Quantization explained with PyTorch - Post-Training Quantization, Quantization-Aware Training

In this video I will introduce and explain quantization: we will first start with a little introduction on numerical representation of integers and floating-point numbers in computers, then see what is quantization and how it works. I will explore topics like Asymmetric and Symmetric Quantization, Quantization Range, Quantization Granularity, Dynamic and Static Quantization, Post-Training Quantization and Quantization-Aware Training.

Code: https://github.com/hkproj/quantization-notes
PDF slides: https://github.com/hkproj/quantization-notes

Chapters
00:00 - Introduction
01:10 - What is quantization?
03:42 - Integer representation
07:25 - Floating-point representation
09:16 - Quantization (details)
13:50 - Asymmetric vs Symmetric Quantization
15:38 - Asymmetric Quantization
18:34 - Symmetric Quantization
20:57 - Asymmetric vs Symmetric Quantization (Python Code)
24:16 - Dynamic Quantization & Calibration
27:57 - Multiply-Accumulate Block
30:05 - Range selection strategies
34:40 - Quantization granularity
35:49 - Post-Training Quantization
43:05 - Training-Aware Quantization

Видео Quantization explained with PyTorch - Post-Training Quantization, Quantization-Aware Training канала Umar Jamil