Improving Inference Speed of Autoregressive Language Models through Blockwise Parallel Decoding Refinement

Core Concepts

Blockwise parallel decoding (BPD) can accelerate inference of autoregressive language models, but the independently generated draft tokens often exhibit unnatural repetition and inconsistency. This work analyzes the predictive dynamics of BPD and proposes algorithms to refine the draft tokens, improving the overall decoding efficiency.

Abstract

This paper explores ways to improve the inference speed of autoregressive language models through the use of blockwise parallel decoding (BPD). BPD is a technique that generates multiple tokens in parallel, rather than sequentially, to speed up the decoding process.
The authors first analyze the properties of BPD drafts and make several key observations:

BPD drafts often contain significant consecutive token repetition, due to the independent prediction of each head.
The confidence of the BPD heads tends to decrease as the position in the draft increases, with earlier heads being more confident.
There is significant headroom for improvement in the "oracle" block efficiency, which represents the theoretical maximum efficiency if the optimal draft could be selected.

Based on these observations, the authors propose two algorithms to refine the BPD drafts and improve the overall decoding efficiency:

Local rescoring via neural models: A small neural language model is used to rescore the top-k predictions from each BPD head, favoring more coherent sequences.
Parallel BPD via global n-gram rescoring: An n-gram language model is used to efficiently rescore all possible draft candidates formed from the top-k predictions, and the top p most probable drafts are verified in parallel.

The authors evaluate these approaches across a range of tasks, including language modeling, question answering, and text summarization. They find that the proposed rescoring methods can significantly improve the block efficiency, with the neural rescoring performing best on tasks with low initial efficiency, and the n-gram rescoring providing consistent gains across all tasks.
The key insights from this work are:

Understanding the predictive dynamics of BPD drafts and their limitations
Leveraging small language models to refine BPD drafts and improve decoding efficiency
Demonstrating the potential for significant improvements in inference speed without compromising output quality

Stats

BPD drafts exhibit 20-75% consecutive token repetition across tasks.
The confidence of BPD heads decreases as the position in the draft increases.
Oracle top-k block efficiency shows significant headroom for improvement over standard BPD.

Quotes

"Blockwise parallel decoding (BPD) was proposed by Stern et al. (2018) as a way to improve inference speed of language models."
"We first offer an analysis of the token distributions produced by the BPD prediction heads. Secondly, we use this analysis to inform algorithms to improve BPD inference speed by refining the BPD drafts using small n-gram or neural language models."

Key Insights Distilled From

Towards Fast Inference: Exploring and Improving Blockwise Parallel Drafts

by Taehyeon Kim... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09221.pdf

Deeper Inquiries

How could the proposed refinement algorithms be extended to work with more complex language models, such as those with cross-attention or retrieval mechanisms

The proposed refinement algorithms could be extended to work with more complex language models by adapting them to handle additional model components such as cross-attention or retrieval mechanisms. For models with cross-attention, the neural rescoring approach could be modified to incorporate information from different parts of the model that interact during decoding. This could involve adjusting the input to the neural rescoring model to include relevant cross-attention information or modifying the interpolation weight to prioritize certain model components during rescoring.
For models with retrieval mechanisms, the n-gram rescoring approach could be enhanced to consider information retrieved from external sources. The n-gram model could be trained on a combination of model-generated text and retrieved information, allowing it to better capture the context and improve the quality of the rescoring process. Additionally, the parallel BPD approach could be modified to integrate retrieved information into the top-k lattice, enabling the model to leverage external sources for more accurate rescoring.

What are the potential trade-offs between the neural and n-gram rescoring approaches in terms of computational complexity, memory usage, and generalization ability

The potential trade-offs between the neural and n-gram rescoring approaches lie in computational complexity, memory usage, and generalization ability.

Computational Complexity: Neural rescoring typically involves running a neural network for each token position, which can be computationally intensive, especially for large models. On the other hand, n-gram rescoring involves dynamic programming to efficiently rescore all paths in the lattice, which can be more computationally efficient but may not capture complex dependencies as effectively as neural rescoring.

Memory Usage: Neural rescoring requires storing and processing the parameters of the neural network, which can consume significant memory resources, especially for large models. In contrast, n-gram rescoring involves storing n-gram language model probabilities, which may be more memory-efficient but could limit the model's ability to capture long-range dependencies.

Generalization Ability: Neural rescoring models have the potential to learn complex patterns and relationships in the data, allowing them to generalize well to unseen examples. However, they may also be prone to overfitting, especially if the training data is limited. N-gram models, while simpler, may struggle to capture nuanced linguistic patterns and may not generalize as effectively to diverse datasets.
Overall, the choice between neural and n-gram rescoring approaches should consider the specific requirements of the task, the available computational resources, and the desired balance between accuracy and efficiency.

Could the insights from this work on BPD dynamics be leveraged to design more efficient language model architectures from the ground up, rather than relying on post-hoc refinement

The insights from this work on BPD dynamics could indeed be leveraged to design more efficient language model architectures from the ground up. By understanding the challenges and limitations of BPD, researchers can incorporate strategies to address these issues directly in the model architecture.
For example, a new language model architecture could be designed to incorporate mechanisms that reduce token repetition during parallel decoding, such as introducing inter-head communication or shared context between prediction heads. Additionally, the model could be optimized to improve the confidence levels of predictions across different heads, ensuring more consistent and accurate drafts during decoding.
Furthermore, the design of new language models could integrate oracle efficiency principles from BPD to maximize block efficiency and reduce the need for post-hoc rescoring. By building these insights into the architecture, the model could achieve faster and more accurate inference without the need for additional refinement steps. This approach would result in more streamlined and efficient language models that are optimized for real-time deployment and large-scale applications.