Pyffic is a simple yet useful Cpp-Python FFI (Foreign Function Interface) system. Pyffic consists of two components: ffi_man on Cpp side and pyffi on Python side.
ffi_man allows you to1:
- Register global functions by recording their registered names, addresses, and generating compile time strings that represent the functions' signatures. (for example, a Cpp function declared as
uint32_t func(fooclass* ptr, int8_t)corresponds to a compile time signature string":*fooclass:i8;u32".) When your Cpp programs are compiled to shared libs, these information could help external programs that load the shared libs know how the functions could be called under specific ABI, avoiding the Cpp name mangling problem. - Register custom Cpp classes by recording their registered name, constructor, and destructor. (for example, registering a class named
fooclassby using macroFFI_REGISTER_CLASS(fooclass, "fooclass", create_fooclass, destroy_fooclass);) These information could help external programs written in different language create the Cpp classes' counterpart objects. Besides, the registered name of the Cpp class could also be used to generate compile time function signature string mentioned in 1. - Register custom Cpp class fields by recording their registered name, offset, and compile time type string. (for example, registering a class field
fooclass::speedby using macroFFI_REGISTER_CLASS_FIELD(fooclass, speed, fooclass::speed, "fooclass.speed");) These information could help external programs access or modify the class value. - Register custom Cpp class methods by generating method wrappers, recording wrapper signatures, and wrapper addresses. (for example, registering a class method
fooclass::double_speedby using macroFFI_REGISTER_CLASS_METHOD(&fooclass::double_speed, "fooclass.double_speed");) These information could help external programs call the class methods.
pyffi allows you to:
- Easily define a Cpp global function counterpart in Python, by automatically reading the ffi_man's recorded information and performing argument conversion, function calling and return value conversion.
- Easily define a Cpp class counterpart in Python, by automatically reading the ffi_man's recorded information, creating proper Python
__init__function,__del__function, normal class methods and descriptors that take care of class field accesses.
- On Cpp side, pyffic requires a modern compiler that supports C++20 standard (e.g. clang14 and higher) to work
- This is because ffi_man used some C++20 specifiers such as
consteval. Actually its core functionalities only require C++14 to work.
- This is because ffi_man used some C++20 specifiers such as
- On Python side, pyffic requires python >= 3.6, and numpy
- Because for pyffi to work it requires
__init_subclass__magic method, which is available only in python >= 3.6 - Because for a Cpp function/method that has pointer type parameters (except for
const char*), pyffi would expectnumpy.ndarrays as arguments
- Because for pyffi to work it requires
- Include
path/to/Pyffic/cpp/ffi_man.hppin your Cpp source files. Note thatffi_man.hppwill include all the header files underpath/to/Pyffic/cpp/siggenso you also would need to ensure that your compiler could find them. It is recommended to keep the directory structure ofPyffic/cppfor convenience. - Add
path/to/Pyffic/cpp/ffi_man.cppto your Cpp source file list.
