mct-quantizers-nightly 1.6.0.20250626.post102857

Infrastructure for support neural networks compression

Model Compression Toolkit (MCT) Quantizers

The MCT Quantizers library is an open-source library developed by researchers and engineers working at Sony Semiconductor Israel.

It provides tools for easily representing a quantized neural network in both Keras and PyTorch. The library offers researchers, developers, and engineers a set of useful quantizers, along with a simple interface for implementing new custom quantizers.

High level description:

The library's quantizers interface consists of two main components:

1. QuantizationWrapper: This object takes a layer with weights and a set of weight quantizers to infer a quantized layer.
2. ActivationQuantizationHolder: An object that holds an activation quantizer to be used during inference.

Users can set the quantizers and all the quantization information for each layer by initializing the weights_quantizer and activation_quantizer API.

Please note that the quantization wrapper and the quantizers are framework-specific.

Quantizers:

The library provides the "Inferable Quantizer" interface for implementing new quantizers. This interface is based on the BaseInferableQuantizer class, which allows the definition of quantizers used for emulating inference-time quantization.

On top of BaseInferableQuantizer the library defines a set of framework-specific quantizers for both weights and activations:

1. Keras Quantizers
2. Pytorch Quantizers

The mark_quantizer Decorator

The @mark_quantizer decorator is used to assign each quantizer with static properties that define its task compatibility. Each quantizer class should be decorated with this decorator, which defines the following properties:

• QuantizationTarget: An Enum that indicates whether the quantizer is intended for weights or activations quantization.
• QuantizationMethod: A list of quantization methods (Uniform, Symmetric, etc.).
• identifier: A unique identifier for the quantizer class. This is a helper property that allows the creation of advanced quantizers for specific tasks.

Getting Started

This section provides a quick guide to getting started. We begin with the installation process, either via source code or the pip server. Then, we provide a short example of usage.

Installation

From PyPi - mct-quantizers package

To install the latest stable release of MCT Quantizer from PyPi, run the following command:

pip install mct-quantizers

If you prefer to use the nightly package (unstable version), you can install it with the following command:

pip install mct-quantizers-nightly

From Source

To work with the MCT Quantizers source code, follow these steps:

git clone https://github.com/sony/mct_quantizers.git
cd mct_quantizers
python setup.py install

Requirements

To use MCT Quantizers, you need to have one of the supported frameworks, Tensorflow or PyTorch, installed.

For use with Tensorflow, please install the following package: tensorflow,

For use with PyTorch, please install the following package: torch

You can also use the requirements file to set up your environment.

License

Apache License 2.0.