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Summary: Fixes https://github.com/pytorch/pytorch/issues/54420 When I tested on master, with the testing code, there were multiple objects on the garbage collector that cannot be printed. Testing code: ``` import torch import gc import os import sys print(torch.__version__) a = torch.rand(10) print(a) objects = gc.get_objects() for i in range(len(objects)): print(objects[i]) ``` ### 1 ``` print(torch.classes) ``` Like SplitInfinity has mentioned in the GitHub issue, the solution here is to set `__file__` for `torch.classes` to something. Similar to [_ops.py](https://github.com/pytorch/pytorch/blob/master/torch/_ops.py#L69), where `__file__` is set to `_ops.py`, we could set `__file__` for torch.classes to `_classes.py`. ### 2 ``` print(torch._ops.ops.quantized) print(torch._ops.ops.atan) ``` When we try to print these two modules, it will call `_OpNamespace::__getattr__`, but the `op_name` is `__file__`. This becomes a problem when `torch._C._jit_get_operation(qualified_op_name)` [(link)](https://github.com/pytorch/pytorch/blob/master/torch/_ops.py#L60) tries to look for an actual op on the native C++ side. Only when we get the attribute for an actual op, e.g. `print(torch._ops.ops.quantized.elu)`, the `op_name` becomes proper (e.g. `elu`). My current solution is to return a hardcoded string (i.e. “torch.ops”) if `op_name` is `"__file__"`. Pull Request resolved: https://github.com/pytorch/pytorch/pull/62447 Reviewed By: saketh-are Differential Revision: D30234654 Pulled By: yidawang-oss fbshipit-source-id: de43a8f599739c749fb3307eea015cc61f1da60e
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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