remove kernelize to prepare for refactors (#13463)

* remove kernelize to prepare for refactors

* less kernelize

* last test

test/test_schedule.py

@@ -672,33 +672,6 @@ class TestSchedule(unittest.TestCase):
     c = (a.sum(2).contiguous() + b).contiguous()
     check_schedule(c, 2)
 
-  def test_kernelize(self):
-    a = Tensor.empty(10)
-    b = Tensor.empty(10)
-    c = (a+b).kernelize()
-    d = c+2
-    check_schedule(d, 2)
-
-  def test_kernelize_view(self):
-    a = Tensor.empty(4,1)
-    b = a*2
-    c = b.kernelize()+Tensor.empty(4,4)
-    check_schedule(c, 2)
-
-  def test_kernelize_diamond(self):
-    a = Tensor([0]).realize()
-    prev_a = (a+1).contiguous()
-    a.assign(Tensor([2]))
-    a.kernelize(prev_a)
-    self.assertEqual((prev_a+a*3).item(), 1+2*3)
-
-  def test_kernelize_sym(self):
-    a = Tensor([1])+Tensor([2])
-    a.kernelize()
-    b = a/a
-    check_schedule(b, 0)
-    self.assertEqual(b.item(), 1)
-
   # TODO: this requires supporting multiple stores in the AST
   @unittest.expectedFailure
   def test_multioutput_ast(self):
@@ -710,35 +683,6 @@ class TestSchedule(unittest.TestCase):
     self.assertEqual(a.buffer.numpy(), [7])
     self.assertEqual(b.buffer.numpy(), [12])
 
-  # unlike schedule, kernelize can be called multiple times on a Tensor
-  def test_double_kernelize(self):
-    a = Tensor.empty(10)
-    b = Tensor.empty(10)
-    c = (a+b)
-    d = c.kernelize()+2
-    e = c.kernelize()+d.kernelize()
-    check_schedule(e, 3)
-
-  def test_kernelize_bw(self):
-    a = Tensor.full((3,), 2.0, requires_grad=True).contiguous()
-    b = Tensor.full((3,), 3.0, requires_grad=True).contiguous()
-    x = (a*b).kernelize()
-    y = Tensor.eye(3, requires_grad=True)
-    z = y.matmul(x).sum()
-    z.backward()
-    self.assertEqual(z.item(), 18.0)
-    self.assertEqual(z.grad.item(), 1.0)
-
-  def test_kernelize_bw_view(self):
-    a = Tensor.full((3,1), 2.0, requires_grad=True).contiguous()
-    b = Tensor.full((3,1), 3.0, requires_grad=True).contiguous()
-    x = (a*b).kernelize()
-    y = Tensor.eye(6, requires_grad=True)
-    z = y.matmul(x.expand(3,2).reshape(6)).sum()
-    z.backward()
-    self.assertEqual(z.item(), 36.0)
-    self.assertEqual(z.grad.item(), 1.0)
-
   @unittest.skip("no longer supported")
   def test_double_from(self):
     x = Tensor([1,2,3,4])
@@ -1915,18 +1859,6 @@ class TestSchedule(unittest.TestCase):
     for X in range(1,N): root = root + bufs[X][vi] + bufs[X][vj]
     self.assertEqual(root.item(), N * 2)
 
-  def test_limit_bufs_kernelize(self):
-    N = 31
-    with Context(TRACK_MATCH_STATS=0, DEBUG=0):
-      bufs = [Tensor(i).contiguous().realize() for i in range(N)]
-    x = bufs[0]
-    for y in bufs[1:]: x = x+y
-    x.kernelize()
-    kcount = len([s for s in x.uop.toposort() if s.op is Ops.KERNEL])
-    z = x+Tensor.empty(1) # z only loads 2 buffers
-    sched = z.schedule()
-    self.assertEqual(len(sched), kcount+1)
-
 class TestSwizzle(unittest.TestCase):
   def test_swizzle_simple(self):
     Tensor.manual_seed(0)
@@ -2118,7 +2050,7 @@ class TestCopyFolding(unittest.TestCase):
     b = Tensor.empty(4, device="CPU")
     add = a+b
     assert all_same([x.device for x in add.uop.src]), f"ALU has different devices! {[x.device for x in add.src]}"
-    add.kernelize()
+    add.schedule()
 
   def test_alu_before_copy(self):
     buf = Tensor.ones(1).contiguous().realize()
@@ -2438,5 +2370,12 @@ class TestUOpBecome(unittest.TestCase):
     b.shrink(((0,4),)).assign(a_view).realize()
     self.assertListEqual(b.tolist(), [0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
 
+class TestSimpleSchedule(unittest.TestCase):
+  def test_reduce_doesnt_split(self):
+    a = Tensor.empty(16,16).sum(axis=1)
+    a1 = a.reshape(4,4)
+    a2 = a.reshape(16,1,1)
+    self.assertEqual(len(Tensor.schedule(a1, a2)), 1)
+
 if __name__ == '__main__':
   unittest.main(verbosity=2)

