[pytorch/pytorch] Feature Request: Extend device checks to support privateuse1 for shared at::native operators logic

### ROOT CAUSE The issue arises because the device checks in the `at::native` operators (core PyTorch functions) do not account for the `privateuse1` device type, which is used for special GPU devices. Currently, the device validation assumes standard integer device indices, but `privateuse1` requires special handling to avoid validation errors. ### CODE FIX ```cpp // In torch/csrc/autograd/functions.cpp, modify the device validation in at::native operators // Example for aten::add: replace the device check with the following logic auto self_device = self.device(); auto other_device = other.device(); // Check device type and index if (self_device.type() != other_device.type()) { // Handle device type mismatch return at::autograd::_VariableFunctionFlagsGuard::allowUnderflow( [&]() -> at::Tensor { return at::add(self, other, out_opt); } ); } if (self_device.type() == DeviceType::CUDA) { // Allow privateuse1 device by checking for its special code point if (self_device.index() == DeviceIndex::PRIVATEUSE1) { // Proceed with the operation, bypassing standard validation return at::autograd::_VariableFunctionFlagsGuard::allowUnderflow( [&]() -> at::Tensor { return at::add(self, other, out_opt); } ); } } // Standard device validation for other devices if (self_device != other_device) { // Raise error for unsupported device combinations throw std::runtime_error("aten::add: Expected self and other to have same device"); } ``` This fix extends device checks to explicitly handle `privateuse1` devices by adding a conditional branch that bypasses standard validation for these special devices.