remove kernelize to prepare for refactors (#13463)

* remove kernelize to prepare for refactors

* less kernelize

* last test

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)