Fixed type instability in tensor operations#1293
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JordiManyer merged 2 commits intomasterfrom May 1, 2026
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- Coverage 88.83% 88.83% -0.01%
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The problem:
Every arithmetic operation on MultiValue types (scalar *, +, -, /) followed the same pattern: extract the independent components as a tuple, apply the operation element-wise, reconstruct
the result. The extraction was written as Tuple(a)[1:Li] where Li = num_indep_components(a) is a plain Int computed at runtime.
The problem is that slicing a tuple with a runtime integer is type-unstable in Julia: the compiler sees NTuple{N,T}[1:Int] and cannot determine the length of the result statically. Everything downstream — the element type, the result type, the constructor call — then cascades into uncertainty. Thus,
Base.promote_opcannot infer the output type and returnsAny.The reason we slice instead of just taking the whole
datais that some types (onlySymTracelessTensorValuein reality) store extra components that are not independent. So the [1:Li] was load-bearing for that type specifically, while being a unnecessary for all others.The fix:
We introduced
get_indep_components(a), which replaces the type-unstable slice: It is equivalent toTuplefor all MultiValues, except for the special ones.