Quantization in Depth from Coursera

In Quantization in Depth you will build model quantization methods to shrink model weights to ¼ their original size, and apply methods to maintain the compressed model’s performance. Your ability to quantize your models can make them more accessible, and also faster at inference time.

Implement and customize linear quantization from scratch so that you can study the tradeoff between space and performance, and then build a general-purpose quantizer in PyTorch that can quantize any open source model. You’ll implement techniques to compress model weights from 32 bits to 8 bits and even 2 bits.

Join this course to:

1. Build and customize linear quantization functions, choosing between two “modes”: asymmetric and symmetric; and three granularities: per-tensor, per-channel, and per-group quantization.

2. Measure the quantization error of each of these options as you balance the performance and space tradeoffs for each option.

3. Build your own quantizer in PyTorch, to quantize any open source model’s dense layers from 32 bits to 8 bits.

4. Go beyond 8 bits, and pack four 2-bit weights into one 8-bit integer.

Quantization in Depth lets you build and customize your own linear quantizer from scratch, going beyond standard open source libraries such as PyTorch and Quanto, which are covered in the short course Quantization Fundamentals, also by Hugging Face.

This course gives you the foundation to study more advanced quantization methods, some of which are recommended at the end of the course.

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Focuses on techniques for model efficiency to minimize cost

Teaches advanced topics in machine learning, such as quantization and lossy compression

Taught by industry professionals with expertise in deep learning optimization

Practical skills and knowledge applicable to real-world ML projects

Reviews summary

Build custom quantizers with pytorch

According to students, this course provides an in-depth exploration of model quantization, focusing on building custom quantizers from scratch in PyTorch. Many highlight the practical, hands-on implementation of techniques to shrink model weights and maintain performance. Learners appreciate the detailed coverage of concepts like asymmetric and symmetric modes and various granularities. While the course is praised for its rigor and ability to equip professionals with valuable skills for model deployment, a notable portion found it challenging, indicating that prerequisites are higher than expected, especially for those without prior advanced ML or strong mathematical foundations. Overall, it's considered a highly valuable resource for those seeking to master quantization beyond standard libraries.

Concepts are generally well-explained, but some advanced sections can be dense.

"The explanations of asymmetric vs. symmetric modes were crystal clear. Highly recommend for ML engineers!"

"Some parts, especially the 2-bit packing, were quite challenging and required rewatching lectures, but the effort was worth it."

"Some explanations were a bit dense, and I had to pause frequently to research external materials."

Logically organized progression, though pacing may be quick for some learners.

"The course is well-structured and the examples are practical."

"The course flow was excellent, each section built well on the previous one... well-paced for someone with a solid ML background."

"While excellent, I think the course assumes more prior knowledge than 'Quantization Fundamentals' provides. Labs were okay, but sometimes the explanations felt too fast."

Equips learners with highly relevant skills for optimizing and deploying ML models.

"Absolutely invaluable for my work! I now feel confident in applying quantization techniques to shrink my models for faster inference."

"The techniques taught here are directly applicable to my work in optimizing large language models for production."

"I can immediately apply these quantization methods to make my models faster and more accessible."

Offers profound insights and practical experience in building custom quantizers.

"This course is a masterpiece for anyone serious about model deployment optimization. Building the PyTorch quantizer from scratch was incredibly rewarding."

"Absolutely invaluable for my work! The focus on implementing from scratch rather than just using libraries really gives you a deep understanding."

"Hands-on, deep, and incredibly relevant. This course taught me not just how to use quantization, but how it actually works under the hood."

Requires a solid background in deep learning, math, and PyTorch for optimal learning.

"The content is good and 'in depth' as promised, but the prerequisites were understated. I struggled a lot without a stronger background in linear algebra."

"Found this course extremely difficult. I spent more time debugging than understanding. This feels more for researchers or highly specialized engineers."

"Good course if you already have a very strong foundation. It's not for those just dabbling. The 2-bit section was particularly opaque."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Quantization in Depth with these activities:

Review Python Basics

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Reinforce your understanding of Python basics, which will provide a solid foundation for the course.

Browse courses on Python Basics

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Review Python data types, operators, and control flow.
Practice writing and running simple Python programs.
Review object-oriented programming concepts in Python.

Linear Quantization Exercises

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Enhance your understanding of linear quantization by completing a series of exercises.

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Implement linear quantization functions with different modes and granularities.
Measure the quantization error of each option.
Analyze the performance and space trade-offs for each option.

Custom PyTorch Quantizer

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Deepen your understanding of quantization by building a custom PyTorch quantizer.

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Design and implement a general-purpose quantizer in PyTorch.
Quantize a specific open source model using your custom quantizer.
Validate the accuracy and performance of the quantized model.

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Quantization Blog Post

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Solidify your understanding of quantization by writing a blog post explaining the concepts.

Browse courses on Quantization

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Research and summarize the key principles of quantization.
Discuss the benefits and limitations of quantization.
Share your insights and learnings with others.

Career center

Learners who complete Quantization in Depth will develop knowledge and skills that may be useful to these careers:

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Quantization in Depth

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Build custom quantizers with pytorch

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