Decorator JITs: Python as a DSL

1. pypy

   1 2 1,291 9.8 Python

   PyPy is a very fast and compliant implementation of the Python language.

   

2. CodeRabbit

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   CodeRabbit: AI Code Reviews for Developers. Revolutionize your code reviews with AI. CodeRabbit offers PR summaries, code walkthroughs, 1-click suggestions, and AST-based analysis. Boost productivity and code quality across all major languages with each PR.

   

3. svd_util

   2 3 9 0.0 Python

   python utulity funcs, classes, small languages, frameworks, scripts. i tend to use most optz, attr, struct, py3, eutf

   i needed to make the "tracing" part - which i called "explain" - without jits, in 2007-8.. using combination of operator-overloading, variables-"declaring", and bytecode hacks [0]. Applied over set of (constrained) functions. Result was well-formed trace of which var got what value because of what expression.

   So can these ~hacks be avoided now - or not really?

   [0] https://github.com/svilendobrev/svd_util/blob/master/tracer....

4. transit-python3

   3 2 1 7.8 Python

   Fork of transit-python2

   

   it was very good. But there was a win only if one can avoid the overhead of function-calls, which is slowest thing in python - magnitude+ more than anything else (well, apart of exception throwing which is even slower, but.. rare). In my case, the speedup in calculations was lost in slowdown because of funccals.. so i ended up grouping and jamming most calculations in one-big-func(TM).. and then that was psyco-assembly-zed.

   btw funccalls are still slowest thing. somedict.get(x) is almost 2x slower than (x in somedict and somedict[x]). In my last-year attempt to optimizing transit-protocol lib [0], bundling / copying few one-line calls in one 5-line func was the biggest win - and of course, not-doing some things at all.

   [0] https://github.com/svilendobrev/transit-python3/blob/master/...

5. attention-gym

   4 2 692 7.9 Python

   Helpful tools and examples for working with flex-attention

   

   Basically this style of code - https://github.com/pytorch-labs/attention-gym/pull/84/files - has issues like this - https://github.com/pytorch/pytorch/pull/137452 https://github.com/pytorch/pytorch/issues/144511 https://github.com/pytorch/pytorch/issues/145869

   For some higher level context, see https://pytorch.org/blog/flexattention/

6. Pytorch

   5 384 88,230 10.0 Python

   Tensors and Dynamic neural networks in Python with strong GPU acceleration

   

   Basically this style of code - https://github.com/pytorch-labs/attention-gym/pull/84/files - has issues like this - https://github.com/pytorch/pytorch/pull/137452 https://github.com/pytorch/pytorch/issues/144511 https://github.com/pytorch/pytorch/issues/145869

   For some higher level context, see https://pytorch.org/blog/flexattention/

7. python-dsls

   6 2 14 6.3 Jupyter Notebook

   Materials for the talk "Python already has a frontend for your compiler" at PiterPy 2023

   It's funny how close it is to the title of my talk "Python already has a frontend for your compiler": https://github.com/true-grue/python-dsls

8. art-deco

   7 1 2 3.2 Jupyter Notebook

   

   Here's another blog post on this theme! https://github.com/kach/art-deco/blob/main/art-deco.ipynb

9. SaaSHub

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10. paip-python

    8 1 356 - Python

    Peter Norvig's Paradigms of AI Programming implemented in Python

    

    Peter Norvig himself has come around to embracing Python as an alternative to Lisp:

    https://norvig.com/python-lisp.html

    https://news.ycombinator.com/item?id=1803815

    and there is indeed a Python implementation for the PAIP programs.

    https://github.com/dhconnelly/paip-python

11. csp

    9 3 261 9.1 C++

    csp is a high performance reactive stream processing library, written in C++ and Python

    

    CSP (https://github.com/Point72/csp) has a healthy amount of AST parsing for their DSL. Looks like they have debug breakpoints working by augmenting the line numbers in the AST generation

12. lightning-thunder

    10 3 1,311 9.8 Python

    Make PyTorch models up to 40% faster! Thunder is a source to source compiler for PyTorch. It enables using different hardware executors at once; across one or thousands of GPUs.

    

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