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ext: Upgrade PyBind11 to version 2.2.1

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This upgrade is necessary for pybind to build with GCC 7.2.

We still need to add the patch for stl.h. MSC_FULL_VER change is no longer
needed.
See https://gem5-review.googlesource.com/c/public/gem5/+/2230

Change-Id: I806729217d022070583994c2dfcaa74476aef30f
Signed-off-by: Jason Lowe-Power <jason@lowepower.com>
Reviewed-on: https://gem5-review.googlesource.com/5801
Reviewed-by: Andreas Sandberg <andreas.sandberg@arm.com>
Maintainer: Andreas Sandberg <andreas.sandberg@arm.com>
This commit is contained in:
Jason Lowe-Power
2017-11-17 17:02:05 -08:00
parent 3f64b374c4
commit f07d5069d8
160 changed files with 15668 additions and 7238 deletions
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49
ext/pybind11/tests/test_numpy_vectorize.cpp
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@@ -16,11 +16,11 @@ double my_func(int x, float y, double z) {
return (float) x*y*z;
}
std::complex<double> my_func3(std::complex<double> c) {
return c * std::complex<double>(2.f);
}
TEST_SUBMODULE(numpy_vectorize, m) {
try { py::module::import("numpy"); }
catch (...) { return; }
test_initializer numpy_vectorize([](py::module &m) {
// 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));
@@ -32,14 +32,45 @@ test_initializer numpy_vectorize([](py::module &m) {
);
// Vectorize a complex-valued function
m.def("vectorized_func3", py::vectorize(my_func3));
m.def("vectorized_func3", py::vectorize(
[](std::complex<double> c) { return c * std::complex<double>(2.f); }
));
/// Numpy function which only accepts specific data types
// test_type_selection
// Numpy function which only accepts specific data types
m.def("selective_func", [](py::array_t<int, py::array::c_style>) { return "Int branch taken."; });
m.def("selective_func", [](py::array_t<float, py::array::c_style>) { return "Float branch taken."; });
m.def("selective_func", [](py::array_t<std::complex<float>, py::array::c_style>) { return "Complex float branch taken."; });
// 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):
struct NonPODClass {
NonPODClass(int v) : value{v} {}
int value;
};
py::class_<NonPODClass>(m, "NonPODClass").def(py::init<int>());
m.def("vec_passthrough", py::vectorize(
[](double *a, double b, py::array_t<double> c, const int &d, int &e, NonPODClass f, const double g) {
return *a + b + c.at(0) + d + e + f.value + g;
}
));
// test_method_vectorization
struct VectorizeTestClass {
VectorizeTestClass(int v) : value{v} {};
float method(int x, float y) { return y + (float) (x + value); }
int value = 0;
};
py::class_<VectorizeTestClass> vtc(m, "VectorizeTestClass");
vtc .def(py::init<int>())
.def_readwrite("value", &VectorizeTestClass::value);
// Automatic vectorizing of methods
vtc.def("method", py::vectorize(&VectorizeTestClass::method));
// test_trivial_broadcasting
// Internal optimization test for whether the input is trivially broadcastable:
py::enum_<py::detail::broadcast_trivial>(m, "trivial")
.value("f_trivial", py::detail::broadcast_trivial::f_trivial)
@@ -50,9 +81,9 @@ test_initializer numpy_vectorize([](py::module &m) {
py::array_t<float, py::array::forcecast> arg2,
py::array_t<double, py::array::forcecast> arg3
) {
size_t ndim;
std::vector<size_t> shape;
ssize_t ndim;
std::vector<ssize_t> shape;
std::array<py::buffer_info, 3> buffers {{ arg1.request(), arg2.request(), arg3.request() }};
return py::detail::broadcast(buffers, ndim, shape);
});
});
}