Posted by f311a 20 hours ago
On one hand, it seemed like JIT work was really progressing in high gear until SC requested a new PEP. Also unfortunate, as it seemed like part of the hope for the JIT was that it'd counter the performance penalty from free threading.
On the other, it did feel (to me) like with all the fancy (and definitely cool) optimizations it got (automatic refcount operation removal, new type-based specializations, inplace math operations), the macrobenchmark benefits weren't _that_ exciting for a JIT. It definitely has potential for more, maybe with even fancier optimizations like unboxing ints, but the complexity just keeps going up.
> It said that the PEP was "overly optimistic" about user-visible differences with the JIT enabled.
The JIT is technically generated from the same source as the interpreter loop itself, so originally the differences were extremely unlikely, but I imagine the risk just kept going up as new JIT-only paths and optimizations were introduced.
And politically, I feel like SC’s reasoning was just “this side project has landed in main branch almost by accident and has been stewing without oversight ever since, with no stabilization in sight”; and the latest PEP was probably still too vague, with a lot of “here’s more things we can add to the stew” - so I can understand if SC wasn’t satisfied.
JITs are big nasty things that by their nature have to do a lot of things. You are basically saying "Give us a PEP for a new python interpreter and compiler".
What they do is fuzzy by their nature because optimizations are very rarely universally beneficial. It's a game of measuring, looking for regressions, and tackling the biggest problem next for whatever sample applications you have available. You always run into the risk of overfitting on top of all that (You may have made django faster, but you've made flask slower).
This is the sort of thing that, IMO, should not go through the PEP process. It's an implementation detail for the runtime.
Imagine, for example, if the JVM needed a bunch of JEPs for JIT improvements. They'd never get done. Or if any javascript engine needed ECMA acceptance before they could do a new JIT optimization.
They do:
Not every optimization requires a JEP, but certainly something as big as "add or remove a JIT" would 100% require a JEP.
FWIW, I definitely can find a bunch of both PEPs and JEPs for internal optimizations, random examples:
1. An outsider to the regular OpenJDK dev team proposing an optimization.
2. Advertising for a cool new performance tweak with potentially large impact.
You'll also notice that the JEP descriptions are very different from PEP descriptions.
Probably the largest JEP for the JVM, Valhalla
Does not have the same level of requirements being asked of the Python JIT. Further, it's well understood that this JEP will "live on main" for quite some time in preview before it's accepted and merged.
What the JEP does, instead, is simply describe the feature and potential risks.
The vast majority of JVM optimizations do not get a JEP.
> The vast majority of JVM optimizations do not get a JEP.
In Python too. Still, the JIT really is just a really big thing. I don’t know if there could be a better way to do it, as either 1. You develop it off-branch and… make an enormous PR when you’re done, with risks of getting rejected anyway? Or 2, you allow more experiments on main like Rust, but then you end up with hundreds of minor half-maintained nightly-only features, possibly conflicting with each other, and eventually go through bureaucracy to remove them anyway.
The Java governance allows for a lot more open ended projects with follow ups. Not that it's reckless, Valhalla is proof of that, but rather that once something is good enough they are pretty open to just shipping it as a preview feature and refining until it is worthy of being stable.
That's why, for example, we saw ZGC get merged, and then JDK after JDK seeing continuing development and improvement on ZGC (Or G1 for that matter). These GCs were fairly rough on their first release and now they are pretty much best in class.
I see a Python JIT as the same thing. Delivering the infrastructure for a JIT would need a PIP, but I wouldn't put in things like "acceptance criteria" beyond "doesn't break older code". I would expect such a project to get continual updates without PEPs.
Just for completeness: the original JIT PR was merged quite enthusiastically and only after the fact it was pointed out that it should have went through a PEP process [1]. The following PEP 744 was of the “informational” type, and the just-withdrawn PEP 836 was the first “standards track” PEP, which means that (in my understanding) it’s technically the first actual PEP that the SC would formally comment on. So in a way it’s not “can we update the JIT” PEP, but the first real “does SC want this JIT as proposed” PEP.
That said, I do agree with most of what you said, especially the acceptance criteria part.
