
.. _extending_theano:

Creating a new Op: Python implementation
========================================

So suppose you have looked through the library documentation and you don't see
a function that does what you want.

If you can implement something in terms of existing Ops, you should do that.
Odds are your function that uses existing Theano expressions is short,
has no bugs, and potentially profits from optimizations that have already been
implemented.

However, if you cannot implement an Op in terms of existing Ops, you have to
write a new one. Don't worry, Theano was designed to make it easy to add new
Ops, Types, and Optimizations.

.. These first few pages will walk you through the definition of a new :ref:`type`,
.. ``double``, and a basic arithmetic :ref:`operations <op>` on that `Type`.

As an illustration, this tutorial shows how to write a simple Python-based
:ref:`operations <op>` which performs operations on
:ref:`type`, ``double<Double>``.
.. It also shows how to implement tests that
.. ensure the proper working of an op.

.. note::

    This is an introductury tutorial and as such it does not cover how to make
    an op that returns a view or modifies the values in its inputs. Thus, all
    ops created with the instructions described here MUST return newly
    allocated memory or reuse the memory provided in the parameter
    ``output_storage`` of the :func:`perform` function. See
    :ref:`views_and_inplace` for an explanation on how to do this.

    If your op returns a view or changes the value of its inputs
    without doing as prescribed in that page, Theano will run, but will
    return correct results for some graphs and wrong results for others.

    It is recommended that you run your tests in DebugMode (Theano *flag*
    ``mode=DebugMode``) since it verifies if your op behaves correctly in this
    regard.


Theano Graphs refresher
-----------------------

.. image:: ../hpcs2011_tutorial/pics/apply_node.png
    :width: 500 px

Theano represents symbolic mathematical computations as graphs. Those graphs
are bi-partite graphs (graphs with 2 types of nodes), they are composed of
interconnected :ref:`apply` and :ref:`variable` nodes.
:ref:`variable` nodes represent data in the graph, either inputs, outputs or
intermediary values. As such, Inputs and Outputs of a graph are lists of Theano
:ref:`variable` nodes. :ref:`apply` nodes perform computation on these
variables to produce new variables. Each :ref:`apply` node has a link to an
instance of :ref:`Op` which describes the computation to perform. This tutorial
details how to write such an Op instance. Please refers to
:ref:`graphstructures` for a more detailed explanation about the graph
structure.


Op's basic methods
------------------

An op is any Python object which inherits from :class:`Op`.
This section provides an overview of the basic methods you typically have to
implement to make a new op.  It does not provide extensive coverage of all the
possibilities you may encounter or need.  For that refer to
:ref:`op_contract`.

.. testcode:: python

    import theano
    from theano.graph.op import Op


    class MyOp(Op):
        # Properties attribute
        __props__ = ()

        #itypes and otypes attributes are
        #compulsory if make_node method is not defined.
        #They're the type of input and output respectively
        itypes = None
        otypes = None

        #Compulsory if itypes and otypes are not defined
        def make_node(self, *inputs):
            pass

        # Python implementation:
        def perform(self, node, inputs_storage, output_storage):
            pass

        # Other type of implementation
        # C implementation: [see theano web site for other functions]
        def c_code(self, node, inputs, outputs, sub):
            pass

        # Other implementations:
        def make_thunk(self, node, storage_map, _, _2, impl=None):
            pass

        # optional:
        check_input = True

        def __init__(self, *args):
            pass

        def grad(self, inputs, g):
            pass

        def R_op(self, inputs, eval_points):
            pass

        def infer_shape(self, fgraph, node, input_shapes):
            pass

An op has to implement some methods defined in the the interface of
:class:`Op`. More specifically, it is mandatory for an op to define either
the method :func:`make_node` or :attr:`itypes`, :attr:`otypes` and one of the
implementation methods, either :func:`perform`, :meth:`Op.c_code`
or :func:`make_thunk`.

  :func:`make_node` method creates an Apply node representing the application
  of the op on the inputs provided. This method is reponsible for three things:

    - it first checks that the input Variables types are compatible
      with the current op. If the op cannot be applied on the provided
      input types, it must raises an exception (such as :class:`TypeError`).
    - it operates on the Variables found in
      ``*inputs`` in Theano's symbolic language to infer the type of
      the symbolic output Variables. It creates output Variables of a suitable
      symbolic `Type` to serve as the outputs of this op's
      application.
    - it creates an Apply instance with the input and output Variable, and
      return the Apply instance.



