Transformer tricks: Removing weights for skipless transformers
Nils Graef
He and Hofmann (arXiv:2311.01906) detailed a skipless transformer without the
V and P (post-attention projection) linear layers, which reduces the total
number of weights. However, this scheme is only applicable to MHA (multi-head
attention), but not for MQA (multi-query attention) and GQA (grouped-query
attention). The latter schemes are used by many popular LLMs such as Llama 2,
Mistral, Mixtral, PaLM, and Gemma. Therefore, this micro-paper proposes
mathematically equivalent versions that are suitable for MQA and GQA. For
example, removing Q and P from a skipless version of Mistral-7B would remove
15% of its weights (and thus reduce its compute and memory complexity). See
arXiv:2402.13388 and https://github.com/OpenMachine-ai/transformer-tricks for
code and more transformer tricks.