MUX-PLMs：面向高吞吐量语言模型的数据多路复用
MUX-PLMs: Data Multiplexing for High-throughput Language Models
AI总结：
本文提出MUX-PLMs，通过数据多路复用与解复用模块训练高吞吐量预训练语言模型，在广泛任务上仅牺牲1-4%性能即可实现2倍/5倍推理加速，性能媲美普通PLMs。
摘要：
诸如ChatGPT和Bard等大型语言模型的广泛采用，导致对这些技术的需求空前高涨。随着模型规模不断扩大，推理成本急剧攀升，加之硬件短缺，限制了人们以可负担的方式获取这些技术，从而迫切需要面向高吞吐量和高性能的效率优化方法。诸如数据多路复用等多输入多输出（MIMO）算法提供了一种极具前景的解决方案，通过以单次输入的代价执行多个输入的推理，实现吞吐量的数倍增长。然而，这些方法目前的性能尚不足以部署在现代系统中。我们通过开发MUX-PLMs改变了这一现状，这是一类利用数据多路复用技术训练的高吞吐量预训练语言模型（PLMs），可针对任何下游任务进行微调，从而实现高吞吐量与高性能。我们新颖的多路复用与解复用模块能够熟练地纠缠与解纠缠输入，并使高性能、高吞吐量的MUX-PLMs具备与普通PLMs相媲美的竞争力，同时在广泛的一系列任务上仅出现1-4%的性能下降，便实现了2倍/5倍的推理加速。
英文摘要：
The widespread adoption of large language models such as ChatGPT and Bard has led to unprecedented demand for these technologies. The burgeoning cost of inference for ever-increasing model sizes coupled with hardware shortages has limited affordable access and poses a pressing need for efficiency approaches geared towards high throughput and performance. Multi-input multi-output (MIMO) algorithms such as data multiplexing, offer a promising solution with a many-fold increase in throughput by performing inference for multiple inputs at the cost of a single input. Yet these approaches are not currently performant enough to be deployed in modern systems. We change that by developing MUX-PLMs, a class of high throughput pre-trained language models (PLMs) trained with data multiplexing, that can be fine-tuned for any downstream task to yield high-throughput high-performance. Our novel multiplexing and demultiplexing modules proficiently entangle and disentangle inputs, and enable high-performance high throughput \muxplms{} that are competitive with vanilla PLMs while achieving 2x/5x inference speedup with only a $1-4\%$ drop on a broad suite of tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