  :func:`perform` method defines the Python implementation of an op.
  It takes several arguments:

    - ``node`` is a reference to an Apply node which was previously
      obtained via the ``Op``'s :func:`make_node` method. It is typically not
      used in simple ops, but it contains symbolic information that
      could be required for complex ops.
    - ``inputs`` is a list of references to data which can be operated on using
      non-symbolic statements, (i.e., statements in Python, Numpy).
    - ``output_storage`` is a list of storage cells where the output
      is to be stored. There is one storage cell for each output of the op.
      The data put in ``output_storage`` must match the type of the
      symbolic output. It is forbidden to change the length of the list(s)
      contained in ``output_storage``.
      A function Mode may allow ``output_storage`` elements to persist
      between evaluations, or it may reset ``output_storage`` cells to
      hold a value of ``None``.  It can also pre-allocate some memory
      for the op to use.  This feature can allow ``perform`` to reuse
      memory between calls, for example. If there is something
      preallocated in the ``output_storage``, it will be of the good
      dtype, but can have the wrong shape and have any stride pattern.

  :func:`perform` method must be determined by the inputs. That is to say,
  when applied to identical inputs the method must return the same outputs.

  :class:`Op` allows some other way to define the op implentation.
  For instance, it is possible to define :meth:`Op.c_code` to provide a
  C-implementation to the op. Please refers to tutorial
  :ref:`extending_theano_c` for a description of :meth:`Op.c_code` and other
  related c_methods. Note that an op can provide both Python and C
  implementation.

  :func:`make_thunk` method is another alternative to :func:`perform`.
  It returns a thunk. A thunk is defined as a zero-arguments
  function which encapsulates the computation to be performed by an
  op on the arguments of its corresponding node. It takes several parameters:

    - ``node`` is the Apply instance for which a thunk is requested,
    - ``storage_map`` is a dict of lists which  maps variables to a one-element
      lists holding the variable's current value. The one-element list acts as
      pointer to the value and allows sharing that "pointer" with other nodes
      and instances.
    - ``compute_map`` is also a  dict of lists.
      It maps variables to one-element lists holding booleans.  If
      the value is 0 then the variable has not been computed and the
      value should not be considered valid.  If the value is 1 the
      variable has been computed and the value is valid.  If the value
      is 2 the variable has been garbage-collected and is no longer
      valid, but shouldn't be required anymore for this call.
      The returned function must ensure that it sets the computed
      variables as computed in the `compute_map`.
    - ``impl`` allow to select between multiple implementation.
      It should have a default value of None.

  :func:`make_thunk` is useful if you want to generate code and compile
  it yourself.

  If :func:`make_thunk()` is defined by an op, it will be used by Theano
  to obtain the op's implementation.
  :func:`perform` and :meth:`Op.c_code` will be ignored.

  If :func:`make_node` is not defined, the :attr:`itypes` and :attr:`otypes`
  are used by the Op's :func:`make_node` method to implement the functionality
  of :func:`make_node` method mentioned above.

Op's auxiliary methods
----------------------

There are other methods that can be optionally defined by the op:

  The :func:`__str__` method provides a meaningful string representation of
  your op.

  :func:`__eq__` and :func:`__hash__` define respectivelly equality
  between two ops and the hash of an op instance.
  They will be used by the optimization
  phase to merge nodes that are doing equivalent computations (same
  inputs, same operation).
  Two ops that are equal according :func:`__eq__`
  should return the same output when they are applied on the same inputs.

  The :attr:`__props__` lists the properties
  that influence how the computation is performed (Ususally these are those
  that you set in  :func:`__init__`). It must be a tuple.
  If you don't have any properties, then you should set this attribute to the
  emtpy tuple `()`.

