Configurable Foundation Models: Building LLMs from a Modular Perspective (2409.02877v1)

Abstract: Advancements in LLMs have recently unveiled challenges tied to computational efficiency and continual scalability due to their requirements of huge parameters, making the applications and evolution of these models on devices with limited computation resources and scenarios requiring various abilities increasingly cumbersome. Inspired by modularity within the human brain, there is a growing tendency to decompose LLMs into numerous functional modules, allowing for inference with part of modules and dynamic assembly of modules to tackle complex tasks, such as mixture-of-experts. To highlight the inherent efficiency and composability of the modular approach, we coin the term brick to represent each functional module, designating the modularized structure as configurable foundation models. In this paper, we offer a comprehensive overview and investigation of the construction, utilization, and limitation of configurable foundation models. We first formalize modules into emergent bricks - functional neuron partitions that emerge during the pre-training phase, and customized bricks - bricks constructed via additional post-training to improve the capabilities and knowledge of LLMs. Based on diverse functional bricks, we further present four brick-oriented operations: retrieval and routing, merging, updating, and growing. These operations allow for dynamic configuration of LLMs based on instructions to handle complex tasks. To verify our perspective, we conduct an empirical analysis on widely-used LLMs. We find that the FFN layers follow modular patterns with functional specialization of neurons and functional neuron partitions. Finally, we highlight several open issues and directions for future research. Overall, this paper aims to offer a fresh modular perspective on existing LLM research and inspire the future creation of more efficient and scalable foundational models.

• The paper introduces a modular LLM framework by decomposing models into emergent and customized bricks for enhanced computational efficiency.
• It details operations like routing, combination, updating, and growing to dynamically assemble model capabilities.
• Empirical results on Llama-3 and Mistral models validate sparse activation, supporting scalable and adaptable AI architectures.

Configurable Foundation Models: Building LLMs from a Modular Perspective

The recent advancements in LLMs have set the foundation for groundbreaking AI applications. However, challenges tied to computational efficiency, scalability, and the necessity to incorporate diverse capabilities continue to pose significant barriers. The paper "Configurable Foundation Models: Building LLMs from a Modular Perspective" addresses these challenges by proposing a novel framework that decomposes LLMs into modular units, termed "bricks," and demonstrates the feasibility and benefits of this modular approach in building more efficient and scalable LLMs.

Overview of Configurable Foundation Models

The paper introduces the concept of configurable foundation models, which view LLMs as composites of numerous functional bricks. These bricks aid in achieving both computational efficiency and dynamic assembly to handle complex tasks. The framework categorizes bricks as "emergent bricks" formed during pre-training and "customized bricks" constructed during post-training. The primary idea is to leverage the flexibility and composability of these bricks to dynamically configure LLMs based on specific instructions.

Emergent Bricks

Emergent bricks refer to the functional partitions that naturally develop during the LLM pre-training phase. These bricks exhibit sparse activation, implying that only a subset of parameters is involved in processing specific inputs, which can enhance computational efficiency significantly. The emergent bricks are further subdivided into human-defined bricks (such as Mixture-of-Experts layers) and self-organized bricks (clusters of neurons that spontaneously form specialized functionalities during pre-training).

Customized Bricks

Customized bricks, or plugins, are designed and trained post pre-training to infuse the LLMs with additional capabilities and knowledge. These bricks can store external knowledge, task-specific skills, understand various modalities, or even address specific application needs efficiently. By constructing these bricks with unified protocols, the model achieves higher flexibility and can be easily updated to adapt to new information without requiring a full model re-training.

Key Operations in Configurable Foundation Models

To effectively utilize these modular bricks, the authors define four primary operations:

1. Routing and Retrieval: Dynamically selecting relevant bricks based on the input instruction.
2. Combination: Merging multiple bricks to achieve composite capabilities, either through parameter averaging or sequential stitching.
3. Updating: Refining bricks over time to incorporate new knowledge while maintaining existing functionalities.
4. Growing: Expanding the brick repository to address emerging requirements, thus ensuring adaptability and scalability.

Empirical Analysis

The empirical analysis emphasizes that widely-used LLMs (Llama-3-8B-Instruct and Mistral-7B-Instruct-v0.3) demonstrate sparse activation and functional specialization of neurons, supporting the modular paradigm proposed. The experiments show that only parts of the model are engaged during specific tasks, thereby validating the potential to optimize computational efficiency through modularity.

Future Research Directions

The paper identifies several vital areas for future research:

• Exploring the correlation and interaction between emergent and customized bricks to minimize redundancy and conflict.
• Developing effective protocols for constructing a wide range of bricks to enable broader community participation in collaborative model training.
• Establishing evaluation metrics tailored to modular architecture, such as sparsity and coupling.
• Enhancing computational frameworks to support efficient brick-based operations and distributed computing.
• Investigating the integration of multiple model-level bricks to create robust and scalable multi-model cooperation systems.

Conclusion

The modular approach presented in the paper promises significant advancements in the development and deployment of LLMs. By dividing LLMs into independent, function-specific bricks, the paper advocates for a more dynamic, efficient, and scalable AI model architecture. The thorough investigation and empirical evidence supporting the brick-based framework lay the groundwork for future innovations in AI research, offering a fresh perspective on the construction and utilization of next-generation LLMs.