diff --git a/mindspore/python/mindspore/numpy/utils_const.py b/mindspore/python/mindspore/numpy/utils_const.py
index bb2f51bad5b..d45eddf8308 100644
--- a/mindspore/python/mindspore/numpy/utils_const.py
+++ b/mindspore/python/mindspore/numpy/utils_const.py
@@ -56,7 +56,7 @@ def _check_dtype(dtype):
     # convert the string dtype to mstype.dtype
     if isinstance(dtype, str):
         dtype = dtype.lower()
-        dtype = dtype_map[dtype]
+        dtype = dtype_map.get(dtype, "")
     elif isinstance(dtype, type):
         if dtype is int:
             dtype = mstype.int32
@@ -264,13 +264,19 @@ def _promote(dtype1, dtype2):
     if dtype1 == dtype2:
         return dtype1
     if (dtype1, dtype2) in promotion_rule:
-        return promotion_rule[dtype1, dtype2]
-    return promotion_rule[dtype2, dtype1]
+        return promotion_rule.get((dtype1, dtype2))
+    res = promotion_rule.get((dtype2, dtype1))
+    if res is None:
+        raise TypeError(f"input dtype: {dtype1}, {dtype2} are not all mindspore datatypes.")
+    return res
 
 
 @constexpr
 def _promote_for_trigonometric(dtype):
-    return rule_for_trigonometric[dtype]
+    res_dtype = rule_for_trigonometric.get(dtype)
+    if res_dtype is None:
+        raise TypeError(f"input dtype: {dtype} is not a mindspore datatype.")
+    return res_dtype
 
 
 @constexpr
diff --git a/mindspore/python/mindspore/ops/_grad/grad_sparse.py b/mindspore/python/mindspore/ops/_grad/grad_sparse.py
index e21ab869815..9923ba2e77c 100644
--- a/mindspore/python/mindspore/ops/_grad/grad_sparse.py
+++ b/mindspore/python/mindspore/ops/_grad/grad_sparse.py
@@ -88,6 +88,7 @@ def get_bprop_sparse_tensor_dense_matmul(self):
         return zeros_like(indices), values_grad, zeros_like(dense_shape), dense_grad
     return bprop
 
+
 @bprop_getters.register(_csr_ops.CSRReduceSum)
 def get_bprop_csr_reduce_sum(self):
     "Back-propagation for CSRReduceSum."
@@ -103,6 +104,7 @@ def get_bprop_csr_reduce_sum(self):
         return F.make_csr_tensor(indptr, indices, values_grad, shape), zeros_like(axis)
     return bprop
 
+
 @bprop_getters.register(_csr_ops.CSRMV)
 def get_bprop_csr_mv(self):
     "Back-propagation for CSRMV."
@@ -129,6 +131,7 @@ def get_bprop_csr_mv(self):
         return F.make_csr_tensor(indptr, indices, values_grad, csr_tensor.shape), dense_grad
     return bprop
 
+
 @bprop_getters.register(_csr_ops.CSRMul)
 def get_bprop_csr_mul(self):
     "Back-propagation for CSRMul."
@@ -153,12 +156,14 @@ def get_bprop_csr_mul(self):
         return csr_tensor_grad, dense_grad
     return bprop
 
+
 @bprop_getters.register(_csr_ops.CSR2COO)
 def get_bprop_csr2coo(self):
     def bprop(indptr, nnz, out, dout):
         return zeros_like(dout)
     return bprop
 
+
 @bprop_getters.register(_csr_ops.COO2CSR)
 def get_bprop_coo2csr(self):
     def bprop(row_indices, height, out, dout):
diff --git a/mindspore/python/mindspore/ops/functional.py b/mindspore/python/mindspore/ops/functional.py
index 616ae69a22e..9103facbecb 100644
--- a/mindspore/python/mindspore/ops/functional.py
+++ b/mindspore/python/mindspore/ops/functional.py
@@ -1006,6 +1006,7 @@ coo_tensor_get_dense_shape = Primitive('COOTensorGetDenseShape')
 
 @constexpr
 def print_info(info):
+    """Print given error info"""
     print(info)