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214 lines
9.3 KiB
ReStructuredText
214 lines
9.3 KiB
ReStructuredText
Frequently asked questions
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##########################
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"ImportError: dynamic module does not define init function"
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===========================================================
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1. Make sure that the name specified in ``pybind::module`` and
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``PYBIND11_PLUGIN`` is consistent and identical to the filename of the
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extension library. The latter should not contain any extra prefixes (e.g.
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``test.so`` instead of ``libtest.so``).
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2. If the above did not fix your issue, then you are likely using an
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incompatible version of Python (for instance, the extension library was
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compiled against Python 2, while the interpreter is running on top of some
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version of Python 3, or vice versa)
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"Symbol not found: ``__Py_ZeroStruct`` / ``_PyInstanceMethod_Type``"
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========================================================================
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See item 2 of the first answer.
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"SystemError: dynamic module not initialized properly"
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======================================================
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See item 2 of the first answer.
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The Python interpreter immediately crashes when importing my module
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===================================================================
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See item 2 of the first answer.
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CMake doesn't detect the right Python version
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=============================================
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The CMake-based build system will try to automatically detect the installed
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version of Python and link against that. When this fails, or when there are
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multiple versions of Python and it finds the wrong one, delete
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``CMakeCache.txt`` and then invoke CMake as follows:
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.. code-block:: bash
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cmake -DPYTHON_EXECUTABLE:FILEPATH=<path-to-python-executable> .
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Limitations involving reference arguments
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=========================================
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In C++, it's fairly common to pass arguments using mutable references or
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mutable pointers, which allows both read and write access to the value
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supplied by the caller. This is sometimes done for efficiency reasons, or to
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realize functions that have multiple return values. Here are two very basic
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examples:
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.. code-block:: cpp
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void increment(int &i) { i++; }
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void increment_ptr(int *i) { (*i)++; }
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In Python, all arguments are passed by reference, so there is no general
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issue in binding such code from Python.
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However, certain basic Python types (like ``str``, ``int``, ``bool``,
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``float``, etc.) are **immutable**. This means that the following attempt
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to port the function to Python doesn't have the same effect on the value
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provided by the caller -- in fact, it does nothing at all.
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.. code-block:: python
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def increment(i):
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i += 1 # nope..
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pybind11 is also affected by such language-level conventions, which means that
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binding ``increment`` or ``increment_ptr`` will also create Python functions
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that don't modify their arguments.
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Although inconvenient, one workaround is to encapsulate the immutable types in
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a custom type that does allow modifications.
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An other alternative involves binding a small wrapper lambda function that
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returns a tuple with all output arguments (see the remainder of the
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documentation for examples on binding lambda functions). An example:
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.. code-block:: cpp
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int foo(int &i) { i++; return 123; }
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and the binding code
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.. code-block:: cpp
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m.def("foo", [](int i) { int rv = foo(i); return std::make_tuple(rv, i); });
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How can I reduce the build time?
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================================
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It's good practice to split binding code over multiple files, as is done in
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the included file :file:`example/example.cpp`.
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.. code-block:: cpp
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void init_ex1(py::module &);
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void init_ex2(py::module &);
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/* ... */
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PYBIND11_PLUGIN(example) {
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py::module m("example", "pybind example plugin");
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init_ex1(m);
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init_ex2(m);
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/* ... */
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return m.ptr();
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}
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The various ``init_ex`` functions should be contained in separate files that
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can be compiled independently from another. Following this approach will
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1. reduce memory requirements per compilation unit.
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2. enable parallel builds (if desired).
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3. allow for faster incremental builds. For instance, when a single class
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definition is changed, only a subset of the binding code will generally need
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to be recompiled.
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How can I create smaller binaries?
