Tensor reference in trace input leads to copies in make_variable

[nunoplopes]

For example, when running zeros(), underneath PyTorch first creates a new tensor and then calls zero_.
So we end up with a trace like:

%0 = <Float> zero_ in<0> #refs E/I=1/2 #output shape=[1, 2]

Inputs:
in<0>: tensor(Float : [1, 2])

We now have a reference to the tensor that is returned by zeros.

Now let's look at the code in torch/csrc/autograd/generated/variable_factories.h:

inline at::Tensor zeros(at::IntArrayRef size, at::TensorOptions options = {}) {
  at::AutoDispatchBelowADInplaceOrView guard;
  return autograd::make_variable(at::zeros(size, at::TensorOptions(options).requires_grad(), /*requires_grad=*/options.requires_grad());
}

And now in torch/csrc/autograd/variable.h:

inline Variable make_variable(at::Tensor data, bool requires_grad = false, bool allow_tensor_metadata_change = true) {
  if (data.defined()) {
    if (data.getIntrusivePtr().use_count() == 1 && data.getIntrusivePtr()->unique_version()) {
      // reuse tensor
    } else {
      auto data_impl_copy = data.getIntrusivePtr()->shallow_copy_and_detach(...);
      return Variable(data_impl_copy); // <-- missing std::move here btw
    }
  }
  return Variable();
}

So now because we have that reference we force this function to create a copy of the tensor unnecessarily.
Can this be fixed?

[nunoplopes]

upstreamed patch: pytorch/pytorch#67018