A Theoretical Perspective for Speculative Decoding Algorithm

• URL: http://arxiv.org/abs/2411.00841v1
• Date: Wed, 30 Oct 2024 01:53:04 GMT
• Title: A Theoretical Perspective for Speculative Decoding Algorithm
• Authors: Ming Yin, Minshuo Chen, Kaixuan Huang, Mengdi Wang,
• Abstract summary: One effective way to accelerate inference is emphSpeculative Decoding, which employs a small model to sample a sequence of draft tokens and a large model to validate. This paper tackles this gap by conceptualizing the decoding problem via markov chain abstraction and studying the key properties, emphoutput quality and inference acceleration, from a theoretical perspective.
• Score: 60.79447486066416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformer-based autoregressive sampling has been the major bottleneck for slowing down large language model inferences. One effective way to accelerate inference is \emph{Speculative Decoding}, which employs a small model to sample a sequence of draft tokens and a large model to validate. Given its empirical effectiveness, the theoretical understanding of Speculative Decoding is falling behind. This paper tackles this gap by conceptualizing the decoding problem via markov chain abstraction and studying the key properties, \emph{output quality and inference acceleration}, from a theoretical perspective. Our analysis covers the theoretical limits of speculative decoding, batch algorithms, and output quality-inference acceleration tradeoffs. Our results reveal the fundamental connections between different components of LLMs via total variation distances and show how they jointly affect the efficiency of decoding algorithms.

