Protocole tampon¶

Certains objets Python enveloppent l’accès à un tableau de mémoire sous-jacente (nommée zone tampon ou simplement tampon, buffer en anglais). Les objets natifs bytes et bytearray en sont des exemples, ainsi que quelques types d’extension comme array.array. Les bibliothèques tierces peuvent définir leurs propres types à des fins spéciales, telles que le traitement d’image ou l’analyse numérique.

Alors que chacun de ces types a sa propre sémantique, ils partagent la caractéristique commune d’être soutenus par un tampon de mémoire important. Il est donc souhaitable, dans certains cas, d’accéder à cette mémoire directement sans l’étape intermédiaire de copie.

Python fournit une telle facilité au niveau du C sous la forme de protocole tampon. Ce protocole comporte deux aspects :

du côté producteur, un type peut exporter une « interface tampon » qui permet aux objets de ce type d’exposer des informations concernant leur tampon sous-jacent. Cette interface est décrite dans la section Buffer Object Structures ;
du côté consommateur, plusieurs moyens sont disponibles pour obtenir un pointeur vers les données sous-jacentes brutes d’un objet (par exemple un paramètre de méthode).

Des objets simples tels que bytes et bytearray exposent leur tampon sous-jacent dans un format orienté octet. D’autres formes sont possibles ; par exemple, les éléments exposés par un array.array peuvent être des valeurs multi-octets.

Un exemple de consommateur de l’interface tampon est la méthode write() des objets fichiers : tout objet qui peut exporter une série d’octets à travers l’interface tampon peut être écrit dans un fichier. Alors que write() n’a besoin que d’un accès lecture au contenu interne de l’objet qui lui est passé, d’autres méthodes telles que readinto() nécessitent un accès écriture au contenu de leur argument. L’interface buffer permet aux objets d’autoriser ou de rejeter sélectivement l’exportation de tampons en mode lecture-écriture et en mode lecture seule.

Un consommateur de l’interface tampon peut acquérir un tampon sur un objet cible de deux manières :

appelez PyObject_GetBuffer() avec les paramètres appropriés ;
appelez PyArg_ParseTuple() (ou l’un de ses fonctions sœurs) avec l’un des y*, w* ou s* format codes.

Dans les deux cas, PyBuffer_Release() doit être appelée quand le tampon n’est plus nécessaire. Ne pas le faire peut conduire à divers problèmes tels que des fuites de ressources.

La structure buffer¶

Les structures tampons (ou simplement les « tampons », buffers en anglais) sont utiles pour exposer les données binaires d’un autre objet au programmeur Python. Elles peuvent également être utilisées comme un mécanisme de découpage sans copie. En utilisant leur capacité à référencer un bloc de mémoire, il est possible d’exposer toutes les données au programmeur Python assez facilement. La mémoire peut être un grand tableau constant dans une extension C, il peut s’agir d’un bloc brut de mémoire à manipuler avant de passer à une bibliothèque de système d’exploitation ou être utilisé pour transmettre des données structurées dans son format natif en mémoire.

Contrairement à la plupart des types de données exposés par l’interpréteur Python, les tampons ne sont pas de simples pointeurs vers PyObject mais plutôt des structures C simples. Cela leur permet d’être créés et copiés très simplement. lorsque vous avez besoin d’une enveloppe générique (wrapper en anglais) pour un tampon, un objet memoryview peut être créé.

For short instructions how to write an exporting object, see Buffer Object Structures. For obtaining a buffer, see PyObject_GetBuffer().

Py_buffer¶

void *buf¶

A pointer to the start of the logical structure described by the buffer fields. This can be any location within the underlying physical memory block of the exporter. For example, with negative strides the value may point to the end of the memory block.

For contiguous arrays, the value points to the beginning of the memory block.

void *obj¶

A new reference to the exporting object. The reference is owned by the consumer and automatically decremented and set to NULL by PyBuffer_Release(). The field is the equivalent of the return value of any standard C-API function.

