LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning (2206.06522v2)

Abstract: Fine-tuning large pre-trained models on downstream tasks has been adopted in a variety of domains recently. However, it is costly to update the entire parameter set of large pre-trained models. Although recently proposed parameter-efficient transfer learning (PETL) techniques allow updating a small subset of parameters (e.g. only using 2% of parameters) inside a pre-trained backbone network for a new task, they only reduce the training memory requirement by up to 30%. This is because the gradient computation for the trainable parameters still requires backpropagation through the large pre-trained backbone model. To address this, we propose Ladder Side-Tuning (LST), a new PETL technique that can reduce training memory requirements by more substantial amounts. Unlike existing parameter-efficient methods that insert additional parameters inside backbone networks, we train a ladder side network, a small and separate network that takes intermediate activations as input via shortcut connections (called ladders) from backbone networks and makes predictions. LST has significantly lower memory requirements than previous methods, because it does not require backpropagation through the backbone network, but instead only through the side network and ladder connections. We evaluate our method with various models (T5 and CLIP-T5) on both NLP (GLUE) and vision-and-language (VQA, GQA, NLVR2 , MSCOCO) tasks. LST saves 69% of the memory costs to fine-tune the whole network, while other methods only save 26% of that in similar parameter usages (hence, 2.7x more memory savings). Moreover, LST achieves higher accuracy than Adapter and LoRA in a low-memory regime. To further show the advantage of this better memory efficiency, we also apply LST to larger T5 models, attaining better GLUE performance than full fine-tuning and other PETL methods. The accuracy-efficiency trade-off also holds on VL tasks.

Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning

The research paper entitled "Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning" introduces a novel approach—named Ladder Side-Tuning (LST)—aimed at addressing the demands of fine-tuning large pre-trained models for downstream tasks. As current methodologies frequently necessitate significant computational resources due to their extensive parameter bases, LST proposes a paradigm shift to achieve parameter efficiency while markedly reducing memory requirements. This essay provides an analysis of the LST technique detailed in the paper, including its methodology, experimental outcomes, and potential implications for future developments in transfer learning.

Overview of Ladder Side-Tuning

Fine-tuning pre-trained models, although effective, is often hindered by high computational costs linked to updating entire parameter sets. Parameter-efficient transfer learning (PETL) offers some relief by only modifying select parameters, but memory consumption remains substantial. LST offers a breakthrough by separating trainable components from the principal model, thereby crafting an independent side network that interacts with the backbone model through shortcut connections aptly termed "ladders." This architecture ensures that backpropagation calculations are limited to the side network and the ladder connections, achieving significant reductions in memory consumption.

Experimentation with various models, including T5 and CLIP-T5, spanning both NLP and Vision-and-Language (VL) tasks, substantiates the efficacy of LST. The LST framework accomplishes memory savings of up to 69%, compared to a mere 26% from traditional PETL techniques, while preserving competitive accuracy rates. Notably, LST excels in a low-memory formulation, surpassing Adapter and LoRA in accuracy, and scales efficiently to larger models such as T5-large and T5-3B, outperforming traditional fine-tuning and other PETL methodologies in terms of both memory utilization and performance.

Methodological Innovations

Central to the LST methodology is the concept of 'ladder connections,' through which intermediate activations from the mainstays of the backbone network are funneled into the trailing side network. Unlike conventional PETL approaches that expand upon existing network layers, LST inserts an entirely separate lightweight network responsible for adapting model responses to new data.

A key innovation is the introduction of a gating mechanism to blend activations from the backbone and side networks, enhancing model robustness. Moreover, the side network's initialization is carefully conducted through network-pruning techniques, enabling it to leverage pivotal parameters from the backbone, thereby ensuring efficient adaptation without full reintegration.

Experimental Results and Implications

Empirically verified across comprehensive experimental setups, LST demonstrates superior memory efficiency across NLP and VL tasks. This positions LST as particularly advantageous for resource-constrained applications such as on-device learning. By circumventing full backpropagation through large pre-trained models, LST not only heightens parameter efficiency but also stands as a more accessible alternative for organizations or individuals without access to expansive computational infrastructure.

The implications of LST extend beyond current paradigms of transfer learning; it accord potential shifts toward leveraging modular networks that can be independently developed and deployed alongside existing architectures. By facilitating efficient model scaling and adaptation, LST may propel advancements in adaptive AI systems, optimizing computational overheads in complex and evolving task environments.

Conclusion

In response to the growing empirical trend favoring large-scale pre-trained models, "Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning" delineates a strategy that promises significant reductions in resource requirements without sacrificing accuracy. As computational demands continue to escalate in AI-driven domains, LST envisions a forward-looking methodology, underscoring the efficacy of integrating task-specific fine-tuning within a minimal computational footprint. The breadth of applications and expansions upon this work foretell a promising avenue for the continued evolution of transfer learning in AI.