.. highlight:: cython .. _fusedtypes: *********************** Fused Types (Templates) *********************** .. include:: ../two-syntax-variants-used Fused types allow you to have one type definition that can refer to multiple types. This allows you to write a single static-typed cython algorithm that can operate on values of multiple types. Thus fused types allow `generic programming`_ and are akin to templates in C++ or generics in languages like Java / C#. .. _generic programming: https://en.wikipedia.org/wiki/Generic_programming .. Note:: Fused types are not currently supported as attributes of extension types. Only variables and function/method arguments can be declared with fused types. Quickstart ========== .. tabs:: .. group-tab:: Pure Python .. literalinclude:: ../../examples/userguide/fusedtypes/char_or_float.py .. group-tab:: Cython .. literalinclude:: ../../examples/userguide/fusedtypes/char_or_float.pyx This gives: .. code-block:: pycon >>> show_me() char -128 float 128.0 ``plus_one(a)`` "specializes" the fused type ``char_or_float`` as a ``char``, whereas ``plus_one(b)`` specializes ``char_or_float`` as a ``float``. Declaring Fused Types ===================== Fused types may be declared as follows: .. tabs:: .. group-tab:: Pure Python .. code-block:: python my_fused_type = cython.fused_type(cython.int, cython.float) .. group-tab:: Cython .. code-block:: cython ctypedef fused my_fused_type: int double This declares a new type called ``my_fused_type`` which can be *either* an ``int`` *or* a ``double``. Only names may be used for the constituent types, but they may be any (non-fused) type, including a typedef. I.e. one may write: .. tabs:: .. group-tab:: Pure Python .. code-block:: python my_double = cython.typedef(cython.double) my_fused_type = cython.fused_type(cython.int, my_double) .. group-tab:: Cython .. code-block:: cython ctypedef double my_double ctypedef fused fused_type: int my_double Using Fused Types ================= Fused types can be used to declare parameters of functions or methods: .. tabs:: .. group-tab:: Pure Python .. code-block:: python @cython.cfunc def cfunc(arg: my_fused_type): return arg + 1 .. group-tab:: Cython .. code-block:: cython cdef cfunc(my_fused_type arg): return arg + 1 If the same fused type appears more than once in the function arguments, then they will all have the same specialised type: .. tabs:: .. group-tab:: Pure Python .. code-block:: python @cython.cfunc def cfunc(arg1: my_fused_type, arg2: my_fused_type): # arg1 and arg2 always have the same type here return arg1 + arg2 .. group-tab:: Cython .. code-block:: cython cdef cfunc(my_fused_type arg1, my_fused_type arg2): # arg1 and arg2 always have the same type here return arg1 + arg2 Here, the type of both parameters is either an int, or a double (according to the previous examples), because they use the same fused type name ``my_fused_type``. Mixing different fused types (or differently named fused types) in the arguments will specialise them independently: .. tabs:: .. group-tab:: Pure Python .. code-block:: python def func(x: A, y: B): ... .. group-tab:: Cython .. code-block:: cython def func(A x, B y): ... This will result in specialized code paths for all combinations of types contained in ``A`` and ``B``, e.g.: .. tabs:: .. group-tab:: Pure Python .. code-block:: python my_fused_type = cython.fused_type(cython.int, cython.double) my_fused_type2 = cython.fused_type(cython.int, cython.double) @cython.cfunc def func(a: my_fused_type, b: my_fused_type2): # a and b may have the same or different types here print("SAME!" if my_fused_type is my_fused_type2 else "NOT SAME!") return a + b .. group-tab:: Cython .. code-block:: cython ctypedef fused my_fused_type: int double ctypedef fused my_fused_type2: int double cdef func(my_fused_type a, my_fused_type2 b): # a and b may have the same or different types here print("SAME!" if my_fused_type is my_fused_type2 else "NOT SAME!") return a + b .. Note:: A simple typedef to rename the fused type does not currently work here. See Github issue :issue:`4302`. Fused types and arrays ---------------------- Note that specializations of only numeric types may not be very useful, as one can usually rely on promotion of