SDSAT: Accelerating LLM Inference through Speculative Decoding with Semantic Adaptive Tokens

• URL: http://arxiv.org/abs/2403.18647v2
• Date: Mon, 1 Apr 2024 09:48:46 GMT
• Title: SDSAT: Accelerating LLM Inference through Speculative Decoding with Semantic Adaptive Tokens
• Authors: Chengbo Liu, Yong Zhu,
• Abstract summary: We propose an acceleration scheme for large language models (LLMs) through Speculative Decoding with Semantic Adaptive Tokens (SDSAT) The primary objective of this design is to enhance the LLM model's ability to generate draft tokens more accurately without compromising its accuracy. Experiments conducted on the CodeLlama-13B and 7B models have yielded speed increases of over 3.5X and 3.0X, respectively.
• Score: 4.5888031410244885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose an acceleration scheme for large language models (LLMs) through Speculative Decoding with Semantic Adaptive Tokens (SDSAT). The primary objective of this design is to enhance the LLM model's ability to generate draft tokens more accurately without compromising the model's accuracy. The core strategies involve: 1) Fine-tune the model by incorporating semantic adaptive tokens that possess flexible decoding capabilities without changing its structure, allowing them to generate high-quality draft tokens. 2) By employing a training method that does not affect the standard tokens, the model can acquire parallel decoding abilities atop its original framework with minimal training overhead. 3) We have designed the "two-step-draft-then-verify" generation strategies using both greedy search and nucleus sampling. Experiments conducted on the CodeLlama-13B and 7B models have yielded speed increases of over 3.5X and 3.0X, respectively. Please refer to https://github.com/hasuoshenyun/SDSAT.

Related papers

• GRIFFIN: Effective Token Alignment for Faster Speculative Decoding [52.905060461479856]
  GRIFFIN is a framework that incorporates a token-alignable training strategy and a token-alignable draft model. Experiments on LLaMA-series and Vicuna models demonstrate that GRIFFIN achieves an average acceptance length improvement of over 7% and a speedup ratio exceeding 8%.
  arXiv  Detail & Related papers  (2025-02-16T07:06:00Z)
• Accelerating LLM Inference with Lossless Speculative Decoding Algorithms for Heterogeneous Vocabularies [10.971976066073442]
  Speculative decoding (SD) methods offer substantial efficiency gains by generating multiple tokens using a single target forward pass. Existing SD approaches require the drafter and target models to share the same vocabulary, thus limiting the pool of possible drafters. We present three new SD methods that remove this shared-vocabulary constraint.
  arXiv  Detail & Related papers  (2025-01-31T19:13:58Z)
• Falcon: Faster and Parallel Inference of Large Language Models through Enhanced Semi-Autoregressive Drafting and Custom-Designed Decoding Tree [7.438117410146904]
  Falcon is an innovative speculative decoding framework fashioned to augment both the drafter's parallelism and output quality. Falcon incorporates the Coupled Sequential Glancing Distillation technique, which fortifies inter-token dependencies within the same block, leading to increased speculation accuracy.
  arXiv  Detail & Related papers  (2024-12-17T08:02:08Z)
• FastDraft: How to Train Your Draft [0.7499722271664144]
  We introduce FastDraft, a novel and efficient approach for pre-training and aligning a draft model to any large language model. We demonstrate FastDraft by training two highly parameter efficient drafts for the popular Phi-3-mini and Llama-3.1-8B models. Using FastDraft, we were able to produce a draft with approximately 10 billion tokens on a single server with 8 Intel$circledR$ Gaudi$circledR$ 2 accelerators in under 24 hours.
  arXiv  Detail & Related papers  (2024-11-17T12:32:44Z)
• ParallelSpec: Parallel Drafter for Efficient Speculative Decoding [62.68430939686566]
  We present ParallelSpec, an alternative to auto-regressive drafting strategies in state-of-the-art speculative decoding approaches. In contrast to auto-regressive drafting in the speculative stage, we train a parallel drafter to serve as an efficient speculative model.
  arXiv  Detail & Related papers  (2024-10-08T01:05:08Z)
• Turning Trash into Treasure: Accelerating Inference of Large Language Models with Token Recycling [53.58854856174773]
  Speculative decoding is an approach to accelerate inference through a guess-and-verify paradigm. Token Recycling stores candidate tokens in an adjacency matrix and employs a breadth-first search algorithm. It significantly outperforms existing train-free methods by 30% and even a training method by 25%.
  arXiv  Detail & Related papers  (2024-08-16T12:20:56Z)
• PEARL: Parallel Speculative Decoding with Adaptive Draft Length [12.166703341906242]
  We propose a conceptually simple, flexible, and general framework to boost speculative decoding, namely Parallel spEculative decoding with Adaptive dRaft Length (PEARL) PEARL proposes pre-verify to verify the first draft token in advance during the drafting phase, and post-verify to generate more draft tokens during the verification phase. Experiments on various text generation benchmarks demonstrate the effectiveness of our PEARL, leading to a superior speed up performance up to 4.43$times$ and 1.50$times$, compared to auto-regressive decoding and vanilla speculative decoding, respectively.
  arXiv  Detail & Related papers  (2024-08-13T08:32:06Z)
• Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines [74.42485647685272]
  We focus on Generative Masked Language Models (GMLMs) We train a model to fit conditional probabilities of the data distribution via masking, which are subsequently used as inputs to a Markov Chain to draw samples from the model. We adapt the T5 model for iteratively-refined parallel decoding, achieving 2-3x speedup in machine translation with minimal sacrifice in quality.
  arXiv  Detail & Related papers  (2024-07-22T18:00:00Z)
• Decoding at the Speed of Thought: Harnessing Parallel Decoding of Lexical Units for LLMs [57.27982780697922]
  Large language models have demonstrated exceptional capability in natural language understanding and generation. However, their generation speed is limited by the inherently sequential nature of their decoding process. This paper introduces Lexical Unit Decoding, a novel decoding methodology implemented in a data-driven manner.
  arXiv  Detail & Related papers  (2024-05-24T04:35:13Z)
• Ouroboros: Generating Longer Drafts Phrase by Phrase for Faster Speculative Decoding [65.94521678103237]
  Speculative decoding is a widely used method that accelerates the generation process of large language models. We introduce Ouroboros, which can generate draft phrases to parallelize the drafting process. Ouroboros can achieve speedups of up to $2.8times$ over speculative decoding and $3.9times$ over vanilla decoding.
  arXiv  Detail & Related papers  (2024-02-21T11:31:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.