[ty] Avoid redundant solve for empty collection context#25527
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Typing conformance resultsNo changes detected ✅Current numbersThe percentage of diagnostics emitted that were expected errors held steady at 91.94%. The percentage of expected errors that received a diagnostic held steady at 87.09%. The number of fully passing files held steady at 92/134. |
Memory usage reportSummary
Significant changesClick to expand detailed breakdownflake8
trio
sphinx
prefect
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| Project | Old Time | New Time | Change |
|---|---|---|---|
pydantic |
2.11s | 0.79s | -62% |
Merging this PR will improve performance by ×2.2
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| Mode | Benchmark | BASE |
HEAD |
Efficiency | |
|---|---|---|---|---|---|
| ⚡ | Simulation | ty_micro[pydantic_core_schema_dict] |
424.6 ms | 94.8 ms | ×4.5 |
| ⚡ | WallTime | pydantic |
34.7 s | 16.9 s | ×2 |
| ⚡ | Memory | ty_micro[pydantic_core_schema_dict] |
19.5 MB | 16.3 MB | +19.73% |
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Comparing cjm/empty-collection-context-fast-path (461b9bd) with main (b82dfe7)
Footnotes
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60 benchmarks were skipped, so the baseline results were used instead. If they were deleted from the codebase, click here and archive them to remove them from the performance reports. ↩
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dcreager
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Jun 1, 2026
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| // Keep this parallel with the slow path below: a covariant context provides | ||
| // only an upper bound, which does not determine the specialization for an empty | ||
| // literal. A contravariant context provides a lower bound, for which inference | ||
| // selects the narrowest valid solution. |
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It would be nice to not have to keep this consistent manually, but I don't see an easy way to do that that doesn't introduce more complexity than it removes...
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Agreed. On the plus side, it seems like something that's unlikely to change?
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Summary
Avoid building and solving a
SpecializationBuilderconstraint set for an empty collection literal when contextual inference has already provided its complete specialization.For this case, directly apply the contextual specialization and return the resulting collection instance.
Motivation
Profiling pydantic exposed an expensive path for empty collection literals with large contextual types. Even when the context completely determines the collection type variables and the literal has no elements that could add information, inference continued through constraint projection and solving.
This is especially expensive for pydantic's large schema unions.
Validation
Existing tests pass, no ecosystem diagnostic changes.