!31540 clean code for cpu

Merge pull request !31540 from liangxhao/cleancode_master

mindspore/ccsrc/plugin/device/cpu/kernel/adam_cpu_kernel.cc

@@ -26,29 +26,39 @@ namespace {
 constexpr size_t kAdamInputsNum = 10;
 constexpr size_t kAdamOutputsNum = 3;
 constexpr size_t kScalarIndex = 0;
+constexpr size_t kIndexVar = 0;
+constexpr size_t kIndexM = 1;
+constexpr size_t kIndexV = 2;
+constexpr size_t kIndexBeta1Power = 3;
+constexpr size_t kIndexBeta2Power = 4;
+constexpr size_t kIndexLr = 5;
+constexpr size_t kIndexBeta1 = 6;
+constexpr size_t kIndexBeta2 = 7;
+constexpr size_t kIndexEpsilon = 8;
+constexpr size_t kIndexGrad = 9;
 constexpr float kAdamBlock = 1000;
 }  // namespace
 
 template <typename T>
 void AdamCpuKernelMod::LaunchAdam(const std::vector<kernel::AddressPtr> &inputs,
                                   const std::vector<kernel::AddressPtr> &) {
-  T *var = reinterpret_cast<T *>(inputs[VAR]->addr);
-  T *m = reinterpret_cast<T *>(inputs[M]->addr);
-  T *v = reinterpret_cast<T *>(inputs[V]->addr);
-  float beta1_power = reinterpret_cast<float *>(inputs[BETA1_POWER]->addr)[kScalarIndex];
-  float beta2_power = reinterpret_cast<float *>(inputs[BETA2_POWER]->addr)[kScalarIndex];
-  float lr = reinterpret_cast<float *>(inputs[LR]->addr)[kScalarIndex];
-  T beta1 = static_cast<T>(reinterpret_cast<float *>(inputs[BETA1]->addr)[kScalarIndex]);
-  T beta2 = static_cast<T>(reinterpret_cast<float *>(inputs[BETA1]->addr)[kScalarIndex]);
-  T epsilon = static_cast<T>(reinterpret_cast<float *>(inputs[EPSILON]->addr)[kScalarIndex]);
-  T *gradient = reinterpret_cast<T *>(inputs[GRAD]->addr);
+  T *var = reinterpret_cast<T *>(inputs[kIndexVar]->addr);
+  T *m = reinterpret_cast<T *>(inputs[kIndexM]->addr);
+  T *v = reinterpret_cast<T *>(inputs[kIndexV]->addr);
+  float beta1_power = reinterpret_cast<float *>(inputs[kIndexBeta1Power]->addr)[kScalarIndex];
+  float beta2_power = reinterpret_cast<float *>(inputs[kIndexBeta2Power]->addr)[kScalarIndex];
+  float lr = reinterpret_cast<float *>(inputs[kIndexLr]->addr)[kScalarIndex];
+  T beta1 = static_cast<T>(reinterpret_cast<float *>(inputs[kIndexBeta1]->addr)[kScalarIndex]);
+  T beta2 = static_cast<T>(reinterpret_cast<float *>(inputs[kIndexBeta2]->addr)[kScalarIndex]);
+  T epsilon = static_cast<T>(reinterpret_cast<float *>(inputs[kIndexEpsilon]->addr)[kScalarIndex]);
+  T *gradient = reinterpret_cast<T *>(inputs[kIndexGrad]->addr);
   constexpr float ONE = 1.0;
   if (beta1_power - ONE == 0) {
     MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the 'beta1_power' can't be set 1.";
   }
   T new_lr = static_cast<T>(lr * std::sqrt(ONE - beta2_power) / (ONE - beta1_power));
   // multithreading
-  size_t lens = inputs[VAR]->size > 0 ? static_cast<size_t>(inputs[VAR]->size / sizeof(T)) : 1;
+  size_t lens = inputs[kIndexVar]->size > 0 ? static_cast<size_t>(inputs[kIndexVar]->size / sizeof(T)) : 1;
   auto task = [this, &var, &m, &v, &gradient, new_lr, beta1, beta2, epsilon](size_t start, size_t end) {
     T one = static_cast<T>(1.0);
     for (size_t i = start; i < end; i++) {
@@ -67,16 +77,16 @@ void AdamCpuKernelMod::LaunchAdam(const std::vector<kernel::AddressPtr> &inputs,
 
