CLIP-UP: A Simple and Efficient Mixture-of-Experts CLIP Training Recipe with Sparse Upcycling (2502.00965v2)

Abstract: Mixture-of-Experts (MoE) models are crucial for scaling model capacity while controlling inference costs. While integrating MoE into multimodal models like CLIP improves performance, training these models is notoriously challenging and expensive. We propose CLIP-Upcycling (CLIP-UP), an efficient alternative training strategy that converts a pre-trained dense CLIP model into a sparse MoE architecture. Through extensive experimentation with various settings and auxiliary losses, we demonstrate that CLIP-UP significantly reduces training complexity and cost. Remarkably, our sparse CLIP B/16 model, trained with CLIP-UP, outperforms its dense counterpart by 7.2% and 6.6% on COCO and Flickr30k text-to-image Recall@1 benchmarks respectively. It even surpasses the larger CLIP L/14 model on this task while using only 30% of the inference FLOPs. We further demonstrate the generalizability of our training recipe across different scales, establishing sparse upcycling as a practical and scalable approach for building efficient, high-performance CLIP models.

• The paper presents a new training method that converts dense CLIP models into sparse MoE architectures, boosting text-to-image retrieval recall by up to 7.2%.
• It leverages sparse upcycling to reduce training and inference costs, cutting up to 70% of computational FLOPs compared to dense models.
• Robust auxiliary losses and scalability across various CLIP architectures ensure consistent performance enhancements in multimodal applications.

Exploring CLIP-UP: Efficient Mixture-of-Experts Training for CLIP

The research paper "CLIP-UP: A Simple and Efficient Mixture-of-Experts CLIP Training Recipe with Sparse Upcycling" explores the complexities and opportunities of adopting Mixture-of-Experts (MoE) architectures for CLIP models. The paper emerges amidst the growing demands to enhance the performance of multimodal models without incurring hefty computational and inference expenses.

Overview of CLIP-UP Approach

CLIP-UP introduces a distinct training methodology that transitions pre-trained dense CLIP models into sparse MoE architectures. This approach, termed sparse upcycling, leverages the advantages of a well-initialized dense model, effectively mitigating the high training costs traditionally associated with MoE models. By capitalizing on this sparsity, CLIP-UP enhances the performance of CLIP models with significant reductions in training FLOPs and inference costs.

Core Contributions

1. Training Efficiency and Performance Gains: The experimental results underscore that CLIP-UP substantially outperforms traditional dense models across benchmarks like COCO and Flickr30k with text-to-image retrieval Recall@1 improvements of 7.2% and 6.6%, respectively. These improvements are achieved with a significant reduction in inference computational requirements—up to 70% less FLOPs compared to some larger models.
2. Scalability: CLIP-UP demonstrates scalability across various CLIP architectures, from B/32 to L/14, consistently improving upon the dense models in retrieval and classification tasks. This scalability implies that CLIP-UP can cater to different operational needs and resource availability.
3. Robustness Across Configurations: The research explores both shared and separated backbone configurations, demonstrating that the sparse upcycling technique consistently bolsters performance, regardless of the underlying architecture.

Technical Insights

The integration of MoE layers in CLIP-UP entails replacing dense MLPs within certain transformer blocks, allowing selective activation of specialized experts for processing inputs. This selective activation drastically improves computational efficiency during inference compared to a fully dense model. Furthermore, the paper explores auxiliary losses like load balance and router z-loss to ensure stable and effective training, highlighting their role in maintaining an even distribution of tokens across the experts.

One of the notable challenges addressed is the initial quality drop observed when upcycling models. CLIP-UP counteracts this by normalizing routing outputs post-routing, thus maintaining token importance for input sequences, especially relevant for nuanced tasks like text-to-image retrieval.

Future Directions

The findings from CLIP-UP signal promising avenues for future research and application in AI:

• Enhanced Training Techniques: Continued exploration of auxiliary losses and capacity factors can further refine training stability and efficiency, unlocking even greater potentials in large-scale multimodal models.
• Broader Applicability: While the focus of this paper is on CLIP, sparse upcycling may be extended to other architectures, potentially revolutionizing how sparsity is applied across different AI paradigms.
• Resource-Constrained Environments: As AI applications venture into more resource-constrained settings such as edge computing, methodologies like CLIP-UP become increasingly pivotal, enabling sophisticated AI functionalities with limited computational power.

In conclusion, this paper presents significant advancements in the efficient training of MoE-based models for multimodal applications, showcasing CLIP-UP as a powerful methodology to enhance performance while curbing computational and financial costs. The robust and scalable nature of CLIP-UP paves the way for broader implementation in AI systems, fostering a new chapter of efficient multimodal learning.