
.. _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:

.. testcode::

    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]

    # 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("target values for D")
    print(D[1])

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

.. testoutput::
   :hide:
   :options: +ELLIPSIS

   Used the cpu
   target values for D
   ...
   prediction on D
   ...

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*).
     * Note 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. So GPU is disabled.
- ``'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.
- ``'NanGuardMode'``: Same optimization as FAST_RUN, but :ref:`check if a node generate nans. <nanguardmode>`

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.
================= =============================================================== ===============================================================================

.. 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 ``NanGuardMode`` and ``DebugMode``, add logic around the
optimizer and linker. ``DebugMode`` uses its 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
NanGuardMode   yes        yes                "++++"     Check if nodes generate NaN
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


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:

.. testcode::

    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.