 void AdamCpuKernelMod::LaunchAdamNnacl(const std::vector<kernel::AddressPtr> &inputs,
                                        const std::vector<kernel::AddressPtr> &) {
-  float *var = reinterpret_cast<float *>(inputs[VAR]->addr);
-  float *m = reinterpret_cast<float *>(inputs[M]->addr);
-  float *v = reinterpret_cast<float *>(inputs[V]->addr);
-  float beta1_power = reinterpret_cast<float *>(inputs[BETA1_POWER]->addr)[kScalarIndex];
-  float beta2_power = reinterpret_cast<float *>(inputs[BETA2_POWER]->addr)[kScalarIndex];
-  float lr = reinterpret_cast<float *>(inputs[LR]->addr)[kScalarIndex];
-  float beta1 = reinterpret_cast<float *>(inputs[BETA1]->addr)[kScalarIndex];
-  float beta2 = reinterpret_cast<float *>(inputs[BETA2]->addr)[kScalarIndex];
-  float epsilon = reinterpret_cast<float *>(inputs[EPSILON]->addr)[kScalarIndex];
-  float *gradient = reinterpret_cast<float *>(inputs[GRAD]->addr);
+  float *var = reinterpret_cast<float *>(inputs[kIndexVar]->addr);
+  float *m = reinterpret_cast<float *>(inputs[kIndexM]->addr);
+  float *v = reinterpret_cast<float *>(inputs[kIndexV]->addr);
+  float beta1_power = reinterpret_cast<float *>(inputs[kIndexBeta1Power]->addr)[kScalarIndex];
+  float beta2_power = reinterpret_cast<float *>(inputs[kIndexBeta2Power]->addr)[kScalarIndex];
+  float lr = reinterpret_cast<float *>(inputs[kIndexLr]->addr)[kScalarIndex];
+  float beta1 = reinterpret_cast<float *>(inputs[kIndexBeta1]->addr)[kScalarIndex];
+  float beta2 = reinterpret_cast<float *>(inputs[kIndexBeta2]->addr)[kScalarIndex];
+  float epsilon = reinterpret_cast<float *>(inputs[kIndexEpsilon]->addr)[kScalarIndex];
+  float *gradient = reinterpret_cast<float *>(inputs[kIndexGrad]->addr);
   constexpr float ONE = 1.0;
   if (beta1_power - ONE == 0) {
     MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the 'beta1_power' can't be set 1.";
@@ -84,7 +94,7 @@ void AdamCpuKernelMod::LaunchAdamNnacl(const std::vector<kernel::AddressPtr> &in
   float new_lr = lr * std::sqrt(ONE - beta2_power) / (ONE - beta1_power);
 
   // multithreading
-  size_t lens = inputs[VAR]->size > 0 ? static_cast<size_t>(inputs[VAR]->size / sizeof(float)) : 1;
+  size_t lens = inputs[kIndexVar]->size > 0 ? static_cast<size_t>(inputs[kIndexVar]->size / sizeof(float)) : 1;
   auto task = [this, &var, &m, &v, &gradient, new_lr, beta1, beta2, epsilon](size_t start, size_t end) {
     int ret = AdamFp32(var, m, v, new_lr, beta1, beta2, epsilon, gradient, start, end, use_nesterov_);
     if (ret != NNACL_OK) {
@@ -110,45 +120,45 @@ bool AdamCpuKernelMod::Launch(const std::vector<kernel::AddressPtr> &inputs, con
   CHECK_KERNEL_INPUTS_NUM(inputs.size(), kAdamInputsNum, kernel_name_);
   CHECK_KERNEL_OUTPUTS_NUM(outputs.size(), kAdamOutputsNum, kernel_name_);
 
