Python Enhancement Proposals

PEP 649 – Deferred Evaluation Of Annotations Using Descriptors

Deferred Evaluation Of Annotations Using Descriptors
Larry Hastings <larry at>
Standards Track
11-Jan-2021, 11-Apr-2021



As of Python 3.9, Python supports two different behaviors for annotations:

  • original or “stock” Python semantics, in which annotations are evaluated at the time they are bound, and
  • PEP 563 semantics, currently enabled per-module by from __future__ import annotations, in which annotations are converted back into strings and must be reparsed and executed by eval() to be used.

Original Python semantics created a circular references problem for static typing analysis. PEP 563 solved that problem–but its novel semantics introduced new problems, including its restriction that annotations can only reference names at module-level scope.

This PEP proposes a third way that embodies the best of both previous approaches. It solves the same circular reference problems solved by PEP 563, while otherwise preserving Python’s original annotation semantics, including allowing annotations to refer to local and class variables.

In this new approach, the code to generate the annotations dict is written to its own function which computes and returns the annotations dict. Then, __annotations__ is a “data descriptor” which calls this annotation function once and retains the result. This delays the evaluation of annotations expressions until the annotations are examined, at which point all circular references have likely been resolved. And if the annotations are never examined, the function is never called and the annotations are never computed.

Annotations defined using this PEP’s semantics have the same visibility into the symbol table as annotations under “stock” semantics–any name visible to an annotation in Python 3.9 is visible to an annotation under this PEP. In addition, annotations under this PEP can refer to names defined after the annotation is defined, as long as the name is defined in a scope visible to the annotation. Specifically, when this PEP is active:

  • An annotation can refer to a local variable defined in the current function scope.
  • An annotation can refer to a local variable defined in an enclosing function scope.
  • An annotation can refer to a class variable defined in the current class scope.
  • An annotation can refer to a global variable.

And in all four of these cases, the variable referenced by the annotation needn’t be defined at the time the annotation is defined–it can be defined afterwards. The only restriction is that the name or variable be defined before the annotation is evaluated.

If accepted, these new semantics for annotations would initially be gated behind from __future__ import co_annotations. However, these semantics would eventually be promoted to be Python’s default behavior. Thus this PEP would supersede PEP 563, and PEP 563’s behavior would be deprecated and eventually removed.



The code presented in this section is simplified for clarity. The intention is to communicate the high-level concepts involved without getting lost in with the details. The actual details are often quite different. See the Implementation section later in this PEP for a much more accurate description of how this PEP works.

Consider this example code:

def foo(x: int = 3, y: MyType = None) -> float:
class MyType:
foo_y_type = foo.__annotations__['y']

As we see here, annotations are available at runtime through an __annotations__ attribute on functions, classes, and modules. When annotations are specified on one of these objects, __annotations__ is a dictionary mapping the names of the fields to the value specified as that field’s annotation.

The default behavior in Python 3.9 is to evaluate the expressions for the annotations, and build the annotations dict, at the time the function, class, or module is bound. At runtime the above code actually works something like this:

annotations = {'x': int, 'y': MyType, 'return': float}
def foo(x = 3, y = "abc"):
foo.__annotations__ = annotations
class MyType:
foo_y_type = foo.__annotations__['y']

The crucial detail here is that the values int, MyType, and float are looked up at the time the function object is bound, and these values are stored in the annotations dict. But this code doesn’t run—it throws a NameError on the first line, because MyType hasn’t been defined yet.

PEP 563’s solution is to decompile the expressions back into strings, and store those strings in the annotations dict. The equivalent runtime code would look something like this:

annotations = {'x': 'int', 'y': 'MyType', 'return': 'float'}
def foo(x = 3, y = "abc"):
foo.__annotations__ = annotations
class MyType:
foo_y_type = foo.__annotations__['y']

This code now runs successfully. However, foo_y_type is no longer a reference to MyType, it is the string 'MyType'. The code would have to be further modified to call eval() or typing.get_type_hints() to convert the string into a useful reference to the actual MyType object.

