diff --git a/paddle/fluid/operators/CMakeLists.txt b/paddle/fluid/operators/CMakeLists.txt
index 4c3b8ec78190723598a56f7633764f10dd5047f3..b395739809dbd187e36c28b3f609a0a08c839643 100644
--- a/paddle/fluid/operators/CMakeLists.txt
+++ b/paddle/fluid/operators/CMakeLists.txt
@@ -273,9 +273,9 @@ op_library(squeeze_op DEPS reshape_op)
 op_library(extract_rows_op DEPS memory)
 op_library(flatten_op DEPS reshape_op)
 
-
 if (WITH_GPU)
     op_library(conv_op DEPS vol2col depthwise_conv im2col)
+    op_library(layer_norm_op DEPS cub)
 else()
     op_library(conv_op DEPS vol2col im2col)
 endif()
diff --git a/paddle/fluid/operators/layer_norm_op.cu b/paddle/fluid/operators/layer_norm_op.cu
index 6840e1e08f3d5bc84a05f15e30982c7cfb59680b..0886c41a1b582881faf24f5531d414db4e4db71c 100644
--- a/paddle/fluid/operators/layer_norm_op.cu
+++ b/paddle/fluid/operators/layer_norm_op.cu
@@ -1,4 +1,4 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -12,8 +12,512 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
+#include <cub/cub.cuh>
 #include "paddle/fluid/operators/layer_norm_op.h"
 
+namespace paddle {
+namespace operators {
+
+inline static int GetDesiredBlockDim(int block_dim) {
+  const int kMaxBlockDim = 512;
+  return block_dim >= kMaxBlockDim
+             ? kMaxBlockDim
+             : (1 << (static_cast<int>(std::log2f(block_dim))));
+}
+
+#define FIXED_BLOCK_DIM_CASE_BASE(log2_block_dim, ...)  \
+  case (1 << (log2_block_dim)): {                       \
+    constexpr auto kBlockDim = (1 << (log2_block_dim)); \
+    __VA_ARGS__;                                        \
+  } break
+
+#define FIXED_BLOCK_DIM_CASE(...)              \
+  FIXED_BLOCK_DIM_CASE_BASE(9, ##__VA_ARGS__); \
+  FIXED_BLOCK_DIM_CASE_BASE(8, ##__VA_ARGS__); \
+  FIXED_BLOCK_DIM_CASE_BASE(7, ##__VA_ARGS__); \
+  FIXED_BLOCK_DIM_CASE_BASE(6, ##__VA_ARGS__); \
+  FIXED_BLOCK_DIM_CASE_BASE(5, ##__VA_ARGS__); \
+  FIXED_BLOCK_DIM_CASE_BASE(4, ##__VA_ARGS__); \
+  FIXED_BLOCK_DIM_CASE_BASE(3, ##__VA_ARGS__); \
+  FIXED_BLOCK_DIM_CASE_BASE(2, ##__VA_ARGS__); \
+  FIXED_BLOCK_DIM_CASE_BASE(1, ##__VA_ARGS__)
+
+static __device__ __forceinline__ float real_sqrt(float x) { return sqrtf(x); }
+static __device__ __forceinline__ double real_sqrt(double x) { return sqrt(x); }
+
+template <typename T>
+struct PairForLayerNorm {
+  __device__ __forceinline__ PairForLayerNorm() {}
+  __device__ __forceinline__ PairForLayerNorm(const T &first, const T &second)
+      : first_(first), second_(second) {}
+
+  T first_;
+  T second_;
+};
+
+template <typename T>
+struct PairForLayerNormAddFunctor {
+  __device__ __forceinline__ PairForLayerNorm<T> operator()(
+      const PairForLayerNorm<T> &p1, const PairForLayerNorm<T> &p2) {
+    return PairForLayerNorm<T>(p1.first_ + p2.first_, p1.second_ + p2.second_);
+  }
+};
+
+template <typename T, int BlockDim>
+__global__ void LayerNormForward(const T *x, const T *scale, const T *bias,
+                                 T *y, T *mean, T *var, float epsilon,
+                                 int feature_size) {
+  using BlockReduce = cub::BlockReduce<PairForLayerNorm<T>, BlockDim>;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+
+  int beg_idx = blockIdx.x * feature_size + threadIdx.x;
+  int end_idx = (blockIdx.x + 1) * feature_size;
+
+  // Step 1: Reduce to calculate mean and var
+  T mean_val = static_cast<T>(0);
+  T var_val = static_cast<T>(0);
