A Practitioner's Guide to Continual Multimodal Pretraining (2408.14471v2)

Abstract: Multimodal foundation models serve numerous applications at the intersection of vision and language. Still, despite being pretrained on extensive data, they become outdated over time. To keep models updated, research into continual pretraining mainly explores scenarios with either (1) infrequent, indiscriminate updates on large-scale new data, or (2) frequent, sample-level updates. However, practical model deployment often operates in the gap between these two limit cases, as real-world applications often demand adaptation to specific subdomains, tasks or concepts -- spread over the entire, varying life cycle of a model. In this work, we complement current perspectives on continual pretraining through a research test bed as well as provide comprehensive guidance for effective continual model updates in such scenarios. We first introduce FoMo-in-Flux, a continual multimodal pretraining benchmark with realistic compute constraints and practical deployment requirements, constructed over 63 datasets with diverse visual and semantic coverage. Using FoMo-in-Flux, we explore the complex landscape of practical continual pretraining through multiple perspectives: (1) A data-centric investigation of data mixtures and stream orderings that emulate real-world deployment situations, (2) a method-centric investigation ranging from simple fine-tuning and traditional continual learning strategies to parameter-efficient updates and model merging, (3) meta learning rate schedules and mechanistic design choices, and (4) the influence of model and compute scaling. Together, our insights provide a practitioner's guide to continual multimodal pretraining for real-world deployment. Our benchmark and code is here: https://github.com/ExplainableML/fomo_in_flux.

• The paper introduces FoMo-in-Flux, a benchmark of 63 diverse datasets designed to evaluate continual multimodal pretraining techniques.
• The study analyzes methods ranging from fine-tuning and parameter-efficient updates to model merging, highlighting trade-offs between knowledge accumulation and retention.
• The authors offer practical guidelines on learning rate meta-schedules and data ordering to enhance real-world model adaptation and performance.

Overview of "A Practitioner's Guide to Continual Multimodal Pretraining"

Introduction

The paper, "A Practitioner's Guide to Continual Multimodal Pretraining," addresses a critical issue prevalent in the deployment of multimodal foundation models: the need for models to adapt continually to new tasks, subdomains, and concepts over time. Traditional pretraining on large datasets often leaves models outdated as newer tasks emerge, necessitating continual pretraining to maintain relevance.

Contributions

The paper makes several notable contributions:

1. Introduction of FoMo-in-Flux: The authors introduce FoMo-in-Flux, a benchmark designed to facilitate the paper and development of continual multimodal pretraining techniques. This benchmark consists of 63 datasets spanning diverse visual and semantic domains, enhanced with captions for multimodal pretraining.
2. Comprehensive Analysis: The paper undertakes a multi-faceted investigation into continual multimodal pretraining from various perspectives:
   • Data-centric: Studies data mixtures and stream orderings.
   • Method-centric: Examines a variety of methods ranging from simple fine-tuning to parameter-efficient updates and model merging.
   • Training strategies: Analyzes the impact of different learning rate schedules and mechanistic design choices.
3. Practical Insights: Offers a practitioner's guide summarizing key insights for performing continual multimodal pretraining in real-world deployment scenarios.

Key Findings

Methodologies for Continual Pretraining

The authors explore and analyze a gamut of approaches:

• Naive Continual Fine-tuning: Provides the strongest knowledge accumulation but suffers from significant retention loss.
• Parameter-efficient Methods (LoRA, DoRA, VeRA): Show differing degrees of accumulation and retention, with adaptive methods like LoRA performing favorably in terms of balancing these trade-offs.
• Traditional Continual Learning Methods (EWC, SI): EWC, with high regularization, results in strong retention but weak accumulation. Conversely, SI offers minimal impact due to low regularization.
• Model Merging: Exhibits promising results, especially with Exponential Moving Average (EMA) and ZeroShot Merge strategies, balancing both knowledge retention and accumulation effectively.

Learning Rate Schedules and Meta-Schedules

Learning rates and their scheduling significantly influence the effectiveness of continual pretraining:

• Meta-Schedules: Introduction of meta learning rate schedules that account for the model's deviation from initial pretraining weights helps mitigate stability issues and improve both retention and accumulation.

Data-Centric Perspectives

• Order of Updates: Different orderings (easy-to-hard, concept frequency, similarity, and random) influence the trajectory of the model's performance but converge to similar endpoints for long-term continual pretraining.
• Data Mixtures: Highlight the importance of replaying previously seen data to "iid-ify" the continual pretraining process, thus improving retention and adaptation.

Implications and Future Directions

The implications of this research are significant for both theoretical and practical aspects of AI deployment:

• Theoretical Implications: The results emphasize the necessity of accounting for the complex interactions between model architecture, data diversity, and training strategies in continual learning.
• Practical Implications: The practical guidelines offered by the authors can directly impact how machine learning practitioners approach model updates, leading to more efficient and effective continuous adaptation strategies.

Future research can build on these insights by:

• Exploring Infinite Learning Rate Schedules: Further investigations into task-specific and order-conditioned learning rate schedules could yield improved continual pretraining methodologies.
• Scaling Model and Compute: Additional studies on scaling up models and compute budgets could offer deeper understanding and more robust continual learning frameworks.
• Text-to-Image Models: Extending the research to continual pretraining of generative models could open up novel applications and improve model robustness.

Conclusion

This paper provides a comprehensive framework and guidelines for conducting continual multimodal pretraining. By introducing the FoMo-in-Flux benchmark and conducting extensive experiments, the authors offer valuable insights into balancing knowledge accumulation and retention, optimizing learning rate schedules, and managing data-centric deployment scenarios. The practical applications of these findings can lead to more cost-effective, scalable, and up-to-date multimodal AI systems.