Speeding up Speculative Decoding via Approximate Verification

• URL: http://arxiv.org/abs/2502.04557v1
• Date: Thu, 06 Feb 2025 23:10:53 GMT
• Title: Speeding up Speculative Decoding via Approximate Verification
• Authors: Meiyu Zhong, Noel Teku, Ravi Tandon,
• Abstract summary: Speculative Decoding (SD) is a recently proposed technique for faster inference using Large Language Models (LLMs) We propose SPRINTER, which utilizes a low-complexity verifier trained to predict if tokens generated from a draft LLM would be accepted by the target LLM. We present a theoretical analysis of SPRINTER, examining the statistical properties of the generated tokens, as well as the expected reduction in latency.
• Score: 7.754712828900729
• License:
• Abstract: Speculative Decoding (SD) is a recently proposed technique for faster inference using Large Language Models (LLMs). SD operates by using a smaller draft LLM for autoregressively generating a sequence of tokens and a larger target LLM for parallel verification to ensure statistical consistency. However, periodic parallel calls to the target LLM for verification prevent SD from achieving even lower latencies. We propose SPRINTER, which utilizes a low-complexity verifier trained to predict if tokens generated from a draft LLM would be accepted by the target LLM. By performing approximate sequential verification, SPRINTER does not require verification by the target LLM and is only invoked when a token is deemed unacceptable. This leads to reducing the number of calls to the larger LLM and can achieve further speedups. We present a theoretical analysis of SPRINTER, examining the statistical properties of the generated tokens, as well as the expected reduction in latency as a function of the verifier. We evaluate SPRINTER on several datasets and model pairs, demonstrating that approximate verification can still maintain high quality generation while further reducing latency. For instance, on Wiki-Summaries dataset, SPRINTER achieves a 1.7x latency speedup and requires 8.3x fewer flops relative to SD, while still generating high-quality responses when using GPT2-Small and GPT2-XL as draft/target models.

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