Mechanistic Design and Scaling of Hybrid Architectures (2403.17844v2)

Abstract: The development of deep learning architectures is a resource-demanding process, due to a vast design space, long prototyping times, and high compute costs associated with at-scale model training and evaluation. We set out to simplify this process by grounding it in an end-to-end mechanistic architecture design (MAD) pipeline, encompassing small-scale capability unit tests predictive of scaling laws. Through a suite of synthetic token manipulation tasks such as compression and recall, designed to probe capabilities, we identify and test new hybrid architectures constructed from a variety of computational primitives. We experimentally validate the resulting architectures via an extensive compute-optimal and a new state-optimal scaling law analysis, training over 500 LLMs between 70M to 7B parameters. Surprisingly, we find MAD synthetics to correlate with compute-optimal perplexity, enabling accurate evaluation of new architectures via isolated proxy tasks. The new architectures found via MAD, based on simple ideas such as hybridization and sparsity, outperform state-of-the-art Transformer, convolutional, and recurrent architectures (Transformer++, Hyena, Mamba) in scaling, both at compute-optimal budgets and in overtrained regimes. Overall, these results provide evidence that performance on curated synthetic tasks can be predictive of scaling laws, and that an optimal architecture should leverage specialized layers via a hybrid topology.

• The paper introduces the Mechanistic Architecture Design (MAD) framework, integrating synthetic tasks to evaluate model capabilities.
• The study finds that hybrid architectures mixing different computational primitives consistently outperform non-hybrid models.
• Results reveal a strong correlation between MAD task outcomes and scaling performance, establishing state-optimal scaling laws.

Scaling Hybrid AI Architectures: A Mechanistic Study

Introduction

Recent advancements in deep learning have been significantly driven by the innovative design of neural network architectures, with the Transformer architecture leading the way in various domains. However, the journey to discovering new, more efficient architectures remains complex, resource-intensive, and largely heuristic. This paper proposes a systematic approach to architecture design, focusing on the integration of mechanistic insight into the design process. The paper introduces a novel framework, dubbed Mechanistic Architecture Design (MAD), that leverages synthetic tasks to evaluate and predict the performance of large-scale models based on their architectural choices. By exploring a mixture of computational primitives and devising hybrid architectures tested across a spectrum of synthetic tasks, the research offers a new pathway to optimize model designs predictively.

Methodology

Mechanistic Architecture Design (MAD)

At the core of this research lies the introduction of Mechanistic Architecture Design (MAD). MAD is composed of a series of synthetic tasks designed to evaluate specific capabilities of a model, such as memory, recall, and compression. These tasks serve as proxies to anticipate the performance and scalability of different architectural designs. Notably, MAD facilitates the rapid prototyping and systematic assessment of new or modified architectures, bringing mechanistic insights directly into the design process.

Architectural Improvements and Hybridization

A significant part of the paper is dedicated to experimenting with and validating various computational primitives to uncover paths for architectural enhancement. The authors explore simple yet effective modifications, such as introducing hybrid topologies that mix different computational primitives in a single architecture. The experimentation reveals that hybridized architectures, leveraging the strengths of distinct primitives, consistently outperform non-hybrid models in the MAD framework.

Results

Scaling Laws of Emerging Architectures

The analysis goes further to validate the MAD framework's predictions against extensive scaling law experiments, encompassing 500 LLMs with parameters ranging from 70 million to 7 billion. The findings confirm that hybrid architectures discovered through MAD not only scale better but also adapt more robustly to regimes where models are trained beyond optimal compute budgets.

State-Optimal Scaling Laws

An intriguing aspect of this paper is the introduction of state-optimal scaling laws, a novel concept aimed at evaluating how model perplexity scales with the state dimension of different architectures. This analysis opens a new dimension in architecture evaluation, emphasizing not just compute efficiency but also memory and latency considerations vital for practical deployment scenarios.

Correlation Between MAD Synthetic Tasks and Scaling Performance

Perhaps one of the most compelling outcomes of this research is the demonstrated correlation between the MAD synthetic tasks' outcomes and large-scale model performance. This correlation underscores the potential of MAD as a predictive tool for architecture design, streamlining the pathway from conception to validation of new architectures.

Implications

The implications of this research are multifold. Theoretically, it offers a structured approach to understanding and leveraging the capabilities of various computational primitives. Practically, it paves the way for more efficient and systematic architecture design, potentially accelerating the advancement of AI technologies. Moreover, by introducing state-optimal scaling laws, the paper highlights the importance of considering inference efficiency in the early stages of architecture design.

Future Directions

The research opens several avenues for future exploration. One immediate direction is extending the MAD framework to encompass a broader range of tasks and model capabilities. Another exciting prospect is applying MAD in conjunction with automated architecture search techniques to further streamline the design process. Moreover, exploring the implications of state-optimal scaling in the context of specific applications could yield practical insights for deploying next-generation AI models.

Conclusion

This paper presents a leap forward in the methodology of neural network architecture design. By integrating mechanistic insights into a systematic design framework, the research not only simplifies the prototyping of new architectures but also provides a predictive lens to their scalability and performance. The findings from this work could significantly impact how future neural network architectures are conceived, designed, and optimized for a wide range of applications.