+  for (int i = beg_idx; i < end_idx; i += BlockDim) {
+    T tmp = x[i];
+    mean_val += tmp;
+    var_val += (tmp * tmp);
+  }
+  auto pair = BlockReduce(temp_storage)
+                  .Reduce(PairForLayerNorm<T>(mean_val, var_val),
+                          PairForLayerNormAddFunctor<T>());
+  if (threadIdx.x == 0) {
+    auto tmp = pair.first_ / feature_size;
+    mean[blockIdx.x] = tmp;
+    var[blockIdx.x] = pair.second_ / feature_size - tmp * tmp;
+  }
+  __syncthreads();
+  mean_val = mean[blockIdx.x];
+  var_val = static_cast<T>(real_sqrt(var[blockIdx.x] + epsilon));
+
+  // Step 2: Calculate y
+  if (scale != nullptr) {
+    if (bias != nullptr) {
+      for (int i = beg_idx, j = threadIdx.x; i < end_idx;
+           i += BlockDim, j += BlockDim) {
+        y[i] = scale[j] * (x[i] - mean_val) / var_val + bias[j];
+      }
+    } else {
+      for (int i = beg_idx, j = threadIdx.x; i < end_idx;
+           i += BlockDim, j += BlockDim) {
+        y[i] = scale[j] * (x[i] - mean_val) / var_val;
+      }
+    }
+  } else {  // scale == nullptr
+    if (bias != nullptr) {
+      for (int i = beg_idx, j = threadIdx.x; i < end_idx;
+           i += BlockDim, j += BlockDim) {
+        y[i] = (x[i] - mean_val) / var_val + bias[j];
+      }
+    } else {
+      for (int i = beg_idx, j = threadIdx.x; i < end_idx;
+           i += BlockDim, j += BlockDim) {
+        y[i] = (x[i] - mean_val) / var_val;
+      }
+    }
+  }
+}
+
+// Make sure that d_scale != nullptr && d_bias != nullptr
+// Since d_scale != nullptr, scale would not be nullptr
+template <typename T, int BlockDim, bool HasDx>
+__global__ void LayerNormBackwardGradientAll(const T *x, const T *d_y,
+                                             T *d_scale, T *d_bias, T *d_x,
+                                             const T *mean, const T *var,
+                                             const T *scale, float epsilon,
+                                             int batch_size, int feature_size) {
+  using BlockReduce = cub::BlockReduce<PairForLayerNorm<T>, BlockDim>;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+
+  int beg_idx = threadIdx.x * feature_size + blockIdx.x;
+  int end_idx = batch_size * feature_size + blockIdx.x;
+  int stride = BlockDim * feature_size;
+
+  T d_scale_partial = 0, d_bias_partial = 0;
+
+  for (int i = beg_idx; i < end_idx; i += stride) {
+    int row_idx = i / feature_size;
+    auto var_val = static_cast<T>(real_sqrt(var[row_idx] + epsilon));
+    d_scale_partial += d_y[i] * (x[i] - mean[row_idx]) / var_val;
+    d_bias_partial += d_y[i];
+    if (HasDx) {
+      d_x[i] = d_y[i] * scale[blockIdx.x] / var_val;
+    }
+  }
+
+  auto pair = BlockReduce(temp_storage)
+                  .Reduce(PairForLayerNorm<T>(d_scale_partial, d_bias_partial),
+                          PairForLayerNormAddFunctor<T>());
+
+  if (threadIdx.x == 0) {
+    d_scale[blockIdx.x] = pair.first_;
+    d_bias[blockIdx.x] = pair.second_;
+  }
+}
+
+// Make sure that there is only one true expression: d_scale != nullptr
+// or d_bias != nullptr
+// Notice: scale may be nullptr
+template <typename T, int BlockDim, bool HasDx, bool HasDScale>
+__global__ void LayerNormBackwardGradientScaleOrBias(
+    const T *x, const T *d_y, T *d_scale, T *d_bias, T *d_x, const T *mean,
+    const T *var, const T *scale, float epsilon, int batch_size,
+    int feature_size) {
