diff --git a/test/test_edgecases.py b/test/test_edgecases.py
index ae4e26b489..d675442162 100644
--- a/test/test_edgecases.py
+++ b/test/test_edgecases.py
@@ -62,7 +62,6 @@ class TestNaNEdgeCases(unittest.TestCase):
 class TestEmptyTensorEdgeCases(unittest.TestCase):
   # we don't need more of these
 
-  @unittest.expectedFailure
   def test_sort_empty(self):
     # Sorting an empty tensor works in PyTorch and should return empty
     # values and indices. tinygrad raises an error instead.
diff --git a/test/test_ops.py b/test/test_ops.py
index fdd81c7b9e..f8d5fe280a 100644
--- a/test/test_ops.py
+++ b/test/test_ops.py
@@ -1093,6 +1093,9 @@ class TestOps(unittest.TestCase):
     helper_test_op(None, lambda x: x.type(torch.int32).argmin().type(torch.int32), lambda x: x.argmin(), forward_only=True, vals=[[True, False]])
 
   def test_sort(self):
+    for shape in [(0,), (0,5), (1,), (1,5)]:
+      helper_test_op([shape], lambda x: x.sort(0).values, lambda x: x.sort(0)[0], forward_only=True)
+      helper_test_op([shape], lambda x: x.sort(0).indices.type(torch.int32), lambda x: x.sort(0)[1], forward_only=True)
     for dim in [-1, 0, 1]:
       for descending in [True, False]:
         helper_test_op([(8,8,6)], lambda x: x.sort(dim, descending).values, lambda x: x.sort(dim, descending)[0], forward_only=True)
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index f66102bc16..b9d71305d0 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -2845,8 +2845,8 @@ class Tensor(MathTrait):
     ```
     """
     x, dim = self, self._resolve_dim(dim)
+    if (orig_len:= x.shape[dim]) <= 1: return x, x.zeros_like(dtype=dtypes.default_int)
     # pad to power of 2
-    orig_len = x.shape[dim]
     n_stages = math.ceil(math.log2(orig_len))
     fill_value = dtypes.min(x.dtype) if descending else dtypes.max(x.dtype)
     pads = tuple((0, 2**n_stages - orig_len) if i == dim else None for i in range(x.ndim))