CLLMs: Consistency Large Language Models (AI summary)

Key Points

1. The article introduces the concept of Consistency Large Language Models (CLLMs) as a refined approach to improving the inference speed of large language models (LLMs) by adapting the models to consistently predict the fixed point given any state as input in a Jacobi decoding framework.

2. CLLMs are specifically designed to address the limitations of existing methods for efficient LLM inference, such as speculative decoding and Medusa, by delivering significant speedup with minimal performance degradation and without introducing extra memory cost or auxiliary model components.

3. The proposed CLLMs approach involves fine-tuning the pre-trained LLMs with two loss terms: a consistency loss that ensures the model maps any arbitrary point on the Jacobi trajectory to the fixed point, and an autoregressive (AR) loss to maintain the generation quality and avoid deviation from the distribution of the target LLM.

4. By refining the target LLM to consistently predict the fixed point given any state as input, CLLMs exhibit the fast forwarding phenomena, where multiple consecutive tokens are correctly predicted in a single forward pass, and the stationary tokens phenomenon, where correctly predicted tokens remain unaltered through subsequent iterations, contributing to a considerable generation speedup.

5. CLLMs demonstrate a 2.0× to 6.8× improvement in the count of fast-forwarded tokens and stationary tokens compared to the original LLM across both domain-specific and open-domain benchmarks, showcasing their effectiveness in reducing inference latency without compromising generation quality.

6. The effectiveness of CLLMs is evaluated across a variety of benchmarks, including domain-specific tasks such as text-to-SQL, Python code generation, and graduate school math, as well as open-domain conversational interactions, demonstrating significant speedup using Jacobi decoding with nearly no loss in accuracy.

7. CLLMs outperform baseline methods, including speculative decoding with distilled draft models and Medusa, in terms of adaptation, integration with lookahead decoding for extra speedup, and resilience across varying n-token sequence lengths while maintaining generation quality, as evidenced by their performance across different tasks and datasets.

8. The article presents empirical evidence of CLLMs' superior performance and robustness across various tasks, highlighting their potential to be trained as pre-trained LLMs with higher inference efficiency using techniques such as on-policy generalized knowledge distillation.

9. The proposed CLLMs approach offers potential as a solution for enhancing the inference speed of LLMs, reducing the complexity associated with additional architectural components, and integrating seamlessly with other techniques for efficient LLM inference, while showcasing strong performance across different tasks and datasets.

Summary

Optimizing parallel decoding for Large Language Models (LLMs)
The research paper discusses an approach aimed at optimizing parallel decoding for Large Language Models (LLMs) inference. The paper introduces a new family of LLMs specialized for the Jacobi decoding method to reduce inference latency. The method aims to achieve faster convergence and higher speedup using the Consistency Large Language Models (CLLMs) with minimal performance degradation. It addresses the limitations of existing methods by refining the target LLM to consistently predict the fixed point given any state as input. The paper extensively demonstrates the effectiveness of CLLMs, showing 2.4× to 3.4× improvements in generation speed while preserving generation quality across both domain-specific and open-domain benchmarks.

Experimental confirmation and data preparation for CLLMs
The paper experimentally confirms the existence of fast forwarding and stationary tokens phenomena in Jacobi decoding of CLLMs, contributing to their fast convergence and significant generation speedup. It explains the data preparation procedure for training CLLMs, including data augmentation and post-processing to ensure high-quality datasets. The training algorithm for CLLMs is detailed, emphasizing the importance of consistency loss and autoregressive loss in training. CLLMs are compared with other baseline methods, including Medusa, speculative decoding, and fine-tuned models using various benchmarks to demonstrate their efficacy.

Training dynamics and comparative analysis of CLLMs
The paper further discusses the training dynamics, including different n-gram sizes and dataset sizes, and evaluates the loss design and its impact on the performance of CLLMs. The research presents a comparative analysis of CLLMs with other baselines across different decoding paradigms and highlights the potential of CLLMs to be trained as pre-trained LLMs for higher inference efficiency. Overall, the paper provides a comprehensive and detailed exploration of CLLMs as an efficient approach for optimizing parallel decoding for LLM inference.

Reference: https://arxiv.org/abs/2403.008...