Parallel Decoding with Hidden Transfer: An Efficient Approach for Accelerating Large Language Model Inference

핵심 개념

A novel parallel decoding approach, named Hidden Transfer, that predicts the pseudo hidden states of future tokens in intermediate layers to enable simultaneous generation of multiple tokens in a single forward pass, thereby improving the inference efficiency of large language models.

초록

The paper proposes a novel parallel decoding approach called Hidden Transfer to accelerate the inference of large language models (LLMs). The key idea is to predict the pseudo hidden states of future tokens in intermediate layers and then pass them through the subsequent transformer layers to generate multiple draft tokens in a single forward propagation.

The authors first analyze the inefficiency of traditional autoregressive decoding, which generates one token per forward pass and fails to fully utilize the parallel computing capabilities of GPUs. They then introduce the Hidden Transfer method, which trains linear projections to transfer the hidden states of the current tokens to the pseudo hidden states of the future tokens in certain intermediate layers. These pseudo hidden states are then passed through the remaining transformer layers, allowing the model to predict multiple draft tokens simultaneously.

The authors conduct extensive experiments to evaluate the effectiveness of their approach. They compare the acceleration ratio of Hidden Transfer with other single-model acceleration techniques, such as Medusa and Self-Speculative Decoding, and demonstrate that Hidden Transfer outperforms these methods in terms of end-to-end time acceleration. Additionally, the authors perform analytical experiments to verify their motivation, showing that the predicted pseudo hidden states are progressively refined through the subsequent transformer layers, gaining more semantic information and improving the accuracy of the draft token predictions.

The paper also discusses the trade-offs in selecting the appropriate transfer layers and the potential limitations of the approach, such as the increased computational resource requirements due to the expansion of the input sequence during both training and inference.

통계

The paper does not contain any explicit numerical data or metrics in the main text. The key results are presented in the form of acceleration ratios and comparative performance against other methods.

인용구

The paper does not contain any direct quotes that are particularly striking or support the key arguments.

핵심 통찰 요약

Parallel Decoding via Hidden Transfer for Lossless Large Language Model Acceleration

by Pengfei Wu,J... 게시일 arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.12022.pdf

더 깊은 질문

How can the tree attention mechanism used in the verification stage be further optimized to improve the overall generation speed

In the context of the verification stage using the tree attention mechanism, there are several ways to optimize it for improved generation speed. One approach could involve implementing a more efficient tree structure representation that minimizes the computational overhead of converting between tree and sequence formats. By streamlining this process, the model can quickly verify multiple candidate sequences without unnecessary computational burden. Additionally, optimizing the attention mask design within the tree attention mechanism can help reduce redundant computations and focus attention on relevant tokens, further enhancing the speed of verification. Furthermore, exploring parallel processing techniques specifically tailored for tree structures can leverage the inherent parallelism in GPUs to accelerate the verification process. By efficiently utilizing hardware resources and optimizing the algorithmic implementation, the overall generation speed can be significantly improved.

What other techniques, beyond the hidden state transfer, could be explored to enhance the quality and accuracy of the draft token predictions

Beyond the hidden state transfer method, several techniques can be explored to enhance the quality and accuracy of draft token predictions. One approach is to incorporate reinforcement learning algorithms to fine-tune the prediction process based on feedback from the model's performance. By rewarding accurate predictions and penalizing errors, the model can learn to generate more precise draft tokens over time. Additionally, leveraging ensemble methods by combining predictions from multiple models or strategies can help mitigate individual model biases and improve overall prediction accuracy. Introducing domain-specific knowledge or constraints into the prediction process can also enhance the quality of draft token predictions by guiding the model towards more contextually relevant outputs. Furthermore, exploring advanced attention mechanisms or incorporating external knowledge sources can provide additional context for the model to make more informed predictions, leading to higher accuracy in draft token generation.

How might the Hidden Transfer approach be extended or adapted to work with other types of language models beyond the Transformer-based architectures considered in this paper

The Hidden Transfer approach can be extended or adapted to work with other types of language models beyond Transformer-based architectures by incorporating the core concept of transferring hidden states to predict future tokens in a single forward pass. For instance, in recurrent neural network (RNN) models, the Hidden Transfer method can be adapted to transfer hidden states across time steps to predict subsequent tokens efficiently. Similarly, in convolutional neural network (CNN) models, the concept of transferring feature maps across layers can be utilized to predict future tokens in a parallel decoding fashion. By adapting the Hidden Transfer approach to suit the architectural nuances of different types of language models, the benefits of accelerated inference and improved prediction accuracy can be extended to a broader range of model architectures. Additionally, exploring hybrid models that combine elements of different architectures, such as Transformers with RNNs or CNNs, can leverage the strengths of each model type while incorporating the efficiency gains of the Hidden Transfer approach for enhanced performance across diverse language modeling tasks.