clean codex

mindspore/ccsrc/backend/kernel_compiler/cpu/slice_cpu_kernel.cc

@@ -111,7 +111,7 @@ bool SliceCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
 
 template <typename T>
 bool SliceCPUKernel::LaunchKernel(const std::vector<kernel::AddressPtr> &inputs,
-                                  const std::vector<kernel::AddressPtr> &outputs) {
+                                  const std::vector<kernel::AddressPtr> &outputs) const {
   T *input_addr = reinterpret_cast<T *>(inputs[0]->addr);
   T *output_addr = reinterpret_cast<T *>(outputs[0]->addr);
   bool can_copy_memory[3] = {CanCopyMemoryOnAxis(0), CanCopyMemoryOnAxis(1), CanCopyMemoryOnAxis(2)};

mindspore/ccsrc/backend/kernel_compiler/cpu/slice_cpu_kernel.h

@@ -34,7 +34,8 @@ class SliceCPUKernel : public CPUKernel {
 
  private:
   template <typename T>
-  bool LaunchKernel(const std::vector<kernel::AddressPtr> &inputs, const std::vector<kernel::AddressPtr> &outputs);
+  bool LaunchKernel(const std::vector<kernel::AddressPtr> &inputs,
+                    const std::vector<kernel::AddressPtr> &outputs) const;
   template <typename T>
   void CopyDataToOutput(const std::vector<kernel::AddressPtr> &inputs, size_t in_offset,
                         const std::vector<kernel::AddressPtr> &outputs, size_t out_offset, size_t copy_num,

mindspore/ccsrc/backend/kernel_compiler/cpu/slice_grad_cpu_kernel.cc

@@ -95,7 +95,7 @@ bool SliceGradCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
 
 template <typename T>
 bool SliceGradCPUKernel::LaunchKernel(const std::vector<kernel::AddressPtr> &inputs,
-                                      const std::vector<kernel::AddressPtr> &outputs) {
+                                      const std::vector<kernel::AddressPtr> &outputs) const {
   T *input_addr = reinterpret_cast<T *>(inputs[0]->addr);
   T *output_addr = reinterpret_cast<T *>(outputs[0]->addr);
 

mindspore/ccsrc/backend/kernel_compiler/cpu/slice_grad_cpu_kernel.h

@@ -34,7 +34,8 @@ class SliceGradCPUKernel : public CPUKernel {
 
  private:
   template <typename T>
-  bool LaunchKernel(const std::vector<kernel::AddressPtr> &inputs, const std::vector<kernel::AddressPtr> &outputs);
+  bool LaunchKernel(const std::vector<kernel::AddressPtr> &inputs,
+                    const std::vector<kernel::AddressPtr> &outputs) const;
   template <typename T>
   void CopyDataToOutput(const std::vector<kernel::AddressPtr> &inputs, size_t in_offset,
                         const std::vector<kernel::AddressPtr> &outputs, size_t out_offset, size_t copy_num,

mindspore/nn/layer/activation.py

@@ -402,7 +402,7 @@ class GELU(Cell):
         TypeError: If dtype of `input_data` is neither float16 nor float32.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)

mindspore/nn/layer/normalization.py

@@ -806,7 +806,7 @@ class LayerNorm(Cell):
         TypeError: If `epsilon` is not a float.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> x = Tensor(np.ones([20, 5, 10, 10]), mindspore.float32)