| | |
| | import types |
| |
|
| | import torch._C |
| |
|
| |
|
| | class _ClassNamespace(types.ModuleType): |
| | def __init__(self, name): |
| | super().__init__("torch.classes" + name) |
| | self.name = name |
| |
|
| | def __getattr__(self, attr): |
| | proxy = torch._C._get_custom_class_python_wrapper(self.name, attr) |
| | if proxy is None: |
| | raise RuntimeError(f"Class {self.name}.{attr} not registered!") |
| | return proxy |
| |
|
| |
|
| | class _Classes(types.ModuleType): |
| | __file__ = "_classes.py" |
| |
|
| | def __init__(self) -> None: |
| | super().__init__("torch.classes") |
| |
|
| | def __getattr__(self, name): |
| | namespace = _ClassNamespace(name) |
| | setattr(self, name, namespace) |
| | return namespace |
| |
|
| | @property |
| | def loaded_libraries(self): |
| | return torch.ops.loaded_libraries |
| |
|
| | def load_library(self, path): |
| | """ |
| | Loads a shared library from the given path into the current process. |
| | |
| | The library being loaded may run global initialization code to register |
| | custom classes with the PyTorch JIT runtime. This allows dynamically |
| | loading custom classes. For this, you should compile your class |
| | and the static registration code into a shared library object, and then |
| | call ``torch.classes.load_library('path/to/libcustom.so')`` to load the |
| | shared object. |
| | |
| | After the library is loaded, it is added to the |
| | ``torch.classes.loaded_libraries`` attribute, a set that may be inspected |
| | for the paths of all libraries loaded using this function. |
| | |
| | Args: |
| | path (str): A path to a shared library to load. |
| | """ |
| | torch.ops.load_library(path) |
| |
|
| |
|
| | |
| | classes = _Classes() |
| |
|
