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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.


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.


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
cd mct_quantizers
python install


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.


Apache License 2.0.


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