We present LayerSkip, an end-to-end solution to speed-up inference of large
language models (LLMs). First, during training we apply layer dropout, with low
dropout rates for earlier layers and higher dropout rates for later layers, and
an early exit loss where all transformer layers share the same exit. Second,
during inference, we show that this training recipe increases the accuracy of
early exit at earlier layers, without adding any auxiliary layers or modules to
the model. Third, we present a novel self-speculative decoding solution where
we exit at early layers and verify and correct with remaining layers of the
model. Our proposed self-speculative decoding approach has less memory
footprint than other speculative decoding approaches and benefits from shared
compute and activations of the draft and verification stages. We run
experiments on different Llama model sizes on different types of training:
pretraining from scratch, continual pretraining, finetuning on specific data
domain, and finetuning on specific task. We implement our inference solution
and show speedups of up to 2.16x on summarization for CNN/DM documents, 1.82x
on coding, and 2.0x on TOPv2 semantic parsing task.

通过应用层丢弃和早期退出损失的训练技术，在推理过程中加快大型语言模型的速度，并推出了一种新颖的自我推测编码解决方案，该解决方案减少了内存占用，并在不同训练任务上实现了高达 2.16 倍的加速。