
.. _using_modes:

==========================================
Configuration Settings and Compiling Modes
==========================================


Configuration
=============

The ``config`` module contains several *attributes* that modify Theano's behavior.  Many of these
attributes are examined during the import of the ``theano`` module and several are assumed to be
read-only.

*As a rule, the attributes in the* ``config`` *module should not be modified inside the user code.*

Theano's code comes with default values for these attributes, but you can
override them from your ``.theanorc`` file, and override those values in turn by
the :envvar:`THEANO_FLAGS` environment variable.

The order of precedence is:

1. an assignment to theano.config.<property>
2. an assignment in :envvar:`THEANO_FLAGS`
3. an assignment in the .theanorc file (or the file indicated in :envvar:`THEANORC`)

You can display the current/effective configuration at any time by printing
theano.config.  For example, to see a list  of all active configuration
variables, type this from the command-line:

.. code-block:: bash

    python -c 'import theano; print theano.config' | less


For more detail, see :ref:`Configuration <libdoc_config>` in the library.


Exercise
========


Consider the logistic regression:

.. code-block:: python
    
    import numpy
    import theano
    import theano.tensor as T
    rng = numpy.random

    N = 400
    feats = 784
    D = (rng.randn(N, feats).astype(theano.config.floatX),
    rng.randint(size=N,low=0, high=2).astype(theano.config.floatX))
    training_steps = 10000

    # Declare Theano symbolic variables
    x = T.matrix("x")
    y = T.vector("y")
    w = theano.shared(rng.randn(feats).astype(theano.config.floatX), name="w")
    b = theano.shared(numpy.asarray(0., dtype=theano.config.floatX), name="b")
    x.tag.test_value = D[0]
    y.tag.test_value = D[1]
    #print "Initial model:"
    #print w.get_value(), b.get_value()

    # Construct Theano expression graph
    p_1 = 1 / (1 + T.exp(-T.dot(x, w)-b)) # Probability of having a one
    prediction = p_1 > 0.5 # The prediction that is done: 0 or 1
    xent = -y*T.log(p_1) - (1-y)*T.log(1-p_1) # Cross-entropy
    cost = xent.mean() + 0.01*(w**2).sum() # The cost to optimize
    gw,gb = T.grad(cost, [w,b])

    # Compile expressions to functions
    train = theano.function(
                inputs=[x,y],
                outputs=[prediction, xent],
                updates={w:w-0.01*gw, b:b-0.01*gb},
                name = "train")
    predict = theano.function(inputs=[x], outputs=prediction,
                name = "predict")

    if any([x.op.__class__.__name__ in ['Gemv', 'CGemv', 'Gemm', 'CGemm'] for x in
            train.maker.fgraph.toposort()]):
        print 'Used the cpu'
    elif any([x.op.__class__.__name__ in ['GpuGemm', 'GpuGemv'] for x in
              train.maker.fgraph.toposort()]):
        print 'Used the gpu'
    else:
        print 'ERROR, not able to tell if theano used the cpu or the gpu'
        print train.maker.fgraph.toposort()

    for i in range(training_steps):
        pred, err = train(D[0], D[1])
    #print "Final model:"
    #print w.get_value(), b.get_value()

    print "target values for D"
    print D[1]

    print "prediction on D"
    print predict(D[0])

   
Modify and execute this example to run on CPU (the default) with floatX=float32 and 
time the execution using the command line ``time python file.py``.  Save your code 
as it will be useful later on. 

.. Note::

   * Apply the Theano flag ``floatX=float32`` (through ``theano.config.floatX``) in your code.
   * Cast inputs before storing them into a shared variable.
   * Circumvent the automatic cast of *int32* with *float32* to *float64*:
    
     * Insert manual cast in your code or use *[u]int{8,16}*.
     * Insert manual cast around the mean operator (this involves division by length, which is an *int64*).
     * Notice that a new casting mechanism is being developed.