This PEP proposes a third approach, delaying the evaluation of the annotations by computing them in their own function. If this PEP was active, the generated code would work something like this:

class function:
    # __annotations__ on a function object is already a
    # "data descriptor" in Python, we're just changing what it does
    def __annotations__(self):
        return self.__co_annotations__()

# ...

def foo_annotations_fn():
    return {'x': int, 'y': MyType, 'return': float}
def foo(x = 3, y = "abc"):
foo.__co_annotations__ = foo_annotations_fn
class MyType:
foo_y_type = foo.__annotations__['y']

The important change is that the code constructing the annotations dict now lives in a function—here, called foo_annotations_fn(). But this function isn’t called until we ask for the value of foo.__annotations__, and we don’t do that until after the definition of MyType. So this code also runs successfully, and foo_y_type now has the correct value–the class MyType–even though MyType wasn’t defined until after the annotation was defined.


Python’s original semantics for annotations made its use for static type analysis painful due to forward reference problems. This was the main justification for PEP 563, and we need not revisit those arguments here.

However, PEP 563’s solution was to decompile code for Python annotations back into strings at compile time, requiring users of annotations to eval() those strings to restore them to their actual Python values. This has several drawbacks:

  • It requires Python implementations to stringize their annotations. This is surprising behavior—unprecedented for a language-level feature. Also, adding this feature to CPython was complicated, and this complicated code would need to be reimplemented independently by every other Python implementation.
  • It requires that all annotations be evaluated at module-level scope. Annotations under PEP 563 can no longer refer to * class variables, * local variables in the current function, or * local variables in enclosing functions.
  • It requires a code change every time existing code uses an annotation, to handle converting the stringized annotation back into a useful value.
  • eval() is slow.
  • eval() isn’t always available; it’s sometimes removed from Python for space reasons.
  • In order to evaluate the annotations on a class, it requires obtaining a reference to that class’s globals, which PEP 563 suggests should be done by looking up that class by name in sys.modules—another surprising requirement for a language-level feature.
  • It adds an ongoing maintenance burden to Python implementations. Every time the language adds a new feature available in expressions, the implementation’s stringizing code must be updated in tandem in order to support decompiling it.

This PEP also solves the forward reference problem outlined in PEP 563 while avoiding the problems listed above:

  • Python implementations would generate annotations as code objects. This is simpler than stringizing, and is something Python implementations are already quite good at. This means: - alternate implementations would need to write less code to implement this feature, and - the implementation would be simpler overall, which should reduce its ongoing maintenance cost.
  • Existing annotations would not need to be changed to only use global scope. Actually, annotations would become much easier to use, as they would now also handle forward references.
  • Code examining annotations at runtime would no longer need to use eval() or anything else—it would automatically see the correct values. This is easier, faster, and removes the dependency on eval().

Backwards Compatibility

PEP 563 changed the semantics of annotations. When its semantics are active, annotations must assume they will be evaluated in module-level scope. They may no longer refer directly to local variables or class attributes.

This PEP removes that restriction; annotations may refer to globals, local variables inside functions, local variables defined in enclosing functions, and class members in the current class. In addition, annotations may refer to any of these that haven’t been defined yet at the time the annotation is defined, as long as the not-yet-defined name is created normally (in such a way that it is known to the symbol table for the relevant block, or is a global or class variable found using normal name resolution). Thus, this PEP demonstrates improved backwards compatibility over PEP 563.

PEP 563 also requires using eval() or typing.get_type_hints() to examine annotations. Code updated to work with PEP 563 that calls eval() directly would have to be updated simply to remove the eval() call. Code using typing.get_type_hints() would continue to work unchanged, though future use of that function would become optional in most cases.

Because this PEP makes semantic changes to how annotations are evaluated, this PEP will be initially gated with a per-module from __future__ import co_annotations before it eventually becomes the default behavior.

Apart from the delay in evaluating values stored in annotations dicts, this PEP preserves nearly all existing behavior of annotations dicts. Specifically:

  • Annotations dicts are mutable, and any changes to them are preserved.
  • The __annotations__ attribute can be explicitly set, and any value set this way will be preserved.
  • The __annotations__ attribute can be deleted using the del statement.

