[Python-3000] Draft pre-PEP: function annotations (original) (raw)

Collin Winter collinw at gmail.com
Thu Aug 10 02:32:19 CEST 2006

After letting the discussions from the Spring stew in my head for a few months, here's my first draft of the proto-PEP for function annotations. This is intended to lay out in a single document the basic ideas for function annotations, to get community feedback on the fundamentals before proceeding to the nitty-gritty. As such, the implementation section isn't filled out; that's still in progress. Also, the list of references is incomplete. Both of these will be completed before the initial submission to the PEP editors.

Without further ado...

PEP: 3XXX Title: Function Annotations Version: Revision:43251Revision: 43251 Revision:43251 Last-Modified: Date:2006−03−2309:28:55−0500(Thu,23Mar2006)Date: 2006-03-23 09:28:55 -0500 (Thu, 23 Mar 2006) Date:2006−03−2309:28:55−0500(Thu,23Mar2006) Author: Collin Winter Discussions-To: python-3000 at python.org Status: Draft Type: Standards Track Requires: 3XXX (Brett Cannon's signature PEP) Content-Type: text/x-rst Created: 03-Aug-2006 Python-Version: 3.0 Post-History:

Abstract

This PEP introduces a syntax for adding annotations to Python functions [#func-term#]_. In addition to annotations for function parameters, the syntax includes support for annotating a function's return value(s).

In section one, I outline the "philosophy" and fundamentals needed to understand function annotations before launching into an in-depth discussion.

In section two, the syntax for function annotations is presented, including a full explanation of the changes needed in Python's grammar.

In section three, I discuss how user code will be able to access the annotation information.

Section four describes a possible implementation of function annotations for Python 3.0.

In section five, a C-language API for use by extension modules is discussed.

Lastly, section six lists a number of ideas that were considered for inclusion but were ultimately rejected.

Rationale

Because Python's 2.x series lacks a standard way of annotating a function's parameters and return values (e.g., with information about a what type a function's return value should be), a variety of tools and libraries have appeared to fill this gap [#tail-examp#]_. Some utilise the decorators introduced in "PEP 318", while others parse a function's doctext strings, looking for annotations there.

This PEP aims to provide a single, standard way of specifying this information, reducing the confusion caused by the wide variation in mechanism and syntax that has existed until this point.

Fundamentals of Function Annotations

Before launching into a discussion of the precise ins and outs of Python 3.0's function annotations, let's first talk broadly about what annotations are and are not:

  1. Function annotations, both for parameters and return values, are completely optional.

  2. Function annotations are nothing more than a way of associating arbitrary Python expressions with various parts of a function at compile-time.

    Re-read that. Once more.

    By itself, Python does not attach any particular meaning or significance to annotations. Left to its own, Python simply takes these expressions and uses them as the values in some theoretical parameter-name-to-annotation-expression mapping.

    The only way that annotations take on meaning is when they are interpreted by third-party libraries. These third-party, annotation-interpreting libraries (TAILs, for short) can do anything they want with a function's annotations. For example, one library might use string-based annotations to provide improved help messages, like so:

    :: def compile(source: "something compilable", filename: "where the compilable thing comes from", mode: "is this a single statement or a suite?"): ... Another library might be used to provide typechecking for Python functions and methods. This library could use annotations to indicate the function's expected input and return types, possibly something like

    :: def sum(*vargs: Number) -> Number: ... where Number is some description of the protocol for numeric types.

    However, neither the strings in the first example nor the type information in the second example have any meaning on their own; meaning comes from third-party libraries alone.

  3. Following from point 2, this PEP makes no attempt to introduce any kind of standard semantics, even for the built-in types. This work will be left to third-party libraries.

    There is no worry that these libraries will assign semantics at random, or that a variety of libraries will appear, each with varying semantics and interpretations of what, say, a tuple of strings means. The difficulty inherent in writing annotation interpreting libraries will keep their number low and their authorship in the hands of people who, frankly, know what they're doing.

