fix tests (#10493)

test/test_schedule.py

@@ -347,14 +347,14 @@ class TestSchedule(unittest.TestCase):
     # a and b share the same underlying device memory
     self.assertIs(a.lazydata.realized, b.lazydata.realized)
 
-  def test_copy_dedups(self):
+  def test_clone_doesnt_dedup(self):
     src = Tensor.ones(4).contiguous().realize()
     a = src.clone()
     b = src.clone()
-    sched = check_schedule([a, b], 1, filter_sink=False)
+    sched = check_schedule([a, b], 2, filter_sink=False)
     run_schedule(sched)
     # a and b are assigned to the same device Buffer
-    self.assertIs(a.lazydata.realized, b.lazydata.realized)
+    self.assertIsNot(a.lazydata.realized, b.lazydata.realized)
 
   # EMPTY is assigned to a unique device Buffer
 
@@ -2337,7 +2337,7 @@ class TestCopyFolding(unittest.TestCase):
     self.assertIs(b.base, a.base)
 
   def test_clone(self):
-    a = Tensor.empty(4).lazydata
+    a = Tensor.empty(4)
     check_schedule(a.clone(), 1, filter_sink=False)
 
   # NOTE: moving copy before view might change this
@@ -2346,7 +2346,7 @@ class TestCopyFolding(unittest.TestCase):
     view = a.shrink(((0, 2),))
     b = view.clone()
     # NOTE: this was sort of a bug making this 2
-    run_schedule(check_schedule(b, 3, filter_sink=False))
+    run_schedule(check_schedule(b, 2, filter_sink=False))
     self.assertEqual(b.lazydata.base.buffer.size, 2)
     self.assertEqual(b.lazydata.size, 2)
     self.assertListEqual(b.tolist(), [0, 1])
@@ -2356,7 +2356,7 @@ class TestCopyFolding(unittest.TestCase):
     view = a.reshape(2, 1).expand(2, 2)
     b = view.clone()
     run_schedule(check_schedule(b, 2, filter_sink=False))
-    self.assertEqual(b.lazydata.base.buffer.size, 2)
+    self.assertEqual(b.lazydata.base.buffer.size, 4)
     self.assertEqual(b.lazydata.size, 4)
     self.assertListEqual(b.tolist(), [[0, 0], [1, 1]])
 

test/test_tensor.py

@@ -688,7 +688,7 @@ class TestZeroShapeTensor(unittest.TestCase):
     np.testing.assert_allclose(a.numpy(), b.numpy())
     self.assertIsNot(a.lazydata.base.buffer, b.lazydata.base.buffer)
 
-    a = Tensor.rand(16, 16).mul(5.0).add(5.0)
+    a = Tensor.rand(16, 16).mul(5.0).add(5.0).realize()
     b = a.clone()
     np.testing.assert_allclose(a.numpy(), b.numpy())
     self.assertIsNot(a.lazydata.base.buffer, b.lazydata.base.buffer)

tinygrad/tensor.py

@@ -291,7 +291,6 @@ class Tensor(MathTrait):
     assert self.shape == x.shape, f"assign shape mismatch {self.shape} != {x.shape}"
     assert self.device == x.device, f"assign device mismatch {self.device} != {x.device}"
     assert self.dtype == x.dtype, f"assign dtype mismatch {self.dtype} != {x.dtype}"
-    assert not x.requires_grad  # self requires_grad is okay?
     self.lazydata = self.lazydata.assign(x.lazydata)
     return self
 
@@ -366,9 +365,9 @@ class Tensor(MathTrait):
     """
     Creates a clone of this tensor allocating a separate buffer for the data.
     """
-    ret = Tensor(self.lazydata.clone(), self.device, requires_grad=self.requires_grad)
+    ret = Tensor.empty(self.shape, device=self.device, dtype=self.dtype)
     if self.grad is not None: ret.grad = self.grad.clone()
-    return ret
+    return ret.assign(self)
 
   def to(self, device:str|tuple[str, ...]|None) -> Tensor:
     """

tinygrad/uop/ops.py

@@ -493,8 +493,6 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     # TODO: this contiguous should not be required!!!
     inp = self if arg is None else UOp(Ops.MSELECT, self.dtype, src=(self.contiguous(),), arg=arg)
     return UOp(Ops.COPY, self.dtype, (inp, UOp(Ops.DEVICE, arg=device) if not isinstance(device, UOp) else device))
-    #return UOp(Ops.COPY, self.dtype, (self, UOp(Ops.DEVICE, arg=device) if not isinstance(device, UOp) else device), arg)
-  def clone(self) -> UOp: return self.copy_to_device(self.device)
   def mselect(self, arg:int) -> UOp: return UOp(Ops.MSELECT, self.dtype, (self,), arg)
   @property
   def metadata(self) -> tuple[Metadata, ...]|None: return all_metadata.get(self, None)