-  if (inputs[VAR]->size != inputs[M]->size) {
+  if (inputs[kIndexVar]->size != inputs[kIndexM]->size) {
     MS_LOG(EXCEPTION) << "For '" << kernel_name_
                       << "', the shape and dtype of 'm' and 'var' should be same, but got the memory size of 'm': "
-                      << inputs[M]->size << " and 'var': " << inputs[VAR]->size;
+                      << inputs[kIndexM]->size << " and 'var': " << inputs[kIndexVar]->size;
   }
-  if (inputs[VAR]->size != inputs[V]->size) {
+  if (inputs[kIndexVar]->size != inputs[kIndexV]->size) {
     MS_LOG(EXCEPTION) << "For '" << kernel_name_
                       << "', the shape and dtype of 'v' and 'var' should be same, but got the memory size of 'v': "
-                      << inputs[V]->size << " and 'var': " << inputs[VAR]->size;
+                      << inputs[kIndexV]->size << " and 'var': " << inputs[kIndexVar]->size;
   }
-  if (inputs[VAR]->size != inputs[GRAD]->size) {
+  if (inputs[kIndexVar]->size != inputs[kIndexGrad]->size) {
     MS_LOG(EXCEPTION) << "For '" << kernel_name_
                       << "', the shape and dtype of 'gradient' and 'var' should be same, but got "
                          "the memory size of 'gradient': "
-                      << inputs[GRAD]->size << " and 'var': " << inputs[VAR]->size;
+                      << inputs[kIndexGrad]->size << " and 'var': " << inputs[kIndexVar]->size;
   }
   size_t f_size = sizeof(float);
-  if (inputs[BETA1_POWER]->size != f_size) {
+  if (inputs[kIndexBeta1Power]->size != f_size) {
     MS_LOG(EXCEPTION) << "For '" << kernel_name_
-                      << "', the 'beta1_power' should be float, but got 'beta1_power': " << inputs[BETA1_POWER];
+                      << "', the 'beta1_power' should be float, but got 'beta1_power': " << inputs[kIndexBeta1Power];
   }
-  if (inputs[BETA2_POWER]->size != f_size) {
+  if (inputs[kIndexBeta2Power]->size != f_size) {
     MS_LOG(EXCEPTION) << "For '" << kernel_name_
-                      << "', the 'beta2_power' should be float, but got 'beta2_power': " << inputs[BETA2_POWER];
+                      << "', the 'beta2_power' should be float, but got 'beta2_power': " << inputs[kIndexBeta2Power];
   }
-  if (inputs[LR]->size != f_size) {
-    MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the 'lr' should be float, but got 'lr': " << inputs[LR];
+  if (inputs[kIndexLr]->size != f_size) {
+    MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the 'lr' should be float, but got 'lr': " << inputs[kIndexLr];
   }
-  if (inputs[BETA1]->size != f_size) {
+  if (inputs[kIndexBeta1]->size != f_size) {
     MS_LOG(EXCEPTION) << "For '" << kernel_name_
-                      << "', the 'beta1' should be float, but got 'beta1': " << inputs[BETA1];
+                      << "', the 'beta1' should be float, but got 'beta1': " << inputs[kIndexBeta1];
   }
-  if (inputs[BETA2]->size != f_size) {
+  if (inputs[kIndexBeta2]->size != f_size) {
     MS_LOG(EXCEPTION) << "For '" << kernel_name_
-                      << "', the 'beta2' should be float, but got 'beta2': " << inputs[BETA2];
+                      << "', the 'beta2' should be float, but got 'beta2': " << inputs[kIndexBeta2];
   }
-  if (inputs[EPSILON]->size != f_size) {
+  if (inputs[kIndexEpsilon]->size != f_size) {
     MS_LOG(EXCEPTION) << "For '" << kernel_name_
-                      << "', the 'epsilon' should be float, but got 'epsilon': " << inputs[EPSILON];
+                      << "', the 'epsilon' should be float, but got 'epsilon': " << inputs[kIndexEpsilon];
   }
 
   if (dtype_ == kNumberTypeFloat32) {

mindspore/ccsrc/plugin/device/cpu/kernel/adam_cpu_kernel.h

@@ -40,7 +40,6 @@ class AdamCpuKernelMod : public NativeCpuKernelMod {
   void LaunchAdamNnacl(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &outputs);
   bool use_nesterov_{false};
   TypeId dtype_{kTypeUnknown};
-  enum input_list_ { VAR, M, V, BETA1_POWER, BETA2_POWER, LR, BETA1, BETA2, EPSILON, GRAD };
 };
 }  // namespace kernel
 }  // namespace mindspore

