diff --git a/paddle/fluid/operators/fused/fused_dropout_helper.h b/paddle/fluid/operators/fused/fused_dropout_helper.h
index 970b2d82e2b15c7c2856db4abb5a8f448f5b0ed5..3972c60e8347b327d7a4587de022534d7b92fa7b 100644
--- a/paddle/fluid/operators/fused/fused_dropout_helper.h
+++ b/paddle/fluid/operators/fused/fused_dropout_helper.h
@@ -250,11 +250,14 @@ class FusedDropoutLayerNormHelper : public FusedDropoutHelper<T, MaskType> {
   }
 
   // out = layernorm(residual + dropout(src + bias))
-  void LayernormResidualDropoutBias(
-      const platform::CUDADeviceContext& ctx, const T* src, const T* residual,
-      const T* bias, const LayerNormParamType<T>* gamma,
-      const LayerNormParamType<T>* beta, T* dropout_out, MaskType* mask, T* out,
-      LayerNormParamType<T>* mean, LayerNormParamType<T>* variance) {
+  template <typename P = LayerNormParamType<T>, bool is_same_type = false>
+  void LayernormResidualDropoutBias(const platform::CUDADeviceContext& ctx,
+                                    const T* src, const T* residual,
+                                    const T* bias, const P* gamma,
+                                    const P* beta, T* dropout_out,
+                                    MaskType* mask, T* out,
+                                    LayerNormParamType<T>* mean,
+                                    LayerNormParamType<T>* variance) {
     using U = LayerNormParamType<T>;
     int vec_size = MAX_CACHE_BYTES / sizeof(T);
     if (this->cols_ % vec_size != 0) {
@@ -263,7 +266,7 @@ class FusedDropoutLayerNormHelper : public FusedDropoutHelper<T, MaskType> {
     int threads = GetDesiredBlockDim(this->cols_ / vec_size);
     int increment = ((this->cols_ - 1) / (threads * vec_size) + 1) * vec_size;
     increment = this->dropout_param_.UpdateSeedAndIncrement(ctx, increment);
-    LaunchLayernormResidualDropoutBias<T, MaskType>(
+    LaunchLayernormResidualDropoutBias<T, MaskType, U, is_same_type>(
         this->rows_, this->cols_, increment, this->dropout_param_.seed,
         this->dropout_param_.dropout_prob, epsilon_,
         this->dropout_param_.is_upscale_in_train, this->dropout_param_.is_test,
@@ -271,17 +274,19 @@ class FusedDropoutLayerNormHelper : public FusedDropoutHelper<T, MaskType> {
         variance, ctx);
   }
 
-  void LayernormResidualDropoutBiasGrad(
-      const platform::CUDADeviceContext& ctx, const T* d_out,
-      const T* layernorm_src, const MaskType* mask,
-      const LayerNormParamType<T>* gamma, const LayerNormParamType<T>* mean,
-      const LayerNormParamType<T>* variance, T* d_layernorm_src,
-      LayerNormParamType<T>* d_scale, LayerNormParamType<T>* d_layernorm_bias,
-      T* d_dropout_src, T* d_bias, T* d_residual) {
+  template <typename P = LayerNormParamType<T>, bool is_same_type = false>
+  void LayernormResidualDropoutBiasGrad(const platform::CUDADeviceContext& ctx,
+                                        const T* d_out, const T* layernorm_src,
+                                        const MaskType* mask, const P* gamma,
+                                        const LayerNormParamType<T>* mean,
+                                        const LayerNormParamType<T>* variance,
+                                        T* d_layernorm_src, P* d_scale,
+                                        P* d_layernorm_bias, T* d_dropout_src,
+                                        T* d_bias, T* d_residual) {
     using U = LayerNormParamType<T>;
-    LayerNormBackward<T, U>(layernorm_src, d_out, gamma, mean, variance,
-                            d_layernorm_src, d_scale, d_layernorm_bias,
-                            epsilon_, this->rows_, this->cols_, ctx);
+    LayerNormBackward<T, U, is_same_type>(
+        layernorm_src, d_out, gamma, mean, variance, d_layernorm_src, d_scale,
+        d_layernorm_bias, epsilon_, this->rows_, this->cols_, ctx);
     this->ResidualDropoutBiasGrad(ctx, d_layernorm_src, mask, d_dropout_src,
                                   d_residual, d_bias);
   }
diff --git a/paddle/fluid/operators/fused/fused_layernorm_residual_dropout_bias.h b/paddle/fluid/operators/fused/fused_layernorm_residual_dropout_bias.h
index 1827e137c15f183ae97552c795df85556ef0cd1a..b27b70dc9dc0c7c08a6b04889be8eb0bc6b29d24 100644
--- a/paddle/fluid/operators/fused/fused_layernorm_residual_dropout_bias.h
+++ b/paddle/fluid/operators/fused/fused_layernorm_residual_dropout_bias.h
@@ -24,46 +24,57 @@ using CudnnDataType = platform::CudnnDataType<T>;
 template <typename T>
 using LayerNormParamType = typename CudnnDataType<T>::BatchNormParamType;
 
