Unit Scaling: Out-of-the-Box Low-Precision Training

• URL: http://arxiv.org/abs/2303.11257v2
• Date: Tue, 30 May 2023 22:05:40 GMT
• Title: Unit Scaling: Out-of-the-Box Low-Precision Training
• Authors: Charlie Blake, Douglas Orr, Carlo Luschi
• Abstract summary: Unit scaling is a paradigm for designing deep learning models that simplifies the use of low-precision number formats. Training in FP16 or the recently proposed FP8 formats offers substantial efficiency gains, but can lack sufficient range for out-of-the-box training. Unit scaling addresses this by introducing a principled approach to model numerics: seeking unit variance of all weights, activations and gradients at initialisation.
• Score: 1.7188280334580197
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We present unit scaling, a paradigm for designing deep learning models that simplifies the use of low-precision number formats. Training in FP16 or the recently proposed FP8 formats offers substantial efficiency gains, but can lack sufficient range for out-of-the-box training. Unit scaling addresses this by introducing a principled approach to model numerics: seeking unit variance of all weights, activations and gradients at initialisation. Unlike alternative methods, this approach neither requires multiple training runs to find a suitable scale nor has significant computational overhead. We demonstrate the efficacy of unit scaling across a range of models and optimisers. We further show that existing models can be adapted to be unit-scaled, training BERT-Large in FP16 and then FP8 with no degradation in accuracy.

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