S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity (2412.06289v3)

Abstract: Current PEFT methods for LLMs can achieve either high quality, efficient training, or scalable serving, but not all three simultaneously. To address this limitation, we investigate sparse fine-tuning and observe a remarkable improvement in generalization ability. Utilizing this key insight, we propose a family of Structured Sparse Fine-Tuning (S$^{2}$FT) methods for LLMs, which concurrently achieve state-of-the-art fine-tuning performance, training efficiency, and inference scalability. S$^{2}$FT accomplishes this by "selecting sparsely and computing densely". It selects a few heads and channels in the MHA and FFN modules for each Transformer block, respectively. Next, it co-permutes weight matrices on both sides of the coupled structures in LLMs to connect the selected components in each layer into a dense submatrix. Finally, S$^{2}$FT performs in-place gradient updates on all submatrices. Through theoretical analysis and empirical results, our method prevents forgetting while simplifying optimization, delivers SOTA performance on both commonsense and arithmetic reasoning with 4.6% and 1.3% average improvements compared to LoRA, and surpasses full FT by 11.5% when generalizing to various domains after instruction tuning. Using our partial backpropagation algorithm, S$^{2}$FT saves training memory up to 3$\times$ and improves latency by 1.5-2.7$\times$ compared to full FT, while delivering an average 10% improvement over LoRA on both metrics. We further demonstrate that the weight updates in S$^{2}$FT can be decoupled into adapters, enabling effective fusion, fast switch, and efficient parallelism for serving multiple fine-tuned models.

• The paper introduces a parameter-efficient fine-tuning method that strategically selects crucial model components to balance performance and resource usage.
• It employs structured sparse optimization with dense computation to reduce memory demands and prevent catastrophic forgetting.
• Empirical results demonstrate up to 4.6% and 11.5% improvements on key benchmarks, underscoring the approach’s scalability and generalizability.

Efficient, Scalable, and Generalizable Fine-Tuning for LLMs by Structured Sparsity

This essay presents an analytical overview of the paper, "S$^2$FT: Efficient, Scalable, and Generalizable LLM Fine-tuning by Structured Sparsity," where the authors propose a novel parameter-efficient fine-tuning (PEFT) methodology specifically for LLMs. The paper addresses key challenges in the fine-tuning of LLMs, such as catastrophic forgetting, memory intensity, and computational demands. It further proposes a novel approach that achieves high fine-tuning performance while enhancing training efficiency and enabling scalable serving.

Introduction to S$^2$FT

The paper identifies a gap in existing PEFT methods, which typically excel only in either high-quality performance, efficient training, or scalable serving, but not concurrently. In response, the authors propose the Structured Sparse Fine-Tuning (S$^2$FT) method. The core innovation of the proposed S$^2$FT framework is its strategic choice of sparsely selecting critical model parts (heads or channels) and then computing these parts densely. This approach captures the structural affinity between model components, thereby not only conserving resources but also mitigating the inefficiencies generally associated with purely sparse methods.

Methodological Contributions

Parameter Efficiency:

S$^2$FT performs sparse selection by identifying crucial attention heads in the multi-head attention (MHA) module and important channels in the feed-forward network (FFN) module. The fine-tuning process is governed by the following strategies:

• S$^2$FT-R: Random selection of crucial heads and channels.
• S$^2$FT-W/A/S/G: Selection based on varying metrics such as weight, activation, weighted combination, or gradient magnitudes using a calibration dataset. The choices aim to balance the fine-tuning process's efficiency and the retention of pre-trained information.

Structured Sparse Optimization:

S$^2$FT utilizes a structured approach to maintain dense computation. The approach is grounded in selecting sparsity in very specific coupled structures within the model, such as linked weight matrices, while ensuring computational efficiency via dense-only operations. Fine-tuning efforts update subsets of model parameters that are crucial for learning without globally modifying the weight matrix, thus avoiding inefficiencies triggered by unstructured forms of sparsity.

In-Place Gradient Updates:

To further enhance efficiency, S$^2$FT incorporates a partial backpropagation algorithm. This approach eliminates the need to compute and store gradients for non-essential components, drastically reducing memory requirements and processing time.

Empirical and Theoretical Evaluation

Performance and Efficiency:

Extensive empirical evaluations are conducted on diverse benchmarks, including commonsense and arithmetic reasoning tasks. S$^2$FT consistently outperforms existing methods like LoRA by significant margins, achieving up to 4.6% improvement on commonsense reasoning tasks and 1.3% on arithmetic reasoning benchmarks. It also shows better generalization after instruction tuning, outperforming traditional full fine-tuning by 11.5%.

Theoretical Insights:

The authors provide a theoretical backing elucidating the role of structured sparsity in preventing overfitting and enabling better generalization. The paper provides mathematical proofs that highlight how maintaining sparsity at structural levels within LLMs can curb catastrophic forgetting during fine-tuning and improve generalization capabilities.

Implications and Future Directions

S$^2$FT illustrates a paradigm shift towards more framework-aware sparse computation methods in AI fine-tuning tasks. This approach can have significant practical implications across domains relying on adaptability and efficiency of LLMs like natural language processing and AI-driven data analytics. The fundamental concept of structured sparsity extends beyond LLMs and can see applications in various neural architecture designs.

Future developments might include enhancements to the selection strategies, optimizing them via reinforcement learning or automated metric-based approaches. Further, adaptation of the S$^2$FT methodology for deployment in edge computing scenarios, where resource allocation is critical, provides another promising direction.

In conclusion, the S$^2$FT methodology bridges the gap in efficient, scalable, and top-tier quality fine-tuning processes for LLMs, achieving a harmonized balance across these crucial factors. The framework's adaptive sparse structuring within intrinsic model architectures serves as a testament to pioneering efforts in realizing parameter-efficient strategies for cutting-edge LLMs.