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ext: Update Pybind to Version 2.10.3

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Updating Pybind11 is necessary for gem5 to compile correctly with
Python 3.11. As of March 9th 2023, 2.10.3 is the latest version of
Pybind11.

Change-Id: I32c68c507770040d3fac2de442d88a8f46b48896
Issue-on: https://gem5.atlassian.net/browse/GEM5-1295
Reviewed-on: https://gem5-review.googlesource.com/c/public/gem5/+/68818
Maintainer: Jason Lowe-Power <power.jg@gmail.com>
Tested-by: kokoro <noreply+kokoro@google.com>
Reviewed-by: Jason Lowe-Power <power.jg@gmail.com>
This commit is contained in:
Bobby R. Bruce
2023-03-09 10:23:23 -08:00
committed by Bobby Bruce
parent b305019ac4
commit 07fca546e6
210 changed files with 17229 additions and 8978 deletions
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32
ext/pybind11/tests/test_numpy_vectorize.cpp
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@@ -8,38 +8,43 @@
BSD-style license that can be found in the LICENSE file.
*/
#include "pybind11_tests.h"
#include <pybind11/numpy.h>
#include "pybind11_tests.h"
#include <utility>
double my_func(int x, float y, double z) {
py::print("my_func(x:int={}, y:float={:.0f}, z:float={:.0f})"_s.format(x, y, z));
return (float) x*y*z;
return (float) x * y * z;
}
TEST_SUBMODULE(numpy_vectorize, m) {
try { py::module_::import("numpy"); }
catch (...) { return; }
try {
py::module_::import("numpy");
} catch (const py::error_already_set &) {
return;
}
// test_vectorize, test_docs, test_array_collapse
// Vectorize all arguments of a function (though non-vector arguments are also allowed)
m.def("vectorized_func", py::vectorize(my_func));
// Vectorize a lambda function with a capture object (e.g. to exclude some arguments from the vectorization)
// Vectorize a lambda function with a capture object (e.g. to exclude some arguments from the
// vectorization)
m.def("vectorized_func2", [](py::array_t<int> x, py::array_t<float> y, float z) {
return py::vectorize([z](int x, float y) { return my_func(x, y, z); })(std::move(x),
std::move(y));
});
// Vectorize a complex-valued function
m.def("vectorized_func3", py::vectorize(
[](std::complex<double> c) { return c * std::complex<double>(2.f); }
));
m.def("vectorized_func3",
py::vectorize([](std::complex<double> c) { return c * std::complex<double>(2.f); }));
// test_type_selection
// NumPy function which only accepts specific data types
// A lot of these no lints could be replaced with const refs, and probably should at some point.
// A lot of these no lints could be replaced with const refs, and probably should at some
// point.
m.def("selective_func",
[](const py::array_t<int, py::array::c_style> &) { return "Int branch taken."; });
m.def("selective_func",
@@ -49,8 +54,8 @@ TEST_SUBMODULE(numpy_vectorize, m) {
});
// test_passthrough_arguments
// Passthrough test: references and non-pod types should be automatically passed through (in the
// function definition below, only `b`, `d`, and `g` are vectorized):
// Passthrough test: references and non-pod types should be automatically passed through (in
// the function definition below, only `b`, `d`, and `g` are vectorized):
struct NonPODClass {
explicit NonPODClass(int v) : value{v} {}
int value;
@@ -76,8 +81,7 @@ TEST_SUBMODULE(numpy_vectorize, m) {
int value = 0;
};
py::class_<VectorizeTestClass> vtc(m, "VectorizeTestClass");
vtc .def(py::init<int>())
.def_readwrite("value", &VectorizeTestClass::value);
vtc.def(py::init<int>()).def_readwrite("value", &VectorizeTestClass::value);
// Automatic vectorizing of methods
vtc.def("method", py::vectorize(&VectorizeTestClass::method));
@@ -99,5 +103,5 @@ TEST_SUBMODULE(numpy_vectorize, m) {
return py::detail::broadcast(buffers, ndim, shape);
});
m.def("add_to", py::vectorize([](NonPODClass& x, int a) { x.value += a; }));
m.def("add_to", py::vectorize([](NonPODClass &x, int a) { x.value += a; }));
}