However, there are two uncommon interactions possible with class and module annotations that work today—both with stock semantics, and with PEP 563 semantics—that would no longer work when this PEP was active. These two interactions would have to be prohibited. The good news is, neither is common, and neither is considered good practice. In fact, they’re rarely seen outside of Python’s own regression test suite. They are:

  • Code that sets annotations on module or class attributes from inside any kind of flow control statement. It’s currently possible to set module and class attributes with annotations inside an if or try statement, and it works as one would expect. It’s untenable to support this behavior when this PEP is active.
  • Code in module or class scope that references or modifies the local __annotations__ dict directly. Currently, when setting annotations on module or class attributes, the generated code simply creates a local __annotations__ dict, then sets mappings in it as needed. It’s also possible for user code to directly modify this dict, though this doesn’t seem like it’s an intentional feature. Although it would be possible to support this after a fashion when this PEP was active, the semantics would likely be surprising and wouldn’t make anyone happy.

Note that these are both also pain points for static type checkers, and are unsupported by those checkers. It seems reasonable to declare that both are at the very least unsupported, and their use results in undefined behavior. It might be worth making a small effort to explicitly prohibit them with compile-time checks.

In addition, there are a few operators that would no longer be valid for use in annotations, because their side effects would affect the annotation function instead of the class/function/module the annotation was nominally defined in:

  • := (aka the “walrus operator”),
  • yield and yield from, and
  • await.

Use of any of these operators in an annotation will result in a compile-time error.

Since delaying the evaluation of annotations until they are evaluated changes the semantics of the language, it’s observable from within the language. Therefore it’s possible to write code that behaves differently based on whether annotations are evaluated at binding time or at access time, e.g.

mytype = str
def foo(a:mytype): pass
mytype = int

This will print <class 'str'> with stock semantics and <class 'int'> when this PEP is active. Since this is poor programming style to begin with, it seems acceptable that this PEP changes its behavior.

Finally, there’s a standard idiom that’s actually somewhat common when accessing class annotations, and which will become more problematic when this PEP is active: code often accesses class annotations via cls.__dict__.get("__annotations__", {}) rather than simply cls.__annotations__. It’s due to a flaw in the original design of annotations themselves. This topic will be examined in a separate discussion; the outcome of that discussion will likely guide the future evolution of this PEP.

Mistaken Rejection Of This Approach In November 2017

During the early days of discussion around PEP 563, using code to delay the evaluation of annotations was briefly discussed, in a November 2017 thread in comp.lang.python-dev. At the time the technique was termed an “implicit lambda expression”.

Guido van Rossum—Python’s BDFL at the time—replied, asserting that these “implicit lambda expression” wouldn’t work, because they’d only be able to resolve symbols at module-level scope:

IMO the inability of referencing class-level definitions from annotations on methods pretty much kills this idea.

This led to a short discussion about extending lambda-ized annotations for methods to be able to refer to class-level definitions, by maintaining a reference to the class-level scope. This idea, too, was quickly rejected.

PEP 563 summarizes the above discussion

What’s puzzling is PEP 563’s own changes to the scoping rules of annotations—it also doesn’t permit annotations to reference class-level definitions. It’s not immediately clear why an inability to reference class-level definitions was enough to reject using “implicit lambda expressions” for annotations, but was acceptable for stringized annotations.

In retrospect there was probably a pivot during the development of PEP 563. It seems that, early on, there was a prevailing assumption that PEP 563 would support references to class-level definitions. But by the time PEP 563 was finalized, this assumption had apparently been abandoned. And it looks like “implicit lambda expressions” were never reconsidered in this new light.

In any case, annotations are still able to refer to class-level definitions under this PEP, rendering the objection moot.


There’s a prototype implementation of this PEP, here:

As of this writing, all features described in this PEP are implemented, and there are some rudimentary tests in the test suite. There are still some broken tests, and the co_annotations repo is many months behind the CPython repo.

from __future__ import co_annotations

In the prototype, the semantics presented in this PEP are gated with:

from __future__ import co_annotations


Python supports runtime metadata for annotations for three different types: function, classes, and modules. The basic approach to implement this PEP is much the same for all three with only minor variations.