  :attr:`__props__` enables the  automatic generation of appropriate
  :func:`__eq__` and :func:`__hash__`.
  Given the method :func:`__eq__`, automatically generated from
  :attr:`__props__`, two ops will be equal if they have the same values for all
  the properties listed in :attr:`__props__`.
  Given to the method :func:`__hash__` automatically generated from
  :attr:`__props__`, two ops will be have the same hash if they have the same
  values for all the properties listed in :attr:`__props__`.
  :attr:`__props__` will also generate a  suitable :func:`__str__` for your op.
  This requires development version after September 1st, 2014 or version 0.7.

  The :func:`infer_shape` method allows an `Op` to infer the shape of its
  output variables without actually computing them.
  It takes as input ``fgraph``, a `FunctionGraph`; ``node``, a reference to the op Apply node;
  and a list of Theano symbolic Varables (``i0_shape``, ``i1_shape``, ...)
  which are the shape of the op input Variables.
  :func:`infer_shape` returns a list where each element is a tuple representing
  the shape of one output.
  This could be helpful if one only
  needs the shape of the output instead of the actual outputs, which
  can be useful, for instance, for optimization procedures.

  The :func:`grad` method is required if you want to differentiate some cost
  whose expression includes your op. The gradient may be
  specified symbolically in this method. It takes two arguments ``inputs`` and
  ``output_gradients`` which are both lists of symbolic Theano Variables and
  those must be operated on using Theano's symbolic language. The grad
  method must return a list containing one Variable for each
  input. Each returned Variable represents the gradient with respect
  to that input computed based on the symbolic gradients with respect
  to each output.
  If the output is not differentiable with respect to an input then
  this method should be defined to return a variable of type NullType
  for that input. Likewise, if you have not implemented the grad
  computation for some input, you may return a variable of type
  NullType for that input. Please refer to :func:`grad` for a more detailed
  view.

  The :func:`R_op` method is needed if you want ``theano.tensor.Rop`` to
  work with your op.
  This function implements the application of the R-operator on the
  function represented by your op. Let assume that function is :math:`f`,
  with input :math:`x`, applying the R-operator means computing the
  Jacobian of :math:`f` and right-multiplying it by :math:`v`, the evaluation
  point, namely: :math:`\frac{\partial f}{\partial x} v`.

  The optional boolean :attr:`check_input` attribute is used to specify
  if you want the types used in your op to check their inputs in their
  c_code. It can be used to speed up compilation, reduce overhead
  (particularly for scalars) and reduce the number of generated C files.


Example: Op definition
----------------------

.. testcode:: example

    import theano
    from theano.graph.op import Op
    from theano.graph.basic import Apply


    class DoubleOp1(Op):
        __props__ = ()

        def make_node(self, x):
            x = theano.tensor.as_tensor_variable(x)
            # Note: using x_.type() is dangerous, as it copies x's broadcasting
            # behaviour
            return Apply(self, [x], [x.type()])

        def perform(self, node, inputs, output_storage):
            x = inputs[0]
            z = output_storage[0]
            z[0] = x * 2

        def infer_shape(self, fgraph, node, i0_shapes):
            return i0_shapes

        def grad(self, inputs, output_grads):
            return [output_grads[0] * 2]

        def R_op(self, inputs, eval_points):
            # R_op can receive None as eval_points.
            # That mean there is no diferientiable path through that input
            # If this imply that you cannot compute some outputs,
            # return None for those.
            if eval_points[0] is None:
                return eval_points
            return self.grad(inputs, eval_points)

    doubleOp1 = DoubleOp1()

    #Using itypes and otypes


    class DoubleOp2(Op):
        __props__ = ()

        itypes = [theano.tensor.dmatrix]
        otypes = [theano.tensor.dmatrix]

        def perform(self, node, inputs, output_storage):
            x = inputs[0]
            z = output_storage[0]
            z[0] = x * 2

        def infer_shape(self, fgraph, node, i0_shapes):
            return i0_shapes

        def grad(self, inputs, output_grads):
            return [output_grads[0] * 2]

        def R_op(self, inputs, eval_points):
            # R_op can receive None as eval_points.
            # That mean there is no diferientiable path through that input
            # If this imply that you cannot compute some outputs,
            # return None for those.
            if eval_points[0] is None:
                return eval_points
            return self.grad(inputs, eval_points)

    doubleOp2 = DoubleOp2()

At a high level, the code fragment declares a class (e.g., ``DoubleOp1``) and then
creates one instance of it (e.g., ``doubleOp1``).

