Release v1.1.0

This is the first released version of this project.

Introduction

MCT Quantizers is an open-source library developed by researchers and engineers at Sony Semiconductor Israel. The library provides tools for easily representing a quantized neural network in both Keras and PyTorch. It offers a set of useful quantizers and a simple interface for implementing new custom quantizers.

Main API and Features

• Quantization Wrapper: A framework-specific object that takes a layer with weights and a set of weight quantizers to infer a quantized layer.

• Activation Quantization Holder: A framework-specific object that holds an activation quantizer to be used during inference.

• 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. A set of framework-specific quantizers for both weights and activations are defined on top of BaseInferableQuantizer.

• 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 properties like QuantizationTarget, QuantizationMethod, and quantizer_type.

• Load quantized model: A framework-specific API that allows loading a model that is quantized using the library's quantization interface.

Getting Started

To get started, you can install MCT Quantizers either from the source or from PyPi. See the repository's README file for more details.

Contributors

@reuvenperetz, @eladc-git, @lior-dikstein, @elad-c, @haihabi, @lapid92, @Idan-BenAmi, @ofirgo

Thank you for your interest in our project. We look forward to your contributions and feedback.

Full Changelog: https://github.com/sony/mct_quantizers/commits/v1.1.0