如何在 C++ 中为 Cython 中的两种类型使用模板化函数？

Question

我有一个模板化的 C++ 函数，我希望能够同时使用这两种类型。由于 Python 不支持重载，我对如何解决这个问题有点困惑。我有一个.pyx，如下所示。如何在 float 和 double 中使用 C++ 函数？

import cython
import numpy as np
cimport numpy as np

# declare the interface to the C code
cdef extern from "diff_cpp.cpp" namespace "diff":
    cdef void diff_cpp[float] (float* at, const float* a, const float visc,
                               const float dxidxi, const float dyidyi, const float dzidzi,
                               const int itot, const int jtot, const int ktot)

cdef extern from "diff_cpp.cpp" namespace "diff":
    cdef void diff_cpp[double] (double* at, const double* a, const double visc,
                                const double dxidxi, const double dyidyi, const double dzidzi,
                                const int itot, const int jtot, const int ktot)

@cython.boundscheck(False)
@cython.wraparound(False)
def diff(np.ndarray[double, ndim=3, mode="c"] at not None,
         np.ndarray[double, ndim=3, mode="c"] a not None,
         double visc, double dxidxi, double dyidyi, double dzidzi):
    cdef int ktot, jtot, itot
    ktot, jtot, itot = at.shape[0], at.shape[1], at.shape[2]
    diff_cpp[double](&at[0,0,0], &a[0,0,0], visc, dxidxi, dyidyi, dzidzi, itot, jtot, ktot)
    return None

@cython.boundscheck(False)
@cython.wraparound(False)
def diff_f(np.ndarray[float, ndim=3, mode="c"] at not None,
           np.ndarray[float, ndim=3, mode="c"] a not None,
           float visc, float dxidxi, float dyidyi, float dzidzi):
    cdef int ktot, jtot, itot
    ktot, jtot, itot = at.shape[0], at.shape[1], at.shape[2]
    diff_cpp[float](&at[0,0,0], &a[0,0,0], visc, dxidxi, dyidyi, dzidzi, itot, jtot, ktot)
    return None

---

更新解决方案

@oz1 的回答提供了正确的方法。对于那些对此特定问题的解决方案感兴趣的人来说，这是有效的代码。

import cython
import numpy as np
cimport numpy as np

# declare the interface to the C code
cdef extern from "diff_cpp.cpp" namespace "diff":
    cdef void diff_cpp[T](T* at, const T* a, const T visc,
                          const T dxidxi, const T dyidyi, const T dzidzi,
                          const int itot, const int jtot, const int ktot)

ctypedef fused float_t:
    cython.float
    cython.double

@cython.boundscheck(False)
@cython.wraparound(False)
def diff(np.ndarray[float_t, ndim=3, mode="c"] at not None,
         np.ndarray[float_t, ndim=3, mode="c"] a not None,
         float_t visc, float_t dxidxi, float_t dyidyi, float_t dzidzi):
    cdef int ktot, jtot, itot
    ktot, jtot, itot = at.shape[0], at.shape[1], at.shape[2]
    diff_cpp(&at[0,0,0], &a[0,0,0], visc, dxidxi, dyidyi, dzidzi, itot, jtot, ktot)
    return None

Answer 1

两个音符：

1. Cython 支持 c++ 模板(http://docs.cython.org/en/latest/src/userguide/wrapping_CPlusPlus.html)
2. Cython 有融合类型(http://docs.cython.org/en/latest/src/userguide/fusedtypes.html)

一个例子：

// lib.cpp
template<typename T>
T arr_sum(T *arr, size_t size)
{
    T temp=0;
    for (size_t i=0; i != size; ++i){
        temp += arr[i];
    }
    return temp;
}

---

# lib_wrapper.pyx
cimport cython

ctypedef fused  float_t:
    cython.float
    cython.double

cdef extern from "lib.cpp" nogil:
    T arr_sum[T](T *arr, size_t size)

def py_arr_sum(float_t[:] arr not None):
    print(sizeof(arr[0]))  # check the element size
    return arr_sum(&arr[0], arr.shape[0])

---

# use.py
import numpy as np
from lib_wrapper import py_arr_sum

print(py_arr_sum(np.array([1,2,3], dtype=np.float32)))
print(py_arr_sum(np.array([1,2,3], dtype=np.float64)))
print(py_arr_sum(np.array([1,2,3], dtype=np.int32)))  # oops