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#### Conditions for allowlisting tensor subclasses We allow tensor subclasses types that (1) Do not override `__setstate__`, `__getattr__`, `__setattr__`, `__get__`, `__set__` or `__getattribute__` of `torch.Tensor` (`torch.Tensor` does not have a definition of `__getattr__`, `__get__` or `__set__` so we check that these are `None`) (2) Use the generic `tp_alloc` (3) Are in a module that *has been imported by the user* to be pushed onto the stack as strings by `GLOBAL` instructions, while storing the type in a dict The strings will be converted to the classes as appropriate when executing `REBUILD` with `_rebuild_from_type_v2` *Note that we use `inspect.getattr_static(sys.modules[module], name)` to get the class/function as this method claims to have no code execution. The rationale for the 3 conditions above is as follows: The rebuild func provided by `Tensor.__reduce_ex__` is `torch._tensor._rebuild_from_type_v2`, which is defined as such (note the call to `getattr`, `Tensor.__setstate__` and the call to `as_subclass` as well as the call to `_set_obj_state` which calls `setattr`) |
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| .. | ||
| amp_examples.rst | ||
| autograd.rst | ||
| broadcasting.rst | ||
| cpu_threading_runtimes.svg | ||
| cpu_threading_torchscript_inference.rst | ||
| cpu_threading_torchscript_inference.svg | ||
| cuda.rst | ||
| ddp.rst | ||
| extending.func.rst | ||
| extending.rst | ||
| faq.rst | ||
| fsdp.rst | ||
| gradcheck.rst | ||
| hip.rst | ||
| large_scale_deployments.rst | ||
| modules.rst | ||
| mps.rst | ||
| multiprocessing.rst | ||
| numerical_accuracy.rst | ||
| randomness.rst | ||
| serialization.rst | ||
| windows.rst | ||
