tico · PyPI

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This release is a pre-release and may not be stable for production use.

TICO — Torch IR to Circle

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 an nn.Module into a ready-to-deploy .circle binary.
.pt2 support — convert saved torch.export programs via the Python API or the pt2-to-circle command-line tool.
Run Circle models in Python — execute converted models directly for quick parity checks against PyTorch.
Circle artifact tools — inspect, check, extract, and clean up exported .circle graphs with the tico.circle API or tico-circle CLI.
Quantization toolkit — a unified prepare/convert API 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 uses torch.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.

Circle artifact tools

tico.circle provides Python APIs and the tico-circle CLI for inspecting and transforming exported .circle files.

tico-circle inspect model.circle --tensors --operators
tico-circle verify model.circle
tico-circle extract model.circle --ops 20-64 -o region.circle

verify performs a static internal-consistency check of the Circle artifact itself, including indices, graph connections, buffers, signatures, and subgraph references. It does not run inference or validate numerical accuracy or backend compatibility.

See the Circle artifact tools guide for the Python API, exact verification rules, extraction semantics, cleanup passes, multi-subgraph and signature behavior, and standard-input/standard-output pipelines.

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 directly in Python
Circle artifact tools	Inspecting, verifying, extracting, and cleaning up exported Circle models
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
Circle artifact tools	Circle pass contracts, index rewriting, verification, and test expectations
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.

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