+  using BlockReduce = cub::BlockReduce<T, BlockDim>;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+  int beg_idx = threadIdx.x * feature_size + blockIdx.x;
+  int end_idx = batch_size * feature_size + blockIdx.x;
+  int stride = BlockDim * feature_size;
+  T d_scale_or_d_bias_partial = 0;
+
+  for (int i = beg_idx; i < end_idx; i += stride) {
+    int row_idx = i / feature_size;
+    auto var_val = static_cast<T>(real_sqrt(var[row_idx] + epsilon));
+    if (HasDScale) {
+      d_scale_or_d_bias_partial += d_y[i] * (x[i] - mean[row_idx]) / var_val;
+    } else {  // d_bias != nullptr
+      d_scale_or_d_bias_partial += d_y[i];
+    }
+
+    if (HasDx) {
+      if (scale != nullptr) {
+        d_x[i] = d_y[i] * scale[blockIdx.x] / var_val;
+      } else {
+        d_x[i] = d_y[i] / var_val;
+      }
+    }
+  }
+
+  d_scale_or_d_bias_partial =
+      BlockReduce(temp_storage).Reduce(d_scale_or_d_bias_partial, cub::Sum());
+
+  if (threadIdx.x == 0) {
+    if (HasDScale) {
+      d_scale[blockIdx.x] = d_scale_or_d_bias_partial;
+    } else {
+      d_bias[blockIdx.x] = d_scale_or_d_bias_partial;
+    }
+  }
+}
+
+template <typename T, int BlockDim>
+__global__ void LayerNormBackwardPostProcessToCalculateDX(const T *x, T *d_x,
+                                                          const T *mean,
+                                                          const T *var,
+                                                          float epsilon,
+                                                          int feature_size) {
+  using BlockReduce = cub::BlockReduce<PairForLayerNorm<T>, BlockDim>;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+  __shared__ T d_x_reduce_tmp[2];
+
+  int beg_idx = blockIdx.x * feature_size + threadIdx.x;
+  int end_idx = (blockIdx.x + 1) * feature_size;
+
+  T block_mean = mean[blockIdx.x];
+  T block_var = var[blockIdx.x];
+  T d_x_mean_partial = 0, d_x_var_partial = 0;
+  for (int i = beg_idx; i < end_idx; i += BlockDim) {
+    d_x_mean_partial += d_x[i];
+    d_x_var_partial += d_x[i] * (x[i] - block_mean);
+  }
+
+  auto pair =
+      BlockReduce(temp_storage)
+          .Reduce(PairForLayerNorm<T>(d_x_mean_partial, d_x_var_partial),
+                  PairForLayerNormAddFunctor<T>());
+
+  if (threadIdx.x == 0) {
+    d_x_reduce_tmp[0] = pair.first_ / feature_size;
+    d_x_reduce_tmp[1] = pair.second_ / (feature_size * (block_var + epsilon));
+  }
+  __syncthreads();
+
+  d_x_mean_partial = d_x_reduce_tmp[0];
+  d_x_var_partial = d_x_reduce_tmp[1];
+  for (int i = beg_idx; i < end_idx; i += BlockDim) {
+    d_x[i] -= d_x_mean_partial;
+    d_x[i] -= (x[i] - block_mean) * d_x_var_partial;
+  }
+}
+
+// Here, we only calculate d_x
+template <typename T, int BlockDim>
+__global__ void LayerNormBackwardGradientOnlyDX(const T *x, const T *d_y,
+                                                T *d_x, const T *mean,
+                                                const T *var, const T *scale,
+                                                float epsilon,
+                                                int feature_size) {
+  using BlockReduce = cub::BlockReduce<PairForLayerNorm<T>, BlockDim>;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+  __shared__ T d_x_reduce_tmp[2];
+
+  int beg_idx = blockIdx.x * feature_size + threadIdx.x;
+  int end_idx = (blockIdx.x + 1) * feature_size;
+
+  T block_mean = mean[blockIdx.x], block_var = var[blockIdx.x];
