ES-dLLM: Efficient Inference for Diffusion Large Language Models by Early-Skipping
arXiv:2603.10088v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) are emerging as a promising alternative to autoregressive models (ARMs) due to their ability to capture bidirectional context and the potential for parallel generation. Despite the advantages, dLLM inference remains computationally expensive as the full input context is processed at every iteration. In this work, we analyze the generation dynamics of dLLMs and find that intermediate representations, including key, value, and hidden states, change only subtly across successive iterations. Leveraging this insight, we propose \textbf{ES-dLLM}, a training-free inference acceleration framework for dLLM that reduces computation by skipping tokens in early layers based on the estimated importance. Token importance is computed with intermediate tensor variation and confidence scores of previous iterations. Experiments on LLaDA-8B and Dream-7B demonstrate that ES-dLLM achieves throughput of up to 226.57 and
arXiv:2603.10088v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) are emerging as a promising alternative to autoregressive models (ARMs) due to their ability to capture bidirectional context and the potential for parallel generation. Despite the advantages, dLLM inference remains computationally expensive as the full input context is processed at every iteration. In this work, we analyze the generation dynamics of dLLMs and find that intermediate representations, including key, value, and hidden states, change only subtly across successive iterations. Leveraging this insight, we propose \textbf{ES-dLLM}, a training-free inference acceleration framework for dLLM that reduces computation by skipping tokens in early layers based on the estimated importance. Token importance is computed with intermediate tensor variation and confidence scores of previous iterations. Experiments on LLaDA-8B and Dream-7B demonstrate that ES-dLLM achieves throughput of up to 226.57 and 308.51 tokens per second (TPS), respectively, on an NVIDIA H200 GPU, delivering 5.6$\times$ to 16.8$\times$ speedup over the vanilla implementation and up to 1.85$\times$ over the state-of-the-art caching method, while preserving generation quality.
Executive Summary
The article proposes ES-dLLM, a training-free inference acceleration framework for diffusion large language models (dLLMs). By analyzing generation dynamics, the authors find that intermediate representations change subtly across iterations, allowing for early-skipping of tokens in lower layers. This approach achieves significant speedup, up to 16.8x, over the vanilla implementation while preserving generation quality. The framework demonstrates potential for efficient inference in dLLMs, making them more viable for practical applications.
Key Points
- ▸ ES-dLLM is a training-free inference acceleration framework for dLLMs
- ▸ The framework skips tokens in early layers based on estimated importance
- ▸ Experiments demonstrate significant speedup over vanilla implementation and state-of-the-art caching method
Merits
Efficient Inference
ES-dLLM reduces computation by skipping tokens in early layers, resulting in significant speedup
Preservation of Generation Quality
The framework preserves generation quality despite the acceleration, making it suitable for practical applications
Demerits
Limited Generalizability
The framework's performance may vary across different dLLM architectures and tasks, requiring further evaluation
Dependence on Intermediate Tensor Variation
The framework's effectiveness relies on the accuracy of intermediate tensor variation and confidence scores, which may not always be reliable
Expert Commentary
The proposed ES-dLLM framework demonstrates a promising approach to accelerating inference in diffusion large language models. By leveraging the subtle changes in intermediate representations, the authors achieve significant speedup while preserving generation quality. However, further evaluation is necessary to ensure the framework's generalizability across different architectures and tasks. Additionally, the reliance on intermediate tensor variation and confidence scores may introduce potential limitations. Overall, ES-dLLM contributes to the ongoing research in efficient language modeling and has potential implications for practical applications and policy decisions.
Recommendations
- ✓ Further evaluation of ES-dLLM across different dLLM architectures and tasks to ensure generalizability
- ✓ Investigation of alternative methods for estimating token importance to reduce reliance on intermediate tensor variation and confidence scores