Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel Decoding
FREE framework significantly reduces inference latency with synchronized parallel decoding, enhancing performance.
Key Findings
Methodology
The FREE framework integrates a shallow-deep module and synchronized parallel decoding to address high inference latency in autoregressive language models. By synchronizing the decoding of current and early-exited tokens, it boosts inference speed. A Beta mixture model is used for adaptive threshold estimation.
Key Results
- On the SAMSum dataset, the FREE framework achieved a 1.47x speedup while maintaining 99% model performance.
- On the CNN/DailyMail dataset, FREE framework scored 40.99 ROUGE-L with a 1.65x speedup.
- On the BIGPATENT dataset, the FREE framework achieved a 1.58x speedup, maintaining over 99% performance.
Significance
The FREE framework significantly reduces inference latency in autoregressive language models, addressing performance degradation issues in early-exiting frameworks. It offers a more efficient solution for real-time applications and provides new directions for future research.
Technical Contribution
By introducing synchronized parallel decoding and adaptive threshold estimation, the FREE framework breaks the performance bottleneck of existing methods, offering new theoretical guarantees and engineering possibilities.
Novelty
The FREE framework is the first to combine a shallow-deep module with synchronized parallel decoding, significantly enhancing inference efficiency compared to existing methods.
Limitations
- FREE framework may experience performance drops when handling extremely long sequences.
- Adaptive threshold estimation requires additional computational resources.
Future Work
Future research can explore the application of the FREE framework in other language models and further optimize the adaptive threshold estimation algorithm.
AI Executive Summary
Autoregressive language models excel in generation tasks, but inference latency limits their application. Existing early-exiting frameworks dynamically adjust computation paths but face performance degradation and threshold sensitivity issues. The FREE framework addresses these issues by introducing a shallow-deep module and synchronized parallel decoding. Experimental results show significant inference speed improvements across multiple datasets while maintaining high performance. It offers a more efficient solution for real-time applications and provides new directions for future research. Although the FREE framework may experience performance drops with extremely long sequences, its innovative approach offers new possibilities for further research.
Deep Analysis
Background
Recent advancements in autoregressive language models have achieved significant progress in generation tasks, but inference latency remains unsolved. Early-exiting frameworks attempt to reduce inference latency by dynamically adjusting computation paths but face performance degradation and threshold sensitivity issues.
Core Problem
Inference latency in autoregressive language models limits their use in real-time applications. Existing early-exiting frameworks dynamically adjust computation paths but face performance degradation and threshold sensitivity issues.
Innovation
The FREE framework introduces a shallow-deep module and synchronized parallel decoding to address performance degradation in early-exiting frameworks. A Beta mixture model is used for adaptive threshold estimation, enhancing inference efficiency.
Methodology
- �� Shallow-deep module: splits computation paths into shallow and deep models.
- �� Synchronized parallel decoding: synchronizes the decoding of current and early-exited tokens.
- �� Adaptive threshold estimation: uses a Beta mixture model to determine suitable confidence thresholds.
Experiments
Experiments were conducted on SAMSum, CNN/DailyMail, Multi-News, and BIGPATENT datasets using T5-large and LongT5-base models. Evaluation metrics included ROUGE-L scores and inference speed.
Results
The FREE framework achieved significant inference speed improvements across multiple datasets while maintaining high performance. Specific data includes a 1.47x speedup on the SAMSum dataset and a 1.65x speedup on the CNN/DailyMail dataset.
Applications
The FREE framework is suitable for real-time generation applications such as automatic summarization and machine translation. It offers a more efficient solution for these fields.
Limitations & Outlook
The FREE framework may experience performance drops when handling extremely long sequences. Adaptive threshold estimation requires additional computational resources.
Plain Language Accessible to non-experts
Imagine a kitchen where a chef needs to quickly prepare each dish. Traditional methods require the chef to complete each step sequentially, while the FREE framework acts like a smart assistant that can handle multiple steps simultaneously. It speeds up the entire process by preemptively judging which steps can be completed quickly.
ELI14 Explained like you're 14
Imagine you're playing a game where your character needs to complete tasks to level up. Traditional methods are like completing tasks one by one, while the FREE framework is like a super tool that can complete multiple tasks at once, allowing your character to level up quickly. Isn't that cool?
Glossary
Autoregressive Model
A generative model that generates each element of a sequence step by step.
Used in generation tasks like text generation.
Early-Exiting Framework
A method that dynamically adjusts computation paths to reduce inference latency.
Used to accelerate inference in autoregressive models.
Beta Mixture Model
A statistical model used for estimating confidence thresholds.
Used in adaptive threshold estimation in the FREE framework.
Synchronized Parallel Decoding
A method to accelerate the decoding process by synchronizing current and early-exited tokens.
Used to improve inference efficiency in the FREE framework.
Shallow-Deep Module
A modular design that splits computation paths into shallow and deep models.
Used to enhance performance in the FREE framework.
Open Questions Unanswered questions from this research
- 1 How can the FREE framework's performance on extremely long sequences be further optimized?
- 2 How can the computational resource requirements for adaptive threshold estimation be reduced?
Applications
Immediate Applications
Real-time Text Generation
The FREE framework can be used for real-time text generation applications like news summarization and social media content generation.
Machine Translation
The FREE framework can be applied to machine translation systems to improve translation speed and quality.
Long-term Vision
Smart Assistants
The FREE framework can be used to develop smarter virtual assistants, providing faster and more accurate responses.
Abstract
To tackle the high inference latency exhibited by autoregressive language models, previous studies have proposed an early-exiting framework that allocates adaptive computation paths for each token based on the complexity of generating the subsequent token. However, we observed several shortcomings, including performance degradation caused by a state copying mechanism or numerous exit paths, and sensitivity to exit confidence thresholds. Consequently, we propose a Fast and Robust Early-Exiting (FREE) framework, which incorporates a shallow-deep module and a synchronized parallel decoding. Our framework enables faster inference by synchronizing the decoding process of the current token with previously stacked early-exited tokens. Furthermore, as parallel decoding allows us to observe predictions from both shallow and deep models, we present a novel adaptive threshold estimator that exploits a Beta mixture model to determine suitable confidence thresholds. We empirically demonstrated the superiority of our proposed framework on extensive generation tasks.