We generalize deep self-attention distillation inMiniLMby 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 ofquery,key, andvaluevectors 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.
Moreover, the fine-grained self-attention relations tend to fully exploit the interaction knowledge learned by Transformer.
In addition, we thoroughly examine the layer selection strategy for teacher models, rather than just relying on the last layer as inMiniLM.
We conduct extensive experiments on compressing both monolingual and multilingual pretrained models.
Experimental results demonstrate that our models111Distilled models and code will be publicly available athttps://aka.ms/minilm.distilled from base-size and large-size teachers (BERT, RoBERTa and XLM-R)  outperform the state-of-the-art.