
.. _faq:

==========================
Frequently Asked Questions
==========================
TypeError: object of type 'TensorVariable' has no len()
-------------------------------------------------------

If you receive the following error, it is because the Python function *__len__* cannot 
be implemented on Theano variables:

.. code-block:: python

   TypeError: object of type 'TensorVariable' has no len()

Python requires that *__len__* returns an integer, yet it cannot be done as Theano's variables are symbolic. However, `var.shape[0]` can be used as a workaround.

This error message cannot be made more explicit because the relevant aspects of Python's 
internals cannot be modified.


Faster gcc optimization
-----------------------

You can enable faster gcc optimization with the ``cxxflags`` option.
This list of flags was suggested on the mailing list::

    -O3 -ffast-math -ftree-loop-distribution -funroll-loops -ftracer

Use it at your own risk. Some people warned that the ``-ftree-loop-distribution`` optimization resulted in wrong results in the past.

In the past we said that if the ``compiledir`` was not shared by multiple
computers, you could add the ``-march=native`` flag. Now we recommend
to remove this flag as Theano does it automatically and safely,
even if the ``compiledir`` is shared by multiple computers with different
CPUs. In fact, Theano asks g++ what are the equivalent flags it uses, and re-uses
them directly.


Faster Theano Function Compilation
----------------------------------

Theano function compilation can be time consuming. It can be sped up by setting
the flag ``mode=FAST_COMPILE`` which instructs Theano to skip most
optimizations and disables the generation of any c/cuda code. This is useful
for quickly testing a simple idea.

If c/cuda code is necessary, as when using a GPU, the flag
``optimizer=fast_compile`` can be used instead. It instructs Theano to skip time
consuming optimizations but still generate c/cuda code. To get the most out of
this flag requires using a development version of Theano instead of the latest
release (0.6).

Similarly using the flag ``optimizer_excluding=inplace`` will speed up
compilation by preventing optimizations that replace operations with a version
that reuses memory where it will not negatively impact the integrity of the
operation. Such optimizations can be time consuming. However using this flag will
result in greater memory usage because space must be allocated for the results
which would be unnecessary otherwise. In short, using this flag will speed up
compilation but it will also use more memory because
``optimizer_excluding=inplace`` excludes inplace optimizations resulting
in a trade off between speed of compilation and memory usage.

Theano flag `reoptimize_unpickled_function` controls if an unpickled theano function
should reoptimize its graph or not. Theano users can use the standard python pickle 
tools to save a compiled theano function. When pickling, both graph before and 
after the optimization are saved, including shared variables. When set to True, 
the graph is reoptimized when being unpickled. Otherwise, skip the graph optimization 
and use directly the optimized graph from the pickled file.

Faster Theano function
----------------------

You can set the Theano flag ``allow_gc`` to ``False`` to get a speed-up by using
more memory. By default, Theano frees intermediate results when we don't need
them anymore. Doing so prevents us from reusing this memory. So disabling the
garbage collection will keep all intermediate results' memory space to allow to
reuse them during the next call to the same Theano function, if they are of the
correct shape. The shape could change if the shapes of the inputs change.

.. _unsafe_optimization:

Unsafe optimization
===================


Some Theano optimizations make the assumption that the user inputs are
valid. What this means is that if the user provides invalid values (like
incompatible shapes or indexing values that are out of bounds) and
the optimizations are applied, the user error will get lost. Most of the
time, the assumption is that the user inputs are valid. So it is good
to have the optimization being applied, but loosing the error is bad.
The newest optimization in Theano with such assumption will add an
assertion in the graph to keep the user error message. Computing
these assertions could take some time. If you are sure everything is valid
in your graph and want the fastest possible Theano, you can enable an
optimization that will remove those assertions with:
``optimizer_including=local_remove_all_assert``


Faster Small Theano function
----------------------------

.. note::

   For Theano 0.6 and up.

For Theano functions that don't do much work, like a regular logistic
regression, the overhead of checking the input can be significant. You
can disable it by setting ``f.trust_input`` to True.
Make sure the types of arguments you provide match those defined when
the function was compiled.

For example, replace the following

.. code-block:: python

    x = theano.tensor.scalar('x')
    f = function([x], x + 1.)
    f(10.)

with

.. code-block:: python

    x = theano.tensor.scalar('x')
    f = function([x], x + 1.)
    f.trust_input = True
    f(numpy.array([10.], dtype=theano.config.floatX))

Also, for small Theano functions, you can remove more Python overhead by
making a Theano function that does not take any input. You can use shared
variables to achieve this. Then you can call it like this: ``f.fn()`` or
``f.fn(n_calls=N)`` to speed it up. In the last case, only the last
function output (out of N calls) is returned.

Out of memory... but not really
-------------------------------

Occasionally Theano may fail to allocate memory when there appears to be more
than enough reporting:

    Error allocating X bytes of device memory (out of memory). Driver report Y
    bytes free and Z total.

where X is far less than Y and Z (i.e. X << Y < Z).

This scenario arises when an operation requires allocation of a large contiguous
block of memory but no blocks of sufficient size are available.

GPUs do not have virtual memory and as such all allocations must be assigned to
a continuous memory region. CPUs do not have this limitation because or their
support for virtual memory. Multiple allocations on a GPU can result in memory
fragmentation which can makes it more difficult to find contiguous regions
of memory of sufficient size during subsequent memory allocations.

A known example is related to writing data to shared variables. When updating a
shared variable Theano will allocate new space if the size of the data does not
match the size of the space already assigned to the variable. This can lead to
memory fragmentation which means that a continugous block of memory of
sufficient capacity may not be available even if the free memory overall is
large enough.

Related Projects
----------------

We try to list in this `wiki page <https://github.com/Theano/Theano/wiki/Related-projects>`_ other Theano related projects.


"What are Theano's Limitations?"
--------------------------------

Theano offers a good amount of flexibility, but has some limitations too.
You must answer for yourself the following question: How can my algorithm be cleverly written 
so as to make the most of what Theano can do?

Here is a list of some of the known limitations:

- *While*- or *for*-Loops within an expression graph are supported, but only via
  the :func:`theano.scan` op (which puts restrictions on how the loop body can
  interact with the rest of the graph).

- Neither *goto* nor *recursion* is supported or planned within expression graphs.


"float32 / int{32, 64} gives float64"
-------------------------------------

It should be noted that using float32 and int{32, 64} together
inside a function would provide float64 as output.

Since the GPU can't compute this kind of output, it would be
preferable not to use those dtypes together.

To help you find where float64 are created, see the
:attr:`warn_float64` Theano flag.
