Project Verona: Fearless Concurrency for Python
(microsoft.github.io)139 points by ptx 3 days ago
139 points by ptx 3 days ago
I've been programming with Python for over 10 years now, and I use type hints whenever I can because of how many bugs they help catch. At this point, I'm beginning to form a rather radical view. As LLMs get smarter and vibe coding (or even more abstract ways of producing software) becomes normalized, we'll be less and less concerned about compatibility with existing codebases because new code will be cheaper, faster to produce, and more disposable. If progress continues at this pace, generating tests with near 100% coverage and fully rewriting libraries against those tests could be feasible within the next decade. Given that, I don't think backward compatibility should be the priority when it comes to language design and improvements. I'm personally ready to embrace a "Python 4" with a strict ownership model like Rust's (hopefully more flexible), fully typed, with the old baggage dropped and all the new bells and whistles. Static typing should also help LLMs produce more correct code and make iteration and refactoring easier.
> I'm personally ready to embrace a "Python 4" with a strict ownership model like Rust's (hopefully more flexible), fully typed, with the old baggage dropped and all the new bells and whistles. Static typing should also help LLMs produce more correct code and make iteration and refactoring easier.
So...a new language? I get it except for borrow checking, just make it GC'ed.
But this doesn't work in practice, if you break compatibility, you are also breaking compatibility with the training data of decades and decades of python code.
Interestingly, I think as we use more and more LLMs, types gets even more and more important as its basically a hint to the program as well.
I think people are still fooling themselves about the relevance of 3GL languages in an AI dominated future.
It is similar to how Assembly developers thought about their relevance until optimising compilers backends turned that into a niche activity.
It is a matter of time, maybe a decade who knows, until we can produce executables directly from AI systems.
Most likely we will still need some kind of formalisation tools to tame natural language uncertainties, however most certainly they won't be Python/Rust like.
We are moving into another abstraction layer, closer to the 4GL, CASE tooling dreams.
> I think people are still fooling themselves about the relevance of 3GL languages in an AI dominated future.
I think, as happens in the AI summer before each AI winter, people are fooling themselves about both the shape and proximity of the “AI dominated future”.
4GL and 5GL are already taken. So this is the 6GL.
https://en.wikipedia.org/wiki/Programming_language_generatio...
But speaking more seriously, how to get this deterministic?
Fair enough, should have taken a look, I stopped counting when computer magazines buzz about 4GLs faded away.
Probably some kind of formal methods inspired approach, declarative maybe, and less imperative coding.
We should take an Alan Kay and Bret Victor like point of view where AI based programming is going to be in a decade from now, not where it is today.
Assemblers and compilers are (practically) deterministic. LLMs are not.
LLMs are deterministic. So far every vendor is giving them random noise in addition to your prompt though. They don't like have a free will or a soul or anything, you feed them exactly the same tokens exactly the same tokens will come out.
Missed the part?
> Most likely we will still need some kind of formalisation tools to tame natural language uncertainties, however most certainly they won't be Python/Rust like
I think the question is: What is the value of that intermediate step? It depends on how long the full path takes.
If we're one year away from realizing a brave new world where everyone is going straight from natural language to machine code or something similar, then any work to make a "python 4" - or any other new programming languages / versions / features - is rearranging deck chairs on the Titanic. But if that's 50 years away, then it's the opposite.
It's hard to know what to work on without being able to predict the future :)
Wild thought: maybe coding is a thing of the past? Given that an llm can get fast&deterministic results if needed, maybe a backend for instance, can be a set of functions which are all textual specifications and by following them it can do actions (validations, calculations, etc), approach apis and connect to databases, then produce output? Then the llm can auto refine the specifications to avoid bugs and roll the changes in real time for the next calls? Like a brain which doesn't need predefined coding instructions to fulfill a task, but just understand its scope, how to approach it and learn from the past.
I really want to meet these people that are letting an LLM touch their db.
I'd think LLMs would be more dependent on compatibility than humans, since they need training data in bulk. Humans can adapt with a book and a list of language changes, and a lot of grumbling about newfangled things. But an LLM isn't going to produce Python++ code without having been trained on a corpus of such code.
It should work if you feed the data yourself, or at the very least the documentation. I do this with niche languages and it seems to work more or less, but you will have to pay attention to your context length, and of course if you start a new chat, you are back to square one.
I don't know if that's a big blocker now we have abundant synthetic data from a RL training loop where language-specific things like syntax can be learned without any human examples. Human code may still be relevant for learning best practices, but even then it's not clear that can't happen via transfer learning from other languages, or it might even emerge naturally if the synthetic problems and rewards are designed well enough. It's still very early days (7-8 months since o1 preview) so to draw conclusions from current difficulties over a 2-year time frame would be questionable.
