Pybind11 and C reference passing mechanism analysis

When using Pybind11 to expose C code to Python, it is crucial to understand how parameter passing in C (by value, by reference, by pointer) maps to object behavior in Python. A common confusion is whether the Python side can detect these changes when a C function modifies an object by reference.

1. The behavior of passing a single object reference

First, let's look at a basic C class and the functions that modify its contents.

C code example:

 // mymodule.cpp
#include <pybind11>
#include <vector>

namespace py = pybind11;

//Define a simple C class A
class A {
public:
    int n = 0;
    double val = 0.0;
    A() = default; //Default constructor};

// Function B: Pass the A object by value inline void B_by_value(A a) {
    an = 1;
    a.val = 0.1;
}

// Function B: Pass the A object by reference inline void B_by_reference(A& a) {
    an = 2;
    a.val = 0.2;
}

// Pybind11 binding code PYBIND11_MODULE(mymodule, m) {
    m.doc() = "Pybind11 example for reference passing";

    py::class_<a>(m, "A")
        .def(py::init())
        .def_readwrite("n", &A::n)
        .def_readwrite("val", &A::val);

    m.def("B_by_value", &B_by_value, "Modifies A by value (no change in Python)");
    m.def("B_by_reference", &B_by_reference, "Modifies A by reference (changes reflected in Python)");
}</a></vector></pybind11>

Python interactive example:

 import mymodule

# 1. Pass a_val by value = mymodule.A()
print(f"Before B_by_value: a_val.n={a_val.n}, a_val.val={a_val.val}")
mymodule.B_by_value(a_val)
print(f"After B_by_value: a_val.n={a_val.n}, a_val.val={a_val.val}")
# Result: a_val has not been modified # 2. Pass a_ref by reference = mymodule.A()
print(f"Before B_by_reference: a_ref.n={a_ref.n}, a_ref.val={a_ref.val}")
mymodule.B_by_reference(a_ref)
print(f"After B_by_reference: a_ref.n={a_ref.n}, a_ref.val={a_ref.val}")
# Result: a_ref is successfully modified

As can be seen from the above example, when a C function receives a single object through a non-const reference, Pybind11 is able to correctly map the Python object to the C reference, allowing modifications on the C side to be reflected on the Python side.

2. Traps of passing list objects by reference

However, the situation becomes complicated when a C function needs to modify a list containing multiple objects (such as std::vector). If a C function receives std::vector& as a parameter and modifies its internal elements, these modifications may not be reflected in the corresponding list object on the Python side.

C code example (problem version):

 // Then mymodule.cpp above
// ... (the binding code between class A and py::class_<a> remains unchanged)

// Function C: pass the A object list by reference inline void C_list_by_reference(std::vector</a><a>& alist) {
    for (auto& a : alist) {
        an = 3;
        a.val = 0.3;
    }
}

// Pybind11 binding code PYBIND11_MODULE(mymodule, m) {
    // ... (the binding between class A and B_by_value, B_by_reference remains unchanged)
    m.def("C_list_by_reference", &C_list_by_reference, "Modifies list of A by reference (elements might not update in Python)");
}</a>

Python interactive example (problem reproduction):

 import mymodule

# Create a Python list containing A objects list_a = [mymodule.A(), mymodule.A()]
print(f"Before C_list_by_reference:")
for i, obj in enumerate(list_a):
    print(f" list_a[{i}]: n={obj.n}, val={obj.val}")

mymodule.C_list_by_reference(list_a)

print(f"After C_list_by_reference:")
for i, obj in enumerate(list_a):
    print(f" list_a[{i}]: n={obj.n}, val={obj.val}")
# Result: The elements in list_a have not been modified

Although the C function C_list_by_reference internally modifies each A object in std::vector by reference, these modifications are not reflected on the A objects in Python's list_a. This is usually because Pybind11, when converting the Python list to std::vector&, may have created a copy of the A object, or its internal mechanism, even though it was a reference, failed to map changes to the elements in the C vector back to the original elements of the Python list.

3. Solution: Use pointer list passing

To solve the problem of std::vector& modification not taking effect, the key is to ensure that the C function operates on the same C object instance referenced by the Python object. This can be achieved by passing std::vector (i.e. a list of pointers to A objects).

C code example (solution):

 // Then mymodule.cpp above
// ... (the binding code between class A and py::class_<a> remains unchanged)

// Function D: pass the A object pointer list by reference inline void D_list_by_pointer_reference(std::vector</a><a>& alist_ptrs) {
    for (A* a_ptr : alist_ptrs) {
        if (a_ptr) { // Check if the pointer is empty a_ptr->n = 4;
            a_ptr->val = 0.4;
        }
    }
}

// Pybind11 binding code PYBIND11_MODULE(mymodule, m) {
    // ... (the binding between class A and B_by_value, B_by_reference, C_list_by_reference remains unchanged)
    m.def("D_list_by_pointer_reference", &D_list_by_pointer_reference, "Modifies list of A using pointers (changes reflected in Python)");
}</a>

Python interactive example (verification solution):

 import mymodule

# Create a Python list containing A objects list_a_solution = [mymodule.A(), mymodule.A()]
print(f"Before D_list_by_pointer_reference:")
for i, obj in enumerate(list_a_solution):
    print(f" list_a_solution[{i}]: n={obj.n}, val={obj.val}")

mymodule.D_list_by_pointer_reference(list_a_solution) # Pybind11 will automatically convert A objects in Python lists to A*

print(f"After D_list_by_pointer_reference:")
for i, obj in enumerate(list_a_solution):
    print(f" list_a_solution[{i}]: n={obj.n}, val={obj.val}")
# Result: The elements in list_a_solution are successfully modified

When a C function receives a std::vector, Pybind11 iterates through the Python list, gets a pointer to the underlying C instance of each A object, and builds a std::vector to pass to the C function. The C function directly modifies the original C object through these pointers, and these modifications will naturally be reflected in the corresponding object on the Python side.

Notes and Summary

1. Parameter type selection:
   • For a single object, it is valid to use T& as the parameter type if a C function needs to modify it.
   • For a container containing multiple objects (such as std::vector), if the C function needs to modify the elements in the container, std::vector should be used as the parameter type. If the C function simply modifies the container itself (such as adding or removing elements), then std::vector& may work, but be aware of the lifecycle and synchronization issues of Python lists.
2. Null pointer check: When handling std::vector in a C function, it is always recommended to perform a null pointer check (if (a_ptr)) to avoid potential runtime errors.
3. Ownership management: In the scenario of this tutorial, the A object in the Python list has its life cycle managed by Python. C functions only access and modify these existing objects through pointers, and do not involve memory allocation or release, so there is no need to worry about the memory ownership issue on the C side. But in more complex scenarios, if a C function needs to create a new object and return it or manage its life cycle, you need to carefully consider Pybind11's ownership policy (py::keep_alive, py::return_value_policy, etc.).
4. Clarity and readability: Although using a list of pointers solves the problem, it also introduces the concept of pointers, which may be slightly complicated for Python developers who are not familiar with C. When designing an API, you should weigh performance, functional requirements, and ease of use of the API.

By understanding the behavior of Pybind11 when dealing with different C parameter types, especially for scenarios where elements in the container are modified, we can choose the correct C parameter type (such as std::vector) to ensure the expected behavior of data synchronization between Python and C.