Summary of Transformer Tricks: Precomputing the First Layer, by Nils Graef
Transformer tricks: Precomputing the first layer
by Nils Graef
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This micro-paper presents a technique to accelerate inference in transformers with RoPE, such as LLaMA, Mistral, PaLM, and Gemma. By precomputing a significant portion of the first transformer layer, latency and cost-per-token are slightly reduced. The relative savings depend on the total number of layers; for instance, a 4-layer model like Whisper tiny can achieve up to 25% maximum savings, whereas a 32-layer model is limited to 3%. This optimization only affects one layer, making it suitable for models with multiple layers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a way to make popular AI models like LLaMA and PaLM work faster. It’s like pre-cooking part of your meal so that when you’re hungry again, it takes less time to eat! They figured out how to do some calculations beforehand in the first layer of these transformer models, which makes them run a bit quicker and use fewer computer resources. The amount of time saved depends on how many layers are used in the model. |
Keywords
* Artificial intelligence * Inference * Llama * Optimization * Palm * Token * Transformer
