
.. _using_modes:

===============================
Using different compiling modes
===============================

Mode
====

Everytime :func:`theano.function <function.function>` is called
the symbolic relationships between the input and output Theano *variables* 
are optimized and compiled. The way this compilation occurs 
is controlled by the value of the ``mode`` parameter.

Theano defines the following modes by name:

- ``'FAST_COMPILE'``: Apply just a few graph optimizations, but use C implementations where possible.
- ``'FAST_RUN'``: Apply all optimizations, and use C implementations where possible.
- ``'DEBUG_MODE'``: Verify the correctness of all optimizations, and compare C and python 
    implementations. This mode can take much longer than the other modes, 
    but can identify many kinds of problems.
- ``'PROFILE_MODE'``: Same optimization then FAST_RUN, put print some profiling information

The default mode is typically ``FAST_RUN``, but it can be controlled via
the configuration variable :attr:`config.mode`, 
which can be overridden by passing the keyword argument to
:func:`theano.function <function.function>`.

================= =============================================================== ===============================================================================
short name        Full constructor                                                What does it do?
================= =============================================================== ===============================================================================
FAST_COMPILE      ``compile.mode.Mode(linker='c|py', optimizer='fast_compile')``  C implementations where available, quick and cheap graph transformations
FAST_RUN          ``compile.mode.Mode(linker='c|py', optimizer='fast_run')``      C implementations where available, all available graph transformations.
DEBUG_MODE        ``compile.debugmode.DebugMode()``                               Both implementations where available, all available graph transformations.
PROFILE_MODE      ``compile.profilemode.ProfileMode()``                           C implementations where available, all available graph transformations, print profile information.
================= =============================================================== ===============================================================================



.. _using_debugmode:

Using DebugMode
===============

While normally you should use the ``FAST_RUN`` or ``FAST_COMPILE`` mode, 
it is useful at first (especially when you are defining new kinds of
expressions or new optimizations) to run your code using the DebugMode  
(available via ``mode='DEBUG_MODE'``). The DebugMode is designed to 
do several self-checks and assertations that can help to diagnose 
possible programming errors that can lead to incorect output. Note that 
``DEBUG_MODE`` is much slower then ``FAST_RUN`` or ``FAST_COMPILE`` so 
use it only during development (not when you launch 1000 process on a 
cluster!).


.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_modes.test_modes_1
DebugMode is used as follows:

.. code-block:: python

    x = T.dvector('x')

    f = theano.function([x], 10*x, mode='DEBUG_MODE')

    f([5]) 
    f([0]) 
    f([7]) 


If any problem is detected, DebugMode will raise an exception according to
what went wrong, either at call time (``f(5)``) or compile time (
``f = theano.function(x, 10*x, mode='DEBUG_MODE')``). These exceptions
should *not* be ignored; talk to your local Theano guru or email the
users list if you cannot make the exception go away.

Some kinds of errors can only be detected for certain input value combinations.
In the example above, there is no way to guarantee that a future call to say,
``f(-1)`` won't cause a problem.  DebugMode is not a silver bullet.

If you instantiate DebugMode using the constructor (see :class:`DebugMode`)
rather than the keyword ``DEBUG_MODE`` you can configure its behaviour via
constructor arguments.  See :ref:`DebugMode <debugMode>` for details.
The keyword version of DebugMode (which you get by using ``mode='DEBUG_MODE``)
is quite strict.


.. _using_profilemode:

ProfileMode
===========

Beside checking for errors, another important task is to profile your 
code. For this Theano uses a special mode called ProfileMode which has 
to be passed as an argument to :func:`theano.function <function.function>`. Using the ProfileMode is a three-step process.

To change the default to it, put the theano flags mode to PROFILE_MODE.

Creating a ProfileMode Instance
-------------------------------

First create a ProfileMode instance. 

