Faster Text Generation with Self-Speculative Decoding
Self-speculative decoding, proposed in
LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding
is a novel approach to text generation. It combines the strengths of speculative decoding with early
exiting from a large language model (LLM). This method allows for efficient generation
by using the same model’s early layers for drafting tokens, and later layers for verification.
LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding
is a novel approach to text generation. It combines the strengths of speculative decoding with early
exiting from a large language model (LLM). This method allows for efficient generation
by using the same model’s early layers for drafting tokens, and later layers for verification.
This technique not only speeds up text generation, but it also achieves significant
memory savings and reduces computational latency. In order to obtain an end-to-end speedup, the
output of the earlier layers need to be close enough to the last layer.