How to Implement Attention Distillation for Transformer Models

Attention distillation is a specialized form of knowledge distillation that focuses on transferring the relational patterns captured in a teacher transformer's attention maps. Instead of just mimicking final outputs, the student learns to replicate the teacher's internal focus—its key-query distributions across layers and heads. This captures richer, structural information about token relationships, which is crucial for tasks requiring nuanced understanding. You'll implement this using attention-based loss functions, such as Mean Squared Error (MSE) or Kullback–Leibler (KL) divergence, between the teacher and student attention matrices.

To implement this, you first load a pre-trained teacher model (e.g., bert-base-uncased) and a smaller student architecture. During training, you pass the same input batch through both models, extract their attention weights, and compute a distillation loss alongside the standard task loss. Use libraries like Hugging Face Transformers and PEFT for efficient fine-tuning. This technique is a core method within our pillar on Knowledge Distillation and Model Pruning for Sustainability, directly reducing the energy required for inference while preserving capability.