- Add
path/to/Pyfficto your$PYTHONPATHto allow your Python interpreter find pyffi - If you use Python environment manager like
conda, make sure that your environment interpreter could find pyffi - Test if
import pyffiis working
1. normal global functions
//Cpp side code that compiles to lib.so
#include "ffi_man.hpp"
double mult(double x, double y){
return x*y;
}
FFI_REGISTER_GLOBAL_FUNCTION(mult, "mult");#Python side code
import pyffi
# this is the ffi manager instance for lib.so
lib = pyffi.Lib("lib.so")
class Mult(lib.FFIGlobalFunc):
def __init__(self) -> None:
super().__init__("mult")
def __call__(self,x,y):
return super().__call__(x,y)
mult = Mult()
result = mult(5,6) #gives 302. functions returning strings
//Cpp side code that compiles to lib.so
#include "ffi_man.hpp"
const char* cstrtester(){
static const char* str = "good";
return str;
}
FFI_REGISTER_GLOBAL_FUNCTION(cstrtester, "cstrtester");#Python side code
import pyffi
# this is the ffi manager instance for lib.so
lib = pyffi.Lib("lib.so")
class CstrTester(lib.FFIGlobalFunc):
def __init__(self) -> None:
super().__init__("cstrtester")
def __call__(self, *args):
return super().__call__(*args)
cstrtester = CstrTester()
result = cstrtester() #gives "good"3. functions accepting pointer arguments
//Cpp side code that compiles to lib.so
#include "ffi_man.hpp"
uint64_t count_zeros(uint32_t* array, uint64_t n){
uint64_t count = 0;
for (uint64_t i=0;i<n;i++){
if (array[i]==0) count++;
}
return count;
}
FFI_REGISTER_GLOBAL_FUNCTION(count_zeros, "count_zeros");Note that pyffi would always expect a numpy.ndarray object for Cpp pointer type parameters other than const char*
#Python side code
import pyffi
import numpy as np
# this is the ffi manager instance for lib.so
lib = pyffi.Lib("lib.so")
class Count_Zeros(lib.FFIGlobalFunc):
def __init__(self) -> None:
super().__init__("count_zeros")
def __call__(self, array:np.ndarray):
# the function is actually provided by super().__call__
# so you could freely wrap the original function
return super().__call__(array,array.size)
count_zeros = Count_Zeros()
# note that the dtype has to be correct
array = np.array([1,2,3,4,5,0,0,0],dtype = np.uint32)
result = count_zeros(array) #gives 31.Class constructor and destructor
//Cpp side code that compiles to class.so
#include "ffi_man.hpp"
class fooclass{
public:
fooclass(float spd, int cnt):speed(spd),count(cnt){
}
~fooclass(){
}
float speed;
int count;
};
// in Cpp getting constructor/destructor addr is strictly prohibited
// so we have to wrap them ourselves.
fooclass* create_fooclass(float spd, int cnt){
auto ptr = new fooclass(spd,cnt);
std::printf("created fooclass!\n");
return ptr;
}
void destroy_fooclass(fooclass* fc){
delete fc;
printf("deleted fooclass\n");
}
FFI_REGISTER_CLASS(fooclass, "fooclass", create_fooclass, destroy_fooclass);#Python side code
import pyffi
# this is the ffi manager instance for class.so
lib = pyffi.Lib("./class.so")
class FooClass(lib.FFIClassBase):
# you have to provide cffi_registered_name manually
cffi_registered_name = "fooclass"
def __init__(self, spd,cnt) -> None:
super().__init__(spd,cnt)
foo = FooClass(100,5)
# running the script gives
# created fooclass!
# deleted fooclass2.Class fields and methods I
Continuing from the previous example, we add a class method double_speed which, well, doubles the field speed's value of fooclass and register both the method and field:
//Cpp side code that compiles to class_1.so
#include "ffi_man.hpp"
class fooclass{
public:
fooclass(float spd, int cnt):speed(spd),count(cnt){
}
~fooclass(){
}
void double_speed(){
speed = speed*2;
}
float speed;
int count;
};
fooclass* create_fooclass(float spd, int cnt){
auto ptr = new fooclass(spd,cnt);
std::printf("created fooclass!\n");
return ptr;
}
void destroy_fooclass(fooclass* fc){
delete fc;
printf("deleted fooclass\n");
}
FFI_REGISTER_CLASS(fooclass, "fooclass", create_fooclass, destroy_fooclass);
FFI_REGISTER_CLASS_FIELD(fooclass, speed, fooclass::speed, "fooclass.speed");
FFI_REGISTER_CLASS_METHOD(&fooclass::double_speed, "fooclass.double_speeed");There are two ways to use the registered methods and fields at Python side. When defining FooClass, pyffi will check if the class already has attributes having the same name as the methods' or the fields' registered name. If not, we could directly access the methods and the fields using their registered names:
