CLLMs: Consistency Large Language Models
CLLMs improve Jacobi decoding to achieve 2.4x to 3.4x speedup while maintaining quality.
Key Findings
Methodology
The paper proposes a novel approach by fine-tuning the target LLM to consistently predict the fixed point from any state on the Jacobi trajectory. This method incorporates the concept of consistency models, requiring the model to accurately predict the fixed point given any state.
Key Results
- On the Spider dataset, CLLMs achieved a 3.4x speedup with almost no accuracy loss.
- On domain-specific benchmarks like GSM8K and CodeSearchNet Python, CLLMs achieved 2.4x to 3.4x speedup.
- On the MT-bench open-domain benchmark, CLLMs achieved a 2.4x speedup on ShareGPT with a score of 6.4.
Significance
This research significantly improves the inference speed of large language models by enhancing the Jacobi decoding method, addressing the high latency issue of traditional autoregressive decoding. It provides substantial speedup without additional memory costs, making it highly impactful for academia and industry.
Technical Contribution
Technically, CLLMs improve the convergence speed of Jacobi decoding by introducing a consistency loss. Compared to existing speculative decoding and Medusa methods, CLLMs do not require additional model components, reducing memory consumption while achieving significant speedup.
Novelty
CLLMs are the first to apply the concept of consistency models to the decoding process of large language models, achieving fast mapping from any intermediate state to the fixed point through consistency loss.
Limitations
- The single-step learning task remains challenging when n is large, leading to slow model convergence.
- Data augmentation and post-processing of Jacobi trajectories are needed to avoid low-quality generations.
Future Work
Future research directions include exploring the application of other sampling strategies and further optimizing CLLMs' performance on different tasks and datasets.
AI Executive Summary
Inference latency of large language models (LLMs) is a critical factor affecting user experience. Traditional autoregressive decoding methods, due to their sequential nature, result in high latency. The proposed CLLMs significantly improve inference speed by enhancing the Jacobi decoding method. CLLMs fine-tune the target LLM to quickly converge from any state to the fixed point, incorporating the concept of consistency models, which requires the model to accurately predict the fixed point given any state.
Experimental results show that CLLMs achieve 2.4x to 3.4x speedup across various domain-specific and open-domain benchmarks while maintaining generation quality. Notably, on the Spider dataset, CLLMs achieved a 3.4x speedup with almost no accuracy loss.
The significant contribution of CLLMs lies in achieving substantial speedup without additional model components, reducing memory consumption. This method provides new possibilities for efficient inference of large language models, making it highly impactful for academia and industry. Future research directions include exploring the application of other sampling strategies and further optimizing CLLMs' performance on different tasks and datasets.
Deep Analysis
Background
Large language models (LLMs) like GPT-4 and LLaMA have made significant advances in AI. However, their inference latency remains a challenge. Traditional autoregressive decoding methods, due to their sequential nature, result in high latency, limiting the efficiency of LLMs in practical applications.
Core Problem
Autoregressive decoding requires generating each token sequentially, leading to high latency. Although Jacobi decoding offers parallelization potential, its practical speedup is limited as each iteration typically predicts only one token accurately.
Innovation
CLLMs fine-tune the target LLM to quickly converge from any state to the fixed point on the Jacobi trajectory. It incorporates the concept of consistency models, requiring the model to accurately predict the fixed point given any state.
Methodology
- �� Fine-tune the target LLM for fast convergence
- �� Introduce consistency loss to improve Jacobi decoding
- �� Data augmentation and post-processing to enhance generation quality
Experiments
Experiments were conducted on various domain-specific and open-domain benchmarks, including Spider, GSM8K, and MT-bench. Evaluation metrics included generation speed and accuracy.
Results
CLLMs achieved a 3.4x speedup on the Spider dataset with almost no accuracy loss. On domain-specific benchmarks like GSM8K and CodeSearchNet Python, CLLMs achieved 2.4x to 3.4x speedup.
Applications
CLLMs can be used in applications requiring fast response generation, such as real-time dialogue systems and code generation tools.
Limitations & Outlook
The single-step learning task remains challenging when n is large, leading to slow model convergence. Data augmentation and post-processing of Jacobi trajectories are needed to avoid low-quality generations.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen. Traditional autoregressive decoding is like adding one ingredient at a time, waiting for each to cook before adding the next. CLLMs are like handling multiple ingredients simultaneously, completing several steps at once. This method greatly speeds up your cooking while ensuring each dish is delicious.
ELI14 Explained like you're 14
Imagine you're playing a game where you can only move one character at a time, waiting for it to finish before moving the next. That's autoregressive decoding. CLLMs let you control multiple characters at once, making the game much faster! Isn't that cool?
Glossary
Jacobi Decoding
A method to parallelize the LLM decoding process by iteratively updating token sequences.
Used to improve LLM inference speed.
Consistency Models
A method to accelerate diffusion models by mapping from any state to the fixed point through consistency loss.
Used to improve the convergence speed of Jacobi decoding.
Fixed Point
In Jacobi decoding, it refers to the token sequence that matches the autoregressive decoding result.
The target of CLLMs is to quickly converge to the fixed point.
Autoregressive Decoding
The traditional LLM decoding method that generates tokens sequentially.
Improved by CLLMs to enhance speed.
Data Augmentation
Improves model learning and generalization by randomly correcting erroneous tokens.
Used to enhance CLLMs' generation quality.
Open Questions Unanswered questions from this research
- 1 How to further increase CLLMs' generation speed without increasing computational complexity?
- 2 How do CLLMs perform on other tasks and datasets?
Applications
Immediate Applications
Real-time Dialogue Systems
CLLMs can significantly improve the response speed of dialogue systems, enhancing user experience.
Long-term Vision
Efficient Code Generation
CLLMs can be used in code generation tools to improve development efficiency.
Abstract
Parallel decoding methods such as Jacobi decoding show promise for more efficient LLM inference as it breaks the sequential nature of the LLM decoding process and transforms it into parallelizable computation. However, in practice, it achieves little speedup compared to traditional autoregressive (AR) decoding, primarily because Jacobi decoding seldom accurately predicts more than one token in a single fixed-point iteration step. To address this, we develop a new approach aimed at realizing fast convergence from any state to the fixed point on a Jacobi trajectory. This is accomplished by refining the target LLM to consistently predict the fixed point given any state as input. Extensive experiments demonstrate the effectiveness of our method, showing 2.4$\times$ to 3.4$\times$ improvements in generation speed while preserving generation quality across both domain-specific and open-domain benchmarks.