python-peps/pep-0649.rst

PEP: 649
Title: Deferred Evaluation Of Annotations Using Descriptors
Author: Larry Hastings <larry@hastings.org>
Status: Draft
Type: Standards Track
Content-Type: text/x-rst
Created: 11-Jan-2021
Post-History: 11-Jan-2021


Abstract
========

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

* original 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 parsed 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.

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 preserving Python's original
straightforward runtime semantics for annotations.

In this new approach, the code to generate the annotations
dict is written to its own callable, and ``__annotations__``
is a "data descriptor" which calls the callable once and
preserves the result.

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 the default behavior.
Thus this PEP would *supersede* PEP 563, and PEP 563's behavior would
be deprecated and eventually removed.

Overview
========

.. note:: The code presented in this section is highly 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
        @property
        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``.


Motivation
==========

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 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 stored with 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 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.

* Code examining annotations at runtime would no longer need
  to use ``eval()`` or anything else—it would automatically
  get the correct values.  This is easier, almost certainly
  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 retains that
semantic change, also requiring that annotations be evaluated in
*module-level* scope.  Thus, code changed so its annotations are
compatible with PEP 563 should *already* compatible with this
aspect of this PEP and would not need further change.  Modules
still using stock semantics would have to be revised so its
annotations evaluate properly in module-level scope, in the same
way they would have to be to achieve compatibility with 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 the same backwards-compatible change
to annotation scoping as PEP 563, this PEP will be initially gated
with a per-module ``from __future__ import co_annotations``
before it eventually becomes the default behavior.

Apart from these two changes already discussed:

* the evaluation of values in annotation dicts will be
  delayed until the ``__annotations__`` attribute is evaluated, and
* annotations are now evaluated in module-level scope,

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'd 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.

There's one more idiom that's actually somewhat common when
dealing with 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.

    https://mail.python.org/pipermail/python-dev/2017-November/150109.html

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 here:

    https://www.python.org/dev/peps/pep-0563/#keeping-the-ability-to-use-function-local-state-when-defining-annotations

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.

PEP 563 semantics have shipped in three major Python releases.
These semantics are now widely used in organizations depending
on static type analysis.  Evaluating annotations at module-level
scope is clearly acceptable to all interested parties.  Therefore,
delayed evaluation of annotations with code using the same scoping
rules is obviously also completely viable.


.. _Implementation:

Implementation
==============

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

    https://github.com/larryhastings/co_annotations/

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
repo is many months behind.


from __future__ import co_annotations
-------------------------------------

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

    from __future__ import co_annotations



__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__``, with 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 a separate internal place to store the current value
of the ``__co_annotations__`` attribute.

``__annotations__`` is also reimplemented 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.  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 an important optimization, for both speed and
memory consumption.  Python processes rarely examine
annotations at runtime. Therefore, pre-binding these
code objects to function objects would be a waste of
resources in nearly all cases.

Note that user code isn't permitted to see 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 is bound, and the resulting function object is
stored as the new value of ``__co_annotations__``.


The annotations function
------------------------

Annotations functions take no arguments and
must return either None or a dict (or subclass of dict).

The bytecode generated for annotations code objects
always uses the ``BUILD_CONST_KEY_MAP`` opcode to build the
dict.  Stock and PEP 563 semantics only uses this bytecode
for function annotations; for class and module annotations,
they generate a longer and slightly-less-efficient stanza
of bytecode.

Also, when generating the bytecode for an annotations code
object, all ``LOAD_*`` opcodes are forced to be ``LOAD_GLOBAL``.


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()``.  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 in place of the
annotations dict for the ``MAKE_FUNCTION`` bytecode.
``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__``.  Naturally the implementation of this
PEP maintains this quirk.


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``.


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.)


Local Annotations Inside Functions
----------------------------------

Python supports syntax for local variable annotations inside
functions. However, these annotations have no runtime effect.
Thus this PEP doesn't need to do anything to support them.


Performance
-----------

Performance with this PEP should be favorable.  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 slightly faster than either
original Python semantics or PEP 563 semantics.  With those, the
annotations dicts are built but never examined; with this PEP,
the annotations dicts won't even be built.  All that happens at
runtime is the loading of a single constant (a simple code
object) which is then set as an attribute on an object.  Since
the annotations are never referenced, the code object is never
bound to a function, the code to create the dict is never
executed, and the dict is never constructed.

When annotations are both defined and referenced, code using
this PEP should be much faster than code using PEP 563 semantics,
and roughly the same as original Python semantics.  PEP 563
semantics requires invoking ``eval()`` for every value inside
an annotations dict, which is much slower.  And, as already
mentioned, this PEP generates more efficient bytecode for class
and module annotations than either stock or PEP 563 semantics.

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; with
the other two semantics, they'll store one unused dictionary.


For Future Discussion
=====================

__globals__
-----------

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
makes do with looking up classes inside ``sys.modules``.  Yet 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``.


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.


Acknowledgements
================

Thanks to Barry Warsaw, Eric V. Smith, and Mark Shannon
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.


Copyright
=========

This document is placed in the public domain or under the
CC0-1.0-Universal license, whichever is more permissive.



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