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We present EE-LLM, a framework for large-scale training and inference ofearly-exit large language models (LLMs). While recent works have shownpreliminary evidence for the efficacy of early exiting in accelerating LLMinference, EE-LLM makes a foundational step towards scaling up early-exit LLMsby supporting their training and inference with massive 3D parallelism. Builtupon Megatron-LM, EE-LLM implements a variety of algorithmic innovations andperformance optimizations tailored to early exiting, including a lightweightmethod that facilitates backpropagation for the early-exit training objectivewith pipeline parallelism, techniques of leveraging idle resources in theoriginal pipeline schedule for computation related to early-exit layers, andtwo approaches of early-exit inference that are compatible with KV caching forautoregressive generation. Our analytical and empirical study shows that EE-LLMachieves great training efficiency with negligible computational overheadcompared to standard LLM training, as well as outstanding inference speedupwithout compromising output quality. To facilitate further research andadoption, we release EE-LLM at https://github.com/pan-x-c/EE-LLM.

EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism