Mailman 3 January 2021 - Python-Dev

Mixed Python/C debugging
by Skip Montanaro 26 Apr '23

26 Apr '23

Having tried comp.lang.python with no response, I turn here... After at least ten years away from Python's run-time interpreter & byte code compiler, I'm getting set to familiarize myself with that again. This will, I think, entail debugging a mixed Python/C environment. I'm an Emacs user and am aware that GDB since 7.0 has support for debugging at the Python code level. Is Emacs+GDB my best bet? Are there any Python IDEs which support C-level breakpoints and debugging? Thanks, Skip

5 6

PEP 448 review
by Guido van Rossum 29 Mar '23

29 Mar '23

I'm back, I've re-read the PEP, and I've re-read the long thread with "(no subject)". I think Georg Brandl nailed it: """ *I like the "sequence and dict flattening" part of the PEP, mostly because itis consistent and should be easy to understand, but the comprehension syntaxenhancements seem to be bad for readability and "comprehending" what the codedoes.The call syntax part is a mixed bag on the one hand it is nice to be consistent with the extended possibilities in literals (flattening), but on the other hand there would be small but annoying inconsistencies anyways (e.g. the duplicate kwarg case above).* """ Greg Ewing followed up explaining that the inconsistency between dict flattening and call syntax is inherent in the pre-existing different rules for dicts vs. keyword args: {'a':1, 'a':2} results in {'a':2}, while f(a=1, a=2) is an error. (This form is a SyntaxError; the dynamic case f(a=1, **{'a': 1}) is a TypeError.) For me, allowing f(*a, *b) and f(**d, **e) and all the other combinations for function calls proposed by the PEP is an easy +1 -- it's a straightforward extension of the existing pattern, and anybody who knows what f(x, *a) does will understand f(x, *a, y, *b). Guessing what f(**d, **e) means shouldn't be hard either. Understanding the edge case for duplicate keys with f(**d, **e) is a little harder, but the error messages are pretty clear, and it is not a new edge case. The sequence and dict flattening syntax proposals are also clean and logical -- we already have *-unpacking on the receiving side, so allowing *x in tuple expressions reads pretty naturally (and the similarity with *a in argument lists certainly helps). From here, having [a, *x, b, *y] is also natural, and then the extension to other displays is natural: {a, *x, b, *y} and {a:1, **d, b:2, **e}. This, too, gets a +1 from me. So that leaves comprehensions. IIRC, during the development of the patch we realized that f(*x for x in xs) is sufficiently ambiguous that we decided to disallow it -- note that f(x for x in xs) is already somewhat of a special case because an argument can only be a "bare" generator expression if it is the only argument. The same reasoning doesn't apply (in that form) to list, set and dict comprehensions -- while f(x for x in xs) is identical in meaning to f((x for x in xs)), [x for x in xs] is NOT the same as [(x for x in xs)] (that's a list of one element, and the element is a generator expression). The basic premise of this part of the proposal is that if you have a few iterables, the new proposal (without comprehensions) lets you create a list or generator expression that iterates over all of them, essentially flattening them: >>> xs = [1, 2, 3] >>> ys = ['abc', 'def'] >>> zs = [99] >>> [*xs, *ys, *zs] [1, 2, 3, 'abc', 'def', 99] >>> But now suppose you have a list of iterables: >>> xss = [[1, 2, 3], ['abc', 'def'], [99]] >>> [*xss[0], *xss[1], *xss[2]] [1, 2, 3, 'abc', 'def', 99] >>> Wouldn't it be nice if you could write the latter using a comprehension? >>> xss = [[1, 2, 3], ['abc', 'def'], [99]] >>> [*xs for xs in xss] [1, 2, 3, 'abc', 'def', 99] >>> This is somewhat seductive, and the following is even nicer: the *xs position may be an expression, e.g.: >>> xss = [[1, 2, 3], ['abc', 'def'], [99]] >>> [*xs[:2] for xs in xss] [1, 2, 'abc', 'def', 99] >>> On the other hand, I had to explore the possibilities here by experimenting in the interpreter, and I discovered some odd edge cases (e.g. you can parenthesize the starred expression, but that seems a syntactic accident). All in all I am personally +0 on the comprehension part of the PEP, and I like that it provides a way to "flatten" a sequence of sequences, but I think very few people in the thread have supported this part. Therefore I would like to ask Neil to update the PEP and the patch to take out the comprehension part, so that the two "easy wins" can make it into Python 3.5 (basically, I am accepting two-thirds of the PEP :-). There is some time yet until alpha 2. I would also like code reviewers (Benjamin?) to start reviewing the patch <http://bugs.python.org/issue2292>, taking into account that the comprehension part needs to be removed. -- --Guido van Rossum (python.org/~guido)

