!6729 clear warnings

Merge pull request !6729 from baihuawei/fixreduce

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

@@ -38,37 +38,7 @@ void ReduceCPUKernel::InitKernel(const CNodePtr &kernel_node) {
     MS_LOG(EXCEPTION) << "Array reduce kernel type " << kernel_name << " is not supported.";
   }
   shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
-  auto axis_addr = AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr(AXIS);
-
-  if (axis_addr->isa<ValueTuple>()) {
-    auto attr_axis = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, AXIS);
-    if (attr_axis.size() > shape_.size()) {
-      MS_LOG(EXCEPTION) << "invalid axis size: " << axis_.size();
-    } else if (attr_axis.empty()) {
-      axis_.push_back(shape_.size() - 1);
-    } else {
-      for (auto axis : attr_axis) {
-        while (axis < 0) {
-          axis += SizeToInt(shape_.size());
-        }
-        if (IntToSize(axis) >= (shape_.size())) {
-          MS_LOG(EXCEPTION) << "axis value is oversize.";
-        }
-        axis_.push_back(IntToSize(axis));
-      }
-    }
-  } else if (axis_addr->isa<Int32Imm>()) {
-    int axis = AnfAlgo::GetNodeAttr<int>(kernel_node, AXIS);
-    while (axis < 0) {
-      axis += SizeToInt(shape_.size());
-    }
-    if (IntToSize(axis) >= shape_.size()) {
-      MS_LOG(EXCEPTION) << "axis value is oversize.";
-    }
-    axis_.push_back(IntToSize(axis));
-  } else {
-    MS_LOG(EXCEPTION) << "Attribute axis type is invalid.";
-  }
+  CheckAxis(kernel_node);
   for (size_t i = 0; i < shape_.size(); ++i) {
     if (shape_[i] <= 0) {
       MS_LOG(EXCEPTION) << "shape value is invalid.";
@@ -114,6 +84,41 @@ bool ReduceCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
   return true;
 }
 
+void ReduceCPUKernel::CheckAxis(const CNodePtr &kernel_node) {
+  auto axis_addr = AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr(AXIS);
+  if (axis_addr->isa<ValueTuple>()) {
+    auto attr_axis = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, AXIS);
+    if (attr_axis.size() > shape_.size()) {
+      MS_LOG(EXCEPTION) << "invalid axis size: " << axis_.size();
+    } else if (attr_axis.empty()) {
+      for (size_t i = 0; i < shape_.size(); ++i) {
+        axis_.push_back(i);
+      }
+    } else {
+      for (auto axis : attr_axis) {
+        while (axis < 0) {
+          axis += SizeToInt(shape_.size());
+        }
+        if (IntToSize(axis) >= (shape_.size())) {
+          MS_LOG(EXCEPTION) << "axis value is oversize.";
+        }
+        axis_.push_back(IntToSize(axis));
+      }
+    }
+  } else if (axis_addr->isa<Int32Imm>()) {
+    int axis = AnfAlgo::GetNodeAttr<int>(kernel_node, AXIS);
+    while (axis < 0) {
+      axis += SizeToInt(shape_.size());
+    }
+    if (IntToSize(axis) >= shape_.size()) {
+      MS_LOG(EXCEPTION) << "axis value is oversize.";
+    }
+    axis_.push_back(IntToSize(axis));
+  } else {
+    MS_LOG(EXCEPTION) << "Attribute axis type is invalid.";
+  }
+}
+
 void ReduceCPUKernel::ConvertDataToOutput(const float *new_input, float *output) {
   if (reduce_type_ == kReduceTypeMax) {
     for (size_t i = 0; i < left_dims_; ++i) {

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

@@ -35,6 +35,7 @@ class ReduceCPUKernel : public CPUKernel {
   void Transpose(const int size, const float *input, const std::vector<size_t> &input_shape,
                  const std::vector<size_t> &input_axis, const int shape_size, float *output);
   void ConvertDataToOutput(const float *input, float *output);
+  void CheckAxis(const CNodePtr &kernel_node);
   size_t reduce_type_ = 0;
   std::vector<size_t> axis_;
   std::vector<size_t> shape_;

tests/st/ops/cpu/test_reduce_op.py

@@ -32,6 +32,8 @@ class NetReduce(nn.Cell):
         self.axis2 = -1
         self.axis3 = (0, 1)
         self.axis4 = (0, 1, 2)
+        self.axis5 = (-1,)
+        self.axis6 = ()
         self.reduce_mean = P.ReduceMean(False)
         self.reduce_sum = P.ReduceSum(False)
         self.reduce_max = P.ReduceMax(False)
@@ -46,7 +48,10 @@ class NetReduce(nn.Cell):
                 self.reduce_sum(indice, self.axis0),
                 self.reduce_sum(indice, self.axis2),
                 self.reduce_max(indice, self.axis0),
-                self.reduce_max(indice, self.axis2))
+                self.reduce_max(indice, self.axis2),
+                self.reduce_max(indice, self.axis5),
+                self.reduce_max(indice, self.axis6))
+
 
 
 @pytest.mark.level0
@@ -69,6 +74,8 @@ def test_reduce():
     print(output[6])
     print(output[7])
     print(output[8])
+    print(output[9])
+    print(output[10])
     expect_0 = np.array([[2., 1., 2., 3., 0., 1], [2., 2., 1., 2., 3., 2.]]).astype(np.float32)
     expect_1 = np.array([[1.5, 1.5, 1.5, 3., 2., 1.], [1.5, 0., 0.5, 4.5, 2., 2.], [3., 3., 2.5, 0., 0.5, 1.5]]).astype(
         np.float32)
@@ -88,6 +95,7 @@ def test_reduce():
     assert (output[6].asnumpy() == expect_6).all()
     assert (output[7].asnumpy() == expect_7).all()
     assert (output[8].asnumpy() == expect_8).all()
-
+    assert (output[9].asnumpy() == expect_8).all()
+    assert (output[10].asnumpy() == 5.0).all()
 
 test_reduce()