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REST: Retrieval-Based Speculative Decoding

About

We introduce Retrieval-Based Speculative Decoding (REST), a novel algorithm designed to speed up language model generation. The key insight driving the development of REST is the observation that the process of text generation often includes certain common phases and patterns. Unlike previous methods that rely on a draft language model for speculative decoding, REST harnesses the power of retrieval to generate draft tokens. This method draws from the reservoir of existing knowledge, retrieving and employing relevant tokens based on the current context. Its plug-and-play nature allows for seamless integration and acceleration of any language models, all without necessitating additional training. When benchmarked on 7B and 13B language models in a single-batch setting, REST achieves a significant speedup of 1.62X to 2.36X on code or text generation. The code of REST is available at https://github.com/FasterDecoding/REST.

Zhenyu He, Zexuan Zhong, Tianle Cai, Jason D. Lee, Di He• 2023

Related benchmarks

TaskDatasetResultRank
Code GenerationHumanEval (test)--
612
Code GenerationHumanEval
Speedup Factor1.84
147
Inference EfficiencyHumanEval
Speedup Factor2.14
90
Multi-turn dialogueMT-Bench
Speedup1.11
80
Code GenerationMBPP
Speedup1.11
79
LLM Inference AccelerationGSM8K
Speedup2.07
61
Mathematical ReasoningGSM8K
Average Length1.5192
61
Speculative DecodingSpec-Bench--
57
Inference AccelerationSpec-Bench
Speedup1.38
53
Instruction FollowingAlpaca
Average Accepted Length1.8594
51
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