With this PEP, each of these types adds a new attribute, __co_annotations__. __co_annotations__ is a function: it takes no arguments, and must return either None or a dict (or subclass of dict). It adds the following semantics:

  • __co_annotations__ is always set, and may contain either None or a callable.
  • __co_annotations__ cannot be deleted.
  • __annotations__ and __co_annotations__ can’t both be set to a useful value simultaneously:
    • If you set __annotations__ to a dict, this also sets __co_annotations__ to None.
    • If you set __co_annotations__ to a callable, this also deletes __annotations__

Internally, __co_annotations__ is a “data descriptor”, where functions are called whenever user code gets, sets, or deletes the attribute. In all three cases, the object has separate internal storage for the current value of the __co_annotations__ attribute.

__annotations__ is also as a data descriptor, with its own separate internal storage for its internal value. The code implementing the “get” for __annotations__ works something like this:

if (the internal value is set)
    return the internal annotations dict
if (__co_annotations__ is not None)
    call the __co_annotations__ function
    if the result is a dict:
        store the result as the internal value
        set __co_annotations__ to None
        return the internal value
do whatever this object does when there are no annotations

Unbound code objects

When Python code defines one of these three objects with annotations, the Python compiler generates a separate code object which builds and returns the appropriate annotations dict. Wherever possible, the “annotation code object” is then stored unbound as the internal value of __co_annotations__; it is then bound on demand when the user asks for __annotations__.

This is a useful optimization for both speed and memory consumption. Python processes rarely examine annotations at runtime. Therefore, pre-binding these code objects to function objects would usually be a waste of resources.

When is this optimization not possible?

  • When an annotation function contains references to free variables, in the current function or in an outer function.
  • When an annotation function is defined on a method (a function defined inside a class) and the annotations possibly refer directly to class variables.

Note that user code isn’t permitted to directly access these unbound code objects. If the user “gets” the value of __co_annotations__, and the internal value of __co_annotations__ is an unbound code object, it immediately binds the code object, and the resulting function object is stored as the new value of __co_annotations__ and returned.

(However, these unbound code objects are stored in the .pyc file. So a determined user could examine them should that be necessary for some reason.)

Function Annotations

When compiling a function, the CPython bytecode compiler visits the annotations for the function all in one place, starting with compiler_visit_annotations() in compile.c. If there are any annotations, they create the scope for the annotations function on demand, and compiler_visit_annotations() assembles it.

The code object is passed in place of the annotations dict for the MAKE_FUNCTION bytecode instruction. MAKE_FUNCTION supports a new bit in its oparg bitfield, 0x10, which tells it to expect a co_annotations code object on the stack. The bitfields for annotations (0x04) and co_annotations (0x10) are mutually exclusive.

When binding an unbound annotation code object, a function will use its own __globals__ as the new function’s globals.

One quirk of Python: you can’t actually remove the annotations from a function object. If you delete the __annotations__ attribute of a function, then get its __annotations__ member, it will create an empty dict and use that as its __annotations__. The implementation of this PEP maintains this quirk for backwards compatibility.

Class Annotations

When compiling a class body, the compiler maintains two scopes: one for the normal class body code, and one for annotations. (This is facilitated by four new functions: compiler.c adds compiler_push_scope() and compiler_pop_scope(), and symtable.c adds symtable_push_scope() and symtable_pop_scope().) Once the code generator reaches the end of the class body, but before it generates the bytecode for the class body, it assembles the bytecode for __co_annotations__, then assigns that to __co_annotations__ using STORE_NAME.

It also sets a new __globals__ attribute. Currently it does this by calling globals() and storing the result. (Surely there’s a more elegant way to find the class’s globals–but this was good enough for the prototype.) When binding an unbound annotation code object, a class will use the value of this __globals__ attribute. When the class drops its reference to the unbound code object–either because it has bound it to a function, or because __annotations__ has been explicitly set–it also deletes its __globals__ attribute.

As discussed above, examination or modification of __annotations__ from within the class body is no longer supported. Also, any flow control (if or try blocks) around declarations of members with annotations is unsupported.

