LoPT: Lossless Parallel Tokenization Acceleration for Long Context Inference of Large Language Model

• URL: http://arxiv.org/abs/2511.04952v1
• Date: Fri, 07 Nov 2025 03:30:34 GMT
• Title: LoPT: Lossless Parallel Tokenization Acceleration for Long Context Inference of Large Language Model
• Authors: Wei Shao, Lingchao Zheng, Pengyu Wang, Peizhen Zheng, Jun Li, Yuwei Fan,
• Abstract summary: Lossless Parallel Tokenization (LoPT) is a novel Lossless Parallel Tokenization framework that ensures output identical to standard sequential tokenization.<n>Our approach employs character-position-based matching and dynamic chunk length adjustment to align and merge tokenized segments accurately.
• Score: 9.978777777704083
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long context inference scenarios have become increasingly important for large language models, yet they introduce significant computational latency. While prior research has optimized long-sequence inference through operators, model architectures, and system frameworks, tokenization remains an overlooked bottleneck. Existing parallel tokenization methods accelerate processing through text segmentation and multi-process tokenization, but they suffer from inconsistent results due to boundary artifacts that occur after merging. To address this, we propose LoPT, a novel Lossless Parallel Tokenization framework that ensures output identical to standard sequential tokenization. Our approach employs character-position-based matching and dynamic chunk length adjustment to align and merge tokenized segments accurately. Extensive experiments across diverse long-text datasets demonstrate that LoPT achieves significant speedup while guaranteeing lossless tokenization. We also provide theoretical proof of consistency and comprehensive analytical studies to validate the robustness of our method.

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