Fix the calculation of layer_norm_bwd (#53229)

* Fix the calculation of layer_norm_bwd

* fix

paddle/phi/kernels/funcs/layer_norm_impl.cu.h

@@ -1603,13 +1603,13 @@ __global__ void LayerNormBackwardGradientAll(
 
   for (int64_t i = beg_idx; i < end_idx; i += stride) {
     int row_idx = i / feature_size;
-    auto var_val = real_sqrt(static_cast<U>(var[row_idx]) + epsilon);
+    auto var_val = rsqrt_(static_cast<U>(var[row_idx]) + epsilon);
     d_scale_partial += static_cast<U>(d_y[i]) *
-                       (static_cast<U>(x[i]) - mean[row_idx]) / var_val;
+                       (static_cast<U>(x[i]) - mean[row_idx]) * var_val;
     d_bias_partial += static_cast<U>(d_y[i]);
     if (HasDx) {
       d_x[i] = static_cast<T>(static_cast<U>(d_y[i]) *
-                              static_cast<U>(scale[blockIdx.x + col_offset]) /
+                              static_cast<U>(scale[blockIdx.x + col_offset]) *
                               var_val);
     }
   }
@@ -1659,10 +1659,10 @@ __global__ void LayerNormBackwardGradientScaleOrBias(
   for (int64_t i = beg_idx; i < end_idx; i += stride) {
     int row_idx = i / feature_size;
     auto var_val =
-        static_cast<U>(real_sqrt(static_cast<float>(var[row_idx]) + epsilon));
+        static_cast<U>(rsqrt_(static_cast<float>(var[row_idx]) + epsilon));
     if (HasDScale) {
       d_scale_or_d_bias_partial += static_cast<U>(d_y[i]) *
-                                   (static_cast<U>(x[i]) - mean[row_idx]) /
+                                   (static_cast<U>(x[i]) - mean[row_idx]) *
                                    var_val;
     } else {  // d_bias != nullptr
       d_scale_or_d_bias_partial += static_cast<U>(d_y[i]);
@@ -1671,10 +1671,10 @@ __global__ void LayerNormBackwardGradientScaleOrBias(
     if (HasDx) {
       if (scale != nullptr) {
         d_x[i] = static_cast<T>(static_cast<U>(d_y[i]) *
-                                static_cast<U>(scale[blockIdx.x + col_offset]) /
+                                static_cast<U>(scale[blockIdx.x + col_offset]) *
                                 var_val);
       } else {
-        d_x[i] = static_cast<T>(static_cast<U>(d_y[i]) / var_val);
+        d_x[i] = static_cast<T>(static_cast<U>(d_y[i]) * var_val);
       }
     }
   }
@@ -1762,13 +1762,13 @@ __global__ void LayerNormBackwardGradientOnlyDX(
   U d_x_mean_partial = static_cast<U>(0), d_x_var_partial = static_cast<U>(0);
   for (int64_t i = beg_idx; i < end_idx; i += BlockDim) {
     auto var_val =
-        static_cast<U>(real_sqrt(static_cast<float>(block_var) + epsilon));
+        static_cast<U>(rsqrt_(static_cast<float>(block_var) + epsilon));
     if (scale != nullptr) {
       int col_idx = i % feature_size;
       d_x[i] = static_cast<T>(static_cast<U>(d_y[i]) *
-                              static_cast<U>(scale[col_idx]) / var_val);
+                              static_cast<U>(scale[col_idx]) * var_val);
     } else {
-      d_x[i] = static_cast<T>(static_cast<U>(d_y[i]) / var_val);
+      d_x[i] = static_cast<T>(static_cast<U>(d_y[i]) * var_val);
     }
     d_x_mean_partial += static_cast<U>(d_x[i]);
     d_x_var_partial +=
@@ -1812,21 +1812,20 @@ __global__ void LayerNormBackwardWhenBatchSizeIsOne(
   int64_t idx = threadIdx.x + blockIdx.x * blockDim.x;
   using ScaleBiasT = LayerNormScaleBiasT<T, U, ScaleBiasWithSameTypeX>;
   if (idx < feature_size) {
-    auto var_val =
-        static_cast<U>(real_sqrt(static_cast<float>(var[0]) + epsilon));
+    auto var_val = static_cast<U>(rsqrt_(static_cast<float>(var[0]) + epsilon));
     if (d_x != nullptr) {
       if (d_scale == nullptr) {
-        d_x[idx] = static_cast<T>(static_cast<U>(d_y[idx]) / var_val);
+        d_x[idx] = static_cast<T>(static_cast<U>(d_y[idx]) * var_val);
       } else {
         d_x[idx] = static_cast<T>(static_cast<U>(d_y[idx]) *
-                                  static_cast<U>(scale[idx]) / var_val);
+                                  static_cast<U>(scale[idx]) * var_val);
       }
     }
 
     if (d_scale != nullptr) {
       d_scale[idx] =
           static_cast<ScaleBiasT>(static_cast<U>(d_y[idx]) *
-                                  (static_cast<U>(x[idx]) - mean[0]) / var_val);
+                                  (static_cast<U>(x[idx]) - mean[0]) * var_val);
     }
 
     if (d_bias != nullptr) {