告警清理

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

@@ -29,8 +29,7 @@ void EluGradCPUKernel::InitKernel(const CNodePtr &kernel_node) {
   }
 }
 
-bool EluGradCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
-                              const std::vector<kernel::AddressPtr> & /*workspace*/,
+bool EluGradCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs, const std::vector<kernel::AddressPtr> &,
                               const std::vector<kernel::AddressPtr> &outputs) {
   if (dtype_ == kNumberTypeFloat32 || dtype_ == kNumberTypeFloat) {
     LaunchKernel<float>(inputs, outputs);
@@ -43,7 +42,8 @@ bool EluGradCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
 }
 
 template <typename T>
-void EluGradCPUKernel::LaunchKernel(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &outputs) {
+void EluGradCPUKernel::LaunchKernel(const std::vector<AddressPtr> &inputs,
+                                    const std::vector<AddressPtr> &outputs) const {
   T *input0 = reinterpret_cast<T *>(inputs[0]->addr);
   T *input1 = reinterpret_cast<T *>(inputs[1]->addr);
   T *output = reinterpret_cast<T *>(outputs[0]->addr);

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

@@ -32,7 +32,7 @@ class EluGradCPUKernel : public CPUKernel {
   bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
               const std::vector<AddressPtr> &outputs) override;
   template <typename T>
-  void LaunchKernel(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &outputs);
+  void LaunchKernel(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &outputs) const;
 
  private:
   TypeId dtype_{kTypeUnknown};

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

@@ -43,9 +43,12 @@ bool SelectCPUKernel<T>::Launch(const std::vector<AddressPtr> &inputs, const std
   auto input_x = reinterpret_cast<T *>(inputs[1]->addr);
   auto input_y = reinterpret_cast<T *>(inputs[2]->addr);
   auto output = reinterpret_cast<T *>(outputs[0]->addr);
-  for (size_t pos = 0; pos < element_num_; pos++) {
-    output[pos] = input_cond[pos] ? input_x[pos] : input_y[pos];
-  }
+  auto task = [=](const size_t start, const size_t end) {
+    for (size_t pos = start; pos < end; pos++) {
+      output[pos] = input_cond[pos] ? input_x[pos] : input_y[pos];
+    }
+  };
+  CPUKernelUtils::ParallelFor(task, element_num_);
   return true;
 }
 }  // namespace kernel