As a special case, for temporary buffers that are wrapped by PyMemoryView_FromBuffer() or PyBuffer_FillInfo() this field is NULL. In general, exporting objects MUST NOT use this scheme.

Py_ssize_t len¶

product(shape) * itemsize. For contiguous arrays, this is the length of the underlying memory block. For non-contiguous arrays, it is the length that the logical structure would have if it were copied to a contiguous representation.

Accessing ((char *)buf)[0] up to ((char *)buf)[len-1] is only valid if the buffer has been obtained by a request that guarantees contiguity. In most cases such a request will be PyBUF_SIMPLE or PyBUF_WRITABLE.

int readonly¶: An indicator of whether the buffer is read-only. This field is controlled by the PyBUF_WRITABLE flag.

Py_ssize_t itemsize¶

Item size in bytes of a single element. Same as the value of struct.calcsize() called on non-NULL format values.

Important exception: If a consumer requests a buffer without the PyBUF_FORMAT flag, format will be set to NULL, but itemsize still has the value for the original format.

If shape is present, the equality product(shape) * itemsize == len still holds and the consumer can use itemsize to navigate the buffer.

If shape is NULL as a result of a PyBUF_SIMPLE or a PyBUF_WRITABLE request, the consumer must disregard itemsize and assume itemsize == 1.

const char *format¶

A NUL terminated string in struct module style syntax describing the contents of a single item. If this is NULL, "B" (unsigned bytes) is assumed.

This field is controlled by the PyBUF_FORMAT flag.

int ndim¶

The number of dimensions the memory represents as an n-dimensional array. If it is 0, buf points to a single item representing a scalar. In this case, shape, strides and suboffsets MUST be NULL.

The macro PyBUF_MAX_NDIM limits the maximum number of dimensions to 64. Exporters MUST respect this limit, consumers of multi-dimensional buffers SHOULD be able to handle up to PyBUF_MAX_NDIM dimensions.

Py_ssize_t *shape¶

An array of Py_ssize_t of length ndim indicating the shape of the memory as an n-dimensional array. Note that shape[0] * ... * shape[ndim-1] * itemsize MUST be equal to len.

Shape values are restricted to shape[n] >= 0. The case shape[n] == 0 requires special attention. See complex arrays for further information.

The shape array is read-only for the consumer.

Py_ssize_t *strides¶

An array of Py_ssize_t of length ndim giving the number of bytes to skip to get to a new element in each dimension.

Stride values can be any integer. For regular arrays, strides are usually positive, but a consumer MUST be able to handle the case strides[n] <= 0. See complex arrays for further information.

The strides array is read-only for the consumer.

Py_ssize_t *suboffsets¶

An array of Py_ssize_t of length ndim. If suboffsets[n] >= 0, the values stored along the nth dimension are pointers and the suboffset value dictates how many bytes to add to each pointer after de-referencing. A suboffset value that is negative indicates that no de-referencing should occur (striding in a contiguous memory block).

If all suboffsets are negative (i.e. no de-referencing is needed, then this field must be NULL (the default value).

This type of array representation is used by the Python Imaging Library (PIL). See complex arrays for further information how to access elements of such an array.

The suboffsets array is read-only for the consumer.

void *internal¶: This is for use internally by the exporting object. For example, this might be re-cast as an integer by the exporter and used to store flags about whether or not the shape, strides, and suboffsets arrays must be freed when the buffer is released. The consumer MUST NOT alter this value.

Buffer request types¶

Buffers are usually obtained by sending a buffer request to an exporting object via PyObject_GetBuffer(). Since the complexity of the logical structure of the memory can vary drastically, the consumer uses the flags argument to specify the exact buffer type it can handle.

