
.. _optimization:

==================
Graph optimization
==================

In this section we will define a couple optimizations on doubles.

.. todo::

   This tutorial goes way too far under the hood, for someone who just wants
   to add yet another pattern to the libraries in tensor.opt for example.

   We need another tutorial that covers the decorator syntax, and explains how
   to register your optimization right away.  That's what you need to get
   going.

   Later, the rest is more useful for when that decorator syntax type thing
   doesn't work. (There are optimizations that don't fit that model).

.. note::

   The optimization tag `cxx_only` is used for optimizations that insert
   Ops which have no Python implementation (so they only have C code).
   Optimizations with this tag are skipped when there is no C++ compiler
   available.

Global and local optimizations
==============================

First, let's lay out the way optimizations work in Theano. There are
two types of optimizations: *global* optimizations and *local*
optimizations. A global optimization takes a ``FunctionGraph`` object (a
FunctionGraph is a wrapper around a whole computation graph, you can see its
:class:`documentation <FunctionGraph>` for more details) and navigates through it
in a suitable way, replacing some Variables by others in the process. A
local optimization, on the other hand, is defined as a function on a
*single* :ref:`apply` node and must return either ``False`` (to mean that
nothing is to be done) or a list of new Variables that we would like to
replace the node's outputs with. A :ref:`navigator` is a special kind
of global optimization which navigates the computation graph in some
fashion (in topological order, reverse-topological order, random
order, etc.) and applies one or more local optimizations at each step.

Optimizations which are holistic, meaning that they must take into
account dependencies that might be all over the graph, should be
global. Optimizations that can be done with a narrow perspective are
better defined as local optimizations. The majority of optimizations
we want to define are local.

.. optimizer:

Global optimization
-------------------

A global optimization (or optimizer) is an object which defines the following
methods:

.. class:: Optimizer

    .. method:: apply(fgraph)

      This method takes a FunctionGraph object which contains the computation graph
      and does modifications in line with what the optimization is meant
      to do. This is one of the main methods of the optimizer.

    .. method:: add_requirements(fgraph)

      This method takes a FunctionGraph object and adds :ref:`features
      <libdoc_gof_fgraphfeature>` to it. These features are "plugins" that are needed
      for the ``apply`` method to do its job properly.

    .. method:: optimize(fgraph)

      This is the interface function called by Theano.

      *Default:* this is defined by Optimizer as ``add_requirement(fgraph);
      apply(fgraph)``.

See the section about :class:`FunctionGraph` to understand how to define these
methods.


Local optimization
------------------

A local optimization is an object which defines the following methods:

.. class:: LocalOptimizer

    .. method:: transform(node)

      This method takes an :ref:`apply` node and returns either ``False`` to
      signify that no changes are to be done or a list of Variables which
      matches the length of the node's ``outputs`` list. When the
      LocalOptimizer is applied by a Navigator, the outputs of the node
      passed as argument to the LocalOptimizer will be replaced by the
      list returned.



One simplification rule
=======================

For starters, let's define the following simplification:

.. math::

   \frac{xy}{y} = x

We will implement it in three ways: using a global optimization, a
local optimization with a Navigator and then using the PatternSub
facility.


Global optimization
-------------------

Here is the code for a global optimization implementing the
simplification described above:

.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_2

.. code-block:: python

   from theano.gof import toolbox

   class Simplify(gof.Optimizer):
       def add_requirements(self, fgraph):
           fgraph.attach_feature(toolbox.ReplaceValidate())
       def apply(self, fgraph):
           for node in fgraph.toposort():
               if node.op == div:
                   x, y = node.inputs
                   z = node.outputs[0]
                   if x.owner and x.owner.op == mul:
                       a, b = x.owner.inputs
                       if y == a:
                           fgraph.replace_validate(z, b)
                       elif y == b:
                           fgraph.replace_validate(z, a)

   simplify = Simplify()

.. todo::

   What is add_requirements? Why would we know to do this? Are there other
   requirements we might want to  know about?

Here's how it works: first, in ``add_requirements``, we add the
``ReplaceValidate`` :ref:`libdoc_gof_fgraphfeature` located in
:ref:`libdoc_gof_toolbox`. This feature adds the ``replace_validate``
method to ``fgraph``, which is an enhanced version of ``replace`` that
does additional checks to ensure that we are not messing up the
computation graph (note: if ``ReplaceValidate`` was already added by
another optimizer, ``extend`` will do nothing). In a nutshell,
``toolbox.ReplaceValidate`` grants access to ``fgraph.replace_validate``,
and ``fgraph.replace_validate`` allows us to replace a Variable with
another while respecting certain validation constraints. You can
browse the list of :ref:`libdoc_gof_fgraphfeaturelist` and see if some of
them might be useful to write optimizations with. For example, as an
exercise, try to rewrite Simplify using :class:`NodeFinder`. (Hint: you
want to use the method it publishes instead of the call to toposort!)

