Contents

• Abstract
• Rationale
• Proposal Overview
• Specification
  • Access flags
  • The Py_buffer struct
  • Releasing the buffer
• New C-API calls are proposed
• Additions to the struct string-syntax
• Examples of Data-Format Descriptions
• Code to be affected
• Issues and Details
• Code
• Examples
  • Ex. 1
  • Ex. 2
  • Ex. 3
  • Ex. 4
• Copyright

Abstract

This PEP proposes re-designing the buffer interface (PyBufferProcs function pointers) to improve the way Python allows memory sharing in Python 3.0

In particular, it is proposed that the character buffer portion of the API be eliminated and the multiple-segment portion be re-designed in conjunction with allowing for strided memory to be shared. In addition, the new buffer interface will allow the sharing of any multi-dimensional nature of the memory and what data-format the memory contains.

This interface will allow any extension module to either create objects that share memory or create algorithms that use and manipulate raw memory from arbitrary objects that export the interface.

Rationale

The Python 2.X buffer protocol allows different Python types to exchange a pointer to a sequence of internal buffers. This functionality is extremely useful for sharing large segments of memory between different high-level objects, but it is too limited and has issues:

1. There is the little used “sequence-of-segments” option (bf_getsegcount) that is not well motivated.
2. There is the apparently redundant character-buffer option (bf_getcharbuffer)
3. There is no way for a consumer to tell the buffer-API-exporting object it is “finished” with its view of the memory and therefore no way for the exporting object to be sure that it is safe to reallocate the pointer to the memory that it owns (for example, the array object reallocating its memory after sharing it with the buffer object which held the original pointer led to the infamous buffer-object problem).
4. Memory is just a pointer with a length. There is no way to describe what is “in” the memory (float, int, C-structure, etc.)
5. There is no shape information provided for the memory. But, several array-like Python types could make use of a standard way to describe the shape-interpretation of the memory (wxPython, GTK, pyQT, CVXOPT, PyVox, Audio and Video Libraries, ctypes, NumPy, data-base interfaces, etc.)
6. There is no way to share discontiguous memory (except through the sequence of segments notion).

   There are two widely used libraries that use the concept of discontiguous memory: PIL and NumPy. Their view of discontiguous arrays is different, though. The proposed buffer interface allows sharing of either memory model. Exporters will typically use only one approach and consumers may choose to support discontiguous arrays of each type however they choose.

   NumPy uses the notion of constant striding in each dimension as its basic concept of an array. With this concept, a simple sub-region of a larger array can be described without copying the data. Thus, stride information is the additional information that must be shared.

   The PIL uses a more opaque memory representation. Sometimes an image is contained in a contiguous segment of memory, but sometimes it is contained in an array of pointers to the contiguous segments (usually lines) of the image. The PIL is where the idea of multiple buffer segments in the original buffer interface came from.

   NumPy’s strided memory model is used more often in computational libraries and because it is so simple it makes sense to support memory sharing using this model. The PIL memory model is sometimes used in C-code where a 2-d array can then be accessed using double pointer indirection: e.g. image[i][j].

   The buffer interface should allow the object to export either of these memory models. Consumers are free to either require contiguous memory or write code to handle one or both of these memory models.

Proposal Overview

• Eliminate the char-buffer and multiple-segment sections of the buffer-protocol.
• Unify the read/write versions of getting the buffer.
• Add a new function to the interface that should be called when the consumer object is “done” with the memory area.
• Add a new variable to allow the interface to describe what is in memory (unifying what is currently done now in struct and array)
• Add a new variable to allow the protocol to share shape information
• Add a new variable for sharing stride information
• Add a new mechanism for sharing arrays that must be accessed using pointer indirection.
• Fix all objects in the core and the standard library to conform to the new interface
• Extend the struct module to handle more format specifiers
• Extend the buffer object into a new memory object which places a Python veneer around the buffer interface.
• Add a few functions to make it easy to copy contiguous data in and out of object supporting the buffer interface.

Specification

While the new specification allows for complicated memory sharing, simple contiguous buffers of bytes can still be obtained from an object. In fact, the new protocol allows a standard mechanism for doing this even if the original object is not represented as a contiguous chunk of memory.

The easiest way to obtain a simple contiguous chunk of memory is to use the provided C-API to obtain a chunk of memory.

Change the PyBufferProcs structure to

typedef struct {
     getbufferproc bf_getbuffer;
     releasebufferproc bf_releasebuffer;
} PyBufferProcs;

Both of these routines are optional for a type object

typedef int (*getbufferproc)(PyObject *obj, PyBuffer *view, int flags)

This function returns 0 on success and -1 on failure (and raises an error). The first variable is the “exporting” object. The second argument is the address to a bufferinfo structure. Both arguments must never be NULL.

