diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index 0b44e83b2f3ef3b46e0046a4f6eb47bb6ddb9223..26f0c98223681ece138fdab8424c10807425b321 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -35,6 +35,7 @@ from ..primitive import Primitive, PrimitiveWithInfer, prim_attr_register, _run_
 from ..._c_expression import signature_rw as sig_rw
 from ..._c_expression import signature_kind as sig_kind
 from ..._c_expression import signature_dtype as sig_dtype
+from ..._c_expression import typing
 
 def _check_infer_attr_reduce(axis, keep_dims, prim_name):
     validator.check_value_type('keep_dims', keep_dims, [bool], prim_name)
@@ -196,8 +197,7 @@ class Cast(PrimitiveWithInfer):
                 data = x.default_input
                 if data.dtype == dtype:
                     return (True, x)
-            return (False, None)
-        raise ValueError(f"Expecting (Tensor, dtype), got : ({x}, {dtype})")
+        return (False, None)
 
     def __infer__(self, x, t):
         src_type = x['dtype']
@@ -1233,10 +1233,8 @@ class Tile(PrimitiveWithInfer):
 
     def check_elim(self, base_tensor, multiplier):
         if (not isinstance(base_tensor, Tensor)) or (not isinstance(multiplier, tuple)):
-            raise ValueError("Expecting (Tensor, tuple), got: ({}, {})".format(base_tensor, multiplier))
-        def is_all_zeros(v_tuple):
-            return all(v == 1 for v in v_tuple)
-        if is_all_zeros(multiplier):
+            raise TypeError("Expecting (Tensor, tuple), got: ({}, {})".format(base_tensor, multiplier))
+        if all(v == 1 for v in multiplier):
             return (True, base_tensor)
         return (False, None)
 
@@ -1246,8 +1244,7 @@ class Tile(PrimitiveWithInfer):
         validator.check_value_type("shape", multiples_v, [tuple], self.name)
         for i, multiple in enumerate(multiples_v):
             validator.check_value_type("multiples[%d]" % i, multiple, [int], self.name)
-        valid_types = [mstype.int16, mstype.int32, mstype.bool_, mstype.float16, mstype.float32]
-        validator.check_tensor_type_same({'x': x['dtype']}, valid_types, self.name)
+        validator.check_value_type("x[\'dtype\']", x["dtype"], typing.TensorType, self.name)
         len_sub = len(multiples_v) - len(x_shp)
         multiples_w = None
         if len_sub == 0: