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, CNNs (Conv1d/2d, pooling, BatchNorm) and a small GPT (Embedding, LayerNorm, GELU, scaled dot product attention). CUDA is next.
Install
pip install raxis
Wheels for Windows (x86-64), Linux (x86-64 and aarch64, e.g. a 64-bit
Raspberry Pi OS) and macOS (Apple silicon, macOS 14+), 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.
raxis.autograd.graph exports the autograd graph of a tensor as Graphviz DOT
or Mermaid text, as autograd nodes (view="backward") or as forward ops
(view="forward"):
from raxis.autograd import graph
print(graph.to_mermaid(loss, dict(model.named_parameters()), view="forward"))
graph.render(loss, "loss.svg") # needs pip install 'raxis[viz]' and Graphviz
examples/autograd_graph.py draws every view and format side by side.
Performance
Measured on a Ryzen 7 9700X (Zen 5, 8 cores, AVX512) with Windows 11, 8 threads, PyTorch 2.14.1 and Intel MKL in both libraries. The scripts and notebooks to reproduce them are in the repo (see Build from source).
Training the examples. Time of trainer.fit in each notebook (training
plus the validation pass of every epoch) with Raxis, PyTorch and the two
earlier projects of the same author:
synapx (autograd in C++ on top of libtorch)
and synapgrad (autograd in pure Python
on top of NumPy).
| Notebook | Raxis | PyTorch | synapx | synapgrad | Params | Train set | Batch | Epochs |
|---|---|---|---|---|---|---|---|---|
| basic_mlp.ipynb | 0.20 s | 1.4 s | 0.53 s | 0.26 s | 337 | 150 | 4 | 50 |
| mnist_mlp.ipynb | 2.9 s | 4.5 s | 5.6 s | 10.0 s | 178,110 | 48,000 | 64 | 10 |
| mnist_cnn.ipynb | 10.8 s | 37.8 s | - | 4 min 55 s | 20,538 | 48,000 | 128 | 5 |
The models reach the same accuracy with every library (MNIST: ~97.6% val with the MLP, ~98.6% with the CNN). Most of PyTorch's time in the CNN is Dropout: its CPU random numbers come from a serial mt19937, Raxis draws them from Philox in parallel. synapx has no Conv2d.
nanoGPT. nanoGPT's
character-level Shakespeare model, trained with examples/nanogpt/train.py
(nanoGPT's model.py with only the imports changed):
| Config | Raxis step | PyTorch step | Raxis val loss | PyTorch val loss |
|---|---|---|---|---|
| nanoGPT README's CPU run: 4 layers, n_embd 128, block 64, batch 12, 2000 iters | 13.2 ms | 14.2 ms | 1.9065 | 1.9121 |
train_shakespeare_char.py: 6 layers, n_embd 384, block 256, batch 64, dropout 0.2 |
2.61 s | 4.00 s | 1.4553 | 1.4680 |
Median time of a training step (forward, backward, gradient clipping and AdamW). The full config would take ~5.1 h with Raxis and ~7.2 h with PyTorch on this CPU for its 5000 iterations, but the val loss stops improving around iteration 2000 and the model overfits afterwards, so both were stopped early: the table has the best val loss up to iteration 2000 (1750 for Raxis, 2000 for PyTorch), against 1.4697 in nanoGPT's README. Random numbers differ between the libraries, so the losses do too.
Ops. A selection of scripts/bench_ops.py (float32, forward only unless
noted). Over its 388 cases the median ratio Raxis/PyTorch is 0.85x; the only
case beyond 2x is the large 3x3 convolution, where PyTorch uses oneDNN's
direct convolution and Raxis im2col plus an MKL gemm.
