The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Quantizing Transformers by Helping Attention Heads Do Nothing with Markus Nagel - #663
Dec 26, 2023
In this discussion, Markus Nagel, a research scientist at Qualcomm AI Research, shares insights from his recent papers at NeurIPS 2023, focusing on machine learning efficiency. He tackles the challenges of quantizing transformers, particularly in minimizing outlier issues in attention mechanisms. The conversation explores the pros and cons of pruning versus quantization for model weight compression and dives into innovative methods for multitask and multidomain learning. Additionally, the use of geometric algebra in enhancing algorithms for robotics is highlighted.
46:49
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Quick takeaways
- Quantizable Transformers address activation quantization issues introduced by attention mechanism.
- Comparing the effectiveness of pruning and quantization methods for model weight compression.
Deep dives
Stable Diffusion: World's Fastest Diffusion Model on Mobile Devices
Qualcomm showcased a demo of stable diffusion, now running in under one second, making it the world's fastest diffusion model on mobile devices. This was achieved through full-stack AI optimizations, including model efficiency techniques such as quantization and knowledge distillation. Multi-stage knowledge distillation, efficient unit pruning, and guidance distillation were introduced to significantly improve the speed of stable diffusion. These optimizations reduced the compute and model size, streamlined the diffusion steps, and improved the overall performance.
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