7 17

PEP 638: Syntactic macros
by Mark Shannon 05 Feb '23

05 Feb '23

Hi everyone, I've submitted my PEP on syntactic macros as PEP 638. https://www.python.org/dev/peps/pep-0638/ All comments and suggestions are welcome. Cheers, Mark

16 25

Speeding up CPython
by Mark Shannon 18 May '21

18 May '21

Hi everyone, CPython is slow. We all know that, yet little is done to fix it. I'd like to change that. I have a plan to speed up CPython by a factor of five over the next few years. But it needs funding. I am aware that there have been several promised speed ups in the past that have failed. You might wonder why this is different. Here are three reasons: 1. I already have working code for the first stage. 2. I'm not promising a silver bullet. I recognize that this is a substantial amount of work and needs funding. 3. I have extensive experience in VM implementation, not to mention a PhD in the subject. My ideas for possible funding, as well as the actual plan of development, can be found here: https://github.com/markshannon/faster-cpython I'd love to hear your thoughts on this. Cheers, Mark.

33 77

PEP 1, PEP Purpose and Guidelines
by barry＠zope.com 18 May '21

18 May '21

It has been a while since I posted a copy of PEP 1 to the mailing lists and newsgroups. I've recently done some updating of a few sections, so in the interest of gaining wider community participation in the Python development process, I'm posting the latest revision of PEP 1 here. A version of the PEP is always available on-line at http://www.python.org/peps/pep-0001.html Enjoy, -Barry -------------------- snip snip -------------------- PEP: 1 Title: PEP Purpose and Guidelines Version: $Revision: 1.36 $ Last-Modified: $Date: 2002/07/29 18:34:59 $ Author: Barry A. Warsaw, Jeremy Hylton Status: Active Type: Informational Created: 13-Jun-2000 Post-History: 21-Mar-2001, 29-Jul-2002 What is a PEP? PEP stands for Python Enhancement Proposal. A PEP is a design document providing information to the Python community, or describing a new feature for Python. The PEP should provide a concise technical specification of the feature and a rationale for the feature. We intend PEPs to be the primary mechanisms for proposing new features, for collecting community input on an issue, and for documenting the design decisions that have gone into Python. The PEP author is responsible for building consensus within the community and documenting dissenting opinions. Because the PEPs are maintained as plain text files under CVS control, their revision history is the historical record of the feature proposal[1]. Kinds of PEPs There are two kinds of PEPs. A standards track PEP describes a new feature or implementation for Python. An informational PEP describes a Python design issue, or provides general guidelines or information to the Python community, but does not propose a new feature. Informational PEPs do not necessarily represent a Python community consensus or recommendation, so users and implementors are free to ignore informational PEPs or follow their advice. PEP Work Flow The PEP editor, Barry Warsaw <peps(a)python.org>, assigns numbers for each PEP and changes its status. The PEP process begins with a new idea for Python. It is highly recommended that a single PEP contain a single key proposal or new idea. The more focussed the PEP, the more successfully it tends to be. The PEP editor reserves the right to reject PEP proposals if they appear too unfocussed or too broad. If in doubt, split your PEP into several well-focussed ones. Each PEP must have a champion -- someone who writes the PEP using the style and format described below, shepherds the discussions in the appropriate forums, and attempts to build community consensus around the idea. The PEP champion (a.k.a. Author) should first attempt to ascertain whether the idea is PEP-able. Small enhancements or patches often don't need a PEP and can be injected into the Python development work flow with a patch submission to the SourceForge patch manager[2] or feature request tracker[3]. The PEP champion then emails the PEP editor <peps(a)python.org> with a proposed title and a rough, but fleshed out, draft of the PEP. This draft must be written in PEP style as described below. If the PEP editor approves, he will assign the PEP a number, label it as standards track or informational, give it status 'draft', and create and check-in the initial draft of the PEP. The PEP editor will