Syntax

Parameters

Annotations for parameters take the form of optional expressions that follow the parameter name. This example indicates that parameters 'a' and 'c' should both be a Number, while parameter 'b' should both be a Mapping:

:: def foo(a: Number, b: Mapping, c: Number = 5): ...

In pseudo-grammar, parameters now look like identifier [: expression] [= expression]. That is, type annotations always precede a parameter's default value and both type annotations and default values are optional. Just like how equal signs are used to indicate a default value, colons are used to mark annotations. All annotation expressions are evaluated at the time the function is compiled.

Annotations for excess parameters (i.e., *vargs and **kwargs) are indicated similarly. In the follow function definition, *vargs is flagged as a list of Numbers, and **kwargs is marked as a dict whose keys are strings and whose values are Sequences.

:: def foo(*vargs: Number, **kwargs: Sequence): ...

Note that, depending on what annotation-interpreting library you're using, the following might also be a valid spelling of the above:

:: def foo(*vargs: [Number], **kwargs: {str: Sequence}): ...

Only the first, however, has the BDFL's blessing [#blessed-excess#]_ as the One Obvious Way.

Return Values

The examples thus far have omitted examples of how to annotate the type of a function's return value. This is done like so:

:: def sum(*vargs: Number) -> Number: ...

The parameter list can now be followed by a literal -> and a Python expression. Like the annotations for parameters, this expression will be evaluated when the function is compiled.

The pseudo-grammar for function definition is now something like

:: vargs = '*' identifier [':' expression] kwargs = '**' identifier [':' expression] parameter = identifier [':' expression] ['=' expression]

funcdef = 'def' identifier '(' [parameter ',']*
                               [vargs ',']
                               [kwargs]
                           ')' ['->' expression] ':' suite

For a complete discussion of the changes to Python's grammar, see the section Grammar Changes_.

Accessing Function Annotations

Once compiled, a function's annotations are available via the function's __signature__ attribute, introduced by PEP 3XXX. Signature objects include an attribute just for annotations, appropriately called annotations. This attribute is a dictionary, mapping parameter names to an object representing the evaluated annotation expression.

There is a special key in the annotations mapping, "return". This key is present only if an annotation was supplied for the function's return value.

For example, the following annotation:

:: def foo(a: Number, b: 5 + 6, c: list) -> String: ...

would result in a __signature__.annotations mapping of

:: {'a': Number, 'b': 11, 'c': list, 'return': String}

The return key was chosen because it cannot conflict with the name of a parameter; any attempt to use return as a parameter name would result in a SyntaxError.

Implementation

XXX This is all very much TODO. Beyond the obvious changes to Python's grammar, the eventual implementation will probably involve a change to the MAKE_FUNCTION opcode, though the details haven't been fully worked out yet.

I'm still working on a sample implementation that works separately from the signature mechanism.

API for Annotations in C-language Extension Modules

XXX TODO

This will probably involve macros around CPython API calls to set and fetch the annotation expression for a given parameter.

Rejected Proposals

Footnotes

.. _[#func-term#] - Unless specifically stated, "function" is generally used as a synonym for "callable" throughout this document.

.. [#tail-examp#] - The author's typecheck library makes use of decorators, while Maxime Bourget's own typechecker_ utilises parsed doctext strings.

References ##########

.. _[#blessed-excess#] - http://mail.python.org/pipermail/python-3000/2006-May/002173.html

.. _[#reject-gen-syn#] - http://mail.python.org/pipermail/python-3000/2006-May/002103.html

.. _typecheck - http://oakwinter.com/code/typecheck/

.. _Maxime Bourget's own typechecker - http://maxrepo.info/taxonomy/term/3,6/all

.. _[#thread-gen#] - http://mail.python.org/pipermail/python-3000/2006-May/002091.html

.. _[#thread-hof#] - http://mail.python.org/pipermail/python-3000/2006-May/001972.html

.. _[#thread-imm-list#] - http://mail.python.org/pipermail/python-3000/2006-May/002105.html

.. _[#thread-mixing#] - http://mail.python.org/pipermail/python-3000/2006-May/002209.html

.. _[#emphasis-tpls#] - http://mail.python.org/pipermail/python-3000/2006-June/002438.html

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