Implementing the buffer protocol¶

Note

This page uses two different syntax variants:

  • Cython specific cdef syntax, which was designed to make type declarations concise and easily readable from a C/C++ perspective.

  • Pure Python syntax which allows static Cython type declarations in pure Python code, following PEP-484 type hints and PEP 526 variable annotations.

    To make use of C data types in Python syntax, you need to import the special cython module in the Python module that you want to compile, e.g.

    import cython

    If you use the pure Python syntax we strongly recommend you use a recent Cython 3 release, since significant improvements have been made here compared to the 0.29.x releases.

Cython objects can expose memory buffers to Python code by implementing the “buffer protocol”. This chapter shows how to implement the protocol and make use of the memory managed by an extension type from NumPy.

A matrix class¶

The following Cython/C++ code implements a matrix of floats, where the number of columns is fixed at construction time but rows can be added dynamically.

# distutils: language = c++

from cython.cimports.libcpp.vector import vector

@cython.cclass
class Matrix:
    ncols: cython.uint
    v: vector[cython.float]

    def __cinit__(self, ncols: cython.uint):
        self.ncols = ncols

    def add_row(self):
        """Adds a row, initially zero-filled."""
        self.v.resize(self.v.size() + self.ncols)

There are no methods to do anything productive with the matrices’ contents. We could implement custom __getitem__, __setitem__, etc. for this, but instead we’ll use the buffer protocol to expose the matrix’s data to Python so we can use NumPy to do useful work.

Implementing the buffer protocol requires adding two methods, __getbuffer__ and __releasebuffer__, which Cython handles specially.

# distutils: language = c++
from cython.cimports.cpython import Py_buffer
from cython.cimports.libcpp.vector import vector

@cython.cclass
class Matrix:
    ncols: cython.Py_ssize_t
    shape: cython.Py_ssize_t[2]
    strides: cython.Py_ssize_t[2]
    v: vector[cython.float]

    def __cinit__(self, ncols: cython.Py_ssize_t):
        self.ncols = ncols

    def add_row(self):
        """Adds a row, initially zero-filled."""
        self.v.resize(self.v.size() + self.ncols)

    def __getbuffer__(self, buffer: cython.pointer(Py_buffer), flags: cython.int):
        itemsize: cython.Py_ssize_t = cython.sizeof(self.v[0])

        self.shape[0] = self.v.size() // self.ncols
        self.shape[1] = self.ncols

        # Stride 1 is the distance, in bytes, between two items in a row;
        # this is the distance between two adjacent items in the vector.
        # Stride 0 is the distance between the first elements of adjacent rows.
        self.strides[1] = cython.cast(cython.Py_ssize_t, (
             cython.cast(cython.p_char, cython.address(self.v[1]))
           - cython.cast(cython.p_char, cython.address(self.v[0]))
           )
       )
        self.strides[0] = self.ncols * self.strides[1]

        buffer.buf = cython.cast(cython.p_char, cython.address(self.v[0]))
        buffer.format = 'f'                     # float
        buffer.internal = cython.NULL           # see References
        buffer.itemsize = itemsize
        buffer.len = self.v.size() * itemsize   # product(shape) * itemsize
        buffer.ndim = 2
        buffer.obj = self
        buffer.readonly = 0
        buffer.shape = self.shape
        buffer.strides = self.strides
        buffer.suboffsets = cython.NULL         # for pointer arrays only

    def __releasebuffer__(self, buffer: cython.pointer(Py_buffer)):
        pass

The method Matrix.__getbuffer__ fills a descriptor structure, called a Py_buffer, that is defined by the Python C-API. It contains a pointer to the actual buffer in memory, as well as metadata about the shape of the array and the strides (step sizes to get from one element or row to the next). Its shape and strides members are pointers that must point to arrays of type and size Py_ssize_t[ndim]. These arrays have to stay alive as long as any buffer views the data, so we store them on the Matrix object as members.

The code is not yet complete, but we can already compile it and test the basic functionality.

>>> from matrix import Matrix
>>> import numpy as np
>>> m = Matrix(10)
>>> np.asarray(m)
array([], shape=(0, 10), dtype=float32)
>>> m.add_row()
>>> a = np.asarray(m)
>>> a[:] = 1
>>> m.add_row()
>>> a = np.asarray(m)
>>> a
array([[ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]], dtype=float32)

Now we can view the Matrix as a NumPy ndarray, and modify its contents using standard NumPy operations.

Memory safety and reference counting¶

The Matrix class as implemented so far is unsafe. The add_row operation can move the underlying buffer, which invalidates any NumPy (or other) view on the data. If you try to access values after an add_row call, you’ll get outdated values or a segfault.

This is where __releasebuffer__ comes in. We can add a reference count to each matrix, and lock it for mutation whenever a view exists.

# distutils: language = c++

from cython.cimports.cpython import Py_buffer
from cython.cimports.libcpp.vector import vector

@cython.cclass
class Matrix:

    view_count: cython.int

    ncols: cython.Py_ssize_t
    v: vector[cython.float]
    # ...

    def __cinit__(self, ncols: cython.Py_ssize_t):
        self.ncols = ncols
        self.view_count = 0

    def add_row(self):
        if self.view_count > 0:
            raise ValueError("can't add row while being viewed")
        self.v.resize(self.v.size() + self.ncols)

    def __getbuffer__(self, buffer: cython.pointer(Py_buffer), flags: cython.int):
        # ... as before

        self.view_count += 1

    def __releasebuffer__(self, buffer: cython.pointer(Py_buffer)):
        self.view_count -= 1

Flags¶

We skipped some input validation in the code. The flags argument to __getbuffer__ comes from np.asarray (and other clients) and is an OR of boolean flags that describe the kind of array that is requested. Strictly speaking, if the flags contain PyBUF_ND, PyBUF_SIMPLE, or PyBUF_F_CONTIGUOUS, __getbuffer__ must raise a BufferError. These macros can be cimport’d from cpython.buffer.

(The matrix-in-vector structure actually conforms to PyBUF_ND, but that would prohibit __getbuffer__ from filling in the strides. A single-row matrix is F-contiguous, but a larger matrix is not.)

References¶

The buffer interface used here is set out in PEP 3118, Revising the buffer protocol.

A tutorial for using this API from C is on Jake Vanderplas’s blog, An Introduction to the Python Buffer Protocol.

Reference documentation is available for Python 3 and Python 2. The Py2 documentation also describes an older buffer protocol that is no longer in use; since Python 2.6, the PEP 3118 protocol has been implemented, and the older protocol is only relevant for legacy code.