Depth-Adaptive Transformer (1910.10073v4)

Abstract: State of the art sequence-to-sequence models for large scale tasks perform a fixed number of computations for each input sequence regardless of whether it is easy or hard to process. In this paper, we train Transformer models which can make output predictions at different stages of the network and we investigate different ways to predict how much computation is required for a particular sequence. Unlike dynamic computation in Universal Transformers, which applies the same set of layers iteratively, we apply different layers at every step to adjust both the amount of computation as well as the model capacity. On IWSLT German-English translation our approach matches the accuracy of a well tuned baseline Transformer while using less than a quarter of the decoder layers.

Overview of "Depth-Adaptive Transformer"

The paper "Depth-Adaptive Transformer" by Maha Elbayad, Jiatao Gu, Edouard Grave, and Michael Auli presents an innovative approach to Transformer models, focusing on adapting the depth of the model's computation to the complexity of the input sequences. This research is situated in the context of neural sequence models, which have grown in complexity and parameter count to address varying input difficulties. However, traditional models apply a uniform computation approach regardless of input complexity, leading to inefficient computational resource utilization.

Core Contribution

The primary contribution of this paper is the introduction of a depth-adaptive mechanism within sequence-to-sequence Transformer architectures. This mechanism enables the model to adjust the number of active layers based on the perceived complexity of each input sequence, thereby optimizing the trade-off between computational efficiency and model accuracy. Unlike the Universal Transformers that use fixed layer applications and recurrent dynamics, the proposed model dynamically selects different layer combinations at each step.

Methodology

1. Model Architecture: The authors extend the conventional Transformer with multiple output classifiers attached to the outputs of various decoding layers, thereby enabling predictions at different network depths.
2. Adaptive Depth Mechanisms: Several adaptive depth mechanisms are explored, including:
   • Sequence-Specific Depth: Utilizes a multinomial classifier conditioned on the source sequence to determine the optimal network depth for the entire input sequence.
   • Token-Specific Depth: Involves predicting a separate exit for each token, with mechanisms for multinomial and geometric-like classifiers determining when to stop processing further layers.
3. Training Strategies: The paper discusses aligned and mixed training strategies for optimizing performance across different exit classifiers within the model.

Experiments and Results

The research demonstrates the proposed model's effectiveness on machine translation tasks (IWSLT14 German-English and WMT'14 English-French). Key results include:

• The model achieves comparable BLEU scores to a standard Transformer with significantly reduced decoder layers, using up to 76% fewer layers without loss of accuracy.
• The experimental findings underscore the potential for substantial improvements in efficiency, particularly for sequences deemed less complex by the model's adaptive mechanisms.

Implications and Future Work

The depth-adaptive approach provides clear implications for computational efficiency in neural architectures, especially as models scale up. This is particularly relevant for deployment in resource-constrained environments where real-time computational savings are critical. The authors suggest future exploration into dynamic architectures for broader NLP tasks and further refinement of adaptive mechanisms, including investigating the potential integration of deep reinforcement learning techniques for more nuanced layer selection processes.

Conclusion

This paper's methodologies and results contribute significantly to the ongoing discourse on efficient neural model architectures, positioning depth-adaptive strategies as a valuable consideration in the design of future neural networks, particularly in facilitating more intelligent resource allocation without compromising performance.