Transformer tricks: Precomputing the first layer
<|reference_start|>Transformer tricks: Precomputing the first layer: This micro-paper describes a trick to speed up inference of transformers with RoPE (such as LLaMA, Mistral, PaLM, and Gemma). For these models, a large portion of the first transformer layer can be precomputed, which results in slightly lower latency and lower cost-per-token. Because this trick optimizes only one layer, the relative savings depend on the total number of layers. For example, the maximum savings for a model with only 4 layers (such as Whisper tiny) is limited to 25%, while a 32-layer model is limited to 3% savings. See https://github.com/OpenMachine-ai/transformer-tricks for code and more transformer tricks.<|reference_end|>
@article{graef2024transformer,
    title={Transformer tricks: Precomputing the first layer},
    author={Nils Graef},
    journal={arXiv preprint arXiv:2402.13388},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13388},
    primaryClass={cs.LG}
}