important 27.02.2025

Ive changed few things, now I preffere to do firstyly something in zig and later on I will look for options to make bindings to python.

mm.zig is main file with matrix multiplication function. the main problem is that i dont know how to build this, to make it work as a independent library in python, I lost an idea to make "faster pytorch", now im focused to something that can be used with or without pytorch.

zig-pytorch

As a man who is trying to understand zig and write usefull code in other language than python (or try to write something usefull), I get an idea of creating something what is using zig to optize pytorch, make is faster, more reliable. I don't know if it's going to work, but I need to do it my own as a motivation do study.

I've seen this in my dream

0. Basic know-how

PyTorch is written in C++ and gives API in this language, BUT ZIG GIVES US built in tools to compile code in C/C++. I can define functions from C++ in zig and use it in my code. 000_example.zig. As you see, something works, and the plan is rewrite/build from scrach pytorch functions in zig and do something with it.

What do I need

Write pytorch functions in zig.
Check it everything work on python

1. Creating basic functions from pytorch

`torch.mm(input, mat2) -> Tensor`

This function takes two tensors 2d (matrix).

Development Workflow

Plan: Define the functionality you want to implement
Test-Driven Development: Write tests in Zig first
Implement: Create the Zig implementation
Benchmark: Compare performance with PyTorch
C API: Expose functionality through C API
Python Bindings: Create Python wrappers
Document: Update documentation

Building the Project

Building the Zig Library

# Build the Zig library
zig build

# Run Zig tests
zig build test

# Build in release mode for better performance
zig build -Drelease-fast

00. notes

zig build command

zig build-obj -OReleaseFast -fPIC mm.zig
zig build-obj -fcompiler-rt mm.zig -fPIC -lpthread

important to create module

pip install -e . // python setup.py install

Building the Python Package

# Install in development mode
pip install -e .

# Build and install
python setup.py install

Adding New functions

ex: Creating a New Operation

create a new file in src/ (e.g., src/add.zig) example add.zig
Implement your function
Create a test file in tests/ (e.g., tests/testadd.zig)
Update build.zig to include your new files
Expose through C API in src/native.zig

# Basic build
zig build
zig build -Doptimize=ReleaseSafe
zig build test

# Clean build artifacts
rm -rf zig-out/

26.01.2026

I've changed my mind, now everytime i will update project to the newest version.

zig loses agains numpy with torch

Important

with torch

Size M×K × K×N         Torch (ms)   NumPy (ms)   Zig (ms)     Zig vs Torch   Zig vs NumPy   Correct
---------------------------------------------------------------------------------------------------
32×32 × 32×32            0.003        0.003        0.017          0.21x          0.18x True
64×64 × 64×64            0.008        0.016        0.085          0.09x          0.19x True
128×128 × 128×128           6.511        0.627        0.462         14.09x          1.36x True
256×256 × 256×256           7.226        0.111        2.773          2.61x          0.04x True
512×512 × 512×512           1.516        0.630       22.216          0.07x          0.03x True
1024×1024 × 1024×1024          7.648        4.324      187.883          0.04x          0.02x False
1024×512 × 512×256           1.249        0.549       21.713          0.06x          0.03x True

zig wins against solo numpy

Important

without TORCH, ONLY NUMPY

python tests/benchmark.py
Size M×K × K×N         Torch (ms)   NumPy (ms)   Zig (ms)     Zig vs Torch   Zig vs NumPy   Correct
---------------------------------------------------------------------------------------------------
32×32 × 32×32              n/a        0.019        0.018            n/a          1.04x True
64×64 × 64×64              n/a        0.143        0.055            n/a          2.62x True
128×128 × 128×128             n/a        1.178        0.358            n/a          3.29x True
256×256 × 256×256             n/a        9.086        2.778            n/a          3.27x True
512×512 × 512×512             n/a       69.996       23.019            n/a          3.04x True
1024×1024 × 1024×1024            n/a      553.027      196.582            n/a          2.81x True
1024×512 × 512×256             n/a       72.071       22.113            n/a          3.26x True

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