support non-Iterable valid_dtypes when check tensor type valid

mindspore/_checkparam.py

@@ -448,12 +448,14 @@ class Validator:
     @staticmethod
     def check_tensors_dtypes_same_and_valid(args, valid_dtypes, prim_name):
         """Checks whether the element types of input tensors are the same and valid."""
+        valid_dtypes = valid_dtypes if isinstance(valid_dtypes, Iterable) else [valid_dtypes]
         tensor_types = [mstype.tensor_type(t) for t in valid_dtypes]
         Validator.check_types_same_and_valid(args, tensor_types, prim_name)
 
     @staticmethod
     def check_tensor_dtype_valid(arg_name, arg_type, valid_dtypes, prim_name):
         """Checks whether the element types of input tensors are valid."""
+        valid_dtypes = valid_dtypes if isinstance(valid_dtypes, Iterable) else [valid_dtypes]
         tensor_types = [mstype.tensor_type(t) for t in valid_dtypes]
         Validator.check_subclass(arg_name, arg_type, tensor_types, prim_name)
 

mindspore/ops/operations/nn_ops.py

@@ -5716,7 +5716,7 @@ class DynamicRNN(PrimitiveWithInfer):
         return y_shape, y_shape, y_shape, y_shape, y_shape, y_shape, y_shape, y_shape
 
     def infer_dtype(self, x_dtype, w_dtype, b_dtype, seq_dtype, h_dtype, c_dtype):
-        tuple(map(partial(validator.check_tensor_dtype_valid, valid_dtypes=mstype.float16, prim_name=self.name),
+        tuple(map(partial(validator.check_tensor_dtype_valid, valid_dtypes=[mstype.float16], prim_name=self.name),
                   ("x", "w", "h", "c"),
                   (x_dtype, w_dtype, h_dtype, c_dtype)))
         validator.check_tensor_dtype_valid("b", b_dtype, (mstype.float16, mstype.float32), self.name)