Consider a language designed only FOR an LLM, and a corresponding LLM designed only FOR that language. You'd imagine there'd be dedicated single tokens for common things like "class" or "def" or "import", which allows more efficient representation. There's a lot to think about ...
It’s just as questionable to declare victory because we had a few early wins and that time will fix everything.
Lots of people had predicted that we wouldn’t have a single human-driven vehicle by now. But many issues happened to be a lot more difficult to solve than previously thought!
You described the thinking behind py2many.
Code in the spirit of rust with python syntax and great devx. Give up on C-API and backward compat with everything.
Re: lifetimes
Py2many has a mojo backend now. You can infer lifetimes for simple cases. See the bubble sort example.
At the point which you describe we could easily write Rust or even just C
Ownership models like Rust require a grester ability for holistic refactoring, otherwise a change in one place causes a lifetime issue elsewhere. This is actually exactly what LLM's are doing the worst at.
Beyond that, a Python with something like lifetimes implies doing away with garbage-collection - there really isn't any need for lifetimes otherwise.
What you are suggesting has nothing to do with Python and completely misses the point of why python became so widely used.
The more general point is that garbage collection is very appealing from a usability standpoint and it removes a whole class of errors. People who don't see that value should look again at the rise of Java vs c/c++. Businesses largely aren't paying for "beautiful", exacting memory management, but for programs which work and hopefully can handle more business concerns with the same development budget.
Rust lifetimes are generally fairly local and don’t impact refactoring too much unless you fundamentally change the ownership structure.
Also a reminder that Rc, Arc, and Box are garbage collection. Indeed rust is a garbage collected language unless you drop to unsafe. It’s best to clarify with tracing GC which is what I think you meant.
While I go into another direction in a sibling comment, lifetimes does not imply not needing garbage collection.
On the contrary, having both allows the productivity of automatic resource management, while providing the necessary tooling to squeeze the ultimate performance when needed.
No need to worry about data structures not friendly to affine/linear types, Pin and Phantom types and so forth.
It is no accident that while Rust has been successful bringing modern lifetime type systems into mainstream, almost everyone else is researching how to combine linear/affine/effects/dependent types with classical automatic resource management approaches.
I mean, why not just write Rust at that point? Required static typing is fundamentally at odds with the design intent of the language.
> a garbage collected Rust
By the way, wouldn't it be possible to have a garbage-collecting container in Rust? Where all the various objects are owned by the container, and available for as long as they are reachable from a borrowed object.
Isn't garbage collected Rust without a borrow checker just OCaml?
> As vibe coding becomes normalized
Just want you to know this heart monitor we gave you was engineered with vibe coding, that's why your insurance was able to cover it. Nobody really knows how the software works (because...vibes), but the AI of course surpasses humans on all current (human-created) benchmarks like SAT and bar exam tests, so there's no reason to think its software isn't superior to human-coded (crusty old non "vibe coded" software) as well. You should be able to resume activity immediately! good luck
What percent of applications require that level of reliability?
Vibe coding will be normalized because the vast, vast majority of code is not life or death. That literally what “normal” means.
Exceptional cases like pacemakers and spaceflight will continue to be produced with rigor. Maybe even 1% of produced code will work that way!
The filename of the formal paper[1] reveals the internal codename: "Pyrona".
1: https://www.microsoft.com/en-us/research/wp-content/uploads/...
I'd rather love to see confluent persistence in python, i.e. a git-like management of an object tree.
so when you create a new call stack ( generator, async sth, thread) you can create a twig/branch, and that is modified in-place, copy on write.
and you decide when and how to merge a data branch,there are support frameworks for this, even defaults but in general merging data is a deliberate operation. like with git.
locally, a python with this option looks and feels single threaded, no brain knots. sharing and merging intermediate results becomes a deliberate operation with synchronisation points that you can reason about.
This is achievable with deepdiff today: https://pypi.org/project/deepdiff/
Maybe not as performant as if you designed your data structures around it. But certainly achievable.
Sounds like a fun job, I’d love to do something like this in my 9 to 5.
It’s also amazing how much work goes into making Python a decent platform because it’s popular. Work that will never be finished and could have been avoided with better design.
Get users first, lock them in, fix problems later seems to be the lesson here.
Python is about 35 years old at this point. It was the better language that had the better design and the fixed problems at some point in time.
Sure, maybe a committee way back in 1990 could have shaved off of some the warts and oopsies that Guido committed.
I’d imagine that said committee would have also shaved off some of the personality that made Python an enjoyable language to use in the first place.
People adopted Python because it was way nicer to use compared to the alternatives in say, 2000
> Get users first, lock them in, fix problems later seems to be the lesson here.
