diff --git a/test/null/test_tensor.py b/test/null/test_tensor.py
index 32ccc255e3..560ce3f5e9 100644
--- a/test/null/test_tensor.py
+++ b/test/null/test_tensor.py
@@ -171,6 +171,11 @@ class TestTensorConstLike(unittest.TestCase):
     t = Tensor.ones(8, 4).shard(("NULL:0", "NULL:1"), axis=0)
     with self.assertRaises(RuntimeError): t.full_like(5, device="NULL")
 
+class TestTensorShape(unittest.TestCase):
+  def test_float_shape_raises(self):
+    for dim in (2.0, 2.5):
+      with self.subTest(dim=dim), self.assertRaisesRegex(RuntimeError, "shape must be int"): Tensor.ones(dim)
+
 class TestTensorDevice(unittest.TestCase):
   def test_create_from_single_device_tuple(self):
     (Tensor([1.0], device=(Device.DEFAULT,)) + Tensor([2.0])).realize()
diff --git a/tinygrad/uop/ops.py b/tinygrad/uop/ops.py
index 49a7429d0a..9a30dbe312 100644
--- a/tinygrad/uop/ops.py
+++ b/tinygrad/uop/ops.py
@@ -94,11 +94,10 @@ def multirange_str(rngs:Iterable[UOp], color=False, pad=None) -> str:
   return ret
 
 def shape_to_shape_arg(arg:tuple[sint, ...]) -> UOp:
-  for x in arg:
-    if isinstance(x, UOp) and not dtypes.is_int(x.dtype): raise RuntimeError(f"shape must be int, got {x.dtype} in {arg}")
-  if len(arg) == 0: return UOp(Ops.STACK)
-  elif len(arg) == 1: return UOp.const(arg[0], dtypes.weakint)
-  else: return UOp(Ops.STACK, src=tuple(UOp.const(x) if isinstance(x, int) else x for x in arg))
+  src = tuple(x if isinstance(x, UOp) else UOp.const(x) for x in arg)
+  for x in src:
+    if not dtypes.is_int(x.dtype): raise RuntimeError(f"shape must be int, got {x.dtype} in {arg}")
+  return src[0] if len(src) == 1 else UOp(Ops.STACK, src=src)
 
 def consumer_map_from_toposort(lst:Iterable[UOp]):
   ret: dict[UOp, dict[UOp, None]] = {}