Enhancing Speculative Decoding via Knowledge Distillation: DistillSpec Improves Alignment Between Draft and Target Language Models

Core Concepts

DistillSpec, a knowledge distillation method, improves the alignment between a small draft model and a large target model to enhance the speed of speculative decoding without compromising performance.

Abstract

The paper proposes DistillSpec, a knowledge distillation (KD) framework that aims to better align a small draft model with a large target model to improve the efficiency of speculative decoding (SD). SD is a technique that uses a compact draft model to generate candidate tokens, which are then verified in parallel by the larger target model, resulting in faster text generation while preserving the target model's output distribution.
The key insights are:

Utilizing on-policy data generated by the draft model during KD is crucial for improving the student-teacher alignment, which is crucial for SD efficiency.
The choice of divergence function in the KD objective should be tailored to the task and decoding strategy (greedy vs. non-greedy).
DistillSpec can be combined with lossy SD to provide fine-grained control over the quality-latency trade-off.
In practical scenarios with multiple models of varying sizes, first distilling a large model into a smaller one as the potential target model, and then applying DistillSpec to train an even smaller draft model, can reduce decoding latency by 6-10x with minimal performance drop.

The paper conducts extensive experiments on diverse language modeling tasks, demonstrating that DistillSpec can improve SD speed by 10-45% compared to standard SD, while preserving model performance. It also shows that the distilled draft model can transfer well to unseen tasks, achieving an average speedup of 26%.

Stats

The paper reports the following key metrics:

Speculative decoding speedup of 10-45% across various datasets compared to standard SD.
Average speedup of 26% when transferring the distilled draft model to 23 unseen BigBenchHard tasks.
Latency reduction of 6-10x in practical scenarios with multiple models, while maintaining minimal performance drop.

Quotes

"DistillSpec makes two key design choices, which we demonstrate via systematic study to be crucial to improving the draft and target alignment: utilizing on-policy data generation from the draft model, and tailoring the divergence function to the task and decoding strategy."
"Notably, DistillSpec yields 10 −45% speedups over standard SD on a range of benchmarks, using both greedy and non-greedy sampling."
"Finally, in practical scenarios with models of varying sizes, first using distillation to boost the performance of the target model and then applying DistillSpec to train a well-aligned draft model can reduce decoding latency by 6 −10× with minimal performance drop, compared to standard decoding without distillation."

Key Insights Distilled From

DistillSpec

by Yong... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2310.08461.pdf

Deeper Inquiries

How can the insights from DistillSpec be extended to other model compression techniques beyond speculative decoding, such as model pruning or quantization

The insights from DistillSpec can be extended to other model compression techniques beyond speculative decoding, such as model pruning or quantization. One key aspect to consider is the importance of model-generated data in the distillation process. Just as DistillSpec leverages on-policy data generated by the draft model to improve alignment with the target model, this approach can be applied to other compression techniques. For model pruning, using on-policy data can help ensure that the pruned model maintains alignment with the original model's distribution. Similarly, in quantization, on-policy data can be used to distill knowledge from the full-precision model to the quantized model, enhancing the alignment and preserving performance.

What are the potential limitations or drawbacks of using on-policy data generated by the draft model during knowledge distillation, and how can they be addressed

Using on-policy data generated by the draft model during knowledge distillation may have potential limitations or drawbacks. One limitation could be the risk of overfitting to the specific data generated by the draft model, leading to a lack of generalization to unseen data. To address this, it is essential to ensure diversity in the on-policy data generation process, covering a wide range of scenarios and inputs. Additionally, incorporating regularization techniques during training, such as dropout or weight decay, can help prevent overfitting. Another drawback could be the computational cost of generating on-policy data, especially for complex models or tasks. This can be mitigated by optimizing the data generation process and leveraging parallel computing resources to speed up data generation.

Can the DistillSpec framework be adapted to other autoregressive generation tasks beyond language modeling, such as image or speech generation

The DistillSpec framework can be adapted to other autoregressive generation tasks beyond language modeling, such as image or speech generation. In image generation tasks, the draft model could be a smaller image generation network, while the target model could be a larger, more complex model. By applying knowledge distillation with on-policy data generation, the draft model can be aligned with the target model's distribution, improving efficiency and performance. Similarly, in speech generation tasks, the draft model could be a smaller speech synthesis model, and the target model could be a larger, more accurate model. DistillSpec can help bridge the gap between model size and performance in these tasks, enabling faster and more efficient generation while maintaining quality.