Related papers

• Generalization Bounds for Transformer Channel Decoders [61.55280736553095]
  This paper studies the generalization performance of ECCT from a learning-theoretic perspective.<n>To the best of our knowledge, this work provides the first theoretical generalization guarantees for this class of decoders.
  arXiv  Detail & Related papers  (2026-01-11T15:56:37Z)
• Accelerate Speculative Decoding with Sparse Computation in Verification [49.74839681322316]
  Speculative decoding accelerates autoregressive language model inference by verifying multiple draft tokens in parallel.<n>Existing sparsification methods are designed primarily for standard token-by-token autoregressive decoding.<n>We propose a sparse verification framework that jointly sparsifies attention, FFN, and MoE components during the verification stage to reduce the dominant computation cost.
  arXiv  Detail & Related papers  (2025-12-26T07:53:41Z)
• The Disparate Impacts of Speculative Decoding [54.98795989404752]
  speculative decoding is a technique for systematically reducing the decoding time of large language models.<n>The paper shows that speed-up gained from speculative decoding is not uniformly distributed across tasks, consistently diminishing for under-fit, and often underrepresented tasks.
  arXiv  Detail & Related papers  (2025-10-02T15:38:57Z)
• READER: Retrieval-Assisted Drafter for Efficient LLM Inference [0.0386965802948046]
  Autoregressive Language Models instantiate a factorized likelihood over token sequences, yet their strictly sequential decoding process imposes an intrinsic lower bound on latency inference.<n>This bottleneck has emerged as a central obstacle to the scalable deployment of large-scale generative models.<n>We present READER, a speculative decoding framework that bypasses the training of the auxiliary draft model.
  arXiv  Detail & Related papers  (2025-08-12T16:47:48Z)
• R-Stitch: Dynamic Trajectory Stitching for Efficient Reasoning [60.37610817226533]
  Chain-of-thought (CoT) reasoning encourages step-by-step intermediate reasoning during inference.<n>CoT introduces substantial computational overhead due to its reliance on autoregressive decoding over long token sequences.<n>We present R-Stitch, a token-level, confidence-based hybrid decoding framework that accelerates CoT inference.
  arXiv  Detail & Related papers  (2025-07-23T08:14:36Z)
• Towards Optimal Multi-draft Speculative Decoding [102.67837141152232]
  Multi-Draft Speculative Decoding (MDSD) is a recent approach where, when generating each token, a small draft model generates multiple drafts. This paper discusses the dual of the optimal transport problem, providing a way to efficiently compute the optimal acceptance rate.
  arXiv  Detail & Related papers  (2025-02-26T03:22:44Z)
• Enhancing Large Language Model Efficiencyvia Symbolic Compression: A Formal Approach Towards Interpretability [3.9122242678047456]
  Large language models (LLMs) face significant token efficiency bottlenecks in code generation and logical reasoning tasks. This paper proposes a formal framework based on symbolic compression,integrating logic, information-theoretic optimal encoding, and context-aware inference techniques.
  arXiv  Detail & Related papers  (2025-01-30T06:40:52Z)
• Compute Optimal Inference and Provable Amortisation Gap in Sparse Autoencoders [0.0]
  We investigate sparse inference and learning in SAEs through the lens of sparse coding. We show that SAEs perform amortised sparse inference with a computationally restricted encoder. We empirically explore conditions where more sophisticated sparse inference methods outperform traditional SAE encoders.
  arXiv  Detail & Related papers  (2024-11-20T08:21:53Z)
• Causal Context Adjustment Loss for Learned Image Compression [72.7300229848778]
  In recent years, learned image compression (LIC) technologies have surpassed conventional methods notably in terms of rate-distortion (RD) performance. Most present techniques are VAE-based with an autoregressive entropy model, which obviously promotes the RD performance by utilizing the decoded causal context. In this paper, we make the first attempt in investigating the way to explicitly adjust the causal context with our proposed Causal Context Adjustment loss.
  arXiv  Detail & Related papers  (2024-10-07T09:08:32Z)
• QGait: Toward Accurate Quantization for Gait Recognition with Binarized Input [17.017127559393398]
  We propose a differentiable soft quantizer, which better simulates the gradient of the round function during backpropagation. This enables the network to learn from subtle input perturbations. We further refine the training strategy to ensure convergence while simulating quantization errors.
  arXiv  Detail & Related papers  (2024-05-22T17:34:18Z)
• A Thorough Examination of Decoding Methods in the Era of LLMs [72.65956436513241]
  Decoding methods play an indispensable role in converting language models from next-token predictors into practical task solvers. This paper provides a comprehensive and multifaceted analysis of various decoding methods within the context of large language models. Our findings reveal that decoding method performance is notably task-dependent and influenced by factors such as alignment, model size, and quantization.
  arXiv  Detail & Related papers  (2024-02-10T11:14:53Z)
• Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding [46.485363806259265]
  Speculative Decoding has emerged as a novel decoding paradigm for Large Language Models (LLMs) inference. In each decoding step, this method first drafts several future tokens efficiently and then verifies them in parallel. This paper presents a comprehensive overview and analysis of this promising decoding paradigm.
  arXiv  Detail & Related papers  (2024-01-15T17:26:50Z)
• Predictive Pipelined Decoding: A Compute-Latency Trade-off for Exact LLM Decoding [12.49711203027534]
  "Predictive Pipelined Decoding (PPD)" is an approach that speeds up greedy decoding in Large Language Models (LLMs) Unlike conventional strategies, PPD employs additional compute resources to parallelize the initiation of subsequent token decoding. We have developed a theoretical framework that allows us to analyze the trade-off between computation and latency.
  arXiv  Detail & Related papers  (2023-07-12T04:28:41Z)
• Learning Quantization in LDPC Decoders [14.37550972719183]
  We propose a floating-point surrogate model that imitates quantization effects as additions of uniform noise. A deep learning-based method is then applied to optimize the message bitwidths. We report an error-rate performance within 0.2 dB of floating-point decoding at an average message quantization bitwidth of 3.1 bits.
  arXiv  Detail & Related papers  (2022-08-10T07:07:54Z)
• Deep Equilibrium Assisted Block Sparse Coding of Inter-dependent Signals: Application to Hyperspectral Imaging [71.57324258813675]
  A dataset of inter-dependent signals is defined as a matrix whose columns demonstrate strong dependencies. A neural network is employed to act as structure prior and reveal the underlying signal interdependencies. Deep unrolling and Deep equilibrium based algorithms are developed, forming highly interpretable and concise deep-learning-based architectures.
  arXiv  Detail & Related papers  (2022-03-29T21:00:39Z)
• A Transformer-based Approach for Source Code Summarization [86.08359401867577]
  We learn code representation for summarization by modeling the pairwise relationship between code tokens. We show that despite the approach is simple, it outperforms the state-of-the-art techniques by a significant margin.
  arXiv  Detail & Related papers  (2020-05-01T23:29:36Z)

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.