[bug] fixed bool check for cast op

Signed-off-by: candanzg <zhangshucheng@huawei.com>

mindspore/ccsrc/operator/composite/do_signature.cc

@@ -137,6 +137,19 @@ void DoAutoCast(const std::vector<Signature>& signature, const abstract::Abstrac
     if (it == dst_type.end() || it->second == i || !arg_value->isa<abstract::AbstractScalar>()) {
       continue;
     }
+    // When scalar is of bool type, the type of tensor must also be of bool type,
+    // otherwise the cast operator will not be added.
+    auto scalar = arg_value->cast<abstract::AbstractScalarPtr>();
+    auto scalar_type = scalar->BuildType();
+    MS_EXCEPTION_IF_NULL(scalar_type);
+    if (scalar_type->type_id() == kNumberTypeBool) {
+      auto tensor = args_spec_list[it->second]->cast<abstract::AbstractTensorPtr>();
+      auto tensor_type = tensor->element()->BuildType();
+      MS_EXCEPTION_IF_NULL(tensor_type);
+      if (tensor_type->type_id() != kNumberTypeBool) {
+        continue;
+      }
+    }
     // get source node for cast
     AnfNodePtr source_node = (*op_inputs)[it->second + 1];
     (*op_inputs)[i + 1] = DoCast((*op_inputs)[i + 1], source_node, graph);

mindspore/ops/operations/array_ops.py

@@ -745,7 +745,7 @@ class Fill(PrimitiveWithInfer):
         out = {
             'value': Tensor(ret),
             'shape': dims['value'],
-            'dtype': x_nptype,
+            'dtype': x_dtype,
         }
         return out
 

tests/ut/python/ops/test_math_ops.py

@@ -30,6 +30,7 @@ from ....mindspore_test_framework.pipeline.forward.compile_forward \
     import pipeline_for_compile_forward_ge_graph_for_case_by_case_config
 from ....mindspore_test_framework.pipeline.forward.verify_exception \
     import pipeline_for_verify_exception_for_case_by_case_config
+import pytest
 
 
 # pylint: disable=W0613
@@ -81,14 +82,29 @@ def test_sqrt():
     assert np.all(result.asnumpy() == expect)
 
 
+class PowNet(nn.Cell):
+    def __init__(self):
+        super(PowNet, self).__init__()
+        self.pow = P.Pow()
+
+    def construct(self, x, y):
+        return self.pow(x, y)
+
+
 def test_pow():
     """ test_pow """
     input_tensor = Tensor(np.array([[2, 2], [3, 3]]))
     power = Tensor(np.array(3.0, np.int64))
+    power2 = Tensor(np.array(True, np.bool))
     testpow = P.Pow()
     expect = np.array([[8, 8], [27, 27]])
     result = testpow(input_tensor, power)
     assert np.all(result.asnumpy() == expect)
+    net = PowNet()
+    with pytest.raises(TypeError):
+        net(input_tensor, True)
+    with pytest.raises(TypeError):
+        net(input_tensor, power2)
 
 
 def test_exp():