Model Pruning Example in PyTorch

This repository contains an example of model pruning using PyTorch. It demonstrates how to perform depth and width pruning on a simple neural network model, SimpleNet, by creating two pruned versions: DepthPrunedNet and WidthPrunedNet.

Table of Contents

• Introduction
• Pruning Techniques
  • Depth Pruning
  • Width Pruning
• Real-World Benefits
• Code Overview
• Installation

Introduction

Pruning is a technique used to reduce the size of a neural network by removing certain parts of it without significantly compromising its performance. This can lead to a more efficient model in terms of computation, memory usage, and inference speed.

In this example, we explore two types of pruning:

• Depth Pruning: Reducing the number of layers.
• Width Pruning: Reducing the number of neurons within a layer.

Pruning Techniques

Depth Pruning

Depth pruning involves removing entire layers from the neural network. In the provided code, DepthPrunedNet is a pruned version of SimpleNet where one of the hidden layers (layer3) has been removed. This reduces the model's depth, potentially decreasing the model's computational complexity and memory usage.

Width Pruning

Width pruning refers to reducing the number of neurons within a layer. In the WidthPrunedNet example, the number of neurons in the second layer has been reduced from 128 to 64. This reduces the model's parameter count, potentially leading to a faster and less resource-intensive model.

Real-World Benefits

Pruning techniques like depth and width pruning offer several benefits in real-world applications:

• Reduced Model Size: Pruned models require less storage space, which is crucial for deployment on devices with limited memory.
• Faster Inference: Smaller models generally lead to faster inference times, which is essential in real-time applications.
• Lower Energy Consumption: Reducing the computational load translates to lower energy usage, which is beneficial for mobile and embedded devices.
• Maintainable Performance: With careful pruning, the performance loss can be minimal, allowing for more efficient models without significant accuracy drops.

Code Overview

The code defines three neural network models:

• SimpleNet: The base model with four linear layers.
• DepthPrunedNet: A pruned version of SimpleNet with one less layer.
• WidthPrunedNet: A pruned version of SimpleNet with fewer neurons in the second layer.

Each model is initialized and summarized using the torchsummary library, and the summaries are logged for inspection.

Key Files

• prune.py: Contains the model definitions, logging setup, and the main function that initializes and summarizes the models.
• environment.yml: Defines the conda environment with all necessary dependencies.

Installation

To set up the environment and install the required dependencies, follow these steps:

1. Create the Conda Environment and run it:

   conda env create -f environment.yml
   Activate the Environment:
   conda activate myenv
   Run the prune.py

Conclusion Pruning is a powerful tool for optimizing neural networks, making them more suitable for real-world deployment where resources are limited. This example provides a starting point for implementing and experimenting with pruning techniques in your models.