Issue when using inplace empty with hpu backend

1

I have a problem when running this simple repro in compile:

def test_empty_tensor_hpu():
    def create_empty_tensor(output, device_type):
        torch.empty(1024,device=device_type, out=output)
        return output * output
    fn_hpu = torch.compile(create_empty_tensor, backend="hpu_backend")
    out_tensor = torch.ones(1024, device="hpu")
    result_h = fn_hpu(out_tensor, "hpu")
    print(result_h)

I get this error:

RuntimeError: Cannot access data pointer of Tensor (e.g. FakeTensor, FunctionalTensor). If you're using torch.compile/export/fx, it is likely that we are erroneously tracing into a custom kernel. To fix this, please wrap the custom kernel into an opaque custom op. Please see the following for details: https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html

While executing %result : [num_users=1] = call_function[target=torch.empty](args = (), kwargs = {size: [1024], dtype: torch.float32, device: hpu, layout: torch.strided, out: %l_op_inputs_dict_out_})

cpp + pythoncallstack: callstack

The issue lies with our backend recieving a FunctionalTensor for the resize_ op during FakeTensorMode which of course throws this error while trying to access its data_ptr. When comparing the resize_ op dispatch on cpu and hpu both seem to be handled similarly. Should our backend be able to handle functional tensors or do we have a problem with how we register ops? / Is there any way to debug this?