#Python side code
import pyffi
lib = pyffi.Lib("./class_1.so")
class FooClass(lib.FFIClassBase):
cffi_registered_name = "fooclass"
def __init__(self, spd,cnt) -> None:
super().__init__(spd,cnt)
foo = FooClass(100,5)
print("foo's speed is {}".format(foo.speed))
foo.speed = 789
print("foo's new speed is {}".format(foo.speed))
foo.double_speed()
print("foo's doubled new speed is {}".format(foo.speed))
# gives:
# foo's speed is 100.0
# foo's new speed is 789.0
# foo's doubled new speed is 1578.0However if pyffi found that there are attributes with the same name as the methods and the fields registered name, the binding names of them would have a prefix _. This is useful if you would like to further wrap them or if you just don't like the aforementioned implicit way:
#Python side code
import pyffi
lib = pyffi.Lib("./class_1.so")
class FooClass(lib.FFIClassBase):
cffi_registered_name = "fooclass"
def __init__(self, spd,cnt) -> None:
super().__init__(spd,cnt)
def double_speed(self):
# since attr "double_speed" already exists
# pyffi now binds the method with a prefix
return self._double_speed()
@property
def speed(self):
# same for class fields
return self._speed
@speed.setter
def speed(self,value):
self._speed = value
foo = FooClass(100,5)
print("foo's speed is {}".format(foo.speed))
foo.speed = 789
print("foo's new speed is {}".format(foo.speed))
foo.double_speed()
print("foo's doubled new speed is {}".format(foo.speed))
# gives:
# foo's speed is 100.0
# foo's new speed is 789.0
# foo's doubled new speed is 1578.03.Class fields and methods II
We now declare another class called anotherclass, slightly modify our fooclass to contain a pointer to an anotherclass object, and observe if pyffi could handle this:
//Cpp side code that compiles to class_2.so
class anotherclass{
public:
anotherclass(uint32_t a, uint32_t b):a(a),b(b){}
uint32_t a;
uint32_t b;
};
anotherclass* create_anaclass(uint32_t a, uint32_t b){
auto ptr = new anotherclass(a,b);
std::printf("created anotherclass!\n");
return ptr;
}
void destroy_anaclass(anotherclass* ptr){
delete ptr;
printf("deleted anotherclass\n");
}
FFI_REGISTER_CLASS(anotherclass, "anotherclass", create_anaclass, destroy_anaclass)
FFI_REGISTER_CLASS_FIELD(anotherclass, a, anotherclass::a, "anotherclass.a")
class fooclass{
public:
fooclass(uint32_t a, uint32_t b){
other = new anotherclass(a,b);
}
~fooclass(){
delete other;
}
anotherclass* other;
};
fooclass* create_fooclass(float spd, int cnt){
auto ptr = new fooclass(spd,cnt);
std::printf("created fooclass!\n");
return ptr;
}
void destroy_fooclass(fooclass* fc){
delete fc;
printf("deleted fooclass\n");
}
FFI_REGISTER_CLASS(fooclass, "fooclass", create_fooclass, destroy_fooclass);
FFI_REGISTER_CLASS_FIELD(fooclass, other, fooclass::other, "fooclass.other")
We define the two classes counterpart in Python:
#Python side code
import pyffi
lib = pyffi.Lib("./class_2.so")
class FooClass(lib.FFIClassBase):
cffi_registered_name = "fooclass"
def __init__(self, a,b) -> None:
super().__init__(a,b)
class AnotherClass(lib.FFIClassBase):
cffi_registered_name = "anotherclass"
def __init__(self, a,b) -> None:
super().__init__(a,b)
foo = FooClass(6,7)
print(foo.other)
print(foo.other.a)
# gives:
# created fooclass!
# <__main__.AnotherClass object at 0x7fef932d2a40>
# 6We see that pyffi could properly handle this case. However, note that pyffi always assumes that only Python objects that are instantiated directly from __init__ function owns the actual Cpp object. Python objects instantiated from class fields (like the above example), function return values are not considered to own the Cpp object.
You could also check Pyffic/testing for above examples' source code.
- Passing raw pointers in pyffi is prohibited
- pyffi will only accept pointer types that has a single level of indirection. (that is, pointers to pointer are not allowed)
Footnotes
-
from the above statements you could see that ffi_man could also be used to establish a FFI system other than a Python-Cpp one. ↩