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-1.4.0.20240108.post1127.tar.gz.

File metadata

File hashes

Hashes for mct-quantizers-nightly-1.4.0.20240108.post1127.tar.gz
Algorithm Hash digest
SHA256 f4dd398e03bcfb25ecd1860ba6b005ee8ab8d264bdf7657ac73ed62196998184
MD5 60719f744f1a01af5a4f71b742356211
BLAKE2b-256 52682991fdf2163cceff14e7074abadeacc24abff1adebae3bcd129c7f209566

See more details on using hashes here.

File details

Details for the file mct_quantizers_nightly-1.4.0.20240108.post1127-py3-none-any.whl.

File metadata

File hashes

Hashes for mct_quantizers_nightly-1.4.0.20240108.post1127-py3-none-any.whl
Algorithm Hash digest
SHA256 1a81bba3ec9c841a0e98f1f9bc53fb9f65c2d977e80586880874ca21d4de927a
MD5 943a00cdbf1d70f76be29dd2e9d85509
BLAKE2b-256 91b23a7b8fc3d6a3c0453d157820aa8ba5d424cb8222c4d8291c2b2738257492

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