diff --git a/paddle/fluid/operators/group_norm_op.cu b/paddle/fluid/operators/group_norm_op.cu
index c08f1920205daa7f6d5d3f032afcf5d832230ced..bb8031b0cc4e6cd97afe5f5584d64d58197d6f9d 100644
--- a/paddle/fluid/operators/group_norm_op.cu
+++ b/paddle/fluid/operators/group_norm_op.cu
@@ -419,23 +419,6 @@ __global__ void GroupNormBackward(const T* x, const T* d_y, const T* scale,
   }
 }
 
-template <typename T, typename AccT, int VecSize>
-__global__ void VectorizedGetDsDbCUDAKernel(int imsize, const T* x, const T* dy,
-                                            T* ds, T* db) {
-  int i = blockIdx.x;
-  AccT ds_sum = static_cast<AccT>(0);
-  AccT db_sum = static_cast<AccT>(0);
-  x += i * imsize;
-  const int input_offset = ((uint64_t)x) % ALIGN_BYTES / sizeof(T);
-
-  phi::Array<const T*, 2> ins;
-  ins[0] = x;
-  ins[1] = dy;
-  ThreadReduce<T, AccT, VecSize, 2>(ins, imsize, input_offset, &db_sum,
-                                    &ds_sum);
-  ReduceMeanAndVar<AccT>(db, ds, db_sum, ds_sum, 1);
-}
-
 template <typename T>
 __global__ void ScalarGetDsDbCUDAKernel(int imsize, const T* x, const T* dy,
                                         T* ds, T* db) {
@@ -622,25 +605,17 @@ class GroupNormGradKernel<platform::CUDADeviceContext, T>
     int flags =
         (scale_data != nullptr) * kHasScale + (bias_data != nullptr) * kHasBias;
     if (data_layout == DataLayout::kNCHW) {
-      using AccT = typename details::MPTypeTrait<T>::Type;
-      constexpr int vec_size = sizeof(float4) / sizeof(T);
       const int max_num_threads = 1024;
-      int max_block_size = std::min(imsize / vec_size, max_num_threads);
+      int max_block_size = std::min(imsize, max_num_threads);
       int block_size_nchw = 1;
       while (block_size_nchw < max_block_size) {
         block_size_nchw *= 2;
       }
       block_size_nchw = std::max(block_size_nchw, kps::details::kWarpSize);
       dim3 blocks(block_size_nchw);
-      if (imsize < vec_size * block_size_nchw) {
-        ScalarGetDsDbCUDAKernel<
-            T><<<x_dims[0] * C, blocks, 0, dev_ctx.stream()>>>(
-            imsize, x_data, dy_data, ds_data, db_data);
-      } else {
-        VectorizedGetDsDbCUDAKernel<
-            T, AccT, vec_size><<<x_dims[0] * C, blocks, 0, dev_ctx.stream()>>>(
-            imsize, x_data, dy_data, ds_data, db_data);
-      }
+      ScalarGetDsDbCUDAKernel<
+          T><<<x_dims[0] * C, blocks, 0, dev_ctx.stream()>>>(
+          imsize, x_data, dy_data, ds_data, db_data);
 
       if (d_scale || d_bias) {
         const int block = 256;