We often gloss over this distinction, but will be precise here:
``doubleOp1`` (the instance) is an Op, not ``DoubleOp1`` (the class which is a
subclass of ``Op``). You can call ``doubleOp1(tensor.vector())`` on a
Variable to build an expression, and in the expression there will be
a ``.op`` attribute that refers to ``doubleOp1``.

.. The first two methods in the Op are relatively boilerplate: ``__eq__``
.. and ``__hash__``.
.. When two Ops are equal, Theano will merge their outputs if they are applied to the same inputs.
.. The base class (Op) says two objects are equal if (and only if)
.. they are the same object.
.. Writing these boilerplate definitions ensures that the logic of the equality comparison is always explicit.

.. It is an essential part of the :ref:`op_contract` that if two Ops compare
.. equal, then they must compute the same result when presented with the same
.. inputs.  Here, if we allocated another instance of ``Fibby`` by typing ``fibby2
.. = Fibby()`` then we would have two Ops that behave identically.
..
.. When should the implementation of ``__eq__`` be more complicated?
.. If ``Fibby.__init__`` had parameters, then we could
.. have configured ``fibby2`` differently from ``fibby`` by passing different
.. arguments to the constructor. If we had done that, and if that different
.. configuration made ``fibby2`` compute different results from ``fibby`` (for the
.. same inputs) then we would have to add logic to the ``__eq__`` and ``__hash__``
.. function so that he two ``Fibby`` Ops would *not be equal*.  The reason why: Theano's merge
.. optimization looks for Ops comparing equal and merges them. If two Ops compare
.. equal but don't always produce equal results from equal inputs, then you might
.. see wrong calculation.

The ``make_node`` method creates a node to be included in the expression graph.
It runs when we apply our Op (``doubleOp1``) to the Variable (``x``), as
in ``doubleOp1(tensor.vector())``.
When an Op has multiple inputs, their order in the inputs argument to ``Apply``
is important:  Theano will call ``make_node(*inputs)`` to copy the graph,
so it is important not to change the semantics of the expression by changing
the argument order.

All the ``inputs`` and ``outputs`` arguments to ``Apply`` must be Variables.
A common and easy way to ensure inputs are variables is to run them through
``as_tensor_variable``. This function leaves TensorType variables alone, raises
an error for non-TensorType variables, and copies any ``numpy.ndarray`` into
the storage for a TensorType Constant. The ``make_node`` method dictates the
appropriate `Type` for all output variables.

The ``perform`` method implements the Op's mathematical logic in Python.
The inputs (here ``x``) are passed by value, but a single output is returned
indirectly as the first element of single-element lists.  If ``doubleOp1`` had
a second output, it would be stored in ``output_storage[1][0]``.

.. jpt: DOn't understand the following

In some execution modes, the output storage might contain the return value of
a previous call.  That old value can be reused to avoid memory re-allocation,
but it must not influence the semantics of the Op output.

You can try the new Op as follows:

.. testcode:: example

    import theano
    x = theano.tensor.matrix()
    f = theano.function([x], DoubleOp1()(x))
    import numpy
    inp = numpy.random.rand(5, 4)
    out = f(inp)
    assert numpy.allclose(inp * 2, out)
    print(inp)
    print(out)

.. testoutput:: example
   :hide:
   :options: +ELLIPSIS, +SKIP

    <BLANKLINE>

.. code-block:: none

    [[ 0.08257206  0.34308357  0.5288043   0.06582951]
     [ 0.65977826  0.10040307  0.5402353   0.55472296]
     [ 0.82358552  0.29502171  0.97387481  0.0080757 ]
     [ 0.77327215  0.65401857  0.76562992  0.94145702]
     [ 0.8452076   0.30500101  0.88430501  0.95818655]]
    [[ 0.16514411  0.68616713  1.0576086   0.13165902]
     [ 1.31955651  0.20080613  1.08047061  1.10944593]
     [ 1.64717104  0.59004341  1.94774962  0.0161514 ]
     [ 1.5465443   1.30803715  1.53125983  1.88291403]
     [ 1.6904152   0.61000201  1.76861002  1.9163731 ]]

