diff --git a/mindspore/ccsrc/backend/kernel_compiler/gpu/math/matmul_gpu_kernel.h b/mindspore/ccsrc/backend/kernel_compiler/gpu/math/matmul_gpu_kernel.h
index d6d547113c..f65b6fd10e 100644
--- a/mindspore/ccsrc/backend/kernel_compiler/gpu/math/matmul_gpu_kernel.h
+++ b/mindspore/ccsrc/backend/kernel_compiler/gpu/math/matmul_gpu_kernel.h
@@ -86,7 +86,7 @@ class MatMulGpuKernel : public GpuKernel {
     dtype_b_ = GetCudaDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 1)));
     dtype_c_ = GetCudaDataType(TypeIdLabel(AnfAlgo::GetOutputDeviceDataType(kernel_node, 0)));
     if (dtype_a_ == CUDA_R_16F && dtype_b_ == CUDA_R_16F && dtype_c_ == CUDA_R_16F) {
-      MS_LOG(WARNING) << "input and output type is float16, allow to use Tensor Core operations if possible";
+      MS_LOG(INFO) << "input and output type is float16, allow to use Tensor Core operations if possible";
       algo_ = CUBLAS_GEMM_DEFAULT_TENSOR_OP;
     }
     auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0);
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 ac78d2cd2a..2e5692775c 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
@@ -86,6 +86,7 @@ class PoolingGradGpuKernel : public GpuKernel {
     auto dout_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 2);
     auto output_shape = AnfAlgo::GetOutputDeviceShape(kernel_node, 0);
     data_format_ = AnfAlgo::GetInputFormat(kernel_node, 0);
+    cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0)));
     is_null_input_ = CHECK_NULL_INPUT(input_shape) || CHECK_NULL_INPUT(input_mask);
     if (is_null_input_) {
       MS_LOG(WARNING) << "PoolingGradGpuKernel input is null.";
@@ -204,7 +205,6 @@ class PoolingGradGpuKernel : public GpuKernel {
                                 "cudnnSetPoolingNdDescriptor failed");
   }
   void SetPoolingMode(const CNodePtr &kernel_node) {
-    cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0)));
     mode_ = AnfAlgo::GetCNodeName(kernel_node);
     if (mode_ == "AvgPoolGradGpu") {
       pooling_mode_ = CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
diff --git a/mindspore/ccsrc/runtime/device/gpu/kernel_info_setter.cc b/mindspore/ccsrc/runtime/device/gpu/kernel_info_setter.cc
index 81832f64fd..8d73a33329 100644
--- a/mindspore/ccsrc/runtime/device/gpu/kernel_info_setter.cc
+++ b/mindspore/ccsrc/runtime/device/gpu/kernel_info_setter.cc
@@ -345,7 +345,6 @@ void SetKernelInfo(const CNodePtr &kernel_node, KernelType kernel_type) {
     SetGraphKernelInfo(kernel_node, func_graph);
     return;
   }
-
   std::vector<std::string> inputs_format;
   std::vector<TypeId> inputs_type;
   for (size_t input_index = 0; input_index < AnfAlgo::GetInputTensorNum(kernel_node); ++input_index) {
@@ -368,12 +367,10 @@ void SetKernelInfo(const CNodePtr &kernel_node, KernelType kernel_type) {
   builder->SetInputsDeviceType(inputs_type);
   builder->SetOutputsFormat(outputs_format);
   builder->SetOutputsDeviceType(outputs_type);
-
   bool result = false;
   if (kernel_type == UNKNOWN_KERNEL_TYPE) {
     result =
       kernel::GpuKernelFactory::GetInstance().SearchRegistered(AnfAlgo::GetCNodeName(kernel_node), builder->Build());
-
     if (!result) {
       result = SelectAkgKernel(kernel_node, builder->Build());
       kernel_type = AKG_KERNEL;
@@ -381,7 +378,6 @@ void SetKernelInfo(const CNodePtr &kernel_node, KernelType kernel_type) {
   } else if (kernel_type == AKG_KERNEL) {
     result = SelectAkgKernel(kernel_node, builder->Build());
   }
-
   if (!result) {
     PrintUnsupportedTypeException(kernel_node, inputs_type, outputs_type);
     return;