Micrograd.jl(liorsinai.github.io) |
Micrograd.jl(liorsinai.github.io) |
And so the perennial cycle continues and another Julia AD-package emerges, and ignores all/most previous work in order to claim novelty.
Without a claim for a complete list: ReverseDiff.jl, ForwardDiff.jl, Zygote.jl, Enzyme.jl, Tangent.jl, Diffractor.jl, and many more whose name has disappeared in the short history of Julia...
Having an alternative to Python would benefit the ML ecosystem, which is too much of a monoculture right now. Julia has some really interesting statistics, probabilistic programming and physics-informed ML packages.
Nothing wrong with this. Very successful languages like Python and JavaScript has tons of reimplementations of the same things because those languages are _easy_. This isn't a loss for the language it's a victory. If a lot of people can use it that's good.
As a consequence there will be lots of garbage libs, like there are lots of garbage libs in Python and JavaScript.
Ironically it's very hard to write actual low-level parallel code (like CUDA) through python, there's really no choice but to call out to Fortran and C libraries for the likes of pytorch.
[0] https://en.wikipedia.org/wiki/Real_Programmers_Don't_Use_Pas...
I’m not really a fan of this convergence but the old school imperative CPU way of thinking about things is dead in this space
Really? Why do you feel the need to say this? Not liking Python, sure, but this kind of comments is just stupid elitism. What's next, the only REAL programmers are the ones that make their own punch cards?
As someone who programs C/C++/Python/Rust/JS you had me curious in the first half of the post. But that comment makes me wonder about the quality of the rest of what you're saying.
(I also remember getting frustrated by frequent uninterruptible kernel hangs in Jupyter, but that might have been a skill issue on my part. But it was definitely a friction I don't encounter with python. When I was developing in Julia I remember feeling anxiety/dread about hitting enter on new cells, double and triple checking my code lest I initiate an uninterruptible error and have to restart my kernel and lose all my compilation progress, meaning I'll have to wait a long time again to run code and generate new graphs.)
Tiny example (which blends Julia-the-language and Julia-the-ecosystem, for better and worse): I just timed reading the most recent CSV I generated in real life, a relatively small 14k rows x 19 columns. 10ms in Julia+CSV+DataFrames, 37ms in Python+Pandas...ie much faster in Julia but also not a pain point either way.
- Lack of multi-GPU support,
- some other weird bugs related to autograd which i never fully figured out,
- and the killer one: none of my coauthors used Julia, so I decided to just go with PyTorch.
PyTorch has been just fine, and it's nice to not have to reinvent to wheel for every new model architecture.
Also, it's now pretty easy to call Python from Julia (and vice versa) [1]. I haven't used it for deep learning, but I've been using it to implement my algorithms in Julia while making use of Jax-based libraries from Python so it's certainly quite smooth and ergonomic.
sometimes that grad student is a brilliantly productive programmer + the libraries reach escape velocity and build a community, and then you get areas where Julia is state of the art like in differential equation solving, or generally other areas of "classical" scientific computing.
in other cases the grad student is merely a very good programmer, and they just sort of float along being "almost but not quite there" for a long time, maybe abandoned depending on the maintainer's career path.
the latter case is pretty common in the machine learning ecosystem. a lot of people get excited about using a fast language for ML, see that Julia can do what they want in a really cool way, and then run into some breaking problem or missing feature ("will be fixed eventually") after investing some time in a project.
The Julia repl is incredibly nice though, I do miss that.
Edit: I see — I think you mean exporting lowered StableHLO code in a shape polymorphic format —- from the docs: https://jax.readthedocs.io/en/latest/export/shape_poly.html
This is not the thing I usually think when someone says dynamic shape support.
In this model, you have to construct a static graph initially —- then you’re allowed to specify a restricted set of input shapes to be symbolic, to avoid the cost of lowering — but you’ll still incur the cost of compilation for any new shapes which the graph hasn’t been specialized for (because those shapes affect the array memory layouts, which XLA needs to know to be aggressive)
Do y'all like understand that this isn't special to Julia and it isn't special to any language and it's 100% due to the fact that nvcc has an open-source counterpart in LLVM?
Like literally there are now dozens of languages that be used to author CUDA kernels because they all just target nvcc's flavor of ll. Eg you can do it in Python like 13 different ways - numba, taichi, etc all do it. You can even use the MLIR python bindings to directly emit nvgpu dialect and then lower to target specific llvmir.
Emulating Python's ML ecosystem is not going to be a viable strategy. The investment into the Python-based standard is just too large.
What I could see happening though is that the continuous evolution of the ML ecosystem will further abstract components of the software stack down to an MLIR/LLVM abstraction level at which point something like Julia could also use this componentry. Sort of a continuum of components, where the "frontend" language and associated programming style is the remaining choice for the user to make.
Same author as the more mature Enzyme.jl, which is AD done at the LLVM level.
Julia's flexibility makes it easy to write a simple AD system, but perhaps we're learning that it's the wrong level to write the program transformations needed for really efficient AD. Perhaps the same is true of "run this on a GPU, or 10" transformations.
https://jax.readthedocs.io/en/latest/export/export.html#supp...
Edit: I think you need to look here as well, the Exported objects do in fact serialize a function and support shape polymorphism:
https://jax.readthedocs.io/en/latest/export/shape_poly.html#...
My definition might be wrong, but I often think of full dynamic shape support as implying something dynamic about the computation graph.
For instance, JAX supports a scan primitive — whose length must be statically known. With full dynamic shape support, this length might be unknown — which would mean one could express loops with shape dependent size.
As far as I can tell, shape polymorphic exports may sort of give you that — but you incur the cost of compilation, which will not be negligible with XLA.
I work with edge devices, so I have also been experimenting with IREE for deployment, that can handle dynamic shapes (at times, it stopped working for a version but may be working again in the development branch).