The article introduces a novel approach to transformer-based language models that allows for dynamic allocation of computational resources, enhancing efficiency. By implementing a top-k routing mechanism, the model learns to optimize compute across different layers and positions within the sequence, adhering to a predefined compute budget. This technique maintains a predictable total computation while enabling the model to flexibly determine which tokens to process, ultimately improving efficiency in training and evaluation processes compared to traditional, uniform FLOPs allocation methods.
In this study, we demonstrate a method for transformer-based language models to dynamically allocate FLOPs, optimizing performance and efficiency across different layers and inputs.
Our approach enforces a compute budget by capping tokens for self-attention and MLP computations, allowing the model to fluidly determine which tokens to process.
This routing mechanism enhances efficiency, making computational expenditure predictable while still being context-sensitive, enabling models to adaptively allocate compute based on sequence demands.
We reveal that through dynamic allocation of compute resources, models can optimize their performance, moving away from uniform FLOPs distribution towards a more efficient allocation strategy.
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