>>> from theano import ProfileMode
>>> profmode = theano.ProfileMode(optimizer='fast_run', linker=theano.gof.OpWiseCLinker())

The ProfileMode constructor takes as input an optimizer and a
linker. Which optimizer and linker to use will depend on the
application. For example, a user wanting to profile the Python
implementation only, should use the gof.PerformLinker (or "py" for
short). On the other hand, a user wanting to profile his graph using C
implementations wherever possible should use the ``gof.OpWiseCLinker``
(or "c|py"). For testing the speed of your code we would recommend 
using the 'fast_run' optimizer and ``gof.OpWiseCLinker`` linker.

Compiling your Graph with ProfileMode
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Once the ProfileMode instance is created, simply compile your graph as you
would normally, by specifying the mode parameter.

>>> # with functions
>>> f = theano.function([input1,input2],[output1], mode=profmode)
>>> # with modules
>>> m = theano.Module()
>>> minst = m.make(mode=profmode)

Retrieving Timing Information
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Once your graph is compiled, simply run the program or operation you wish to
profile, then call ``profmode.print_summary()``. This will provide you with
the desired timing information, indicating where your graph is spending most
of its time.

This is best shown through an example.
Lets use the example of logistic
regression.  (Code for this example is in the file
``benchmark/regression/regression.py``.) 

Compiling the module with ProfileMode and calling ``profmode.print_summary()``
generates the following output:

.. code-block:: python
    
    """
    ProfileMode.print_summary()
    ---------------------------

    local_time 0.0749197006226 (Time spent running thunks)
    Apply-wise summary: <fraction of local_time spent at this position> (<Apply position>, <Apply Op name>)
            0.069   15      _dot22
            0.064   1       _dot22
            0.053   0       InplaceDimShuffle{x,0}
            0.049   2       InplaceDimShuffle{1,0}
            0.049   10      mul
            0.049   6       Elemwise{ScalarSigmoid{output_types_preference=<theano.scalar.basic.transfer_type object at 0x171e650>}}[(0, 0)]
            0.049   3       InplaceDimShuffle{x}
            0.049   4       InplaceDimShuffle{x,x}
            0.048   14      Sum{0}
            0.047   7       sub
            0.046   17      mul
            0.045   9       sqr
            0.045   8       Elemwise{sub}
            0.045   16      Sum
            0.044   18      mul
       ... (remaining 6 Apply instances account for 0.25 of the runtime)
    Op-wise summary: <fraction of local_time spent on this kind of Op> <Op name>
            0.139   * mul
            0.134   * _dot22
            0.092   * sub
            0.085   * Elemwise{Sub{output_types_preference=<theano.scalar.basic.transfer_type object at 0x1779f10>}}[(0, 0)]
            0.053   * InplaceDimShuffle{x,0}
            0.049   * InplaceDimShuffle{1,0}
            0.049   * Elemwise{ScalarSigmoid{output_types_preference=<theano.scalar.basic.transfer_type object at 0x171e650>}}[(0, 0)]
            0.049   * InplaceDimShuffle{x}
            0.049   * InplaceDimShuffle{x,x}
            0.048   * Sum{0}
            0.045   * sqr
            0.045   * Sum
            0.043   * Sum{1}
            0.042   * Elemwise{Mul{output_types_preference=<theano.scalar.basic.transfer_type object at 0x17a0f50>}}[(0, 1)]
            0.041   * Elemwise{Add{output_types_preference=<theano.scalar.basic.transfer_type object at 0x1736a50>}}[(0, 0)]
            0.039   * Elemwise{Second{output_types_preference=<theano.scalar.basic.transfer_type object at 0x1736d90>}}[(0, 1)]
       ... (remaining 0 Ops account for 0.00 of the runtime)
    (*) Op is running a c implementation

    """


The summary has two components to it. In the first section called the
Apply-wise summary, timing information is provided for the worst
offending Apply nodes. This corresponds to individual Op applications
within your graph which take the longest to execute (so if you use
``dot`` twice, you will see two entries there). In the second portion,
the Op-wise summary, the execution time of all Apply nodes executing
the same Op are grouped together and the total execution time per Op
is shown (so if you use ``dot`` twice, you will see only one entry
there corresponding to the sum of the time spent in each of them).

Note that the ProfileMode also shows which Ops were running a c
implementation.