not unreasonably deny a PEP. Reasons for denying PEP status include duplication of effort, being technically unsound, not providing proper motivation or addressing backwards compatibility, or not in keeping with the Python philosophy. The BDFL (Benevolent Dictator for Life, Guido van Rossum) can be consulted during the approval phase, and is the final arbitrator of the draft's PEP-ability. If a pre-PEP is rejected, the author may elect to take the pre-PEP to the comp.lang.python newsgroup (a.k.a. python-list(a)python.org mailing list) to help flesh it out, gain feedback and consensus from the community at large, and improve the PEP for re-submission. The author of the PEP is then responsible for posting the PEP to the community forums, and marshaling community support for it. As updates are necessary, the PEP author can check in new versions if they have CVS commit permissions, or can email new PEP versions to the PEP editor for committing. Standards track PEPs consists of two parts, a design document and a reference implementation. The PEP should be reviewed and accepted before a reference implementation is begun, unless a reference implementation will aid people in studying the PEP. Standards Track PEPs must include an implementation - in the form of code, patch, or URL to same - before it can be considered Final. PEP authors are responsible for collecting community feedback on a PEP before submitting it for review. A PEP that has not been discussed on python-list(a)python.org and/or python-dev(a)python.org will not be accepted. However, wherever possible, long open-ended discussions on public mailing lists should be avoided. Strategies to keep the discussions efficient include, setting up a separate SIG mailing list for the topic, having the PEP author accept private comments in the early design phases, etc. PEP authors should use their discretion here. Once the authors have completed a PEP, they must inform the PEP editor that it is ready for review. PEPs are reviewed by the BDFL and his chosen consultants, who may accept or reject a PEP or send it back to the author(s) for revision. Once a PEP has been accepted, the reference implementation must be completed. When the reference implementation is complete and accepted by the BDFL, the status will be changed to `Final.' A PEP can also be assigned status `Deferred.' The PEP author or editor can assign the PEP this status when no progress is being made on the PEP. Once a PEP is deferred, the PEP editor can re-assign it to draft status. A PEP can also be `Rejected'. Perhaps after all is said and done it was not a good idea. It is still important to have a record of this fact. PEPs can also be replaced by a different PEP, rendering the original obsolete. This is intended for Informational PEPs, where version 2 of an API can replace version 1. PEP work flow is as follows: Draft -> Accepted -> Final -> Replaced ^ +----> Rejected v Deferred Some informational PEPs may also have a status of `Active' if they are never meant to be completed. E.g. PEP 1. What belongs in a successful PEP? Each PEP should have the following parts: 1. Preamble -- RFC822 style headers containing meta-data about the PEP, including the PEP number, a short descriptive title (limited to a maximum of 44 characters), the names, and optionally the contact info for each author, etc. 2. Abstract -- a short (~200 word) description of the technical issue being addressed. 3. Copyright/public domain -- Each PEP must either be explicitly labelled as placed in the public domain (see this PEP as an example) or licensed under the Open Publication License[4]. 4. Specification -- The technical specification should describe the syntax and semantics of any new language feature. The specification should be detailed enough to allow competing, interoperable implementations for any of the current Python platforms (CPython, JPython, Python .NET). 5. Motivation -- The motivation is critical for PEPs that want to change the Python language. It should clearly explain why the existing language specification is inadequate to address the problem that the PEP solves. PEP submissions without sufficient motivation may be rejected outright. 6. Rationale -- The rationale fleshes out the specification by describing what motivated the design and why particular design decisions were made. It should describe alternate designs that were considered and related work, e.g. how the feature is supported in other languages. The rationale should provide evidence of consensus within the community and discuss important objections or concerns raised during discussion. 