3. Définir les types d'extension : divers sujets¶
This section aims to give a quick fly-by on the various type methods you can implement and what they do.
Here is the definition of PyTypeObject
, with some fields only used in
debug builds omitted:
typedef struct _typeobject {
PyObject_VAR_HEAD
const char *tp_name; /* For printing, in format "<module>.<name>" */
Py_ssize_t tp_basicsize, tp_itemsize; /* For allocation */
/* Methods to implement standard operations */
destructor tp_dealloc;
printfunc tp_print;
getattrfunc tp_getattr;
setattrfunc tp_setattr;
PyAsyncMethods *tp_as_async; /* formerly known as tp_compare (Python 2)
or tp_reserved (Python 3) */
reprfunc tp_repr;
/* Method suites for standard classes */
PyNumberMethods *tp_as_number;
PySequenceMethods *tp_as_sequence;
PyMappingMethods *tp_as_mapping;
/* More standard operations (here for binary compatibility) */
hashfunc tp_hash;
ternaryfunc tp_call;
reprfunc tp_str;
getattrofunc tp_getattro;
setattrofunc tp_setattro;
/* Functions to access object as input/output buffer */
PyBufferProcs *tp_as_buffer;
/* Flags to define presence of optional/expanded features */
unsigned long tp_flags;
const char *tp_doc; /* Documentation string */
/* call function for all accessible objects */
traverseproc tp_traverse;
/* delete references to contained objects */
inquiry tp_clear;
/* rich comparisons */
richcmpfunc tp_richcompare;
/* weak reference enabler */
Py_ssize_t tp_weaklistoffset;
/* Iterators */
getiterfunc tp_iter;
iternextfunc tp_iternext;
/* Attribute descriptor and subclassing stuff */
struct PyMethodDef *tp_methods;
struct PyMemberDef *tp_members;
struct PyGetSetDef *tp_getset;
struct _typeobject *tp_base;
PyObject *tp_dict;
descrgetfunc tp_descr_get;
descrsetfunc tp_descr_set;
Py_ssize_t tp_dictoffset;
initproc tp_init;
allocfunc tp_alloc;
newfunc tp_new;
freefunc tp_free; /* Low-level free-memory routine */
inquiry tp_is_gc; /* For PyObject_IS_GC */
PyObject *tp_bases;
PyObject *tp_mro; /* method resolution order */
PyObject *tp_cache;
PyObject *tp_subclasses;
PyObject *tp_weaklist;
destructor tp_del;
/* Type attribute cache version tag. Added in version 2.6 */
unsigned int tp_version_tag;
destructor tp_finalize;
} PyTypeObject;
Now that's a lot of methods. Don't worry too much though -- if you have a type you want to define, the chances are very good that you will only implement a handful of these.
As you probably expect by now, we're going to go over this and give more information about the various handlers. We won't go in the order they are defined in the structure, because there is a lot of historical baggage that impacts the ordering of the fields. It's often easiest to find an example that includes the fields you need and then change the values to suit your new type.
const char *tp_name; /* For printing */
The name of the type -- as mentioned in the previous chapter, this will appear in various places, almost entirely for diagnostic purposes. Try to choose something that will be helpful in such a situation!
Py_ssize_t tp_basicsize, tp_itemsize; /* For allocation */
These fields tell the runtime how much memory to allocate when new objects of
this type are created. Python has some built-in support for variable length
structures (think: strings, tuples) which is where the tp_itemsize
field
comes in. This will be dealt with later.
const char *tp_doc;
Ici vous pouvez mettre une chaîne (ou son adresse) que vous voulez renvoyer lorsque le script Python référence obj.__doc__
pour récupérer le docstring.
Nous en arrivons maintenant aux méthodes de type basiques -- celles que la plupart des types d'extension mettront en œuvre.
