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# Miscellaneous

## General notes regarding convenience macros

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.

## Global Interpreter Lock (GIL)

The Python C API dictates that the Global Interpreter Lock (GIL) must always be held by the current thread to safely access Python objects. As a result, when Python calls into C++ via pybind11 the GIL must be held, and pybind11 will never implicitly release the GIL.

    void my_function() {
        /* GIL is held when this function is called from Python */
    }

    PYBIND11_MODULE(example, m) {
        m.def("my_function", &my_function);
    }

pybind11 will ensure that the GIL is held when it knows that it is calling Python code. For example, if a Python callback is passed to C++ code via `std::function`, when C++ code calls the function the built-in wrapper will acquire the GIL before calling the Python callback. Similarly, the `PYBIND11_OVERRIDE` family of macros will acquire the GIL before calling back into Python.

When writing C++ code that is called from other C++ code, if that code accesses Python state, it must explicitly acquire and release the GIL. A separate document on deadlocks [1] elaborates on a particularly subtle interaction with C++’s block-scope static variable initializer guard mutexes.

[1] (1,2)

See docs/advanced/deadlock.md

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, but great care must be taken when any `gil_scoped_release` appear: if there is any way that the C++ code can access Python objects, `gil_scoped_acquire` should be used to reacquire the GIL. Taking Overriding virtual functions in Python as an example, this could be realized as follows (important changes highlighted):

    class PyAnimal : public Animal, public py::trampoline_self_life_support {
    public:
        /* Inherit the constructors */
        using Animal::Animal;

        /* Trampoline (need one for each virtual function) */
        std::string go(int n_times) {
            /* PYBIND11_OVERRIDE_PURE will acquire the GIL before accessing Python state */
            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, py::smart_holder> animal(m, "Animal");
        animal
            .def(py::init<>())
            .def("go", &Animal::go);

        py::class_<Dog, py::smart_holder>(m, "Dog", animal)
            .def(py::init<>());

        m.def("call_go", [](Animal *animal) -> std::string {
            // GIL is held when called from Python code. 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 Additional call policies) which yields the same result:

    m.def("call_go", &call_go, py::call_guard<py::gil_scoped_release>());

## Common Sources Of Global Interpreter Lock Errors

Failing to properly hold the Global Interpreter Lock (GIL) is one of the more common sources of bugs within code that uses pybind11. If you are running into GIL related errors, we highly recommend you consult the following checklist.

- Do you have any global variables that are pybind11 objects or invoke pybind11 functions in either their constructor or destructor? You are generally not allowed to invoke any Python function in a global static context. We recommend using lazy initialization and then intentionally leaking at the end of the program.

- Do you have any pybind11 objects that are members of other C++ structures? One commonly overlooked requirement is that pybind11 objects have to increase their reference count whenever their copy constructor is called. Thus, you need to be holding the GIL to invoke the copy constructor of any C++ class that has a pybind11 member. This can sometimes be very tricky to track for complicated programs Think carefully when you make a pybind11 object a member in another struct.

- C++ destructors that invoke Python functions can be particularly troublesome as destructors can sometimes get invoked in weird and unexpected circumstances as a result of exceptions.

- C++ static block-scope variable initialization that calls back into Python can cause deadlocks; see [1] for a detailed discussion.

- You should try running your code in a debug build. That will enable additional assertions within pybind11 that will throw exceptions on certain GIL handling errors (reference counting operations).

## Free-threading support

pybind11 supports the experimental free-threaded builds of Python versions 3.13+. pybind11’s internal data structures are thread safe. To enable your modules to be used with free-threading, pass the `mod_gil_not_used` tag as the third argument to `PYBIND11_MODULE`.

For example:

    PYBIND11_MODULE(example, m, py::mod_gil_not_used()) {
        py::class_<Animal> animal(m, "Animal");
        // etc
    }

Importantly, enabling your module to be used with free-threading is also your promise that your code is thread safe. Modules must still be built against the Python free-threading branch to enable free-threading, even if they specify this tag. Adding this tag does not break compatibility with non-free-threaded Python.

## Sub-interpreter support

pybind11 supports isolated sub-interpreters, which are stable in Python 3.12+. pybind11’s internal data structures are sub-interpreter safe. To enable your modules to be imported in isolated sub-interpreters, pass the `multiple_interpreters::per_interpreter_gil()` tag as the third or later argument to `PYBIND11_MODULE`.

For example:

    PYBIND11_MODULE(example, m, py::multiple_interpreters::per_interpreter_gil()) {
        py::class_<Animal> animal(m, "Animal");
        // etc
    }

Best Practices for Sub-interpreter Safety:

- Your initialization function will run for each interpreter that imports your module.

- Never share Python objects across different sub-interpreters.

- Avoid global/static state whenever possible. Instead, keep state within each interpreter, such as in instance members tied to Python objects, `globals()`, and the interpreter state dict.

- Modules without any global/static state in their C++ code may already be sub-interpreter safe without any additional work!

- Avoid trying to “cache” Python objects in C++ variables across function calls (this is an easy way to accidentally introduce sub-interpreter bugs).

- While sub-interpreters each have their own GIL, there can now be multiple independent GILs in one program, so concurrent calls into a module from two different sub-interpreters are still possible. Therefore, your module still needs to consider thread safety.

pybind11 also supports “legacy” sub-interpreters which shared a single global GIL. You can enable legacy-only behavior by using the `multiple_interpreters::shared_gil()` tag in `PYBIND11_MODULE`.

You can explicitly disable sub-interpreter support in your module by using the `multiple_interpreters::not_supported()` tag. This is the default behavior if you do not specify a multiple_interpreters tag.

## Concurrency and Parallelism in Python with pybind11

Sub-interpreter support does not imply free-threading support or vice versa. Free-threading safe modules can still have global/static state (as long as access to them is thread-safe), but sub-interpreter safe modules cannot. Likewise, sub-interpreter safe modules can still rely on the GIL, but free-threading safe modules cannot.

Here is a simple example module which has a function that calculates a value and returns the result of the previous calculation.

    PYBIND11_MODULE(example, m) {
        static size_t seed = 0;
        m.def("calc_next", []() {
            auto old = seed;
            seed = (seed + 1) * 10;
            return old;
        });

This module is not free-threading safe because there is no synchronization on the number variable. It is relatively easy to make this free-threading safe. One way is by using atomics, like this:

    PYBIND11_MODULE(example, m, py::mod_gil_not_used()) {
        static std::atomic<size_t> seed(0);
        m.def("calc_next", []() {
            size_t old, next;
            do {
                old = seed.load();
                next = (old + 1) * 10;
            } while (!seed.compare_exchange_weak(old, next));
            return old;
        });
    }

The atomic variable and the compare-exchange guarantee a consistent behavior from this function even when called currently from multiple threads at the same time.

However, the global/static integer is not sub-interpreter safe, because the calls in one sub-interpreter will change what is seen in another. To fix it, the state needs to be specific to each interpreter. One way to do that is by storing the state on another Python object, such as a member of a class. For this simple example, we will store it in `globals()`.

    PYBIND11_MODULE(example, m, py::multiple_interpreters::per_interpreter_gil()) {
        m.def("calc_next", []() {
            if (!py::globals().contains("myseed"))
                py::globals()["myseed"] = 0;
            size_t old = py::globals()["myseed"];
            py::globals()["myseed"] = (old + 1) * 10;
            return old;
        });
    }

This module is sub-interpreter safe, for both `shared_gil` (“legacy”) and `per_interpreter_gil` (“default”) varieties. Multiple sub-interpreters could each call this same function concurrently from different threads. This is safe because each sub-interpreter’s GIL protects it’s own Python objects from concurrent access.

However, the module is no longer free-threading safe, for the same reason as before, because the calculation is not synchronized. We can synchronize it using a Python critical section. This will do nothing if not in free-threaded Python. You can have it lock one or two Python objects. You cannot nest it.

Warning

When using a `py::scoped_critical_section`, make sure it is not nested and that no other synchronization primitives (such as a `std::mutex`) are held, which could lead to deadlocks. In 3.13, taking the same lock causes it to release then reacquire, which means you can’t use it to, for example, read and write to a dictionary, because the dictionary uses a critical section internally in CPython. Use a `std::mutex` instead if you need this on Python 3.13. In 3.14, taking a lock on a locked object no longer releases and relocks as an optimization, which also fixes this case.

    #include <pybind11/critical_section.h>
    // ...

    PYBIND11_MODULE(example, m, py::multiple_interpreters::per_interpreter_gil(), py::mod_gil_not_used()) {
        m.def("calc_next", []() {
            size_t old;
            py::dict g = py::globals();
            py::scoped_critical_section guard(g);
            if (!g.contains("myseed"))
                g["myseed"] = 0;
            old = g["myseed"];
            g["myseed"] = (old + 1) * 10;
            return old;
        });
    }

The module is now both sub-interpreter safe and free-threading safe.

## Binding sequence data types, iterators, the slicing protocol, etc.

Please refer to the supplemental example for details.

See also

The file `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.

## Partitioning code over multiple extension modules

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 Inheritance and automatic downcasting.

    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 `py::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 `py::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 [2] 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.

[2]

https://docs.python.org/3/extending/extending.html#using-capsules

## Module Destructors

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();

Note

PyPy does not garbage collect objects when the interpreter exits. An alternative approach (which also works on CPython) is to use the `atexit` module [3], for example:

    auto atexit = py::module_::import("atexit");
    atexit.attr("register")(py::cpp_function([]() {
        // perform cleanup here -- this function is called with the GIL held
    }));

[3]

https://docs.python.org/3/library/atexit.html

## Generating documentation using Sphinx

Sphinx [4] 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 [5] 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");
    }

pybind11 also appends all members of an enum to the resulting enum docstring. This default behavior can be disabled by using the `disable_enum_members_docstring()` function of the `options` class.

With `disable_user_defined_docstrings()` all user defined docstrings of `module_::def()`, `class_::def()` and `enum_()` are disabled, but the function signatures and enum members are included in the docstring, unless they are disabled separately.

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.

[4]

http://www.sphinx-doc.org

[5]

http://github.com/pybind/python_example

## Avoiding C++ types in docstrings

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&>());
    }

## Setting inner type hints in docstrings

When you use pybind11 wrappers for `list`, `dict`, and other generic python types, the docstring will just display the generic type. You can convey the inner types in the docstring by using a special ‘typed’ version of the generic type.

    PYBIND11_MODULE(example, m) {
        m.def("pass_list_of_str", [](py::typing::List<py::str> arg) {
            // arg can be used just like py::list
        ));
    }

The resulting docstring will be `pass_list_of_str(arg0: list[str]) -> None`.

The following special types are available in `pybind11/typing.h`:

- `py::Tuple<Args...>`

- `py::Dict<K, V>`

- `py::List<V>`

- `py::Set<V>`

- `py::Callable<Signature>`

Warning

Just like in python, these are merely hints. They don’t actually enforce the types of their contents at runtime or compile time.