+  T d_x_mean_partial = 0, d_x_var_partial = 0;
+  for (int i = beg_idx; i < end_idx; i += BlockDim) {
+    auto var_val = static_cast<T>(real_sqrt(block_var + epsilon));
+    if (scale != nullptr) {
+      int col_idx = i % feature_size;
+      d_x[i] = d_y[i] * scale[col_idx] / var_val;
+    } else {
+      d_x[i] = d_y[i] / var_val;
+    }
+    d_x_mean_partial += d_x[i];
+    d_x_var_partial += d_x[i] * (x[i] - block_mean);
+  }
+
+  auto pair =
+      BlockReduce(temp_storage)
+          .Reduce(PairForLayerNorm<T>(d_x_mean_partial, d_x_var_partial),
+                  PairForLayerNormAddFunctor<T>());
+
+  if (threadIdx.x == 0) {
+    d_x_reduce_tmp[0] = pair.first_ / feature_size;
+    d_x_reduce_tmp[1] = pair.second_ / (feature_size * (block_var + epsilon));
+  }
+  __syncthreads();
+
+  d_x_mean_partial = d_x_reduce_tmp[0];
+  d_x_var_partial = d_x_reduce_tmp[1];
+  for (int i = beg_idx; i < end_idx; i += BlockDim) {
+    d_x[i] -= d_x_mean_partial;
+    d_x[i] -= (x[i] - block_mean) * d_x_var_partial;
+  }
+}
+
+template <typename T>
+__global__ void LayerNormBackwardWhenBatchSizeIsOne(
+    const T *x, const T *d_y, T *d_x, T *d_scale, T *d_bias, const T *mean,
+    const T *var, const T *scale, float epsilon, int feature_size) {
+  int idx = threadIdx.x + blockIdx.x * blockDim.x;
+  if (idx < feature_size) {
+    auto var_val = static_cast<T>(real_sqrt(var[idx] + epsilon));
+    if (d_x != nullptr) {
+      if (d_scale == nullptr) {
+        d_x[idx] = d_y[idx] / var_val;
+      } else {
+        d_x[idx] = d_y[idx] * scale[idx] / var_val;
+      }
+    }
+
+    if (d_scale != nullptr) {
+      d_scale[idx] = d_y[idx] * (x[idx] - mean[idx]) / var_val;
+    }
+
+    if (d_bias != nullptr) d_bias[idx] = d_y[idx];
+  }
+}
+
+template <typename T>
+static void LayerNormBackward(const T *x, const T *d_y, const T *scale,
+                              const T *mean, const T *var, T *d_x, T *d_scale,
+                              T *d_bias, float epsilon, int batch_size,
+                              int feature_size, cudaStream_t stream) {
+  const int kMaxBlockDim = 512;
+  int gradient_flag = ((d_x != nullptr ? 1 : 0) << 2) |
+                      ((d_scale != nullptr ? 1 : 0) << 1) |
+                      ((d_bias != nullptr ? 1 : 0));
+  if (gradient_flag == 0) return;
+
+  if (batch_size == 1) {
+    LayerNormBackwardWhenBatchSizeIsOne<
+        T><<<(feature_size + kMaxBlockDim - 1) / kMaxBlockDim, kMaxBlockDim, 0,
+             stream>>>(x, d_y, d_x, d_scale, d_bias, mean, var, scale, epsilon,
+                       feature_size);
+
+    if (d_x != nullptr) {
+      switch (GetDesiredBlockDim(feature_size)) {
+        FIXED_BLOCK_DIM_CASE(LayerNormBackwardPostProcessToCalculateDX<
+                             T, kBlockDim><<<1, kBlockDim, 0, stream>>>(
+            x, d_x, mean, var, epsilon, feature_size));
+      }
+    }
+    return;
+  }
+
+  auto block_dim = GetDesiredBlockDim(batch_size);
+  switch (gradient_flag) {
+    case 1:  // d_x == nulptr, d_scale == nullptr, d_bias != nullptr
+      switch (block_dim) {
+        FIXED_BLOCK_DIM_CASE(LayerNormBackwardGradientScaleOrBias<
+                             T, kBlockDim, false,
+                             false><<<feature_size, kBlockDim, 0, stream>>>(
+            x, d_y, d_scale, d_bias, d_x, mean, var, scale, epsilon, batch_size,
+            feature_size));
+      }
+      break;
