Skip to main content

Infrastructure for support neural networks compression

Project description

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.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

Built Distribution

File details

Details for the file mct-quantizers-nightly-1.5.2.20241113.post1316.tar.gz.

File metadata

File hashes

Hashes for mct-quantizers-nightly-1.5.2.20241113.post1316.tar.gz
Algorithm Hash digest
SHA256 832404a6ac128d83017c73cc61a826086290e4553ab7cf062520c450db0adb2c
MD5 09ec8a504c072bee1449c86baeeada8d
BLAKE2b-256 42a3f7c5c698103154c0622c0bdb61704158c5e32ad5f23d2671a12684fb7733

See more details on using hashes here.

File details

Details for the file mct_quantizers_nightly-1.5.2.20241113.post1316-py3-none-any.whl.

File metadata

File hashes

Hashes for mct_quantizers_nightly-1.5.2.20241113.post1316-py3-none-any.whl
Algorithm Hash digest
SHA256 ad834bc734b6f2380479154e709d60c05085a09351614255d75e1967eeb39ba8
MD5 5d3a8e3ef70aab45288a105116aa0ba3
BLAKE2b-256 604ac31a844f181cda3f8a3f0e8a45b863c9752a27eb4398b10b3ec25be7618e

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page