+template <typename T, typename U, bool ScaleBiasWithSameTypeX>
+using LayerNormScaleBiasT =
+    typename std::conditional<ScaleBiasWithSameTypeX, T, U>::type;
+
 /**
  * @brief fused add_bias, dropout, add residual and leyer_norm into one
  * operators. Currently only support forward
  */
 
-template <typename T, int VecSize>
-__device__ void CalcLayernormY(const LayerNormParamType<T> *scale,
-                               const LayerNormParamType<T> *bias, const T *x,
-                               T *y, const int row_id, const int col_id,
-                               const int cols,
-                               const LayerNormParamType<T> mean_val,
-                               const LayerNormParamType<T> invvar) {
-  using U = LayerNormParamType<T>;
+template <typename T, int VecSize, typename U,
+          bool ScaleBiasWithSameTypeX = false>
+__device__ void CalcLayernormY(
+    const LayerNormScaleBiasT<T, U, ScaleBiasWithSameTypeX> *scale,
+    const LayerNormScaleBiasT<T, U, ScaleBiasWithSameTypeX> *bias, const T *x,
+    T *y, const int row_id, const int col_id, const int cols,
+    const LayerNormParamType<T> mean_val, const LayerNormParamType<T> invvar) {
   using LoadT = platform::AlignedVector<T, VecSize>;
   using StoreT = platform::AlignedVector<T, VecSize>;
   using LoadU = platform::AlignedVector<U, VecSize>;
+  using LoadScaleOrBias =
+      platform::AlignedVector<LayerNormScaleBiasT<T, U, ScaleBiasWithSameTypeX>,
+                              VecSize>;
   for (int i = col_id * VecSize; i < cols; i += blockDim.x * VecSize) {
-    LoadU scale_vec;
-    LoadU bias_vec;
+    LoadScaleOrBias scale_vec;
+    LoadScaleOrBias bias_vec;
     LoadT x_vec;
 #pragma unroll
     for (int ii = 0; ii < VecSize; ii++) {
-      scale_vec[ii] = static_cast<U>(1);
-      bias_vec[ii] = static_cast<U>(0);
+      scale_vec[ii] =
+          static_cast<LayerNormScaleBiasT<T, U, ScaleBiasWithSameTypeX>>(1);
+      bias_vec[ii] =
+          static_cast<LayerNormScaleBiasT<T, U, ScaleBiasWithSameTypeX>>(0);
     }
     // vectorize load data from global
     platform::Load<T, VecSize>(&x[row_id * cols + i], &x_vec);
 
     if (scale != nullptr) {
-      platform::Load<U, VecSize>(&scale[i], &scale_vec);
+      platform::Load<LayerNormScaleBiasT<T, U, ScaleBiasWithSameTypeX>,
+                     VecSize>(&scale[i], &scale_vec);
     }
     if (bias != nullptr) {
-      platform::Load<U, VecSize>(&bias[i], &bias_vec);
+      platform::Load<LayerNormScaleBiasT<T, U, ScaleBiasWithSameTypeX>,
+                     VecSize>(&bias[i], &bias_vec);
     }
 