.. testcode:: example

    import theano
    x = theano.tensor.matrix()
    f = theano.function([x], DoubleOp2()(x))
    import numpy
    inp = numpy.random.rand(5, 4)
    out = f(inp)
    assert numpy.allclose(inp * 2, out)
    print(inp)
    print(out)


.. testoutput:: example
   :hide:
   :options: +ELLIPSIS, +SKIP

    <BLANKLINE>

.. code-block:: none

    [[ 0.02443785  0.67833979  0.91954769  0.95444365]
     [ 0.60853382  0.7770539   0.78163219  0.92838837]
     [ 0.04427765  0.37895602  0.23155797  0.4934699 ]
     [ 0.20551517  0.7419955   0.34500905  0.49347629]
     [ 0.24082769  0.49321452  0.24566545  0.15351132]]
    [[ 0.04887571  1.35667957  1.83909538  1.90888731]
     [ 1.21706764  1.55410779  1.56326439  1.85677674]
     [ 0.08855531  0.75791203  0.46311594  0.9869398 ]
     [ 0.41103034  1.48399101  0.69001811  0.98695258]
     [ 0.48165539  0.98642904  0.4913309   0.30702264]]


Example: __props__ definition
-----------------------------

We can modify the previous piece of code in order to demonstrate
the usage of the :attr:`__props__` attribute.

We create an Op that takes a variable ``x`` and returns ``a*x+b``.
We want to say that two such ops are equal when their values of ``a``
and ``b`` are equal.

.. testcode:: properties

    import theano
    from theano.graph.op import Op
    from theano.graph.basic import Apply


    class AXPBOp(Op):
        """
        This creates an Op that takes x to a*x+b.
        """
        __props__ = ("a", "b")

        def __init__(self, a, b):
            self.a = a
            self.b = b
            super().__init__()

        def make_node(self, x):
            x = theano.tensor.as_tensor_variable(x)
            return Apply(self, [x], [x.type()])

        def perform(self, node, inputs, output_storage):
            x = inputs[0]
            z = output_storage[0]
            z[0] = self.a * x + self.b

        def infer_shape(self, fgraph, node, i0_shapes):
            return i0_shapes

        def grad(self, inputs, output_grads):
            return [a * output_grads[0] + b]


The use of :attr:`__props__` saves
the user the trouble of implementing :func:`__eq__` and :func:`__hash__`
manually. It also generates a default :func:`__str__` method that prints the
attribute names and their values.

We can test this by running the following segment:

.. testcode:: properties

    mult4plus5op = AXPBOp(4, 5)
    another_mult4plus5op = AXPBOp(4, 5)
    mult2plus3op = AXPBOp(2, 3)

    assert mult4plus5op == another_mult4plus5op
    assert mult4plus5op != mult2plus3op

    x = theano.tensor.matrix()
    f = theano.function([x], mult4plus5op(x))
    g = theano.function([x], mult2plus3op(x))

    import numpy
    inp = numpy.random.rand(5, 4).astype(numpy.float32)
    assert numpy.allclose(4 * inp + 5, f(inp))
    assert numpy.allclose(2 * inp + 3, g(inp))


How To Test it
--------------

Theano has some functionalities to simplify testing. These help test the
``infer_shape``, ``grad`` and ``R_op`` methods. Put the following code
in a file and execute it with the ``pytest`` program.

Basic Tests
^^^^^^^^^^^

Basic tests are done by you just by using the op and checking that it
returns the right answer. If you detect an error, you must raise an
*exception*. You can use the ``assert`` keyword to automatically raise an
``AssertionError``.

.. testcode:: tests

    import numpy
    import theano

    from tests import unittest_tools as utt
    from theano.configdefaults import config
    class TestDouble(utt.InferShapeTester):
        def setup_method(self):
            super().setup_method()
            self.op_class = DoubleOp
            self.op = DoubleOp()

        def test_basic(self):
            x = theano.tensor.matrix()
            f = theano.function([x], self.op(x))
            inp = numpy.asarray(numpy.random.rand(5, 4), dtype=config.floatX)
            out = f(inp)
            # Compare the result computed to the expected value.
            utt.assert_allclose(inp * 2, out)

We call ``utt.assert_allclose(expected_value, value)`` to compare
NumPy ndarray.This raise an error message with more information. Also,
the default tolerance can be changed with the Theano flags
``config.tensor__cmp_sloppy`` that take values in 0, 1 and 2. The
defaul value do the most strict comparison, 1 and 2 make less strict
comparison.

