# Implementing some specific Ops¶

This page is a guide on the implementation of some specific types of Ops, and points to some examples of such implementations.

For the random number generating Ops, it explains different possible implementation strategies.

## Scalar/Elemwise/Reduction Ops¶

Implementing an Aesara scalar Op allows that scalar operation to be reused by our elemwise operations on tensors. If the scalar operation has C code, the elemwise implementation will automatically have C code too. This will enable the fusion of elemwise operations using your new scalar operation. It is similar for reduction operations.

Be careful about some possible problems in the definition of the
`grad`

method, and about dependencies that may not be available. In
particular, see the following fixes:
Fix to grad() methods
and impl() methods related to SciPy.

## Sparse Ops¶

There are a few differences to keep in mind if you want to make an op
that uses sparse inputs or outputs, rather than the
usual dense tensors. In particular, in the
`make_node()`

function, you have to call
`Aesara.sparse.as_sparse_variable(x)`

on sparse input variables,
instead of `as_tensor_variable(x)`

.

Another difference is that you need to use `SparseVariable`

and
`SparseTensorType`

instead of `TensorVariable`

and `TensorType`

.

Do not forget that we support only sparse matrices (so only 2 dimensions)
and (like in SciPy) they do not support broadcasting operations by default
(although a few Ops do it when called manually). Also, we support only two
formats for sparse type: `csr`

and `csc`

. So in `make_mode()`

,
you can create output variables like this:

```
out_format = inputs[0].format # or 'csr' or 'csc' if the output format is fixed
SparseTensorType(dtype=inputs[0].dtype, format=out_format).make_variable()
```

See the sparse `Aesara.sparse.basic.Cast`

`Op`

code for a good example of
a sparse `Op`

with Python code.

Note

From the definition of CSR and CSC formats, CSR column indices are
not necessarily sorted. Likewise for CSC row indices. Use
`EnsureSortedIndices`

if your code does not
support it.

Also, there can be explicit zeros in your inputs. Use
`Remove0`

or `remove0`

to
make sure they aren’t present in your input if you don’t support
that.

To remove explicit zeros and make sure indices are sorted, use
`clean`

.

### Sparse Gradient¶

There are 2 types of gradients for sparse
operations: `normal`

gradient and `structured`

gradient. Please document what your op
implements in its docstring. It is important that the user knows it, and
it is not always easy to infer from the code. Also make clear which
inputs/outputs are sparse and which ones are dense.

### Sparse C code¶

Aesara does not have a native C code interface for sparse matrices. The
reason is simple: we use the SciPy sparse matrix objects and they don’t
have a C object. So we use a simple trick: a sparse matrix is made of
4 fields that are NumPy vector arrays: `data`

, `indices`

, `indptr`

and `shape`

. So to make
an op with C code that has sparse variables as inputs, we actually make an op
that takes as input the needed fields of those sparse variables.

You can extract the 4 fields with
`Aesara.sparse.basic.csm_properties()`

. You can use
`Aesara.sparse.basic.csm_data()`

,
`Aesara.sparse.basic.csm_indices()`

,
`Aesara.sparse.basic.csm_indptr()`

and
`Aesara.sparse.basic.csm_shape()`

to extract the individual
fields.

You can look at the `AddSD`

sparse `Op`

for an example with C code. It implements
the addition of a sparse matrix with a dense matrix.

### Sparse Tests¶

You can reuse the test system for tensor variables. To generate the
needed sparse variable and data, you can use
`tests.sparse.test_basic.sparse_random_inputs()`

. It takes
many parameters, including parameters for the format (csr or csc), the shape, the
dtype, whether to have explicit 0 and whether to have unsorted indices.

## Random distribution¶

We have 3 base random number generators. One that wraps NumPy’s random generator, one that implements MRG31k3p and one that wraps CURAND.

The recommended and 2nd faster is MRG. It works on the CPU and has more implemented distributions.

The slowest is our wrapper on NumPy’s random generator.

We explain and provide advice on 3 possibles implementations of new distributions here:

- Extend our wrapper around NumPy random functions. See this PR as an example.
- Extend MRG implementation by reusing existing Aesara Op. Look into
the
`Aesara/sandbox/rng_mrg.py`

file and grep for all code about binomial(). This distribution uses the output of the uniform distribution and converts it to a binomial distribution with existing Aesara operations. The tests go in`Aesara/sandbox/test_rng_mrg.py`

- Extend MRG implementation with a new Op that takes a uniform sample as
input. Look in the
`Aesara/sandbox/{rng_mrg,multinomial}.py`

file and its test in`Aesara/sandbox/test_multinomal.py`

. This is recommended when current Aesara ops aren’t well suited to modify the uniform to the target distribution. This can happen in particular if there is a loop or complicated condition.

Note

In all cases, you must reuse the same interface as NumPy for compatibility.

## OpenMP Ops¶

To allow consistent interface of Ops that support OpenMP, we have some helper code. Doing this also allows to enable/disable OpenMP globally or per op for fine-grained control.

Your Op needs to inherit from `aesara.link.c.op.OpenMPOp`

. If it overrides
the `__init__()`

method, it must have an `openmp=None`

parameter
and must call `super(MyOpClass, self).__init__(openmp=openmp)`

.

The `OpenMPOp`

class also implements `c_compile_args`

and
`make_thunk`

. This makes it add the correct g++ flags to compile with
OpenMP. It also disables OpenMP and prints a warning if the version of
g++ does not support it.

The Aesara flag `openmp`

is currently False by default as we do not
have code that gets sped up with it. The only current implementation
is ConvOp. It speeds up some cases, but slows down others. That is why
we disable it by default. But we have all the code to have it enabled
by default if there is more than 1 core and the environment
variable OMP_NUM_THREADS is not 1. This allows Aesara to respect the
current convention.

## Numba Ops¶

Want C speed without writing C code for your new Op? You can use Numba to generate the C code for you! Here is an example Op doing that.

## Alternate Aesara Types¶

Most ops in Aesara are used to manipulate tensors. However, Aesara also supports many other variable types. The supported types are listed below, along with pointers to the relevant documentation.

`TensorType`

: Aesara type that represents a multidimensional array containing elements that all have the same type. Variables of this Aesara type are represented in C as objects of class PyArrayObject.- TypedList : Aesara type that represents a typed list (a list where every element in the list has the same Aesara type). Variables of this Aesara type are represented in C as objects of class PyListObject.
- ScalarType : Aesara type that represents a C primitive type. The C type associated with this Aesara type is the represented C primitive itself.
- SparseTensorType : Aesara
`Type`

used to represent sparse tensors. There is no equivalent C type for this Aesara`Type`

but you can split a sparse variable into its parts as TensorVariables. Those can then be used as inputs to an op with C code. `Generic`

: Aesara type that represents a simple Python Object. Variables of this Aesara type are represented in C as objects of class PyObject.`CDataType`

: Aesara type that represents a C data type. The C type associated with this Aesara type depends on the data being represented.