Scaling Laws for Precision

Low precision training and inference affect both the quality and cost of language models, but current scaling laws do not account for this. In this work, we devise “precision-aware” scaling laws for both training and inference. We propose that training in lower precision reduces the model’s effective parameter count, allowing us to predict the additional loss incurred from training in low precision and post-train quantization. For inference, we find that the degradation introduced by post-training quantization increases as models are trained on more data, eventually making additional pretraining data actively harmful. For training, our scaling laws allow us to predict the loss of a model with different parts in different precisions, and suggest that training larger models in lower precision may be compute optimal. We unify the scaling laws for post and pretraining quantization to arrive at a single functional form that predicts degradation from training and inference in varied precisions. We fit on over 465 pretraining runs and validate our predictions on model sizes up to 1.7B parameters trained on up to 26B tokens.

Focus: Methods or Design
Source: arXiv
Readability: Expert
Type: PDF Article
Open Source: Yes
Keywords: N/A
Learn Tags: AI and Machine Learning Design/Methods Bias Ethics Fairness
Summary: Low precision training and inference affect both the quality and cost of language models, but current scaling laws do not account for this. This paper offers “precision-aware” scaling laws for both training and inference.