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Align mlx::core::max op nan propagation with NumPy (#2339)

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* Make max op NaN propagation rules align with numpy

* Adding benchmarks and testing for max op nanpropagation

* Pre-commit formatting

* Fix max complex64 nan propagation and add test

* Improve the cpp unittest

* Only check nans on non-integral types in simd_reduce_impl.

* Cleanup using namespace alias

* Add cpu Max nanpropagation. Fix a small fib in cpu max dispatch data types for int8/int16.

* Make the max nanpropagation test more meaningful for integer types

* Remove tuple unpacking syntax to comply with earlier python versions. Add cuda skip to nanpropagation tests, fix cuda implementation in a separate PR.
This commit is contained in:
jhavukainen
2025-07-09 11:26:27 -07:00
committed by GitHub
parent fb4e8b896b
commit 8b9a3f3cea
7 changed files with 131 additions and 5 deletions
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11
benchmarks/cpp/single_ops.cpp
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@@ -192,6 +192,17 @@ void time_reductions() {
auto argmin_along_1 = [&a]() { return mx::argmin(a, 1, false); };
TIME(argmin_along_1);
auto indices = mx::array({1});
auto updates = mx::reshape(mx::array({NAN}), {1, 1, 1});
std::vector<int> axes{0};
auto b = scatter(a, {indices}, updates, axes);
mx::eval(b);
auto max_along_0 = [&b]() { return mx::max(b, 0, false); };
TIME(max_along_0);
auto max_along_1 = [&b]() { return mx::max(b, 1, false); };
TIME(max_along_1);
}
void time_gather_scatter() {