check parameter types of uniform

mindspore/ops/composite/multitype_ops/_constexpr_utils.py

@@ -131,6 +131,13 @@ def is_same_type(inst, type_):
     return inst == type_
 
 
+@constexpr
+def check_valid_type(data_type, value_type, name):
+    if not data_type in value_type:
+        raise TypeError(
+            f"For {name}, valid type include {value_type}, {data_type} is invalid")
+
+
 def slice_expand(input_slices, shape):
     """
     Converts slice to indices.

mindspore/ops/composite/random_ops.py

@@ -92,6 +92,9 @@ def uniform(shape, minval, maxval, seed=0, dtype=mstype.float32):
           If dtype is int32, only one number is allowed.
         seed (int): Seed is used as entropy source for Random number engines generating pseudo-random numbers.
           Must be non-negative. Default: 0.
+        dtype (mindspore.dtype): type of the Uniform distribution. If it is int32, it generates numbers from discrete
+          uniform distribution; if it is float32, it generates numbers from continuous uniform distribution. It only
+          supports these two data types. Default: mstype.float32.
 
     Returns:
         Tensor. The shape should be the broadcasted shape of Input "shape" and shapes of minval and maxval.
@@ -112,6 +115,7 @@ def uniform(shape, minval, maxval, seed=0, dtype=mstype.float32):
     """
     minval_dtype = F.dtype(minval)
     maxval_dtype = F.dtype(maxval)
+    const_utils.check_valid_type(dtype, [mstype.int32, mstype.float32], 'uniform')
     const_utils.check_tensors_dtype_same(minval_dtype, dtype, "uniform")
     const_utils.check_tensors_dtype_same(maxval_dtype, dtype, "uniform")
     const_utils.check_non_negative("seed", seed, "uniform")