diff --git a/extra/torch_backend/backend.py b/extra/torch_backend/backend.py
index 846e290fe9..5d0b06cf38 100644
--- a/extra/torch_backend/backend.py
+++ b/extra/torch_backend/backend.py
@@ -22,6 +22,33 @@ torch.utils.rename_privateuse1_backend("tiny")
 torch._register_device_module("tiny", TinyBackend())
 torch.utils.generate_methods_for_privateuse1_backend()
 
+# *** bad functions on CPU ***
+
+@torch.library.impl("aten::masked_select", "privateuseone")
+def masked_select(self, mask):
+  # err, bad
+  return wrap(Tensor(self.cpu().numpy()[mask.cpu().numpy()]))
+
+@torch.library.impl("aten::topk", "privateuseone")
+def topk(self, k, dim=-1, largest=True, sorted=True):
+  # TODO: move to tinygrad
+  t1, t2 = torch.topk(self.cpu(), k, dim, largest, sorted)
+  return torch.return_types.topk((t1.tiny(), t2.tiny()))
+
+@torch.library.impl("aten::_index_put_impl_", "privateuseone")
+def _index_put_impl_(self, indices, values, accumulate=False, unsafe=False):
+  # TODO: move to tinygrad
+  return aten._index_put_impl_(self.cpu(), [x.cpu() for x in indices], values.cpu(), accumulate, unsafe).tiny()
+
+@torch.library.impl("aten::index.Tensor", "privateuseone")
+def index_tensor(x, y):
+  return aten.index(x.cpu(), [z.cpu() if isinstance(z, torch.Tensor) else None for z in y]).tiny()
+
+@torch.library.impl("aten::randperm.generator_out", "privateuseone")
+def randperm_generator(n, generator=None, out=None): out.copy_(torch.randperm(n, generator=generator, device="cpu").tiny())
+
+# *** end bad functions on CPU ***
+
 @torch.library.impl("aten::zero_", "privateuseone")
 def zero_(x):
   tt = unwrap(x)
@@ -35,11 +62,6 @@ def fill_scalar(x, y):
 @torch.library.impl("aten::_local_scalar_dense", "privateuseone")
 def _local_scalar_dense(tensor): return unwrap(tensor).item()
 
-@torch.library.impl("aten::masked_select", "privateuseone")
-def masked_select(self, mask):
-  # err, bad
-  return wrap(Tensor(self.cpu().numpy()[mask.cpu().numpy()]))
-
 @functools.lru_cache(None)
 def cached_to_movement_ops(shape, st) -> list:
   mops = to_movement_ops(st)
@@ -99,24 +121,6 @@ def arange_start(start, end, dtype=None, device=None, pin_memory=None):
 def arange_start_step(start, end, step, dtype=None, device=None, pin_memory=None):
   return wrap(Tensor.arange(start, end, step, dtype=_from_torch_dtype(dtype or torch.get_default_dtype())))
 
-@torch.library.impl("aten::topk", "privateuseone")
-def topk(self, k, dim=-1, largest=True, sorted=True):
-  # TODO: move to tinygrad
-  t1, t2 = torch.topk(self.cpu(), k, dim, largest, sorted)
-  return torch.return_types.topk((t1.tiny(), t2.tiny()))
-
-@torch.library.impl("aten::_index_put_impl_", "privateuseone")
-def _index_put_impl_(self, indices, values, accumulate=False, unsafe=False):
-  # TODO: move to tinygrad
-  return aten._index_put_impl_(self.cpu(), [x.cpu() for x in indices], values.cpu(), accumulate, unsafe).tiny()
-
-@torch.library.impl("aten::randperm.generator_out", "privateuseone")
-def randperm_generator(n, generator=None, out=None): out.copy_(torch.randperm(n, generator=generator).tiny())
-
-@torch.library.impl("aten::index.Tensor", "privateuseone")
-def index_tensor(x, y):
-  return aten.index(x.cpu(), [z.cpu() if isinstance(z, torch.Tensor) else None for z in y]).tiny()
-
 @torch.library.impl("aten::convolution_overrideable", "privateuseone")
 def convolution_overrideable(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups):
   if TORCH_DEBUG >= 1: