LANTERN++: Enhanced Relaxed Speculative Decoding with Static Tree Drafting for Visual Auto-regressive Models

• URL: http://arxiv.org/abs/2502.06352v1
• Date: Mon, 10 Feb 2025 11:05:18 GMT
• Title: LANTERN++: Enhanced Relaxed Speculative Decoding with Static Tree Drafting for Visual Auto-regressive Models
• Authors: Sihwan Park, Doohyuk Jang, Sungyub Kim, Souvik Kundu, Eunho Yang,
• Abstract summary: LANTERN++ is a novel framework that integrates static tree drafting with a relaxed acceptance condition, allowing drafts to be selected independently of low-confidence predictions. Experiments on state-of-the-art visual AR models demonstrate that LANTERN++ significantly accelerates inference, achieving up to $mathbftimes 2.56$ speedup over standard AR decoding.
• Score: 31.1717739483817
• License:
• Abstract: Speculative decoding has been widely used to accelerate autoregressive (AR) text generation. However, its effectiveness in visual AR models remains limited due to token selection ambiguity, where multiple tokens receive similarly low probabilities, reducing acceptance rates. While dynamic tree drafting has been proposed to improve speculative decoding, we show that it fails to mitigate token selection ambiguity, resulting in shallow draft trees and suboptimal acceleration. To address this, we introduce LANTERN++, a novel framework that integrates static tree drafting with a relaxed acceptance condition, allowing drafts to be selected independently of low-confidence predictions. This enables deeper accepted sequences, improving decoding efficiency while preserving image quality. Extensive experiments on state-of-the-art visual AR models demonstrate that LANTERN++ significantly accelerates inference, achieving up to $\mathbf{\times 2.56}$ speedup over standard AR decoding while maintaining high image quality.

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)
• 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)
• LANTERN: Accelerating Visual Autoregressive Models with Relaxed Speculative Decoding [30.630803933771865]
  Experimental results demonstrate the efficacy of our method in providing a substantial speed-up over speculative decoding. LANTERN increases speed-ups by $mathbf1.75times$ and $mathbf1.82times$, as compared to greedy decoding and random sampling.
  arXiv  Detail & Related papers  (2024-10-04T12:21:03Z)
• Accelerating Auto-regressive Text-to-Image Generation with Training-free Speculative Jacobi Decoding [60.188309982690335]
  We propose a training-free probabilistic parallel decoding algorithm, Speculative Jacobi Decoding (SJD), to accelerate auto-regressive text-to-image generation. By introducing a probabilistic convergence criterion, our SJD accelerates the inference of auto-regressive text-to-image generation while maintaining the randomness in sampling-based token decoding.
  arXiv  Detail & Related papers  (2024-10-02T16:05:27Z)
• FRAP: Faithful and Realistic Text-to-Image Generation with Adaptive Prompt Weighting [18.708185548091716]
  FRAP is a simple, yet effective approach based on adaptively adjusting the per-token prompt weights to improve prompt-image alignment and authenticity of the generated images. We show FRAP generates images with significantly higher prompt-image alignment to prompts from complex datasets. We also explore combining FRAP with prompt rewriting LLM to recover their degraded prompt-image alignment.
  arXiv  Detail & Related papers  (2024-08-21T15:30:35Z)
• NAMER: Non-Autoregressive Modeling for Handwritten Mathematical Expression Recognition [80.22784377150465]
  Handwritten Mathematical Expression Recognition (HMER) has gained considerable attention in pattern recognition for its diverse applications in document understanding. This paper makes the first attempt to build a novel bottom-up Non-AutoRegressive Modeling approach for HMER, called NAMER. NAMER comprises a Visual Aware Tokenizer (VAT) and a Parallel Graph (PGD)
  arXiv  Detail & Related papers  (2024-07-16T04:52:39Z)
• Chimera: A Lossless Decoding Method for Accelerating Large Language Models Inference by Fusing all Tokens [15.566726645722657]
  We propose a novel framework specifically designed for speculative sampling. Within this framework, we introduce a lightweight draft model that effectively utilizes previously generated tokens to predict subsequent words. We demonstrate impressive results, achieving an average latency speedup ratio of 2.7x compared to the vanilla auto-regressive decoding approach.
  arXiv  Detail & Related papers  (2024-02-24T08:10:39Z)
• GliDe with a CaPE: A Low-Hassle Method to Accelerate Speculative Decoding [81.01996600734616]
  We introduce GliDe and CaPE, two low-hassle modifications to vanilla speculative decoding. GliDe is a modified draft model architecture that reuses the cached keys and values from the target LLM. We will release our code, data, and the trained draft models.
  arXiv  Detail & Related papers  (2024-02-03T08:44:11Z)
• Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel Decoding [43.659680579686544]
  We propose a Fast and Robust Early-Exiting framework, which incorporates a shallow-deep module and a synchronized parallel decoding. Our framework enables faster inference by synchronizing the decoding process of the current token with previously stacked early-exited tokens. As parallel decoding allows us to observe predictions from both shallow and deep models, we present a novel adaptive threshold estimator.
  arXiv  Detail & Related papers  (2023-10-09T05:53:05Z)
• Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation [80.2267931231335]
  We propose Speculative Decoding (SpecDec) to study exploiting the idea of speculative execution to accelerate autoregressive (AR) decoding. SpecDec has two innovations: Spec-Drafter -- an independent model specially optimized for efficient drafting, and Spec-Verification -- a reliable method for verifying the drafted tokens efficiently.
  arXiv  Detail & Related papers  (2022-03-30T17:27:09Z)
• Semi-Autoregressive Image Captioning [153.9658053662605]
  Current state-of-the-art approaches for image captioning typically adopt an autoregressive manner. Non-autoregressive image captioning with continuous iterative refinement can achieve comparable performance to the autoregressive counterparts with a considerable acceleration. We propose a novel two-stage framework, referred to as Semi-Autoregressive Image Captioning (SAIC) to make a better trade-off between performance and speed.
  arXiv  Detail & Related papers  (2021-10-11T15:11:54Z)

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.