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==================================
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To do its job, pybind11 extensively relies on a programming technique known as
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*template metaprogramming*, which is a way of performing computation at compile
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time using type information. Template metaprogamming usually instantiates code
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involving significant numbers of deeply nested types that are either completely
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removed or reduced to just a few instructions during the compiler's optimization
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phase. However, due to the nested nature of these types, the resulting symbol
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names in the compiled extension library can be extremely long. For instance,
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the included test suite contains the following symbol:
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.. only:: html
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.. code-block:: none
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__ZN8pybind1112cpp_functionC1Iv8Example2JRNSt3__16vectorINS3_12basic_stringIwNS3_11char_traitsIwEENS3_9allocatorIwEEEENS8_ISA_EEEEEJNS_4nameENS_7siblingENS_9is_methodEA28_cEEEMT0_FT_DpT1_EDpRKT2_
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.. only:: not html
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.. code-block:: cpp
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__ZN8pybind1112cpp_functionC1Iv8Example2JRNSt3__16vectorINS3_12basic_stringIwNS3_11char_traitsIwEENS3_9allocatorIwEEEENS8_ISA_EEEEEJNS_4nameENS_7siblingENS_9is_methodEA28_cEEEMT0_FT_DpT1_EDpRKT2_
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which is the mangled form of the following function type:
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.. code-block:: cpp
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pybind11::cpp_function::cpp_function<void, Example2, std::__1::vector<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> >, std::__1::allocator<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> > > >&, pybind11::name, pybind11::sibling, pybind11::is_method, char [28]>(void (Example2::*)(std::__1::vector<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> >, std::__1::allocator<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> > > >&), pybind11::name const&, pybind11::sibling const&, pybind11::is_method const&, char const (&) [28])
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The memory needed to store just the mangled name of this function (196 bytes)
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is larger than the actual piece of code (111 bytes) it represents! On the other
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hand, it's silly to even give this function a name -- after all, it's just a
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tiny cog in a bigger piece of machinery that is not exposed to the outside
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world. So we'll generally only want to export symbols for those functions which
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are actually called from the outside.
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This can be achieved by specifying the parameter ``-fvisibility=hidden`` to GCC
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and Clang, which sets the default symbol visibility to *hidden*. It's best to
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do this only for release builds, since the symbol names can be helpful in
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debugging sessions. On Visual Studio, symbols are already hidden by default, so
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nothing needs to be done there. Needless to say, this has a tremendous impact
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on the final binary size of the resulting extension library.
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Another aspect that can require a fair bit of code are function signature
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descriptions. pybind11 automatically generates human-readable function
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signatures for docstrings, e.g.:
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.. code-block:: none
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| __init__(...)
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| __init__(*args, **kwargs)
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| Overloaded function.
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| 1. __init__(example.Example1) -> NoneType
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| Docstring for overload #1 goes here
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| 2. __init__(example.Example1, int) -> NoneType
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| Docstring for overload #2 goes here
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| 3. __init__(example.Example1, example.Example1) -> NoneType
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| Docstring for overload #3 goes here
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In C++11 mode, these are generated at run time using string concatenation,
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which can amount to 10-20% of the size of the resulting binary. If you can,
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enable C++14 language features (using ``-std=c++14`` for GCC/Clang), in which
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case signatures are efficiently pre-generated at compile time. Unfortunately,
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Visual Studio's C++14 support (``constexpr``) is not good enough as of April
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2016, so it always uses the more expensive run-time approach.
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Working with ancient Visual Studio 2009 builds on Windows
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=========================================================
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The official Windows distributions of Python are compiled using truly
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ancient versions of Visual Studio that lack good C++11 support. Some users
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implicitly assume that it would be impossible to load a plugin built with
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Visual Studio 2015 into a Python distribution that was compiled using Visual
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Studio 2009. However, no such issue exists: it's perfectly legitimate to
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interface DLLs that are built with different compilers and/or C libraries.
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Common gotchas to watch out for involve not ``free()``-ing memory region
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that that were ``malloc()``-ed in another shared library, using data
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structures with incompatible ABIs, and so on. pybind11 is very careful not
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to make these types of mistakes.
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