Value Residual Learning For Alleviating Attention Concentration In Transformers (2410.17897v3)

Abstract: Transformers can capture long-range dependencies using self-attention, allowing tokens to attend to all others directly. However, stacking multiple attention layers leads to attention concentration. One natural way to address this issue is to use cross-layer attention, allowing information from earlier layers to be directly accessible to later layers. However, this approach is computationally expensive. To address this problem, we propose Transformer with residual value (ResFormer) which approximates cross-layer attention through adding a residual connection from the values of the the first layer to all subsequent layers. Based on this method, one variant is the Transformer with single layer value (SVFormer), where all layers share the same value embedding from first layer. Comprehensive empirical evidence demonstrates ResFormer achieves equivalent validation loss with 10.4% fewer model parameters and 13.6% less training data compared to Transformer, while maintaining similar memory usage and computational cost. Besides, SVFormer reduces KV cache size by nearly half with only a small performance penalty and can be integrated with other KV-efficient methods, yielding further reductions in KV cache, with performance influenced by sequence length and cumulative learning rate. Further visualization results suggest that Resformer and SVFormer alleviate attention concentration in deeper layers through avoiding value-state drains and enhance representation across most layers.

Value Residual Learning for Alleviating Attention Concentration in Transformers

The paper by Zhou et al. proposes a novel approach to address the issue of attention concentration in transformer networks. Despite the success of transformers across various domains such as LLMing and computer vision, their tendency to focus attention on fewer tokens as layers increase presents challenges in model efficiency and generalization. The authors introduce ResFormer, a Transformer variant integrating residual value connections, aimed at circumventing these challenges while conserving computational resources.

The core contribution of ResFormer lies in its method of approximating cross-layer attention, traditionally an effective but computationally expensive solution to attention concentration. Instead of directly implementing cross-layer attention, ResFormer introduces a residual connection from the value embeddings of the first layer to all subsequent layers. This method benefits from the informational foundation laid by early layers, mitigating the drift of attention towards select tokens as depth increases. Notably, ResFormer achieves comparable validation loss metrics with reduced parameters (by 10.4%) and less training data consumption (by 13.6%) relative to standard transformers, without increasing memory footprint or computational demands.

Additionally, the SVFormer variant enforces an even tighter storage constraint by allowing layers to share the same value embedding from the initial layer. This reduces the key-value (KV) cache size significantly. SVFormer can combine with other KV-efficient strategies, achieving further KV cache reduction, notably alleviating the computational load prevalent in using long sequences.

Through extensive empirical evaluations, ResFormer and its variant, SVFormer, demonstrate substantial improvement in preventing attention concentration across deep transformer layers. The experiments underscore that ResFormer maintains a more uniform distribution of attention, as evidenced by entropy and similarity measurements, revealing its resistance to the typical over-smoothing effect seen in deeply stacked layers.

The implications of this research extend both practically and theoretically. The approach offers a pathway towards more efficient transformer architectures, crucial in contexts where model size and speed are paramount, such as mobile applications and latency-sensitive tasks. Theoretically, the work underlines the potential of value residual connections as a paradigm shift in interpreting and handling hierarchical dependencies within neural networks.

Looking forward, further investigation into the underlying mechanisms and applications of value residual connections could unveil deeper insights, potentially fostering advancements in models with even greater capacity and efficiency. As AI models continue to scale, methodologies like those proposed here will be central in maintaining balanced model growth, ensuring that efficiency, interpretability, and generalization coalesce.