+    case 2:  // d_x == nullptr, d_scale != nullptr, d_bias == nullptr
+      switch (block_dim) {
+        FIXED_BLOCK_DIM_CASE(LayerNormBackwardGradientScaleOrBias<
+                             T, kBlockDim, false,
+                             true><<<feature_size, kBlockDim, 0, stream>>>(
+            x, d_y, d_scale, d_bias, d_x, mean, var, scale, epsilon, batch_size,
+            feature_size));
+      }
+      break;
+    case 3:  // d_x == nullptr, d_scale != nulptr, d_bias != nullptr
+      switch (block_dim) {
+        FIXED_BLOCK_DIM_CASE(
+            LayerNormBackwardGradientAll<
+                T, kBlockDim, false><<<feature_size, kBlockDim, 0, stream>>>(
+                x, d_y, d_scale, d_bias, d_x, mean, var, scale, epsilon,
+                batch_size, feature_size));
+      }
+      break;
+    case 4:  // d_x != nullptr, d_scale == nullptr, d_bias == nullptr
+      switch (GetDesiredBlockDim(feature_size)) {
+        FIXED_BLOCK_DIM_CASE(
+            LayerNormBackwardGradientOnlyDX<
+                T, kBlockDim><<<batch_size, kBlockDim, 0, stream>>>(
+                x, d_y, d_x, mean, var, scale, epsilon, feature_size));
+      }
+      break;
+    case 5:  // d_x != nulptr, d_scale == nullptr, d_bias != nullptr
+      switch (block_dim) {
+        FIXED_BLOCK_DIM_CASE(LayerNormBackwardGradientScaleOrBias<
+                             T, kBlockDim, true,
+                             false><<<feature_size, kBlockDim, 0, stream>>>(
+            x, d_y, d_scale, d_bias, d_x, mean, var, scale, epsilon, batch_size,
+            feature_size));
+      }
+      switch (GetDesiredBlockDim(feature_size)) {
+        FIXED_BLOCK_DIM_CASE(
+            LayerNormBackwardPostProcessToCalculateDX<
+                T, kBlockDim><<<batch_size, kBlockDim, 0, stream>>>(
+                x, d_x, mean, var, epsilon, feature_size));
+      }
+      break;
+    case 6:  // d_x != nullptr, d_scale != nullptr, d_bias == nullptr
+      switch (block_dim) {
+        FIXED_BLOCK_DIM_CASE(LayerNormBackwardGradientScaleOrBias<
+                             T, kBlockDim, true,
+                             true><<<feature_size, kBlockDim, 0, stream>>>(
+            x, d_y, d_scale, d_bias, d_x, mean, var, scale, epsilon, batch_size,
+            feature_size));
+      }
+      switch (GetDesiredBlockDim(feature_size)) {
+        FIXED_BLOCK_DIM_CASE(
+            LayerNormBackwardPostProcessToCalculateDX<
+                T, kBlockDim><<<batch_size, kBlockDim, 0, stream>>>(
+                x, d_x, mean, var, epsilon, feature_size));
+      }
+      break;
+    case 7:  // d_x != nullptr, d_scale != nullptr, d_bias != nullptr
+      switch (block_dim) {
+        FIXED_BLOCK_DIM_CASE(
+            LayerNormBackwardGradientAll<
+                T, kBlockDim, true><<<feature_size, kBlockDim, 0, stream>>>(
+                x, d_y, d_scale, d_bias, d_x, mean, var, scale, epsilon,
+                batch_size, feature_size));
+      }
+      switch (GetDesiredBlockDim(feature_size)) {
+        FIXED_BLOCK_DIM_CASE(
+            LayerNormBackwardPostProcessToCalculateDX<
+                T, kBlockDim><<<batch_size, kBlockDim, 0, stream>>>(
+                x, d_x, mean, var, epsilon, feature_size));
+      }
+      break;
+    default:
+      break;
+  }
+}
+
+template <typename T>
+class LayerNormKernel<platform::CUDADeviceContext, T>
+    : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    const float epsilon = ctx.Attr<float>("epsilon");