Then, in ``apply`` we do the actual job of simplification. We start by
iterating through the graph in topological order. For each node
encountered, we check if it's a ``div`` node. If not, we have nothing
to do here. If so, we put in ``x``, ``y`` and ``z`` the numerator,
denominator and quotient (output) of the division.
The simplification only occurs when the numerator is a multiplication,
so we check for that. If the numerator is a multiplication we put the
two operands in ``a`` and ``b``, so
we can now say that ``z == (a*b)/y``. If ``y==a`` then ``z==b`` and if
``y==b`` then ``z==a``. When either case happens then we can replace
``z`` by either ``a`` or ``b`` using ``fgraph.replace_validate`` - else we do
nothing. You might want to check the documentation about :ref:`variable`
and :ref:`apply` to get a better understanding of the
pointer-following game you need to get ahold of the nodes of interest
for the simplification (``x``, ``y``, ``z``, ``a``, ``b``, etc.).


.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_2

Test time:

>>> x = double('x')
>>> y = double('y')
>>> z = double('z')
>>> a = add(z, mul(div(mul(y, x), y), div(z, x)))
>>> e = gof.FunctionGraph([x, y, z], [a])
>>> e
[add(z, mul(div(mul(y, x), y), div(z, x)))]
>>> simplify.optimize(e)
>>> e
[add(z, mul(x, div(z, x)))]

Cool! It seems to work. You can check what happens if you put many
instances of :math:`\frac{xy}{y}` in the graph. Note that it sometimes
won't work for reasons that have nothing to do with the quality of the
optimization you wrote. For example, consider the following:

>>> x = double('x')
>>> y = double('y')
>>> z = double('z')
>>> a = div(mul(add(y, z), x), add(y, z))
>>> e = gof.FunctionGraph([x, y, z], [a])
>>> e
[div(mul(add(y, z), x), add(y, z))]
>>> simplify.optimize(e)
>>> e
[div(mul(add(y, z), x), add(y, z))]

Nothing happened here. The reason is: ``add(y, z) != add(y,
z)``. That is the case for efficiency reasons. To fix this problem we
first need to merge the parts of the graph that represent the same
computation, using the ``merge_optimizer`` defined in
``theano.gof.opt``.

>>> from theano.gof.opt import merge_optimizer
>>> merge_optimizer.optimize(e)
>>> e
[div(mul(*1 -> add(y, z), x), *1)]
>>> simplify.optimize(e)
>>> e
[x]

Once the merge is done, both occurrences of ``add(y, z)`` are
collapsed into a single one and is used as an input in two
places. Note that ``add(x, y)`` and ``add(y, x)`` are still considered
to be different because Theano has no clue that ``add`` is
commutative. You may write your own global optimizer to identify
computations that are identical with full knowledge of the rules of
arithmetics that your Ops implement. Theano might provide facilities
for this somewhere in the future.

.. note::

   :class:`FunctionGraph` is a Theano structure intended for the optimization
   phase. It is used internally by function and is rarely
   exposed to the end user. You can use it to test out optimizations,
   etc. if you are comfortable with it, but it is recommended to use
   the function frontend and to interface optimizations with
   :class:`optdb` (we'll see how to do that soon).


Local optimization
------------------

The local version of the above code would be the following:

.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_2


.. code-block:: python

   class LocalSimplify(gof.LocalOptimizer):
       def transform(self, node):
           if node.op == div:
               x, y = node.inputs
               if x.owner and x.owner.op == mul:
                   a, b = x.owner.inputs
                   if y == a:
                       return [b]
                   elif y == b:
                       return [a]
           return False
       def tracks(self):
           # This should be needed for the EquilibriumOptimizer
           # but it isn't now
           # TODO: do this and explain it
           return [] # that's not what you should do

   local_simplify = LocalSimplify()

.. todo::

    Fix up previous example... it's bad and incomplete.

The definition of transform is the inner loop of the global optimizer,
where the node is given as argument. If no changes are to be made,
``False`` must be returned. Else, a list of what to replace the node's
outputs with must be returned. This list must have the same length as
node.ouputs. If one of node.outputs don't have clients(it is not used
in the graph), you can put None in the returned list to remove it.

In order to apply the local optimizer we must use it in conjunction
with a :ref:`navigator`. Basically, a :ref:`navigator` is a global
optimizer that loops through all nodes in the graph (or a well-defined
subset of them) and applies one or several local optimizers on them.

.. If you modify this code, also change :
.. theano/tests/test_tutorial.py:T_extending.test_extending_2

>>> x = double('x')
>>> y = double('y')
>>> z = double('z')
>>> a = add(z, mul(div(mul(y, x), y), div(z, x)))
>>> e = gof.FunctionGraph([x, y, z], [a])
>>> e
[add(z, mul(div(mul(y, x), y), div(z, x)))]
>>> simplify = gof.TopoOptimizer(local_simplify)
>>> simplify.optimize(e)
>>> e
[add(z, mul(x, div(z, x)))]


OpSub, OpRemove, PatternSub
+++++++++++++++++++++++++++

Theano defines some shortcuts to make LocalOptimizers:

.. function:: OpSub(op1, op2)

  Replaces all uses of *op1* by *op2*. In other
  words, the outputs of all :ref:`apply` involving *op1* by the outputs
  of Apply nodes involving *op2*, where their inputs are the same.

