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Cython Memory Helper

cymem provides two small memory-management helpers for Cython. They make it easy to tie memory to a Python object's life-cycle, so that the memory is freed when the object is garbage collected.

The most useful is cymem.Pool, which acts as a thin wrapper around the calloc function:

>>> from cymem.cymem cimport Pool
>>> cdef Pool mem = Pool()
>>> data1 = <int*>mem.alloc(10, sizeof(int))
>>> data2 = <float*>mem.alloc(12, sizeof(float))

The Pool object saves the memory addresses internally, and frees them when the object is garbage collected. Typically you'll attach the Pool to some cdef'd class. This is particularly handy for deeply nested structs, which have complicated initialization functions. Just pass the pool object into the initializer, and you don't have to worry about freeing your struct at all --- all of the calls to Pool.alloc will be automatically freed when the Pool expires.

Installation

Installation is via pip, and requires Cython.

pip install cymem

Example Use Case: An array of structs

Let's say we want a sequence of sparse matrices. We need fast access, and a Python list isn't performing well enough. So, we want a C-array or C++ vector, which means we need the sparse matrix to be a C-level struct --- it can't be a Python class. We can write this easily enough in Cython:

.. code-block:: cython

"""Example without Cymem

To use an array of structs, we must carefully walk the data structure when
we deallocate it.
"""

from libc.stdlib cimport calloc, free

cdef struct SparseRow:
    size_t length
    size_t* indices
    double* values

cdef struct SparseMatrix:
    size_t length
    SparseRow* rows

cdef class MatrixArray:
    cdef size_t length
    cdef SparseMatrix** matrices

    def __cinit__(self, list py_matrices):
        self.length = 0
        self.matrices = NULL

    def __init__(self, list py_matrices):
        self.length = len(py_matrices)
        self.matrices = <SparseMatrix**>calloc(len(py_matrices), sizeof(SparseMatrix*))

        for i, py_matrix in enumerate(py_matrices):
            self.matrices[i] = sparse_matrix_init(py_matrix)

    def __dealloc__(self):
        for i in range(self.length):
            sparse_matrix_free(self.matrices[i])
        free(self.matrices)


cdef SparseMatrix* sparse_matrix_init(list py_matrix) except NULL:
    sm = <SparseMatrix*>calloc(1, sizeof(SparseMatrix))
    sm.length = len(py_matrix)
    sm.rows = <SparseRow*>calloc(sm.length, sizeof(SparseRow))
    cdef size_t i, j
    cdef dict py_row
    cdef size_t idx
    cdef double value
    for i, py_row in enumerate(py_matrix):
        sm.rows[i].length = len(py_row)
        sm.rows[i].indices = <size_t*>calloc(sm.rows[i].length, sizeof(size_t))
        sm.rows[i].values = <double*>calloc(sm.rows[i].length, sizeof(double))
        for j, (idx, value) in enumerate(py_row.items()):
            sm.rows[i].indices[j] = idx
            sm.rows[i].values[j] = value
    return sm


cdef void* sparse_matrix_free(SparseMatrix* sm) except *:
    cdef size_t i
    for i in range(sm.length):
        free(sm.rows[i].indices)
        free(sm.rows[i].values)
    free(sm.rows)
    free(sm)

We wrap the data structure in a Python ref-counted class at as low a level as we can, given our performance constraints. This allows us to allocate and free the memory in the cinit and dealloc Cython special methods.

However, it's very easy to make mistakes when writing the dealloc and sparsematrixfree functions, leading to memory leaks. cymem prevents you from writing these deallocators at all. Instead, you write as follows:

.. code-block:: cython

"""Example with Cymem.

Memory allocation is hidden behind the Pool class, which remembers the
addresses it gives out.  When the Pool object is garbage collected, all of
its addresses are freed.

We don't need to write MatrixArray.__dealloc__ or sparse_matrix_free,
eliminating a common class of bugs.
"""
from cymem.cymem cimport Pool

cdef struct SparseRow:
    size_t length
    size_t* indices
    double* values

cdef struct SparseMatrix:
    size_t length
    SparseRow* rows


cdef class MatrixArray:
    cdef size_t length
    cdef SparseMatrix** matrices
    cdef Pool mem

    def __cinit__(self, list py_matrices):
        self.mem = None
        self.length = 0
        self.matrices = NULL

    def __init__(self, list py_matrices):
        self.mem = Pool()
        self.length = len(py_matrices)
        self.matrices = <SparseMatrix**>self.mem.alloc(self.length, sizeof(SparseMatrix*))
        for i, py_matrix in enumerate(py_matrices):
            self.matrices[i] = sparse_matrix_init(self.mem, py_matrix)

cdef SparseMatrix* sparse_matrix_init_cymem(Pool mem, list py_matrix) except NULL:
    sm = <SparseMatrix*>mem.alloc(1, sizeof(SparseMatrix))
    sm.length = len(py_matrix)
    sm.rows = <SparseRow*>mem.alloc(sm.length, sizeof(SparseRow))
    cdef size_t i, j
    cdef dict py_row
    cdef size_t idx
    cdef double value
    for i, py_row in enumerate(py_matrix):
        sm.rows[i].length = len(py_row)
        sm.rows[i].indices = <size_t*>mem.alloc(sm.rows[i].length, sizeof(size_t))
        sm.rows[i].values = <double*>mem.alloc(sm.rows[i].length, sizeof(double))
        for j, (idx, value) in enumerate(py_row.items()):
            sm.rows[i].indices[j] = idx
            sm.rows[i].values[j] = value
    return sm

All that the Pool class does is remember the addresses it gives out. When the MatrixArray object is garbage-collected, the Pool object will also be garbage collected, which triggers a call to Pool.dealloc. The Pool then frees all of its addresses. This saves you from walking back over your nested data structures to free them, eliminating a common class of errors.