Modularformer

Modularformer is a neural network architecture that integrates modular design with the Transformer model, enabling the composition of specialized functional modules within a single model. It aims to combine a strong global modeling capability of transformers with the flexibility and reuse of modular components.

Architecturally, a modularformer comprises a transformer backbone augmented with a set of modular blocks or experts. A routing mechanism selects which modules participate in processing a given input, allowing dynamic assembly of task- or domain-specific pathways. Modules can be implemented as adapters, small feedforward networks, or more specialized neural modules. The routing controller can be based on gating networks, reinforcement learning, or soft/hard attention, and may operate at the layer or sublayer level.

Training and optimization: Depending on design goals, modules can be trained jointly with the backbone or through staged training and fine-tuning. Techniques include sparsity-regularized mixture of experts, selective dropout, and module reuse constraints to promote parameter efficiency and transfer. In continual or multi-task settings, new tasks can be mapped to a subset of modules with minimal retraining.

Advantages and use cases: Modularformer aims to improve parameter efficiency, enable rapid adaptation to new tasks, and reduce interference between tasks by isolating modules. It is potentially beneficial for multi-task NLP, multimodal tasks, and code-related workloads where reusable components can be combined in different ways.

Limitations: The routing layer adds computational overhead and can complicate training. There is a risk of underutilized modules, catastrophic forgetting among modules, and design complexity requiring careful hyperparameter tuning and data management.