ResiDual: Transformer with Dual Residual Connections (2304.14802v1)

Abstract: Transformer networks have become the preferred architecture for many tasks due to their state-of-the-art performance. However, the optimal way to implement residual connections in Transformer, which are essential for effective training, is still debated. Two widely used variants are the Post-Layer-Normalization (Post-LN) and Pre-Layer-Normalization (Pre-LN) Transformers, which apply layer normalization after each residual block's output or before each residual block's input, respectively. While both variants enjoy their advantages, they also suffer from severe limitations: Post-LN causes gradient vanishing issue that hinders training deep Transformers, and Pre-LN causes representation collapse issue that limits model capacity. In this paper, we propose ResiDual, a novel Transformer architecture with Pre-Post-LN (PPLN), which fuses the connections in Post-LN and Pre-LN together and inherits their advantages while avoids their limitations. We conduct both theoretical analyses and empirical experiments to verify the effectiveness of ResiDual. Theoretically, we prove that ResiDual has a lower bound on the gradient to avoid the vanishing issue due to the residual connection from Pre-LN. Moreover, ResiDual also has diverse model representations to avoid the collapse issue due to the residual connection from Post-LN. Empirically, ResiDual outperforms both Post-LN and Pre-LN on several machine translation benchmarks across different network depths and data sizes. Thanks to the good theoretical and empirical performance, ResiDual Transformer can serve as a foundation architecture for different AI models (e.g., LLMs). Our code is available at https://github.com/microsoft/ResiDual.

• The paper introduces a dual residual mechanism combining Pre- and Post-LN paths to balance gradient flow and mitigate representational collapse.
• It provides rigorous theoretical analysis, proving lower bounds on gradient norms that safeguard the training of deeper layers.
• Empirical results on machine translation benchmarks show that ResiDual outperforms standard Transformer models under varying depths and data scales.

Insights on "ResiDual: Transformer with Dual Residual Connections"

The paper of residual connections within Transformer architectures continues to be a pivotal area of research in deep learning due to their impact on training efficacy and model capacity. The paper "ResiDual: Transformer with Dual Residual Connections" introduces an innovative approach to addressing significant challenges associated with the two predominant residual connection strategies used in Transformers: Post-Layer Normalization (Post-LN) and Pre-Layer Normalization (Pre-LN).

Theoretical and Empirical Contributions

The authors propose a novel Transformer architecture, "ResiDual," which incorporates both Pre-LN and Post-LN residual pathways to synthesize their benefits while mitigating their respective limitations. This work offers a dual-residual approach termed Pre-Post-LN (PPLN). The theoretical analysis of ResiDual reveals crucial insights into its advantages. A significant contribution is the proof that ResiDual can achieve a balance between gradient flow and representational diversity, counteracting both the gradient vanishing problem encountered by Post-LN architectures and the representational collapse issue associated with Pre-LN Transformers.

From a mathematical standpoint, the authors provide an in-depth analysis of how the dual residual paths foster better gradient distribution and model robustness. The paper establishes a lower bound on gradient norms in ResiDual, which safeguards against gradient vanishing—a common difficulty in training deeper layers in Post-LN models. Furthermore, their studies show that the preserved representational diversity in ResiDual avoids the collapse issues prevalent in Pre-LN models, maintaining the model's representational capacity across all layers.

Empirically, the paper validates the ResiDual mechanism through rigorous experimentation across several machine translation benchmarks, including IWSLT-14 EN$\to$DE, WMT DE$\to$EN, and the large-scale OPUS-100 dataset. Results consistently demonstrate that the ResiDual Transformer exceeds the performance of both Post-LN and Pre-LN architectures in a range of conditions, including varying model depths and data scales. Notably, ResiDual's superiority is more pronounced in deeper models, showcasing its potential as a robust foundational architecture for AI models requiring large capacities, such as LLMs.

Implications and Future Prospects

The architecture proposed in this paper not only demonstrates improvements in stability and performance of deep networks but also poses potential implications for the design and training of large-scale AI models. By effectively balancing training dynamics and model expressivity, ResiDual could set a new standard for the implementation of residual connections in future Transformer-based models.

Potential future directions might include extending ResiDual beyond machine translation to explore its effectiveness in other domains, such as image processing or speech synthesis. Moreover, the paper sets a foundation for further exploration into dynamically adaptive residual connections that could adjust based on task-specific requirements, potentially yielding even greater performance gains.

In conclusion, the ResiDual model stands as a noteworthy advancement in the field of neural network architecture, addressing long-standing challenges with a novel dual-residual approach. As the field of AI evolves, such architectures will likely play a crucial role in maximizing the efficacy and scalability of deep learning systems across various applications.