HPy API introduction


HPy is still in the early stages of development and the API may change.


The “H” in HPy stands for handle, which is a central concept: handles are used to hold a C reference to Python objects, and they are represented by the C HPy type. They play the same role as PyObject * in the Python/C API, albeit with some important differences which are detailed below.

When they are no longer needed, handles must be closed by calling HPy_Close, which plays more or less the same role as Py_DECREF. Similarly, if you need a new handle for an existing object, you can duplicate it by calling HPy_Dup, which plays more or less the same role as Py_INCREF.

The HPy API strictly follows these rules:

  • HPy handles returned by a function are never borrowed, i.e., the caller must either close or return it.

  • HPy handles passed as function arguments are never stolen; if you receive a HPy handle argument from your caller, you should never close it.

These rules makes the code simpler to reason about. Moreover, no reference borrowing enables the Python implementations to use whatever internal representation they wish. For example, the object returned by HPy_GetItem_i may be created on demand from some compact internal representation, which does not need to convert itself to full blown representation in order to hold onto the borrowed object.

We strongly encourage the users of HPy to also internally follow these rules for their own internal APIs and helper functions. For the sake of simplicity and easier local reasoning and also because in the future, code adhering to those rules may be suitable target for some scalable and precise static analysis tool.

The concept of handles is certainly not unique to HPy. Other examples include Unix file descriptors, where you have dup() and close(), and Windows’ HANDLE, where you have DuplicateHandle() and CloseHandle().

Handles vs PyObject *

In order to fully understand the way HPy handles work, it is useful to discuss the Python/C API Pyobject * pointer. These pointers always point to the same object, and a python object’s identity is completely given by its address in memory, and two pointers with the same address can be passed to Python/C API functions interchangeably. As a result, Py_INCREF and Py_DECREF can be called with any reference to an object as long as the total number of calls of incref is equal to the number of calls of decref at the end of the object lifetime.

Whereas using HPy API, each handle must be closed independently.

Thus, the following perfectly valid piece of Python/C code:

void foo(void)
    PyObject *x = PyLong_FromLong(42);  // implicit INCREF on x
    PyObject *y = x;
    Py_INCREF(y);                       // INCREF on y
    /* ... */
    Py_DECREF(x);                       // two DECREF on x

Becomes using HPy API:

void foo(HPyContext *ctx)
    HPy x = HPyLong_FromLong(ctx, 42);
    HPy y = HPy_Dup(ctx, x);
    /* ... */
    // we need to close x and y independently
    HPy_Close(ctx, x);
    HPy_Close(ctx, y);

Calling any HPy function on a closed handle is an error. Calling HPy_Close() on the same handle twice is an error. Forgetting to call HPy_Close() on a handle results in a memory leak. When running in Debug Mode, HPy actively checks that you don’t close a handle twice and that you don’t forget to close any.


Debug mode is a good example of how powerful it is to decouple the identity and therefore the lifetime of handles and those of objects. If you find a memory leak on CPython, you know that you are missing a Py_DECREF somewhere but the only way to find the corresponding Py_INCREF is to manually and carefully study the source code. On the other hand, if you forget to call HPy_Close(), debug mode is able to identify the precise code location which created the unclosed handle. Similarly, if you try to operate on a closed handle, it will identify the precise code locations which created and closed it. This is possible because handles are associated with a single call to a C/API function. As a result, given a handle that is leaked or used after freeing, it is possible to identify exactly the C/API function that producted it.

Remember that Python/C guarantees that multiple references to the same object results in the very same PyObject * pointer. Thus, it is possible to compare the pointer addresses to check whether they refer to the same object:

int is_same_object(PyObject *x, PyObject *y)
    return x == y;

On the other hand, in HPy, each handle is independent and it is common to have two different handles which point to the same underlying object, so comparing two handles directly is ill-defined. To prevent this kind of common error (especially when porting existing code to HPy), the HPy C type is opaque and the C compiler actively forbids comparisons between them. To check for identity, you can use HPy_Is():

int is_same_object(HPyContext *ctx, HPy x, HPy y)
    // return x == y; // compilation error!
    return HPy_Is(ctx, x, y);


The main benefit of opaque handle semantics is that implementations are allowed to use very different models of memory management. On CPython, implementing handles is trivial because HPy is basically PyObject * in disguise, and HPy_Dup() and HPy_Close() are just aliases for Py_INCREF and Py_DECREF.

Unlike CPython, PyPy does not use reference counting to manage memory: instead, it uses a moving GC, which means that the address of an object might change during its lifetime, and this makes it hard to implement semantics like PyObject *’s where the address identifies the object, and this is directly exposed to the user. HPy solves this problem: on PyPy, handles are integers which represent indices into a list, which is itself managed by the GC. When an address changes, the GC edits the list, without having to touch all the handles which have been passed to C.


All HPy function calls take an HPyContext as a first argument, which represents the Python interpreter all the handles belong to. Strictly speaking, it would be possible to design the HPy API without using HPyContext: after all, all HPy function calls are ultimately mapped to Python/C function call, where there is no notion of context.

