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1022443168
Test Plan: revert-hammer
Differential Revision:
D30279364 (b004307252)
Original commit changeset: c1ed77dfe43a
fbshipit-source-id: eab50857675c51e0088391af06ec0ecb14e2347e
52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
import types
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import torch._C
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class _ClassNamespace(types.ModuleType):
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def __init__(self, name):
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super(_ClassNamespace, self).__init__('torch.classes' + name)
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self.name = name
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def __getattr__(self, attr):
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proxy = torch._C._get_custom_class_python_wrapper(self.name, attr)
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if proxy is None:
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raise RuntimeError(f'Class {self.name}.{attr} not registered!')
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return proxy
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class _Classes(types.ModuleType):
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__file__ = '_classes.py'
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def __init__(self):
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super(_Classes, self).__init__('torch.classes')
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def __getattr__(self, name):
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namespace = _ClassNamespace(name)
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setattr(self, name, namespace)
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return namespace
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@property
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def loaded_libraries(self):
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return torch.ops.loaded_libraries
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def load_library(self, path):
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"""
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Loads a shared library from the given path into the current process.
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The library being loaded may run global initialization code to register
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custom classes with the PyTorch JIT runtime. This allows dynamically
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loading custom classes. For this, you should compile your class
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and the static registration code into a shared library object, and then
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call ``torch.classes.load_library('path/to/libcustom.so')`` to load the
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shared object.
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After the library is loaded, it is added to the
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``torch.classes.loaded_libraries`` attribute, a set that may be inspected
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for the paths of all libraries loaded using this function.
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Args:
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path (str): A path to a shared library to load.
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"""
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torch.ops.load_library(path)
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# The classes "namespace"
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classes = _Classes()
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