The third argument indicates what kind of buffer the consumer is prepared to deal with and therefore what kind of buffer the exporter is allowed to return. The new buffer interface allows for much more complicated memory sharing possibilities. Some consumers may not be able to handle all the complexity but may want to see if the exporter will let them take a simpler view to its memory.

In addition, some exporters may not be able to share memory in every possible way and may need to raise errors to signal to some consumers that something is just not possible. These errors should be PyErr_BufferError unless there is another error that is actually causing the problem. The exporter can use flags information to simplify how much of the PyBuffer structure is filled in with non-default values and/or raise an error if the object can’t support a simpler view of its memory.

The exporter should always fill in all elements of the buffer structure (with defaults or NULLs if nothing else is requested). The PyBuffer_FillInfo function can be used for simple cases.

struct bufferinfo {
     void *buf;
     Py_ssize_t len;
     int readonly;
     const char *format;
     int ndim;
     Py_ssize_t *shape;
     Py_ssize_t *strides;
     Py_ssize_t *suboffsets;
     Py_ssize_t itemsize;
     void *internal;
} Py_buffer;

typedef void (*releasebufferproc)(PyObject *obj, Py_buffer *view)

New C-API calls are proposed

int PyObject_CheckBuffer(PyObject *obj)

Return 1 if the getbuffer function is available otherwise 0.

int PyObject_GetBuffer(PyObject *obj, Py_buffer *view,
                       int flags)

This is a C-API version of the getbuffer function call. It checks to make sure object has the required function pointer and issues the call. Returns -1 and raises an error on failure and returns 0 on success.

void PyBuffer_Release(PyObject *obj, Py_buffer *view)

This is a C-API version of the releasebuffer function call. It checks to make sure the object has the required function pointer and issues the call. This function always succeeds even if there is no releasebuffer function for the object.

PyObject *PyObject_GetMemoryView(PyObject *obj)

Return a memory-view object from an object that defines the buffer interface.

A memory-view object is an extended buffer object that could replace the buffer object (but doesn’t have to as that could be kept as a simple 1-d memory-view object). Its C-structure is

typedef struct {
    PyObject_HEAD
    PyObject *base;
    Py_buffer view;
} PyMemoryViewObject;

This is functionally similar to the current buffer object except a reference to base is kept and the memory view is not re-grabbed. Thus, this memory view object holds on to the memory of base until it is deleted.

This memory-view object will support multi-dimensional slicing and be the first object provided with Python to do so. Slices of the memory-view object are other memory-view objects with the same base but with a different view of the base object.

When an “element” from the memory-view is returned it is always a bytes object whose format should be interpreted by the format attribute of the memoryview object. The struct module can be used to “decode” the bytes in Python if desired. Or the contents can be passed to a NumPy array or other object consuming the buffer protocol.

The Python name will be

__builtin__.memoryview

Methods:

Attributes (taken from the memory of the base object):

• format
• itemsize
• shape
• strides
• suboffsets
• readonly
• ndim

Py_ssize_t PyBuffer_SizeFromFormat(const char *)

Return the implied itemsize of the data-format area from a struct-style description.

PyObject * PyMemoryView_GetContiguous(PyObject *obj,  int buffertype,
                                      char fortran)

Return a memoryview object to a contiguous chunk of memory represented by obj. If a copy must be made (because the memory pointed to by obj is not contiguous), then a new bytes object will be created and become the base object for the returned memory view object.

The buffertype argument can be PyBUF_READ, PyBUF_WRITE, PyBUF_UPDATEIFCOPY to determine whether the returned buffer should be readable, writable, or set to update the original buffer if a copy must be made. If buffertype is PyBUF_WRITE and the buffer is not contiguous an error will be raised. In this circumstance, the user can use PyBUF_UPDATEIFCOPY to ensure that a writable temporary contiguous buffer is returned. The contents of this contiguous buffer will be copied back into the original object after the memoryview object is deleted as long as the original object is writable. If this is not allowed by the original object, then a BufferError is raised.

If the object is multi-dimensional, then if fortran is ‘F’, the first dimension of the underlying array will vary the fastest in the buffer. If fortran is ‘C’, then the last dimension will vary the fastest (C-style contiguous). If fortran is ‘A’, then it does not matter and you will get whatever the object decides is more efficient. If a copy is made, then the memory must be freed by calling PyMem_Free.