3.1. Finalisation et de-allocation¶
destructor tp_dealloc;
This function is called when the reference count of the instance of your type is reduced to zero and the Python interpreter wants to reclaim it. If your type has memory to free or other clean-up to perform, you can put it here. The object itself needs to be freed here as well. Here is an example of this function:
static void
newdatatype_dealloc(newdatatypeobject *obj)
{
free(obj->obj_UnderlyingDatatypePtr);
Py_TYPE(obj)->tp_free(obj);
}
One important requirement of the deallocator function is that it leaves any
pending exceptions alone. This is important since deallocators are frequently
called as the interpreter unwinds the Python stack; when the stack is unwound
due to an exception (rather than normal returns), nothing is done to protect the
deallocators from seeing that an exception has already been set. Any actions
which a deallocator performs which may cause additional Python code to be
executed may detect that an exception has been set. This can lead to misleading
errors from the interpreter. The proper way to protect against this is to save
a pending exception before performing the unsafe action, and restoring it when
done. This can be done using the PyErr_Fetch()
and
PyErr_Restore()
functions:
static void
my_dealloc(PyObject *obj)
{
MyObject *self = (MyObject *) obj;
PyObject *cbresult;
if (self->my_callback != NULL) {
PyObject *err_type, *err_value, *err_traceback;
/* This saves the current exception state */
PyErr_Fetch(&err_type, &err_value, &err_traceback);
cbresult = PyObject_CallObject(self->my_callback, NULL);
if (cbresult == NULL)
PyErr_WriteUnraisable(self->my_callback);
else
Py_DECREF(cbresult);
/* This restores the saved exception state */
PyErr_Restore(err_type, err_value, err_traceback);
Py_DECREF(self->my_callback);
}
Py_TYPE(obj)->tp_free((PyObject*)self);
}
Note
There are limitations to what you can safely do in a deallocator function.
First, if your type supports garbage collection (using tp_traverse
and/or tp_clear
), some of the object's members can have been
cleared or finalized by the time tp_dealloc
is called. Second, in
tp_dealloc
, your object is in an unstable state: its reference
count is equal to zero. Any call to a non-trivial object or API (as in the
example above) might end up calling tp_dealloc
again, causing a
double free and a crash.
Starting with Python 3.4, it is recommended not to put any complex
finalization code in tp_dealloc
, and instead use the new
tp_finalize
type method.
Voir aussi
PEP 442 explique le nouveau schéma de finalisation.
3.2. Présentation de l'objet¶
In Python, there are two ways to generate a textual representation of an object:
the repr()
function, and the str()
function. (The print()
function just calls str()
.) These handlers are both optional.
reprfunc tp_repr;
reprfunc tp_str;
The tp_repr
handler should return a string object containing a
representation of the instance for which it is called. Here is a simple
example:
static PyObject *
newdatatype_repr(newdatatypeobject * obj)
{
return PyUnicode_FromFormat("Repr-ified_newdatatype{{size:%d}}",
obj->obj_UnderlyingDatatypePtr->size);
}
If no tp_repr
handler is specified, the interpreter will supply a
representation that uses the type's tp_name
and a uniquely-identifying
value for the object.
The tp_str
handler is to str()
what the tp_repr
handler
described above is to repr()
; that is, it is called when Python code calls
str()
on an instance of your object. Its implementation is very similar
to the tp_repr
function, but the resulting string is intended for human
consumption. If tp_str
is not specified, the tp_repr
handler is
used instead.
Voici un exemple simple :
static PyObject *
newdatatype_str(newdatatypeobject * obj)
{
return PyUnicode_FromFormat("Stringified_newdatatype{{size:%d}}",
obj->obj_UnderlyingDatatypePtr->size);
}
3.3. Gestion des attributs¶
For every object which can support attributes, the corresponding type must
provide the functions that control how the attributes are resolved. There needs
to be a function which can retrieve attributes (if any are defined), and another
to set attributes (if setting attributes is allowed). Removing an attribute is
a special case, for which the new value passed to the handler is NULL
.
Python supports two pairs of attribute handlers; a type that supports attributes
only needs to implement the functions for one pair. The difference is that one
pair takes the name of the attribute as a char*
, while the other
accepts a PyObject*
. Each type can use whichever pair makes more
sense for the implementation's convenience.
getattrfunc tp_getattr; /* char * version */
setattrfunc tp_setattr;
/* ... */
getattrofunc tp_getattro; /* PyObject * version */
setattrofunc tp_setattro;
If accessing attributes of an object is always a simple operation (this will be
explained shortly), there are generic implementations which can be used to
provide the PyObject*
version of the attribute management functions.
The actual need for type-specific attribute handlers almost completely
disappeared starting with Python 2.2, though there are many examples which have
not been updated to use some of the new generic mechanism that is available.