7. Backwards Compatibility -- All PEPs that introduce backwards incompatibilities must include a section describing these incompatibilities and their severity. The PEP must explain how the author proposes to deal with these incompatibilities. PEP submissions without a sufficient backwards compatibility treatise may be rejected outright. 8. Reference Implementation -- The reference implementation must be completed before any PEP is given status 'Final,' but it need not be completed before the PEP is accepted. It is better to finish the specification and rationale first and reach consensus on it before writing code. The final implementation must include test code and documentation appropriate for either the Python language reference or the standard library reference. PEP Template PEPs are written in plain ASCII text, and should adhere to a rigid style. There is a Python script that parses this style and converts the plain text PEP to HTML for viewing on the web[5]. PEP 9 contains a boilerplate[7] template you can use to get started writing your PEP. Each PEP must begin with an RFC822 style header preamble. The headers must appear in the following order. Headers marked with `*' are optional and are described below. All other headers are required. PEP: <pep number> Title: <pep title> Version: <cvs version string> Last-Modified: <cvs date string> Author: <list of authors' real names and optionally, email addrs> * Discussions-To: <email address> Status: <Draft | Active | Accepted | Deferred | Final | Replaced> Type: <Informational | Standards Track> * Requires: <pep numbers> Created: <date created on, in dd-mmm-yyyy format> * Python-Version: <version number> Post-History: <dates of postings to python-list and python-dev> * Replaces: <pep number> * Replaced-By: <pep number> The Author: header lists the names and optionally, the email addresses of all the authors/owners of the PEP. The format of the author entry should be address(a)dom.ain (Random J. User) if the email address is included, and just Random J. User if the address is not given. If there are multiple authors, each should be on a separate line following RFC 822 continuation line conventions. Note that personal email addresses in PEPs will be obscured as a defense against spam harvesters. Standards track PEPs must have a Python-Version: header which indicates the version of Python that the feature will be released with. Informational PEPs do not need a Python-Version: header. While a PEP is in private discussions (usually during the initial Draft phase), a Discussions-To: header will indicate the mailing list or URL where the PEP is being discussed. No Discussions-To: header is necessary if the PEP is being discussed privately with the author, or on the python-list or python-dev email mailing lists. Note that email addresses in the Discussions-To: header will not be obscured. Created: records the date that the PEP was assigned a number, while Post-History: is used to record the dates of when new versions of the PEP are posted to python-list and/or python-dev. Both headers should be in dd-mmm-yyyy format, e.g. 14-Aug-2001. PEPs may have a Requires: header, indicating the PEP numbers that this PEP depends on. PEPs may also have a Replaced-By: header indicating that a PEP has been rendered obsolete by a later document; the value is the number of the PEP that replaces the current document. The newer PEP must have a Replaces: header containing the number of the PEP that it rendered obsolete. PEP Formatting Requirements PEP headings must begin in column zero and the initial letter of each word must be capitalized as in book titles. Acronyms should be in all capitals. The body of each section must be indented 4 spaces. Code samples inside body sections should be indented a further 4 spaces, and other indentation can be used as required to make the text readable. You must use two blank lines between the last line of a section's body and the next section heading. You must adhere to the Emacs convention of adding two spaces at the end of every sentence. You should fill your paragraphs to column 70, but under no circumstances should your lines extend past column 79. If your code samples spill over column 79, you should rewrite them. Tab characters must never appear in the document at all. A PEP should include the standard Emacs stanza included by example at the bottom of this PEP. A PEP must contain a Copyright section, and it is strongly recommended to put the PEP in the public domain. When referencing an external web page in the body of a PEP, you should include the title of the page in the text, with a footnote reference to the URL. Do not include the URL in the body text of the PEP. E.g. Refer to the Python Language web site [1] for more details. ... [1] http://www.python.org When referring to another PEP, include the PEP number in the body text, such as "PEP 1". The title may optionally appear. Add a footnote reference that includes the PEP's title and author. It may optionally include the explicit URL on a separate line, but only in the References section. Note that the pep2html.py script will calculate URLs automatically, e.g.: ... Refer to PEP 1 [7] for more information about PEP style ... References [7] PEP 1, PEP Purpose and Guidelines, Warsaw, Hylton http://www.python.org/peps/pep-0001.html If you decide to provide an explicit URL for a PEP, please use this as the URL template: http://www.python.org/peps/pep-xxxx.html PEP numbers in URLs must be padded with zeros from the left, so as to be exactly 4 characters wide, however PEP numbers in text are never padded. Reporting PEP Bugs, or Submitting PEP Updates How you report a bug, or submit a PEP update depends on several factors, such as the maturity of the PEP, the preferences of the PEP author, and the nature of your comments. For the early draft stages of the PEP, it's probably best to send your comments and changes directly to the PEP author. For more mature, or finished PEPs you may want to submit corrections to the SourceForge bug manager[6] or better yet, the SourceForge patch manager[2] so that your changes don't get lost. If the PEP author is a SF developer, assign the bug/patch to him, otherwise assign it to the PEP editor. When in doubt about where to send your changes, please check first with the PEP author and/or PEP editor. PEP authors who are also SF committers, can update the PEPs themselves by using "cvs commit" to commit their changes. Remember to also push the formatted PEP text out to the web by doing the following: % python pep2html.py -i NUM where NUM is the number of the PEP you want to push out. See % python pep2html.py --help for details. Transferring PEP Ownership It occasionally becomes necessary to transfer ownership of PEPs to a new champion. In general, we'd like to retain the original author as a co-author of the transferred PEP, but that's really up to the original author. A good reason to transfer ownership is because the original author no longer has the time or interest in updating it or following through with the PEP process, or has fallen off the face of the 'net (i.e. is unreachable or not responding to email). A bad reason to transfer ownership is because you don't agree with the direction of the PEP. We try to build consensus around a PEP, but if that's not possible, you can always submit a competing PEP. If you are interested assuming ownership of a PEP, send a message asking to take over, addressed to both the original author and the PEP editor <peps(a)python.org>. If the original author doesn't respond to email in a timely manner, the PEP editor will make a unilateral decision (it's not like such decisions can be reversed. :). References and Footnotes [1] This historical record is available by the normal CVS commands for retrieving older revisions. For those without direct access to the CVS tree, you can browse the current and past PEP revisions via the SourceForge web site at http://cvs.sourceforge.net/cgi-bin/cvsweb.cgi/python/nondist/peps/?cvsroot=… [2] http://sourceforge.net/tracker/?group_id=5470&atid=305470 [3] http://sourceforge.net/tracker/?atid=355470&group_id=5470&func=browse [4] http://www.opencontent.org/openpub/ [5] The script referred to here is pep2html.py, which lives in the same directory in the CVS tree as the PEPs themselves. Try "pep2html.py --help" for details. The URL for viewing PEPs on the web is http://www.python.org/peps/ [6] http://sourceforge.net/tracker/?group_id=5470&atid=305470 [7] PEP 9, Sample PEP Template http://www.python.org/peps/pep-0009.html Copyright This document has been placed in the public domain. Local Variables: mode: indented-text indent-tabs-mode: nil sentence-end-double-space: t fill-column: 70 End:

