# Sparse#

In general, sparse matrices provide the same functionality as regular matrices. The difference lies in the way the elements of sparse matrices are represented and stored in memory. Only the non-zero elements of the latter are stored. This has some potential advantages: first, this may obviously lead to reduced memory usage and, second, clever storage methods may lead to reduced computation time through the use of sparse specific algorithms. We usually refer to the generically stored matrices as dense matrices.

PyTensor’s sparse package provides efficient algorithms, but its use is not recommended in all cases or for all matrices. As an obvious example, consider the case where the sparsity proportion is very low. The sparsity proportion refers to the ratio of the number of zero elements to the number of all elements in a matrix. A low sparsity proportion may result in the use of more space in memory since not only the actual data is stored, but also the position of nearly every element of the matrix. This would also require more computation time whereas a dense matrix representation along with regular optimized algorithms might do a better job. Other examples may be found at the nexus of the specific purpose and structure of the matrices. More documentation may be found in the SciPy Sparse Reference.

Since sparse matrices are not stored in contiguous arrays, there are several ways to represent them in memory. This is usually designated by the so-called format of the matrix. Since PyTensor’s sparse matrix package is based on the SciPy sparse package, complete information about sparse matrices can be found in the SciPy documentation. Like SciPy, PyTensor does not implement sparse formats for arrays with a number of dimensions different from two.

So far, PyTensor implements two formats of sparse matrix: csc and csr. Those are almost identical except that csc is based on the columns of the matrix and csr is based on its rows. They both have the same purpose: to provide for the use of efficient algorithms performing linear algebra operations. A disadvantage is that they fail to give an efficient way to modify the sparsity structure of the underlying matrix, i.e. adding new elements. This means that if you are planning to add new elements in a sparse matrix very often in your computational graph, perhaps a tensor variable could be a better choice.

More documentation may be found in the Sparse Library Reference.

Before going further, here are the import statements that are assumed for the rest of the tutorial:

>>> import pytensor
>>> import numpy as np
>>> import scipy.sparse as sp
>>> from pytensor import sparse

## Compressed Sparse Format#

PyTensor supports two compressed sparse formats: csc and csr, respectively based on columns and rows. They have both the same attributes: data, indices, indptr and shape.

• The data attribute is a one-dimensional ndarray which contains all the non-zero elements of the sparse matrix.

• The indices and indptr attributes are used to store the position of the data in the sparse matrix.

• The shape attribute is exactly the same as the shape attribute of a dense (i.e. generic) matrix. It can be explicitly specified at the creation of a sparse matrix if it cannot be inferred from the first three attributes.

### Which format should I use?#

At the end, the format does not affect the length of the data and indices attributes. They are both completely fixed by the number of elements you want to store. The only thing that changes with the format is indptr. In csc format, the matrix is compressed along columns so a lower number of columns will result in less memory use. On the other hand, with the csr format, the matrix is compressed along the rows and with a matrix that have a lower number of rows, csr format is a better choice. So here is the rule:

Note

If shape[0] > shape[1], use csc format. Otherwise, use csr.

Sometimes, since the sparse module is young, ops does not exist for both format. So here is what may be the most relevant rule:

Note

Use the format compatible with the ops in your computation graph.

The documentation about the ops and their supported format may be found in the Sparse Library Reference.

## Handling Sparse in PyTensor#

Most of the ops in PyTensor depend on the format of the sparse matrix. That is why there are two kinds of constructors of sparse variables: csc_matrix and csr_matrix. These can be called with the usual name and dtype parameters, but no broadcastable flags are allowed. This is forbidden since the sparse package, as the SciPy sparse module, does not provide any way to handle a number of dimensions different from two. The set of all accepted dtype for the sparse matrices can be found in sparse.all_dtypes.

>>> sparse.all_dtypes
set(['int8', 'int16', 'int32', 'int64', 'uint8', 'uint16', 'uint32', 'uint64',
'float32', 'float64', 'complex64', 'complex128'])

### To and Fro#

To move back and forth from a dense matrix to a sparse matrix representation, PyTensor provides the dense_from_sparse, csr_from_dense and csc_from_dense functions. No additional detail must be provided. Here is an example that performs a full cycle from sparse to sparse:

>>> x = sparse.csc_matrix(name='x', dtype='float32')
>>> y = sparse.dense_from_sparse(x)
>>> z = sparse.csc_from_dense(y)

### Properties and Construction#

Although sparse variables do not allow direct access to their properties, this can be accomplished using the csm_properties function. This will return a tuple of one-dimensional tensor variables that represents the internal characteristics of the sparse matrix.

In order to reconstruct a sparse matrix from some properties, the functions CSC and CSR can be used. This will create the sparse matrix in the desired format. As an example, the following code reconstructs a csc matrix into a csr one.

>>> x = sparse.csc_matrix(name='x', dtype='int64')
>>> data, indices, indptr, shape = sparse.csm_properties(x)
>>> y = sparse.CSR(data, indices, indptr, shape)
>>> f = pytensor.function([x], y)
>>> a = sp.csc_matrix(np.asarray([[0, 1, 1], [0, 0, 0], [1, 0, 0]]))
>>> print(a.toarray())
[[0 1 1]
[0 0 0]
[1 0 0]]
>>> print(f(a).toarray())
[[0 0 1]
[1 0 0]
[1 0 0]]

The last example shows that one format can be obtained from transposition of the other. Indeed, when calling the transpose function, the sparse characteristics of the resulting matrix cannot be the same as the one provided as input.

### Structured Operation#

Several ops are set to make use of the very peculiar structure of the sparse matrices. These ops are said to be structured and simply do not perform any computations on the zero elements of the sparse matrix. They can be thought as being applied only to the data attribute of the latter. Note that these structured ops provide a structured gradient. More explication below.

>>> x = sparse.csc_matrix(name='x', dtype='float32')
>>> f = pytensor.function([x], y)
>>> a = sp.csc_matrix(np.asarray([[0, 0, -1], [0, -2, 1], [3, 0, 0]], dtype='float32'))
>>> print(a.toarray())
[[ 0.  0. -1.]
[ 0. -2.  1.]
[ 3.  0.  0.]]
>>> print(f(a).toarray())
[[ 0.  0.  1.]
[ 0.  0.  3.]
[ 5.  0.  0.]]

The gradients of the ops in the sparse module can also be structured. Some ops provide a flag to indicate if the gradient is to be structured or not. The documentation can be used to determine if the gradient of an op is regular or structured or if its implementation can be modified. Similarly to structured ops, when a structured gradient is calculated, the computation is done only for the non-zero elements of the sparse matrix.

More documentation regarding the gradients of specific ops can be found in the Sparse Library Reference.