Doubling Neural Network Finetuning Efficiency with 16-bit Precision Techniques

A PyTorch vision transformer fine-tuning example demonstrates how Lightning Fabric can add advanced training options with minimal code changes while preserving the baseline’s performance of 94.06% test accuracy, 16.88 minutes of training time, and 16.70 GB of GPU memory on a single A100 GPU. Enabling 16-bit mixed precision through Fabric reduces training time by more than 30% and memory use by over 25% without reducing accuracy, because it combines fast low-precision computations with 32-bit operations for stability. Full FP16 training can produce NaN losses due to its limited numerical range, whereas full bfloat16 training offers a wider range and, in these experiments, maintained accuracy while roughly halving both training time and peak memory consumption relative to standard 32-bit training. The techniques are presented as broadly applicable to other architectures, although performance and accuracy should be verified for each model and bfloat16 requires compatible hardware such as NVIDIA A100-class GPUs.

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