LLM Inference Unveiled: Survey and Roofline Model Insights
- URL: http://arxiv.org/abs/2402.16363v6
- Date: Wed, 1 May 2024 20:42:28 GMT
- Title: LLM Inference Unveiled: Survey and Roofline Model Insights
- Authors: Zhihang Yuan, Yuzhang Shang, Yang Zhou, Zhen Dong, Zhe Zhou, Chenhao Xue, Bingzhe Wu, Zhikai Li, Qingyi Gu, Yong Jae Lee, Yan Yan, Beidi Chen, Guangyu Sun, Kurt Keutzer,
- Abstract summary: Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges.
Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model.
This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems.
- Score: 62.92811060490876
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The field of efficient Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Although the field has expanded and is vibrant, there hasn't been a concise framework that analyzes the various methods of LLM Inference to provide a clear understanding of this domain. Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model for systematic analysis of LLM inference techniques. This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems, such as why LLMs are memory-bound, how much memory and computation they need, and how to choose the right hardware. We systematically collate the latest advancements in efficient LLM inference, covering crucial areas such as model compression (e.g., Knowledge Distillation and Quantization), algorithm improvements (e.g., Early Exit and Mixture-of-Expert), and both hardware and system-level enhancements. Our survey stands out by analyzing these methods with roofline model, helping us understand their impact on memory access and computation. This distinctive approach not only showcases the current research landscape but also delivers valuable insights for practical implementation, positioning our work as an indispensable resource for researchers new to the field as well as for those seeking to deepen their understanding of efficient LLM deployment. The analyze tool, LLM-Viewer, is open-sourced.
Related papers
- From Selection to Generation: A Survey of LLM-based Active Learning [153.8110509961261]
Large Language Models (LLMs) have been employed for generating entirely new data instances and providing more cost-effective annotations.
This survey aims to serve as an up-to-date resource for researchers and practitioners seeking to gain an intuitive understanding of LLM-based AL techniques.
arXiv Detail & Related papers (2025-02-17T12:58:17Z) - Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey [39.82566660592583]
Large Language Models (LLMs) have demonstrated remarkable success in various tasks such as natural language understanding, text summarization, and machine translation.
Their general-purpose nature often limits their effectiveness in domain-specific applications that require specialized knowledge, such as healthcare, chemistry, or legal analysis.
To address this, researchers have explored diverse methods to enhance LLMs by integrating domain-specific knowledge.
arXiv Detail & Related papers (2025-02-15T07:43:43Z) - Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search [57.28671084993782]
Large language models (LLMs) have demonstrated remarkable reasoning capabilities across diverse domains.
Recent studies have shown that increasing test-time computation enhances LLMs' reasoning capabilities.
We propose a two-stage training paradigm: 1) a small-scale format tuning stage to internalize the COAT reasoning format and 2) a large-scale self-improvement stage leveraging reinforcement learning.
arXiv Detail & Related papers (2025-02-04T17:26:58Z) - A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering [12.504438766461027]
Large Language Models (LLMs) have transformed Software Engineering (SE) by providing innovative methods for analyzing software repositories.
Our research packages a framework, coined Prompt Refinement and Insights for Mining Empirical Software repositories (PRIMES)
Our findings indicate that standardizing prompt engineering and using PRIMES can enhance the reliability and accuracy of studies utilizing LLMs.
arXiv Detail & Related papers (2024-11-15T06:08:57Z) - EVOLvE: Evaluating and Optimizing LLMs For Exploration [76.66831821738927]
Large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty.
We measure LLMs' (in)ability to make optimal decisions in bandits, a state-less reinforcement learning setting relevant to many applications.
Motivated by the existence of optimal exploration algorithms, we propose efficient ways to integrate this algorithmic knowledge into LLMs.
arXiv Detail & Related papers (2024-10-08T17:54:03Z) - A Survey on Efficient Inference for Large Language Models [25.572035747669275]
Large Language Models (LLMs) have attracted extensive attention due to their remarkable performance across various tasks.
The substantial computational and memory requirements of LLM inference pose challenges for deployment in resource-constrained scenarios.
This paper presents a comprehensive survey of the existing literature on efficient LLM inference.
arXiv Detail & Related papers (2024-04-22T15:53:08Z) - LLM In-Context Recall is Prompt Dependent [0.0]
A model's ability to do this significantly influences its practical efficacy and dependability in real-world applications.
This study demonstrates that an LLM's recall capability is not only contingent upon the prompt's content but also may be compromised by biases in its training data.
arXiv Detail & Related papers (2024-04-13T01:13:59Z) - Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs [60.40396361115776]
This paper introduces a novel collaborative approach, namely SlimPLM, that detects missing knowledge in large language models (LLMs) with a slim proxy model.
We employ a proxy model which has far fewer parameters, and take its answers as answers.
Heuristic answers are then utilized to predict the knowledge required to answer the user question, as well as the known and unknown knowledge within the LLM.
arXiv Detail & Related papers (2024-02-19T11:11:08Z) - Editing Large Language Models: Problems, Methods, and Opportunities [51.903537096207]
This paper embarks on a deep exploration of the problems, methods, and opportunities related to model editing for LLMs.
We provide an exhaustive overview of the task definition and challenges associated with model editing, along with an in-depth empirical analysis of the most progressive methods currently at our disposal.
Our objective is to provide valuable insights into the effectiveness and feasibility of each editing technique, thereby assisting the community in making informed decisions on the selection of the most appropriate method for a specific task or context.
arXiv Detail & Related papers (2023-05-22T16:00:00Z)
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.