This release is a pre-release and may not be stable for production use.
TICO (Torch IR to Circle ONE) is a Python library that converts PyTorch modules into Circle models — a lightweight and efficient representation in ONE designed for optimized on-device neural network inference.
Highlights
- One-call conversion —
tico.convert()turns annn.Moduleinto a ready-to-deploy.circlebinary. .pt2support — convert savedtorch.exportprograms via the Python API or thept2-to-circlecommand-line tool.- Run Circle models in Python — execute converted models directly for quick parity checks against PyTorch.
- Quantization toolkit — a unified
prepare/convertAPI with GPTQ, PTQ (WrapQ), SmoothQuant, SpinQuant, and CLE, plus config-driven CLI recipes for LLMs and VLMs.
Installation
Prerequisites
- Python 3.10+
- (Optional) one-compiler — only required to run inference with converted Circle models. Conversion itself does not need it.
We highly recommend using a virtual environment (e.g., conda, venv).
From PyPI
pip install tico
From source
git clone https://github.com/Samsung/TICO.git
cd TICO
./ccex build # generates build/ and dist/
./ccex install # installs the package
./ccex install options
| Option | Description |
|---|---|
--dist |
Install from the built wheel (default is editable mode) |
--torch_ver <ver> |
Torch version to install: a family (2.5 ~ 2.10), an exact version (e.g. 2.7.0+cu118), or nightly. Default: 2.7 |
--cuda_ver <maj.min> |
Override the detected CUDA version (e.g. 12.1) |
--cpu_only |
Force a CPU-only Torch installation |
Quick start
import tico
import torch
class AddModule(torch.nn.Module):
def forward(self, x, y):
return x + y
torch_module = AddModule()
example_inputs = (torch.ones(4), torch.ones(4))
circle_model = tico.convert(torch_module.eval(), example_inputs)
circle_model.save('add.circle')
[!NOTE] Call
eval()on the module before conversion. TICO internally usestorch.export, so the module must be export-able.
Converting a saved .pt2 file from the command line:
pt2-to-circle -i add.pt2 -o add.circle
See the Getting Started guide for compile configurations,
.pt2 conversion, and running Circle models directly in Python.
Quantization
The tico.quantization module provides a unified,
modular interface for quantizing neural networks — including large language models —
through a simple two-step prepare → convert workflow:
from tico.quantization import prepare, convert
from tico.quantization.config.gptq import GPTQConfig
prepared_model = prepare(model.eval(), GPTQConfig())
for d in dataset: # calibration
prepared_model(d)
quantized_model = convert(prepared_model, GPTQConfig())
- Quantization overview — API, architecture, and how to add a new algorithm
- Quantization algorithms — GPTQ, SmoothQuant, SpinQuant, CLE, …
- Config-driven CLI examples — quantize, evaluate, and inspect LLM/VLM recipes from the command line
Documentation
For users
| Document | Description |
|---|---|
| Getting Started | Converting modules and .pt2 files, compile configuration, running Circle models in Python |
| Quantization | The prepare/convert quantization API and toolkit |
| Quantization examples | Command-line quantization, evaluation, and debugging workflows |
For developers
| Document | Description |
|---|---|
| Development guide | Environment setup, testing, and code formatting with ./ccex |
| System design | Architecture, pass pipeline, invariants, and behavior design |
| Requirements | Functional and non-functional requirements |
| System tests | System-level test coverage |
Contributing
Contributions are welcome! For quantization algorithms, start with the quantization contribution guide and the recipes developer guide. Before submitting a PR, set up the development environment and run the tests and formatter as described in the development guide.
License
Licensed under the Apache License 2.0 — see LICENSE.
1 maintainer
Release files for tico 0.2.0.dev260707
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Total release size: 1.5 MB
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