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.
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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.
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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Activities
Review Python Basics
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Reinforce your understanding of Python basics, which will provide a solid foundation for the course.
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Python Basics
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Review Python data types, operators, and control flow.
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Practice writing and running simple Python programs.
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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.
Show steps
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Implement linear quantization functions with different modes and granularities.
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Measure the quantization error of each option.
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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.
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Quantize a specific open source model using your custom quantizer.
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Validate the accuracy and performance of the quantized model.
One other activity
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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.
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Discuss the benefits and limitations of quantization.
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Share your insights and learnings with others.
Review Python Basics
Show steps
Reinforce your understanding of Python basics, which will provide a solid foundation for the course.
Browse courses on
Python Basics
Show steps
- 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
Show steps
Enhance your understanding of linear quantization by completing a series of exercises.
Show steps
- 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
Show steps
Deepen your understanding of quantization by building a custom PyTorch quantizer.
Show steps
- 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.
Quantization Blog Post
Show steps
Solidify your understanding of quantization by writing a blog post explaining the concepts.
Browse courses on
Quantization
Show steps
- Research and summarize the key principles of quantization.
- Discuss the benefits and limitations of quantization.
- Share your insights and learnings with others.
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