Addax: A Memory-Efficient Optimization Algorithm for Fine-Tuning Large Language Models

Core Concepts

Addax is a novel optimization algorithm designed for fine-tuning large language models (LLMs) that addresses the memory limitations of traditional methods like Adam while achieving faster convergence and better performance than memory-efficient alternatives like MeZO.

Abstract

• Bibliographic Information: Li, Z., Zhang, X., Zhong, P., Deng, Y., Razaviyayn, M., & Mirrokni, V. (2024). Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models. arXiv preprint arXiv:2410.06441v1.

• Research Objective: This paper introduces Addax, a novel optimization algorithm designed to address the memory challenges associated with fine-tuning large language models (LLMs). The authors aim to demonstrate that Addax can achieve comparable or superior performance to existing methods while significantly reducing memory requirements.

• Methodology: Addax combines first-order and zeroth-order gradient estimates to optimize memory usage during fine-tuning. It strategically assigns data batches to either in-place Stochastic Gradient Descent (IP-SGD) or Memory-Efficient Zeroth-order Optimizer (MeZO) based on the input sequence length. Shorter sequences, requiring less memory for gradient computation, are assigned to IP-SGD, while longer sequences are assigned to MeZO. This approach allows Addax to maintain a memory footprint similar to MeZO while benefiting from the faster convergence of IP-SGD.

• Key Findings: The authors conducted extensive experiments on various LLM architectures (masked LM and autoregressive LM), model scales (350M to 70B parameters), and tasks (classification, multiple-choice questions, content generation). Their results demonstrate that Addax consistently outperforms MeZO in terms of accuracy and convergence speed while maintaining a comparable memory footprint. Notably, Addax successfully fine-tuned the OPT-13B model on a single A100 GPU across all nine tasks, while SGD and IP-SGD failed due to memory limitations. Furthermore, Addax achieved superior performance compared to MeZO, SGD, and IP-SGD when fine-tuning larger models like OPT-30B, OPT-66B, and Llama-2-70B.

• Main Conclusions: Addax offers a practical and efficient solution for fine-tuning LLMs, particularly in resource-constrained settings. Its ability to balance memory efficiency, convergence speed, and model performance makes it a valuable tool for researchers and practitioners working with large-scale language models.

• Significance: This research addresses a crucial bottleneck in LLM fine-tuning, enabling wider accessibility to advanced language processing capabilities. By reducing the hardware requirements for fine-tuning, Addax facilitates research and development in the field, particularly for individuals and institutions with limited resources.

• Limitations and Future Research: While Addax introduces a new hyperparameter (α), requiring tuning for optimal performance, the authors acknowledge the need for further investigation into the regularization effects of zeroth-order gradient estimates on first-order methods. Future research could explore the application of Addax in pre-training tasks and its potential synergy with other memory-efficient techniques like PEFT, quantization, and memory-efficient attention mechanisms.

Stats

Fine-tuning a 13-billion-parameter model like OPT in mixed precision requires over 316 GB of memory.
Fine-tuning OPT-13B with Adam with a batch size of 8 requires 4×H100 GPUs (316GB total).
Utilizing PEFT decreases the memory requirement for fine-tuning OPT-13B to 2×H100 GPUs (158GB total) with a batch size of 16.
Model inference for OPT-13B requires 25GB of memory.
Addax outperforms MeZO in terms of accuracy/F1 score by 14% and runs 15× faster on average for the OPT-13B model on a single A100 GPU.
Addax outperforms MeZO in terms of accuracy/F1 score by > 16% and runs 30× faster on average for the OPT-30B model on a single H100 GPU.

Quotes

Key Insights Distilled From

Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models

by Zeman Li, Xi... at arxiv.org 10-10-2024

https://arxiv.org/pdf/2410.06441.pdf

Deeper Inquiries

How might the principles behind Addax's memory-efficient optimization be applied to other memory-intensive machine learning tasks beyond language modeling?

The core principles of Addax, centered around memory-aware gradient estimation, hold significant potential for broader application in memory-intensive machine learning tasks beyond language modeling. Here's how:

Identifying Memory Bottlenecks: Similar to analyzing sequence length in language models, other domains can be analyzed to identify specific data characteristics or model components that disproportionately contribute to memory consumption. For instance, in computer vision, image resolution or the presence of complex objects could be key factors.

Adaptive Gradient Estimation: Addax's strategy of combining zeroth-order and first-order gradients can be adapted to other tasks. Memory-intensive components or data samples could leverage the memory efficiency of zeroth-order methods, while less demanding parts could benefit from the accuracy of first-order optimization.

