diff --git a/paddle/phi/kernels/gpu/batch_norm_grad_kernel.cu b/paddle/phi/kernels/gpu/batch_norm_grad_kernel.cu
index 252fbe7d21b74406fa2a89d10f1ed77230555f09..cfad86506c9099d08b92471edf27cf437ba85dd9 100644
--- a/paddle/phi/kernels/gpu/batch_norm_grad_kernel.cu
+++ b/paddle/phi/kernels/gpu/batch_norm_grad_kernel.cu
@@ -858,15 +858,20 @@ void BatchNormGradRawKernel(const Context &ctx,
 //             ctx.GetPlace()),
 //         epsilon, saved_mean_data, saved_var_data));
 #else
-      // CUDNN only support small batch size
-      // const size_t CUDNN_PER_ACTIVATION_THRESHOLD = 131070;
-      const size_t CUDNN_PER_ACTIVATION_THRESHOLD = 10240;
-      const size_t CUDNN_SPATIAL_THRESHOLD = 880801;
-      const bool use_native_kernel =
-          ((x_dims.size() == 2 && N >= CUDNN_PER_ACTIVATION_THRESHOLD) ||
-           (x_dims.size() == 3 && N >= CUDNN_SPATIAL_THRESHOLD));
-      if (use_native_kernel) {
-        if (x_dims.size() == 2) {
+    }
+    // CUDNN only support small batch size
+    // const size_t CUDNN_PER_ACTIVATION_THRESHOLD = 131070;
+    const size_t CUDNN_PER_ACTIVATION_THRESHOLD = 10240;
+    const size_t CUDNN_SPATIAL_THRESHOLD = 880801;
+    bool use_native_nhwc =
+        d_x ? (x_dims.size() == 4 && compute_format == DataLayout::kNHWC)
+            : false;
+    const bool use_native_kernel =
+        ((x_dims.size() == 2 && N >= CUDNN_PER_ACTIVATION_THRESHOLD) ||
+         (x_dims.size() == 3 && N >= CUDNN_SPATIAL_THRESHOLD));
+    if (use_native_nhwc || (d_x && d_scale && d_bias)) {
+      if (use_native_kernel || use_native_nhwc) {
+        if (x_dims.size() == 2 || use_native_nhwc) {
           dim3 block;
           dim3 grid;
           const int block_size = 512;
@@ -937,6 +942,21 @@ void BatchNormGradRawKernel(const Context &ctx,
                     flag_ptr);
           }
           // 2. reduce_sum(x, dy, mean) => dscale, dbias
+          BatchNormParamType<T> *dscale = nullptr;
+          BatchNormParamType<T> *dbias = nullptr;
+          bool with_scale = false;
+          if (d_scale && d_bias) {
+            dscale = ctx.template Alloc<BatchNormParamType<T>>(d_scale);
+            dbias = ctx.template Alloc<BatchNormParamType<T>>(d_bias);
+          } else {
+            DenseTensor dscale_mem =
+                phi::Empty<BatchNormParamType<T>, Context>(ctx, {C});
+            DenseTensor dbias_mem =
+                phi::Empty<BatchNormParamType<T>, Context>(ctx, {C});
+            dscale = dscale_mem.data<BatchNormParamType<T>>();
+            dbias = dbias_mem.data<BatchNormParamType<T>>();
+          }
+
           BNBackward2DChannelLastStage2<T, block_size>
               <<<grid, block, 0, ctx.stream()>>>(
                   transformed_d_y.template data<T>(),
@@ -948,8 +968,8 @@ void BatchNormGradRawKernel(const Context &ctx,
                   H * W * D,
                   epsilon,
                   block_data_ptr,
-                  ctx.template Alloc<BatchNormParamType<T>>(d_scale),
-                  ctx.template Alloc<BatchNormParamType<T>>(d_bias),
+                  dscale,
+                  dbias,
                   flag_ptr);
 
           // 3. elementwise_mul(scale, mean, inv_var, dy, dscale, dbias) => dx
@@ -958,8 +978,8 @@ void BatchNormGradRawKernel(const Context &ctx,
                   transformed_d_y.template data<T>(),
                   transformed_x.template data<T>(),
                   scale.template data<BatchNormParamType<T>>(),
-                  d_scale->data<BatchNormParamType<T>>(),
-                  d_bias->data<BatchNormParamType<T>>(),
+                  dscale,
+                  dbias,
                   mean_ptr,
                   variance_ptr,
                   C,
@@ -1169,6 +1189,7 @@ void BatchNormGradRawKernel(const Context &ctx,
         paddle::platform::dynload::cudnnDestroyTensorDescriptor(
             bn_param_desc_));
 #endif
+
   } else {
     const auto *running_mean = mean.get_ptr();
     const auto *running_var = variance.get_ptr();