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In this paper, we introduce a new task for code completion that focuses onhandling long code input and propose a sparse Transformer model, calledLongCoder, to address this task. LongCoder employs a sliding window mechanismfor self-attention and introduces two types of globally accessible tokens -bridge tokens and memory tokens - to improve performance and efficiency. Bridgetokens are inserted throughout the input sequence to aggregate localinformation and facilitate global interaction, while memory tokens are includedto highlight important statements that may be invoked later and need to bememorized, such as package imports and definitions of classes, functions, orstructures. We conduct experiments on a newly constructed dataset that containslonger code context and the publicly available CodeXGLUE benchmark.Experimental results demonstrate that LongCoder achieves superior performanceon code completion tasks compared to previous models while maintainingcomparable efficiency in terms of computational resources during inference. Allthe codes and data are available at https://github.com/microsoft/CodeBERT.

LongCoder: A Long-Range Pre-trained Language Model for Code Completion