diff --git a/test/test_linearizer.py b/test/test_linearizer.py
index 7d38f7dbd3..7ce727729f 100644
--- a/test/test_linearizer.py
+++ b/test/test_linearizer.py
@@ -190,7 +190,10 @@ class TestLinearizer(unittest.TestCase):
       a, b = Tensor.rand(tc.dims[1], tc.dims[2], dtype=tc.dtype_in), Tensor.rand(tc.dims[2], tc.dims[0], dtype=tc.dtype_in)
       np_a, np_b = a.numpy(), b.numpy()
       r = a.matmul(b, acc_dtype=tc.dtype_out)
-      realized_ast, _ = helper_realized_ast(r)
+      sched = create_schedule([r.lazydata])
+      realized_ast = sched[-1].ast[0]
+      run_schedule(sched)
+      out = r.numpy()
       k = Linearizer(realized_ast)
       k.apply_tensor_cores(1)
       k.linearize()
@@ -198,7 +201,7 @@ class TestLinearizer(unittest.TestCase):
       assert len([x for x in k.applied_opts if x.op is OptOps.TC]) == 1, "tensor core opt not included"
       np_c = np_a @ np_b
       (tc_atol, tc_rtol) = (1e-2, 1e-3) if tc.dtype_out == dtypes.half else (5e-3, 1e-4)
-      np.testing.assert_allclose(np_c, r.numpy(), atol=tc_atol, rtol=tc_rtol)
+      np.testing.assert_allclose(np_c, out, atol=tc_atol, rtol=tc_rtol)
 
   def test_limit_dims_to_max_5d_global(self):
     t = Tensor.empty(3, 4, 5, 6, 7).pad(((1, 1), (1, 1), (1, 1), (1, 1), (1, 1))) + 1
@@ -228,11 +231,13 @@ class TestLinearizer(unittest.TestCase):
     a.assign(b.where(2, a))
     sched = create_schedule([a.lazydata])
     assert len(sched) == 1
-    lin = Linearizer(*sched[-1].ast)
+    sched_copy = sched[:]
+    run_schedule(sched)
+    np.testing.assert_equal(a.flatten().numpy(), [1.,1.,1.,1.,2.,2.,2.,2.,1.,1.,1.,1.,1.,1.,1.,1.])
+    lin = Linearizer(*sched_copy[-1].ast)
     lin.hand_coded_optimizations()
     lin.linearize()
     assert not any(u.arg == TernaryOps.WHERE for u in lin.uops), "found where where where should be folded"
-    np.testing.assert_equal(a.flatten().numpy(), [1.,1.,1.,1.,2.,2.,2.,2.,1.,1.,1.,1.,1.,1.,1.,1.])
 
   def test_simplify_uop(self):
     def helper_test_simplify(uop, dtype, vin, arg=None):
diff --git a/tinygrad/engine/realize.py b/tinygrad/engine/realize.py
index 0b1fa1d0c8..10be6b2e65 100644
--- a/tinygrad/engine/realize.py
+++ b/tinygrad/engine/realize.py
@@ -44,7 +44,6 @@ def run_schedule(schedule:List[ScheduleItem]):
     for out in si.outputs:
       # we don't have an output buffer, we have to create it, and create to max size if it has symbolic shape
       if out.size > 0 and not dont_allocate and out.op is not LoadOps.ASSIGN: out.buffer.allocate()
-      del out.srcs
 
     # run the function (put it in JIT)
     real_buffers = [x.buffer for x in si.outputs+si.inputs if x.size != 0]
diff --git a/tinygrad/engine/schedule.py b/tinygrad/engine/schedule.py
index 508ef9a5cc..ac7b1354cc 100644
--- a/tinygrad/engine/schedule.py
+++ b/tinygrad/engine/schedule.py
@@ -197,6 +197,7 @@ def create_schedule(outs:List[LazyBuffer], seen:Optional[Set[LazyBuffer]]=None)
         graph[out].append(assign_targets[x])
         in_degree[assign_targets[x]] += 1
       if x in prescheduled: in_degree[out] += 1
+    del out.srcs  # can only schedule once
 
   queue = deque(out for out in prescheduled if in_degree[out] == 0)
   schedule: List[ScheduleItem] = []