Self-Speculative Decoding with LayerSkip enables faster text generation in Transformers

AI Impact Summary

Self-speculative decoding reuses early layers of a large decoder to draft tokens, with deeper layers verifying them, yielding end-to-end speedups and memory reductions when early outputs align with final results. A LayerSkip training recipe (early exit loss and increasing layer dropout) is required to make intermediate-layer logits usable, and Hugging Face transformers can enable this via the assistant_early_exit parameter, enabling deployment on existing workflows. Benchmarks show meaningful speedups and memory savings across several LayerSkip checkpoints (e.g., facebook/layerskip-llama2-7B/13B/70B and layerskip-llama3 variants) on multi-GPU setups, though gains vary by model size (limited gains for 70B). Adoption hinges on using LayerSkip-trained checkpoints and validating performance on target hardware, with integration supported through the transformers library.