If you delete the __annotations__ attribute of a class, then get its __annotations__ member, it will return the annotations dict of the first base class with annotations set. If no base classes have annotations set, it will raise AttributeError.

Although it’s an implementation-specific detail, currently classes store the internal value of __co_annotations__ in their tp_dict under the same name.

Module Annotations

Module annotations work much the same as class annotations. The main difference is, a module uses its own dict as the __globals__ when binding the function.

If you delete the __annotations__ attribute of a class, then get its __annotations__ member, the module will raise AttributeError.

Annotations With Closures

It’s possible to write annotations that refer to free variables, and even free variables that have yet to be defined. For example:

from __future__ import co_annotations

def outer():
    def middle():
        def inner(a:mytype, b:mytype2): pass
        mytype = str
        return inner
    mytype2 = int
    return middle()

fn = outer()

At the time fn is set, inner.__co_annotations__() hasn’t been run. So it has to retain a reference to the future definitions of mytype and mytype2 if it is to correctly evaluate its annotations.

If an annotation function refers to a local variable from the current function scope, or a free variable from an enclosing function scope–if, in CPython, the annotation function code object contains one or more LOAD_DEREF opcodes–then the annotation code object is bound at definition time with references to these variables. LOAD_DEREF instructions require the annotation function to be bound with special run-time information (in CPython, a freevars array). Rather than store that separately and use that to later lazy-bind the function object, the current implementation simply early-binds the function object.

Note that, since the annotation function inner.__co_annotations__() is defined while parsing outer(), from Python’s perspective the annotation function is a “nested function”. So “local variable inside the ‘current’ function” and “free variable from an enclosing function” are, from the perspective of the annotation function, the same thing.

Annotations That Refer To Class Variables

It’s possible to write annotations that refer to class variables, and even class variables that haven’t yet been defined. For example:

from __future__ import co_annotations

class C:
    def method(a:mytype): pass
    mytype = str


Internally, annotation functions are defined as a new type of “block” in CPython’s symbol table called an AnnotationBlock. An AnnotationBlock is almost identical to a FunctionBlock. It differs in that it’s permitted to see names from an enclosing class scope. (Again: annotation functions are functions, and they’re defined inside the same scope as the thing they’re being defined on. So in the above example, the annotation function for C.method() is defined inside C.)

If it’s possible that an annotation function refers to class variables–if all these conditions are true:

  • The annotation function is being defined inside a class scope.
  • The generated code for the annotation function has at least one LOAD_NAME instruction.

Then the annotation function is bound at the time it’s set on the class/function, and this binding includes a reference to the class dict. The class dict is pushed on the stack, and the MAKE_FUNCTION bytecode instruction takes a new second bitfield (0x20) indicating that it should consume that stack argument and store it as __locals__ on the newly created function object.

Then, at the time the function is executed, the f_locals field of the frame object is set to the function’s __locals__, if set. This permits LOAD_NAME opcodes to work normally, which means the code generated for annotation functions is nearly identical to that generated for conventional Python functions.

Interactive REPL Shell

Everything works the same inside Python’s interactive REPL shell, except for module annotations in the interactive module (__main__) itself. Since that module is never “finished”, there’s no specific point where we can compile the __co_annotations__ function.

For the sake of simplicity, in this case we forego delayed evaluation. Module-level annotations in the REPL shell will continue to work exactly as they do today, evaluating immediately and setting the result directly inside the __annotations__ dict.

(It might be possible to support delayed evaluation here. But it gets complicated quickly, and for a nearly-non-existent use case.)

Annotations On Local Variables Inside Functions

Python supports syntax for local variable annotations inside functions. However, these annotations have no runtime effect–they’re discarded at compile-time. Therefore, this PEP doesn’t need to do anything to support them, the same as stock semantics and PEP 563.

Performance Comparison

Performance with this PEP should be favorable, when compared with either stock behavior or PEP 563. In general, resources are only consumed on demand—“you only pay for what you use”.

There are three scenarios to consider:

  • the runtime cost when annotations aren’t defined,
  • the runtime cost when annotations are defined but not referenced, and
  • the runtime cost when annotations are defined and referenced.