     StoreT y_vec;
     for (int ii = 0; ii < VecSize; ii++) {
-      y_vec[ii] = static_cast<T>(
-          scale_vec[ii] * (static_cast<U>(x_vec[ii]) - mean_val) * invvar +
-          bias_vec[ii]);
+      y_vec[ii] =
+          static_cast<T>(static_cast<U>(scale_vec[ii]) *
+                             (static_cast<U>(x_vec[ii]) - mean_val) * invvar +
+                         static_cast<U>(bias_vec[ii]));
     }
     platform::Store<T, VecSize>(y_vec, &y[row_id * cols + i]);
   }
@@ -85,15 +96,17 @@ __device__ void CalcLayernormY(const LayerNormParamType<T> *scale,
  * means: [rows]: layernorm means
  * vars: [rows]: layernorm vars
  */
-template <typename T, typename MaskType, int VecSize>
+template <typename T, typename MaskType, int VecSize, typename U,
+          bool ScaleBiasWithSameTypeX = false>
 __global__ void FusedLayernormResidualDropoutBias(
     const size_t rows, const size_t cols, uint64_t seed,
     const float dropout_prob, const bool is_upscale_in_train,
     const bool is_test, const uint64_t increment, const float epsilon,
     const T *src, const T *residual, const T *bias,
-    const LayerNormParamType<T> *scale,
-    const LayerNormParamType<T> *layernorm_bias, MaskType *mask, T *dst,
-    T *layernorm_dst, LayerNormParamType<T> *mean, LayerNormParamType<T> *var) {
+    const LayerNormScaleBiasT<T, U, ScaleBiasWithSameTypeX> *scale,
+    const LayerNormScaleBiasT<T, U, ScaleBiasWithSameTypeX> *layernorm_bias,
+    MaskType *mask, T *dst, T *layernorm_dst, LayerNormParamType<T> *mean,
+    LayerNormParamType<T> *var) {
   int col_id = threadIdx.x;
   int row_id = blockIdx.x;
   int idx = row_id * cols + col_id;
@@ -101,7 +114,6 @@ __global__ void FusedLayernormResidualDropoutBias(
   curand_init(seed, idx, increment, &state);
 
   T factor = GetFactor<T>(dropout_prob, is_upscale_in_train, is_test);
-  using U = LayerNormParamType<T>;
 
   __shared__ U mean_share;
   __shared__ U var_share;
@@ -121,10 +133,12 @@ __global__ void FusedLayernormResidualDropoutBias(
   mean_val = BlockReduceSum<U>(mean_val, shared_mean);
   var_val = BlockReduceSum<U>(var_val, shared_var);
   if (threadIdx.x == 0) {
-    auto scale = static_cast<float>(1.) / static_cast<float>(cols);
-    auto tmp = mean_val * scale;
+    auto scale = static_cast<LayerNormScaleBiasT<T, U, ScaleBiasWithSameTypeX>>(
+        static_cast<float>(1.) / static_cast<float>(cols));
+    auto tmp = mean_val * static_cast<U>(scale);
     mean[row_id] = mean_share = static_cast<U>(tmp);
-    var_share = static_cast<U>(var_val * scale - mean_share * mean_share);
+    var_share = static_cast<U>(var_val * static_cast<U>(scale) -
+                               mean_share * mean_share);
     var_share = var_share > U(0) ? var_share : U(0);
     var[row_id] = var_share;
   }
@@ -134,8 +148,9 @@ __global__ void FusedLayernormResidualDropoutBias(
   U invvar = rsqrt_<U>(var_share + static_cast<U>(epsilon));
 
   // calculate layernorm_dst
-  CalcLayernormY<T, VecSize>(scale, layernorm_bias, dst, layernorm_dst, row_id,
-                             col_id, cols, mean_val, invvar);
+  CalcLayernormY<T, VecSize, U, ScaleBiasWithSameTypeX>(
+      scale, layernorm_bias, dst, layernorm_dst, row_id, col_id, cols, mean_val,
+      invvar);
 }
 
