Philip Meier a926be39d4 torch.jit.script escape hatch (#106229) ... Although the sun is setting for torchscript, it is not [officially deprecated](https://github.com/pytorch/pytorch/issues/103841#issuecomment-1605017153) since nothing currently fully replaces it. Thus, "downstream" libraries like TorchVision, that started offering torchscript support still need to support it for BC. torchscript has forced us to use workaround after workaround since forever. Although this makes the code harder to read and maintain, we made our peace with it. However, we are currently looking into more elaborate API designs that are severely hampered by our torchscript BC guarantees. Although likely not intended as such, while looking for ways to enable our design while keeping a subset of it scriptable, we found the undocumented `__prepare_scriptable__` escape hatch: 0cf918947d/torch/jit/_script.py (L977) One can define this method and if you call `torch.jit.script` on the object, the returned object of the method will be scripted rather than the original object. In TorchVision we are using exactly [this mechanism to enable BC](3966f9558b/torchvision/transforms/v2/_transform.py (L122-L136)) while allowing the object in eager mode to be a lot more flexible (`*args, **kwargs`, dynamic dispatch, ...). Unfortunately, this escape hatch is only available for `nn.Module`'s 0cf918947d/torch/jit/_script.py (L1279-L1283) This was fine for the example above since we were subclassing from `nn.Module` anyway. However, we recently also hit a case [where this wasn't the case](https://github.com/pytorch/vision/pull/7747#issuecomment-1642045479). Given the frozen state on JIT, would it be possible to give us a general escape hatch so that we can move forward with the design unconstrained while still keeping BC? This PR implements just this by re-using the `__prepare_scriptable__` hook. Pull Request resolved: https://github.com/pytorch/pytorch/pull/106229 Approved by: https://github.com/lezcano, https://github.com/ezyang		2023-08-11 18:24:46 +00:00
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_passes	Apply UFMT to low traffic torch modules (#106249)	2023-07-29 23:37:30 +00:00
mobile	Apply UFMT to low traffic torch modules (#106249)	2023-07-29 23:37:30 +00:00
__init__.py	Apply UFMT to low traffic torch modules (#106249)	2023-07-29 23:37:30 +00:00
_async.py	Apply UFMT to low traffic torch modules (#106249)	2023-07-29 23:37:30 +00:00
_await.py	Apply UFMT to low traffic torch modules (#106249)	2023-07-29 23:37:30 +00:00
_builtins.py	Apply UFMT to low traffic torch modules (#106249)	2023-07-29 23:37:30 +00:00
_check.py	Apply UFMT to low traffic torch modules (#106249)	2023-07-29 23:37:30 +00:00
_dataclass_impls.py	Apply UFMT to low traffic torch modules (#106249)	2023-07-29 23:37:30 +00:00
_decomposition_utils.py	Apply UFMT to low traffic torch modules (#106249)	2023-07-29 23:37:30 +00:00
_decompositions.py	Apply UFMT to low traffic torch modules (#106249)	2023-07-29 23:37:30 +00:00
_freeze.py	Apply UFMT to low traffic torch modules (#106249)	2023-07-29 23:37:30 +00:00
_fuser.py	Apply UFMT to low traffic torch modules (#106249)	2023-07-29 23:37:30 +00:00
_ir_utils.py	Apply UFMT to low traffic torch modules (#106249)	2023-07-29 23:37:30 +00:00
_logging.py
_monkeytype_config.py	Apply UFMT to low traffic torch modules (#106249)	2023-07-29 23:37:30 +00:00
_pickle.py	Apply UFMT to low traffic torch modules (#106249)	2023-07-29 23:37:30 +00:00
_recursive.py	Apply UFMT to low traffic torch modules (#106249)	2023-07-29 23:37:30 +00:00
_script.py	torch.jit.script escape hatch (#106229)	2023-08-11 18:24:46 +00:00
_serialization.py
_shape_functions.py	Apply UFMT to low traffic torch modules (#106249)	2023-07-29 23:37:30 +00:00
_state.py	Apply UFMT to low traffic torch modules (#106249)	2023-07-29 23:37:30 +00:00
_trace.py	Apply UFMT to low traffic torch modules (#106249)	2023-07-29 23:37:30 +00:00
annotations.py	Apply UFMT to low traffic torch modules (#106249)	2023-07-29 23:37:30 +00:00
frontend.py	UFMT torch/jit/frontend.py, manually fix mypy suppression (#106268)	2023-07-30 19:10:59 +00:00
generate_bytecode.py	Apply UFMT to low traffic torch modules (#106249)	2023-07-29 23:37:30 +00:00
quantized.py	Apply UFMT to low traffic torch modules (#106249)	2023-07-29 23:37:30 +00:00
supported_ops.py	Apply UFMT to low traffic torch modules (#106249)	2023-07-29 23:37:30 +00:00
unsupported_tensor_ops.py	Apply UFMT to low traffic torch modules (#106249)	2023-07-29 23:37:30 +00:00