test/unit/test_kernelize.py

@@ -1,37 +0,0 @@
-import unittest
-from tinygrad import Tensor
-from tinygrad.uop import Ops
-
-class TestKernelize(unittest.TestCase):
-  def test_add_reshaped(self):
-    a = Tensor.ones(16,16).contiguous()
-    b = Tensor.zeros(16,16).contiguous()
-    ret = (a+b).sum(axis=1)
-    ret_reshaped_1 = ret.reshape(4,4)
-    ret_reshaped_2 = ret.reshape(2,8)
-    ret.kernelize()
-    self.assertIs(ret_reshaped_1.uop.src[0], ret_reshaped_2.uop.src[0])
-
-  def test_two_reduce(self):
-    a = Tensor.ones(16,16).contiguous()
-    a1 = a.sum(axis=1)
-    a0 = a1.sum(axis=0)
-    a0.kernelize()
-    self.assertEqual(len([s for s in a0.uop.toposort() if s.op is Ops.KERNEL]), 2)
-    self.assertIs(a1.uop.base.op, Ops.REDUCE_AXIS)
-    # input Tensor and user contiguous kernelize
-    self.assertIs(a0.uop.base.op, Ops.AFTER)
-    self.assertIs(a.uop.base.op, Ops.AFTER)
-
-  def test_two_reduce_w_add(self):
-    a = Tensor.ones(16,16).contiguous()
-    a1 = a.sum(axis=1)
-    a0 = (a1+1).sum(axis=0)
-    a0.kernelize()
-    # NOTE: the +1 is fused with a1, so a1 is not kernelized
-    self.assertIs(a1.uop.base.op, Ops.REDUCE_AXIS)
-    # the input to the REDUCE_AXIS is an ASSIGN though
-    self.assertIs(a1.uop.base.src[0].base.op, Ops.AFTER)
-
-if __name__ == '__main__':
-  unittest.main()

test/unit/test_simple_schedule.py

@@ -1,15 +0,0 @@
-import unittest
-from tinygrad import Tensor
-from tinygrad.uop.ops import Ops
-
-class TestSimpleSchedule(unittest.TestCase):
-  def test_reduce_doesnt_split(self):
-    a = Tensor.empty(16,16).sum(axis=1)
-    a1 = a.reshape(4,4)
-    a2 = a.reshape(16,1,1)
-    Tensor.kernelize(a1, a2)
-    kernels = [x for x in a1.uop.sink(a2.uop).toposort() if x.op is Ops.KERNEL]
-    self.assertEqual(len(kernels), 1)
-
-if __name__ == '__main__':
-  unittest.main()

test/unit/test_tinyfs.py

@@ -3,19 +3,19 @@ from tinygrad import Tensor
 
 class TestLoadStore(unittest.TestCase):
   def test_load_shape(self):
-    t = Tensor(bytes(16)).fs_load(1024).kernelize()
+    t = Tensor(bytes(16)).fs_load(1024)
     assert t.shape == (1024,), t.shape
 
   def test_store_shape(self):
-    t = Tensor.zeros(1024).fs_store().kernelize()
+    t = Tensor.zeros(1024).fs_store()
     assert t.shape == (16,), t.shape
 
   def test_load_large_shape(self):
-    t = Tensor(bytes(16)).fs_load(10_000_000).kernelize()
+    t = Tensor(bytes(16)).fs_load(10_000_000)
     assert t.shape == (10_000_000,), t.shape
 
   def test_store_large_shape(self):
-    t = Tensor.zeros(10_000_000).fs_store().kernelize()
+    t = Tensor.zeros(10_000_000).fs_store()
     assert t.shape == (16,), t.shape
 
 if __name__ == "__main__":

test/unit/test_viz.py

@@ -262,14 +262,6 @@ from tinygrad import Tensor, Device
 from tinygrad.engine.realize import get_program
 
 class TestVizIntegration(BaseTestViz):
-  # kernelize has a custom name function in VIZ
-  def test_kernelize_tracing(self):
-    a = Tensor.empty(4, 4)
-    Tensor.kernelize(a+1, a+2)
-    lst = get_viz_list()
-    self.assertEqual(len(lst), 1)
-    self.assertEqual(lst[0]["name"], "Schedule 2 Kernels n1")
-
   # codegen supports rendering of code blocks
   def test_codegen_tracing(self):
     ast = Tensor.schedule(Tensor.empty(4)+Tensor.empty(4))[0].ast
@@ -284,7 +276,7 @@ class TestVizIntegration(BaseTestViz):
       a = Tensor.empty(1)
       b = Tensor.empty(1)
       metadata = (alu:=a+b).uop.metadata
-      alu.kernelize()
+      alu.schedule()
       graph = next(get_viz_details(0, 0))["graph"]
     self.assertEqual(len([n for n in graph.values() if repr(metadata) in n["label"]]), 1)
 

tinygrad/tensor.py

@@ -220,25 +220,6 @@ class Tensor(OpMixin):
 
   # ***** data handlers ****
 
-  def kernelize(self, *lst:Tensor) -> Tensor:
-    """
-    Creates the kernels and buffers needed to realize these Tensor(s).
-
-    NOTE: Kernelize can be called multiple times on a Tensor
-    """
-    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")
-    return self
-
   def custom_kernel(self, *lst:Tensor, fxn:Callable, grad_fxn:Callable|None=None) -> list[Tensor]:
     """
     Call into a custom kernel written in UOps. Returns the Tensors after the Kernel has been applied.
@@ -254,7 +235,19 @@ class Tensor(OpMixin):
     NOTE: A Tensor can only be scheduled once.
     """
     st = time.perf_counter()
-    self.kernelize(*lst)
+    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
@@ -1853,8 +1846,7 @@ class Tensor(OpMixin):
         # χ and ι step
         state = state.bitwise_xor(~state.roll(shifts=-1, dims=2) & state.roll(shifts=-2, dims=2))
         state = state.flatten(1) ^ rnd_const_masks[i]
-      # NOTE: kernelize here to prevent internal stack from growing propotional to data size
-      state = state.kernelize()
+      # NOTE: there was a kernelize here to prevent internal stack from growing propotional to data size, do we need something else?
     return state.bitcast(dtypes.uint8)[:,:(obytes:=(200 - rate) // 2)].reshape(*self.shape[:-1], obytes)
 
   def _hash_1mb(self) -> Tensor: