Mailman 3 February 2022 - Python-Dev

Proposal: Allow non-default after default arguments
by Sebastian Rittau 16 Oct '23

16 Oct '23

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 31 Jul '23

31 Jul '23

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 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

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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)

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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

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How about using modern C++ in development of CPython ?
by redradist＠gmail.com 19 Oct '22

19 Oct '22

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 21 Sep '22

21 Sep '22

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 22 Jul '22

22 Jul '22

(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 29 May '22

29 May '22

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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Slowly bend the C API towards the limited API to get a stable ABI for everyone
by Victor Stinner 20 Apr '22

20 Apr '22

Hi, There is a reason why I'm bothering C extensions maintainers and Python core developers with my incompatible C API changes since Python 3.8. Let me share my plan with you :-) In 2009 (Python 3.2), Martin v. Löwis did an amazing job with the PEP 384 "Defining a Stable ABI" to provide a "limited C API" and a "stable ABI" for C extensions: build an extension once, use it on multiple Python versions. Some projects like PyQt5 and cryptograpy use it, but it is just a drop in the PyPI ocean (353,084 projects). I'm trying to bend the "default" C API towards this "limited C API" to make it possible tomorrow to build *more* C extensions for the stable ABI. My goal is that the stable ABI would be the default, and only a minority of C extensions would opt-out because they need to access to more functions for best performance. The basic problem is that at the ABI level, C extensions must only call functions, rather than getting and setting directly to structure members. Structures changes frequently in Python (look at changes between Python 3.2 and Python 3.11), and any minor structure change breaks the ABI. The limited C API hides structures and only use function calls to solve this problem. Since 2020, I'm modifying the C API, one function by one, to slowly hide implementations (prepare the API to make strutures opaque). I focused on the following structures: * PyObject and PyVarObject (bpo-39573) * PyTypeObject (bpo-40170) * PyFrameObject (bpo-40421) * PyThreadState (bpo-39947) The majority of C extensions use functions and macros, they don't access directly structure members. There are a few members which are sometimes accessed directly which prevents making these structures opaque. For example, some old C extensions use obj->ob_type rather than Py_TYPE(obj). Fixing the minority of C extensions should benefit to the majority which may become compatible with the stable ABI. I am also converting macros to static inline functions to fix their API: define parameter types, result type and avoid surprising macros side effects ("macro pitfalls"). I wrote the PEP 670 "Convert macros to functions in the Python C API" for these changes. I wrote the upgrade_pythoncapi.py tool in my pythoncapi_project (*) which modify C code to use Py_TYPE(), Py_SIZE() and Py_REFCNT() rather than accessing directly PyObject and PyVarObject members. (*) https://github.com/pythoncapi/pythoncapi_compat In this tool, I also added "Borrow" variant of functions like PyFrame_GetCode() which returns a strong reference, to replace frame->f_code with _PyFrame_GetCodeBorrow(). In Python 3.11, you cannot use the frame->f_code member anymore, since it has been removed! You must call PyFrame_GetCode() (or pythoncapi_compat _PyFrame_GetCodeBorrow() variant). There are also a few macros which can be used as l-values like Py_TYPE(): "Py_TYPE(type1) = type2" must now be written "Py_SET_TYPE(type1, type2)" to avoid setting directly the tp_type type at the ABI level. I proposed the PEP 674 "Disallow using Py_TYPE() and Py_SIZE() macros as l-values" to solve these issues. Currently, many "functions" are still implemented as macros or static inline functions, so C extensions still access structure members at the ABI level for best Python performance. Converting these to regular functions has an impact on performance and I would prefer to first write a PEP giving the rationale for that. Today, it is not possible yet to build numpy for the stable ABI. The gap is just too large for this big C extension. But step by step, the C API becomes closer to the limited API, and more and more code is ready to be built for the stable ABI. Well, these C API changes have other advantages, like preparing Python for further optimizations, ease Python maintenance, clarify the seperation between the limited C API and the default C API, etc. ;-) Victor -- Night gathers, and now my watch begins. It shall not end until my death.

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