 /**
@@ -154,16 +169,17 @@ __global__ void FusedLayernormResidualDropoutBias(
  * means: [rows]: layernorm means
  * vars: [rows]: layernorm vars
  */
-template <typename T, typename MaskType>
+template <typename T, typename MaskType, typename U,
+          bool ScaleBiasWithSameTypeX = false>
 void LaunchLayernormResidualDropoutBias(
     const uint32_t rows, const uint32_t cols, const int increment,
     uint64_t seed, const float dropout_prob, const float epsilon,
     const bool is_upscale_in_train, const bool is_test, const T *src,
-    const T *residual, const T *bias, const LayerNormParamType<T> *scale,
-    const LayerNormParamType<T> *layernorm_bias, MaskType *mask_data, T *dst,
-    T *layernorm_dst, LayerNormParamType<T> *mean, LayerNormParamType<T> *var,
-    const platform::CUDADeviceContext &ctx) {
-  using U = LayerNormParamType<T>;
+    const T *residual, const T *bias,
+    const LayerNormScaleBiasT<T, U, ScaleBiasWithSameTypeX> *scale,
+    const LayerNormScaleBiasT<T, U, ScaleBiasWithSameTypeX> *layernorm_bias,
+    MaskType *mask_data, T *dst, T *layernorm_dst, LayerNormParamType<T> *mean,
+    LayerNormParamType<T> *var, const platform::CUDADeviceContext &ctx) {
   // dropout_prob == 1.0f
   if (std::abs(dropout_prob - 1.0f) < 1e-5) {
     auto cuda_place = BOOST_GET_CONST(platform::CUDAPlace, ctx.GetPlace());
@@ -175,8 +191,9 @@ void LaunchLayernormResidualDropoutBias(
     // call layernorm forward
     switch (GetDesiredBlockDim(cols)) {
       FIXED_BLOCK_DIM_CASE(
-          LayerNormForward<T, U,
-                           kBlockDim><<<rows, kBlockDim, 0, ctx.stream()>>>(
+          LayerNormForward<
+              T, U, kBlockDim,
+              ScaleBiasWithSameTypeX><<<rows, kBlockDim, 0, ctx.stream()>>>(
               dst, scale, layernorm_bias, layernorm_dst, mean, var, epsilon,
               cols));
       default:
@@ -184,21 +201,24 @@ void LaunchLayernormResidualDropoutBias(
             "Product from begin_norm_axis to end must be larger than 1"));
         break;
     }
+
     return;
   }
 
   const int VecSize = MAX_CACHE_BYTES / sizeof(T);
   if (cols % VecSize != 0) {
     int blockDim = GetDesiredBlockDim(cols);
-    FusedLayernormResidualDropoutBias<T, uint8_t,
-                                      1><<<rows, blockDim, 0, ctx.stream()>>>(
+    FusedLayernormResidualDropoutBias<
+        T, uint8_t, 1, U,
+        ScaleBiasWithSameTypeX><<<rows, blockDim, 0, ctx.stream()>>>(
         rows, cols, seed, dropout_prob, is_upscale_in_train, is_test, increment,
         epsilon, src, residual, bias, scale, layernorm_bias, mask_data, dst,
         layernorm_dst, mean, var);
   } else {
     int blockDim = GetDesiredBlockDim(cols / VecSize);
     FusedLayernormResidualDropoutBias<
-        T, uint8_t, VecSize><<<rows, blockDim, 0, ctx.stream()>>>(
+        T, uint8_t, VecSize, U,
+        ScaleBiasWithSameTypeX><<<rows, blockDim, 0, ctx.stream()>>>(
         rows, cols, seed, dropout_prob, is_upscale_in_train, is_test, increment,
         epsilon, src, residual, bias, scale, layernorm_bias, mask_data, dst,
         layernorm_dst, mean, var);
diff --git a/paddle/fluid/operators/fused/fused_layernorm_residual_dropout_bias_test.cu b/paddle/fluid/operators/fused/fused_layernorm_residual_dropout_bias_test.cu
index 50e3555b4bcd629c85aca27fd3f2999b6694ecb2..57d3fc94dc88a0699b103c081642757798719332 100644
--- a/paddle/fluid/operators/fused/fused_layernorm_residual_dropout_bias_test.cu
+++ b/paddle/fluid/operators/fused/fused_layernorm_residual_dropout_bias_test.cu
@@ -223,7 +223,7 @@ struct TestFusedLayernormResidualDropoutBias {
       layernorm_bias_ptr = layernorm_bias.data<U>();
     }
 
-    paddle::operators::LaunchLayernormResidualDropoutBias<T, uint8_t>(
+    paddle::operators::LaunchLayernormResidualDropoutBias<T, uint8_t, U, false>(
         rows, cols, increment, seed, dropout_prob, epsilon, is_upscale_in_train,
         is_test, src.data<T>(), residual.data<T>(), bias_ptr, scale_ptr,
         layernorm_bias_ptr, mask.data<uint8_t>(), out.data<T>(),