QONNX
Note
QONNX is currently under active development. APIs will likely change.
QONNX (Quantized ONNX) introduces three new custom operators – Quant, BipolarQuant and Trunc – in order to represent arbitrary-precision uniform quantization in ONNX. This enables:
Representation of binary, ternary, 3-bit, 4-bit, 6-bit or any other quantization.
Quantization is an operator itself, and can be applied to any parameter or layer input.
Flexible choices for scaling factor and zero-point granularity.
Quantized values are carried using standard float datatypes to remain ONNX protobuf-compatible.
This repository contains a set of Python utilities to work with QONNX models, including but not limited to:
executing QONNX models for (slow) functional verification
shape inference, constant folding and other basic optimizations
summarizing the inference cost of a QONNX model in terms of mixed-precision MACs, parameter and activation volume
Python infrastructure for writing transformations and defining executable, shape-inferencable custom ops
(experimental) data layout conversion from standard ONNX NCHW to custom QONNX NHWC ops
Quickstart
Operator definitions
Quant for 2-to-arbitrary-bit quantization, with scaling and zero-point
BipolarQuant for 1-bit (bipolar) quantization, with scaling and zero-point
Trunc for truncating to a specified number of bits, with scaling and zero-point
Installation
Install latest release from PyPI:
pip install qonnx
Development
Install in editable mode in a venv:
git clone https://github.com/fastmachinelearning/qonnx
cd qonnx
virtualenv -p python3.8 venv
source venv/bin/activate
pip install -e .[testing, docs, notebooks]
Run entire test suite, parallelized across CPU cores:
pytest -n auto --verbose
Run a particular test and fall into pdb if it fails:
pytest --pdb -k "test*extend*partition.py::test*extend*partition[extend_id1-2]"
QONNX also uses GitHub actions to run the full test suite on PRs.