Use vectorcall for all-positional-argument calls #5896
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If a handle or object is called with only positional arguments, it is
straightforward to use PyObject_Vectorcall instead of
PyObject_CallObject.
Benchmarked by adding a trivial function to pybind11_benchmark:
```
m.def("call_func_with_int", [](py::object func) {
return func(py::cast(1));
});
```
and then running `python -m timeit --setup 'from pybind11_benchmark import call_func_with_int; f = lambda x: x + 1' 'call_func_with_int(f)'`.
Before on M4 mac: 57.6 nsec per loop
After on M4 mac: 48.4 nsec per loop
For comparison, the included collatz benchmark takes 33.1 nsec per
loop, just calling `f(1)` directly takes 17.8 nec per loop, and simply
running `pass` takes 4.19 nsec per loop.
swolchok
commented
Nov 14, 2025
include/pybind11/cast.h
Outdated
| // Disable warnings about useless comparisons when N == 0. | ||
| PYBIND11_WARNING_PUSH | ||
| PYBIND11_WARNING_DISABLE_GCC("-Wtype-limits") | ||
| PYBIND11_WARNING_DISABLE_INTEL(186) |
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not sure why suppressing the icc warning didn't work :(
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If a handle or object is called with only positional arguments, it is straightforward to use PyObject_Vectorcall instead of PyObject_CallObject.
Benchmarked by adding a trivial function to pybind11_benchmark:
and then running
python -m timeit --setup 'from pybind11_benchmark import call_func_with_int; f = lambda x: x + 1' 'call_func_with_int(f)'.Before on M4 mac: 57.6 nsec per loop
After on M4 mac: 48.4 nsec per loop
For comparison, the included collatz benchmark takes 33.1 nsec per loop, just calling
f(1)directly takes 17.8 nec per loop, and simply runningpasstakes 4.19 nsec per loop.Suggested changelog entry: