Torch-based tool for quantizing high-dimensional vectors using additive codebooks

Last update: Jan 7, 2023

Related tags

Overview

Trainable multi-codebook quantization

This repository implements a utility for use with PyTorch, and ideally GPUs, for training an efficient quantizer based on multiple single-byte codebooks. The prototypical scenario is that you have some distribution over vectors in some space, say, of dimension 512, that might come from a neural net embedding, and you want a means of encoding a vector into a short sequence of bytes (say, 4 or 8 bytes) that can be used to reconstruct the vector with minimal expected loss, measured as squared distance, i.e. squared l2 loss.

This repository provides Quantizer object that lets you do this quantization, and an associated QuantizerTrainer object that you can use to train the Quantizer. For example, you might invoke the QuantizerTrainer with 20,000 minibatches of vectors.

Usage

Installation

python3 setup.py install

Example

import torch
import quantization

trainer = quantization.QuantizerTrainer(dim=256, bytes_per_frame=4,
                                        device=torch.device('cuda'))
while not trainer.done():
   # let x be some tensor of shape (*, dim), that you will train on
   # (should not be the same on each minibatch)
   trainer.step(x)
quantizer = trainer.get_quantizer()

# let x be some tensor of shape (*, dim)..
encoded = quantizer.encode(x)  # (*, 4), dtype=uint8
x_approx = quantizer.decode(quantizer.encode(x))

To avoid versioning issues and so on, it may be easier to just include quantization.py in your repository directly (and add its requirements to your requirements.txt).

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Comments

fix_circular_import

These modifications solve "circular import" error when installed by command:

pip install git+https://github.com/danpovey/quantization.git@master
python -c "import quantization"

Error message:

from quantization import Quantizer
ImportError: cannot import name 'Quantizer' from partially initialized module 'quantization' (most likely due to a circular import) (/ceph-ly/env/torchvi/lib/python3.8/site-packages/quantization/__init__.py)

opened by glynpu 0

Prediction

This branch adds a module which is a convenient way to predict these codebook entries, regressing on something. (In the test code, we regress on the vector itself, that we encoded, but in general it would be something else that we are trying to learn).

This supports also regressing on previous codebook entries in the list of codebooks, so in effect it is jointly predicting the whole set of codebook entries, rather than modeling them independently. We may find that this improves results.

opened by danpovey 0

Torch-based tool for quantizing high-dimensional vectors using additive codebooks

Related tags

Overview

Trainable multi-codebook quantization

Usage

Installation

Example

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Prediction

Owner

Daniel Povey

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