     U sum_loss2 = U(0);
@@ -867,9 +867,8 @@ static void LayerNormBackward(const T *x, const T *d_y, const U *scale,
       constexpr int BDIMX1 = 32;
       constexpr int BDIMY1 = 4;
       dim3 threads1(BDIMX1, BDIMY1, 1);
-      const dim3 blocks1(1, batch_size, 1);
       LayerNormBackwardComputeGradInput<
-          T, U, BDIMX1, BDIMY1><<<blocks1, threads1, 0, stream>>>(
+          T, U, BDIMX1, BDIMY1><<<batch_size, threads1, 0, stream>>>(
           d_y, x, batch_size, feature_size, mean, var, epsilon, scale, d_x);
       break;
     }