[jit] Fix reflection on weak modules, copy attributes #20190
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* Constructs a new type at runtime so that `isinstance` checks work for weak modules assigned to `ScriptModule`s * Register `int`, `float`, `str`, and `bool` properties of `nn.Module` classes as attributes of the corresponding weak module * Fix some issues where `bias` was added to `__constants__` even though it's a parameter
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eellison
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May 7, 2019
eellison
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May 9, 2019
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Looks good, have a couple of qauestions
| >>> print(output.size()) | ||
| torch.Size([128, 30]) | ||
| """ | ||
| __constants__ = ['bias', 'in_features', 'out_features'] |
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Not related to this diff but: how did this work without constants previously? Should there have been a test failure somewhere that didn't happen?
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They're not actually used in nn.Linear except in __repr__
| return getattr(self.__dict__["_original"](), attr) | ||
| # unwrap the original | ||
| original_module = self.__dict__["_original"]() | ||
| if original_module and self.__dict__["_initialized"]: |
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Why do we need the "initialized" check here?
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So the __getattr__ only looks at the original model after __init__ is done
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This was referenced May 23, 2019
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isinstancechecks work forweak modules assigned to
ScriptModules__constants__in_featuresandout_featurestonn.Linear__constants__Fixes #19363
Differential Revision: D15302350