Summary of Normalization Layer Per-example Gradients Are Sufficient to Predict Gradient Noise Scale in Transformers, by Gavia Gray and Aman Tiwari and Shane Bergsma and Joel Hestness
Normalization Layer Per-Example Gradients are Sufficient to Predict Gradient Noise Scale in Transformers
by Gavia Gray, Aman Tiwari, Shane Bergsma, Joel Hestness
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 A novel method for estimating gradient noise scale (GNS) with minimal variance is proposed, enabling accurate observation of GNS in various layers of transformer models. By simultaneously computing per-example gradient norms and parameter gradients, the FLOPs required are minimized, particularly in 3D or greater tensor regimes. The GNS of normalization layers is found to be a strong predictor of the total GNS of contemporary transformer models. A custom kernel is developed to compute per-example gradient norms during the LayerNorm backward pass with zero throughput overhead. This approach enables a practical batch size schedule that reduces training time by 18% on a Chinchilla-optimal language model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to measure how much noise is present in the gradients of transformer models. By doing this, they can help train these models more efficiently and accurately. They achieve this by developing a method that calculates gradient norms while also calculating the gradients themselves, all with minimal extra computation required. This means they can get more accurate measurements without slowing down the training process. |
Keywords
* Artificial intelligence * Language model * Transformer
