.. _scan_internals:

Internal documentation of the scan op
=====================================

Top-level description of scan
-----------------------------

The `scan` operation is meant to be able to describe symbolically loops,
recurrent relations or dynamical systems. In general, we will say that the
scan op implements system of equations of the following form:

.. math::

    \mathbf{x}_1(t) = f_{\mathbf{x}_1}
        (\mathbf{u}_1(t), \mathbf{u}_1(t-1), \ldots, \mathbf{u}_1(t-l_1), 
         \mathbf{u}_2(t), \ldots, \mathbf{u}_2(t-l_2),
         \ldots,
         \mathbf{u}_M(t), \ldots, \mathbf{u}_M(t - l_M),
         \mathbf{x}_1(t-1), \ldots, \mathbf{x}_1(t-k_1),
         \ldots,
         \mathbf{x}_N(t-1), \ldots, \mathbf{x}_N(t-k_N), 
         \mathbf{w}_1, \ldots, \mathbf{w}_Q)

    \vdots

    \mathbf{x}_N(t) = f_{\mathbf{x}_N}
        (\mathbf{u}_1(t), \mathbf{u}_1(t-1), \ldots, \mathbf{u}_1(t-l_1), 
         \mathbf{u}_2(t), \ldots, \mathbf{u}_2(t-l_2),
         \ldots,
         \mathbf{u}_M(t), \ldots, \mathbf{u}_M(t - l_M),
         \mathbf{x}_1(t-1), \ldots, \mathbf{x}_1(t-k_1),
         \ldots,
         \mathbf{x}_N(t-1), \ldots, \mathbf{x}_N(t-k_N), 
         \mathbf{w}_1, \ldots, \mathbf{w}_Q)
    
    \mathbf{y}_1(t) = f_{\mathbf{y}_1}
        (\mathbf{u}_1(t), \mathbf{u}_1(t-1), \ldots, \mathbf{u}_1(t-l_1), 
         \mathbf{u}_2(t), \ldots, \mathbf{u}_2(t-l_2),
         \ldots,
         \mathbf{u}_M(t), \ldots, \mathbf{u}_M(t - l_M),
         \mathbf{x}_1(t-1), \ldots, \mathbf{x}_1(t-k_1),
         \ldots,
         \mathbf{x}_N(t-1), \ldots, \mathbf{x}_N(t-k_N), 
         \mathbf{w}_1, \ldots, \mathbf{w}_Q)

      \vdots

    \mathbf{y}_M(t) = f_{\mathbf{y}_M}
        (\mathbf{u}_1(t), \mathbf{u}_1(t-1), \ldots, \mathbf{u}_1(t-l_1), 
         \mathbf{u}_2(t), \ldots, \mathbf{u}_2(t-l_2),
         \ldots,
         \mathbf{u}_M(t), \ldots, \mathbf{u}_M(t - l_M),
         \mathbf{x}_1(t-1), \ldots, \mathbf{x}_1(t-k_1),
         \ldots,
         \mathbf{x}_N(t-1), \ldots, \mathbf{x}_N(t-k_N), 
         \mathbf{w}_1, \ldots, \mathbf{w}_Q)

The equations describe a system evolving in time, where :math:`t` represents the
current step. The system is described by inputs, states, outputs and
parameteres. 

The inputs, denoted by :math:`\mathbf{u}` are time-varying quantities, 
hence indexed by :math:`t`. They however only influence the system, but are
not influenced by the system. 

The states :math:`\mathbf{x}` are time-varying quantities, whose value at
time :math:`t` depends on its (or other state) previous values as well as
the inputs and parameters. Note that the first few values of the states are
always provided, otherwise we could not imploy the recurrent equation to
generate these sequence of values without a starting point. 

The outputs, :math:`\mathbf{y}` are outputs of the system, i.e. values that
depend on the previous values of the states and inputs. The difference
between outputs and states is that outputs do not feed back into the system. 

The parameters :math:`\mathbf{w}` are fixed quantities that are re-used at
every time step of the evolution of the system. 

Each of the equations above are implemented by the **inner function** of scan. You 
can think of the **inner function** as a theano function that gets executed
at each step to get the new values. This **inner function** should not be
confused with the **constructive function**, which is what the user gives to
the scan function. The **constructive function** is used to construct the
computational graph that is afterwards compiled into the **inner function**.


Naming conventions
------------------

* ``input_state`` will stand for a state :math:`\mathbf{x}`, when it is
  provided as an input to the recurrent formula (the inner function) that 
  will generate the new value of the state
* ``output_state`` will stand for a state :math:`\math{x}` when it refers 
  to the result of the recurrent formula (the output of the inner function)
* ``output`` will stand for an output :math:`\mathbf{y}`
* ``input`` will be an input :math:`\mathbf{u}`
* ``parameter`` will stand for a parameter tensor :math:`\mathbf{w}` that stays 
  constant at each step of the inner function 
* ``non_numeric_input_state`` will stand for states that are not numeric in nature, 
  more specifically *random states*, when they are provided as an input. The
  same holds for ``non_numeric_output_state``.
* ``t`` is the time index (the current step in the evolution of the system).
* ``T`` is the total number of steps in the evolution of the system.
* the suffix ``_slices`` added to either ``x`` or ``u`` will mean the list of
  variables representing slices of states or inputs. These are the arguments
  given to the constructive function of scan (see above). 
* the suffix ``_inner`` added to ``x``, ``y``, ``xy``, ``u``, ``w`` or ``z``
  will mean the variables representing the state/output/input/weights in the
  inner function
* the suffix ``_outer`` added to ``x``, ``y``, ``xy``, ``u``, ``w`` or ``z``
  will mean the variables representing the state/output/input/weights in the
  main computational graph (the one containing the scan op).

Files 
-----

The implementation of scan is spread over several files. The different
files, and section of the code they deal with, are :

* ``scan.py`` implements the ``scan`` function. The ``scan`` function
  arranges the arguments of scan correctly, constructs the scan op and
  afterwards calls the constructed scan op on the arguments. This function
  takes care of figuring out missing inputs and shared variables. 

* ``scan_op.py`` implements the ``scanOp`` class. The ``scanOp`` respects
  the ``Op`` interface, and contains most of the logic of the scan operator.

* ``scan_utils.py`` contains several helpful functions used through out the
  other files that are specific of the scan operator.

* ``scan_views.py`` contains different views of the scan op that have
  simpler and easier signatures to be used in specific cases.

* ``scan_opt.py`` contains the list of all optimizations for the scan
  operator.

The logical flow
----------------

First the scan arguments are parsed by the function ``canonical_arguments``, 
that wraps them into lists and adds default values for the arguments. One
important step that happens in this function is that the inputs arguments
are converted such that they all have a single tap, namely 0. For example 
if you have ``[{'input':u, 'taps':[0, 4]}]`` as the list of inputs arguments
to scan, it gets converted into ``[{'input':u, 'taps':[0]}, {'input':u[4:],
'taps':[0]}]``.

The second step is to check if ``n_steps`` is a constant and has the value 1
or -1. If that is true then the function ``one_step_scan`` is called which 
unwraps the computation of the inner function into the outer graph without 
adding any scan op in the graph.