All Py_buffer fields are unambiguously defined by the request type.

request-independent fields¶

The following fields are not influenced by flags and must always be filled in with the correct values: obj, buf, len, itemsize, ndim.

readonly, format¶

PyBUF_WRITABLE¶

Controls the readonly field. If set, the exporter MUST provide a writable buffer or else report failure. Otherwise, the exporter MAY provide either a read-only or writable buffer, but the choice MUST be consistent for all consumers.

PyBUF_FORMAT¶

Controls the format field. If set, this field MUST be filled in correctly. Otherwise, this field MUST be NULL.

PyBUF_WRITABLE can be |”d to any of the flags in the next section. Since PyBUF_SIMPLE is defined as 0, PyBUF_WRITABLE can be used as a stand-alone flag to request a simple writable buffer.

PyBUF_FORMAT can be |”d to any of the flags except PyBUF_SIMPLE. The latter already implies format B (unsigned bytes).

shape, strides, suboffsets¶

The flags that control the logical structure of the memory are listed in decreasing order of complexity. Note that each flag contains all bits of the flags below it.

Request	shape	strides	suboffsets
`PyBUF_INDIRECT`¶	oui	oui	if needed
`PyBUF_STRIDES`¶	oui	oui	NULL
`PyBUF_ND`¶	oui	NULL	NULL
`PyBUF_SIMPLE`¶	NULL	NULL	NULL

contiguity requests¶

C or Fortran contiguity can be explicitly requested, with and without stride information. Without stride information, the buffer must be C-contiguous.

Request	shape	strides	suboffsets	contig
`PyBUF_C_CONTIGUOUS`¶	oui	oui	NULL	C
`PyBUF_F_CONTIGUOUS`¶	oui	oui	NULL	F
`PyBUF_ANY_CONTIGUOUS`¶	oui	oui	NULL	C or F
`PyBUF_ND`	oui	NULL	NULL	C

compound requests¶

All possible requests are fully defined by some combination of the flags in the previous section. For convenience, the buffer protocol provides frequently used combinations as single flags.

In the following table U stands for undefined contiguity. The consumer would have to call PyBuffer_IsContiguous() to determine contiguity.

Request	shape	strides	suboffsets	contig	readonly	format
`PyBUF_FULL`¶	oui	oui	if needed	U	0	oui
`PyBUF_FULL_RO`¶	oui	oui	if needed	U	1 or 0	oui
`PyBUF_RECORDS`¶	oui	oui	NULL	U	0	oui
`PyBUF_RECORDS_RO`¶	oui	oui	NULL	U	1 or 0	oui
`PyBUF_STRIDED`¶	oui	oui	NULL	U	0	NULL
`PyBUF_STRIDED_RO`¶	oui	oui	NULL	U	1 or 0	NULL
`PyBUF_CONTIG`¶	oui	NULL	NULL	C	0	NULL
`PyBUF_CONTIG_RO`¶	oui	NULL	NULL	C	1 or 0	NULL

Complex arrays¶

NumPy-style: shape and strides¶

The logical structure of NumPy-style arrays is defined by itemsize, ndim, shape and strides.

If ndim == 0, the memory location pointed to by buf is interpreted as a scalar of size itemsize. In that case, both shape and strides are NULL.

If strides is NULL, the array is interpreted as a standard n-dimensional C-array. Otherwise, the consumer must access an n-dimensional array as follows:

ptr = (char *)buf + indices[0] * strides[0] + ... + indices[n-1] * strides[n-1] item = *((typeof(item) *)ptr);

As noted above, buf can point to any location within the actual memory block. An exporter can check the validity of a buffer with this function:

def verify_structure(memlen, itemsize, ndim, shape, strides, offset):
    """Verify that the parameters represent a valid array within
       the bounds of the allocated memory:
           char *mem: start of the physical memory block
           memlen: length of the physical memory block
           offset: (char *)buf - mem
    """
    if offset % itemsize:
        return False
    if offset < 0 or offset+itemsize > memlen:
        return False
    if any(v % itemsize for v in strides):
        return False

    if ndim <= 0:
        return ndim == 0 and not shape and not strides
    if 0 in shape:
        return True