3.3.1. Gestion des attributs génériques¶
Most extension types only use simple attributes. So, what makes the attributes simple? There are only a couple of conditions that must be met:
Le nom des attributs doivent être déjà connus lorsqu'on lance
PyType_Ready()
.No special processing is needed to record that an attribute was looked up or set, nor do actions need to be taken based on the value.
Note that this list does not place any restrictions on the values of the attributes, when the values are computed, or how relevant data is stored.
When PyType_Ready()
is called, it uses three tables referenced by the
type object to create descriptors which are placed in the dictionary of the
type object. Each descriptor controls access to one attribute of the instance
object. Each of the tables is optional; if all three are NULL
, instances of
the type will only have attributes that are inherited from their base type, and
should leave the tp_getattro
and tp_setattro
fields NULL
as
well, allowing the base type to handle attributes.
Les tables sont déclarées sous la forme de trois champs de type objet :
struct PyMethodDef *tp_methods;
struct PyMemberDef *tp_members;
struct PyGetSetDef *tp_getset;
If tp_methods
is not NULL
, it must refer to an array of
PyMethodDef
structures. Each entry in the table is an instance of this
structure:
typedef struct PyMethodDef {
const char *ml_name; /* method name */
PyCFunction ml_meth; /* implementation function */
int ml_flags; /* flags */
const char *ml_doc; /* docstring */
} PyMethodDef;
One entry should be defined for each method provided by the type; no entries are
needed for methods inherited from a base type. One additional entry is needed
at the end; it is a sentinel that marks the end of the array. The
ml_name
field of the sentinel must be NULL
.
The second table is used to define attributes which map directly to data stored in the instance. A variety of primitive C types are supported, and access may be read-only or read-write. The structures in the table are defined as:
typedef struct PyMemberDef {
const char *name;
int type;
int offset;
int flags;
const char *doc;
} PyMemberDef;
For each entry in the table, a descriptor will be constructed and added to the
type which will be able to extract a value from the instance structure. The
type
field should contain one of the type codes defined in the
structmember.h
header; the value will be used to determine how to
convert Python values to and from C values. The flags
field is used to
store flags which control how the attribute can be accessed.
The following flag constants are defined in structmember.h
; they may be
combined using bitwise-OR.
Constante |
Signification |
---|---|
|
Jamais disponible en écriture. |
|
Non disponible en lecture, dans le mode restreint. |
|
Non disponible en écriture dans le mode restreint. |
|
Non disponible en lecture ou écriture, en mode restreint. |
Un avantage intéressant de l'utilisation de la table tp_members
pour construire les descripteurs qui sont utilisés à l'exécution, est que à tout attribut défini de cette façon on peut associer un docstring, en écrivant simplement le texte dans la table. Une application peut utiliser l'API d'introspection pour récupérer le descripteur de l'objet de classe, et utiliser son attribut __doc__
pour renvoyer le docstring.
As with the tp_methods
table, a sentinel entry with a name
value
of NULL
is required.
3.3.2. Gestion des attributs de type spécifiques¶
For simplicity, only the char*
version will be demonstrated here; the
type of the name parameter is the only difference between the char*
and PyObject*
flavors of the interface. This example effectively does
the same thing as the generic example above, but does not use the generic
support added in Python 2.2. It explains how the handler functions are
called, so that if you do need to extend their functionality, you'll understand
what needs to be done.
The tp_getattr
handler is called when the object requires an attribute
look-up. It is called in the same situations where the __getattr__()
method of a class would be called.
Voici un exemple :
static PyObject *
newdatatype_getattr(newdatatypeobject *obj, char *name)
{
if (strcmp(name, "data") == 0)
{
return PyLong_FromLong(obj->data);
}
PyErr_Format(PyExc_AttributeError,
"'%.50s' object has no attribute '%.400s'",
tp->tp_name, name);
return NULL;
}
The tp_setattr
handler is called when the __setattr__()
or
__delattr__()
method of a class instance would be called. When an
attribute should be deleted, the third parameter will be NULL
. Here is an
example that simply raises an exception; if this were really all you wanted, the
tp_setattr
handler should be set to NULL
.
static int
newdatatype_setattr(newdatatypeobject *obj, char *name, PyObject *v)
{
PyErr_Format(PyExc_RuntimeError, "Read-only attribute: %s", name);
return -1;
}
3.4. Comparaison des objets¶
richcmpfunc tp_richcompare;
The tp_richcompare
handler is called when comparisons are needed. It is
analogous to the rich comparison methods, like
__lt__()
, and also called by PyObject_RichCompare()
and
PyObject_RichCompareBool()
.
