Beyond the Speculative Game: A Survey of Speculative Execution in Large Language Models

• URL: http://arxiv.org/abs/2404.14897v1
• Date: Tue, 23 Apr 2024 10:25:45 GMT
• Title: Beyond the Speculative Game: A Survey of Speculative Execution in Large Language Models
• Authors: Chen Zhang, Zhuorui Liu, Dawei Song,
• Abstract summary: Speculative execution is introduced to LLM decoding in a textitdraft-then-verify style. As the costly inference is parallelized, decoding speed can be significantly boosted. We present the first survey paper that reviews and unifies literature of speculative execution in LLMs.
• Score: 9.121458241884444
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the increasingly giant scales of (causal) large language models (LLMs), the inference efficiency comes as one of the core concerns along the improved performance. In contrast to the memory footprint, the latency bottleneck seems to be of greater importance as there can be billions of requests to a LLM (e.g., GPT-4) per day. The bottleneck is mainly due to the autoregressive innateness of LLMs, where tokens can only be generated sequentially during decoding. To alleviate the bottleneck, the idea of speculative execution, which originates from the field of computer architecture, is introduced to LLM decoding in a \textit{draft-then-verify} style. Under this regime, a sequence of tokens will be drafted in a fast pace by utilizing some heuristics, and then the tokens shall be verified in parallel by the LLM. As the costly sequential inference is parallelized, LLM decoding speed can be significantly boosted. Driven by the success of LLMs in recent couple of years, a growing literature in this direction has emerged. Yet, there lacks a position survey to summarize the current landscape and draw a roadmap for future development of this promising area. To meet this demand, we present the very first survey paper that reviews and unifies literature of speculative execution in LLMs (e.g., blockwise parallel decoding, speculative decoding, etc.) in a comprehensive framework and a systematic taxonomy. Based on the taxonomy, we present a critical review and comparative analysis of the current arts. Finally we highlight various key challenges and future directions to further develop the area.

Related papers

• FIRP: Faster LLM inference via future intermediate representation prediction [54.897493351694195]
  FIRP generates multiple tokens instead of one at each decoding step. We conduct extensive experiments, showing a speedup ratio of 1.9x-3x in several models and datasets.
  arXiv  Detail & Related papers  (2024-10-27T15:53:49Z)
• 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)
• Graph-Structured Speculative Decoding [52.94367724136063]
  Speculative decoding has emerged as a promising technique to accelerate the inference of Large Language Models. We introduce an innovative approach utilizing a directed acyclic graph (DAG) to manage the drafted hypotheses. We observe a remarkable speedup of 1.73$times$ to 1.96$times$, significantly surpassing standard speculative decoding.
  arXiv  Detail & Related papers  (2024-07-23T06:21:24Z)
• What's Wrong with Your Code Generated by Large Language Models? An Extensive Study [80.18342600996601]
  Large language models (LLMs) produce code that is shorter yet more complicated as compared to canonical solutions. We develop a taxonomy of bugs for incorrect codes that includes three categories and 12 sub-categories, and analyze the root cause for common bug types. We propose a novel training-free iterative method that introduces self-critique, enabling LLMs to critique and correct their generated code based on bug types and compiler feedback.
  arXiv  Detail & Related papers  (2024-07-08T17:27:17Z)
• Parallel Decoding via Hidden Transfer for Lossless Large Language Model Acceleration [54.897493351694195]
  We propose a novel parallel decoding approach, namely textithidden transfer, which decodes multiple successive tokens simultaneously in a single forward pass. In terms of acceleration metrics, we outperform all the single-model acceleration techniques, including Medusa and Self-Speculative decoding.
  arXiv  Detail & Related papers  (2024-04-18T09:17:06Z)
• 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)
• Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approach [12.214585409361126]
  Large language models (LLMs)- based code generation is a complex and powerful black-box model. We propose a novel causal graph-based representation of the prompt and the generated code. We illustrate the insights that our framework can provide by studying over 3 popular LLMs with over 12 prompt adjustment strategies.
  arXiv  Detail & Related papers  (2023-10-10T14:56:26Z)
• LLMCad: Fast and Scalable On-device Large Language Model Inference [11.103824752113148]
  Generative tasks, such as text generation and question answering, hold a crucial position in the realm of mobile applications. Currently, the execution of these generative tasks heavily depends on Large Language Models (LLMs) We introduce LLMCad, an on-device inference engine specifically designed for efficient generative Natural Language Processing (NLP) tasks.
  arXiv  Detail & Related papers  (2023-09-08T10:44:19Z)
• Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation [23.65270067167911]
  This work aims at decreasing the end-to-end generation latency of large language models (LLMs) We propose Skeleton-of-Thought (SoT), which first guides LLMs to generate the skeleton of the answer, and then conducts parallel API calls or batched decoding to complete the contents of each skeleton point in parallel. SoT is an initial attempt at data-centric optimization for inference efficiency, and showcases the potential of eliciting high-quality answers by explicitly planning the answer structure in language.
  arXiv  Detail & Related papers  (2023-07-28T06:31:34Z)
• Inference with Reference: Lossless Acceleration of Large Language Models [97.04200102556551]
  LLMA is an accelerator to speed up Large Language Model (LLM) inference with references. It is motivated by the observation that there are abundant identical text spans between the decoding result by an LLM and the reference that is available in many real world scenarios.
  arXiv  Detail & Related papers  (2023-04-10T09:55:14Z)

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.