    imin = sum(strides[j]*(shape[j]-1) for j in range(ndim)
               if strides[j] <= 0)
    imax = sum(strides[j]*(shape[j]-1) for j in range(ndim)
               if strides[j] > 0)

    return 0 <= offset+imin and offset+imax+itemsize <= memlen

PIL-style: shape, strides and suboffsets¶

In addition to the regular items, PIL-style arrays can contain pointers that must be followed in order to get to the next element in a dimension. For example, the regular three-dimensional C-array char v[2][2][3] can also be viewed as an array of 2 pointers to 2 two-dimensional arrays: char (*v[2])[2][3]. In suboffsets representation, those two pointers can be embedded at the start of buf, pointing to two char x[2][3] arrays that can be located anywhere in memory.

Here is a function that returns a pointer to the element in an N-D array pointed to by an N-dimensional index when there are both non-NULL strides and suboffsets:

void *get_item_pointer(int ndim, void *buf, Py_ssize_t *strides,
                       Py_ssize_t *suboffsets, Py_ssize_t *indices) {
    char *pointer = (char*)buf;
    int i;
    for (i = 0; i < ndim; i++) {
        pointer += strides[i] * indices[i];
        if (suboffsets[i] >=0 ) {
            pointer = *((char**)pointer) + suboffsets[i];
        }
    }
    return (void*)pointer;
}

Fonctions relatives aux tampons¶

int PyObject_CheckBuffer(PyObject *obj)¶: Return 1 if obj supports the buffer interface otherwise 0. When 1 is returned, it doesn’t guarantee that PyObject_GetBuffer() will succeed.

int PyObject_GetBuffer(PyObject *exporter, Py_buffer *view, int flags)¶

Send a request to exporter to fill in view as specified by flags. If the exporter cannot provide a buffer of the exact type, it MUST raise PyExc_BufferError, set view->obj to NULL and return -1.

On success, fill in view, set view->obj to a new reference to exporter and return 0. In the case of chained buffer providers that redirect requests to a single object, view->obj MAY refer to this object instead of exporter (See Buffer Object Structures).

Successful calls to PyObject_GetBuffer() must be paired with calls to PyBuffer_Release(), similar to malloc() and free(). Thus, after the consumer is done with the buffer, PyBuffer_Release() must be called exactly once.

void PyBuffer_Release(Py_buffer *view)¶

Release the buffer view and decrement the reference count for view->obj. This function MUST be called when the buffer is no longer being used, otherwise reference leaks may occur.

It is an error to call this function on a buffer that was not obtained via PyObject_GetBuffer().

Py_ssize_t PyBuffer_SizeFromFormat(const char *)¶: Return the implied itemsize from format. This function is not yet implemented.

int PyBuffer_IsContiguous(Py_buffer *view, char order)¶: Return 1 if the memory defined by the view is C-style (order is 'C') or Fortran-style (order is 'F') contiguous or either one (order is 'A'). Return 0 otherwise.

void PyBuffer_FillContiguousStrides(int ndim, Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t itemsize, char order)¶: Fill the strides array with byte-strides of a contiguous (C-style if order is 'C' or Fortran-style if order is 'F') array of the given shape with the given number of bytes per element.

int PyBuffer_FillInfo(Py_buffer *view, PyObject *exporter, void *buf, Py_ssize_t len, int readonly, int flags)¶

Handle buffer requests for an exporter that wants to expose buf of size len with writability set according to readonly. buf is interpreted as a sequence of unsigned bytes.

The flags argument indicates the request type. This function always fills in view as specified by flags, unless buf has been designated as read-only and PyBUF_WRITABLE is set in flags.

On success, set view->obj to a new reference to exporter and return 0. Otherwise, raise PyExc_BufferError, set view->obj to NULL and return -1;

If this function is used as part of a getbufferproc, exporter MUST be set to the exporting object and flags must be passed unmodified. Otherwise, exporter MUST be NULL.