This function is called with two Python objects and the operator as arguments,
where the operator is one of Py_EQ
, Py_NE
, Py_LE
, Py_GT
,
Py_LT
or Py_GT
. It should compare the two objects with respect to the
specified operator and return Py_True
or Py_False
if the comparison is
successful, Py_NotImplemented
to indicate that comparison is not
implemented and the other object's comparison method should be tried, or NULL
if an exception was set.
Here is a sample implementation, for a datatype that is considered equal if the size of an internal pointer is equal:
static PyObject *
newdatatype_richcmp(PyObject *obj1, PyObject *obj2, int op)
{
PyObject *result;
int c, size1, size2;
/* code to make sure that both arguments are of type
newdatatype omitted */
size1 = obj1->obj_UnderlyingDatatypePtr->size;
size2 = obj2->obj_UnderlyingDatatypePtr->size;
switch (op) {
case Py_LT: c = size1 < size2; break;
case Py_LE: c = size1 <= size2; break;
case Py_EQ: c = size1 == size2; break;
case Py_NE: c = size1 != size2; break;
case Py_GT: c = size1 > size2; break;
case Py_GE: c = size1 >= size2; break;
}
result = c ? Py_True : Py_False;
Py_INCREF(result);
return result;
}
3.5. Support pour le protocole abstrait¶
Python supports a variety of abstract 'protocols;' the specific interfaces provided to use these interfaces are documented in Couche d'abstraction des objets.
A number of these abstract interfaces were defined early in the development of
the Python implementation. In particular, the number, mapping, and sequence
protocols have been part of Python since the beginning. Other protocols have
been added over time. For protocols which depend on several handler routines
from the type implementation, the older protocols have been defined as optional
blocks of handlers referenced by the type object. For newer protocols there are
additional slots in the main type object, with a flag bit being set to indicate
that the slots are present and should be checked by the interpreter. (The flag
bit does not indicate that the slot values are non-NULL
. The flag may be set
to indicate the presence of a slot, but a slot may still be unfilled.)
PyNumberMethods *tp_as_number;
PySequenceMethods *tp_as_sequence;
PyMappingMethods *tp_as_mapping;
If you wish your object to be able to act like a number, a sequence, or a
mapping object, then you place the address of a structure that implements the C
type PyNumberMethods
, PySequenceMethods
, or
PyMappingMethods
, respectively. It is up to you to fill in this
structure with appropriate values. You can find examples of the use of each of
these in the Objects
directory of the Python source distribution.
hashfunc tp_hash;
This function, if you choose to provide it, should return a hash number for an instance of your data type. Here is a simple example:
static Py_hash_t
newdatatype_hash(newdatatypeobject *obj)
{
Py_hash_t result;
result = obj->some_size + 32767 * obj->some_number;
if (result == -1)
result = -2;
return result;
}
Py_hash_t
is a signed integer type with a platform-varying width.
Returning -1
from tp_hash
indicates an error,
which is why you should be careful to avoid returning it when hash computation
is successful, as seen above.
ternaryfunc tp_call;
This function is called when an instance of your data type is "called", for
example, if obj1
is an instance of your data type and the Python script
contains obj1('hello')
, the tp_call
handler is invoked.
Cette fonction prend trois arguments :
self is the instance of the data type which is the subject of the call. If the call is
obj1('hello')
, then self isobj1
.args is a tuple containing the arguments to the call. You can use
PyArg_ParseTuple()
to extract the arguments.kwds is a dictionary of keyword arguments that were passed. If this is non-
NULL
and you support keyword arguments, usePyArg_ParseTupleAndKeywords()
to extract the arguments. If you do not want to support keyword arguments and this is non-NULL
, raise aTypeError
with a message saying that keyword arguments are not supported.
