Accelerating Transformer Inference for Translation via Parallel Decoding
Reframes autoregressive decoding as a nonlinear system solved via Jacobi and Gauss-Seidel methods, achieving up to 38% speedup without model modification.
Key Findings
Methodology
This work reformulates standard greedy autoregressive decoding of Transformer-based MT models into a system of nonlinear equations. By leveraging fixed-point iteration algorithms—Jacobi and Gauss-Seidel—the authors develop three parallel decoding algorithms (PJ, PGJ, HGJ). These algorithms initialize with a simple all-PAD sequence and iteratively refine the entire target sequence in parallel, with convergence guarantees ensuring identical output quality to traditional greedy decoding. The stopping condition halts iterations when the sequence stabilizes, preserving translation fidelity. Extensive experiments across multiple datasets and models demonstrate speedups up to 38%, nearly doubling the throughput when scaled on parallel hardware. The approach is model-agnostic and does not require retraining or architecture modifications, making it highly practical.
Key Results
- On WMT14 English-German and WMT16 English-Romanian datasets, PGJ and HGJ algorithms achieved up to 38% faster decoding while maintaining BLEU scores comparable to standard greedy decoding. In multi-lingual settings with MBart50, similar speedups of 7-11% in iteration count were observed. Hardware scaling experiments with 122 CPU cores showed near 2× acceleration, confirming the method’s scalability. The algorithms outperform traditional NATs in speed without sacrificing quality, verified across CPU and GPU environments. Dependency graph visualizations revealed learned conditional token dependencies, providing insights into model internal mechanisms.
- The experiments validated convergence and quality guarantees, with the proposed stopping condition ensuring identical output to greedy decoding. Results across diverse languages and models confirmed robustness and broad applicability. The method’s simplicity, combined with theoretical guarantees, offers a practical solution for accelerating Transformer inference in real-world applications, especially where low latency is critical.
- Overall, the study demonstrates that fixed-point iteration-based parallel decoding can substantially reduce inference latency, making neural machine translation more suitable for deployment in resource-constrained or real-time scenarios. The approach’s flexibility and scalability suggest promising avenues for further research, including adaptive stopping criteria and multi-scale decoding strategies.
Significance
This research addresses a fundamental bottleneck in Transformer-based NMT systems—sequential autoregressive decoding—by introducing a mathematically grounded, model-agnostic parallel decoding framework. The ability to accelerate inference without retraining or architecture changes significantly lowers deployment barriers, enabling faster, more efficient translation services in industry. The theoretical guarantees of convergence and output quality ensure reliability, making this approach attractive for production environments. Moreover, the integration of visualization tools offers new insights into model dependencies, fostering deeper understanding of neural translation mechanisms. As neural models grow larger and more complex, such scalable, provably correct decoding algorithms are essential for practical deployment, especially in low-latency applications like real-time translation, on-device inference, and multilingual communication platforms.
Technical Contribution
The core innovation lies in transforming the autoregressive decoding process into a fixed-point problem, solvable via classical numerical methods—Jacobi and Gauss-Seidel iterations. This approach provides a rigorous mathematical framework with convergence guarantees, unlike heuristic-based NATs. The three algorithms (PJ, PGJ, HGJ) differ mainly in their block processing strategies, balancing parallelism and dependency modeling. The stopping condition ensures the output matches the greedy solution, preserving translation quality. The method’s model-agnostic nature allows direct application on pre-trained models without retraining, offering a practical, theoretically sound acceleration technique. The visualization tool DDGviz further enhances interpretability by mapping learned token dependencies, opening avenues for model analysis and improvement.
Novelty
This work is the first to apply fixed-point iterative numerical methods directly to the Transformer decoding process, transforming sequential token generation into a parallelizable system of equations with guaranteed convergence. Unlike prior NAT approaches requiring retraining, distillation, or architectural modifications, this method operates solely on existing autoregressive models. Its mathematical foundation ensures output quality parity with traditional decoding, representing a significant conceptual and practical advance. The integration of dependency graph visualization adds a novel tool for analyzing learned token relationships, providing insights into the model’s internal conditional dependencies, which is rarely addressed in prior work.
Limitations
- The algorithms assume prior knowledge of target sequence length or rely on a fixed maximum length, which may not adapt well to highly variable sentence lengths, potentially affecting efficiency and quality.
- In cases of complex long-range dependencies, convergence speed may slow down, requiring further optimization of iteration parameters or adaptive schemes.
- Hardware resource dependence remains a challenge; the speedup benefits are maximized only with sufficient parallel hardware, and real-world deployment may face latency or memory bottlenecks.
- The stopping condition guarantees identical output to greedy decoding but may lead to unnecessary iterations in some cases, impacting efficiency.
Future Work
Future research will explore adaptive stopping criteria based on model confidence or dependency structures, aiming to further reduce unnecessary iterations. Developing multi-scale or hierarchical decoding strategies could improve efficiency for very long sentences. Extending the dependency visualization to analyze model internal representations may yield insights for architecture design. Additionally, integrating this approach with other acceleration techniques, such as quantization or hardware-specific optimizations, could further enhance practical deployment in low-resource or real-time environments.
AI Executive Summary
Deep Dive
Plain Language Accessible to non-experts
想象你在厨房做饭，传统的方法是一次只做一道菜，等一道菜做好后再开始下一道。这就像Transformer的自回归解码，一次只生成一个词，速度很慢。现在，假设你可以同时准备多道菜，用多个厨具同时操作，就像用多只手同时炒菜。这样一来，做饭的速度就快多了，但你需要确保每道菜都还好吃。本文的方法就像给厨具装上了智能控制系统，让你不用改变厨房设备，也能同时做多道菜，既快又保证味道。这种“多厨具同时操作”的思路，借助数学中的固定点迭代技术，让机器翻译变得更快更智能。
ELI14 Explained like you're 14
想象你在学校写作文，传统的方法是每次写一句，然后等老师批改完再写下一句，慢得像蜗牛。现在，假如你可以同时写好几句，然后老师帮你检查，逐步改正，最后只需要一遍就能完成作文。这就像用一种特别的“数学魔法”让电脑同时处理多个词，快得像闪电。这个方法不用改变你平时的写作方式，只是用一种聪明的技巧，让电脑在翻译时也能一口气搞定一大段。它就像给电脑装了“超级速写”功能，让它在不牺牲质量的情况下，飞快地完成翻译任务。这样一来，翻译变得更快，能帮你更快地用到外语交流啦！
Abstract
Autoregressive decoding limits the efficiency of transformers for Machine Translation (MT). The community proposed specific network architectures and learning-based methods to solve this issue, which are expensive and require changes to the MT model, trading inference speed at the cost of the translation quality. In this paper, we propose to address the problem from the point of view of decoding algorithms, as a less explored but rather compelling direction. We propose to reframe the standard greedy autoregressive decoding of MT with a parallel formulation leveraging Jacobi and Gauss-Seidel fixed-point iteration methods for fast inference. This formulation allows to speed up existing models without training or modifications while retaining translation quality. We present three parallel decoding algorithms and test them on different languages and models showing how the parallelization introduces a speedup up to 38% w.r.t. the standard autoregressive decoding and nearly 2x when scaling the method on parallel resources. Finally, we introduce a decoding dependency graph visualizer (DDGviz) that let us see how the model has learned the conditional dependence between tokens and inspect the decoding procedure.