Optimized Multi-Token Joint Decoding with Auxiliary Model for LLM Inference

The rapid development of large language models (LLMs) like GPT-4, Llama-2, and OPT has significantly advanced NLP capabilities. These models, based on transformer architectures, excel in tasks such as translation, summarization, and question answering. Despite their success, their inference process remains computationally intensive due to autoregressive decoding, which generates tokens sequentially. Early efforts to improve efficiency include non-autoregressive models and speculative decoding, which use smaller auxiliary models to draft tokens and verify them in parallel. However, these methods often compromise output quality or still require substantial computation. Recent research focuses on joint decoding strategies that generate multiple tokens simultaneously, aiming to reduce perplexity and improve performance, but the high computational cost of sampling from joint distributions limits practical deployment. This paper builds on these developments, proposing a scalable, theoretically grounded approach that balances efficiency and effectiveness.

The main challenge is to accelerate large model inference without degrading output quality. Sequential token generation leads to high latency and energy consumption, especially for models with billions of parameters. While multi-token joint decoding offers a promising solution, directly sampling from the joint distribution incurs exponential computational costs. Existing methods like greedy or beam search do not fully leverage token interactions, resulting in suboptimal perplexity and downstream performance. The key bottleneck is designing an approximation that is both computationally feasible and maintains high fidelity to the true joint distribution. Achieving this balance is critical for deploying large models in real-world, resource-constrained environments.

The paper introduces multi-token joint decoding (MTJD), which generates multiple tokens simultaneously based on their joint probability, reducing perplexity. To make this feasible, a smaller auxiliary model MTAD approximates the joint distribution, combined with a verification mechanism that bounds approximation errors. Extending this, multi-candidate MTAD (MMTAD) employs tree attention to verify multiple candidate sequences in parallel, increasing acceptance rates and decoding speed. Theoretical analysis guarantees bounds on the approximation error, and the approach is validated empirically on large models, demonstrating significant improvements in speed, perplexity, and energy efficiency. This integrated framework effectively bridges the gap between speed and quality in large-scale decoding.

• �� Define multi-token joint decoding (MTJD) to generate multiple tokens per step based on their joint distribution, aiming to lower perplexity.
• �� Design a smaller auxiliary model MTAD to approximate the large model’s joint distribution efficiently.
• �� Implement a verification mechanism that compares joint likelihoods with a threshold, ensuring high-quality token acceptance.
• �� Extend to multi-candidate MTAD (MMTAD), employing tree attention to verify multiple candidate sequences simultaneously.
• �� Use beam sampling to generate draft tokens, then validate and select the longest high-likelihood sequence.
• �� Theoretically, establish bounds on the approximation error and convergence conditions.
• �� Empirically, evaluate on Llama-2 and OPT models, measuring perplexity, downstream task performance, speed, and energy consumption.
• �� Conduct ablation studies to analyze the impact of different components, including the auxiliary model size and verification thresholds.

The evaluation involved large models like Llama-2-13B and OPT-70B across diverse NLP tasks, including text generation, question answering, and dialogue. Baselines included standard multinomial sampling, greedy decoding, and existing speculative decoding methods. Metrics such as perplexity, Rouge-L, and downstream task accuracy were used. Hyperparameters included joint token count γ, auxiliary model size, and verification thresholds. Ablation experiments tested the impact of each component, and energy consumption was measured via GPU profiling. Results consistently showed that MTAD reduced perplexity by 21.2%, improved downstream performance by 43%, and achieved a 1.42× speed-up while consuming 23.6% less energy, validating the approach's robustness and scalability.

The proposed MTAD framework significantly outperforms traditional decoding methods, with perplexity dropping by 21.2%, and downstream task accuracy increasing by 43%. Speed improvements of 1.42× were observed, alongside a 23.6% reduction in energy consumption. The multi-candidate verification further enhanced output quality and robustness, with theoretical bounds on approximation errors confirmed empirically. These results demonstrate the effectiveness of joint multi-token generation combined with auxiliary models, establishing a new benchmark for efficient large-scale inference. The experiments also revealed that increasing the joint token count γ improves perplexity and downstream performance but at higher computational costs, which the verification mechanism effectively mitigates.

This approach is directly applicable to real-time NLP applications such as chatbots, virtual assistants, and automated content creation, where inference speed and quality are critical. It enables deployment of large models in resource-constrained environments like edge devices or mobile platforms. Long-term, integrating multi-modal data (images, audio) with joint decoding could revolutionize multi-sensory AI systems, enabling more natural and efficient human-AI interactions. Additionally, the energy efficiency gains support sustainable AI development, reducing carbon footprint and operational costs in large-scale AI services.

The method relies on the auxiliary model’s quality; significant discrepancies between auxiliary and large models can reduce effectiveness. Verification overhead may become substantial for very long sequences or low-resource hardware. The approach still requires considerable computational resources, especially for high joint token counts. Further research is needed to optimize the auxiliary model training, verification thresholds, and hardware acceleration techniques. Additionally, the theoretical bounds assume certain model properties, which may not hold in all practical scenarios, necessitating adaptive strategies for diverse tasks.