Use faster algorithm for topological sort#20790
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ilevkivskyi
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Feb 12, 2026
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Interesting. I guess this will also help with ordering large SCCs (like in case of torch) where this may be called multiple times.
| from mypy.errors import Errors | ||
| from mypy.fscache import FileSystemCache | ||
| from mypy.graph_utils import strongly_connected_components, topsort | ||
| from mypy.graph_utils import strongly_connected_components, topsort, topsort2 |
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If this will be around for a while, we may choose a better name, otherwise this is fine.
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|'m planning to rename the new one to topsort and drop the old one pretty soon, once I've measured the performance in our large codebase (probably within a week or so).
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According to mypy_primer, this change doesn't affect type check results on a corpus of open source code. ✅ |
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In a large codebase, up to 9% of CPU was used in
topsortwhen doing a small incremental run. This should make it significantly faster (I've verified that it's faster at least when using synthetic data).Use Kahn's algorithm, since it's O(V + E) rather than O(depth * V) for the original algorithm.
Description of the algorithm: https://www.geeksforgeeks.org/dsa/topological-sorting-indegree-based-solution/
perf_compare.pyshowed a small improvement in self check performance, but the difference is below the noise floor. This will likely mostly help with larger codebases.Keep the old
topsortfunction around for now, so that we can test that the new and old functions behave identically in tests. I'll remove the old one afterwards.I used coding agent assist for this, but I did the implementation in multiple small increments.