Essentials-of-Compilation VS jax

if you like compilers you should this book out https://github.com/IUCompilerCourse/Essentials-of-Compilation.

Essentials of compilation by Dr. Siek. There’s a GitHub repo. Just navigate to the releases and you will find a pdf https://github.com/IUCompilerCourse/Essentials-of-Compilation/releases/tag/python-MIT-press. This book is really good and it’s practical. There’s a lot of code and it guides you along the way. So it’s a great book to self study. To supplement this you can buy Engineering a Compiler by cooper. This is more comprehensive but there’s no code in this book, only pseudo code. Start with essentials of compilation my friend. It will teach you everything you need.

You can try this book if you want something that came out this year https://github.com/IUCompilerCourse/Essentials-of-Compilatio.... Go to the releases to either get the racket version or python version. But I mean cmu uses the dragon book second edition for a graduate level compiler optimization class.

This paper is my favorite introduction to compilers, it's short and hands-on: http://scheme2006.cs.uchicago.edu/11-ghuloum.pdf

There is a book-length expansion of this paper that goes into more detail: https://github.com/IUCompilerCourse/Essentials-of-Compilatio...

Introduction to Computing"

https://dcic-world.org/

# Programming Language Theory:

"Programming Languages: Application and Interpretation"

https://www.plai.org/

# Compilation:

"Essentials of Compilation: An Incremental Approach in Python"

https://github.com/IUCompilerCourse/Essentials-of-Compilatio...

# Database Systems:

"CMU: Intro to Database Systems"

https://15445.courses.cs.cmu.edu/

"CMU: Advanced Database Systems"

https://15721.courses.cs.cmu.edu/

# Calculus I/II & Real Analysis

"A Course in Calculus and Real Analysis"

https://link.springer.com/book/10.1007/978-3-030-01400-1

"A Course in Multivariable Calculus and Analysis"

https://link.springer.com/book/10.1007/978-1-4419-1621-1

# Linear Algebra & ML:

* A Series of books by prof. Joe Suzuki without using any external library for the implementations *

"Statistical Learning with Math and Python"

https://link.springer.com/book/10.1007/978-981-15-7877-9

"Sparse Estimation with Math and Python"

https://link.springer.com/book/10.1007/978-981-16-1438-5

"Kernel Methods for Machine Learning with Math and Python"

https://link.springer.com/book/10.1007/978-981-19-0401-1

# Discrete Mathematics:

"CMU 21-228 Discrete Mathematics (prof. Poh-Shen Loh"

https://www.math.cmu.edu/~ploh/2021-228.shtml

# Cryptography:

"Serious Cryptography: A Practical Introduction to Modern Encryption"

https://nostarch.com/seriouscrypto

# Problem Solving:

"Math 235: Mathematical Problem Solving"

https://www.cip.ifi.lmu.de/~grinberg/t/20f/

This compiler book explains monadic normal form which it’s anf but not 100 percent because of the difference in how let expressions are represented. https://github.com/IUCompilerCourse/Essentials-of-Compilation

I learned to write compilers by reading "Essentials of Compilation." You can find a free pdf in the book's repo https://github.com/IUCompilerCourse/Essentials-of-Compilation/releases/tag/python-MIT-press. The book is published my MIT Press although right now the racket version is out. the python version is coming out soon. the link that I just shared is for the python version. This is a great book . I recommend it

One of the best (free/open source) books for learning how to write a compiler is Essentials of Compilation. It comes in two flavors: Racket and Python. I'm less familiar with the Python version, but it might be what you're looking for.

Not sure about specifics, but maybe https://github.com/IUCompilerCourse/Essentials-of-Compilation is worth a look?

There are pdfs in the releases section: Python - https://github.com/IUCompilerCourse/Essentials-of-Compilation/releases/download/python-MIT-press/book.pdf

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.

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...

The design of MLX is inspired by frameworks like NumPy, PyTorch, Jax, and ArrayFire.

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

>

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

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!

For a similar "benchmark" (also Mandelbrot) but took place in Jax repo discussion: https://github.com/google/jax/discussions/11078#discussionco...

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 *

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!