Course Description

In this advanced course, titled "Quantization in Depth", you will delve deeper into the intricacies of model quantization. This comprehensive course provides hands-on experience in implementing and customizing linear quantization methodologies. You will explore different quantization modes and granularities with Pytorch tools, aiming to achieve up to 4x compression on dense layers of any open source model. Furthermore, you'll experiment with techniques such as weights packing to enhance model efficiency and performance during inference.

What you will learn

• Understand and implement various linear quantization techniques, including symmetric and asymmetric modes.
• Customize quantization granularity with options like per-tensor, per-channel, and per-group settings.
• Quantize dense layers of open source models in PyTorch from 32 bits to smaller bit representations like 8 bits and 2 bits.
• Pack multiple lower-bit weights into single integers to achieve effective model compression.
• Measure and evaluate the quantization error and balance the trade-offs between model size reduction and performance.

Prerequisites

• Basic understanding of machine learning models and neural network architecture.
• Familiarity with Python programming and PyTorch framework.
• Previous experience with basic quantization methods is beneficial, ideally having completed a foundational course like "Quantization Fundamentals with Hugging Face".

Course Coverage

• Introduction to linear quantization: symmetric vs. asymmetric modes.
• Differentiating between per-tensor, per-channel, and per-group quantization techniques.
• Building a custom quantizer in PyTorch to handle dense layers.
• Advanced weight packing methods to reduce data storage requirements.
• Hands-on projects and case studies using real-world datasets and models.

Who this course is for

This course is designed for data scientists, AI researchers, and machine learning engineers who have a foundational knowledge of quantization processes and are looking to deepen their expertise in model optimization. Accommodating learners aiming for proficiency in model compression and efficiency, this course serves as a perfect progression for those familiar with basic concepts introduced in earlier quantization courses.

Application of Skills in the Real World

• Optimize machine learning models for better performance in resource-constrained environments such as mobile devices and embedded systems.
• Reduce computational costs and increase the speed of model inference in production environments.
• Enhance the accessibility of advanced machine learning models by reducing their operational requirements.
• Contribute to eco-friendly AI development by curbing the energy consumption associated with running larger models.