diff --git a/tinygrad/engine/schedule.py b/tinygrad/engine/schedule.py
index 0171b3b5d3..7f619f67bf 100644
--- a/tinygrad/engine/schedule.py
+++ b/tinygrad/engine/schedule.py
@@ -1,9 +1,11 @@
+import time
 from typing import cast
 from dataclasses import dataclass, field, replace
 from collections import deque, defaultdict
 from tinygrad.uop.ops import UOp, Ops, buffers
+from tinygrad.uop.spec import type_verify, tensor_spec
 from tinygrad.device import Device, Buffer, MultiBuffer
-from tinygrad.helpers import Metadata, all_same
+from tinygrad.helpers import Metadata, all_same, DEBUG, cpu_profile, TracingKey, SPEC, flatten
 
 # **** ScheduleItem return type
 
@@ -114,3 +116,35 @@ def create_schedule_with_vars(sched_sink:UOp) -> tuple[list[ScheduleItem], dict[
       real_schedule.append(replace(si, fixedvars=si.fixedvars | {s.src[0].arg[0]:in_ranges[s.src[1]] for s in si.bound_ranges}, bound_ranges=()))
     sched_ptr += 1
   return real_schedule, var_vals
+
+from tinygrad.engine.memory import memory_planner
+from tinygrad.schedule.rangeify import get_rangeify_map
+from tinygrad.schedule.multi import get_multi_map
+
+def complete_create_schedule_with_vars(big_sink:UOp) -> tuple[dict[UOp, UOp], list[ScheduleItem], dict[str, int]]:
+  # big_sink srcs are all the Tensors
+  st = time.perf_counter()
+
+  # verify Tensors match the spec
+  if SPEC: type_verify(big_sink, tensor_spec)
+
+  # tensor map is what we return
+  tensor_map: dict[UOp, UOp] = {}
+
+  if any(isinstance(x._device, tuple) for x in big_sink.toposort()):
+    tensor_map |= get_multi_map(big_sink)
+    big_sink = big_sink.substitute(tensor_map, name="Apply Multi Map")
+    big_sink = UOp.sink(*flatten([x.src if x.op is Ops.MULTI else [x] for x in big_sink.src]))
+
+  tensor_map |= get_rangeify_map(big_sink)
+  big_sink = big_sink.substitute(tensor_map, name="Apply Kernelize Map")
+
+  # create the schedule
+  with cpu_profile(TracingKey("toposort schedule")): schedule, var_vals = create_schedule_with_vars(big_sink)
+  with cpu_profile(TracingKey("memory planner")): schedule = memory_planner(schedule)
+
+  # remove all AFTERs, after scheduling, the tensors are just buffers
+  tensor_map |= {u:u.buf_uop for u in big_sink.toposort() if u.op is Ops.AFTER}
+
+  if (DEBUG >= 1 and len(schedule) > 1) or DEBUG >= 3: print(f"scheduled {len(schedule)} kernels in {(time.perf_counter()-st)*1000:.2f} ms")
+  return tensor_map, schedule, var_vals
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index 5247214476..8659727ec3 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -6,19 +6,15 @@ from typing import Callable, ClassVar, Sequence, cast, get_args, Literal, Suppor
 from tinygrad.dtype import DType, DTypeLike, dtypes, ImageDType, ConstType, least_upper_float, least_upper_dtype, sum_acc_dtype, to_dtype, truncate
 from tinygrad.dtype import _from_np_dtype, _to_np_dtype
 from tinygrad.helpers import argfix, make_tuple, flatten, prod, all_int, round_up, merge_dicts, argsort, getenv, all_same, fully_flatten
-from tinygrad.helpers import IMAGE, WINO, Metadata, TRACEMETA, ceildiv, fetch, polyN, DEBUG, is_numpy_ndarray, SPEC, TracingKey, cpu_profile
+from tinygrad.helpers import IMAGE, WINO, Metadata, TRACEMETA, ceildiv, fetch, polyN, is_numpy_ndarray, TracingKey, cpu_profile
 from tinygrad.helpers import suppress_finalizing
 from tinygrad.gradient import compute_gradient
 from tinygrad.mixin import OpMixin
 from tinygrad.mixin.movement import _align_left
 from tinygrad.uop.ops import smax, smin, resolve, UOp, Ops, sint, identity_element, all_metadata, _index_to_concrete_int, sint_to_uop
-from tinygrad.uop.spec import type_verify, tensor_spec
+from tinygrad.engine.schedule import ScheduleItem, complete_create_schedule_with_vars
 from tinygrad.device import Device, Buffer
 from tinygrad.engine.realize import run_schedule
-from tinygrad.engine.memory import memory_planner
-from tinygrad.engine.schedule import ScheduleItem, create_schedule_with_vars
-from tinygrad.schedule.rangeify import get_rangeify_map
-from tinygrad.schedule.multi import get_multi_map
 
 # TODO: this should be the only usage of Device
 def canonicalize_device(device:str|None) -> str: return Device.canonicalize(device)
@@ -234,30 +230,9 @@ class Tensor(OpMixin):
 
     NOTE: A Tensor can only be scheduled once.
     """
-    st = time.perf_counter()
     big_sink = UOp.sink(*[x.uop for x in (self,)+lst])
-
-    # verify Tensors match the spec
-    if SPEC: type_verify(big_sink, tensor_spec)
-
-    if any(isinstance(x._device, tuple) for x in big_sink.toposort()):
-      _apply_map_to_tensors(get_multi_map(big_sink), name="Apply Multi Map")
-      big_sink = UOp.sink(*flatten([x.uop.src if x.uop.op is Ops.MULTI else [x.uop] for x in (self,)+lst]))
-
-    becomes_map = get_rangeify_map(big_sink)
-    _apply_map_to_tensors(becomes_map, name="Apply Kernelize Map")
-
-    # get new sink
-    sink = UOp.sink(*[x.uop for x in (self,)+lst])
-
-    # remove all AFTERs, after scheduling, the tensors are just buffers
-    remove_assign_map = {u:u.buf_uop for u in sink.toposort() if u.op is Ops.AFTER}
-    _apply_map_to_tensors(remove_assign_map, name="Remove After")
-
-    # create the schedule
-    with cpu_profile(TracingKey("toposort schedule")): schedule, var_vals = create_schedule_with_vars(sink)
-    with cpu_profile(TracingKey("memory planner")): schedule = memory_planner(schedule)
-    if (DEBUG >= 1 and len(schedule) > 1) or DEBUG >= 3: print(f"scheduled {len(schedule)} kernels in {(time.perf_counter()-st)*1000:.2f} ms")
+    becomes_map, schedule, var_vals = complete_create_schedule_with_vars(big_sink)
+    _apply_map_to_tensors(becomes_map, name="Apply Schedule Map")
     return schedule, var_vals
 
   def schedule(self, *lst:Tensor) -> list[ScheduleItem]: