Mailman 3 December 2021 - Python-Dev

Proposal: Allow non-default after default arguments
by Sebastian Rittau Oct. 16, 2023

Oct. 16, 2023

Currently, Python doesn't allow non-default arguments after default arguments: >>> def foo(x=None, y): pass File "<stdin>", line 1 def foo(x=None, y): pass ^ SyntaxError: non-default argument follows default argument I believe that at the time this was introduced, no use cases for this were known and this is is supposed to prevent a source of bugs. I have two use cases for this, one fringe, but valid, the other more important: The fringe use case: Suppose you have a function that takes a 2D coordinate value as separate "x" and "y" arguments. The "x" argument is optional, the "y" argument isn't. Currently there are two ways to do this, none of them particularly great: def foo(y, x): # reverse x and y arguments, confusing ... def foo(x, y=None): # Treat the x argument as y if only one argument is provided if y is None: x, y = y, x ... To me, the "natural" solution looks like this: def foo(x=None, y): ... # Called like this: foo(1, 2) foo(y=2) This could also be useful when evolving APIs. For example there is a function "bar" that takes two required arguments. In a later version, the first argument gains a useful default, the second doesn't. There is no sensible way to evolve the API at the moment. The more important use case involves @overloads. A condensed example of a function where the return type depends on an "encoding" parameter, followed by further parameters that could be called like this: foo(123, "utf-8") # would return bytes foo(encoding="utf-8") foo(123, None) # would return str foo(encoding=None) foo(x=123) # would return str foo() This could ideally be written as: @overload def foo(x: int = ..., encoding: None = ...) -> str: ... @overload def foo(x: int = ..., encoding: str) -> bytes: ... # plus the actual implementation But due to the syntax constraint, this needs to be hacked around with a third overload: @overload def foo(x: int = ... encoding: None = ...) -> str: ... @overload def foo(x: int, encoding: str) -> bytes: ... # for foo(123, "utf-8") @overload def foo(*, encoding: str) -> bytes: ... # for foo(encoding="utf-8") Not only is this hard to read, real examples in typeshed are usually more complex, with many arguments before or after the affected argument or even multiple affected arguments. This often becomes too complex to write or maintain. Here is one example from the wild: https://github.com/python/typeshed/blob/b95b729b9e07ab21d252701af0f5b740467… Allowing non-default arguments after default arguments would solve both use cases above and eliminates a special case. I'm also not sure what exactly the current SyntaxError really protects us from. Adding a non-default after a default argument can't really lead bugs. - Sebastian

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Python multithreading without the GIL
by Sam Gross July 31, 2023

July 31, 2023

Hi, I've been working on changes to CPython to allow it to run without the global interpreter lock. I'd like to share a working proof-of-concept that can run without the GIL. The proof-of-concept involves substantial changes to CPython internals, but relatively few changes to the C-API. It is compatible with many C extensions: extensions must be rebuilt, but usually require small or no modifications to source code. I've built compatible versions of packages from the scientific Python ecosystem, and they are installable through the bundled "pip". Source code: https://github.com/colesbury/nogil Design overview: https://docs.google.com/document/d/18CXhDb1ygxg-YXNBJNzfzZsDFosB5e6BfnXLlej… My goal with the proof-of-concept is to demonstrate that removing the GIL is feasible and worthwhile, and that the technical ideas of the project could serve as a basis of such an effort. I'd like to start a discussion about these ideas and gauge the community's interest in this approach to removing the GIL. Regards, Sam Gross colesbury(a)gmail.com / sgross(a)fb.com

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Mixed Python/C debugging
by Skip Montanaro April 26, 2023

April 26, 2023

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

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PEP 448 review
by Guido van Rossum March 29, 2023

March 29, 2023

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)

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PEP 638: Syntactic macros
by Mark Shannon Feb. 5, 2023

Feb. 5, 2023

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

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How about using modern C++ in development of CPython ?
by redradist＠gmail.com Oct. 19, 2022

Oct. 19, 2022

Hi all, What about of using modern C++ in developing CPython ? With C++ we can get the following benefits: 1) Readability - less code, most code is hidden by abstraction without losing performance 2) Maintainability - no code duplication in favor of using reusable classes 3) RAII - Resource Acquisition Is Initialization, predictable allocation and free resources ...

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PEP 505 (None-aware operators) for Python 3.11
by Doug Swarin Sept. 21, 2022

Sept. 21, 2022

Hello, I've been following PEP 505 (https://www.python.org/dev/peps/pep-0505/) since it was first proposed for Python 3.8. It's been deferred for some time and I'm interested in seeing it in Python 3.11, but I know there were also a number of objections which resulted in it being deferred (including by one of the original authors, Mr. Dower). I did email both Mr. Dower and Mr. Haase and they graciously gave me their permission to bring it up on this list for discussion and hopefully final pronouncement one way or the other. I personally believe that the PEP will result in a significant reduction in boilerplate code, and it is substantially similar to the same operators now found in a number of other languages, especially C# and JavaScript (https://wikipedia.org/wiki/Null_coalescing_operator and https://wikipedia.org/wiki/Safe_navigation_operator). I believe strong and valid arguments can be made about the use of None being a fundamental flaw in some types of coding (and that adding additional support for it to the language will increase the use of None in this way), but I also believe there are many use cases in programming where it is by far the simplest way to express various semantics, and the fact exists that None is already used extensively in large quantities of code, and further that there is already a great deal of code written to constantly test against None and break out of a statement without throwing an error. I also understand the argument that especially the maybe-dot (?.) and maybe-subscript (?[) operators can decrease readability of code and also believe these are valid arguments against it. While I believe the existence and use of these operators in other languages definitely helps the case that these can be used and understood successfully, I think it is entirely valid to either consider other syntax (though I prefer the chosen syntax of PEP 505), or even to reduce PEP 505 to having only the coalesce operator (??) and the maybe-assign operator (??=). Separately, I have implemented a pure-Python solution for PEP505 (which is definitely rather beta) which might help test the waters for a final implementation in CPython (though the CPython implementation would of course be much more efficient). It can be found at https://pypi.org/project/pep505/ Thanks, Doug

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PEP 563 and Python 3.10.
by Thomas Wouters July 22, 2022

July 22, 2022

(Starting a new thread so as not to derail any of the ongoing discussions.) Thanks, everyone, for your thoughts on Python 3.10 and the impact of PEP 563 (postponed evaluation of annotations) becoming the default. The Steering Council has considered the issue carefully, along with many of the proposed alternatives and solutions, and we’ve decided that at this point, we simply can’t risk the compatibility breakage of PEP 563. We need to roll back the change that made stringified annotations the default, at least for 3.10. (Pablo is already working on this.) To be clear, we are not reverting PEP 563 itself. The future import will keep working like it did since Python 3.7. We’re delaying making PEP 563 string-based annotations the default until Python 3.11. This will give us time to find a solution that works for everyone (or to find a feasible upgrade path for users who currently rely on evaluated annotations). Some considerations that led us to this decision: - PEP 563’s default change is clearly too disruptive to downstream users and third-party libraries to happen right now. We can’t risk breaking even a small subset of the FastAPI/pydantic users, not to mention other uses of evaluated type annotations that we’re not aware of yet. - PEP 563 provides no warning to users of the feature it’s disabling. Without that, we can’t expect users to be aware of the upcoming breakage. The lack of a warning was by design, and made sense in a world where type annotations were only consumed by static type checkers --- but that’s not actually the situation we’re in. There are clearly existing real-world, run-time uses of type annotations that would be adversely affected by this change. - Originally, PEP 563 was scheduled to take effect in Python 4, and this changed recently (after the discussion in the Language Summit of 2020). It's possible that third-party libraries and users didn’t plan to react in the current time frame as they were not aware of this change in timing. - There isn’t enough time to properly discuss PEP 649 or any of the alternatives before the beta 1 deadline, and we really need to make sure we don’t compound errors here. We need to look for a long term solution, which isn’t possible while still maintaining the release deadlines of Python 3.10. That means we’re also deferring PEP 649 to Python 3.11. In the Steering Council’s unanimous opinion, rolling back the default flip for stringified annotations in Python 3.10 is the least disruptive of all the options. We need to continue discussing the issue and potential solutions, since this merely postpones the problem until 3.11. (For the record, postponing the change further is not off the table, either, for example if the final decision is to treat evaluated annotations as a deprecated feature, with warnings on use.) For what it’s worth, the SC is also considering what we can do to reduce the odds of something like this happening again, but that’s a separate consideration, and a multi-faceted one at that. For the Steering Council, Thomas. -- Thomas Wouters <thomas(a)python.org> Hi! I'm an email virus! Think twice before sending your email to help me spread!

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compiled python3.10 is unable to find _ssl
by Sandeep Gupta May 29, 2022

May 29, 2022

I am having compilation issues again with python3.10 with ssl . The ./configure was invoked with ssl options and ssl modules seems to be build successfully. """ The following modules found by detect_modules() in setup.py, have been built by the Makefile instead, as configured by the Setup files: _abc _hashlib _ssl pwd time """ However, when I do import ssl from python, I get the error: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/shared/Builds/Python-3.10.0/lib/python3.10/ssl.py", line 98, in <module> import _ssl # if we can't import it, let the error propagate ModuleNotFoundError: No module named '_ssl' I can't find _ssl.so in build or install directory. Thanks -S

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"immortal" objects and how they would help per-interpreter GIL
by Eric Snow Feb. 8, 2022

Feb. 8, 2022

Most of the work toward interpreter isolation and a per-interpreter GIL involves moving static global variables to _PyRuntimeState or PyInterpreterState (or module state). Through the effort of quite a few people, we've made good progress. However, many globals still remain, with the majority being objects and most of those being static strings (e.g. _Py_Identifier), static types (incl. exceptions), and singletons. On top of that, a number of those objects are exposed in the public C-API and even in the limited API. :( Dealing with this specifically is probably the trickiest thing I've had to work through in this project. There is one solution that would help both of the above in a nice way: "immortal" objects. The idea of objects that never get deallocated isn't new and has been explored here several times. Not that long ago I tried it out by setting the refcount really high. That worked. Around the same time Eddie Elizondo at Facebook did something similar but modified Py_INCREF() and Py_DECREF() to keep the refcount from changing. Our solutions were similar but with different goals in mind. (Facebook wants to avoid copy-on-write in their pre-fork model.) A while back I concluded that neither approach would work for us. The approach I had taken would have significant cache performance penalties in a per-interpreter GIL world. The approach that modifies Py_INCREF() has a significant performance penalty due to the extra branch on such a frequent operation. Recently I've come back to the idea of immortal objects because it's much simpler than the alternate (working) solution I found. So how do we get around that performance penalty? Let's say it makes CPython 5% slower. We have some options: * live with the full penalty * make other changes to reduce the penalty to a more acceptable threshold than 5% * eliminate the penalty (e.g. claw back 5% elsewhere) * abandon all hope Mark Shannon suggested to me some things we can do. Also, from a recent conversation with Dino Viehland it sounds like Eddie was able to reach performance-neutral with a few techniques. So here are some things we can do to reduce or eliminate that penalty: * reduce refcount operations on high-activity objects (e.g. None, True, False) * reduce refcount operations in general * walk the heap at the end of runtime initialization and mark all objects as immortal * mark all global objects as immortal (statics or in _PyRuntimeState; for PyInterpreterState not needed) What do you think? Does this sound realistic? Are there additional things we can do to counter that penalty? -eric

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