8 14

Let's Fix Class Annotations -- And Maybe Annotations Generally
by Larry Hastings 24 Apr '21

24 Apr '21

Howdy howdy. While working on my PEP I stumbled over a lot of behavior by annotations that I found inconsistent and inconvenient. I think there are several problems here that needs fixing. This discussion will probably evolve into a PEP, and I'll be happy to steer that process. But I'm less certain about what the right thing to do is. (Although I do know what I'd prefer!) So let's talk about it! Annotations are represented in Python as a dictionary. They can be present on functions, classes, and modules as an attribute called "__annotations__". We start with: how do you get the annotations from one of these objects? Surely it's as easy as this line from Lib/inspect.py shows us: return func.__annotations__ And yes, that's best practice for getting an annotation from a function object. But consider this line from Lib/functools.py: ann = getattr(cls, '__annotations__', {}) Huh. Why doesn't it simply look at cls.__annotations__? It's because the language declares that __annotations__ on a class or module is optional. Since cls.__annotations__ may not be defined, evaluating that might throw an exception. Three-argument getattr() is much safer, and I assert it's best practice for getting the annotations from a module. But consider this line from Lib/dataclasses.py: cls_annotations = cls.__dict__.get('__annotations__', {}) And a very similar line from Lib/typing.py: ann = base.__dict__.get('__annotations__', {}) Huh! Why is this code skipping the attribute entirely, and examining cls.__dict__? It's because the getattr() approach has a subtle bug when dealing with classes. Consider this example: class A: ax:int=3 class B(A): pass print(getattr(B, '__annotations__', {})) That's right, B *inherits* A.__annotations__! So this prints {'ax': int}. This *can't* the intended behavior of __annotations__ on classes. It's only supposed to contain annotations for the fields of B itself, not those of one of its randomly-selected base classes. But that's how it behaves today--and people have had to work around this behavior for years. Examining the class dict is, sadly, best practice for getting __annotations__ from a class. So, already: three different objects can have __annotations__, and there are three different best practices for getting their __annotations__. Let's zoom out for a moment. Here's the list of predefined data fields you can find on classes: __annotations__ __bases__ __class__ __dict__ __doc__ __module__ __mro__ __name__ __qualname__ All of these describe metadata about the class. In every case *except one*, the field is mandatory, which also means it's never inherited. And in every case *except one*, you cannot delete the field. (Though you *are* allowed to overwrite some of them.) You guessed it: __annotations__ is the exception. It's optional, and you're allowed to delete it. And these exceptions are causing problems. It seems to me that, if the only way to correctly use a language-defined attribute of classes is by rooting around in its __dict__, the design is a misfire. (Much of the above also applies to modules, too. The big difference: since modules lack inheritance, you don't need to look in their __dict__.) Now consider what happens if my "delayed annotations of annotations using descriptors" PEP is accepted. If that happens, pulling __annotations__ out of the class dict won't work if they haven't been generated yet. So today's "best practice" becomes tomorrow's "this code doesn't work". To correctly examine class annotations, code would have to do something like this, which should work correctly in any Python 3.x version: if (getattr(cls, '__co_annotations__', None) or ('__annotations__' in cls.__dict__)): ann = cls.__annotations__ else: ann = {} This is getting ridiculous. Let's move on to a related topic. For each of the objects that can have annotations, what happens if o.__annotations__ is set, and you "del o.__annotations__", then you access o.__annotations__? It depends on what the object is, because each of them behaves differently. You already know what happens with classes: if any of the base classes has __annotations__ set, you'll get the first one you find in the MRO. If none of the bases have __annotations__ set you'll get an AttributeError. For a module, if you delete it then try to access it, you'll always get an AttributeError. For a function, if you delete it then try to get it, the function will create a new empty dict, store it as its new annotations dict, and return that. Why does it do that? I'm not sure. The relevent PEP (3107) doesn't specify this behavior. So, annotations can be set on three different object types, and each of those three have a different behavior when you delete the annotations then try to get them again. As a final topic: what are the permitted types for __annotations__? If you say "o.__annotations__ = <x>", what types are and aren't allowed for <x>? For functions, __annotations__ may be assigned to either None or a dict (an object that passes PyDict_Check). Anything else throws a TypeError. For classes and modules, no checking is done whatsoever, and you can set __annotations__ on those two to any Python object. While "a foolish consistency is the hobgoblin of little minds", I don't see the benefit of setting a module's __annotations__ to 2+3j. I think it's long past time that we cleaned up the behavior of annotations. They should be simple and consistent across all objects that support them. At the very least, I think we should make cls.__annotations__ required rather than optional, so that it's never inherited. What should its default value be? An empty dict would be more compatible, but None would be cheaper. Note that creating the empty dict on the fly, the way function objects do, wouldn't really help--because current best practice means looking in cls.__dict__. I also think you shouldn't be able to delete __annotations__ on any of the three objects (function, class, module). It should always be set, so that the best practice for accessing annotations on an object is always o.__annotations__. If I could wave my magic wand and do whatever I wanted, I'd change the semantics for __annotations__ to the following: * Functions, classes, and modules always have an __annotations__ member set. * "del o.__annotations__" always throws a TypeError. * The language will set __annotations__ to a dict if the object has annotations, or None if it has no annotations. * You may set __annotations__, but you can only set it to either None or a dict (passes PyDict_Check). * You may only access __annotations__ as an attribute, and because it's always set, best practice is to use "o.__annotations__" (though getattr will always work too). * How __annotations__ is stored is implementation-specific behavior; looking in the relevant __dict__ is unsupported. This would grant sanity and consistency to __annotations__ in a way it's never so far enjoyed. The problem is, it's a breaking change. But the existing semantics are kind of terrible, so at this point my goal is to break them. I think the best practice needs to stop requiring examining cls.__dict__; in fact I'd prefer people stop doing it altogether. If we change the behavior as part of a new release of Python, code that examines annotations on classes can do a version check: if (sys.version_info.major >=3 and sys.version_info.minor >= 10): def get_annotations(o): return o.__annotations__ or {} else: def get_annotations(o): # eight or ten lines of complex code goes here ... Or code can just use inspect.get_type_hints(), which is tied to the Python version anyway and should always do the right thing. //arry/

