noniml 0.1.2

Noni — a tiny tensor library with autograd, for humans.

Noni (WIP)

A minimal tensor library with autograd flexible for building good enough deep learning models.

Familiar API

a = Tensor([[1., 2.], [3., 4.]], requires_grad=True)
b = Tensor([[0.5, -1.], [2., 0.]], requires_grad=True)

# Each op records its backward function
c = a * b        # op="*",  backward: dc/da = b, dc/db = a
d = c.sum()      # op="sum", backward: ones

d.backward()     # topological sort → apply each _backward in reverse

print(a.grad)    # dL/da = b.data = [[0.5, -1.], [2., 0.]]
print(b.grad)    # dL/db = a.data = [[1., 2.], [3., 4.]]

Common Modules for everything

from noni.nn import Linear, LayerNorm, MultiHeadAttention, CrossEntropyLoss

# A simple 2-layer MLP
W1 = Linear(784, 256)
W2 = Linear(256, 10)

x = Tensor(some_batch)
h = W1(x).relu()
logits = W2(h)

loss = CrossEntropyLoss()(logits, targets)
loss.backward()   # gradients in W1.weight.grad, W2.weight.grad etc.

Build your own

Noni has three built-in backends:

Backend	Device tag	Notes
NumPy	cpu	Always available, pure Python/NumPy
OpenCL	opencl	Cross-platform GPU (NVIDIA, AMD, Intel)
MLX	mlx	Apple Silicon GPU via Metal — recommended for M-series Macs

Move tensors and modules to any backend with .to():

from noni import Tensor
from noni.nn import Linear

# Apple Silicon — runs matmul through Metal Performance Shaders
lin = Linear(512, 256)
lin.to("mlx")
x = Tensor(data, device="mlx")
y = lin(x)

There is also work going on to support CUDA natively as well as Vulkan compute and Triton. You can always implement and register your own backend if you prefer.

from noni.backends import Backend, register_backend


class MyDevice(Backend):
	...

register_backend("mygpu", MyDevice())

Module	Description
Linear	Fully connected layer with weight + bias parameters, initialized using Kaiming initialization
Embedding	Lookup table for token embeddings with scatter-add backward pass
LayerNorm	Normalizes across the last N dimensions with learned affine parameters
Dropout	Inverted dropout applied during training for regularization
MultiHeadAttention	Multi-head self-attention module with optional causal mask for autoregressive models
FeedForward	Position-wise feedforward network using GELU activation
TransformerBlock	Pre-norm residual block combining Multi-Head Attention and FeedForward layers
CrossEntropyLoss	Numerically stable implementation using log-softmax + negative log likelihood
Optimizers	Includes SGD, Adam, AdamW, and CosineAnnealingLR scheduler

Building wheels

python -m build
twine upload dist/*