Large Language Model Inference Acceleration Based on Hybrid Model Branch Prediction

🌈 Abstract

The paper proposes a hybrid model acceleration inference method based on branch prediction to reduce the validation time during hybrid model inference and accelerate the inference speed. The key points are:

• A branch-prediction function is constructed based on the binomial distribution assumption to fit the empirical distribution, further accelerating the inference speed.
• Experiments demonstrate that the proposed algorithm achieves better acceleration effects in tasks of generating combinations of models of different scales.

🙋 Q&A

[01] Introduction

1. Questions related to the content of the section?

• The paper proposes a hybrid model acceleration inference method based on branch prediction to reduce the validation time and accelerate the inference speed.
• It constructs a branch-prediction function based on the binomial distribution assumption to further accelerate the inference speed.
• Experiments show the proposed algorithm achieves better acceleration effects when generating combinations of models of different scales.

[02] Related Work

No specific questions or answers provided, as the section title "Related Work" was not included in the document.

[03] Prior Knowledge

1. What is speculative sampling?

• The smaller model is used to quickly generate a series of inference output draft characters (tokens).
• These drafts are then verified and, if necessary, corrected by the original large model.
• The number of accepted drafts is defined as the starting point for the next round of small model inference.

2. What is branch prediction?

• Static prediction relies on simple rules, such as always predicting a branch will go in a specific direction.
• Dynamic prediction depends on information collected at runtime, predicting future branch decisions based on historical branch behavior.

[04] Methods

1. What are the key steps of the hybrid model inference acceleration algorithm based on branch prediction?

• Small Model Draft Generation: The small model generates draft tokens as potential generation output.
• Branch Prediction: A prediction function is used to predict the number of acceptable drafts.
• Large Model Validation: The large model evaluates the draft tokens and determines the actual number of accepted drafts.
• Branch-Prediction Result Check: The prediction for the current round is checked after large model validation.

2. How is the prediction function designed for acceleration?

• The prediction function is based on the binomial distribution assumption and the empirical distribution analysis.
• It aims to fit the observed frequency distribution and accurately predict the number of accepted drafts.

[05] Analysis

No specific questions or answers provided, as the section title "Analysis" was not included in the document.

[06] Experiments

1. What are the key findings from the experiments?

• The branch-prediction algorithm achieves better acceleration effects compared to speculative sampling, especially when combining models of different scales.
• The single-round token generation quantity has an impact on the acceleration effect.
• An extreme trade-off strategy exploring exhaustive methods is also explored.

[07] Conclusions

No specific questions or answers provided, as the section title "Conclusions" was not included in the document.