types. This is not true for arrays, pointers and typed views of memory however. Indeed, one may write: .. tabs:: .. group-tab:: Pure Python .. code-block:: python @cython.cfunc def myfunc(x: A[:, :]): ... # and @cython.cfunc cdef otherfunc(x: cython.pointer(A)): ... .. group-tab:: Cython .. code-block:: cython cdef myfunc(A[:, :] x): ... # and cdef otherfunc(A *x): ... Following code snippet shows an example with pointer to the fused type: .. tabs:: .. group-tab:: Pure Python .. literalinclude:: ../../examples/userguide/fusedtypes/pointer.py .. group-tab:: Cython .. literalinclude:: ../../examples/userguide/fusedtypes/pointer.pyx .. Note:: In Cython 0.20.x and earlier, the compiler generated the full cross product of all type combinations when a fused type was used by more than one memory view in a type signature, e.g. :: def myfunc(A[:] a, A[:] b): # a and b had independent item types in Cython 0.20.x and earlier. ... This was unexpected for most users, unlikely to be desired, and also inconsistent with other structured type declarations like C arrays of fused types, which were considered the same type. It was thus changed in Cython 0.21 to use the same type for all memory views of a fused type. In order to get the original behaviour, it suffices to declare the same fused type under different names, and then use these in the declarations:: ctypedef fused A: int long ctypedef fused B: int long def myfunc(A[:] a, B[:] b): # a and b are independent types here and may have different item types ... To get only identical types also in older Cython versions (pre-0.21), a ``ctypedef`` can be used:: ctypedef A[:] A_1d def myfunc(A_1d a, A_1d b): # a and b have identical item types here, also in older Cython versions ... Selecting Specializations ========================= You can select a specialization (an instance of the function with specific or specialized (i.e., non-fused) argument types) in two ways: either by indexing or by calling. .. _fusedtypes_indexing: Indexing -------- You can index functions with types to get certain specializations, i.e.: .. tabs:: .. group-tab:: Pure Python .. literalinclude:: ../../examples/userguide/fusedtypes/indexing.py :caption: indexing.py .. group-tab:: Cython .. literalinclude:: ../../examples/userguide/fusedtypes/indexing.pyx :caption: indexing.pyx Indexed functions can be called directly from Python: .. code-block:: pycon >>> import cython >>> import indexing cfunc called: double 5.0 double 1.0 cpfunc called: float 1.0 double 2.0 func called: float 1.0 double 2.0 >>> indexing.cpfunc[cython.float, cython.float](1, 2) cpfunc called: float 1.0 float 2.0 >>> indexing.func[cython.float, cython.float](1, 2) func called: float 1.0 float 2.0 If a fused type is used as a component of a more complex type (for example a pointer to a fused type, or a memoryview of a fused type), then you should index the function with the individual component and not the full argument type: .. tabs:: .. group-tab:: Pure Python .. code-block:: python @cython.cfunc def myfunc(x: cython.pointer(A)): ... # Specialize using int, not int * myfunc[cython.int](myint) .. group-tab:: Cython .. code-block:: cython cdef myfunc(A *x): ... # Specialize using int, not int * myfunc[int](myint) For memoryview indexing from python space we can do the following: .. tabs:: .. group-tab:: Pure Python .. code-block:: python my_fused_type = cython.fused_type(cython.int[:, ::1], cython.float[:, ::1]) def func(array: my_fused_type): print("func called:", cython.typeof(array)) my_fused_type[cython.int[:, ::1]](myarray) .. group-tab:: Cython .. code-block:: cython ctypedef fused my_fused_type: int[:, ::1] float[:, ::1] def func(my_fused_type array): print("func called:", cython.typeof(array)) my_fused_type[cython.int[:, ::1]](myarray) The same goes for when using e.g. ``cython.numeric[:, :]``. Calling ------- A fused function can also be called with arguments, where the dispatch is figured out automatically: .. tabs:: .. group-tab:: Pure Python .. code-block:: python def main(): p1: cython.double = 1.0 p2: cython.float = 2.0 cfunc(p1, p1) # prints "cfunc called: double 1.0 double 1.0" cpfunc(p1, p2) # prints "cpfunc called: double 1.0 float 2.0" .. group-tab:: Cython .. code-block:: cython