You receive a new reference to the memoryview object.

int PyObject_CopyToObject(PyObject *obj, void *buf, Py_ssize_t len,
                          char fortran)

Copy len bytes of data pointed to by the contiguous chunk of memory pointed to by buf into the buffer exported by obj. Return 0 on success and return -1 and raise an error on failure. If the object does not have a writable buffer, then an error is raised. If fortran is ‘F’, then if the object is multi-dimensional, then the data will be copied into the array in Fortran-style (first dimension varies the fastest). If fortran is ‘C’, then the data will be copied into the array in C-style (last dimension varies the fastest). If fortran is ‘A’, then it does not matter and the copy will be made in whatever way is more efficient.

int PyObject_CopyData(PyObject *dest, PyObject *src)

These last three C-API calls allow a standard way of getting data in and out of Python objects into contiguous memory areas no matter how it is actually stored. These calls use the extended buffer interface to perform their work.

int PyBuffer_IsContiguous(Py_buffer *view, char fortran)

Return 1 if the memory defined by the view object is C-style (fortran = ‘C’) or Fortran-style (fortran = ‘F’) contiguous or either one (fortran = ‘A’). Return 0 otherwise.

void PyBuffer_FillContiguousStrides(int ndim, Py_ssize_t *shape,
                                    Py_ssize_t *strides, Py_ssize_t itemsize,
                                    char fortran)

Fill the strides array with byte-strides of a contiguous (C-style if fortran is ‘C’ or Fortran-style if fortran is ‘F’ array of the given shape with the given number of bytes per element.

int PyBuffer_FillInfo(Py_buffer *view, void *buf,
                      Py_ssize_t len, int readonly, int infoflags)

Fills in a buffer-info structure correctly for an exporter that can only share a contiguous chunk of memory of “unsigned bytes” of the given length. Returns 0 on success and -1 (with raising an error) on error.

PyExc_BufferError

A new error object for returning buffer errors which arise because an exporter cannot provide the kind of buffer that a consumer expects. This will also be raised when a consumer requests a buffer from an object that does not provide the protocol.

Additions to the struct string-syntax

The struct string-syntax is missing some characters to fully implement data-format descriptions already available elsewhere (in ctypes and NumPy for example). The Python 2.5 specification is at http://docs.python.org/library/struct.html.

Here are the proposed additions:

The struct module will be changed to understand these as well and return appropriate Python objects on unpacking. Unpacking a long-double will return a decimal object or a ctypes long-double. Unpacking ‘u’ or ‘w’ will return Python unicode. Unpacking a multi-dimensional array will return a list (of lists if >1d). Unpacking a pointer will return a ctypes pointer object. Unpacking a function pointer will return a ctypes call-object (perhaps). Unpacking a bit will return a Python Bool. White-space in the struct-string syntax will be ignored if it isn’t already. Unpacking a named-object will return some kind of named-tuple-like object that acts like a tuple but whose entries can also be accessed by name. Unpacking a nested structure will return a nested tuple.

Endian-specification (‘!’, ‘@’,’=’,’>’,’<’, ‘^’) is also allowed inside the string so that it can change if needed. The previously-specified endian string is in force until changed. The default endian is ‘@’ which means native data-types and alignment. If un-aligned, native data-types are requested, then the endian specification is ‘^’.

According to the struct-module, a number can precede a character code to specify how many of that type there are. The (k1,k2,...,kn) extension also allows specifying if the data is supposed to be viewed as a (C-style contiguous, last-dimension varies the fastest) multi-dimensional array of a particular format.

Functions should be added to ctypes to create a ctypes object from a struct description, and add long-double, and ucs-2 to ctypes.

Examples of Data-Format Descriptions

Here are some examples of C-structures and how they would be represented using the struct-style syntax.

<named> is the constructor for a named-tuple (not-specified yet).

float

'd' <–> Python float

complex double

'Zd' <–> Python complex

RGB Pixel data

'BBB' <–> (int, int, int) 'B:r: B:g: B:b:' <–> <named>((int, int, int), (‘r’,’g’,’b’))

Mixed endian (weird but possible)

'>i:big: <i:little:' <–> <named>((int, int), (‘big’, ‘little’))

Nested structure

struct {
     int ival;
     struct {
         unsigned short sval;
         unsigned char bval;
         unsigned char cval;
     } sub;
}
"""i:ival:
   T{
      H:sval:
      B:bval:
      B:cval:
    }:sub:
"""

Nested array

struct {
     int ival;
     double data[16*4];
}
"""i:ival:
   (16,4)d:data:
"""

Note, that in the last example, the C-structure compared against is intentionally a 1-d array and not a 2-d array data[16][4]. The reason for this is to avoid the confusions between static multi-dimensional arrays in C (which are laid out contiguously) and dynamic multi-dimensional arrays which use the same syntax to access elements, data[0][1], but whose memory is not necessarily contiguous. The struct-syntax always uses contiguous memory and the multi-dimensional character is information about the memory to be communicated by the exporter.

