Fix I5IZU7 optimize log && clean pclint-plus warnings

Add loss to cfg instead train_cfg

include/api/cfg.h

@@ -60,8 +60,8 @@ class TrainCfg {
   ~TrainCfg() = default;
 
   OptimizationLevel optimization_level_ = kO0;
-  std::vector<std::string> loss_name_ = {"loss_fct",
-                                         "_loss_fn"}; /**< Set part of the name that identify a loss kernel */
+  std::vector<std::string> loss_name_ = {
+    "loss_fct", "_loss_fn", "SigmoidCrossEntropy"}; /**< Set part of the name that identify a loss kernel */
   MixPrecisionCfg mix_precision_cfg_;               /**< Mix precision configuration */
   bool accumulate_gradients_ = false;
 };

mindspore/ccsrc/pipeline/pynative/pynative_cache.h

@@ -20,11 +20,11 @@
 #include <vector>
 #include <memory>
 #include <unordered_map>
+#include "pybind11/pytypes.h"
 #include "utils/hash_map.h"
 #include "utils/ms_utils.h"
 #include "ir/anf.h"
 #include "ir/signature.h"
-#include "pybind11/pytypes.h"
 
 namespace mindspore::pynative {
 struct AbsCacheKey {

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

@@ -17,6 +17,7 @@
 #include <algorithm>
 #include <utility>
 #include <limits>
+#include <cmath>
 #include "plugin/device/cpu/hal/device/cpu_device_address.h"
 
 namespace mindspore {
@@ -27,11 +28,7 @@ static constexpr int MAX_DIMS = 7;
 template <typename T>
 T GetDivZeroVal(const T &v) {
   auto zero = static_cast<T>(0.0);
-  if (std::numeric_limits<T>::has_infinity) {
   return v > zero ? std::numeric_limits<T>::infinity() : -std::numeric_limits<T>::infinity();
-  } else {
-    return v > zero ? std::numeric_limits<T>::max() : std::numeric_limits<T>::min();
-  }
 }
 
 template <>
@@ -40,28 +37,42 @@ complex128 GetDivZeroVal(const complex128 &v) {
 }
 
 template <>
-complex64 GetDivZeroVal(const complex64 &v) {
+complex64 GetDivZeroVal(const complex64 &) {
   return std::numeric_limits<complex64>::quiet_NaN();
 }
 
+template <class T>
+bool isZero(T val) {
+  return val == T(0.0f);
+}
+
+template <>
+bool isZero(float val) {
+  return std::fpclassify(val) == FP_ZERO;
+}
+
+template <>
+bool isZero(double val) {
+  return std::fpclassify(val) == FP_ZERO;
+}
+
 template <typename T>
 bool XdivyCpuKernelMod::LaunchKernel(const std::vector<kernel::AddressPtr> &inputs,
                                      const std::vector<kernel::AddressPtr> &,
                                      const std::vector<kernel::AddressPtr> &outputs) {
   CHECK_KERNEL_INPUTS_NUM(inputs.size(), INPUT_NUM, kernel_name_);
   CHECK_KERNEL_OUTPUTS_NUM(outputs.size(), OUTPUT_NUM, kernel_name_);
-  auto x_addr = reinterpret_cast<T *>(inputs[0]->addr);
-  auto y_addr = reinterpret_cast<T *>(inputs[1]->addr);
-  auto output_addr = reinterpret_cast<T *>(outputs[0]->addr);
+  auto x_addr = static_cast<T *>(inputs[0]->addr);
+  auto y_addr = static_cast<T *>(inputs[1]->addr);
+  auto output_addr = static_cast<T *>(outputs[0]->addr);
   size_t output_size = outputs[0]->size / sizeof(T);
   auto sameShapeTask = [&x_addr, &y_addr, &output_addr](size_t start, size_t end) {
     for (size_t i = start; i < end; i++) {
       auto dividend = x_addr[i];
       auto divisor = y_addr[i];
-      auto zero = (T)0;
-      if (divisor == zero) {
-        if (dividend == zero) {
-          output_addr[i] = zero;
+      if (isZero(divisor)) {
+        if (isZero(dividend)) {
+          output_addr[i] = static_cast<T>(0.0);
           continue;
         }
         output_addr[i] = GetDivZeroVal(dividend);
@@ -217,7 +228,7 @@ int XdivyCpuKernelMod::Resize(const BaseOperatorPtr &base_operator, const std::v
     GetBroadCastIndex(x_shape, out_shape, &index_listx_);
     GetBroadCastIndex(y_shape, out_shape, &index_listy_);
   }
-  return KRET_OK;
+  return 0;
 }
 
 std::vector<KernelAttr> XdivyCpuKernelMod::GetOpSupport() { return support_ops_; }

