ConsistentEE: A Reinforcement Learning-Based Early Exiting Method for Efficient Language Model Inference

Core Concepts

ConsistentEE formulates the early exiting process as a reinforcement learning problem, where a policy network decides whether to exit or continue at each intermediate layer. The training objective only requires each instance to be predicted correctly by one internal classifier, in contrast to existing methods that impose all internal classifiers to predict all instances correctly.

Abstract

The paper proposes ConsistentEE, an early exiting method that is consistent in training and inference for accelerating language model inference.
Key highlights:

Current early exiting methods adopt the (weighted) sum of the cross entropy loss of all internal classifiers as the training objective, which imposes all classifiers to predict all instances correctly. However, during inference, as long as one internal classifier predicts an instance correctly, it can accelerate without losing accuracy.
ConsistentEE formulates the early exiting process as a reinforcement learning problem, where a policy network decides whether to exit or continue at each intermediate layer. The training objective only requires each instance to be predicted correctly by one internal classifier.
The authors introduce the concept of "Memorized Layer" to measure the hardness of an instance and incorporate it into the reward function design. This allows "easy" instances to focus more on acceleration while "hard" instances to focus more on accuracy.
Experimental results show that ConsistentEE outperforms various baselines on natural language understanding and generation tasks using PLMs and LLMs as backbones.

Stats

The scale of pre-trained language models (PLMs) and large language models (LLMs) continues to grow, improving their performance but slowing down their inference speed.
Early exiting is one of the most popular methods to achieve efficient inference, where internal classifiers are added to intermediate layers to allow instances to stop model inference early.
Current early exiting methods adopt the (weighted) sum of the cross entropy loss of all internal classifiers as the training objective, which imposes all classifiers to predict all instances correctly.

Quotes

"As long as one internal classifier predicts an instance correctly, it can accelerate without losing accuracy."
"We propose a concept named Memorized Layer to measure the hardness of an instance. We incorporate it into the reward function to allow an instance to balance the accuracy and acceleration depending on individual hardness."

Key Insights Distilled From

ConsistentEE

by Ziqian Zeng,... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2312.11882.pdf

Deeper Inquiries

How can the concept of Memorized Layer be extended to other types of language models beyond PLMs and LLMs

The concept of Memorized Layer can be extended to other types of language models beyond PLMs and LLMs by adapting it to the specific architecture and characteristics of the model in question. For instance, in models with a different layer structure or token representation, the Memorized Layer could be defined based on the point at which a certain level of understanding or context is achieved. This could involve tracking the layer at which key features are captured or the token at which critical information is encoded. By customizing the definition of the Memorized Layer to suit the unique properties of the language model, it can still serve as a measure of instance hardness and guide the trade-off between accuracy and acceleration in the early exiting process.

What are the potential limitations of the reinforcement learning-based approach in ConsistentEE, and how can they be addressed

One potential limitation of the reinforcement learning-based approach in ConsistentEE is the complexity and computational cost associated with training a policy network for each intermediate layer. This could lead to longer training times and increased resource requirements, especially for models with a large number of layers. To address this limitation, techniques such as transfer learning or model distillation could be employed to pre-train the policy networks on a smaller dataset or a simpler task before fine-tuning them on the target dataset. This can help reduce the training time and resource overhead while still achieving effective early exiting performance.

Can the principles of ConsistentEE be applied to other domains beyond natural language processing to achieve efficient inference

The principles of ConsistentEE can be applied to other domains beyond natural language processing to achieve efficient inference by adapting the concept of early exiting and the reward function design to suit the specific characteristics of the domain. For example, in computer vision tasks, the concept of Memorized Layer could be translated to the point at which key visual features are extracted or understood. The reinforcement learning framework could be used to determine when to exit the inference process based on the level of confidence in the predictions. By customizing the approach to the requirements of the domain, it is possible to achieve faster inference without compromising accuracy in various applications such as image recognition, object detection, and video analysis.