Longformer › Window Size and Its Effect on Coverage

Michael BrenndoerferUpdated June 25, 20252 min read
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Part 17 of 67Efficient Attention
Chapter 121Longformer › Window Size and Its Effect on Coverage
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The window size is the single most consequential hyperparameter in Longformer. Choosing it well requires understanding the trade-off between local coverage, memory usage, and the number of layers needed for full-sequence reasoning. The following visualization illustrates how different window sizes produce different attention density and coverage profiles.

Out[10]:
Visualization
Line chart showing attention density and per-layer memory usage as a function of window size for a 4096-token sequence.
Effect of window size on attention density and effective receptive field at different sequence lengths. Larger windows provide denser coverage per layer but increase memory usage; smaller windows require more layers for full-document coverage but reduce per-layer memory costs.

Notice that as window size increases, attention density rises and the number of layers needed for full-sequence coverage falls. A window of 64 tokens covers only 1.6% of a 4,096-token sequence per layer and would require 64 layers for a token to be influenced by the full document. A window of 512 tokens covers 12.5% per layer and requires only 8 layers for full propagation. A window of 2,048 tokens covers 50% but uses half as much memory as full attention, making it worth considering only for very dense tasks where far more than just local context matters.