Ceci est une implémentation tp_call
très simple :
static PyObject *
newdatatype_call(newdatatypeobject *self, PyObject *args, PyObject *kwds)
{
PyObject *result;
const char *arg1;
const char *arg2;
const char *arg3;
if (!PyArg_ParseTuple(args, "sss:call", &arg1, &arg2, &arg3)) {
return NULL;
}
result = PyUnicode_FromFormat(
"Returning -- value: [%d] arg1: [%s] arg2: [%s] arg3: [%s]\n",
obj->obj_UnderlyingDatatypePtr->size,
arg1, arg2, arg3);
return result;
}
/* Iterators */
getiterfunc tp_iter;
iternextfunc tp_iternext;
These functions provide support for the iterator protocol. Both handlers
take exactly one parameter, the instance for which they are being called,
and return a new reference. In the case of an error, they should set an
exception and return NULL
. tp_iter
corresponds
to the Python __iter__()
method, while tp_iternext
corresponds to the Python __next__()
method.
Tout objet iterable doit implémenter le gestionnaire tp_iter
, qui doit renvoyer un objet de type iterator. Ici, les mêmes directives s'appliquent de la même façon que pour les classes Python :
Pour les collections (telles que les listes et les n-uplets) qui peuvent implémenter plusieurs itérateurs indépendants, un nouvel itérateur doit être créé et renvoyé par chaque appel de type
tp_iter
.Objects which can only be iterated over once (usually due to side effects of iteration, such as file objects) can implement
tp_iter
by returning a new reference to themselves -- and should also therefore implement thetp_iternext
handler.
Any iterator object should implement both tp_iter
and tp_iternext
. An iterator's
tp_iter
handler should return a new reference
to the iterator. Its tp_iternext
handler should
return a new reference to the next object in the iteration, if there is one.
If the iteration has reached the end, tp_iternext
may return NULL
without setting an exception, or it may set
StopIteration
in addition to returning NULL
; avoiding
the exception can yield slightly better performance. If an actual error
occurs, tp_iternext
should always set an exception
and return NULL
.
3.6. Prise en charge de la référence faible¶
L'un des objectifs de l'implémentation de la référence faible de Python est de permettre à tout type d'objet de participer au mécanisme de référence faible sans avoir à supporter le surcoût de la performance critique des certains objets, tels que les nombres.
Voir aussi
Documentation pour le module weakref
.
Pour qu'un objet soit faiblement référençable, le type d'extension doit faire deux choses :
Include a
PyObject*
field in the C object structure dedicated to the weak reference mechanism. The object's constructor should leave itNULL
(which is automatic when using the defaulttp_alloc
).Définissez le membre de type
tp_weaklistoffset
à la valeur de décalage (offset) du champ susmentionné dans la structure de l'objet C, afin que l'interpréteur sache comment accéder à ce champ et le modifier.
Concrètement, voici comment une structure d'objet simple serait complétée par le champ requis :
typedef struct {
PyObject_HEAD
PyObject *weakreflist; /* List of weak references */
} TrivialObject;
Et le membre correspondant dans l'objet de type déclaré statiquement :
static PyTypeObject TrivialType = {
PyVarObject_HEAD_INIT(NULL, 0)
/* ... other members omitted for brevity ... */
.tp_weaklistoffset = offsetof(TrivialObject, weakreflist),
};
The only further addition is that tp_dealloc
needs to clear any weak
references (by calling PyObject_ClearWeakRefs()
) if the field is
non-NULL
:
static void
Trivial_dealloc(TrivialObject *self)
{
/* Clear weakrefs first before calling any destructors */
if (self->weakreflist != NULL)
PyObject_ClearWeakRefs((PyObject *) self);
/* ... remainder of destruction code omitted for brevity ... */
Py_TYPE(self)->tp_free((PyObject *) self);
}
3.7. Plus de suggestions¶
Pour savoir comment mettre en œuvre une méthode spécifique pour votre nouveau type de données, téléchargez le code source CPython. Allez dans le répertoire Objects
, puis cherchez dans les fichiers sources C la fonction tp_
plus la fonction que vous voulez (par exemple, tp_richcompare
). Vous trouverez des exemples de la fonction que vous voulez implémenter.
Lorsque vous avez besoin de vérifier qu'un objet est une instance concrète du type que vous implémentez, utilisez la fonction PyObject_TypeCheck()
. Voici un exemple de son utilisation :
if (!PyObject_TypeCheck(some_object, &MyType)) {
PyErr_SetString(PyExc_TypeError, "arg #1 not a mything");
return NULL;
}
Voir aussi
- Télécharger les versions sources de CPython.
- Le projet CPython sur GitHub, où se trouve le code source CPython.