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#.
Fused types are not currently supported as attributes of extension types. Only variables and function/method arguments can be declared with fused types.
from __future__ import print_function ctypedef fused char_or_float: char float cpdef char_or_float plus_one(char_or_float var): return var + 1 def show_me(): cdef: char a = 127 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_or_float as a
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 same fused type appears more than once in the function arguments, then they will all have the same specialised type:
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
my_fused_type. Mixing different fused types (or differently named
fused types) in the arguments will specialise them independently:
def func(A x, B y): ...
This will result in specialized code paths for all combinations of types
ctypedef fused my_fused_type: cython.int cython.double ctypedef fused my_fused_type2: cython.int cython.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 that a simple typedef to rename the fused type does not currently work here. See Github 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:
def myfunc(A[:, :] x): ... # and cdef otherfunc(A *x): ...
Note that 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
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 ...
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)
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
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¶
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 not and
!= to see if a fused type is equal to a certain
other non-fused type (to check the specialization), or use
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!")
Conditional GIL Acquiring / Releasing¶
Acquiring and releasing the GIL can be controlled by a condition which is known at compile time (see Conditional Acquiring / Releasing the GIL).
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).:
cimport cython ctypedef fused double_or_object: cython.double object def increment(double_or_object x): with nogil(double_or_object is cython.double): # Same code handles both cython.double (GIL is released) # and python object (GIL is not released). x = x + 1 return x
Finally, function objects from
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
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
Python space, as they are not type identifiers but builtin types there. Passing
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.