EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism

• URL: http://arxiv.org/abs/2312.04916v3
• Date: Sun, 16 Jun 2024 08:37:25 GMT
• Title: EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism
• Authors: Yanxi Chen, Xuchen Pan, Yaliang Li, Bolin Ding, Jingren Zhou,
• Abstract summary: We present EE-LLM, a framework for large-scale training and inference of early-exit large language models (LLMs) Built upon Megatron-LM, EE-LLM implements a variety of algorithmic innovations and performance optimizations tailored to early exiting. Our analytical and empirical study shows that EE-LLM achieves great training efficiency with negligible computational overhead.
• Score: 70.07661254213181
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present EE-LLM, a framework for large-scale training and inference of early-exit large language models (LLMs). While recent works have shown preliminary evidence for the efficacy of early exiting in accelerating LLM inference, EE-LLM makes a foundational step towards scaling up early-exit LLMs by supporting their training and inference with massive 3D parallelism. Built upon Megatron-LM, EE-LLM implements a variety of algorithmic innovations and performance optimizations tailored to early exiting, including a lightweight method that facilitates backpropagation for the early-exit training objective with pipeline parallelism, techniques of leveraging idle resources in the original pipeline schedule for computation related to early-exit layers, and two approaches of early-exit inference that are compatible with KV caching for autoregressive generation. Our analytical and empirical study shows that EE-LLM achieves great training efficiency with negligible computational overhead compared to standard LLM training, as well as outstanding inference speedup without compromising output quality. To facilitate further research and adoption, we release EE-LLM at https://github.com/pan-x-c/EE-LLM.

Related papers

• R-SFLLM: Jamming Resilient Framework for Split Federated Learning with Large Language Models [83.77114091471822]
  Split federated learning (SFL) is a compute-efficient paradigm in distributed machine learning (ML) A challenge in SFL, particularly when deployed over wireless channels, is the susceptibility of transmitted model parameters to adversarial jamming. This is particularly pronounced for word embedding parameters in large language models (LLMs), which are crucial for language understanding. A physical layer framework is developed for resilient SFL with LLMs (R-SFLLM) over wireless networks.
  arXiv  Detail & Related papers  (2024-07-16T12:21:29Z)
• Efficient Continual Pre-training by Mitigating the Stability Gap [68.49269649759005]
  We study the behavior of Large Language Models (LLMs) during continual pre-training. We propose three effective strategies to enhance LLM performance within a fixed compute budget. Our strategies improve the average medical task performance of the OpenLlama-3B model from 36.2% to 40.7% with only 40% of the original training budget.
  arXiv  Detail & Related papers  (2024-06-21T02:28:37Z)
• Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models [90.14693869269519]
  MoE LLMs can achieve higher performance with fewer parameters, but it is still hard to deploy them due to their immense parameter sizes. This paper mainly aims to enhance the deployment efficiency of MoE LLMs by introducing plug-and-play expert-level sparsification techniques.
  arXiv  Detail & Related papers  (2024-02-22T18:56:07Z)
• EE-Tuning: An Economical yet Scalable Solution for Tuning Early-Exit Large Language Models [75.1814102438065]
  EE-Tuning is a solution to training/tuning early-exit large language models (LLMs) It augments any pre-trained (and possibly fine-tuned) standard LLM with additional early-exit layers that are tuned in a parameter-efficient manner. Our implementation achieves outstanding training efficiency via extensive performance optimizations.
  arXiv  Detail & Related papers  (2024-02-01T11:39:04Z)
• GrowLength: Accelerating LLMs Pretraining by Progressively Growing Training Length [65.24730341801468]
  This paper introduces a novel, simple, and effective method named growlength'' to accelerate the pretraining process of Large Language Models. Our method progressively increases the training length throughout the pretraining phase, thereby mitigating computational costs and enhancing efficiency.
  arXiv  Detail & Related papers  (2023-10-01T05:25:24Z)
• FwdLLM: Efficient FedLLM using Forward Gradient [8.520892692833293]
  This work introduces FwdLLM, an innovative FL protocol designed to enhance the FedLLM efficiency. FwdLLM employs backpropagation (BP)-free training methods, requiring devices only to execute perturbed inferences''
  arXiv  Detail & Related papers  (2023-08-26T14:36:30Z)
• Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline [22.08897444328099]
  Large language models (LLMs) have revolutionized the field of AI, demonstrating unprecedented capacity across various tasks. In this paper, we propose an efficient LLM inference pipeline that harnesses the power of LLMs.
  arXiv  Detail & Related papers  (2023-05-22T15:36:06Z)

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.