
=============================================
Proposal for pfunc  Function Interface [DONE]
=============================================

.. note::

  This proposal was implemented some time around summer 2009, and merged into
  the trunk around new years 2010.

Following discussion on theano-dev (titled TheanoObject), the following
changes are proposed to make function-construction calls more
readable and intuitive, and to make it easier to share values between
functions.

The strategy is to 

- introduce a new kind of ``Variable`` (``SharedVariable``) that has a container
  associated with it, and can allow multiple functions to share a value.

- introduce a class called ``Param`` to serve a role similar to that of ``In``,

- introduce a friendlier version of function (tentative name ``pfunc``),

The following code gives a very quick idea of what is being proposed:

..code-block:: python

    a = lscalar()
    b = shared(1) #NEW: create a shared variable

    f1 = pfunc([a], a+b)
    f2 = pfunc([Param(a, default=44)], a + b, updates={b: b + 1})

    b.value # -> 1

    f1(3)   # -> 4
    f2(3)   # -> 4 (but update b.value with += 1)
    b.value # -> 2

    f1(3)   # -> 5

    b.value = 0
    f1(3)   # -> 3


Declaring a Shared Variable
===========================

The proposal is for two new ways of creating a *shared* variable:

.. code-block:: python

    class SharedVariable(Variable):
        """
        Variable with a value that is (defaults to being) shared between functions that it appears in.
        """

        def __init__(self, name, type, value, strict):
            """
            :param name: The name for this variable (see `Variable`).
    
            :param type: The type for this variable (see `Variable`).
    
            :param value: A value to associate with this variable (a new container will be created).
    
            :param strict: True -> assignments to .value will not be casted or copied, so they must
            have the correct type.
    
            :param container: The container to use for this variable. Illegal to pass this as well
            as a value.
    
            For more user-friendly constructor, see `shared`

            """
            ...



        value = property(...)
        """Read/write the non-symbolic value associated with this SharedVariable.
        
        If the SharedVariable is shared, changes to this value will be visible to all functions using
        this SharedVariable.  If this SharedVariable is not shared, a change will not be visible to
        functions that were created before the change.
    
        """

    def shared(value, name=None, strict=False, **kwargs):
        """Return a SharedVariable Variable, initialized with a copy or reference of `value`.
    
        This function iterates over constructor functions (see `shared_constructor`) to find a
        suitable SharedVariable subclass.
    
        :note: 
        By passing kwargs, you effectively limit the set of potential constructors to those that
        can accept those kwargs.
        
        """
        ...

The function `shared` is a factory-method intended for end-users.

Direct construction of a ``SharedVariable`` is probably not going to be a common
pattern, it will be more common to subclass it (i.e. ``TensorSharedVariable``,
``SparseSharedVariable``, etc.) and to register a constructor so that these
subclasses will be instantiated by the `shared` factory method.

A ``SharedVariable`` instance is meant to change over the duration of a program,
either because of the updates of a function call, or because of direct
assignment to its ``.value`` field.
At any time, the ``.value`` field can be be used to access the current value
associated with the shared value.

Using SharedVariables as pfunc Parameters
=========================================

A ``SharedVariable`` instance has a ``value`` property that can be used to get and
set the value associated with that shared variable in all the ``pfunc``
functions that use it.

.. code-block:: python

    a = tensor.lscalar()
    b = shared(7)

    # create two functions that use `b` as an implicit input
    f1 = pfunc([a], a + b)
    f2 = pfunc([a], a * b)

    f1(5) # -> 12
    b.value = 8    # modify the shared variable's value

    f1(5) # -> 13   # the new value is reflected in any compiled functions
    f2(4) # -> 32   # f2 uses the latest value in b's container

However, SharedVariables cannot be used as inputs to theano functions.
This is because doing it may yield code that would be either ambiguous, or
prone to easy mistakes (e.g. accidentally overwriting the content of a shared
variable).


Param and pfunc
===============

The examples above give the general flavour of what pfunc and Param are for.
Their signatures are below.
Corner cases and exotic examples can be found in the tests.

.. code-block:: python

    def pfunc(params, outputs, mode=None, givens={}, updates=[])
        """Function-constructor for graphs with shared variables.
    
        :type params: list of either Variable or Param instances.
        :param params: function parameters, these are not allowed to be shared
        variables
    
        :type outputs: list of Variables or Out instances
        :param outputs: expressions to compute
    
        :param mode: compilation mode
    
        :type updates: iterable over pairs (shared_variable, new_expression). List, tuple or dict.
        :param updates: update the values for SharedVariable inputs according to these expressions
    
        :rtype: theano.compile.Function
        :returns: a callable object that will compute the outputs (given the inputs)
        and update the implicit function arguments according to the `updates`.
    
        """
        ...

.. code-block:: python

    class Param(object):
        def __init__(self, variable, default=None, mutable=False, strict=False):
            """
            :param variable: A node in an expression graph to set with each function call.
    
            :param default: The default value to use at call-time (can also be a Container where
            the function will find a value at call-time.)
    
            :param name: A string to identify this parameter from function kwargs.
    
            :param mutable: True -> function is allowed to modify this argument.
    
            :param strict: False -> function arguments may be copied or casted to match the
            type required by the parameter `variable`.  True -> function arguments must exactly match the type
            required by `variable`.
    
            :param implicit: see help(theano.io.In)
    
            """

Note that if some update value is not a variable, it will be cast into
a ``SharedVariable`` using the ``shared`` function. This ensures it is
properly taken into account to build the Theano function underlying the
``pfunc``. A consequence of this is that if this update value is mutable
(e.g. a Numpy array), it may be modified after the function is created.


NNet Example
============

Of course there are lots of ways to write the following code, but this is one
simple one.

.. code-block:: python


    import numpy, theano
   
    from pfunc import pfunc
    from sharedvalue import shared
    from theano import tensor
    from theano.tensor.nnet import sigmoid
    
    class NNet(object):
    
        def __init__(self, 
                input = tensor.dvector('input'),
                target = tensor.dvector('target'),
                n_input=1, n_hidden=1, n_output=1, lr=1e-3, **kw):
            super(NNet, self).__init__(**kw)
        
            self.input = input
            self.target = target
            self.lr = shared(lr, 'learning_rate')
            self.w1 = shared(numpy.zeros((n_hidden, n_input)), 'w1')
            self.w2 = shared(numpy.zeros((n_output, n_hidden)), 'w2')
        
            self.hidden = sigmoid(tensor.dot(self.w1, self.input))
            self.output = tensor.dot(self.w2, self.hidden)
            self.cost = tensor.sum((self.output - self.target)**2)
        
            self.sgd_updates = {
                        self.w1: self.w1 - self.lr * tensor.grad(self.cost, self.w1),
                        self.w2: self.w2 - self.lr * tensor.grad(self.cost, self.w2)}
        
            self.sgd_step = pfunc(
                    params = [self.input, self.target],
                    outputs = [self.output, self.cost],
                    updates = self.sgd_updates)
        
            self.compute_output = pfunc([self.input],  self.output)
        
            self.output_from_hidden = pfunc([self.hidden], self.output)
                            
