用预判式解码提速大模型推理，最多快4倍
Tutorial Proposal: Speculative Decoding for Efficient LLM Inference
- 先预估多个未来词元，再并行验证，打破逐词生成瓶颈
- 实测可实现2~4倍加速，且保持原输出分布不变
- 适合想提升大模型推理效率的研究者与工程师
本教程系统介绍推测解码（Speculative Decoding, SD）这一先进大模型推理加速技术。SD作为应对大模型自回归解码高延迟问题的创新范式，在每一步解码中高效预拟多个未来词元，并行验证其正确性。该方法突破传统逐词生成限制，可在单步内完成多词元解码，实现2至4倍的推理加速，同时保持原始输出分布一致。教程深入探讨了当前SD的最新进展，包括草稿模型架构与验证策略，并展望其加速潜力与未来研究方向。旨在帮助研究人员全面了解该领域现状，推动更高效的大型语言模型推理技术发展。
原文摘要 · Abstract (English)
This tutorial presents a comprehensive introduction to Speculative Decoding (SD), an advanced technique for LLM inference acceleration that has garnered significant research interest in recent years. SD is introduced as an innovative decoding paradigm to mitigate the high inference latency stemming from autoregressive decoding in LLMs. At each decoding step, SD efficiently drafts several future tokens and then verifies them in parallel. This approach, unlike traditional autoregressive decoding, facilitates the simultaneous decoding of multiple tokens per step, thereby achieving promising 2x-4x speedups in LLM inference while maintaining original distributions. This tutorial delves into the latest techniques in SD, including draft model architectures and verification strategies. Additionally, it explores the acceleration potential and future research directions in this promising field. We aim for this tutorial to elucidate the current research landscape and offer insights for researchers interested in Speculative Decoding, ultimately contributing to more efficient LLM inference.
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