The issue I have and find it hard to resolve is that since the code was already on main, the feature was already in “sunk cost” mode. It’s hard when the code is already on main and another 3 optimization PRs are always there behind the corner, but it needs to be possible for SC to say “we decided it’s not actually worth all this complexity for <some small number>% speed improvement” (I’m not commenting whether in this particular case it’d have been a right call), and the PEP (if not withdrawn) was the way to exercise that control.
[1] https://github.com/python/cpython/pull/113465#issuecomment-1...
And even then, Hotspot did not got a JSR, because all that work was done before Sun started collaborating with industry partners for Java evolution.
They "simply" ported the JIT compiler from the Strongtalk acquisition into Java.
Zero chance of shoehorning that into a standards-compliant Python runtime. Some things are too fundamental to change without affecting the user-visible language: JITting, lazy imports, threading, copying gc, ref counting, etc.
Dunno how much investment is needed into language optimization anymore.
A fairly massive undertaking for existing code, which jit was supposed to be useful for.
Which honestly, is the right approach.
Because having just the hot loop in the performant language is often deemed good enough. And there's tons of Python code out there where the "hot loop" is already automatically in C (the various operations implemented in C by the runtime) or where you can get close enough e.g. with Numpy. Consider that things like Pygame can exist; for a lot of the games people want to make, the only thing that really needs to happen anywhere near "bare-metal" speed is blitting.
It was done plenty. Just with extensions to python like numpy and opencv. There is no reason to translate everything versus just the critical pieces.
This way I can easily write inlined functions with proper C speed, in a Python esque language.
Reasons why I rarely use Cython without this:
- unmodified Python code running in Cython is often faster, but not that much. 50% or so is my rule of thumb.
- but the development style is very different for Cython and Python, adding a lot of impedance. Eg reloading doesn’t quite work, profiling is different, you need a build directory and a build step at deployment etc.
- Cython proper is different enough and I’d argue unintuitive enough that if I have to have all these things in place, I might as well wrap my function in Rust.
Such a decorator would give you a low friction way to make things faster when you need it, with scope for making it much faster via all the funny Cython, while keeping Python-esque semantics, in particular "development semantics".
I think it would require some language modification since Cython was definitely a superset of Python; I think some of that syntax has been streamlined, but maybe still a strict superset.
Objective-Smalltalk runs on the Objective-C runtime, so Objective-Smalltalk methods look like Objective-C methods accessed via objc_msgSend() to the entire rest of the system, including to other Objective-Smalltalk code.
That means it doesn't really matter to the rest of the system if any particular method is interpreted, AOT-compiled or JIT-compiled. They can't tell, except for execution speed.
And of course the execution model includes machine primitives, which the interpreter also deals with but doesn't make faster, but the native compilers do.
On another note, I'm working on a (big) PEP for python, and looking for maintainers and sponsors (as per python's rules). Do someone know where I could find such amazing persons? (I already have the code, works in cython,graalVM and pypy, and it's kind of a big change but I believe it will help a lot the language)
Python already compiles to bytecode (the .pyc files are a cache thereof). It's just that interpreting even the simple bytecode instructions can take dozens of clock cycles each. I imagine that a lot of this is not in the interpreter loop (which has seen quite a bit of optimization, IIRC up to the level of "predictive" gotos within a big switch statement within a loop) but just unwrapping objects and moving values to and from registers, dealing with the fact that there is basically never any cache coherency for anything etc.
Basically I'm not convinced that writing out the set of machine-code instructions needed to interpret the bytecodes would be a big win, because it takes too many instructions for simple tasks.
[EDIT - I assume you are commenting on Dartmouth BASIC.]
Since we have to prove everything on Internet forums,
https://archive.org/details/bitsavers_dartmouthB_3679804/mod...
=> Saving of Compiled Programs
A pain point that is big enough that at least two programming languages, Julia and Mojo, were created to move beyond it.
Which would not even be an issue, if Lisp, Scheme and Prolog had not lost their aura among AI devs.
Mojo in particular recognized to compete with Python they'd have to be a superset, then they backed of that goal when they realized it would be too hard.
