2097826346
Problem ======= The template trampoline pattern documented in PR #322 has a problem with virtual method overloads in intermediate classes in the inheritance chain between the trampoline class and the base class. For example, consider the following inheritance structure, where `B` is the actual class, `PyB<B>` is the trampoline class, and `PyA<B>` is an intermediate class adding A's methods into the trampoline: PyB<B> -> PyA<B> -> B -> A Suppose PyA<B> has a method `some_method()` with a PYBIND11_OVERLOAD in it to overload the virtual `A::some_method()`. If a Python class `C` is defined that inherits from the pybind11-registered `B` and tries to provide an overriding `some_method()`, the PYBIND11_OVERLOADs declared in PyA<B> fails to find this overloaded method, and thus never invoke it (or, if pure virtual and not overridden in PyB<B>, raises an exception). This happens because the base (internal) `PYBIND11_OVERLOAD_INT` macro simply calls `get_overload(this, name)`; `get_overload()` then uses the inferred type of `this` to do a type lookup in `registered_types_cpp`. This is where it fails: `this` will be a `PyA<B> *`, but `PyA<B>` is neither the base type (`B`) nor the trampoline type (`PyB<B>`). As a result, the overload fails and we get a failed overload lookup. The fix ======= The fix is relatively simple: we can cast `this` passed to `get_overload()` to a `const B *`, which lets get_overload look up the correct class. Since trampoline classes should be derived from `B` classes anyway, this cast should be perfectly safe. This does require adding the class name as an argument to the PYBIND11_OVERLOAD_INT macro, but leaves the public macro signatures unchanged. |
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| CONTRIBUTING.md | ||
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pybind11 — Seamless operability between C++11 and Python
pybind11 is a lightweight header-only library that exposes C++ types in Python and vice versa, mainly to create Python bindings of existing C++ code. Its goals and syntax are similar to the excellent Boost.Python library by David Abrahams: to minimize boilerplate code in traditional extension modules by inferring type information using compile-time introspection.
The main issue with Boost.Python—and the reason for creating such a similar project—is Boost. Boost is an enormously large and complex suite of utility libraries that works with almost every C++ compiler in existence. This compatibility has its cost: arcane template tricks and workarounds are necessary to support the oldest and buggiest of compiler specimens. Now that C++11-compatible compilers are widely available, this heavy machinery has become an excessively large and unnecessary dependency.
Think of this library as a tiny self-contained version of Boost.Python with everything stripped away that isn't relevant for binding generation. Without comments, the core header files only require ~2.5K lines of code and depend on Python (2.7 or 3.x) and the C++ standard library. This compact implementation was possible thanks to some of the new C++11 language features (specifically: tuples, lambda functions and variadic templates). Since its creation, this library has grown beyond Boost.Python in many ways, leading to dramatically simpler binding code in many common situations.
Tutorial and reference documentation is provided at http://pybind11.readthedocs.org/en/latest. A PDF version of the manual is available here.
Core features
pybind11 can map the following core C++ features to Python
- Functions accepting and returning custom data structures per value, reference, or pointer
- Instance methods and static methods
- Overloaded functions
- Instance attributes and static attributes
- Arbitrary exception types
- Enumerations
- Callbacks
- Custom operators
- STL data structures
- Iterators and ranges
- Smart pointers with reference counting like
std::shared_ptr - Internal references with correct reference counting
- C++ classes with virtual (and pure virtual) methods can be extended in Python
Goodies
In addition to the core functionality, pybind11 provides some extra goodies:
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pybind11 uses C++11 move constructors and move assignment operators whenever possible to efficiently transfer custom data types.
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It is possible to bind C++11 lambda functions with captured variables. The lambda capture data is stored inside the resulting Python function object.
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It's easy to expose the internal storage of custom data types through Pythons' buffer protocols. This is handy e.g. for fast conversion between C++ matrix classes like Eigen and NumPy without expensive copy operations.
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pybind11 can automatically vectorize functions so that they are transparently applied to all entries of one or more NumPy array arguments.
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Python's slice-based access and assignment operations can be supported with just a few lines of code.
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Everything is contained in just a few header files; there is no need to link against any additional libraries.
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Binaries are generally smaller by a factor of at least 2 compared to equivalent bindings generated by Boost.Python. A recent pybind11 conversion of PyRosetta, an enourmous Boot.Python binding project, reported a binary size reduction of 5.4x and compile time reduction by 5.8x.
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When supported by the compiler, two new C++14 features (relaxed constexpr and return value deduction) are used to precompute function signatures at compile time, leading to smaller binaries.
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With little extra effort, C++ types can be pickled and unpickled similar to regular Python objects.
Supported compilers
- Clang/LLVM (any non-ancient version with C++11 support)
- GCC (any non-ancient version with C++11 support)
- Microsoft Visual Studio 2015 or newer
- Intel C++ compiler 16 or newer (15 with a workaround)
- Cygwin/GCC (tested on 2.5.1)
About
This project was created by Wenzel Jakob. Significant features and/or improvements to the code were contributed by Jonas Adler, Sylvain Corlay, Axel Huebl, @hulucc, Sergey Lyskov Johan Mabille, Tomasz Miąsko, Dean Moldovan, Ben Pritchard, Jason Rhinelander, Boris Schäling, Pim Schellart, and Ivan Smirnov.
License
pybind11 is provided under a BSD-style license that can be found in the
LICENSE file. By using, distributing, or contributing to this project,
you agree to the terms and conditions of this license.