Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey
(2403.14608)Abstract
Large models represent a groundbreaking advancement in multiple application fields, enabling remarkable achievements across various tasks. However, their unprecedented scale comes with significant computational costs. These models, often consisting of billions of parameters, require vast amounts of computational resources for execution. Especially, the expansive scale and computational demands pose considerable challenges when customizing them for particular downstream tasks, particularly over the hardware platforms constrained by computational capabilities. Parameter Efficient Fine-Tuning (PEFT) provides a practical solution by efficiently adapt the large models over the various downstream tasks. In particular, PEFT refers to the process of adjusting the parameters of a pre-trained large models to adapt it to a specific task while minimizing the number of additional parameters introduced or computational resources required. This approach is particularly important when dealing with LLMs with high parameter counts, as fine-tuning these models from scratch can be computationally expensive and resource-intensive, posing considerable challenges in the supporting system platform design. In this survey, we present comprehensive studies of various PEFT algorithms, examining their performance and computational overhead. Moreover, we provide an overview of applications developed using different PEFT algorithms and discuss common techniques employed to mitigate computation costs for PEFT. In addition to the algorithmic perspective, we overview various real-world system designs to investigate the implementation costs associated with different PEFT algorithms. This survey serves as an indispensable resource for researchers aiming to understand both the PEFT algorithm and its system implementation, offering detailed insights into recent advancements and practical applications.
Overview
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The paper provides a thorough review of Parameter Efficient Fine-Tuning (PEFT) methods, focusing on their classification, algorithmic features, and practical applications for large-scale machine learning models.
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PEFT methods are categorized into four main types: Additive Fine-Tuning, Selective Fine-Tuning, Reparameterized Fine-Tuning, and Hybrid Fine-Tuning, each with distinct strategies to optimize large models.
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The survey discusses the computational efficiency of PEFT methods, detailing strategies like KV-cache management, pruning, and quantization, and explores applications across various domains including NLP, Computer Vision, continual learning, and visual task adaptations.
Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey
The paper "Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey" by Zeyu Han, Chao Gao, Jinyang Liu, Jeff (Jun) Zhang, and Sai Qian Zhang offers a meticulous review of the landscape of Parameter Efficient Fine-Tuning (PEFT) methodologies, particularly in optimizing large-scale machine learning models. This comprehensive survey endeavors to categorize, analyze, and elucidate the diverse PEFT strategies, emphasizing their algorithmic distinctions, computational efficiencies, and practical implementations across various application domains.
Introduction
Large Models (LMs) have made significant strides in multiple domains, such as NLP and Computer Vision (CV), achieving remarkable task performance. Despite these advancements, the accompanying computational costs necessitate innovative solutions like PEFT to adapt these models efficiently to specific downstream tasks. PEFT addresses the challenges posed by the extensive scale and computational demands of fine-tuning large models by minimizing the number of additional parameters introduced and the computational resources required.
PEFT Algorithms
The authors delineate PEFT algorithms into four primary categories, each with its unique approach to handling LMs efficiently:
Additive Fine-Tuning:
- Adapters: Introduces small adapter layers within the Transformer blocks, with variants like Serial Adapter, Parallel Adapter, and Multi-task Adaptation approaches.
- Soft Prompt: Involves appending learnable prompt vectors to the input sequences or key/value matrices, as seen in methods like Prefix-tuning and p-tuning.
- Others: Techniques such as $\text{(IA)}3$ and SSF introduce learnable scale vectors to rescale activations, achieving comparable performance with minimized training overhead.
Selective Fine-Tuning:
- Unstructural Masking: Selects a subset of parameters based on criteria like Fisher information or magnitude to be fine-tuned.
- Structural Masking: Fine-tunes specific structural components or groups of parameters within the model, such as bias terms or specific layers.
Reparameterized Fine-Tuning:
- Low-rank Decomposition: Reparameterizes the model parameters into low-dimensional matrices, as exemplified by LoRA and its derivatives, enabling efficient tuning.
- LoRA Derivatives: Expands upon LoRA with dynamic rank adaptation and Bayesian approaches to improve performance across tasks.
Hybrid Fine-Tuning: Combines multiple PEFT strategies, optimizing their collective advantages for enhanced performance and efficiency, such as UniPELT and NOAH.
Efficiency Strategies
To balance computational cost and memory usage, various strategies are explored:
- KV-cache Management: Essential for optimizing autoregressive token generation processes, focusing on memory efficiency and computational throughput.
- Pruning: Techniques like AdapterDrop and SparseAdapter trim unnecessary parameters to enhance computational efficiency.
- Quantization: Methods such as QLoRA and LQ-LoRA reduce precision to save memory without significantly compromising performance.
- Memory-efficient Methods: Approaches like Side-Tuning and LoRA-FA minimize the memory overhead during training by optimizing the backpropagation process or leveraging alternative training pathways.
Applications
The survey extends beyond conventional NLP and CV tasks, addressing domains such as:
- Visual Instruct Following: Adapting LLMs to comprehend and generate responses to visual inputs using techniques like LLaMA-Adapter.
- Continual Learning: Ensuring retention of acquired knowledge across sequential tasks using strategies like AdapterCL.
- Context Window Extension: Extending the context length in LLMs efficiently with methods like LongLoRA.
Additionally, the adaptation of PEFT to Vision Transformers (ViTs) for tasks like image classification and video recognition, as well as applications in vision-language alignment models (VLAs) and diffusion models, underscores the versatility and broad applicability of PEFT.
System Design Challenges
The survey discusses system-level optimizations for deploying PEFT in both centralized and distributed computing environments. Solutions like DLoRA and PetS highlight methods to balance computational loads, optimize query serving, and enhance system throughput while addressing the specific requirements of multiple PEFT tasks.
Conclusion and Future Directions
In conclusion, the survey articulates the significance of PEFT in making large-scale models more accessible and efficient. It suggests several future research directions, including simplifying hyperparameter tuning, establishing unified benchmarks, enhancing training efficiency, exploring scaling laws, serving more models, and improving data privacy and model compression efficiencies.
By identifying these focal areas, the survey not only consolidates existing knowledge but also sets a pathway for future innovations in the field of parameter-efficient fine-tuning, ensuring the continued relevance and efficacy of large-scale models in an ever-evolving computational landscape.
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