jax
the-pile
jax | the-pile | |
---|---|---|
82 | 15 | |
28,082 | 1,403 | |
2.0% | 1.6% | |
10.0 | 0.0 | |
2 days ago | about 1 year ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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jax
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The Elements of Differentiable Programming
The dual numbers exist just as surely as the real numbers and have been used well over 100 years
https://en.m.wikipedia.org/wiki/Dual_number
Pytorch has had them for many years.
https://pytorch.org/docs/stable/generated/torch.autograd.for...
JAX implements them and uses them exactly as stated in this thread.
https://github.com/google/jax/discussions/10157#discussionco...
As you so eloquently stated, "you shouldn't be proclaiming things you don't actually know on a public forum," and doubly so when your claimed "corrections" are so demonstrably and totally incorrect.
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Julia GPU-based ODE solver 20x-100x faster than those in Jax and PyTorch
On your last point, as long as you jit the topmost level, it doesn't matter whether or not you have inner jitted functions. The end result should be the same.
Source: https://github.com/google/jax/discussions/5199#discussioncom...
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Apple releases MLX for Apple Silicon
The design of MLX is inspired by frameworks like NumPy, PyTorch, Jax, and ArrayFire.
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MLPerf training tests put Nvidia ahead, Intel close, and Google well behind
I'm still not totally sure what the issue is. Jax uses program transformations to compile programs to run on a variety of hardware, for example, using XLA for TPUs. It can also run cuda ops for Nvidia gpus without issue: https://jax.readthedocs.io/en/latest/installation.html
There is also support for custom cpp and cuda ops if that's what is needed: https://jax.readthedocs.io/en/latest/Custom_Operation_for_GP...
I haven't worked with float4, but can imagine that new numerical types would require some special handling. But I assume that's the case for any ml environment.
But really you probably mean fixed point 4bit integer types? Looks like that has had at least some work done in Jax: https://github.com/google/jax/issues/8566
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MatX: Efficient C++17 GPU numerical computing library with Python-like syntax
>
Are they even comparing apples to apples to claim that they see these improvements over NumPy?
> While the code complexity and length are roughly the same, the MatX version shows a 2100x over the Numpy version, and over 4x faster than the CuPy version on the same GPU.
NumPy doesn't use GPU by default unless you use something like Jax [1] to compile NumPy code to run on GPUs. I think more honest comparison will mainly compare MatX running on same CPU like NumPy as focus the GPU comparison against CuPy.
[1] https://github.com/google/jax
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JAX – NumPy on the CPU, GPU, and TPU, with great automatic differentiation
Actually that never changed. The README has always had an example of differentiating through native Python control flow:
https://github.com/google/jax/commit/948a8db0adf233f333f3e5f...
The constraints on control flow expressions come from jax.jit (because Python control flow can't be staged out) and jax.vmap (because we can't take multiple branches of Python control flow, which we might need to do for different batch elements). But autodiff of Python-native control flow works fine!
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Julia and Mojo (Modular) Mandelbrot Benchmark
For a similar "benchmark" (also Mandelbrot) but took place in Jax repo discussion: https://github.com/google/jax/discussions/11078#discussionco...
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Functional Programming 1
2. https://github.com/fantasyland/fantasy-land (A bit heavy on jargon)
Note there is a python version of Ramda available on pypi and there’s a lot of FP tidbits inside JAX:
3. https://pypi.org/project/ramda/ (Worth making your own version if you want to learn, though)
4. For nested data, JAX tree_util is epic: https://jax.readthedocs.io/en/latest/jax.tree_util.html and also their curry implementation is funny: https://github.com/google/jax/blob/4ac2bdc2b1d71ec0010412a32...
Anyway don’t put FP on a pedestal, main thing is to focus on the core principles of avoiding external mutation and making helper functions. Doesn’t always work because some languages like Rust don’t have legit support for currying (afaik in 2023 August), but in those cases you can hack it with builder methods to an extent.
Finally, if you want to understand the middle of the midwit meme, check out this wiki article and connect the free monoid to the Kleene star (0 or more copies of your pattern) and Kleene plus (1 or more copies of your pattern). Those are also in regex so it can help you remember the regex symbols. https://en.wikipedia.org/wiki/Free_monoid?wprov=sfti1
The simplest example might be {0}^* in which case
0: “” // because we use *
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Best Way to Learn JAX
Hello! I'm trying to learn JAX over the next couple of weeks. Ideally, I want to be comfortable with using it for projects after about 3 weeks to a month, although I understand that may not be realistic. I currently have experience with PyTorch and TensorFlow. How should I go about learning JAX? Is there a specific YouTube tutorial or online course I should use, or should I just use the tutorial on https://jax.readthedocs.io/? Any information, advice, or experience you can share would be much appreciated!
- Codon: Python Compiler
the-pile
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The Pile
[2] https://github.com/EleutherAI/the-pile/issues/56
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The Pile: a dataset for language modeling [pdf]
I came so close to getting my dataset DebateSum (https://huggingface.co/datasets/Hellisotherpeople/DebateSum) into the pile, but they decided at the last minute not to add it: https://github.com/EleutherAI/the-pile/issues/56
I'm still a tiny bit salty about that.
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Sarah Silverman is suing OpenAI and Meta for copyright infringement
Anyone want to check if the book in question is in ThePile dataset?:
https://github.com/EleutherAI/the-pile/blob/master/the_pile/...
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What Types Of Websites Are Typically Scraped To Train LLMs?
All of it, it’s quite diverse. Especially the commoncrawl bit, https://github.com/EleutherAI/the-pile.
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Can anyone answer some questions on how GPT-NeoX-20B was developed, and future models?
For example, before this I didn't realize one of the sources of data that the pile uses is a massive number of emails gathered during the Enron lawsuits. Weird, but cool I guess.
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How do I add AI modules?
NovelAI's Krake and Euterpe, and the rest, are finetuned versions of existing models. The original models were trained on a mass of text. Krake is a finetune of Neo-X 20b, which was trained on The Pile. NovelAI's finetunes involve further training but on various works of fiction rather than more text trawled from the internet. The statistical rules in the existing models are thus shifted in a (slightly) new direction. Modules refine those statistical rules, or weights, just a little bit more.
- GitHub - EleutherAI/the-pile
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Sounds about right 😂 /s
Literally The Pile.
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What is the difference between OpenAI and the gpt3 algorithm?
The parameters are taken from large datasets like The Pile.
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Official Beta AMA @ June 14th, 12pm EST
We use the GPT-Neo as our base model which trained on The Pile and you can see it's contents in their github repo: https://github.com/EleutherAI/the-pile
What are some alternatives?
Numba - NumPy aware dynamic Python compiler using LLVM
mesh-transformer-jax - Model parallel transformers in JAX and Haiku
functorch - functorch is JAX-like composable function transforms for PyTorch.
datasets - 🤗 The largest hub of ready-to-use NLP datasets for ML models with fast, easy-to-use and efficient data manipulation tools
julia - The Julia Programming Language
opendyslexic - OpenDyslexic, a typeface that uses typeface shapes & features to help offset some visual symptoms of Dyslexia. Now in SIL-OFL.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
mesh-transformer-jax - Model parallel transformers in JAX and Haiku
Cython - The most widely used Python to C compiler
DALLE-mtf - Open-AI's DALL-E for large scale training in mesh-tensorflow.
jax-windows-builder - A community supported Windows build for jax.