We’ll examine each of these scenarios in the context of all three semantics for annotations: stock, PEP 563, and this PEP.

When there are no annotations, all three semantics have the same runtime cost: zero. No annotations dict is created and no code is generated for it. This requires no runtime processor time and consumes no memory.

When annotations are defined but not referenced, the runtime cost of Python with this PEP should be roughly equal to or slightly better than PEP 563 semantics, and slightly better than “stock” Python semantics. The specifics depend on the object being annotated:

  • With stock semantics, the annotations dict is always built, and set as an attribute of the object being annotated.
  • In PEP 563 semantics, for function objects, a single constant (a tuple) is set as an attribute of the function. For class and module objects, the annotations dict is always built and set as an attribute of the class or module.
  • With this PEP, a single object is set as an attribute of the object being annotated. Most often, this object is a constant (a code object). In cases where the annotation refers to local variables or class variables, the code object will be bound to a function object, and the function object is set as the attribute of the object being annotated.

When annotations are both defined and referenced, code using this PEP should be much faster than code using PEP 563 semantics, and equivalent to or slightly improved over original Python semantics. PEP 563 semantics requires invoking eval() for every value inside an annotations dict, which is enormously slow. And, as already mentioned, this PEP generates measurably more efficient bytecode for class and module annotations than stock semantics; for function annotations, this PEP and stock semantics should be roughly equivalent.

Memory use should also be comparable in all three scenarios across all three semantic contexts. In the first and third scenarios, memory usage should be roughly equivalent in all cases. In the second scenario, when annotations are defined but not referenced, using this PEP’s semantics will mean the function/class/module will store one unused code object (possibly bound to an unused function object); with the other two semantics, they’ll store one unused dictionary (or constant tuple).

Bytecode Comparison

The bytecode generated for annotations functions with this PEP uses the efficient BUILD_CONST_KEY_MAP opcode to build the dict for all annotatable objects: functions, classes, and modules.

Stock semantics also uses BUILD_CONST_KEY_MAP bytecode for function annotations. PEP 563 has an even more efficient method for building annotations dicts on functions, leveraging the fact that its annotations dicts only contain strings for both keys and values. At compile-time it constructs a tuple containing pairs of keys and values at compile-time, then at runtime it converts that tuple into a dict on demand. This is a faster technique than either stock semantics or this PEP can employ, because in those two cases annotations dicts can contain Python values of any type. Of course, this performance win is negated if the annotations are examined, due to the overhead of eval().

For class and module annotations, both stock semantics and PEP 563 generate a longer and slightly-less-efficient stanza of bytecode, creating the dict and setting the annotations individually.

For Future Discussion

Circular Imports

There is one unfortunately-common scenario where PEP 563 currently provides a better experience, and it has to do with large code bases, with circular dependencies and imports, that examine their annotations at run-time.

PEP 563 permitted defining and examining invalid expressions as annotations. Its implementation requires annotations to be legal Python expressions, which it then converts into strings at compile-time. But legal Python expressions may not be computable at runtime, if for example the expression references a name that isn’t defined. This is a problem for stringized annotations if they’re evaluated, e.g. with typing.get_type_hints(). But any stringized annotation may be examined harmlessly at any time–as long as you don’t evaluate it, and only examine it as a string.

Some large organizations have code bases that unfortunately have circular dependency problems with their annotations–class A has methods annotated with class B, but class B has methods annotated with class A–that can be difficult to resolve. Since PEP 563 stringizes their annotations, it allows them to leave these circular dependencies in place, and they can sidestep the circular import problem by never importing the module that defines the types used in the annotations. Their annotations can no longer be evaluated, but this appears not to be a concern in practice. They can then examine the stringized form of the annotations at runtime and this seems to be sufficient for their needs.

This PEP allows for many of the same behaviors. Annotations must be legal Python expressions, which are compiled into a function at compile-time. And if the code never examines an annotation, it won’t have any runtime effect, so here too annotations can harmlessly refer to undefined names. (It’s exactly like defining a function that refers to undefined names–then never calling that function. Until you call the function, nothing bad will happen.)