Testing the infer_shape
^^^^^^^^^^^^^^^^^^^^^^^

When a class inherits from the ``InferShapeTester`` class, it gets the
``self._compile_and_check`` method that tests the op's ``infer_shape``
method. It tests that the op gets optimized out of the graph if only
the shape of the output is needed and not the output
itself. Additionally, it checks that the optimized graph computes
the correct shape, by comparing it to the actual shape of the computed
output.

``self._compile_and_check`` compiles a Theano function. It takes as
parameters the lists of input and output Theano variables, as would be
provided to ``theano.function``, and a list of real values to pass to the
compiled function. It also takes the op class as a parameter
in order to verify that no instance of it appears in the shape-optimized graph.

If there is an error, the function raises an exception. If you want to
see it fail, you can implement an incorrect ``infer_shape``.

When testing with input values with shapes that take the same value
over different dimensions (for instance, a square matrix, or a tensor3
with shape (n, n, n), or (m, n, m)), it is not possible to detect if
the output shape was computed correctly, or if some shapes with the
same value have been mixed up. For instance, if the infer_shape uses
the width of a matrix instead of its height, then testing with only
square matrices will not detect the problem. This is why the
``self._compile_and_check`` method prints a warning in such a case. If
your op works only with such matrices, you can disable the warning with the
``warn=False`` parameter.

.. testcode:: tests

    from tests import unittest_tools as utt
    from theano.configdefaults import config
    class TestDouble(utt.InferShapeTester):
        # [...] as previous tests.
        def test_infer_shape(self):
            x = theano.tensor.matrix()
            self._compile_and_check([x],  # theano.function inputs
                                    [self.op(x)],  # theano.function outputs
                                    # Always use not square matrix!
                                    # inputs data
                                    [numpy.asarray(numpy.random.rand(5, 4),
                                                   dtype=config.floatX)],
                                    # Op that should be removed from the graph.
                                    self.op_class)

Testing the gradient
^^^^^^^^^^^^^^^^^^^^

The function :ref:`verify_grad <validating_grad>`
verifies the gradient of an op or Theano graph. It compares the
analytic (symbolically computed) gradient and the numeric
gradient (computed through the Finite Difference Method).

If there is an error, the function raises an exception. If you want to
see it fail, you can implement an incorrect gradient (for instance, by removing
the multiplication by 2).

.. testcode:: tests

        def test_grad(self):
            tests.unittest_tools.verify_grad(self.op,
                                             [numpy.random.rand(5, 7, 2)])

Testing the Rop
^^^^^^^^^^^^^^^

.. TODO: repair defective links in the following paragraph

The class :class:`RopLop_checker` defines the functions
:func:`RopLop_checker.check_mat_rop_lop`, :func:`RopLop_checker.check_rop_lop` and
:func:`RopLop_checker.check_nondiff_rop`. These allow to test the
implementation of the Rop method of a particular op.

For instance, to verify the Rop method of the DoubleOp, you can use this:

.. testcode:: tests

   import numpy
   import tests
   from tests.test_rop import RopLop_checker
   class TestDoubleRop(RopLop_checker):
       def setUp(self):
           super(TestDoubleRop, self).setUp()
       def test_double_rop(self):
           self.check_rop_lop(DoubleRop()(self.x), self.in_shape)

Running Your Tests
^^^^^^^^^^^^^^^^^^

To perform your tests, simply run ``pytest``.

In-file
"""""""

One may also add a block of code similar to the following at the end
of the file containing a specific test of interest and run the
file. In this example, the test *TestDoubleRop* in the class
*test_double_op* would be performed.

.. testcode:: tests

    if __name__ == '__main__':
       t = TestDoubleRop("test_double_rop")
       t.setUp()
       t.test_double_rop()

We recommend that when we execute a file, we run all tests in that
file. This can be done by adding this at the end of your test files:

.. testcode:: tests

    if __name__ == '__main__':
        unittest.main()

Exercise
""""""""

Run the code of the *DoubleOp* example above.

Modify and execute to compute: x * y.

