MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers

Understand

We generalize deep self-attention distillation in MiniLM (Wang et al., 2020) by only using self-attention relation distillation for task-agnostic compression of pretrained Transformers.

• In particular, we define multi-head self-attention relations as scaled dot-product between the pairs of query, key, and value vectors within each self-attention module.
• Then we employ the above relational knowledge to train the student model.
• Besides its simplicity and unified principle, more favorably, there is no restriction in terms of the number of student's attention heads, while most previous work has to guarantee the same head number between teacher and student.