Styjun

How do I pickle an instance of a frozen dataclass with __slots__? For example, the following code raises an exception in Python 3.7.0:

import pickle
from dataclasses import dataclass

@dataclass(frozen=True)
class A:
 __slots__ = ('a',)
 a: int

b = pickle.dumps(A(5))
pickle.loads(b)

Traceback (most recent call last):
 File "<stdin>", line 1, in <module>
 File "<string>", line 3, in __setattr__
dataclasses.FrozenInstanceError: cannot assign to field 'a'

This works if I remove either the frozen or the __slots__. Is this just a bug?

The problem comes from pickle using the __setattr__ method of the instance when setting the state of the slots.

The default __setsate__ is defined in load_build in _pickle.c line 6220.

For the items in the state dict, the instance __dict__ is updated directly:

 if (PyObject_SetItem(dict, d_key, d_value) < 0)

whereas for the items in the slotstate dict, the instance's __setattr__ is used:

if (PyObject_SetAttr(inst, d_key, d_value) < 0)

Now because the instance is frozen, __setattr__ raises FrozenInstanceError when loading.

To circumvent this, you can define your own __setstate__ method which will use object.__setattr__, and not the instance's __setattr__.

The docs give some sort of warning for this:

There is a tiny performance penalty when using frozen=True: __init__() cannot use simple assignment to initialize fields, and must use object.__setattr__().

It may also be good to define __getstate__ as the instance __dict__ is always None in your case. If you don't, the state argument of __setstate__ will be a tuple (None, 'a': 5), the first value being the value of the instance's __dict__ and the second the slotstate dict.

import pickle
from dataclasses import dataclass

@dataclass(frozen=True)
class A:
 __slots__ = ('a',)
 a: int

 def __getstate__(self):
 return dict(
 (slot, getattr(self, slot))
 for slot in self.__slots__
 if hasattr(self, slot)
 )

 def __setstate__(self, state):
 for slot, value in state.items():
 object.__setattr__(self, slot, value) # <- use object.__setattr__


b = pickle.dumps(A(5))
pickle.loads(b)

I personally would not call it a bug as the pickling process is designed to be flexible, but there is room for a feature enhancement. A revision of the pickling protocol could fix this in future. Unless I am missing something and aside of the tiny performance penalty, using PyObject_GenericSetattr for all the slots might be a reasonable fix?