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AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment

Yonggan Fu, Zhongzhi Yu, Junwei Li, Jiayi Qian, Yongan Zhang, Xiangchi Yuan, Dachuan Shi, Roman Yakunin, Yingyan Celine Lin

TL;DR

AmoebaLLM is proposed, a novel framework designed to enable the instant derivation of LLM subnets of arbitrary shapes, which achieve the accuracy-efficiency frontier and can be extracted immediately after a one-time fine-tuning.

Abstract

Motivated by the transformative capabilities of large language models (LLMs) across various natural language tasks, there has been a growing demand to deploy these models effectively across diverse real-world applications and platforms. However, the challenge of efficiently deploying LLMs has become increasingly pronounced due to the varying application-specific performance requirements and the rapid evolution of computational platforms, which feature diverse resource constraints and deployment flows. These varying requirements necessitate LLMs that can adapt their structures (depth and width) for optimal efficiency across different platforms and application specifications. To address this critical gap, we propose AmoebaLLM, a novel framework designed to enable the instant derivation of LLM subnets of arbitrary shapes, which achieve the accuracy-efficiency frontier and can be extracted immediately after a one-time fine-tuning. In this way, AmoebaLLM significantly facilitates rapid deployment tailored to various platforms and applications. Specifically, AmoebaLLM integrates three innovative components: (1) a knowledge-preserving subnet selection strategy that features a dynamic-programming approach for depth shrinking and an importance-driven method for width shrinking; (2) a shape-aware mixture of LoRAs to mitigate gradient conflicts among subnets during fine-tuning; and (3) an in-place distillation scheme with loss-magnitude balancing as the fine-tuning objective. Extensive experiments validate that AmoebaLLM not only sets new standards in LLM adaptability but also successfully delivers subnets that achieve state-of-the-art trade-offs between accuracy and efficiency.

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment

TL;DR

AmoebaLLM is proposed, a novel framework designed to enable the instant derivation of LLM subnets of arbitrary shapes, which achieve the accuracy-efficiency frontier and can be extracted immediately after a one-time fine-tuning.

Abstract

Motivated by the transformative capabilities of large language models (LLMs) across various natural language tasks, there has been a growing demand to deploy these models effectively across diverse real-world applications and platforms. However, the challenge of efficiently deploying LLMs has become increasingly pronounced due to the varying application-specific performance requirements and the rapid evolution of computational platforms, which feature diverse resource constraints and deployment flows. These varying requirements necessitate LLMs that can adapt their structures (depth and width) for optimal efficiency across different platforms and application specifications. To address this critical gap, we propose AmoebaLLM, a novel framework designed to enable the instant derivation of LLM subnets of arbitrary shapes, which achieve the accuracy-efficiency frontier and can be extracted immediately after a one-time fine-tuning. In this way, AmoebaLLM significantly facilitates rapid deployment tailored to various platforms and applications. Specifically, AmoebaLLM integrates three innovative components: (1) a knowledge-preserving subnet selection strategy that features a dynamic-programming approach for depth shrinking and an importance-driven method for width shrinking; (2) a shape-aware mixture of LoRAs to mitigate gradient conflicts among subnets during fine-tuning; and (3) an in-place distillation scheme with loss-magnitude balancing as the fine-tuning objective. Extensive experiments validate that AmoebaLLM not only sets new standards in LLM adaptability but also successfully delivers subnets that achieve state-of-the-art trade-offs between accuracy and efficiency.

Paper Structure

This paper contains 15 sections, 3 equations, 3 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Motivation and Profiling
  3. The Proposed AmoebaLLM Framework
  4. AmoebaLLM: Methodology Overview
  5. AmoebaLLM: The Proposed Knowledge-Preserving Subnet Selection Strategy
  6. AmoebaLLM: The Proposed Shape-aware Mixture of LoRAs
  7. AmoebaLLM: The Fine-tuning Objective
  8. Experimental Results
  9. Experiment Setup
  10. Benchmark with SOTA LLM Compression Methods
  11. Ablation Study: Effectiveness of Each Component
  12. Ablation Study: The Selection of Calibration Datasets
  13. Limitations and Future Work
  14. Related Work
  15. Conclusion

Figures (3)

  • Figure 1: The latency of LLaMA2 7B with scaled depth/width on various devices/deployment flows.
  • Figure 2: An overview of our AmoebaLLM framework: (a) Stage 1: Generate the subnet selection strategy; (b) Stage 2: One-for-all fine-tuning. Zoom in for a better view.
  • Figure 3: Benchmark AmoebaLLM's achieved accuracy-latency trade-offs with SOTA LLM compression methods on an NVIDIA A5000 GPU.