10 44

Boundaries between numbers and identifiers
by Serhiy Storchaka 15 Apr '21

15 Apr '21

In Python 2.5 `0or[]` was accepted by the Python parser. It became an error in 2.6 because "0o" became recognizing as an incomplete octal number. `1or[]` still is accepted. On other hand, `1if 2else 3` is accepted despites the fact that "2e" can be recognized as an incomplete floating point number. In this case the tokenizer pushes "e" back and returns "2". Shouldn't it do the same with "0o"? It is possible to make `0or[]` be parseable again. Python implementation is able to tokenize this example: $ echo '0or[]' | ./python -m tokenize 1,0-1,1: NUMBER '0' 1,1-1,3: NAME 'or' 1,3-1,4: OP '[' 1,4-1,5: OP ']' 1,5-1,6: NEWLINE '\n' 2,0-2,0: ENDMARKER '' On other hand, all these examples look weird. There is an assymmetry: `1or 2` is a valid syntax, but `1 or2` is not. It is hard to recognize visually the boundary between a number and the following identifier or keyword, especially if numbers can contain letters ("b", "e", "j", "o", "x") and underscores, and identifiers can contain digits. On both sides of the boundary can be letters, digits, and underscores. I propose to change the Python syntax by adding a requirement that there should be a whitespace or delimiter between a numeric literal and the following keyword.

8 10

minor PR 22981 waiting review ~20 days: https://github.com/python/cpython/pull/22981
by Alfred Perlstein 22 Feb '21

22 Feb '21

PR 22981 is a minor update to doctest to allow it to safely consume modules which contain objects that cause inspect.unwrap to throw. I believe the review comments in the PR have been addressed at this point for ~20 days. The patch is relatively small, reviews are done, but it is unmerged. Is there a way to "bump" the request for review? thank you, -Alfred

2 2

PEP: Deferred Evaluation Of Annotations Using Descriptors
by Larry Hastings 16 Feb '21

16 Feb '21

I've written a new PEP. Please find it below. Happy reading! //arry/ ---------- PEP: XXXX Title: Deferred Evaluation Of Annotations Using Descriptors Version: $Revision$ Last-Modified: $Date$ Author: Larry Hastings <larry(a)hastings.org> Discussions-To: Python-Dev <python-dev(a)python.org> Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 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 *supercede* 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: @property # __annotations__ on a function object is already a # "data descriptor", 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``. 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 de-compile code for Python annotations back into strings at compile time, requiring users of annotations to ``eval()`` those strings to turn them back into Python values. This has several drawbacks: * It requires Python implementations to stringize their annotations. This is surprising—unprecedented behavior 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 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-functi… 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 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 / modification of ``__annotations__`` from within the class body is no longer supported. Also, any flow control (``if`` / ``try``) 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 permissable 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. .. Local Variables: mode: indented-text indent-tabs-mode: nil sentence-end-double-space: t fill-column: 70 coding: utf-8 End:

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Comments on PEP 558
by Mark Shannon 08 Feb '21

08 Feb '21

Hi Nick, Regarding `f_locals` PEP 558 states: """ Instead of being a direct reference to the internal dynamic snapshot used to populate the independent snapshots returned by locals(), frame.f_locals will be updated to instead return a dedicated proxy type (implemented as a private subclass of the existing types.MappingProxyType) that has two internal attributes not exposed as part of the Python runtime API: * mapping: an implicitly updated snapshot of the function local variables and closure references, as well as any arbitrary items that have been set via the mapping API, even if they don't have storage allocated for them on the underlying frame * frame: the underlying frame that the snapshot is for """ This seems rather complex, and consequently fragile. I fear that this is just going to result in different bugs, rather than fixing the bugs it proposes to fix. Why not just make `f_local` a direct view on the underlying frame? It would be simpler to understand, more robust, and should perform better. Cheers, Mark.

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