diff --git a/tinygrad/function.py b/tinygrad/function.py
index 0f71b90923..c7823b223c 100644
--- a/tinygrad/function.py
+++ b/tinygrad/function.py
@@ -17,7 +17,9 @@ pm_transform_unique_const = PatternMatcher([
 ])
 
 pm_ctx = PatternMatcher([
-  (UPat((Ops.BUFFER, Ops.BIND), name="x"), add_to_ctx),
+  (UPat(Ops.BUFFER, src=(UPat(Ops.UNIQUE), UPat(Ops.DEVICE)), name="x"),
+   lambda ctx,x: x.replace(src=(UOp(Ops.LUNIQUE, arg=next(ctx[1])), x.src[1])) if x.src[0].arg > ctx[2] else add_to_ctx(ctx,x)),
+  (UPat(Ops.BIND, name="x"), add_to_ctx),
   (UPat((Ops.AFTER, Ops.CONTIGUOUS), name="x"),
    lambda ctx,x: add_to_ctx(ctx,x) if not x.op_in_backward_slice_with_self(Ops.PARAM) and x.op_in_backward_slice_with_self(Ops.BUFFER) else None),
 ])+pm_transform_unique_const
@@ -46,6 +48,7 @@ class _function(Generic[ReturnType]):
     # run it and do surgery later
     with Context(ALLOW_DEVICE_USAGE=getenv("DEVICE_IN_FUNCTION_BUG", 0)):
       _function.depth += 1
+      unique_start = next(UOp.unique_num)
       ret = self.fxn(*args, **kwargs)
       _function.depth -= 1
     if isinstance(ret, Tensor):
@@ -65,7 +68,7 @@ class _function(Generic[ReturnType]):
 
     # the BUFFERs that are left are the implicit inputs
     num_explicit = len(call_uops)
-    uret = graph_rewrite(uret, pm_ctx, (call_uops, itertools.count(0)), bottom_up=True, name="get_implicit_inputs")
+    uret = graph_rewrite(uret, pm_ctx, (call_uops, itertools.count(0), unique_start), bottom_up=True, name="get_implicit_inputs")
     name = getattr(self.fxn, '__qualname__', None) or type(self.fxn).__qualname__
     if not self.allow_implicit:
       implicit_buffers = [x for x in call_uops[num_explicit:] if x.op is Ops.BUFFER]
diff --git a/tinygrad/mixin/__init__.py b/tinygrad/mixin/__init__.py
index d24a7d9d60..07b9123178 100644
--- a/tinygrad/mixin/__init__.py
+++ b/tinygrad/mixin/__init__.py
@@ -7,7 +7,7 @@ from tinygrad.mixin.reduce import ReduceMixin
 from tinygrad.uop import Ops
 from tinygrad.uop.ops import _broadcast_shape, resolve, smax, smin, identity_element
 from tinygrad.device import canonicalize_device
-from tinygrad.dtype import ConstType, DType, DTypeLike, Invalid, InvalidType, PtrDType, PyConst, dtypes, least_upper_dtype, sum_acc_dtype, to_dtype
+from tinygrad.dtype import ConstType, DType, DTypeLike, InvalidType, PtrDType, PyConst, dtypes, least_upper_dtype, sum_acc_dtype, to_dtype
 from tinygrad.helpers import all_int, argfix, ceildiv, flatten, flat_to_grouped, make_tuple, prod, resolve_pool_pads, round_up
 
 if TYPE_CHECKING:
@@ -20,6 +20,8 @@ class OpMixin(ElementwiseMixin, ReduceMixin):
   @staticmethod
   def unique_const(fill_value:ConstType, **kwargs): raise NotImplementedError("creation helpers are only supported on Tensor and UOp")
   @staticmethod
+  def empty(*shape, **kwargs): raise NotImplementedError("creation helpers are only supported on Tensor and UOp")
+  @staticmethod
   def const(dtype, b, device=None): raise NotImplementedError("creation helpers are only supported on Tensor and UOp")
 
   @classmethod
@@ -46,13 +48,11 @@ class OpMixin(ElementwiseMixin, ReduceMixin):
   @classmethod
   def invalids(cls, *shape, **kwargs) -> Self:
     """
-    Creates a tensor with the given shape, filled with Invalid.
+    Creates an anonymous uninitialized buffer with the given shape.
 
-    This is an alternative to Tensor.empty when you want an "anonymous" buffer.
-
-    Eventually Tensor.empty will be replaced by this.
+    You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.
     """
-    return cls.full(argfix(*shape), Invalid, **kwargs)
+    return cls.empty(argfix(*shape), **kwargs)
 
   @classmethod
   def zeros(cls, *shape, **kwargs) -> Self: