Parallel Decoding via Hidden Transfer for Lossless Large Language Model Acceleration

arXiv:2404.12022v1 Announce Type: new
Abstract: Large language models (LLMs) have recently shown remarkable performance across a wide range of tasks. However, the substantial number of parameters in LLMs contributes to significant latency during model inference. This is particularly evident when utilizing autoregressive decoding methods, which generate one token in a single forward process, thereby not fully capitalizing on the parallel computing capabilities of GPUs. In this paper, we propose a novel parallel decoding approach, namely \textit{hidden transfer}, which decodes multiple …

abstract arxiv autoregressive cs.cl decoding generate hidden however inference language language model language models large language large language model large language models latency llms parameters performance tasks token transfer type via