EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty

• URL: http://arxiv.org/abs/2401.15077v2
• Date: Sun, 4 Feb 2024 17:18:34 GMT
• Title: EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty
• Authors: Yuhui Li, Fangyun Wei, Chao Zhang, Hongyang Zhang
• Abstract summary: Autoregression at the feature (second-to-top-layer) level is more straightforward than at the token level. The inherent uncertainty in feature (second-to-top-layer) level autoregression constrains its performance.
• Score: 28.07947754770082
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autoregressive decoding makes the inference of Large Language Models (LLMs) time-consuming. In this paper, we reconsider speculative sampling and derive two key observations. Firstly, autoregression at the feature (second-to-top-layer) level is more straightforward than at the token level. Secondly, the inherent uncertainty in feature (second-to-top-layer) level autoregression constrains its performance. Based on these insights, we introduce EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency), a simple yet highly efficient speculative sampling framework. By incorporating a token sequence advanced by one time step, EAGLE effectively resolves the uncertainty, enabling precise second-to-top-layer feature prediction with minimal overhead. We conducted comprehensive evaluations of EAGLE, including all models from the Vicuna and LLaMA2-Chat series, the MoE model Mixtral 8x7B Instruct, and tasks in dialogue, code generation, mathematical reasoning, and instruction following. For LLaMA2-Chat 70B, EAGLE achieved a latency speedup ratio of 2.7x-3.5x, doubled throughput, while maintaining the distribution of the generated text.

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