So at the end of the day, Python has a value proposition and it cannot be entirely subsumed by other languages, because those languages make tradeoffs that move away from Python's value.
Maybe also then, importing module as Cython?
import django with Cython
https://github.com/py2many/py2many
https://github.com/adsharma/fquery/blob/main/tests/django_example_model.pyFolks, numba is great. To first approximation: write your python function to "look like C"[1] and work over the numpy arrays you probably already have and understand, put a "@jit" decorator on it, and you're done. It JITs and runs your code natively at native performance.
With some extra effort, there are features for SIMD vectorization and thread parallelism, and even GPU backends (that, full disclosure, I've been pretty frustrated by -- but they do work).
[1] Which is to say, don't touch Python interpreter data structures. No dicts or objects or lists or strings, etc... If you're bound by the interpreter[2] you're bound by the interpreter and nothing is going to help you.
[2] A corrolary here is that the kind of C code numba is NOT good for is "following pointers between structs", which is something that can't be expressed in either python or numpy.
That's a really intense restriction. Sounds like the name fits; it's not general purpose, it's jit for number crunching and little else.
I'm not sure exactly what you mean by "bound by the interpreter" but there's nothing about touching those data structures that requires the interpreter to be involved in most cases. The problem of slow APIs can be solved by the JIT having intimate knowledge of python's internals, can't it?
Is it? An object field access in python is a dynamic hash lookup followed by a method call as a fallback (itself involving hashed lookups up the class hierarchy). That's not an "interpreter vs. native" kind of thing, that's just the semantics of the language that have to be preserved. And that's just not going to be "fast" in the way that the user of a JIT would want. Code dominated by Python datastructures is going to be "slow" in ways that JITs can't fix, ever.
Which is why Python appears to have given up on the effort in this space. PyPy too[1] really never delivered on the desired performance gains (like 5x or so on typical code) without forcing the user into careful optimization.
Numba, to its credit, doesn't even try. It falls back to python if it must for python stuff, but is quite clear that it's providing no benefit if that's what you're doing. And the stuff it does do is obvious and clean and extremely useful.
[1] PyPy is also looking pretty moribund recently.
Maybe, but the world is still waiting for the counterexample. Python is simply not a runtime well-suited to optimization.
In the meantime, those of us who actually enjoy real HPC problems are using numba and rolling our eyes about all the confuffery around JITs. Maybe one will arrive that proves us wrong, but numba is fast today.
I don't have enough experience with PyPy to say much about it.
I don’t know how people who’ve worked with Python and been frustrated with its performance don’t know about Numba.
* Typing is a non-obligatory add-on to the syntax
* SQLAlchemy and other ORMs override basic operators like "=" and employ metaclass programming heavily to create Python-like SQL query builders
* Pytest rewrites all the assertions as it side-steps bytecode generation (so your .pyc files are different than what they would be without pytest)
* Many libraries depend on monkey-patching live library calls, including ones to external dependencies
* The way async was added in is another bolt-on where the same code behaves differently in different contexts
* Decorators can introduce any side-effect you imagine, and nobody (including core libraries) follow the golden rule of "don't do, only decorate"
* Many debugging and profiling mechanisms (including REPL) depend on these properties to function — including observability tooling like Sentry, Datadog etc.
...
Have you seen Ruby?
I don't know Python's innards well enough to understand why it's so especially difficult in this regard, but I gather around the edges that its object model may not be as well founded.
All just as dynamic as Python, and with quite usable JIT compilers.
Or for that matter, PyPy and GraalPy do exist.
Can you just replace CPython with PyPy or GraalPy in every project? And why not? :)
JavaScript also has monkey-patching, and it's also very common. E.g. SolidJS replaces objects with dependency-tracking proxies.
There are really no fundamental language reasons why Python should be more complex. From what I heard from a JIT developer, the main issue is not the code itself but the module system and code loading.
JavaScript was designed from the start to operate inside a sealed environment with only few external handles, with the code loaded upfront or added on-demand via "eval". Not so for Python, where native code modules can (and will) result in code loaded dynamically and it's hard to track what depends on what.
Yet both languages are on the genesis of modern JIT compilers, going beyond what was already being done for Lisps, Prolog, BASIC.
And the proxies make it slow as Python. Plain objects cannot suddenly become proxies, so the runtime can treat them as a special case.
someone please think of the monkey patchers!
If all python had was __slots__ then we would not be struggling to JIT python.
Tight numerical loops is where the JIT adds performance.
Most performance sensitive features are already available in python between numpy, numba and Cython without really leaving the language core.
You'll mostly be speeding up the slow parts of python, not supercharging the fast parts.
Heh, how about how `__class__` works? Not that I recommend actually doing this, but you can literally change the type of an already instantiated object by reassigning `__class__`:
>>> class Dog:
... def hi(self):
... print("woof")
...
>>> class Cat:
... def hi(self):
... print("meow")
...
>>> dog = Dog()
>>> dog.hi()
woof
>>> dog.__class__ = Cat
>>> dog.hi()
meow
>>> isinstance(dog, Dog)
False
>>> isinstance(dog, Cat)
True
>>> >>> Dog.__bases__ = (Cat,)
>>> isinstance(dog, Dog)
True
>>> isinstance(dog, Cat)
True
>>>See also the warning here:
https://developer.mozilla.org/en-US/docs/Web/JavaScript/Refe...
And this related article:
I saw this presented as new tech at either OOPSLA or OSCon almost exactly 20 years ago.
CPU branch prediction is still pretty good even with the loss of function in recent years.
Not since before Ruby 1.9, no.
Not to say that the technical objections were without merit, they likely very much weren't, and the debugger and instrumentation support and maintenance work sounds fairly painful on its own.
If having to put all that in writing and get a bunch of people to agree feels too burdensome to you, maybe that’s a sign that you would be happier working on something other than core changes to the world’s second most popular programming language.
(edit: That wasn’t meant to be a jab at the core developers working on the JIT. Based on what’s in the public discussion threads, they have been handling things very well throughout the process. It’s unfortunate that the SC let the project get this far before realizing the project had grown well beyond its original PIP’s mandate, and I’m sure that is deeply frustrating to the people leading the project. I know I would not be handling the setback nearly as gracefully as they are.)
As they themselves pointed out, the problem isn’t that there’s anything wrong with an open-ended experiment. It’s just that open-ended experimentation doesn’t belong in main, of all places. They have an ongoing duty to ensure that a critical piece of global infrastructure remains stable and trustworthy to consider.
The scope of a JIT is the entire python runtime. Not at the beginning, it can be more narrow, but certainly it can evolve to cover everything. JITs touch everything.
> success criteria
Impossible to measure. JITs don't universally make things faster and unless the SC was willing to accept "some programs will see around x% improvement" then it would be impossible to actually give them the criteria they want. There's a reason C++ compilers and their optimizations aren't defined by the ISO committee.
> support plan
Like most JITs, support for a JIT is an ongoing and ever evolving process. What's needed depends entirely on what's needed today. You usually need more than 1 or 2 dev working on it, which may be the issue here.
> It’s just that open-ended experimentation doesn’t belong in main, of all places.
Very strong disagree. That's simply the nature of performance optimizations for JITs. They belong on main because new optimizations and heuristics will always be generated. The role of doing a compiler is a long term experiment in performance, seeing what your runtime is currently doing and how it's being used and tweaking it to work better.
pypy isn't in a worse place because it's been running a longterm performance experiment.
> They have an ongoing duty to ensure that a critical piece of global infrastructure remains stable and trustworthy to consider.
None of the things mentioned actually do that, other than perhaps a support plan. There are plenty of critical infrastructure projects who all can manage to develop JITs. Python is the special snowflake that has decided to do JIT development incorrectly.
This is a runtime optimization/implementation detail. It's foolish to send it through a committee approval process. That's why you won't be able to find a single language with a JIT or advanced optimizations that has do it by committee.
> This is a runtime optimization/implementation detail.
I view JITs as a critical architectural component. Like you said, it impacts the whole system. Optimization and implementation details don't usually do that.
It’s kind of wild how nice Python’s C extension system, which enables basically zero cost interop with native code, is for certain purposes. It supports a situation that’s comparable to the games industry’s tradition of putting a Lua or Lisp tip on top of a C++ iceberg.
Perhaps these priorities were always irreconcilable.
Perhaps that’s why the existence of competing Python JIT projects that weren’t trying to commit directly into CPython’s main branch kept coming up in the conversation.
The experiments had long been done. The topic at hand was including it as a core feature.
Bureaucracy doesn't definitionally mean Kafkaesque. It's popular to hate on it, but good or neutral bureaucracy does exist.
In this case a single committee directly engaged with both core developers and the community, through a fairly open process, explicitly bent several of their own official rules to suit the occasion, and indicated cases where further flexibility might be needed depending on how things develop.
I would call that a pretty stellar example of non-bureaucratic governance.
It’s true that that decision didn’t involve everybody getting their way. But that’s not organizational hell, that’s just organization. If you don’t want to contend with other people’s competing interests, the solution to that is solo development.
HN when an organization achieves its intended goals: this can't be bureaucracy because it's good.
In this case, it seems like the human bureaucracy made wise and measured decisions to contain the software bureaucracy. They made reasonable requests to clarify the scope and impact of a major, major core feature. The proposers didn't feel like that was doable, so a complexity nuke was avoided.
There are already many way of addressing Python performance (leaving aside whether Python is a good choice if performance matters) such as Cython, Numba, Pypy and C extensions.
And all of them are lacking in different ways, we ALSO, want faster pure Python.
Pypy does provide faster pure Python. Its a compatible implementation with JIT.
And from what I understand there are limitations with using libraries that use the CPython API to interface with native code.
A bit like Chrome there's a fully open source version and a proprietary version with a few extra bells and whistles, but the latter is entirely free for both open and commercial usage (you can't redistribute it for a fee). So you may as well use it.
It has other interesting features
• Scalable pauseless GC
• Single binary distribution of Python programs (like Go)
• Fancy and optimized interop with other languages including Java, JavaScript, Kotlin, Ruby and any other Truffle language.
• Supports native extensions. Tested and works with common libs like SciPy, NumPy, PyTorch, Pandas etc: https://graalpy.org/python-developers/compatibility/
• They're working on a GIL-free mode
In recent weeks there has been a release of a new web framework for Python that only runs on GraalPy, called Pyronaut (https://pyronaut.io/). It takes advantage of JVM interop to expose the Micronaut framework, which is similar to Spring for those who know it. Thanks to the JIT and well optimized base libraries it runs extremely fast, being about 3x faster than FastAPI+Granian, or 7x faster than Flask+Gunicorn and having many more features.
https://www.youtube.com/watch?v=zo6L7bxn4OY
(that was presented at Devoxx but the Pyronaut devs will start touring Python specific conferences soon)
So although CPython may not have made it past the JIT milestone, that doesn't mean Python as a language or community has to fail it. It does require openness to alternative implementations, but historically Pythonistas have been fairly good about that (e.g. Pypy was embraced back when it was more active).
This stuff does require you to accept that it's made by Oracle, but as it's open source, that should be OK.
Disclosure: I sit next to some of the people who work on this stuff.
They are Python libraries. Don't make the mistake of screwing up the cause and effect here. C, Rust etc - those are the replaceable parts of the chain. There is an official C++ pytorch library and virtually nobody uses it for ML Research. People want to code in python and build those libraries so they can.
PyTorch in C++ naturally gets little use, because most people don't want to use C++, and although possible, e.g. CINT, very few care about C++ REPLs.
Also these libraries only exist because Python cannot deliver on its own, like Lisp or Scheme, and needs such libraries in first place.
Even BASIC has better options to do machine code and system programming than Python, starting with the original Dartmouth BASIC.
They are python libraries because people reached for those languages so they could continue work in python. They are there to serve python and nothing else. It's the same with Rust. It's by no means a C++ thing.
EDIT: Not saying this is always the case. Qt>Pyside is an example where it's both technically and functionally bindings, but the vast majority of the time they are serving python. Some don't even have a native library to speak of.
