void initialize_python_bindings() {
// Initialize the at::Type* pointers, name, and properties of the PyTensorType
// vector. After this call, the vector must not be resized.
initialize_aten_types(tensor_types);
// Initialize the Python metaclass for the torch.FloatTensor, etc. types.
// The metaclass handles __instancecheck__ checks and binds the dtype property
// on the type objects.
py_initialize_metaclass(metaclass);
// Get the tp_dict of the Variable class. We copy function definitions
// onto each Tensor type object so that they can be accessed via e.g.
// `torch.FloatTensor.add`.
auto tensor_dict = get_tensor_dict();
// Initialize each Python type object torch.FloatTensor, torch.DoubleTensor, etc.
for (auto& tensor_type : tensor_types) {
py_initialize_tensor_type(tensor_type.py_type, tensor_type.name, tensor_dict.get());
}
// Add the type objects to their corresponding modules. e.g. torch.FloatTensor
// is added to the `torch` module as `FloatTensor`. Also add all the type
// objects to the set torch._tensor_classes.
py_bind_tensor_types(tensor_types);
// Use torch.float32 as the default tensor type
set_default_tensor_type(torch::CPU(kFloat));
}
void py_set_default_tensor_type(PyObject* obj) {
PyTensorType *type;
if (PyTensorType_Check(obj)) {
type = (PyTensorType*)obj;
} else {
throw TypeError("invalid type object");
}
if (!type->aten_type) {
throw unavailable_type(*type);
}
set_default_tensor_type(*type->aten_type);
}
void py_set_default_dtype(PyObject* obj) {
PyTensorType *type;
if (THPDtype_Check(obj)) {
auto ¤t_default = get_default_tensor_type();
type = &get_tensor_type((THPDtype*)obj, torch::getLayout(current_default.backend()),
torch::getDeviceType(current_default) == DeviceType::CUDA);
} else {
throw TypeError("invalid type object");
}
if (!type->aten_type) {
throw unavailable_type(*type);
}
set_default_tensor_type(*type->aten_type);
}
