Efficient Transformers with Dynamic Token Pooling (2211.09761v2)

Abstract: Transformers achieve unrivalled performance in modelling language, but remain inefficient in terms of memory and time complexity. A possible remedy is to reduce the sequence length in the intermediate layers by pooling fixed-length segments of tokens. Nevertheless, natural units of meaning, such as words or phrases, display varying sizes. To address this mismatch, we equip LLMs with a dynamic-pooling mechanism, which predicts segment boundaries in an autoregressive fashion. We compare several methods to infer boundaries, including end-to-end learning through stochastic re-parameterisation, supervised learning (based on segmentations from subword tokenizers or spikes in conditional entropy), as well as linguistically motivated boundaries. We perform character-level evaluation on texts from multiple datasets and morphologically diverse languages. The results demonstrate that dynamic pooling, which jointly segments and models language, is both faster and more accurate than vanilla Transformers and fixed-length pooling within the same computational budget.

• The paper introduces a dynamic pooling mechanism that learns variable-length token segments to enhance Transformer efficiency.
• It uses stochastic re-parameterisation and supervised boundary predictions to adaptively align segmentation with linguistic cues.
• Experimental results show improved bits per character scores and reduced computational cost across diverse language tasks.

Efficient Transformers with Dynamic Token Pooling: An Overview

The research paper presents an exploration into enhancing the efficiency of Transformer models through a novel dynamic pooling mechanism that segments token sequences into variable-length segments while maintaining autoregressive generation capabilities. This approach addresses a significant limitation in the Transformer architecture—its memory and computation inefficiency, which scales quadratically with sequence length.

Problem Context and Motivation

Transformers are the backbone of numerous state-of-the-art generative models due to their capacity to process large datasets and exhibit emergent capabilities. However, their application to long sequences remains problematic due to the $\mathcal{O}(l^2n)$ complexity, where $l$ is the sequence length and $n$ is the number of layers. The literature has proposed several solutions, such as sparsifying self-attention or introducing architectures like Hourglass Transformers, which shorten sequences in intermediate layers using fixed-size token pooling. Nonetheless, the static nature of fixed pooling can misalign with the linguistic structure, where semantic units inherently vary in size.

Proposed Solution

The authors propose a Transformer variant that uses dynamic pooling to learn variable-length token segments, aimed at enhancing both computational efficiency and model performance. This method involves a boundary prediction mechanism using several techniques including:

• Stochastic Re-parameterisation via Gumbel-Sigmoid: Allows end-to-end differentiable learning of segment boundaries.
• Supervised Techniques: Utilize segmentations from subword tokenizers like Unigram or derived from spikes in conditional entropy.
• Linguistic Boundaries: Leverage natural boundaries such as whitespaces in language scripts.

This approach ensures the derived segmentations are both linguistically meaningful and adaptive, addressing the suboptimality of fixed-size pooling by dynamically allocating resources based on segment uncertainty and information content.

Experimental Evaluation

The paper emphasizes a thorough experimental setup, validating the effectiveness of dynamic pooling on several character-level LLMing tasks across varied linguistic datasets (English, Finnish, Hebrew, and Vietnamese). Notably, dynamic pooling models outperform vanilla and fixed-size pooling Transformers, suggesting superior inductive biases for language representation. Results show statistically significant improvements in bits per character (BPC) scores and computational efficiency with enhanced shortening factors (SF), reducing resource consumption and training duration.

Implications and Future Directions

This dynamic pooling approach not only yields a more computationally efficient model but also opens avenues for more human-like hierarchical processing of language. The framework demonstrates scalability potential and positions itself as a favorable solution for long-context tasks and modalities beyond text, such as speech and vision.

The future research direction could involve exploring:

• Integration with other efficient self-attention methods to further decrease Transformer complexity.
• Extending dynamic pooling to non-contiguous and higher linguistic structures, capturing more complex language dependencies.
• Broadening application across diverse data modalities, further testing its adaptability and efficacy.

Conclusion

The paper contributes significantly by introducing a novel, adaptive dynamic pooling mechanism in Transformer models, demonstrating the potential for boosting efficiency and performance simultaneously. By refining the way Transformer's underlying computations align with linguistic structures, this research paves the way for more versatile and computationally economical AI models.