Task-Specific Hybrid Optimization: The concept of Addax can be extended to create hybrid optimization pipelines. For example, in reinforcement learning, where experience replay buffers are memory intensive, Addax's principles could be used to selectively update experiences using a combination of zeroth and first-order methods.

Hardware-Aware Optimization: Addax's focus on memory efficiency naturally lends itself to hardware-aware optimization. By profiling memory usage on different hardware platforms, the balance between zeroth and first-order gradient computation can be dynamically adjusted to maximize resource utilization.

Examples beyond Language Modeling:

Computer Vision: In high-resolution image segmentation, Addax's principles could be applied to process different regions of an image with varying levels of detail using a combination of zeroth and first-order methods.
Recommendation Systems: When dealing with large user-item interaction matrices, Addax's approach could be used to update user and item embeddings with varying accuracy based on their frequency and contribution to memory usage.

Could the reliance on zeroth-order gradients in Addax potentially limit its ability to reach the same level of fine-grained accuracy as traditional first-order methods in certain complex NLP tasks?

Yes, the reliance on zeroth-order gradients in Addax could potentially limit its ability to reach the same level of fine-grained accuracy as traditional first-order methods in certain complex NLP tasks. This limitation stems from the inherent nature of zeroth-order optimization:

Noisy Gradient Estimates: Zeroth-order gradients are inherently noisy estimates of the true gradient, obtained through finite differences. This noise can hinder the optimizer's ability to converge to sharp minima, potentially leading to suboptimal solutions compared to the more precise gradient information provided by first-order methods.

Sensitivity to Hyperparameters: Zeroth-order methods often exhibit higher sensitivity to hyperparameters like the perturbation size (ϵ). Careful tuning is required to balance exploration and noise, which can be challenging in complex NLP tasks with high-dimensional parameter spaces.
Situations where the limitations might be pronounced:

Tasks requiring fine-grained linguistic distinctions: Tasks like natural language inference, semantic role labeling, or machine translation, which rely on capturing subtle linguistic nuances, might suffer from the noise introduced by zeroth-order gradients.
Highly non-convex optimization landscapes: In NLP tasks with complex loss surfaces characterized by numerous local minima or saddle points, the noisy gradient estimates of zeroth-order methods might make it difficult to navigate efficiently towards a global optimum.
Mitigating the limitations:

Hybrid approaches: Addax's own hybrid approach, combining zeroth and first-order gradients, is a step towards mitigating these limitations. Further research could explore more sophisticated strategies for dynamically adjusting the balance between the two based on task complexity and available memory.
Variance reduction techniques: Incorporating variance reduction techniques within the zeroth-order gradient estimation process could help reduce noise and improve the accuracy of the optimization process.

If the trend of ever-growing language models continues, how might the development of memory-efficient algorithms like Addax influence the accessibility and democratization of advanced NLP technologies?

The development of memory-efficient algorithms like Addax is crucial for the democratization of advanced NLP technologies, especially as language models continue to grow in size. Here's how Addax can influence accessibility:

Reduced Hardware Requirements: By significantly reducing the memory footprint of fine-tuning, Addax enables researchers and practitioners with limited resources to work with larger, more powerful language models. This lowers the barrier to entry for individuals and institutions without access to massive compute clusters.

Enabling Fine-tuning on Commodity Hardware: Addax's ability to fine-tune large models on a single or a few GPUs paves the way for utilizing more widely available and affordable hardware. This is in stark contrast to traditional methods that often necessitate specialized and expensive hardware setups.

Faster Experimentation and Iteration: Reduced memory requirements translate to faster training times, allowing for quicker experimentation and iteration cycles. This is particularly beneficial for smaller teams or individual researchers who can now explore a wider range of ideas and model configurations without being bottlenecked by computational constraints.

Decentralization of NLP Research and Development: As memory-efficient algorithms like Addax become more prevalent, we can expect a shift away from centralized control over large language models. Smaller entities will be empowered to fine-tune and adapt these models for their specific needs and domains, fostering innovation and diversity in NLP applications.

New Possibilities for On-Device NLP: The memory efficiency of Addax could potentially enable the fine-tuning and deployment of powerful language models on resource-constrained devices like smartphones or edge devices. This opens up exciting possibilities for personalized and privacy-preserving NLP applications.
In conclusion, Addax and similar memory-efficient algorithms are essential for ensuring that the benefits of advanced NLP are not limited to a select few with vast resources. By lowering the barriers to entry and enabling wider participation, these algorithms have the potential to democratize NLP technologies and unlock a new wave of innovation across various domains.