Or with a less cynical spin: deliver something that's useful and solves a problem for your potential users, and iterate over that without dying in the process (and Python suffered a lot already in the 2 to 3 transition)
Imo it is less about locking anyone in (in this case) and more about what Python actually enables: exceedingly fast prototyping and iteration. Turns out the ability to ship fast and iterate is actually more useful that performance, esp in a web context where the bottlenecks are frequently not program execution speed.
Python has compounding problems that make it extremely tricky though.
If it was just slow because it was interpreted they could easily have added a good JIT or transpiler by now, but it's also extremely dynamic so anything can change at any time, and the type mess doesn't help.
If it was just slow one could parallelise, but it has a GIL (although they're finally trying to fix it), so one needs multiple processes.
If it just had a GIL but was somewhat fast, multiple processes would be OK, but as it is also terribly slow, any single process can easily hit its performance limit if one request or task is slow. If you make the code async to fix that you either get threads or extremely complex cooperative multitasking code that keeps breaking when there's some bit of slow performance or blocking you missed
If the problem was just the GIL, but it was OK fast and had a good async model, you could run enough processes to cope, but it's slow so you need a ridiculous number, which has knock-on effects on needing a silly number of database/api connections
I've tried very hard to make this work, but when you can replace 100 servers struggling to serve the load on python with 3 servers running Java (and you only have 3 because of redundancy as a single one can deal with the load), you kinda give up on using python for a web context
If you want a dynamic web backend language that's fast to write, typescript is a much better option, if you can cope with the dependency mess
If it's a tiny thing that won't need to scale or is easy to rewrite if it does, I guess python is ok
> If it was just slow because it was interpreted they could easily have added a good JIT or transpiler by now, but it's also extremely dynamic so anything can change at any time, and the type mess doesn't help.
See Smalltalk, Common Lisp, Self.
Their dynamism, image based development, break-edit-compile-redo.
What to change everything in Smalltalk in a single call?
a becomes: b
Now every single instance of a in a Smalltalk image, has been replaced by b.
Just one example, there is hardly anything that one can do in Python that those languages don't do as well.
Smalltalk and Self are the genesis of JIT research that eventually gave birth to Hotspot and V8.
Or Elixir / Erlang instead of Java / Kotlin, and Go instead of Python, for this use case.
I agree that fast iteration and the „easy to get something working” factor is a huge asset in Python, which contributed to its growth. A whole lot of things were done right from that point of view.
An additional asset was the friendliness of the language to non-programmers, and features enabling libraries that are similarly friendly.
Python is also unnecessarily slow - 50x slower than Java, 20x slower than Common Lisp and 10x slower than JavaScript. It’s iterative development is worse than Common Lisp’s.
I’d say that the biggest factor is simply that American higher education adopted Python as the introductory learning language.
For American higher education, It was Pascal ages ago, and then it was Java for quite a while.
But Java is too bureaucratic to be an introductory language, especially for would-be-non-programmers. Python won on “intorudctoriness” merits - capable of getting everything done in every field (bio, chem, stat, humanities) while still being (relatively) friendly. I remember days it was frowned upon for being a “script language” (thus not a real language). But it won on merit.
Isn't this so common in computer science, haven't you heard of Worse is Better [1]?
I wish Python had moved to the BEAM or something similar as part of the 2 to 3 transition. This other stuff makes me cringe.
I'm a big BEAM person, but python 3.0.0 was released december 2008. At that time, I believe OTP R12 was current, and it only gained SMP support in R11. [1] In 2008, I don't know that it would have been clear that the BEAM would be a good target. And I don't know how switching to BEAM then would have addressed what I think is the core issue python 3 was working on, unicode strings; BEAM didn't start taking on unicode until R13 and IMHO, is kind of on the slow end of unicode adoption (which isn't always bad... being late means adopting industry consensus with less of the intermediate false steps)
At the time of the original Py3 release, PyPy was not ready for wide use. Otherwise maybe there could have been a chance of it replacing CPython. They were in too big a hurry to ship Py3 though. Tragedy.
Which is a pity, Python ends up being the only major dynamic language, where for all pratical purposes there is no JIT support, because while there are alternative implementations with great JIT achievements, the comunity behaves as if all that effort was for nothing other than helping PhD students doing their thesis.
This is the true Python concurrency effort! I know, I have followed many! (Life of Brian)
So they sounded out the Faster CPython team, which is now fired (was van Rossum fired, too?):
"Over the last two years, we have been engaging with the Faster CPython team at Microsoft as a sounding board for our ideas."
And revive the subinterpreter approach yet again.
Microsoft laid off the Faster CPython lead Mark Shannon and ended support for the project, where does this leave the Verona project?