gpu codex fix

mindspore/ccsrc/kernel/gpu/nn/pooling_grad_gpu_kernel.cc

@@ -24,27 +24,27 @@ MS_REG_GPU_KERNEL_ONE(MaxPoolGrad,
                         .AddInputAttr(kNumberTypeFloat32)
                         .AddInputAttr(kNumberTypeFloat32)
                         .AddOutputAttr(kNumberTypeFloat32),
-                      PoolingGradGpuFwdKernel, float)
+                      PoolingGradGpuKernel, float)
 MS_REG_GPU_KERNEL_ONE(MaxPoolGrad,
                       KernelAttr()
                         .AddInputAttr(kNumberTypeFloat16)
                         .AddInputAttr(kNumberTypeFloat16)
                         .AddInputAttr(kNumberTypeFloat16)
                         .AddOutputAttr(kNumberTypeFloat16),
-                      PoolingGradGpuFwdKernel, half)
+                      PoolingGradGpuKernel, half)
 MS_REG_GPU_KERNEL_ONE(AvgPoolGradGpu,
                       KernelAttr()
                         .AddInputAttr(kNumberTypeFloat32)
                         .AddInputAttr(kNumberTypeFloat32)
                         .AddInputAttr(kNumberTypeFloat32)
                         .AddOutputAttr(kNumberTypeFloat32),
-                      PoolingGradGpuFwdKernel, float)
+                      PoolingGradGpuKernel, float)
 MS_REG_GPU_KERNEL_ONE(AvgPoolGradGpu,
                       KernelAttr()
                         .AddInputAttr(kNumberTypeFloat16)
                         .AddInputAttr(kNumberTypeFloat16)
                         .AddInputAttr(kNumberTypeFloat16)
                         .AddOutputAttr(kNumberTypeFloat16),
-                      PoolingGradGpuFwdKernel, half)
+                      PoolingGradGpuKernel, half)
 }  // namespace kernel
 }  // namespace mindspore

mindspore/ccsrc/kernel/gpu/nn/pooling_grad_gpu_kernel.h

@@ -28,9 +28,9 @@
 namespace mindspore {
 namespace kernel {
 template <typename T>
-class PoolingGradGpuFwdKernel : public GpuKernel {
+class PoolingGradGpuKernel : public GpuKernel {
  public:
-  PoolingGradGpuFwdKernel()
+  PoolingGradGpuKernel()
       : cudnn_handle_(nullptr),
         pooling_descriptor_(nullptr),
         y_descriptor_(nullptr),
@@ -55,7 +55,7 @@ class PoolingGradGpuFwdKernel : public GpuKernel {
         padded_size_(0),
         workspace_size_(0),
         use_pad_(true) {}
-  ~PoolingGradGpuFwdKernel() override { DestroyResource(); }
+  ~PoolingGradGpuKernel() override { DestroyResource(); }
 
   const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
   const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
@@ -108,7 +108,7 @@ class PoolingGradGpuFwdKernel : public GpuKernel {
     auto input_mask = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
     is_null_input_ = CHECK_NULL_INPUT(input_shape) || CHECK_NULL_INPUT(input_mask);
     if (is_null_input_) {
-      MS_LOG(WARNING) << "PoolingGradGpuFwdKernel input is null.";
+      MS_LOG(WARNING) << "PoolingGradGpuKernel input is null.";
       InitSizeLists();
       return true;
     }
@@ -196,7 +196,7 @@ class PoolingGradGpuFwdKernel : public GpuKernel {
   bool CheckParam(const CNodePtr &kernel_node) {
     size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
     if (input_num != 3) {
-      MS_LOG(ERROR) << "Input number is " << input_num << ", but PoolingGradGpuFwdKernel needs 3 inputs.";
+      MS_LOG(ERROR) << "Input number is " << input_num << ", but PoolingGradGpuKernel needs 3 inputs.";
       return false;
     }
     return true;

mindspore/ccsrc/kernel/gpu/nn/relu_grad_kernel.cc

@@ -21,10 +21,10 @@ namespace kernel {
 MS_REG_GPU_KERNEL_ONE(
   ReluGrad,
   KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
-  ReluGradGpuFwdKernel, float)
+  ReluGradGpuKernel, float)
 MS_REG_GPU_KERNEL_ONE(
   ReluGrad,
   KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
-  ReluGradGpuFwdKernel, half)
+  ReluGradGpuKernel, half)
 }  // namespace kernel
 }  // namespace mindspore

mindspore/ccsrc/kernel/gpu/nn/relu_grad_kernel.h

@@ -25,9 +25,9 @@
 namespace mindspore {
 namespace kernel {
 template <typename T>
-class ReluGradGpuFwdKernel : public GpuKernel {
+class ReluGradGpuKernel : public GpuKernel {
  public:
-  ReluGradGpuFwdKernel()
+  ReluGradGpuKernel()
       : cudnn_handle_(nullptr),
         activation_desc_(nullptr),
         mode_(CUDNN_ACTIVATION_RELU),
@@ -35,7 +35,7 @@ class ReluGradGpuFwdKernel : public GpuKernel {
         is_null_input_(false),
         cudnn_data_type_(CUDNN_DATA_FLOAT),
         input_size_(0) {}
-  ~ReluGradGpuFwdKernel() override { DestroyResource(); }
+  ~ReluGradGpuKernel() override { DestroyResource(); }
   const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
   const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
   const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
@@ -63,14 +63,14 @@ class ReluGradGpuFwdKernel : public GpuKernel {
     cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0)));
     size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
     if (input_num != 2) {
-      MS_LOG(ERROR) << "Argument number is " << input_num << ", but ReluGradGpuFwdKernel needs 2.";
+      MS_LOG(ERROR) << "Argument number is " << input_num << ", but ReluGradGpuKernel needs 2.";
       return false;
     }
     auto input_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0);
     mode_ = CUDNN_ACTIVATION_RELU;
     is_null_input_ = CHECK_NULL_INPUT(input_shape);
     if (is_null_input_) {
-      MS_LOG(WARNING) << "ReluGradGpuFwdKernel input is null.";
+      MS_LOG(WARNING) << "ReluGradGpuKernel input is null.";
       InitSizeLists();
       return true;
     }