diff --git a/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/pooling_grad_gpu_kernel.h b/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/pooling_grad_gpu_kernel.h
index c8354087b1..945e6a000e 100644
--- a/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/pooling_grad_gpu_kernel.h
+++ b/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/pooling_grad_gpu_kernel.h
@@ -85,10 +85,10 @@ class PoolingGradGpuKernel : public GpuKernel {
     auto input_mask = AnfAlgo::GetInputDeviceShape(kernel_node, 1);
     auto dout_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 2);
     auto output_shape = AnfAlgo::GetOutputDeviceShape(kernel_node, 0);
-    data_format_ = AnfAlgo::GetInputFormat(kernel_node, 0);
-    auto format_attr = GetAttr<std::string>(kernel_node, "data_format");
-    if (format_attr == kOpFormat_NHWC) {
-      data_format_ = kOpFormat_NHWC;
+    auto data_format = AnfAlgo::GetInputFormat(kernel_node, 0);
+    format_attr_ = GetAttr<std::string>(kernel_node, "data_format");
+    if (format_attr_ == kOpFormat_NHWC) {
+      data_format = kOpFormat_NHWC;
     }
     cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0)));
     is_null_input_ = CHECK_NULL_INPUT(input_shape) || CHECK_NULL_INPUT(input_mask);
@@ -97,7 +97,7 @@ class PoolingGradGpuKernel : public GpuKernel {
       InitSizeLists();
       return true;
     }
-    SetNCHW(input_shape, &n_, &c_, &old_height_, &old_width_, data_format_);
+    SetNCHW(input_shape, &n_, &c_, &old_height_, &old_width_, data_format);
     const int nbDims = 4;
     int dimA[4];
     int strideAin[4];
@@ -107,14 +107,14 @@ class PoolingGradGpuKernel : public GpuKernel {
     int strideAdy[4];
     int dimAout[4];
     int strideAout[4];
-    SetDimA(input_shape, dimA, 4, data_format_);
-    SetStrideA(input_shape, strideAin, 4, data_format_);
-    SetDimA(input_mask, dimAy, 4, data_format_);
-    SetStrideA(input_mask, strideAiny, 4, data_format_);
-    SetDimA(dout_shape, dimAdy, 4, data_format_);
-    SetStrideA(dout_shape, strideAdy, 4, data_format_);
-    SetDimA(output_shape, dimAout, 4, data_format_);
-    SetStrideA(output_shape, strideAout, 4, data_format_);
+    SetDimA(input_shape, dimA, 4, data_format);
+    SetStrideA(input_shape, strideAin, 4, data_format);
+    SetDimA(input_mask, dimAy, 4, data_format);
+    SetStrideA(input_mask, strideAiny, 4, data_format);
+    SetDimA(dout_shape, dimAdy, 4, data_format);
+    SetStrideA(dout_shape, strideAdy, 4, data_format);
+    SetDimA(output_shape, dimAout, 4, data_format);
+    SetStrideA(output_shape, strideAout, 4, data_format);
     CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetTensorNdDescriptor(y_descriptor_, cudnn_data_type_, nbDims, dimAy, strideAiny),
                                 "cudnnSetTensor4dDescriptor failed");
     CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetTensorNdDescriptor(dy_descriptor_, cudnn_data_type_, nbDims, dimAdy, strideAdy),
@@ -180,7 +180,7 @@ class PoolingGradGpuKernel : public GpuKernel {
     int window_width = window[3];
     int stride_h = stride_[2];
     int stride_w = stride_[3];
-    if (data_format_ == kOpFormat_NHWC) {
+    if (format_attr_ == kOpFormat_NHWC) {
       window_height = window[1];
       window_width = window[2];
       stride_h = stride_[1];
@@ -247,7 +247,7 @@ class PoolingGradGpuKernel : public GpuKernel {
   std::vector<size_t> workspace_size_list_;
   std::string mode_;
   std::string pad_mode_;
-  std::string data_format_ = kOpFormat_NCHW;
+  std::string format_attr_ = kOpFormat_NCHW;
   cudnnDataType_t cudnn_data_type_;
   cudnnTensorFormat_t compute_format_;
   int old_height_;