In other words, the struct-syntax description does not have to match the C-syntax exactly as long as it describes the same memory layout. The fact that a C-compiler would think of the memory as a 1-d array of doubles is irrelevant to the fact that the exporter wanted to communicate to the consumer that this field of the memory should be thought of as a 2-d array where a new dimension is considered after every 4 elements.

Code to be affected

All objects and modules in Python that export or consume the old buffer interface will be modified. Here is a partial list.

• buffer object
• bytes object
• string object
• unicode object
• array module
• struct module
• mmap module
• ctypes module

Anything else using the buffer API.

Issues and Details

It is intended that this PEP will be back-ported to Python 2.6 by adding the C-API and the two functions to the existing buffer protocol.

Previous versions of this PEP proposed a read/write locking scheme, but it was later perceived as a) too complicated for common simple use cases that do not require any locking and b) too simple for use cases that required concurrent read/write access to a buffer with changing, short-living locks. It is therefore left to users to implement their own specific locking scheme around buffer objects if they require consistent views across concurrent read/write access. A future PEP may be proposed which includes a separate locking API after some experience with these user-schemes is obtained

The sharing of strided memory and suboffsets is new and can be seen as a modification of the multiple-segment interface. It is motivated by NumPy and the PIL. NumPy objects should be able to share their strided memory with code that understands how to manage strided memory because strided memory is very common when interfacing with compute libraries.

Also, with this approach it should be possible to write generic code that works with both kinds of memory without copying.

Memory management of the format string, the shape array, the strides array, and the suboffsets array in the bufferinfo structure is always the responsibility of the exporting object. The consumer should not set these pointers to any other memory or try to free them.

Several ideas were discussed and rejected:

Having a “releaser” object whose release-buffer was called. This was deemed unacceptable because it caused the protocol to be asymmetric (you called release on something different than you “got” the buffer from). It also complicated the protocol without providing a real benefit.

Passing all the struct variables separately into the function. This had the advantage that it allowed one to set NULL to variables that were not of interest, but it also made the function call more difficult. The flags variable allows the same ability of consumers to be “simple” in how they call the protocol.

Code

The authors of the PEP promise to contribute and maintain the code for this proposal but will welcome any help.

Examples

struct rgba {
    unsigned char r, g, b, a;
};

struct ImageObject {
    PyObject_HEAD;
    ...
    struct rgba** lines;
    Py_ssize_t height;
    Py_ssize_t width;
    Py_ssize_t shape_array[2];
    Py_ssize_t stride_array[2];
    Py_ssize_t view_count;
};

int Image_getbuffer(PyObject *self, Py_buffer *view, int flags) {

    static Py_ssize_t suboffsets[2] = { 0, -1};

    view->buf = self->lines;
    view->len = self->height*self->width;
    view->readonly = 0;
    view->ndims = 2;
    self->shape_array[0] = height;
    self->shape_array[1] = width;
    view->shape = &self->shape_array;
    self->stride_array[0] = sizeof(struct rgba*);
    self->stride_array[1] = sizeof(struct rgba);
    view->strides = &self->stride_array;
    view->suboffsets = suboffsets;

    self->view_count ++;

    return 0;
}


int Image_releasebuffer(PyObject *self, Py_buffer *view) {
    self->view_count--;
    return 0;
}

int myobject_getbuffer(PyObject *self, Py_buffer *view, int flags) {

    void *buf;
    Py_ssize_t len;
    int readonly=0;

    buf = /* Point to buffer */
    len = /* Set to size of buffer */
    readonly = /* Set to 1 if readonly */

    return PyObject_FillBufferInfo(view, buf, len, readonly, flags);
}

/* No releasebuffer is necessary because the memory will never
   be re-allocated
*/

Py_buffer view;
int ret;

if (PyObject_GetBuffer(obj, &view, Py_BUF_SIMPLE) < 0) {
     /* error return */
}

/* Now, view.buf is the pointer to memory
        view.len is the length
        view.readonly is whether or not the memory is read-only.
 */


/* After using the information and you don't need it anymore */

if (PyBuffer_Release(obj, &view) < 0) {
        /* error return */
}

void *buf;
Py_ssize_t len;
char *format;
int copy;

copy = PyObject_GetContiguous(obj, &buf, &len, &format, 0, 'A');
if (copy < 0) {
   /* error return */
}

/* process memory pointed to by buffer if format is correct */

/* Optional:

   if, after processing, we want to copy data from buffer back
   into the object

   we could do
   */

if (PyObject_CopyToObject(obj, buf, len, 'A') < 0) {
       /*        error return */
}

/* Make sure that if a copy was made, the memory is freed */
if (copy == 1) PyMem_Free(buf);

Copyright

This PEP is placed in the public domain.