raxis
raxis is a from-scratch re-implementation of the core of PyTorch, the ATen tensor library and the autograd engine, in C++20 with Python bindings. It is distilled from the PyTorch source: it keeps the techniques that make ATen fast (TensorIterator, vectorized kernels with runtime CPU dispatch, BLAS/MKL) without the breadth and indirection that make the original hard to read.
The goal is a codebase that someone curious about how PyTorch works inside can read end to end, while running at PyTorch speed on CPU.
Status: alpha. CPU only. Tensors with views and broadcasting, type promotion, elementwise ops, reductions, matmul, autograd, and an
nn/optimpackage with enough to train MLPs. CNN layers and CUDA are next.
Install
pip install raxis
Wheels for Windows and Linux (x86-64), Python 3.10 to 3.14. raxis has no
runtime dependencies; numpy is optional (.numpy(), raxis.from_numpy).
import raxis
from raxis import nn, optim
model = nn.Sequential(nn.Linear(784, 128), nn.ReLU(), nn.Linear(128, 10))
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
x, y = raxis.randn((64, 784)), raxis.randint(0, 10, (64,))
loss = nn.functional.cross_entropy(model(x), y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
The API follows PyTorch's, so most code only needs torch replaced by
raxis.
Inside
| Path | What it holds |
|---|---|
libraxis/raxis/tensor |
TensorImpl and storage, views and strides, type promotion, TensorIterator |
libraxis/raxis/ops |
Operators and their CPU kernels, compiled once per ISA (AVX2, NEON, ...) and picked at runtime |
libraxis/raxis/autograd |
Graph, engine and backward functions |
libraxis/bindings |
pybind11 module raxis._C |
raxis/ |
Python package: nn, optim, tensor printing |
Code taken from PyTorch keeps a // From: aten/src/ATen/... comment pointing
to the original.
Build from source
Requirements: a C++20 compiler (MSVC 17+, GCC 12+, Clang 14+), CMake 3.22+ and uv.
git clone --recursive https://github.com/pgmesa/raxis.git
cd raxis
uv sync --extra dev --extra cpu # or --extra cu130 for CUDA PyTorch
uv run python scripts/build_libraxis.py
uv run pytest tests
uv sync installs the pinned build and test dependencies from uv.lock,
including Intel MKL from PyPI and PyTorch, the reference the tests compare
against. build_libraxis.py builds libraxis with CMake and installs it into
the raxis/ package. To work on the C++ side, libraxis/CMakePresets.json
has debug and release presets; CMakeLists.txt lists the build options
(RAXIS_USE_*).
scripts/bench_ops.py benchmarks each op against PyTorch and NumPy, and
examples/ has notebooks that train MLPs on two moons and MNIST with either
raxis or PyTorch.
License
MIT. raxis includes code derived from PyTorch (BSD-3-Clause) and links SLEEF, mimalloc and Intel MKL: see THIRD_PARTY_NOTICES.txt.
1 maintainer
Metadata
1 maintainer
Release files for raxis 0.1.0
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