Mixed-Precision Inference Effects on Long-Context Model Efficiency-Accuracy Trade-Offs

Abstract

Abstract: With many real-world applications of Natural Language Processing (NLP) comprising of long texts, there has been a rise in NLP benchmarks that measure the accuracy of models that can handle longer input sequences. However, these benchmarks do not consider the trade-offs between accuracy, speed, and power consumption as input sizes or model sizes are varied. In this work, we perform a systematic study of this accuracy vs. efficiency trade-off on two widely used long-sequence models - Longformer-Encoder-Decoder (LED) and Big Bird - during fine-tuning and inference on four datasets from the SCROLL

Research Question

What is the impact of mixed-precision inference (e.g., FP16 vs. BF16) on the efficiency-accuracy trade-off for long-context models like Longformer-En on tasks requiring domain-specific knowledge versus general knowledge?

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