def main(): cdef double p1 = 1.0 cdef float p2 = 2.0 cfunc(p1, p1) # prints "cfunc called: double 1.0 double 1.0" cpfunc(p1, p2) # prints "cpfunc called: double 1.0 float 2.0" For a ``cdef`` or ``cpdef`` function called from Cython this means that the specialization is figured out at compile time. For ``def`` functions the arguments are typechecked at runtime, and a best-effort approach is performed to figure out which specialization is needed. This means that this may result in a runtime ``TypeError`` if no specialization was found. A ``cpdef`` function is treated the same way as a ``def`` function if the type of the function is unknown (e.g. if it is external and there is no cimport for it). The automatic dispatching rules are typically as follows, in order of preference: * try to find an exact match * choose the biggest corresponding numerical type (biggest float, biggest complex, biggest int) Built-in Fused Types ==================== There are some built-in fused types available for convenience, these are:: cython.integral # short, int, long cython.floating # float, double cython.numeric # short, int, long, float, double, float complex, double complex Casting Fused Functions ======================= .. note:: Pointers to functions are currently not supported by pure Python mode. (GitHub issue :issue:`4279`) Fused ``cdef`` and ``cpdef`` functions may be cast or assigned to C function pointers as follows:: cdef myfunc(cython.floating, cython.integral): ... # assign directly cdef object (*funcp)(float, int) funcp = myfunc funcp(f, i) # alternatively, cast it ( myfunc)(f, i) # This is also valid funcp = myfunc[float, int] funcp(f, i) Type Checking Specializations ============================= Decisions can be made based on the specializations of the fused parameters. False conditions are pruned to avoid invalid code. One may check with ``is``, ``is not`` and ``==`` and ``!=`` to see if a fused type is equal to a certain other non-fused type (to check the specialization), or use ``in`` and ``not in`` to figure out whether a specialization is part of another set of types (specified as a fused type). In example: .. tabs:: .. group-tab:: Pure Python .. literalinclude:: ../../examples/userguide/fusedtypes/type_checking.py .. group-tab:: Cython .. literalinclude:: ../../examples/userguide/fusedtypes/type_checking.pyx .. _fused_gil_conditional: Conditional GIL Acquiring / Releasing ===================================== Acquiring and releasing the GIL can be controlled by a condition which is known at compile time (see :ref:`gil_conditional`). This is most useful when combined with fused types. A fused type function may have to handle both cython native types (e.g. cython.int or cython.double) and python types (e.g. object or bytes). Conditional Acquiring / Releasing the GIL provides a method for running the same piece of code either with the GIL released (for cython native types) and with the GIL held (for python types): .. tabs:: .. group-tab:: Pure Python .. literalinclude:: ../../examples/userguide/fusedtypes/conditional_gil.py .. group-tab:: Cython .. literalinclude:: ../../examples/userguide/fusedtypes/conditional_gil.pyx __signatures__ ============== Finally, function objects from ``def`` or ``cpdef`` functions have an attribute ``__signatures__``, which maps the signature strings to the actual specialized functions. This may be useful for inspection: .. tabs:: .. group-tab:: Pure Python .. literalinclude:: ../../examples/userguide/fusedtypes/indexing.py :lines: 1-9,14-16 :caption: indexing.py .. group-tab:: Cython .. literalinclude:: ../../examples/userguide/fusedtypes/indexing.pyx :lines: 1-9,14-16 :caption: indexing.pyx .. code-block:: pycon >>> from indexing import cpfunc >>> cpfunc.__signatures__, ({'double|double': , 'double|float': , 'float|double': , 'float|float': },) Listed signature strings may also be used as indices to the fused function, but the index format may change between Cython versions .. code-block:: pycon >>> specialized_function = cpfunc["double|float"] >>> specialized_function(5.0, 1.0) cpfunc called: double 5.0 float 1.0 However, the better way how to index is by providing list of types as mentioned in :ref:`fusedtypes_indexing` section.