Accelerating Production LLMs with Combined Token/Embedding Speculators

• URL: http://arxiv.org/abs/2404.19124v2
• Date: Thu, 6 Jun 2024 18:38:34 GMT
• Title: Accelerating Production LLMs with Combined Token/Embedding Speculators
• Authors: Davis Wertheimer, Joshua Rosenkranz, Thomas Parnell, Sahil Suneja, Pavithra Ranganathan, Raghu Ganti, Mudhakar Srivatsa,
• Abstract summary: This report describes the design and training of novel speculative decoding draft models. By conditioning draft predictions on both context vectors and sampled tokens, we can train our speculators to efficiently predict high-quality n-grams.
• Score: 4.649953910785797
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This technical report describes the design and training of novel speculative decoding draft models, for accelerating the inference speeds of large language models in a production environment. By conditioning draft predictions on both context vectors and sampled tokens, we can train our speculators to efficiently predict high-quality n-grams, which the base model then accepts or rejects. This allows us to effectively predict multiple tokens per inference forward pass, accelerating wall-clock inference speeds of highly optimized base model implementations by a factor of 2-3x. We explore these initial results and describe next steps for further improvements.

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