Fused Types (Templates)
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#.
Support is experimental and new in this release, there may be bugs!
cimport cython ctypedef fused char_or_float: cython.char cython.float cpdef char_or_float plus_one(char_or_float var): return var + 1 def show_me(): cdef: cython.char a = 127 cython.float b = 127 print 'char', plus_one(a) print 'float', plus_one(b)
>>> 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:
cimport cython ctypedef fused my_fused_type: cython.int cython.double
This declares a new type called my_fused_type which can be either an int or a double. Alternatively, the declaration may be written as:
my_fused_type = cython.fused_type(cython.int, cython.float)
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:
ctypedef double my_double my_fused_type = cython.fused_type(cython.int, my_double)
Using Fused Types
Fused types can be used to declare parameters of functions or methods:
cdef cfunc(my_fused_type arg): return arg + 1
If the you use the same fused type more than once in an argument list, then each specialization of the fused type must be the same:
cdef cfunc(my_fused_type arg1, my_fused_type arg2): return cython.typeof(arg1) == cython.typeof(arg2)
In this case, the type of both parameters is either an int, or a double (according to the previous examples). However, because these arguments are the same fused type of my_fused_type, both arg1 and arg2 must be specialized to the same type. Therefore this function returns True for every possible valid invocation. You are allowed to mix fused types however:
def func(A x, B y): ...
where A and B are different fused types. This will result in specialized code paths for all combinations of types contained in A and B.
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:
def myfunc(A[:, :] x): ... # and cdef otherfunc(A *x): ...
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.
You can index functions with types to get certain specializations, i.e.:
cfunc[cython.p_double](p1, p2) # From Cython space func[float, double](myfloat, mydouble) # From Python space func[cython.float, cython.double](myfloat, mydouble)
If a fused type is used as a base type, this will mean that the base type is the fused type, so the base type is what needs to be specialized:
cdef myfunc(A *x): ... # Specialize using int, not int * myfunc[int](myint)
A fused function can also be called with arguments, where the dispatch is figured out automatically:
cfunc(p1, p2) func(myfloat, mydouble)
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
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 (<object (*)(float, int)> 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:
ctypedef fused bunch_of_types: ... ctypedef fused string_t: cython.p_char bytes unicode cdef cython.integral myfunc(cython.integral i, bunch_of_types s): cdef int *int_pointer cdef long *long_pointer # Only one of these branches will be compiled for each specialization! if cython.integral is int: int_pointer = &i else: long_pointer = &i if bunch_of_types in string_t: print "s is a string!"
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. Listed signature strings may also be used as indices to the fused function, but the index format may change between Cython versions:
specialized_function = fused_function["MyExtensionClass|int|float"]
It would usually be preferred to index like this, however:
specialized_function = fused_function[MyExtensionClass, int, float]
Although the latter will select the biggest types for int and float from Python space, as they are not type identifiers but builtin types there. Passing cython.int and cython.float would resolve that, however.
For memoryview indexing from python space we can do the following:
ctypedef fused my_fused_type: int[:, ::1] float[:, ::1] def func(my_fused_type array): ... my_fused_type[cython.int[:, ::1]](myarray)
The same goes for when using e.g. cython.numeric[:, :].