Benefits of Embedded pybind11 Tests

Oct 13, 2025 by orovajewels.com 47 views

Introduction

As developers, we often encounter complex issues in our code that require a deeper understanding of the underlying C++ implementation. In this article, we will explore the concept of embedded pybind11 tests, which allow us to drill into C++ in problematic Python cases. This feature is particularly useful when onboarding unit tests, as it enables us to investigate and debug C++ code more effectively.

Motivation

The motivation for this feature comes from the need to write unit tests while learning RoughPy in Jupyter. During this process, I encountered some Python bugs with unclear exceptions in the FreeTensor constructor and a segfault that stopped the Jupyter kernel. After reviewing these bugs with Sam, I suggested using pybind11::embed as a means of launching Python from C++ tests to drill into underlying data if required.

Benefits of Embedded pybind11 Tests

Embedded pybind11 tests offer several benefits, including:

Convenient debugging: By launching Python from C++ tests, we can debug C++ code more effectively and identify issues that may not be apparent in Python.
Deep introspection: The pybind functions allow us to drill into particular variables in C++ and cast pybind objects back to the underlying C++ class, making it easier to investigate and debug complex issues.
Improved testing: Embedded pybind11 tests enable us to write more comprehensive unit tests that cover both Python and C++ code, ensuring that our code is more robust and reliable.

Implementing Embedded pybind11 Tests

To implement embedded pybind11 tests, we need to follow these steps:

Link pybind11::embed: In CMakeLists, we need to link in pybind11::embed to enable the use of pybind11::embed.
Find Development.Module: We need to find Development.Module in our python find_package to enable the use of pybind11::embed.
Write test code: We can write test code that executes Python directly using py::scoped_interpreter and py::exec.
Break down code into separate steps: To make the code more useful, we can break it down into separate C++ evaluated steps using py::module_, py::object, and py::list.

Example Code

Here is an example of how to write a test that executes Python directly using py::scoped_interpreter and py::exec:

TEST(TestPythonAPI, PythonExecl)
{
    py::scoped_interpreter guard{};
    py::exec(R"(
        import roughpy as rp
        context = rp.get_context(width=2, depth=3, coeffs=rp.RationalPoly)
        int N = context.tensor_size()
        ones = [1 for _ in range(N)]
        a = rp.FreeTensor(
            ones,
            ctx=context
        )
        print(a)
    )");
}

And here is an example of how to break down the code into separate C++ evaluated steps:

TEST(TestPythonAPI, PythonExecl)
{
    py::scoped_interpreter guard{};
    py::module_ rp = py::module_::import("roughpy");
    py::object context = rp.attr("get_context")(
        "width"_a = 2,
        "depth"_a = 3,
        "coeffs"_a = rp.attr("RationalPoly")
    );
    int N = context.attr("tensor_size")().cast<int>();
    py::list ones;
    for (int i = 0; i < N; ++i) {
        ones.append(1);
    }
    py::object a = rp.attr("FreeTensor")(
        ones,
        "ctx"_a = context
    );
    std::cout << py::str(a).cast<std::string>() << std::endl;

    // Get underlying C++ FreeTensor ptr for testing
    auto* a_ptr = a.cast<rpy::algebra::FreeTensor*>();
}

Conclusion

In conclusion, embedded pybind11 tests offer a powerful way to drill into C++ in problematic Python cases. By following the steps outlined in this article, we can implement embedded pybind11 tests that enable us to write more comprehensive unit tests and debug C++ code more effectively. Whether you're a seasoned developer or just starting out, embedded pybind11 tests are an essential tool to have in your toolkit.

Future Work

In the future, we plan to raise new tickets for specific bugs and continue to explore the use of pybind11::embed in our codebase. We also plan to investigate the use of pybind11::embed in other areas of our codebase, such as in the RoughPy library.

References

pybind11 documentation
pybind11::embed documentation
Embedded pybind11 Tests: A Q&A Guide =====================================

Q: What is embedded pybind11 testing?

A: Embedded pybind11 testing is a feature that allows us to drill into C++ in problematic Python cases. It enables us to write unit tests that cover both Python and C++ code, making it easier to identify and debug issues.

Q: Why do we need embedded pybind11 testing?

A: We need embedded pybind11 testing because it provides a convenient way to debug C++ code in Python. It also enables us to write more comprehensive unit tests that cover both Python and C++ code, making our code more robust and reliable.

Q: How do I implement embedded pybind11 testing?

A: To implement embedded pybind11 testing, you need to follow these steps:

Link pybind11::embed: In CMakeLists, you need to link in pybind11::embed to enable the use of pybind11::embed.
Find Development.Module: You need to find Development.Module in your python find_package to enable the use of pybind11::embed.
Write test code: You can write test code that executes Python directly using py::scoped_interpreter and py::exec.
Break down code into separate steps: To make the code more useful, you can break it down into separate C++ evaluated steps using py::module_, py::object, and py::list.

Q: What are the benefits of embedded pybind11 testing?

A: The benefits of embedded pybind11 testing include:

Convenient debugging: By launching Python from C++ tests, you can debug C++ code more effectively and identify issues that may not be apparent in Python.
Deep introspection: The pybind functions allow you to drill into particular variables in C++ and cast pybind objects back to the underlying C++ class, making it easier to investigate and debug complex issues.
Improved testing: Embedded pybind11 tests enable you to write more comprehensive unit tests that cover both Python and C++ code, ensuring that your code is more robust and reliable.

Q: Can I use embedded pybind11 testing with other libraries?

A: Yes, you can use embedded pybind11 testing with other libraries. However, you need to ensure that the library is compatible with pybind11 and that you have the necessary dependencies installed.

Q: How do I troubleshoot issues with embedded pybind11 testing?

A: To troubleshoot issues with embedded pybind11 testing, you can follow these steps:

Check the CMakeLists: Ensure that you have linked pybind11::embed correctly in your CMakeLists.
Check the python find_package: Ensure that you have found Development.Module correctly in your python find_package.
Check the test code: Ensure that your test code is correct and that you are using the correct pybind functions.
Use a debugger: Use a debugger to step through your code and identify the issue.

Q: Can I use embedded pybind11 testing in production code?

A: Yes, you can use embedded pybind11 testing in production code. However, you need to ensure that the code is well-tested and that you have a good understanding of the pybind functions and their limitations.

Q: What are the limitations of embedded pybind11 testing?

A: The limitations of embedded pybind11 testing include:

Performance overhead: Embedded pybind11 testing can introduce a performance overhead due to the use of Python and the pybind functions.
Complexity: Embedded pybind11 testing can be complex to implement and debug, especially for large and complex codebases.
Compatibility issues: Embedded pybind11 testing can introduce compatibility issues with other libraries and dependencies.

Conclusion

In conclusion, embedded pybind11 testing is a powerful tool that enables us to drill into C++ in problematic Python cases. By following the steps outlined in this article, you can implement embedded pybind11 testing and write more comprehensive unit tests that cover both Python and C++ code. Whether you're a seasoned developer or just starting out, embedded pybind11 testing is an essential tool to have in your toolkit.