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

@@ -18,7 +18,6 @@
 #include <complex>
 #include <vector>
 #include <map>
-#include <utility>
 #include "plugin/device/cpu/kernel/cpu_kernel.h"
 #include "plugin/factory/ms_factory.h"
 
@@ -41,9 +40,9 @@ class XdivyCpuKernelMod : public NativeCpuKernelMod {
   int Resize(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs,
              const std::vector<KernelTensorPtr> &outputs, const std::map<uint32_t, tensor::TensorPtr> &) override;
 
- protected:
   std::vector<KernelAttr> GetOpSupport() override;
 
+ protected:
   void ResetResource() noexcept {
     input_size_list_.clear();
     output_size_list_.clear();

mindspore/core/ir/manager.cc

@@ -781,8 +781,8 @@ void FuncGraphManager::CommitChanges(std::vector<change::ChangePtr> &&changes) {
 
 void FuncGraphManager::EraseOneGraph(const FuncGraphPtr &fg) {
   MS_EXCEPTION_IF_NULL(fg);
-  size_t erase_cnt = func_graphs_.erase(fg->shared_from_base<FuncGraph>());
-  if (!erase_cnt) {
+  bool erase_ret = func_graphs_.erase(fg->shared_from_base<FuncGraph>());
+  if (!erase_ret) {
     return;
   }
   fg->DecAttachedMngCnt();

mindspore/core/ir/pattern_matcher.h

@@ -617,7 +617,7 @@ class PConstant : public PBase<PConstant<T> > {
     MS_EXCEPTION_IF_NULL(source_data);
     if (x_tensor_ptr->DataSize() == 1) {
       auto tensor_type_byte = GetTypeByte(tensor_type_ptr);
-      char *data = reinterpret_cast<char *>(new_tensor_ptr->data_c());
+      char *data = static_cast<char *>(new_tensor_ptr->data_c());
       for (int i = 0; i < new_tensor_ptr->ElementsNum(); i++) {
         ret = memcpy_s(data + IntToSize(i) * tensor_type_byte, tensor_type_byte, source_data, tensor_type_byte);
         if (ret != 0) {
@@ -626,7 +626,7 @@ class PConstant : public PBase<PConstant<T> > {
         }
       }
     } else {
-      char *data = reinterpret_cast<char *>(new_tensor_ptr->data_c());
+      char *data = static_cast<char *>(new_tensor_ptr->data_c());
       ret = memcpy_s(data, new_tensor_ptr->Size(), source_data, new_tensor_ptr->Size());
       if (ret != 0) {
         MS_LOG(INFO) << "memcpy_s error, error no " << ret << ", source size " << new_tensor_ptr->Size()

mindspore/core/ops/extract_volume_patches.cc

@@ -49,10 +49,10 @@ abstract::ShapePtr ExtractVolumePatchesInferShape(const PrimitivePtr &primitive,
                                            primitive->name());
   auto padding = GetValue<std::string>(primitive->GetAttr(kPadding));
   for (auto &item : strides) {
-    (void)CheckAndConvertUtils::Check("strides", item, kGreaterThan, 0, primitive->name());
+    CheckAndConvertUtils::Check("strides", item, kGreaterThan, 0, primitive->name());
   }
   for (auto &item : kernel_size) {
-    (void)CheckAndConvertUtils::Check("kernel_size", item, kGreaterThan, 0, primitive->name());
+    CheckAndConvertUtils::Check("kernel_size", item, kGreaterThan, 0, primitive->name());
   }
   std::vector<int64_t> y_shape(5);
   int64_t padding_needed = 0;

mindspore/python/mindspore/ops/composite/multitype_ops/_constexpr_utils.py

@@ -291,9 +291,9 @@ def check_type_valid(dtype, target_type, op_name):
     if dtype != target_type and (isinstance(target_type, (list, tuple)) and dtype not in target_type):
         if op_name in (TENSOR_GETITEM, TENSOR_SETITEM):
             raise IndexError(
-                f"The '{op_name}' doesn't support {dtype}' and expect to receive {target_type}.")
+                f"The '{op_name}' doesn't support '{dtype}' and expect to receive {target_type}.")
         raise TypeError(
-            f"The '{op_name}' doesn't support {dtype}' and expect to receive {target_type}.")
+            f"The '{op_name}' doesn't support '{dtype}' and expect to receive {target_type}.")
 
 
 @constexpr