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8ed1dd528e
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/32508 Test Plan: Imported from OSS Differential Revision: D19525175 Pulled By: jamesr66a fbshipit-source-id: b9f07113f551bdfb56d49d24d12989be2b8fc7e4
40 lines
1.3 KiB
Python
40 lines
1.3 KiB
Python
import types
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import torch._C
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class _Classes(types.ModuleType):
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def __init__(self):
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super(_Classes, self).__init__('torch.classes')
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def __getattr__(self, attr):
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proxy = torch._C._get_custom_class_python_wrapper(attr)
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if proxy is None:
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raise RuntimeError('Class {} not registered!'.format(attr))
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return proxy
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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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Arguments:
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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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