Specialize mapreduce + sparsevec on function arguments - #753
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`Base._mapreduce`, `_mapreducezeros` and `Base._mapreducedim!` for sparse matrices, their sparse-vector counterparts, and the `sparsevec` methods with a `combine` argument took their function arguments without a type parameter. Where such an argument is only passed through, Julia compiles the method for `Function` rather than for the concrete function, and every call below that point becomes dynamic. Under juliac's `--trim` this made `sum(A)` for a sparse matrix, and the other reductions with it, unbuildable. Give each of these function arguments a type parameter, as JuliaSparse#751 did for the `combine` argument of `sparse`, so the methods specialize and the reduction call graph resolves statically. This commit was written with the assistance of generative AI (Claude). Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
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You could write a comment explaining why the extra type parameters are needed - they look otherwise useless and somebody else might remove them. |
fredrikekre
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You could write a comment explaining why the extra type parameters are needed
I don't think that is needed. This is a well known pattern and is used all over the place (https://docs.julialang.org/en/v1/manual/performance-tips/#Be-aware-of-when-Julia-avoids-specializing).
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Another small trim fix-up, follow-up to #751 . Expect ~1 or 2 more PRs in this series.
I do not think it is easy to add a test here since the problem this fixes has to do with the "compileable" specializations chosen by the compiler - those affect codegen and cause
--trimto fail but they don't generally impact inference.