diff --git a/test/helpers.py b/test/helpers.py
index 15ee0c56..be2472b1 100644
--- a/test/helpers.py
+++ b/test/helpers.py
@@ -1,4 +1,5 @@
 import sys
+import numpy as np
 from tinygrad import Tensor, Device, dtypes
 from tinygrad.device import Runner
 from tinygrad.dtype import DType
@@ -38,3 +39,12 @@ def is_dtype_supported(dtype: DType, device: str = Device.DEFAULT):
     if device == "PYTHON": return sys.version_info >= (3, 12)
   if dtype == dtypes.float64: return device != "METAL" and not (OSX and device == "GPU")
   return True
+
+def rand_for_dtype(dt:DType, size:int):
+  if dtypes.is_unsigned(dt):
+    return np.random.randint(0, 100, size=size, dtype=dt.np)
+  elif dtypes.is_int(dt):
+    return np.random.randint(-100, 100, size=size, dtype=dt.np)
+  elif dt == dtypes.bool:
+    return np.random.choice([True, False], size=size)
+  return np.random.uniform(-10, 10, size=size).astype(dt.np)
diff --git a/test/test_dtype.py b/test/test_dtype.py
index 216952cb..a9f0cbbf 100644
--- a/test/test_dtype.py
+++ b/test/test_dtype.py
@@ -6,7 +6,7 @@ from tinygrad.helpers import getenv, DEBUG
 from tinygrad.dtype import DType, DTYPES_DICT, ImageDType, PtrDType, least_upper_float, least_upper_dtype
 from tinygrad import Device, Tensor, dtypes
 from hypothesis import given, settings, strategies as strat
-from test.helpers import is_dtype_supported
+from test.helpers import is_dtype_supported, rand_for_dtype
 
 settings.register_profile("my_profile", max_examples=200, deadline=None)
 settings.load_profile("my_profile")
@@ -59,15 +59,7 @@ class TestDType(unittest.TestCase):
   @classmethod
   def setUpClass(cls):
     if not cls.DTYPE or not is_dtype_supported(cls.DTYPE): raise unittest.SkipTest("dtype not supported")
-    DATA_SIZE = 10
-    if dtypes.is_unsigned(cls.DTYPE):
-      cls.DATA = np.random.randint(0, 100, size=DATA_SIZE, dtype=cls.DTYPE.np)
-    elif dtypes.is_int(cls.DTYPE):
-      cls.DATA = np.random.randint(-100, 100, size=DATA_SIZE, dtype=cls.DTYPE.np)
-    elif cls.DTYPE == dtypes.bool:
-      cls.DATA = np.random.choice([True, False], size=DATA_SIZE)
-    else:
-      cls.DATA = np.random.uniform(-10, 10, size=DATA_SIZE).astype(cls.DTYPE.np)
+    cls.DATA = rand_for_dtype(cls.DTYPE, 10)
   def setUp(self):
     if self.DTYPE is None: raise unittest.SkipTest("base class")