Compressing Large Language Models Using a Linear Combination of Random Basis

Conceitos essenciais

NOLA, a novel reparameterization technique, enables efficient fine-tuning of large language models by decoupling the number of trainable parameters from the model architecture and the chosen rank, breaking the rank-one decomposition limit of existing methods like LoRA.

Resumo

The content discusses a novel method called NOLA (Networks as Linear Combination of Low Rank Random Basis) for efficiently fine-tuning large language models like GPT-2 and vision transformers like ViT.

Key highlights:

• Large pre-trained neural networks like GPT and ViT have shown remarkable generalization abilities, but fine-tuning and storing the entire set of model parameters for each task is impractical due to their massive size.
• Existing methods like LoRA (Low-Rank Adaptation) enable efficient fine-tuning by optimizing a low-rank decomposition of the weight changes, but are limited by the rank-one decomposition and the dependence on the model architecture.
• NOLA overcomes these limitations by reparameterizing the low-rank matrices in LoRA as a linear combination of randomly generated basis matrices. This allows decoupling the number of trainable parameters from both the choice of rank and the network architecture.
• NOLA achieves comparable or better performance than LoRA with significantly fewer trainable parameters, especially in the low-data regime. It can halve the parameters in larger models compared to LoRA with rank one, without sacrificing performance.
• NOLA can also be further improved by quantizing the coefficients of the linear combination, reducing both computation and storage.
• Experiments on natural language generation tasks using GPT-2 and image classification tasks using ViT show the effectiveness of NOLA.

Estatísticas

GPT-2 Medium with NOLA (0.036M parameters) achieves a BLEU score of 70.12 on the E2E NLG Challenge, which is 20 times more compact compared to LoRA with rank 4 (0.77M parameters) that achieves a BLEU score of 70.4.
On the CIFAR-10 dataset with 5 training samples per class, NOLA with 47K parameters outperforms LoRA with 141K parameters.
On the CUB-200-2011 dataset with 5 training samples per class, NOLA with 94K parameters matches the performance of LoRA with 375K parameters.

Citações

"NOLA allows one to decouple the number of trainable parameters from both the choice of rank and the network architecture, and it breaks the rank-one decomposition limit of LoRA."
"NOLA offers a more compact reparameterization solution that can be stored effectively in GPU memory, allowing for on-demand reconstruction directly on the GPU itself when a new task arises."

Principais Insights Extraídos De

NOLA: Compressing LoRA using Linear Combination of Random Basis

by Soroush Abba... às arxiv.org 05-01-2024

https://arxiv.org/pdf/2310.02556.pdf

Perguntas Mais Profundas

How can the theoretical properties of NOLA, such as its ability to cover a higher dimensional subspace compared to PRANC, be further analyzed and understood

To further analyze and understand the theoretical properties of NOLA, particularly its ability to cover a higher dimensional subspace compared to PRANC, one could delve into a more detailed exploration of the solution space. This could involve conducting a systematic study on the rank distribution of the solutions generated by both NOLA and PRANC across a range of scenarios. By varying parameters such as the number of basis vectors, the dimensionality of weight matrices, and the total number of parameters, one can gain insights into how the solutions span the entire space. Analyzing the rank distribution in different settings can provide a clearer picture of the expressive power and flexibility of NOLA compared to PRANC. Additionally, conducting experiments with different datasets and model architectures can help validate the consistency of these properties across various contexts.

What are the potential trade-offs or limitations of NOLA compared to other parameter-efficient fine-tuning methods like prompt tuning or adapter-based approaches

While NOLA offers significant advantages in terms of decoupling the number of trainable parameters from the rank choice and network architecture, there are potential trade-offs and limitations to consider compared to other parameter-efficient fine-tuning methods. Prompt tuning and adapter-based approaches, for instance, may offer more task-specific customization and adaptability by optimizing specific input tokens or adding small modules to intermediate layers. These methods may excel in scenarios where fine-tuning requires intricate adjustments at a granular level. In contrast, NOLA's strength lies in its ability to compress models efficiently without compromising performance, making it ideal for scenarios where storage and computational efficiency are paramount. However, NOLA may not offer the same level of task-specific fine-tuning capabilities as prompt tuning or adapters, which could be a limitation in certain specialized applications that require highly tailored adjustments.

Could the NOLA reparameterization technique be extended or adapted to other types of neural network architectures beyond transformers, such as convolutional or recurrent models

The NOLA reparameterization technique can indeed be extended or adapted to other types of neural network architectures beyond transformers, such as convolutional or recurrent models. The key concept of NOLA, which involves reparameterizing weight matrices as a linear combination of randomly generated basis matrices, can be applied to various network structures. For convolutional models, the weight tensors can be reshaped into 2D matrices, similar to how it is done in transformers, and compressed using the NOLA approach. By constraining the rank and optimizing the linear mixture coefficients, NOLA can effectively reduce the number of parameters needed for fine-tuning in convolutional networks. Similarly, in recurrent models, the weight matrices can be restructured and compressed using the NOLA technique, enabling efficient adaptation and storage for task-specific models. This adaptability showcases the versatility and applicability of NOLA across different neural network architectures.