Otherwise they are bindings to native libraries.
We can keep discussing in circles.
So if you can't call/use the 'library' from the 'native' language, it's still a library? Whatever floats your boat. Lol.
Otherwise it’s just a sparkling FFI.
I think if the Python community wants an implementation that has a proven JIT and where the remaining work is easily scaled compatibility testing, then there's one available and ready. Whether they're calling into C or Rust or FORTRAN or Java shouldn't make a big difference in principle.
Now, I've met lots of people who think that binding to Rust will always be the fastest thing to do but that underestimates Java significantly, especially once you factor in the fact that GraalPy can optimize out interop overheads.
Consider LLM tokenization. It's one of the most heavily optimized pieces of code on the planet. Yet, toknroll written in Java can beat tiktoken which is written in Rust:
https://github.com/qxoticai/qxotic/tree/main/toknroll
Moreover the Rust code has some unsafe{} blocks as Rust often does, whereas toknroll has no unsafe code anywhere.
So the scripting language communities need to think hard about where they go next, IMHO. DHH already got up and announced at a Rails conference that Ruby is over, you should use Rust for everything. I don't think that's quite true, but it's going to be a meme that with agents you should just pick the fastest language and go. If Python/Ruby/etc want to stay relevant then they need to care a lot more about their performance story. Just being more pleasant to read and write for humans doesn't cut it anymore. CPython is clearly not going to happen in this regard, so alternative implementations need to start being taken a lot more seriously than they were in the past.
How long does that take?
https://pyronaut.io/performance/
> Latency lines come from the load ramp (+25% per 15-second step); throughput from 45-second validation steps (+2% each) after a 180-second warm-up. Each framework ran twice and we show its best run. No profilers were attached.
So that's a "realistic" web serving benchmark if you're willing to grant the use of a GraalPy-optimized framework rather than Flask/FastAPI.
Warmup times for other benchmarks are currently quite unstable because there's a big focus on interpreter performance at the moment, which is one of the main influences on warmup. There's a blog post here from August about how the compiler is being taught to recognize and optimize bytecode interpreter loops, to get results that normally require handcrafted assembly:
https://medium.com/graalvm/escaping-the-giant-switch-dec20b5...
So as you know already, warmup was historically a weakness of PE based stuff - although in this case the fair comparison is with CPython which never warms up at all - but a fast interpreter will make a big difference. In some benchmarks the GraalPy interpreter is already faster than the CPython interpreter, which is heavily optimized.
Python is a mature production language with a lot of users. Even before it was huge - like 20 years ago when I was in college - getting a small backwards compatible API change into a python extension module (gnu readline in my case) required convincing GvR personally that the API change was necessary.
Every JIT attempt I've seen for python just tries to cut too deep in the first pass, and scope themselves too large.
I believe any successful attempt at optimizing python performance from JIT will FIRST start with a 100% semantically compatible hidden type model for optimizing object property access and method dispatch.
The single biggest performance delta in most dynamic language jits come from that, not from optimizing guards off of bytecode operations or from compiling to machine code. It's from converting hashtable lookups into constant-time guard + offset lookups.
The hidden type model is also a hard nut to crack. It needs to perserve dict semantics which have very dynamic behaviour via the various `__*__` methods, and handle exotics scope objects since scoped name lookups are the most common ones which have their own exotic behaviours (I remember python providing some ability for callee frames to inspect caller scopes in some limited ways).
Python already does it in a lot of cases, see opcode specializations like LOAD_ATTR_INSTANCE_VALUE, LOAD_ATTR_SLOT.
More digging led to this: https://peps.python.org/pep-0659/ which seems to be what the effort is organized around?
Python's object semantics are broadly similar to Javascript, bar the prototypical inheritance chain, but with the extra complexity of potentially needing to handle dynamic `__*attr__` overrides. Optimizing for this would require a tree of hidden map objects much like JS JIT engines use. I dug around the codebase a while back looking for something like that but didn't find it. `OrderedDict` seemed promising but after more investigation that turned out to just be adding property ordering to dictionaries.
