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 quant…
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Chapters (15)
Introduction
1:10
What is quantization?
3:42
Integer representation
7:25
Floating-point representation
9:16
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
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