One of the reasons to include HPyContext from the day one is to be future-proof: it is conceivable to use it to hold the interpreter or the thread state in the future, in particular when there will be support for sub-interpreters. Another possible usage could be to embed different versions or implementations of Python inside the same process. In addition, the HPyContext may also be extended by adding new functions to the end without breaking any extensions built against the current HPyContext.

Moreover, HPyContext is used by the HPy Universal ABI to contain a sort of virtual function table which is used by the C extensions to call back into the Python interpreter.

A simple example

In this section, we will see how to write a simple C extension using HPy. It is assumed that you are already familiar with the existing Python/C API, so we will underline the similarities and the differences with it.

We want to create a function named myabs which takes a single argument and computes its absolute value:

#include "hpy.h"

HPyDef_METH(myabs, "myabs", myabs_impl, HPyFunc_O)
static HPy myabs_impl(HPyContext *ctx, HPy self, HPy arg)
    return HPy_Absolute(ctx, arg);

There are a couple of points which are worth noting:

  • We use the macro HPyDef_METH to declare we are going to define a HPy function called myabs.

  • The function will be available under the name "myabs" in our Python module.

  • The actual C function which implements myabs is called myabs_impl.

  • It uses the HPyFunc_O calling convention. Like METH_O in Python/C API, HPyFunc_O means that the function receives a single argument on top of self.

  • myabs_impl takes two arguments of type HPy: handles for self and the argument, which are guaranteed to be valid. They are automatically closed by the caller, so there is no need to call HPy_Close on them.

  • myabs_impl returns a handle, which has to be closed by the caller.

  • HPy_Absolute is the equivalent of PyNumber_Absolute and computes the absolute value of the given argument.

  • We also do not call HPy_Close on the result returned to the caller. We must return a valid handle.


Among other things, the HPyDef_METH macro is needed to maintain compatibility with CPython. In CPython, C functions and methods have a C signature that is different to the one used by HPy: they don’t receive an HPyContext and their arguments have the type PyObject * instead of HPy. The macro automatically generates a trampoline function whose signature is appropriate for CPython and which calls the myabs_impl. This trampoline is then used from both the CPython ABI and the CPython implementation of the universal ABI, but other implementations of the universal ABI will usually call directly the HPy function itself.

Now, we can define our module:

static HPyDef *SimpleMethods[] = {

static HPyModuleDef simple = {
        .name = "simple",
        .doc = "HPy Example",
        .size = -1,
        .defines = SimpleMethods,
        .legacy_methods = NULL

This part is very similar to the one you would write in Python/C. Note that we specify myabs (and not myabs_impl) in the method table. There is also the .legacy_methods field, which allows to add methods that use the Python/C API, i.e., the value should be an array of PyMethodDef. This feature enables support for hybrid extensions in which some of the methods are still written using the Python/C API.

Finally, HPyModuleDef is basically the same as the old PyModuleDef:

HPy init_simple_impl(HPyContext *ctx) {
    return HPyModule_Create(ctx, &simple);

Building the module

Let’s write a setup.py to build our extension:

from setuptools import setup, Extension
from os import path

        Extension('simple', sources=[path.join(path.dirname(__file__), 'simple.c')]),

We can now build the extension by running python setup.py build_ext -i. On CPython, it will target the CPython ABI by default, so you will end up with a file named e.g. simple.cpython-37m-x86_64-linux-gnu.so which can be imported directly on CPython with no dependency on HPy.

To target the HPy Universal ABI instead, it is possible to pass the option --hpy-abi=universal to setup.py. The following command will produce a file called simple.hpy.so (note that you need to specify --hpy-abi before build_ext, since it is a global option):

python setup.py --hpy-abi=universal build_ext -i


This command will also produce a Python file named simple.py, which loads the HPy module using the universal.load function from the hpy Python package.

VARARGS calling convention

If we want to receive more than a single arguments, we need the HPy_METH_VARARGS calling convention. Let’s add a function add_ints which adds two integers:

HPyDef_METH(add_ints, "add_ints", add_ints_impl, HPyFunc_VARARGS)
static HPy add_ints_impl(HPyContext *ctx, HPy self, HPy *args, HPy_ssize_t nargs)
    long a, b;
    if (!HPyArg_Parse(ctx, NULL, args, nargs, "ll", &a, &b))
        return HPy_NULL;
    return HPyLong_FromLong(ctx, a+b);

There are a few things to note:

  • The C signature is different than the corresponding Python/C METH_VARARGS: in particular, instead of taking a PyObject *args, we take an array of HPy and its size. This allows e.g. PyPy to do a call more efficiently, because you don’t need to create a tuple just to pass the arguments.

  • We call HPyArg_Parse to parse the arguments. Contrarily to almost all the other HPy functions, this is not a thin wrapper around PyArg_ParseTuple because as stated above we don’t have a tuple to pass to it, although the idea is to mimic its behavior as closely as possible. The parsing logic is implemented from scratch inside HPy, and as such there might be missing functionality during the early stages of HPy development.

  • If an error occurs, we return HPy_NULL: we cannot simply return NULL because HPy is not a pointer type.

Once we have written our function, we can add it to the SimpleMethods[] table, which now becomes:

static HPyDef *SimpleMethods[] = {