dex-lang
Co-dfns
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dex-lang | Co-dfns | |
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25 | 19 | |
1,534 | 642 | |
0.1% | 1.4% | |
8.8 | 9.6 | |
18 days ago | 6 days ago | |
Haskell | APL | |
BSD 3-clause "New" or "Revised" License | GNU Affero General Public License v3.0 |
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dex-lang
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Thinking in an Array Language
A really nice approach to this I've seen recently is Google's research on [Dex](https://github.com/google-research/dex-lang).
- Function Composition in Programming Languages – Conor Hoekstra – CppNorth 2023 [video]
- Dex Lang: Research language for array processing in the Haskell/ML family
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[D] Have their been any attempts to create a programming language specifically for machine learning?
Dex
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[D] PyTorch 2.0 Announcement
Have you tried Dex? https://github.com/google-research/dex-lang It is in a relatively early stage, but it is exploring some interesting parts of the design space.
- Mangle, a programming language for deductive database programming
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Looking for languages that combine algebraic effects with parallel execution
I think [Dex](https://github.com/google-research/dex-lang) might be along the lines of what you're looking for, although its focus is on SIMD GPU-style parallelism rather than thread-level parallelism.
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“Why I still recommend Julia”
Dex proves indexing correctness without a full dependent type system, including loops.
See: https://github.com/google-research/dex-lang/pull/969
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Haskell for Artificial Intelligence?
In case you want to see one research direction that's combining practical machine learning and functional programming, one of the authors of JAX (and the main author of its predecessor, Autograd) is writing Dex (https://github.com/google-research/dex-lang), a functional language for array processing. The compiler itself is written in Haskell. JAX is one of the most popular libraries for doing a lot of machine learning these days, along with Tensorflow and PyTorch. You might also want to see the bug in the JAX repo about adding Haskell support, for some context: https://github.com/google/jax/issues/185
Co-dfns
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Tacit Programming
And if anyone wants an absolute masterclass in tacit programming, have a look at Aaron's Co-dfns compiler. The README has extensive reference material. https://github.com/Co-dfns/Co-dfns/
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YAML Parser for Dyalog APL
I don't put a lot of stock in the "write-only" accusation. I think it's mostly used by those who don't know APL because, first, it's clever, and second, they can't read the code. However, if I remember I implemented something in J 10 years ago, I will definitely dig out the code because that's the fastest way by far for me to remember how it works.
This project specifically looks to be done in a flat array style similar to Co-dfns[0]. It's not a very common way to use APL. However, I've maintained an array-based compiler [1] for several years, and don't find that reading is a particular difficulty. Debugging is significantly easier than a scalar compiler, because the computation works on arrays drawn from the entire source code, and it's easy to inspect these and figure out what doesn't match expectations. I wrote most of [2] using a more traditional compiler architecture and it's easier to write and extend but feels about the same for reading and small tweaks. See also my review [3] of the denser compiler and precursor Co-dfns.
As for being read by others, short snippets are definitely fine. Taking some from the last week or so in the APL Farm, {⍵÷⍨+/|-/¯9 ¯11+.○?2⍵2⍴0} and {(⍸⍣¯1+\⎕IO,⍺)⊂[⎕IO]⍵} seemed to be easily understood. Forum links at [4]; the APL Orchard is viewable without signup and tends to have a lot of code discussion. There are APL codebases with many programmers, but they tend to be very verbose with long names. Something like the YAML parser here with no comments and single-letter names would be hard to get into. I can recognize, say, that c⌿¨⍨←(∨⍀∧∨⍀U⊖)∘(~⊢∊LF⍪WS⍨)¨c trims leading and trailing whitespace from each string in a few seconds, but in other places there are a lot of magic numbers so I get the "what" but not the "why". Eh, as I look over it things are starting to make sense, could probably get through this in an hour or so. But a lot of APLers don't have experience with the patterns used here.
[0] https://github.com/Co-dfns/Co-dfns
[1] https://github.com/mlochbaum/BQN/blob/master/src/c.bqn
[2] https://github.com/mlochbaum/Singeli/blob/master/singeli.bqn
[3] https://mlochbaum.github.io/BQN/implementation/codfns.html
[4] https://aplwiki.com/wiki/Chat_rooms_and_forums
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HVM updates: simplifications, finally runs on GPUs, 80x speedup on RTX 4090
This always seemed like a very interesting project; we need to get to the point where, if things can run in parallel, they must run in parallel to make software more efficient on modern cpu/gpu.
It won't attract funds, I guess, but it would be far more trivial to make this work with an APL or a Lisp/Scheme. There already is great research for APL[0] and looking at the syntax of HVM-core it seems it is rather easy to knock up a CL DSL. If only there were more hours in a day.
[0] https://github.com/Co-dfns/Co-dfns
- Co-Dfns
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APL: An Array Oriented Programming Language (2018)
There are many styles of APL, not just due to its long history, but also because APL is somewhat agnostic to architecture paradigms. You can see heavily imperative code with explicit branching all over the place, strongly functional-style with lots of small functions, even object-oriented style.
However, given the aesthetic that you express, I think you might like https://github.com/Co-dfns/Co-dfns/. This is hands-down my favorite kind of APL, in which the data flow literally follows the linear code flow.
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Franz Inc. has moved the whole Allegro CL IDE to a browser-based user interface. Incl. all their Lisp development tools. One can check that out with their Allegro CL Express Edition.
Which is, as far as I know, unused. (Similarly the gpu compiler.)
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What would make you try a new language?
You might be familiar with iKe (grahics), SpecialK (GLSL) and Co-dfns. Also, I am working on bastardized APL for GPU – Fluent. Fluent 1 had backend implemented through Apple Metal Performance Shaders Graph and Fluent 2 has TensorFlowJS backend for now. I care more about having auto differentiation in the lang than running on GPU and do graphics, to be honest.
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APL9 from Outer Space
Not that I am aware of. I think the closest project is co-dfns[1] which is being developed by Aaron Hsu (he did a presentation as well). It aims to compile a subset of APL so that it can be executed on GPUs for instance, possibly with other backends. I imagine an XLA backend could be possible there.
[1] https://github.com/Co-dfns/Co-dfns
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Who is researching array languages these days?
Aaron hsu did his dissertation on this topic (compiler, thesis), at indiana university in the us.
- Researchers Develop Transistor-Free Compute-in-Memory Architecture
What are some alternatives?
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
BQN - An APL-like programming language. Self-hosted!
futhark - :boom::computer::boom: A data-parallel functional programming language
chibicc - A small C compiler
julia - The Julia Programming Language
tigerbeetle - A distributed financial accounting database designed for mission critical safety and performance. [Moved to: https://github.com/tigerbeetledb/tigerbeetle]
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
ngn-apl - An APL interpreter written in JavaScript. Runs in a browser or NodeJS.
hasktorch - Tensors and neural networks in Haskell
uemacs - Random version of microemacs with my private modificatons
CIPs
april - The APL programming language (a subset thereof) compiling to Common Lisp.