Inference from large autoregressive models like Transformers is slow - decodingtokens takesserial runs of the model.
In this work we introducespeculative decoding- an algorithm to sample from autoregressive models fasterwithout any changes to the outputs,
by computing several tokens in parallel.
At the heart of our approach lie the observations that (1)  hard language-modeling tasks often include easier subtasks that can be approximated well by more efficient
models, and (2)  using speculative execution and a novel sampling method, we can make exact decoding from the large models faster, by running them in parallel on the outputs of the approximation models, potentially generating several tokens concurrently, and without changing the distribution.
Our method can accelerate existing off-the-shelf models without retraining or architecture changes.
We demonstrate it on T5-XXL and show a2X-3Xacceleration compared to the standard T5X implementation, with identical outputs.