pybind11 provides a few convenience macros such as
PYBIND11_DECLARE_HOLDER_TYPE()
and
PYBIND11_OVERRIDE_*
. Since these are “just” macros that are evaluated in the preprocessor (which has no concept of types), they
will
get confused by commas in a template argument; for example, consider:
PYBIND11_OVERRIDE(MyReturnType<T1, T2>, Class<T3, T4>, func)
The limitation of the C preprocessor interprets this as five arguments (with new arguments beginning after each comma) rather than three. To get around this, there are two alternatives: you can use a type alias, or you can wrap the type using the
PYBIND11_TYPE
macro:
// Version 1: using a type alias using ReturnType = MyReturnType<T1, T2>; using ClassType = Class<T3, T4>; PYBIND11_OVERRIDE(ReturnType, ClassType, func); // Version 2: using the PYBIND11_TYPE macro: PYBIND11_OVERRIDE(PYBIND11_TYPE(MyReturnType<T1, T2>), PYBIND11_TYPE(Class<T3, T4>), func)
The
PYBIND11_MAKE_OPAQUE
macro does
not
require the above workarounds.
When calling a C++ function from Python, the GIL is always held. The classes
gil_scoped_release
and
gil_scoped_acquire
can be used to acquire and release the global interpreter lock in the body of a C++ function call. In this way, long-running C++ code can be parallelized using multiple Python threads. Taking
覆写 Python 虚函数
as an example, this could be realized as follows (important changes highlighted):
class PyAnimal : public Animal { public: /* Inherit the constructors */ using Animal::Animal; /* Trampoline (need one for each virtual function) */ std::string go(int n_times) { /* Acquire GIL before calling Python code */ py::gil_scoped_acquire acquire; PYBIND11_OVERRIDE_PURE( std::string, /* Return type */ Animal, /* Parent class */ go, /* Name of function */ n_times /* Argument(s) */ ); } }; PYBIND11_MODULE(example, m) { py::class_<Animal, PyAnimal> animal(m, "Animal"); animal .def(py::init<>()) .def("go", &Animal::go); py::class_<Dog>(m, "Dog", animal) .def(py::init<>()); m.def("call_go", [](Animal *animal) -> std::string { /* Release GIL before calling into (potentially long-running) C++ code */ py::gil_scoped_release release; return call_go(animal); }); }
The
call_go
wrapper can also be simplified using the
call_guard
policy (see
额外调用策略
) which yields the same result:
m.def("call_go", &call_go, py::call_guard<py::gil_scoped_release>());
Please refer to the supplemental example for details.
另请参阅
文件
tests/test_sequences_and_iterators.cpp
contains a complete example that shows how to bind a sequence data type, including length queries (
__len__
), iterators (
__iter__
), the slicing protocol and other kinds of useful operations.
It’s straightforward to split binding code over multiple extension modules, while referencing types that are declared elsewhere. Everything “just” works without any special precautions. One exception to this rule occurs when extending a type declared in another extension module. Recall the basic example from Section 继承和自动向下铸造 .
py::class_<Pet> pet(m, "Pet"); pet.def(py::init<const std::string &>()) .def_readwrite("name", &Pet::name); py::class_<Dog>(m, "Dog", pet /* <- specify parent */) .def(py::init<const std::string &>()) .def("bark", &Dog::bark);
Suppose now that
Pet
bindings are defined in a module named
basic
, whereas the
Dog
bindings are defined somewhere else. The challenge is of course that the variable
pet
is not available anymore though it is needed to indicate the inheritance relationship to the constructor of
class_<Dog>
. However, it can be acquired as follows:
py::object pet = (py::object) py::module_::import("basic").attr("Pet"); py::class_<Dog>(m, "Dog", pet) .def(py::init<const std::string &>()) .def("bark", &Dog::bark);
Alternatively, you can specify the base class as a template parameter option to
class_
, which performs an automated lookup of the corresponding Python type. Like the above code, however, this also requires invoking the
import
function once to ensure that the pybind11 binding code of the module
basic
has been executed:
py::module_::import("basic"); py::class_<Dog, Pet>(m, "Dog") .def(py::init<const std::string &>()) .def("bark", &Dog::bark);
Naturally, both methods will fail when there are cyclic dependencies.
