f97cf54db7
This updates Pybind11 from version 2.2.1 to version 2.4.1. This fixes
warning/error received when "<experiment/optional>" is used when
compiling using c++14 with clang. It should be noted that
"ext/pybind11/include/pybind11/std.h" has been changed to include a fix
added by commit ba42457254. This is
necessary to avoid build errors.
Built: Linux (gcc, c++11) and MacOS (clang, c++14).
Tested: Ran quick tests for X86, ARM, and RISC-V.
Deprecates: https://gem5-review.googlesource.com/c/public/gem5/+/21019
Change-Id: Ie9783511cb6be50136076a55330e645f4f36d075
Reviewed-on: https://gem5-review.googlesource.com/c/public/gem5/+/21119
Reviewed-by: Jason Lowe-Power <jason@lowepower.com>
Reviewed-by: Andreas Sandberg <andreas.sandberg@arm.com>
Maintainer: Jason Lowe-Power <jason@lowepower.com>
Maintainer: Andreas Sandberg <andreas.sandberg@arm.com>
Tested-by: kokoro <noreply+kokoro@google.com>
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94 lines
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.. image:: pybind11-logo.png
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About this project
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==================
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**pybind11** is a lightweight header-only library that exposes C++ types in Python
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and vice versa, mainly to create Python bindings of existing C++ code. Its
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goals and syntax are similar to the excellent `Boost.Python`_ library by David
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Abrahams: to minimize boilerplate code in traditional extension modules by
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inferring type information using compile-time introspection.
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.. _Boost.Python: http://www.boost.org/doc/libs/release/libs/python/doc/index.html
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The main issue with Boost.Python—and the reason for creating such a similar
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project—is Boost. Boost is an enormously large and complex suite of utility
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libraries that works with almost every C++ compiler in existence. This
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compatibility has its cost: arcane template tricks and workarounds are
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necessary to support the oldest and buggiest of compiler specimens. Now that
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C++11-compatible compilers are widely available, this heavy machinery has
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become an excessively large and unnecessary dependency.
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Think of this library as a tiny self-contained version of Boost.Python with
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everything stripped away that isn't relevant for binding generation. Without
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comments, the core header files only require ~4K lines of code and depend on
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Python (2.7 or 3.x, or PyPy2.7 >= 5.7) and the C++ standard library. This
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compact implementation was possible thanks to some of the new C++11 language
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features (specifically: tuples, lambda functions and variadic templates). Since
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its creation, this library has grown beyond Boost.Python in many ways, leading
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to dramatically simpler binding code in many common situations.
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Core features
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*************
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The following core C++ features can be mapped to Python
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- Functions accepting and returning custom data structures per value, reference, or pointer
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- Instance methods and static methods
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- Overloaded functions
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- Instance attributes and static attributes
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- Arbitrary exception types
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- Enumerations
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- Callbacks
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- Iterators and ranges
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- Custom operators
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- Single and multiple inheritance
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- STL data structures
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- Smart pointers with reference counting like ``std::shared_ptr``
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- Internal references with correct reference counting
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- C++ classes with virtual (and pure virtual) methods can be extended in Python
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Goodies
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*******
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In addition to the core functionality, pybind11 provides some extra goodies:
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- Python 2.7, 3.x, and PyPy (PyPy2.7 >= 5.7) are supported with an
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implementation-agnostic interface.
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- It is possible to bind C++11 lambda functions with captured variables. The
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lambda capture data is stored inside the resulting Python function object.
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- pybind11 uses C++11 move constructors and move assignment operators whenever
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possible to efficiently transfer custom data types.
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- It's easy to expose the internal storage of custom data types through
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Pythons' buffer protocols. This is handy e.g. for fast conversion between
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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
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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
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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
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against any additional libraries.
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- Binaries are generally smaller by a factor of at least 2 compared to
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equivalent bindings generated by Boost.Python. A recent pybind11 conversion
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of `PyRosetta`_, an enormous Boost.Python binding project, reported a binary
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size reduction of **5.4x** and compile time reduction by **5.8x**.
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- Function signatures are precomputed at compile time (using ``constexpr``),
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leading to smaller binaries.
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- With little extra effort, C++ types can be pickled and unpickled similar to
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regular Python objects.
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.. _PyRosetta: http://graylab.jhu.edu/RosettaCon2016/PyRosetta-4.pdf
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Supported compilers
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*******************
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1. Clang/LLVM (any non-ancient version with C++11 support)
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2. GCC 4.8 or newer
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3. Microsoft Visual Studio 2015 or newer
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4. Intel C++ compiler v17 or newer (v16 with pybind11 v2.0 and v15 with pybind11 v2.0 and a `workaround <https://github.com/pybind/pybind11/issues/276>`_ )
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