static Tensor dispatch_type(const Tensor & self, const at::Type & type, int device, bool non_blocking) {
if (type.is_cuda()) {
torch::utils::cuda_lazy_init();
}
AutoNoGIL no_gil;
AutoGPU auto_gpu(device);
int64_t tensor_device = self.is_cuda() ? self.get_device() : -1;
if (self.is_cuda() && type.is_cuda() && tensor_device != at::current_device()) {
// copy if the devices are different even if the types are the same
return type.copy(self, non_blocking);
}
return self.toType(type, non_blocking);
}
void set_default_tensor_type(const at::Type& type) {
if (!at::isFloatingType(type.scalarType())) {
throw TypeError("only floating-point types are supported as the default type");
}
if (!type.is_variable_or_undefined()) {
throw TypeError("only variable types are supported");
}
if (type.is_sparse()) {
throw TypeError("only dense types are supported as the default type");
}
// get the storage first, so if it doesn't exist we don't change the default tensor type
THPObjectPtr storage = get_storage_obj(type);
default_tensor_type = const_cast<Type*>(&type);
auto torch_module = THPObjectPtr(PyImport_ImportModule("torch"));
if (!torch_module) throw python_error();
if (PyObject_SetAttrString(torch_module.get(), "Storage", storage) != 0) {
// technically, we should undo the change of default tensor type.
throw python_error();
}
}
at::Device::Type getDeviceType(const at::Type& type) {
return type.is_cuda() ? at::Device::Type::CUDA : at::Device::Type::CPU;
}