Note that pybind11 code compiled with hidden-by-default symbol visibility (e.g. via the command line flag
-fvisibility=hidden
on GCC/Clang), which is required for proper pybind11 functionality, can interfere with the ability to access types defined in another extension module. Working around this requires manually exporting types that are accessed by multiple extension modules; pybind11 provides a macro to do just this:
class PYBIND11_EXPORT Dog : public Animal { ... };
Note also that it is possible (although would rarely be required) to share arbitrary C++ objects between extension modules at runtime. Internal library data is shared between modules using capsule machinery 1 which can be also utilized for storing, modifying and accessing user-defined data. Note that an extension module will “see” other extensions’ data if and only if they were built with the same pybind11 version. Consider the following example:
auto data = reinterpret_cast<MyData *>(py::get_shared_data("mydata")); if (!data) data = static_cast<MyData *>(py::set_shared_data("mydata", new MyData(42)));
If the above snippet was used in several separately compiled extension modules, the first one to be imported would create a
MyData
instance and associate a
"mydata"
key with a pointer to it. Extensions that are imported later would be then able to access the data behind the same pointer.
pybind11 does not provide an explicit mechanism to invoke cleanup code at module destruction time. In rare cases where such functionality is required, it is possible to emulate it using Python capsules or weak references with a destruction callback.
auto cleanup_callback = []() { // perform cleanup here -- this function is called with the GIL held }; m.add_object("_cleanup", py::capsule(cleanup_callback));
This approach has the potential downside that instances of classes exposed within the module may still be alive when the cleanup callback is invoked (whether this is acceptable will generally depend on the application).
Alternatively, the capsule may also be stashed within a type object, which ensures that it not called before all instances of that type have been collected:
auto cleanup_callback = []() { /* ... */ }; m.attr("BaseClass").attr("_cleanup") = py::capsule(cleanup_callback);
Both approaches also expose a potentially dangerous
_cleanup
attribute in Python, which may be undesirable from an API standpoint (a premature explicit call from Python might lead to undefined behavior). Yet another approach that avoids this issue involves weak reference with a cleanup callback:
// Register a callback function that is invoked when the BaseClass object is collected py::cpp_function cleanup_callback( [](py::handle weakref) { // perform cleanup here -- this function is called with the GIL held weakref.dec_ref(); // release weak reference } ); // Create a weak reference with a cleanup callback and initially leak it (void) py::weakref(m.attr("BaseClass"), cleanup_callback).release();
注意
PyPy does not garbage collect objects when the interpreter exits. An alternative approach (which also works on CPython) is to use the
atexit
模块
2
，例如：
auto atexit = py::module_::import("atexit"); atexit.attr("register")(py::cpp_function([]() { // perform cleanup here -- this function is called with the GIL held }));
Sphinx 3 has the ability to inspect the signatures and documentation strings in pybind11-based extension modules to automatically generate beautiful documentation in a variety formats. The python_example repository 4 contains a simple example repository which uses this approach.
There are two potential gotchas when using this approach: first, make sure that the resulting strings do not contain any TAB characters, which break the docstring parsing routines. You may want to use C++11 raw string literals, which are convenient for multi-line comments. Conveniently, any excess indentation will be automatically be removed by Sphinx. However, for this to work, it is important that all lines are indented consistently, i.e.:
// ok m.def("foo", &foo, R"mydelimiter( The foo function Parameters ---------- )mydelimiter"); // *not ok* m.def("foo", &foo, R"mydelimiter(The foo function Parameters ---------- )mydelimiter");
By default, pybind11 automatically generates and prepends a signature to the docstring of a function registered with
module_::def()
and
class_::def()
. Sometimes this behavior is not desirable, because you want to provide your own signature or remove the docstring completely to exclude the function from the Sphinx documentation. The class
options
allows you to selectively suppress auto-generated signatures:
PYBIND11_MODULE(example, m) { py::options options; options.disable_function_signatures(); m.def("add", [](int a, int b) { return a + b; }, "A function which adds two numbers"); }
Note that changes to the settings affect only function bindings created during the lifetime of the
options
instance. When it goes out of scope at the end of the module’s init function, the default settings are restored to prevent unwanted side effects.
Docstrings are generated at the time of the declaration, e.g. when
.def(...)
is called. At this point parameter and return types should be known to pybind11. If a custom type is not exposed yet through a
py::class_
constructor or a custom type caster, its C++ type name will be used instead to generate the signature in the docstring:
| __init__(...)
| __init__(self: example.Foo, arg0: ns::Bar) -> None
^^^^^^^
This limitation can be circumvented by ensuring that C++ classes are registered with pybind11 before they are used as a parameter or return type of a function:
PYBIND11_MODULE(example, m) { auto pyFoo = py::class_<ns::Foo>(m, "Foo"); auto pyBar = py::class_<ns::Bar>(m, "Bar"); pyFoo.def(py::init<const ns::Bar&>()); pyBar.def(py::init<const ns::Foo&>()); }