Automated Quantization of Neural Networks

Bachelor thesis - Miroslav Šafář ([email protected])

File structure

• src/ - source code of proposed system
• karolina/ - scripts used for running experiments on Karolina supercomputer
• nsga_runs/ - folder that contains system runs referred to in the paper
• latex/ - source code of the paper
• automated_quantization_of_neural_networks_paper.pdf - bachelor thesis paper
• README.md - README
• tiny_imagenet100.tar - dataset used for experiments
• environment_linux_x86.yml - conda environment file for Linux
• environment_macos_arm64.yml - conda environment file for macOS with Apple Silicon

Project Setup

Preferred: Conda

We provide conda environment file to setup conda environment.

On Apple Silicon computers:

$ conda env create --file environment_macos_arm64.yml

On Linux:

$ conda env create --file environment_linux_x86.yml

WARNING: Linux environment does not contains libraries for GPU acceleration.

Then you can activate created conda environment by using:

$ conda activate bachelor_thesis

PIP

There is another option to setup the project environment using pip. We recommend using Python 3.10.9 and a virtual environment. You can install all required packages using pip:

$ pip install -r requirements_macos_arm64.txt

WARNING: You can use this option only with Apple Silicon computer. With linux please use conda environment.

Tinyimagenet dataset

For testing purposes, we provide our tiny-imagenet dataset. To use it you need to extract tiny_imagenet100.tar into your TensorFlow Datamodels folder (default: ~/tensorflow_datasets).

Create pre-trained Mobilenet model

Switch to the source directory:

$ cd src

To create and train MobileNet model on tiny-imagenet dataset use:

$ python3 mobilenet_tinyimagenet_train.py --alpha 0.25 --save-as mobilenet_tinyimagenet_025.keras

If you already have a weights file for a model, you can create it using:

$ python3 mobilenet_tinyimagenet_create.py --alpha 0.25 --weights-path weights_025.hfd5 --destination mobilenet_tinyimagenet_025.keras

Run NSGA-II

Switch to the source directory:

$ cd src

To run NSGA-II for per-layer asymmetric quantization with the approximate solution for batch normalization folding use:

$ python3 run_nsga.py --generations 20 --parent-size 16 --offspring-size 16 --logs-dir <nsga_run_log_dir> --approx

By default, it uses pre-trained MobileNet model saved as mobilenet_tinyimagenet_025.keras, to use different pre-trained model, specify parameter --base-model-path.

Other important parameters:

• --per-channel use per-channel weight quantization for convolutional layers
• --symmetric use symmetric quantization for weights
• --batch-size batch size for quantization-aware training
• --epochs number of epochs for partial tuning of models
• --multigpu run on multiple gpus
• --help to print list of all script parameters

Then evaluate the final results with full fine-tuning of the quantized models using:

For evaluation of per-layer asymmetric quantization using more accurate method for batch normalization folding use:

python3 nsga_evaluate.py --run <nsga_run_log_dir>/<run_file>

For evaluation of per-layer asymmetric quantization using approximate method for batch normalization folding use:

python3 nsga_evaluate.py --run <nsga_run_log_dir>/<run_file> --approx

For evaluation of per-channel symmetric quantization use:

python3 nsga_evaluate.py --run <nsga_run_log_dir> --approx --per-channel --symmetric

Other important parameters:

• --batch-size batch size for quantization-aware training
• --epochs number of epochs for final fine-tuning
• --multigpu run on multiple gpus
• --help to print a list of all script parameters

Visualization of results

To view system results use:

python3 show_layer_configuration.py --run <nsga_run_log_dir> [--per-channel] [--symmetric]

This script allows you to choose between best-found configurations and then shows you the bit-width for each layer.