[cherry-pick] Fix bn performance degradation (#50382)

att, cherry-pick: #48563 , #50287

paddle/phi/kernels/gpu/batch_norm_grad_kernel.cu

@@ -855,7 +855,8 @@ void BatchNormGradRawKernel(const Context &ctx,
     }
     // CUDNN only support small batch size
     bool use_native_nhwc =
-        d_x ? (x_dims.size() == 4 && compute_format == DataLayout::kNHWC)
+        d_x ? (x_dims.size() == 4 && compute_format == DataLayout::kNHWC &&
+               H * W >= CUDNN_SPATIAL_THRESHOLD_EVAL)
             : false;
     const bool use_native_kernel =
         ((x_dims.size() == 2 && N >= CUDNN_PER_ACTIVATION_THRESHOLD) ||
@@ -933,6 +934,21 @@ 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>(),
@@ -944,8 +960,8 @@ 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
@@ -954,8 +970,8 @@ 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,
@@ -1165,6 +1181,7 @@ 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();