But examining an annotation when this PEP is active means evaluating it, which means the names evaluated in that expression must be defined. An undefined name will throw a NameError in an annotation function, just as it would with a stringized annotation passed in to typing.get_type_hints(), and just like any other context in Python where an expression is evaluated.

In discussions we have yet to find a solution to this problem that makes all the participants in the conversation happy. There are various avenues to explore here:

  • One workaround is to continue to stringize one’s annotations, either by hand or done automatically by the Python compiler (as it does today with from __future__ import annotations). This might mean preserving Python’s current stringizing annotations going forward, although leaving it turned off by default, only available by explicit request (though likely with a different mechanism than from __future__ import annotations).
  • Another possible workaround involves importing the circularly-dependent modules separately, then externally adding (“monkey-patching”) their dependencies to each other after the modules are loaded. As long as the modules don’t examine their annotations until after they are completely loaded, this should work fine and be maintainable with a minimum of effort.
  • A third and more radical approach would be to change the semantics of annotations so that they don’t raise a NameError when an unknown name is evaluated, but instead create some sort of proxy “reference” object.
  • Of course, even if we do deprecate PEP 563, it will be several releases before the functionality is removed, giving us several years in which to research and innovate new solutions for this problem.

In any case, the participants of the discussion agree that this PEP should still move forward, even as this issue remains currently unresolved [1].


cls.__globals__ and fn.__locals__

Is it permissible to add the __globals__ reference to class objects as proposed here? It’s not clear why this hasn’t already been done; PEP 563 could have made use of class globals, but instead made do with looking up classes inside sys.modules. Python seems strangely allergic to adding a __globals__ reference to class objects.

If adding __globals__ to class objects is indeed a bad idea (for reasons I don’t know), here are two alternatives as to how classes could get a reference to their globals for the implementation of this PEP:

  • The generate code for a class could bind its annotations code object to a function at the time the class is bound, rather than waiting for __annotations__ to be referenced, making them an exception to the rule (even though “special cases aren’t special enough to break the rules”). This would result in a small additional runtime cost when annotations were defined but not referenced on class objects. Honestly I’m more worried about the lack of symmetry in semantics. (But I wouldn’t want to pre-bind all annotations code objects, as that would become much more costly for function objects, even as annotations are rarely used at runtime.)
  • Use the class’s __module__ attribute to look up its module by name in sys.modules. This is what PEP 563 advises. While this is passable for userspace or library code, it seems like a little bit of a code smell for this to be defined semantics baked into the language itself.

Also, the prototype gets globals for class objects by calling globals() then storing the result. I’m sure there’s a much faster way to do this, I just didn’t know what it was when I was prototyping. I’m sure we can revise this to something much faster and much more sanitary. I’d prefer to make it completely internal anyway, and not make it visible to the user (via this new __globals__ attribute). There’s possibly already a good place to put it anyway–ht_module.

Similarly, this PEP adds one new dunder member to functions, classes, and modules (__co_annotations__), and a second new dunder member to functions (__locals__). This might be considered excessive.

Bikeshedding the name

During most of the development of this PEP, user code actually could see the raw annotation code objects. __co_annotations__ could only be set to a code object; functions and other callables weren’t permitted. In that context the name co_annotations makes a lot of sense. But with this last-minute pivot where __co_annotations__ now presents itself as a callable, perhaps the name of the attribute and the name of the from __future__ import needs a re-think.


Thanks to Barry Warsaw, Eric V. Smith, Mark Shannon, and Guido van Rossum for feedback and encouragement. Thanks in particular to Mark Shannon for two key suggestions—build the entire annotations dict inside a single code object, and only bind it to a function on demand—that quickly became among the best aspects of this proposal. Also, thanks in particular to Guido van Rossum for suggesting that __co_annotations__ functions should duplicate the name visibility rules of annotations under “stock” semantics–this resulted in a sizeable improvement to the second draft. Finally, special thanks to Jelle Zijlstra, who contributed not just feedback–but code!


Last modified: 2022-01-21 11:03:51 GMT