:download:`Solution<modes_solution_1.py>`

-------------------------------------------

Mode
====

Every time :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 and only use Python implementations.
- ``'FAST_RUN'``: Apply all optimizations and use C implementations where possible.
- ``'DebugMode``: 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
    several kinds of problems.
- ``'ProfileMode'``: Same optimization as FAST_RUN, but 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='py', optimizer='fast_compile')``    Python implementations only, quick and cheap graph transformations
``FAST_RUN``      ``compile.mode.Mode(linker='cvm', optimizer='fast_run')``       C implementations where available, all available graph transformations.
``DebugMode``     ``compile.debugmode.DebugMode()``                               Both implementations where available, all available graph transformations.
``ProfileMode``   ``compile.profilemode.ProfileMode()``                           C implementations where available, all available graph transformations, print profile information.
================= =============================================================== ===============================================================================

.. Note::

    For debugging purpose, there also exists a ``MonitorMode`` (which has no
    short name). It can be used to step through the execution of a function:
    see :ref:`the debugging FAQ<faq_monitormode>` for details.


Linkers
=======

A mode is composed of 2 things: an optimizer and a linker. Some modes,
like ``ProfileMode`` and ``DebugMode``, add logic around the optimizer and
linker. ``ProfileMode`` and ``DebugMode`` use their own linker.

You can select which linker to use with the Theano flag :attr:`config.linker`.
Here is a table to compare the different linkers.

=============  =========  =================  =========  ===
linker         gc [#gc]_  Raise error by op  Overhead   Definition
=============  =========  =================  =========  ===
cvm            yes        yes                "++"       As c|py, but the runtime algo to execute the code is in c
cvm_nogc       no         yes                "+"        As cvm, but without gc
c|py [#cpy1]_  yes        yes                "+++"      Try C code. If none exists for an op, use Python
c|py_nogc      no         yes                "++"       As c|py, but without gc
c              no         yes                "+"        Use only C code (if none available for an op, raise an error)
py             yes        yes                "+++"      Use only Python code
c&py [#cpy2]_  no         yes                "+++++"    Use C and Python code
ProfileMode    no         no                 "++++"     Compute some extra profiling info
DebugMode      no         yes                VERY HIGH  Make many checks on what Theano computes
=============  =========  =================  =========  ===


.. [#gc] Garbage collection of intermediate results during computation.
         Otherwise, their memory space used by the ops is kept between
         Theano function calls, in order not to
         reallocate memory, and lower the overhead (make it faster...).
.. [#cpy1] Default
.. [#cpy2] Deprecated


For more detail, see :ref:`Mode<libdoc_compile_mode>` in the library.


.. _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='DebugMode``). The DebugMode is designed to
run several self-checks and assertions that can help diagnose
possible programming errors leading to incorrect output. Note that
``DebugMode`` is much slower than ``FAST_RUN`` or ``FAST_COMPILE`` so
use it only during development (not when you launch 1000 processes 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='DebugMode')

    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='DebugMode')``). 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.

.. TODO: repair the following link

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

For more detail, see :ref:`DebugMode<debugmode>` in the library.

.. _using_profilemode:


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

Besides 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.

.. note::

    To switch the default accordingly, set the Theano flag
    :attr:`config.mode` to ProfileMode.  In that case, when the Python
    process exits, it will automatically print the profiling
    information on the standard output.

    The memory profile of the output of each ``apply`` node can be enabled with the 
    Theano flag :attr:`config.ProfileMode.profile_memory`.

For more detail, see :ref:`ProfileMode <profilemode>` in the library.

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 the ``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. Let's use our logistic
regression example.

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

    """


This output has two components. In the first section called
*Apply-wise summary*, timing information is provided for the worst
offending ``Apply`` nodes. This corresponds to individual op applications
within your graph which took 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).
Finally, notice that the ``ProfileMode`` also shows which ops were running a C
implementation.


For more detail, see :ref:`ProfileMode<libdoc_compile_mode>` in the library.