mindspore/ccsrc/plugin/device/cpu/kernel/argmax_with_value_cpu_kernel.cc

@@ -64,9 +64,7 @@ template <typename T>
 bool ArgMaxWithValueCpuKernelMod::LaunchKernel(const std::vector<kernel::AddressPtr> &inputs,
                                                const std::vector<kernel::AddressPtr> &,
                                                const std::vector<kernel::AddressPtr> &outputs) {
-  if (!check_validation<T>(shape_, num_before_axis_, num_after_axis_, inputs, outputs)) {
-    return false;
-  }
+  (void)check_validation<T>(shape_, num_before_axis_, num_after_axis_, inputs, outputs);
 
   auto input = reinterpret_cast<T *>(inputs[0]->addr);
   auto output0 = reinterpret_cast<int32_t *>(outputs[0]->addr);

mindspore/ccsrc/plugin/device/cpu/kernel/boundingbox_encode_cpu_kernel.cc

@@ -58,7 +58,7 @@ void BoundingBoxEncodeCpuKernelMod::InitKernel(const CNodePtr &kernel_node) {
   } else if (common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("means")->isa<FloatImm>()) {
     float mean = common::AnfAlgo::GetNodeAttr<float>(kernel_node, "means");
     for (size_t i = 0; i < coordinate_size; i++) {
-      means_.emplace_back(mean);
+      (void)means_.emplace_back(mean);
     }
   } else {
     MS_LOG(EXCEPTION) << "For '" << kernel_name_
@@ -71,7 +71,7 @@ void BoundingBoxEncodeCpuKernelMod::InitKernel(const CNodePtr &kernel_node) {
   } else if (common::AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("stds")->isa<FloatImm>()) {
     float std = common::AnfAlgo::GetNodeAttr<float>(kernel_node, "stds");
     for (size_t i = 0; i < coordinate_size; i++) {
-      stds_.emplace_back(std);
+      (void)stds_.emplace_back(std);
     }
   } else {
     MS_LOG(EXCEPTION) << "For '" << kernel_name_

mindspore/ccsrc/plugin/device/cpu/kernel/layer_norm_grad_cpu_kernel.cc

@@ -142,7 +142,7 @@ void LayerNormGradCpuKernelMod::LaunchKernel(const std::vector<AddressPtr> &inpu
       task1(i);
       return common::SUCCESS;
     };
-    tasks1.emplace_back(block);
+    (void)tasks1.emplace_back(block);
   }
   ParallelLaunch(tasks1);
   for (size_t i = 0; i < thread_num2; ++i) {
@@ -150,7 +150,7 @@ void LayerNormGradCpuKernelMod::LaunchKernel(const std::vector<AddressPtr> &inpu
       task2(i);
       return common::SUCCESS;
     };
-    tasks2.emplace_back(block);
+    (void)tasks2.emplace_back(block);
   }
   ParallelLaunch(tasks2);
 }

mindspore/ccsrc/plugin/device/cpu/kernel/masked_select_grad_cpu_kernel.cc

@@ -23,14 +23,17 @@ namespace kernel {
 namespace {
 constexpr size_t kMaskedSelectGradInputsNum = 3;
 constexpr size_t kMaskedSelectGradOutputsNum = 1;
+constexpr size_t kIndexInput = 0;
+constexpr size_t kIndexMask = 1;
+constexpr size_t kIndexGrad = 2;
 }  // namespace
 
