loopformer

Loopformer is a type of transformer model designed to address the quadratic computational complexity and memory usage of traditional transformers, which can be prohibitive for long sequences. The core idea behind Loopformer is to reduce the complexity of the self-attention mechanism by introducing a loop mechanism that iteratively refines the attention scores.

In a standard transformer, the self-attention mechanism computes attention scores for all pairs of tokens in a sequence, resulting in a complexity of O(n^2), where n is the sequence length. Loopformer mitigates this by using a loop mechanism that iteratively updates the attention scores. This is achieved by maintaining a set of learnable parameters that are updated during training, allowing the model to focus on the most relevant parts of the sequence.

The loop mechanism in Loopformer works as follows: for each token, the model computes an initial set of attention scores based on the token's position and the learnable parameters. These scores are then iteratively refined by applying a series of linear transformations and non-linear activations. The final attention scores are used to compute the weighted sum of the token embeddings, which are then passed through the feed-forward network.

Loopformer has been shown to achieve comparable performance to traditional transformers on various natural language processing tasks while significantly reducing computational complexity and memory usage. Its loop mechanism allows it to handle longer sequences more efficiently, making it a promising alternative for applications that require processing long texts or sequences.