Key Points

1. The paper presents a systematic literature review (SLR) on optimization and acceleration techniques for large language models (LLMs).

2. It identifies challenges associated with training, inference, and system serving for LLMs with billions or trillions of parameters.

3. The paper develops a structured taxonomy to categorize LLM optimization techniques into three classes: training optimization, hardware optimization, and scalability and reliability.

4. It reviews and evaluates recent libraries and frameworks designed for LLM optimization, providing a comprehensive overview.

5. The paper identifies promising areas for future research in LLM development, focusing on efficiency, scalability, and flexibility.

6. It provides a detailed analysis of recent optimization and acceleration strategies, including a taxonomy and comparative evaluation.

7. Two in-depth case studies demonstrate practical approaches to optimizing model training and enhancing inference efficiency.

8. The case studies showcase how resource limitations can be addressed while maintaining LLM performance.

9. The paper explores a range of future directions for LLM development, including enhancing efficiency, scalability, and flexibility.

Summary

The paper provides an overview of the development of language modeling, from n-gram models to neural language models and transformer-based large language models (LLMs). LLMs have achieved remarkable success in natural language processing (NLP) tasks, but they require an enormous number of parameters, often in the billions or trillions, which creates significant computational and memory challenges.

To address these challenges, the paper presents a systematic review of recent optimization and acceleration techniques for LLMs. The authors introduce a taxonomy that categorizes LLM optimization strategies into three main classes: LLM training, LLM inference, and system serving.

For LLM training, the paper discusses various optimization techniques, including model optimization (e.g. algorithmic optimizations, layer-specific kernels, model partitioning), size reduction (e.g. quantization, pruning, hyperparameter tuning), and distributed training (e.g. data parallelism, model parallelism, combined parallelism). These techniques aim to improve the efficiency, speed, and scalability of LLM training.

In the LLM inference domain, the review covers frameworks and libraries that focus on accelerating the deployment and execution of LLMs. Key strategies include hardware-aware optimizations (e.g. offloading, mixed precision training), algorithmic improvements (e.g. custom GPU kernels, memory management), and distributed inference (e.g. separating compute-intensive and memory-intensive phases).

For system serving, the paper discusses approaches to enable efficient and scalable deployment of LLMs, such as memory management, sequence-length-aware scheduling, and collaborative distributed inference platforms.

The paper also presents two in-depth case studies. The first case study demonstrates the effectiveness of the SparseGPT framework, which uses a one-shot pruning technique to significantly reduce the size of massive language models like OPT-175B and BLOOM-176B, without compromising their performance. The second case study examines the QMoE framework, which employs novel compression methods to enable the efficient execution of trillion-parameter Mixture-of-Experts (MoE) models on commodity hardware.

Overall, this systematic review provides a comprehensive understanding of the current state-of-the-art in LLM optimization and acceleration. The proposed taxonomy and the detailed analysis of frameworks, libraries, and techniques offer valuable insights for researchers and practitioners working towards more efficient, scalable, and accessible large language models.

Reference: https://arxiv.org/abs/2409.048...