 void MaskedSelectGradCpuKernelMod::InitKernel(const CNodePtr &kernel_node) {
   MS_EXCEPTION_IF_NULL(kernel_node);
   kernel_name_ = common::AnfAlgo::GetCNodeName(kernel_node);
-  input_shape_a_ = AnfAlgo::GetInputDeviceShape(kernel_node, INPUT);
-  input_shape_b_ = AnfAlgo::GetInputDeviceShape(kernel_node, MASK);
-  grad_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, GRAD);
+  input_shape_a_ = AnfAlgo::GetInputDeviceShape(kernel_node, kIndexInput);
+  input_shape_b_ = AnfAlgo::GetInputDeviceShape(kernel_node, kIndexMask);
+  grad_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, kIndexGrad);
   output_shape_ = CPUKernelUtils::GetBroadcastShape(input_shape_a_, input_shape_b_);
   for (const uint64_t &d : output_shape_) {
     tensor_size_ *= d;
@@ -49,9 +52,9 @@ bool MaskedSelectGradCpuKernelMod::LaunchKernel(const std::vector<kernel::Addres
                                                 const std::vector<kernel::AddressPtr> &outputs) {
   CHECK_KERNEL_INPUTS_NUM(inputs.size(), kMaskedSelectGradInputsNum, kernel_name_);
   CHECK_KERNEL_OUTPUTS_NUM(outputs.size(), kMaskedSelectGradOutputsNum, kernel_name_);
-  auto mask = reinterpret_cast<bool *>(inputs[MASK]->addr);
-  auto grad = reinterpret_cast<T *>(inputs[GRAD]->addr);
-  auto dx = reinterpret_cast<T *>(outputs[INPUT]->addr);
+  auto mask = reinterpret_cast<bool *>(inputs[kIndexMask]->addr);
+  auto grad = reinterpret_cast<T *>(inputs[kIndexGrad]->addr);
+  auto dx = reinterpret_cast<T *>(outputs[kIndexInput]->addr);
 
   auto ret = memset_s(dx, outputs[0]->size, 0, outputs[0]->size);
   if (ret != EOK) {

mindspore/ccsrc/plugin/device/cpu/kernel/masked_select_grad_cpu_kernel.h

@@ -53,7 +53,6 @@ class MaskedSelectGradCpuKernelMod : public NativeCpuKernelMod {
   std::vector<size_t> grad_shape_;
   std::vector<size_t> output_shape_;
   uint64_t tensor_size_ = 1;
-  enum input_list_ { INPUT, MASK, GRAD };
 };
 }  // namespace kernel
 }  // namespace mindspore

mindspore/ccsrc/plugin/device/cpu/kernel/nms_with_mask_cpu_kernel.cc

@@ -20,6 +20,15 @@
 
 namespace mindspore {
 namespace kernel {
+namespace {
+const size_t kIndexDataBuff = 0;
+const size_t kIndexIndexBuff = 1;
+const size_t kIndexRowMask = 2;
+const size_t kIndexOutput = 0;
+const size_t kIndexSelIdx = 1;
+const size_t kIndexSelBoxes = 2;
+}  // namespace
+
 uint32_t NmsRoundUpPower2(int v) {
   constexpr uint32_t ONE = 1, TWO = 2, FOUR = 4, EIGHT = 8, SIXTEEN = 16;
   v--;
@@ -54,7 +63,7 @@ void NMSWithMaskCpuKernelMod::NmsBitonicSortByKeyKernel(const int inner, const s
 
   for (size_t i = 2; i <= ceil_power2; i <<= 1) {
     for (size_t j = (i >> 1); j > 0; j >>= 1) {
-      auto task2 = [&](size_t start, size_t end) {
+      auto task2 = [i, j, &data_buff, &index_buff](size_t start, size_t end) {
         for (size_t tid = start; tid < end; tid++) {
           size_t tid_comp = tid ^ j;
           if (tid_comp > tid) {
@@ -237,12 +246,12 @@ bool NMSWithMaskCpuKernelMod::LaunchKernel(const std::vector<kernel::AddressPtr>
                                            const std::vector<kernel::AddressPtr> &workspace,
                                            const std::vector<kernel::AddressPtr> &outputs) {
   auto input = reinterpret_cast<T *>(inputs[0]->addr);
-  auto data_buff = reinterpret_cast<T *>(workspace[DATA_BUFF]->addr);
-  auto index_buff = reinterpret_cast<int *>(workspace[INDEX_BUFF]->addr);
-  auto row_mask = reinterpret_cast<bool *>(workspace[ROW_MASK]->addr);
-  auto output = reinterpret_cast<T *>(outputs[OUTPUT]->addr);
-  auto sel_idx = reinterpret_cast<int *>(outputs[SEL_IDX]->addr);
-  auto sel_boxes = reinterpret_cast<bool *>(outputs[SEL_BOXES]->addr);
+  auto data_buff = reinterpret_cast<T *>(workspace[kIndexDataBuff]->addr);
+  auto index_buff = reinterpret_cast<int *>(workspace[kIndexIndexBuff]->addr);
+  auto row_mask = reinterpret_cast<bool *>(workspace[kIndexRowMask]->addr);
+  auto output = reinterpret_cast<T *>(outputs[kIndexOutput]->addr);
+  auto sel_idx = reinterpret_cast<int *>(outputs[kIndexSelIdx]->addr);
+  auto sel_boxes = reinterpret_cast<bool *>(outputs[kIndexSelBoxes]->addr);
 