The thing is, you can build that entire hidden type structure out, AND optimize against it, without ever having to mess with things like bytecode replacement. Once you have the hidden type model, you can modify the property lookup algorithm in the VM itself (as in cpython c code) to use some annotation structure hanging off of the bytecode to look up inline caches of type-guarded fastpaths. It would be an optimized fastpath that integrated directly into the C code that interprets the GETATTR opcode.
I don’t really think that’s true or even that relevant , at least in context of classes (especially with slots - the class, by definition, _is_ the “hidden map”) and not generic dicts.
> The thing is, you can build that entire hidden type structure out, AND optimize against it, without ever having to mess with things like bytecode replacement.
Pretty sure the motivation was exactly the opposite - that a simple “if a LOAD_ATTR encounters the same class with slots N times in a row, swap it out for LOAD_ATTR_SLOT with inline cached slot index, and swap back to generic one if something changes” is way easier (both to implement and reason about) than any alternative that’d interact with the object model. (Also it means you skip having to branch between a slow and fast path at all)
> you can modify the property lookup algorithm in the VM itself (as in cpython c code) to use some annotation structure hanging off of the bytecode to look up inline caches of type-guarded fastpaths. It would be an optimized fastpath that integrated directly into the C code that interprets the GETATTR opcode.
Sorry, that sounds a bit vague and not too different from what CPython already is doing.
> Sorry, that sounds a bit vague and not too different from what CPython already is doing.
With slots being available, I can see where I was misled. I went looking for a type structure that modeled the dynamic properties on an object which would be used as a typeguard, but the class itself functions as that if you're restricting the optimization to slotted properties.
Appreciate the clarfication!
https://peps.python.org/pep-3000/ - PEP 3000 says it will be a derived from the Python 2 codebase and not a complete rewrite (referencing Joel Spolsky's views on complete rewrites and Netscape Navigator).
https://peps.python.org/pep-3099/ - PEP 3099 is changes Guido van Rossum rejected from Python 3 (including := as a binding operator), parallel iteration, programmable syntax, parser going beyond LL(1), changing the for/else construct.
https://peps.python.org/pep-3100/ - PEP 3100 is things he set as goals for it, things like all strings are Unicode and byte arrays are a separate type, optional type annotations, 'exec' and 'print' change from statements to functions, None/True/False become keywords, old-style classes are gone, changing inequality comparison from <> to != , require C99, drop BeOS support, drop old standard library modules, and more.
actually, more changes than I remember.
Probably also during the 2 to 3 migration, but that goal appears to have now been abandoned.
I wonder how the authors of other implementations like pypy will be able to keep up as the reference implementation becomes more and more complex.
The problem was he exposed too much of the implementation into the language itself, destroying its ability to be a simple reference implementation that others could re-implement.
The python documentation has historically also not been especially clear at distinguishing between what the language guarantees, vs what is just implementation details of the cPython implementation. So even if libraries wanted to try to avoid depending on implementation specifics, it isn't always clear what those are.
The net result is that for the end user python the language is effectively: whatever cpython does. This really, really discourages creating alternate implementations, as the only way to be an alternative implementation with good compatibility is to be a living fork of cpython, constantly merging in all the changes from it, while also maintaining your improvements.
Today there are far more people using it, and everyone has a different perspective on what it is. No longer is it a niche glue language, now it's a backbone of AI technology at major tech firms, so it's understandable they'd like it to be faster on its own.
Are the JIT developers the same people who were working on Faster CPython at Microsoft? No surprise that they don't have the same amount of energy to dedicate to the JIT now that it's gone from a full-time job to a hobby project.
I wouldn't be surprised if they all went nodejs instead :/
His current focus seems to be speeding up GC and free threading.
It would have been more honest to just cut the feature instead of haranguing a developer until they withdrew it.
To be clear, such a request doesn't even mean that someone is consciously attempting to kill the project (although in the corporate world, ime it probably does). It just means that something went critically wrong. In this case, it appears like python's development process really required a PEP for this sort of work, especially since it was happening on the main branch.
>> For example, rather than proposing one single concrete JIT implementation, it may make more sense for the PEP to describe a JIT infrastructure that can support multiple implementation strategies.