use const_like in uop zero folding [pr] (#8470)

test/test_schedule.py

@@ -2054,14 +2054,12 @@ class TestBigGraph(unittest.TestCase):
     assert UPat(Ops.CONST, arg=0).match(sink, {}), f"expected {sink} to collapse to a const 0"
     assert sink.shape == a.shape
 
-  @unittest.expectedFailure
   def test_const_folding_ne(self):
     a = Tensor([1])
     sink = tensor_rewrite(a != a)
     assert UPat(Ops.CONST, arg=False).match(sink, {}), f"expected {sink} to collapse to a const False"
     assert sink.shape == a.shape
 
-  @unittest.expectedFailure
   def test_const_folding_lt(self):
     a = Tensor([1])
     sink = tensor_rewrite(a < a)

tinygrad/ops.py

@@ -1192,9 +1192,9 @@ symbolic_simple = PatternMatcher([
   (UPat.var("x", dtype=dtypes.bool).logical_not().logical_not(), lambda x: x),
   (UPat.var("x", dtype=dtypes.bool).where(UPat.const(dtypes.bool, True), UPat.const(dtypes.bool, False)), lambda x: x),
   # ** zero folding **
-  (UPat.var("x") < UPat.var("x"), lambda x: UOp.const(dtypes.bool.vec(x.dtype.count), False)), # x < x -> False
+  (UPat.var("x") < UPat.var("x"), lambda x: x.const_like(False).cast(dtypes.bool.vec(x.dtype.count))), # x < x -> False
   (UPat.var("x", dtype=dtypes.ints) != UPat.var("x", dtype=dtypes.ints),
-   lambda x: UOp.const(dtypes.bool.vec(x.dtype.count), False)), # x != x -> False (only ints)
+   lambda x: x.const_like(False).cast(dtypes.bool.vec(x.dtype.count))), # x != x -> False (only ints)
   # x*0 -> 0 or 0*x -> 0
   # if x is nan or inf it should render the nan value.
   # NOTE: this can be wrong for loaded NaN