   NmsBitonicSortByKeyKernel<T>(num_input_, ceil_power_2, input, data_buff, index_buff, box_size_);
   size_t total_val = IntToSize(num_input_ * num_input_);

mindspore/ccsrc/plugin/device/cpu/kernel/nms_with_mask_cpu_kernel.h

@@ -80,8 +80,6 @@ class NMSWithMaskCpuKernelMod : public NativeCpuKernelMod {
   float iou_value_{0.0};
   size_t ceil_power_2{0};
   static const int box_size_ = 5;  //  pre_defined box width
-  enum workspace_list_ { DATA_BUFF, INDEX_BUFF, ROW_MASK };
-  enum output_list_ { OUTPUT, SEL_IDX, SEL_BOXES };
 };
 }  // namespace kernel
 }  // namespace mindspore

mindspore/ccsrc/plugin/device/cpu/kernel/random_choice_with_mask_cpu_kernel.cc

@@ -180,12 +180,11 @@ bool RandomChoiceWithMaskCpuKernelMod::Launch(const std::vector<kernel::AddressP
     }
   }
 
-  int32_t copy_output_length = 0;
   if (output_length * input_dim_size >= INT_MAX || output_length * input_dim_size < 0) {
     MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', output size exceed INT_MAX.";
   }
 
-  copy_output_length = output_length * input_dim_size;
+  int32_t copy_output_length = output_length * input_dim_size;
   (void)memset_s(output, IntToSize(copy_output_length), 0X00, IntToSize(copy_output_length));
   ParseOutputCoordinate(dims, output_length, input_dim_size, input_total_count, tmp_output, output);
 

mindspore/ccsrc/plugin/device/cpu/kernel/random_choice_with_mask_cpu_kernel.h

@@ -37,9 +37,9 @@ class RandomChoiceWithMaskCpuKernelMod : public NativeCpuKernelMod {
   bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
               const std::vector<AddressPtr> &outputs) override;
 
+ protected:
   void InitInputOutputSize(const CNodePtr &kernel_node) override;
 
- protected:
   std::vector<KernelAttr> GetOpSupport() override {
     static std::vector<KernelAttr> support_list = {
       KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeBool)};

mindspore/ccsrc/plugin/device/cpu/kernel/resize_bilinear_cpu_kernel.cc

@@ -97,6 +97,9 @@ bool ResizeBilinearCpuKernelMod::LaunchKernel(const std::vector<AddressPtr> &inp
     MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dtype of input should be float16 or float32, but got "
                       << TypeIdLabel(dtype_);
   }
+  MS_EXCEPTION_IF_NULL(output_addr_T2);
+  MS_EXCEPTION_IF_NULL(float_input_addr);
+  MS_EXCEPTION_IF_NULL(float_output_addr);
 
   size_t batch_size = shape_[0];
   size_t channel = shape_[1];

mindspore/ccsrc/plugin/device/cpu/kernel/resize_bilinear_grad_cpu_kernel.cc

@@ -108,6 +108,10 @@ bool ResizeBilinearGradCpuKernelMod::LaunchKernel(const std::vector<AddressPtr>
                       << TypeIdLabel(dtype_);
   }
 
