Cython code is much more complicated than Pythran code… We won’t be able to support all Cython features!
However, a descent set of Cython can be supported. We need to find Python syntaxes for the most useful Cython special syntaxes.
Note that some Cython features are useless in Pythran (for example cdef of
local variables, or
Note that ideally, we want to write Cython code that can be executed without the Cython package and without Cython compilation. It is possible with the “pure Python mode” of Cython. Therefore, we first need examples of Cython code written in this mode.
Note however, that this mode is currently still experimental and that we hit simple Cython bugs which limit a lot what can be done in practice with the Cython backend. For example:
Pure-Python mode and fused types https://github.com/cython/cython/issues/3142
Incompatibility ccall/nogil in pure-Python mode: https://github.com/cython/cython/issues/3169
nogil and pxd in pure-Python mode: https://github.com/cython/cython/issues/3170
More generally, there are many known bugs in Cython which do not help! For example:
ctypedefand buffer https://github.com/cython/cython/issues/754
Defining a fused type using a fused type https://stackoverflow.com/questions/57887972
I think at least some of these bugs have to be solved upstream…
Cython syntaxes already supported
cpdef signature with simple (basic and array) types for arguments
cdef for type declaration of local variables:
In “pure Python mode”, one can write
@cython.locals(result=np.float64_t, i=cython.int, n=cython.int) cpdef mysum(np.float64_t[:] arr_input)
With variables annotations (which are removed for Pythran / Numba):
from transonic import boost @boost def mysum(arr_input: "float"): i: int n: int = arr_input.shape result: float = 0. for i in range(n): result += arr_input[i] return result
Currently only for simple functions (no methods).
Cython syntaxes partly supported
Function definition (cdef, cpdef, inline, nogil, return type)
from transonic import boost @boost(inline=True, nogil=True) def func(a: "float", n: int) -> "void": ...
which would translate in Cython as something like:
cpdef inline void func(np.ndarray[np.float_t, ndim=1] a, cython.int n) nogil
all function signatures use
boost(inline=True)is supported for functions, see this example.
Return type is supported and there is a void type (
We already have fused types in Transonic. With Transonic, we can already do:
import numpy as np from transonic import Array, Type, NDim np_floats = Type(np.float32, np.float64) N = NDim(2, 3, 4) A = Array[np_floats, N] A1 = Array[np_floats, N + 1] # or simply A3d = Array[np_floats, "3d"]
However, Cython Fused types are currently very limited.
Even with something as simple as that
from transonic import Array, Type A = Array[Type(np.float64, np.complex128), "1d"] def mysum(arr: A): result: A.dtype = arr.dtype.type(0.) i: int for i in range(arr.shape): result += arr[i] return result
should be translated to this (not supported, see https://github.com/cython/cython/issues/754) Cython code:
import cython import numpy as np cimport numpy as np ctypedef fused T0: np.complex128_t np.float64_t ctypedef np.ndarray[T0, ndim=1] A def mysum(A arr): cdef T0 ret = arr.dtype.type(0.) cdef cython.int i for i in range(arr.shape): ret += arr[i] return ret
Note that it works with a memoryview… (but not in pure-Python mode!)
Note that another working alternative is:
import cython import numpy as np cimport numpy as np ctypedef fused T0: np.complex128_t np.float64_t def mysum(np.ndarray[T0, ndim=1] arr): cdef T0 ret = arr.dtype.type(0.) cdef cython.int i for i in range(arr.shape): ret += arr[i] return ret
But the corresponding pure-Python version does not work!
More array types (contiguous arrays, C or F order, memoryviews)
I think we should support:
Array[int, NDim(3), "C"] Array[int, "3d", "C"] transonic.typeof(np.empty((2, 2, 2))) Array["int[:, :, ::1]"] Array[int, "[:, :, ::1]"] transonic.str2type("int[:, :, ::1]")
and maybe also:
transonic.int64[:, :, ::1]
I tend to think that the default (
"int[:,:]") should correspond to
"order=C". “Fortran” order and “any” order (contiguous C or F) could be
Strided arrays could be obtained with
Array[int, NDim(3), "strided"] or
str2type("int[::, ::, ::]").
Note that for Pythran, we could also support:
A_fixed_dim = Array[Type(np.float32, float), "[:, :, 3]"]
For Cython, we need to be able to specify if an array is a
np.ndarray or a
memoryview. By default, we will use
memoryview could be
Array[int, "[:, :, ::1]", "memview"]
Special C types
np.intp, which is supported) and
from ctypes import c_ssize_t as Py_ssize_t from transonic import boost @boost def func(arr: "float", index: Py_ssize_t): if n > 1: a[n-1] = 0
from transonic import boost @boost def func(arr: "float", index: "Py_ssize_t"): ...
Cython syntaxes that can be supported quite easily
We could support something like
from transonic import boost, nogil @boost def func(n: int): with nogil: result = n**2 return result
Of course there is no equivalent in Pythran, so the Pythran backend would have
to suppress the
return <DTYPE_t*> myvar
I guess we should follow Cython and its pure Python mode function
Cython syntaxes that will be difficult to support
Pointers and addresses
cdef Py_ssize_t *p_indexer
cdef struct Heap: Py_ssize_t items Py_ssize_t space Heapitem *data Heapitem **ptrs
cdef class Foo:
features_carr = <MBLBP*>malloc(features_number * sizeof(MBLBP))