- The paper introduces DeepCrossAttention (DCA) to dynamically weight and combine layer outputs, mitigating information dilution in transformers.
- It employs depth-wise cross-attention with a generalized residual network that adaptively fuses input-dependent signals.
- Empirical results on LM1B and C4 show improved perplexity and faster training, achieving model quality up to three times more efficiently than baselines.
The paper "DeepCrossAttention: Supercharging Transformer Residual Connections" presents a novel enhancement to transformer architectures through an approach called DeepCrossAttention (DCA). The core innovation of DCA is a sophisticated modification to traditional residual connections within transformers, which are crucial for model convergence and stability across deep learning architectures. Residual connections, as initially popularized by the ResNet architecture, increase the flow of information but do not differentiate among information from different layers, which can lead to the undesirable effect of information dilution.
Key Contributions and Methodology
- DeepCrossAttention (DCA): The paper introduces DCA, a mechanism that employs learnable, input-dependent weights to dynamically combine outputs from different layers. This allows the model to focus on the most pertinent information, mitigating the uniform treatment of outputs in conventional residuals that potentially dilutes important signals.
- Depth-wise Cross-Attention: DCA incorporates depth-wise cross-attention to facilitate richer inter-layer interactions. This cross-attention is achieved by enabling queries, keys, and values within each transformer block to independently synthesize layer outputs.
- Generalized Residual Network (GRN): DCA builds on a generalized form of residual networks, which the authors refer to as GRN. The GRN consists of three progressive generalizations:
- GRN-v1: Utilizes dimension-independent weights for linear combinations of outputs.
- GRN-v2: Further generalizes these weights to be dimension-dependent.
- GRN-v3: Allows the weights to be both dimension and input dependent, bringing in non-linear transformations and input-specific adaptivity.
Theoretical Insights
The theoretical analysis highlights DCA's compelling trade-off between model accuracy and size, particularly when the ratio of collective layer ranks to the ambient dimension remains below a critical threshold. This improved trade-off is attributed to the mitigation of information dilution and enhanced capacity for selectivity in model learning.
- The GRN formulation provides a general framework where models achieve higher expressivity by optimizing the combination of previous layer outputs. The paper theoretically characterizes the expressivity of these models in terms of their capacity to represent certain function classes compared to standard models.
- Theoretical results demonstrate that both GRN-v1 and GRN-v2 achieve superior trade-offs under specific conditions related to the collective rank and complexity of the target task.
Empirical Results
The authors validate the effectiveness of DCA through comprehensive experiments on LLMing tasks using the LM1B and C4 datasets. The empirical evaluation demonstrates:
- Improved Perplexity: DCA achieves improved perplexity over base transformer models for similar parameter budgets and training times. This suggests a more parameter-efficient architecture than increasing model depth or width.
- Training Efficiency: DCA demonstrates faster convergence and training stability, reducing the occurrence of loss spikes that often hamper the training of large models.
- Parameter Efficiency: Experiments highlight that DCA attains requisite model quality up to three times faster than conventional counterparts, evidencing its efficiency in both parameter usage and computational demands.
Conclusion
The introduction of DCA reflects a significant advance in the architectural design of transformers. By addressing the bottleneck inherent in uniform residual connections, DCA not only enhances model accuracy and convergence but also optimizes computational resources. This contribution is particularly relevant for large-scale models, where efficient use of parameters translates directly into practical performance gains. The paper underscores the potential for DCA to be retrofitted into existing architectures, extending its utility across various domains within deep learning.
