ultragraph-1bit 0.10.0

A pure-Python (+numpy) byte-graph that is a 1-bit (ternary) LLM: dunder API, autograd, ultra-edges, visualization.

ultragraph

A pure-Python (+ numpy) byte-graph that is a 1-bit (ternary) LLM.

genesis 251e6ea · themed after pocoo.vaked.dev

Three levels:

level	unit	storage
micro	node / edge	1 byte each — int8 activation / ternary weight {-1,0,+1}
meso	tree	a whole graph == one net/module (a Linear/MLP block)
macro	ultra-edge (===)	typed wiring between trees → the ultra-graph = the model

Weights are ternary (BitNet b1.58 style); activations are int8. Full-precision "master" weights live in an ad-hoc side store during training; the byte buffers are the deployed state. Training uses a straight-through estimator (STE).

Illustrations

Real outputs from a trained ternary mini-GPT — regenerate with uv run python assets/make_figures.py:

ultra-graph	causal attention	ternary weight bytes

Left: the model as an ultra-graph — trees wired by ultra-edges (===), with residual skips. Middle: real causal self-attention weights (lower-triangular → no peeking at the future). Right: a trained query projection's weight bytes, each ∈ {−1, 0, +1}.

Install

pip install ultragraph-1bit    # then: import ultragraph
# or from source (Python >=3.11):
uv sync

Dunder API

>> is overloaded by operand type:

import numpy as np
from ultragraph import Tree, UltraGraph, Tensor, mlp, SGD

# micro-edges inside a sparse tree
g = Tree(4, "g")
g[0] >> g[1]        # node >> node  -> micro-edge
g[2] = 7            # set a node byte
print(len(g), 2 in g, list(g))

# ultra-edges between trees
ug = UltraGraph()
a = ug.add(Tree.dense(8, 16, "a"))
b = ug.add(Tree.dense(16, 4, "b", act="none"))
a >> b              # tree >> tree -> ultra-edge (plain)
a.wire(b, "residual")

Train a tiny ternary net

ug = mlp([4, 16, 2])                 # dense ternary linear trees wired plain
opt = SGD(ug, lr=0.3, momentum=0.9)
x = Tensor(np.random.randn(32, 4).astype("float32"))
for _ in range(300):
    loss = ug.forward(x).cross_entropy(y)
    opt.zero_grad(); loss.backward(); opt.step()   # step() re-quantizes weights

See examples/char_lm.py (MLP LM), examples/transformer_lm.py (single-head attention), examples/mini_gpt.py (batched multi-head attention + RMSNorm + Adam), and examples/gpt_lm.py (the whole stack: ByteTokenizer → GPT → train → stream) for end-to-end char/byte-level ternary language models.

from ultragraph import Embedding, MultiHeadAttention, RMSNorm, linear_tree, Adam
# pre-norm transformer block over a [B, T, d_model] sequence:
#   x = x + mha(norm1(x));  x = x + ff2(ff1(norm2(x)))

A whole ternary GPT

from ultragraph import GPT

m = GPT(vocab=256, d_model=128, n_layers=4, n_heads=4, max_len=256)  # RoPE + KV-cache
logits = m(ids)                       # ids [B, T] -> logits [B, T, vocab]
out = m.generate([72, 105], n_new=64, temperature=0.8, top_k=40, top_p=0.9,
                 repetition_penalty=1.3, stop=10, seed=0)   # stop on newline byte

for tok in m.generate([72, 105], n_new=64, temperature=0.8, stream=True):
    print(tok, end=" ", flush=True)   # token-by-token
m.save("gpt.npz")                     # fp32 masters; reload onto the same architecture

# a true 1-bit-on-disk checkpoint: bit-packed ternary bytes, no fp32 masters.
m.save_deployed("gpt.q.npz")          # ~10x smaller, inference-only
deployed = GPT.load_deployed("gpt.q.npz")   # byte-exact logits, runs from the trits

The deployed checkpoint stores weights at their true ~1.6 bits/weight density (5 ternary values per byte) plus the tiny fp32 pieces (embedding, norm gains, biases). On an 858k-param model that's 3.4 MB → 334 KB, and deployed(ids) gives logits identical to the trained model — Tree.forward runs straight from the stored bytes.

generate decodes with a per-layer KV-cache; since activations are quantized per token, a cached step is byte-for-byte the full-forward result at that position. Positions come from RoPE (rotary embeddings) — relative, and offset-aware so they line up across cached steps.

Tasks

just test        # pytest
just test-fast   # dependency-free runner (stdlib + numpy)
just demo        # char-LM end-to-end
just viz         # render example SVGs

Layout

ultragraph/quant.py     ternary + int8 quantization, STE
ultragraph/autograd.py  numpy autograd tape; ternary_linear (STE); exp/tanh/sigmoid/gelu/silu
ultragraph/core.py      Node/Edge/Tree/UltraEdge/UltraGraph + dunder API
ultragraph/nn.py        linear_tree, mlp, Attention, MultiHeadAttention, RoPE, RMSNorm, LayerNorm, LearnedPositionalEmbedding, MoE, Dropout, Sequential
ultragraph/model.py     TransformerBlock + GPT (embedding + RoPE + pre-norm blocks + ternary head) with cached .generate()
ultragraph/optim.py     SGD + Adam (grad clip, weight decay) + CosineSchedule, re-quantize after step
ultragraph/pack.py      dense ternary bit-packing (5 values/byte, ~1.58-bit)
ultragraph/tokenize.py  byte-level tokenizer (ByteTokenizer, vocab 256)
ultragraph/vaked.py      optional vaked lowering (lower_graph, compile_vaked via vendored vakedc)
ultragraph/viz/         svg.py (pure-SVG) + mpl.py (optional matplotlib) — micro / macro / byte-heatmap
ultragraph/io.py        byte-exact save / load (optional packed weights); save_params/load_params

Design spec: docs/superpowers/specs/2026-07-10-ultragraph-design.md. Graph-theory reading list (Erdős classics): docs/references.md.

Install from source

git clone https://github.com/peterlodri-sec/ultra-graph
cd ultra-graph
uv sync
just test

License

MIT — see LICENSE.