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 PyTensor 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 PyTensor.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 PyTensor.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#

PyTensor 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 PyTensor.sparse.basic.csm_properties(). You can use PyTensor.sparse.basic.csm_data(), PyTensor.sparse.basic.csm_indices(), PyTensor.sparse.basic.csm_indptr() and PyTensor.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 PyTensor Op. Look into the PyTensor/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 PyTensor operations. The tests go in PyTensor/sandbox/test_rng_mrg.py
Extend MRG implementation with a new Op that takes a uniform sample as input. Look in the PyTensor/sandbox/{rng_mrg,multinomial}.py file and its test in PyTensor/sandbox/test_multinomal.py. This is recommended when current PyTensor 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 pytensor.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 PyTensor 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 PyTensor 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 PyTensor Types#

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

TensorType : PyTensor type that represents a multidimensional array containing elements that all have the same type. Variables of this PyTensor type are represented in C as objects of class PyArrayObject.
TypedList : PyTensor type that represents a typed list (a list where every element in the list has the same PyTensor type). Variables of this PyTensor type are represented in C as objects of class PyListObject.
ScalarType : PyTensor type that represents a C primitive type. The C type associated with this PyTensor type is the represented C primitive itself.
SparseTensorType : PyTensor Type used to represent sparse tensors. There is no equivalent C type for this PyTensor 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 : PyTensor type that represents a simple Python Object. Variables of this PyTensor type are represented in C as objects of class PyObject.
CDataType : PyTensor type that represents a C data type. The C type associated with this PyTensor type depends on the data being represented.