| Op | Shape | Raxis (ms) | PyTorch (ms) |
|---|---|---|---|
| add | 1000 x 1000 | 0.017 | 0.017 |
| exp | 1000 x 1000 | 0.123 | 0.130 |
| sum(dim=1) | 1000 x 1000 | 0.010 | 0.018 |
| mm | 512 x 512 @ 512 x 512 | 0.459 | 0.464 |
| softmax | 16384 x 384 | 0.872 | 0.885 |
| layer_norm | 16384 x 384 | 0.726 | 1.182 |
| gelu | 16384 x 384 | 0.814 | 0.800 |
| sdpa (causal) | 64 x 6 x 256 x 64 | 13.98 | 15.23 |
| sort | 1000 x 1000 | 5.98 | 11.03 |
| x[idx] backward | 1000 x 1000 | 0.050 | 0.317 |
| normal_ | 1000 x 1000 | 0.548 | 3.111 |
| dropout | 1000 x 1000 | 0.123 | 6.880 |
| conv2d 3x3, 32 -> 64 ch | 64 x 32 x 26 x 26 | 2.98 | 1.49 |
| conv2d 3x3, 64 -> 64 ch | 64 x 64 x 32 x 32 | 8.94 | 4.32 |
ARM. On GitHub's ARM runners (Apple M1 and Neoverse, shared machines, so no table) the median ratio Raxis/PyTorch over the same cases is 0.79x to 0.88x in float32 and float64.
Against NumPy. Raxis wheels are 8-10 MB, smaller than NumPy's (13-17 MB),
while PyTorch's CPU wheels take 110-180 MB. On 1000 x 1000 elementwise ops
Raxis and PyTorch both run 3 to 30x faster than NumPy (add 0.017 vs 0.565
ms, exp 0.123 vs 0.944 ms), mostly because they split the work between
threads: NumPy's own functions are
single-threaded by design,
only its BLAS calls use several. On 100 x 100 arrays the three are on par.
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, AVX512, NEON) and picked at runtime |
libraxis/raxis/autograd |
Graph, engine and backward functions |
libraxis/bindings |
pybind11 module raxis._C |
raxis/ |
Python package: nn, optim, autograd.graph, 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,
scripts/bench_training.py times a training step of the example models, and
examples/ has notebooks that train MLPs on two moons and MNIST and a CNN on
MNIST with either Raxis or PyTorch, and examples/nanogpt trains
nanoGPT's character-level Shakespeare
model, with its model.py unchanged except for the imports, and samples from
it. raxis.save and raxis.load use PyTorch's checkpoint format, so a model
trained with one library can be sampled with the other.
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
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Transparency logRelease files / raxis-0.3.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
| Download URL | raxis-0.3.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl |
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| Size | 4.6 MB |
| Tags | CPython 3.11 Linux glibc 2.27+ ARM64 Linux glibc 2.28+ ARM64 |
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Transparency logRelease files / raxis-0.3.0-cp311-cp311-macosx_14_0_arm64.whl
| Download URL | raxis-0.3.0-cp311-cp311-macosx_14_0_arm64.whl |
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| Size | 1.5 MB |
| Tags | CPython 3.11 macOS 14.0+ ARM64 |
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Transparency logRelease files / raxis-0.3.0-cp310-cp310-win_amd64.whl
| Download URL | raxis-0.3.0-cp310-cp310-win_amd64.whl |
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| Size | 7.7 MB |
| Tags | CPython 3.10 Windows x86-64 |
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Transparency logRelease files / raxis-0.3.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
| Download URL | raxis-0.3.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl |
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| Size | 10.1 MB |
| Tags | CPython 3.10 Linux glibc 2.27+ x86-64 Linux glibc 2.28+ x86-64 |
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Transparency logRelease files / raxis-0.3.0-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
| Download URL | raxis-0.3.0-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl |
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| Size | 4.6 MB |
| Tags | CPython 3.10 Linux glibc 2.27+ ARM64 Linux glibc 2.28+ ARM64 |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Transparency logRelease files / raxis-0.3.0-cp310-cp310-macosx_14_0_arm64.whl
| Download URL | raxis-0.3.0-cp310-cp310-macosx_14_0_arm64.whl |
|---|---|
| Size | 1.5 MB |
| Tags | CPython 3.10 macOS 14.0+ ARM64 |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Transparency logSecurity for raxis 0.3.0
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