+  MS_EXCEPTION_IF_NULL(output_addr);
+  MS_EXCEPTION_IF_NULL(float_dloss_addr);
+  MS_EXCEPTION_IF_NULL(float_output_addr);
+
   size_t batch_size = shape_[0];
   size_t channel = shape_[1];
   size_t in_height = shape_[2];

mindspore/ccsrc/plugin/device/cpu/kernel/roi_align_cpu_kernel.cc

@@ -240,7 +240,7 @@ void ROIAlignCpuKernelFunc<T>::bin_box(int thread_idx, const T *roi_boxes, int r
   const T *roi_box = roi_boxes + (*n) * roi_cols;
   int roi_batch_ind = 0;
   if (roi_cols == ROIS_COLS) {
-    roi_batch_ind = FloatToInt(rint(static_cast<float>(roi_box[0]) + eps));
+    roi_batch_ind = FloatToInt(rintf(static_cast<float>(roi_box[0]) + eps));
     roi_box++;
   }
 

mindspore/ccsrc/plugin/device/cpu/kernel/roi_align_grad_cpu_kernel.cc

@@ -96,6 +96,8 @@ void AtomicAdd(T *const address, const T val) {
       AtomicAddTask<T, int64_t>(address, val);
       break;
     }
+    default:
+      MS_LOG(EXCEPTION) << "For 'ROIAlignGrad', the dtype " << typeid(T).name() << " is unsupported.";
   }
 }
 
@@ -299,7 +301,7 @@ void ROIAlignGradCpuKernelFunc<T>::bin_box(int thread_idx, const T *roi_boxes, i
   const T *roi_box = roi_boxes + (*n) * roi_cols;
   int roi_batch_ind = 0;
   if (roi_cols == ROIS_COLS) {
-    roi_batch_ind = FloatToInt(rint(static_cast<float>(roi_box[0]) + eps));
+    roi_batch_ind = FloatToInt(rintf(static_cast<float>(roi_box[0]) + eps));
     roi_box++;
   }
 

mindspore/ccsrc/plugin/device/cpu/kernel/sgd_cpu_kernel.cc

@@ -25,6 +25,12 @@ namespace kernel {
 namespace {
 constexpr size_t kSGDInputsNum = 6;
 constexpr size_t kSGDOutputsNum = 1;
+constexpr size_t kIndexParm = 0;
+constexpr size_t kIndexGrad = 1;
+constexpr size_t kIndexLr = 2;
+constexpr size_t kIndexAccum = 3;
+constexpr size_t kIndexMomentum = 4;
+constexpr size_t kIndexStat = 5;
 }  // namespace
 void SGDCpuKernelMod::InitKernel(const CNodePtr &kernel_node) {
   MS_EXCEPTION_IF_NULL(kernel_node);
@@ -45,12 +51,12 @@ template <typename T>
 bool SGDCpuKernelMod::LaunchKernel(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &outputs) {
   CHECK_KERNEL_INPUTS_NUM(inputs.size(), kSGDInputsNum, kernel_name_);
   CHECK_KERNEL_OUTPUTS_NUM(outputs.size(), kSGDOutputsNum, kernel_name_);
-  auto param = reinterpret_cast<T *>(inputs[PARAM]->addr);
-  auto grad = reinterpret_cast<T *>(inputs[GRAD]->addr);
-  auto lr = reinterpret_cast<T *>(inputs[LR]->addr);
-  auto accum = reinterpret_cast<T *>(inputs[ACCUM]->addr);
-  auto momentum = reinterpret_cast<T *>(inputs[MOMENTUM]->addr);
-  auto stat = reinterpret_cast<T *>(inputs[STAT]->addr);
+  auto param = reinterpret_cast<T *>(inputs[kIndexParm]->addr);
+  auto grad = reinterpret_cast<T *>(inputs[kIndexGrad]->addr);
+  auto lr = reinterpret_cast<T *>(inputs[kIndexLr]->addr);
+  auto accum = reinterpret_cast<T *>(inputs[kIndexAccum]->addr);
+  auto momentum = reinterpret_cast<T *>(inputs[kIndexMomentum]->addr);
+  auto stat = reinterpret_cast<T *>(inputs[kIndexStat]->addr);
   auto output_param = reinterpret_cast<T *>(outputs[0]->addr);
   size_t elem_num = inputs[0]->size / sizeof(T);
 

mindspore/ccsrc/plugin/device/cpu/kernel/sgd_cpu_kernel.h

@@ -51,7 +51,6 @@ class SGDCpuKernelMod : public NativeCpuKernelMod {
   float dampening_{0.0};
   float weight_decay_{0.0};
   bool nesterov_{true};
-  enum input_list_ { PARAM, GRAD, LR, ACCUM, MOMENTUM, STAT };
 };
 }  // namespace kernel
 }  // namespace mindspore