+    auto *scale = ctx.Input<Tensor>("Scale");
+    auto *bias = ctx.Input<Tensor>("Bias");
+    auto *x = ctx.Input<Tensor>("X");
+
+    auto *y = ctx.Output<Tensor>("Y");
+    auto *mean = ctx.Output<Tensor>("Mean");
+    auto *var = ctx.Output<Tensor>("Variance");
+    const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
+
+    const auto x_dims = x->dims();
+    auto *x_data = x->data<T>();
+    auto *y_data = y->mutable_data<T>(ctx.GetPlace());
+    auto *mean_data = mean->mutable_data<T>(ctx.GetPlace());
+    auto *var_data = var->mutable_data<T>(ctx.GetPlace());
+    auto *scale_data = (scale == nullptr ? nullptr : scale->data<T>());
+    auto *bias_data = (bias == nullptr ? nullptr : bias->data<T>());
+
+    auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
+    int batch_size = static_cast<int>(matrix_dim[0]);
+    int feature_size = static_cast<int>(matrix_dim[1]);
+
+    auto stream = ctx.cuda_device_context().stream();
+
+    switch (GetDesiredBlockDim(feature_size)) {
+      FIXED_BLOCK_DIM_CASE(
+          LayerNormForward<T, kBlockDim><<<batch_size, kBlockDim, 0, stream>>>(
+              x_data, scale_data, bias_data, y_data, mean_data, var_data,
+              epsilon, feature_size));
+      default:
+        PADDLE_THROW(
+            "Product from begin_norm_axis to end must be larger than 1");
+        break;
+    }
+  }
+};
+
+template <typename T>
+class LayerNormGradKernel<platform::CUDADeviceContext, T>
+    : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    const float epsilon = ctx.Attr<float>("epsilon");
+    // d_x, d_scale, d_bias may be nullptr
+    auto *d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto *d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
+    auto *d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
+
+    auto *x = ctx.Input<Tensor>("X");
+    auto *mean = ctx.Input<Tensor>("Mean");
+    auto *var = ctx.Input<Tensor>("Variance");
+    auto *scale = ctx.Input<Tensor>("Scale");
+    auto *d_y = ctx.Input<Tensor>(framework::GradVarName("Y"));
+
+    auto *x_data = x->data<T>();
+    auto *d_y_data = d_y->data<T>();
+    auto *mean_data = mean->data<T>();
+    auto *var_data = var->data<T>();
+    auto *scale_data = (scale == nullptr ? nullptr : scale->data<T>());
+    auto *d_scale_data =
+        (d_scale == nullptr ? nullptr
+                            : d_scale->mutable_data<T>(ctx.GetPlace()));
+    auto *d_bias_data =
+        (d_bias == nullptr ? nullptr : d_bias->mutable_data<T>(ctx.GetPlace()));
+    auto *d_x_data =
+        (d_x == nullptr ? nullptr : d_x->mutable_data<T>(ctx.GetPlace()));
+
+    const auto &x_dims = x->dims();
+    const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
+    auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
+    int batch_size = static_cast<int>(matrix_dim[0]);
+    int feature_size = static_cast<int>(matrix_dim[1]);
+
+    auto stream = ctx.cuda_device_context().stream();
+
+    LayerNormBackward<T>(x_data, d_y_data, scale_data, mean_data, var_data,
+                         d_x_data, d_scale_data, d_bias_data, epsilon,
+                         batch_size, feature_size, stream);
+  }
+};
+
+#undef FIXED_BLOCK_DIM_CASE_BASE
+#undef FIXED_BLOCK_DIM_CASE
+}  // namespace operators
+}  // namespace paddle
+
 namespace ops = paddle::operators;
 REGISTER_OP_CUDA_KERNEL(
     layer_norm,