Modify and execute the example to return two outputs: x + y and x - y.

You can omit the Rop functions. Try to implement the testing apparatus
described above.

(Notice that Theano's current *elemwise fusion* optimization is
only applicable to computations involving a single output. Hence, to gain
efficiency over the basic solution that is asked here, the two operations would
have to be jointly optimized explicitly in the code.)

Random numbers in tests
"""""""""""""""""""""""

Making tests errors more reproducible is a good practice. To make your
tests more reproducible, you need a way to get the same random
numbers. You can do this by seeding NumPy's random number
generator.

For convenience, the classes InferShapeTester and RopLop_checker
already do this for you. If you implement your own ``setUp`` function,
don't forget to call the parent ``setUp`` function.

For more details see :ref:`random_value_in_tests`.


:download:`Solution<extending_theano_solution_1.py>`


as_op
-----

as_op is a python decorator that converts a python function into a
basic Theano op that will call the supplied function during execution.

This isn't the recommended way to build an op, but allows for a quick
implementation.

It takes an optional :func:`infer_shape` parameter that must have this
signature:

.. code-block:: none

    def infer_shape(fgraph, node, input_shapes):
        # ...
        return output_shapes

  - `input_shapes` and `output_shapes` are lists of tuples that
    represent the shape of the corresponding inputs/outputs, and `fgraph`
    is a `FunctionGraph`.

.. note::

    Not providing the `infer_shape` method prevents shape-related
    optimizations from working with this op. For example
    `your_op(inputs, ...).shape` will need the op to be executed just
    to get the shape.

.. note::

    As no grad is defined, this means you won't be able to
    differentiate paths that include this op.

.. note::

    It converts the Python function to a callable object that takes as
    inputs Theano variables that were declared.

.. note::
    The python function wrapped by the `as_op` decorator needs to return a new
    data allocation, no views or in place modification of the input.

as_op Example
^^^^^^^^^^^^^

.. testcode:: asop

    import theano
    import numpy
    from theano import function
    from theano.compile.ops import as_op

    def infer_shape_numpy_dot(fgraph, node, input_shapes):
        ashp, bshp = input_shapes
        return [ashp[:-1] + bshp[-1:]]

    @as_op(itypes=[theano.tensor.fmatrix, theano.tensor.fmatrix],
           otypes=[theano.tensor.fmatrix], infer_shape=infer_shape_numpy_dot)
    def numpy_dot(a, b):
       return numpy.dot(a, b)

You can try it as follows:

.. testcode:: asop

    x = theano.tensor.fmatrix()
    y = theano.tensor.fmatrix()
    f = function([x, y], numpy_dot(x, y))
    inp1 = numpy.random.rand(5, 4).astype('float32')
    inp2 = numpy.random.rand(4, 7).astype('float32')
    out = f(inp1, inp2)


Exercise
^^^^^^^^

Run the code of the *numpy_dot* example above.

Modify and execute to compute: numpy.add and numpy.subtract.

Modify and execute the example to return two outputs: x + y
    and x - y.

.. _Documentation:

Documentation and Coding Style
------------------------------
Please always respect the :ref:`quality_contributions` or your contribution
will not be accepted.

NanGuardMode and AllocEmpty
---------------------------

NanGuardMode help users find where in the graph NaN appear. But
sometimes, we want some variables to not be checked. For example, in
the old GPU back-end, we use a float32 CudaNdarray to store the MRG
random number generator state (they are integers). So if NanGuardMode
check it, it will generate false positive. Another case is related to
[Gpu]AllocEmpty or some computation on it (like done by Scan).

You can tell NanGuardMode to do not check a variable with:
``variable.tag.nan_guard_mode_check``. Also, this tag automatically
follow that variable during optimization. This mean if you tag a
variable that get replaced by an inplace version, it will keep that
tag.


Final Note
----------

A more extensive discussion of this section's content may be found in
the advanced tutorial :ref:`Extending Theano<extending>`.

The section :ref:`Other ops <other_ops>` includes more instructions for
the following specific cases:

 - :ref:`scalar_ops`
 - :ref:`scipy_ops`
 - :ref:`sparse_ops`
 - :ref:`Random ops <random_ops>`
 - :ref:`openmp_ops`
 - :ref:`numba_ops`