.. function:: OpRemove(op)

  Removes all uses of *op* in the following way:
  if ``y = op(x)`` then ``y`` is replaced by ``x``. *op* must have as many
  outputs as it has inputs. The first output becomes the first input,
  the second output becomes the second input, and so on.

.. function:: PatternSub(pattern1, pattern2)

  Replaces all occurrences of the first pattern by the second pattern.
  See :class:`PatternSub`.


.. code-block:: python

   from theano.gof.opt import OpSub, OpRemove, PatternSub

   # Replacing add by mul (this is not recommended for primarily
   # mathematical reasons):
   add_to_mul = OpSub(add, mul)

   # Removing identity
   remove_identity = OpRemove(identity)

   # The "simplify" operation we've been defining in the past few
   # sections. Note that we need two patterns to account for the
   # permutations of the arguments to mul.
   local_simplify_1 = PatternSub((div, (mul, 'x', 'y'), 'y'),
                                 'x')
   local_simplify_2 = PatternSub((div, (mul, 'x', 'y'), 'x'),
                                 'y')

.. note::

   ``OpSub``, ``OpRemove`` and ``PatternSub`` produce local optimizers, which
   means that everything we said previously about local optimizers
   apply: they need to be wrapped in a Navigator, etc.

.. todo::

   wtf is a navigator?

When an optimization can be naturally expressed using ``OpSub``, ``OpRemove``
or ``PatternSub``, it is highly recommended to use them.

WRITEME: more about using PatternSub (syntax for the patterns, how to
use constraints, etc. - there's some decent doc at
:class:`PatternSub` for those interested)



.. _optdb:

The optimization database (optdb)
=================================

Theano exports a symbol called ``optdb`` which acts as a sort of
ordered database of optimizations. When you make a new optimization,
you must insert it at the proper place in the database. Furthermore,
you can give each optimization in the database a set of tags that can
serve as a basis for filtering.

The point of optdb is that you might want to apply many optimizations
to a computation graph in many unique patterns. For example, you might
want to do optimization X, then optimization Y, then optimization
Z. And then maybe optimization Y is an EquilibriumOptimizer containing
LocalOptimizers A, B and C which are applied on every node of the
graph until they all fail to change it. If some optimizations act up,
we want an easy way to turn them off. Ditto if some optimizations are
very CPU-intensive and we don't want to take the time to apply them.

The optdb system allows us to tag each optimization with a unique name
as well as informative tags such as 'stable', 'buggy' or
'cpu_intensive', all this without compromising the structure of the
optimizations.


Definition of optdb
-------------------

optdb is an object which is an instance of
:class:`SequenceDB <optdb.SequenceDB>`,
itself a subclass of :class:`DB <optdb.DB>`.
There exist (for now) two types of DB, SequenceDB and EquilibriumDB.
When given an appropriate Query, DB objects build an Optimizer matching
the query.

A SequenceDB contains Optimizer or DB objects. Each of them has a
name, an arbitrary number of tags and an integer representing their
order in the sequence. When a Query is applied to a SequenceDB, all
Optimizers whose tags match the query are inserted in proper order in
a SequenceOptimizer, which is returned. If the SequenceDB contains DB
instances, the Query will be passed to them as well and the optimizers
they return will be put in their places.

An EquilibriumDB contains LocalOptimizer or DB objects. Each of them
has a name and an arbitrary number of tags. When a Query is applied to
an EquilibriumDB, all LocalOptimizers that match the query are
inserted into an EquilibriumOptimizer, which is returned. If the
SequenceDB contains DB instances, the Query will be passed to them as
well and the LocalOptimizers they return will be put in their places
(note that as of yet no DB can produce LocalOptimizer objects, so this
is a moot point).

Theano contains one principal DB object, :class:`optdb`, which
contains all of Theano's optimizers with proper tags. It is
recommended to insert new Optimizers in it. As mentioned previously,
optdb is a SequenceDB, so, at the top level, Theano applies a sequence
of global optimizations to the computation graphs.


Query
-----

A Query is built by the following call:

.. code-block:: python

   theano.gof.Query(include, require = None, exclude = None, subquery = None)

.. class:: Query

    .. attribute:: include

       A set of tags (a tag being a string) such that every
       optimization obtained through this Query must have **one** of the tags
       listed. This field is required and basically acts as a starting point
       for the search.

    .. attribute:: require

       A set of tags such that every optimization obtained
       through this Query must have **all** of these tags.

    .. attribute:: exclude

       A set of tags such that every optimization obtained
       through this Query must have **none** of these tags.

    .. attribute:: subquery

       optdb can contain sub-databases; subquery is a
       dictionary mapping the name of a sub-database to a special Query.
       If no subquery is given for a sub-database, the original Query will be
       used again.

Furthermore, a Query object includes three methods, ``including``,
``requiring`` and ``excluding`` which each produce a new Query object
with include, require and exclude sets refined to contain the new [WRITEME]


Examples
--------

Here are a few examples of how to use a Query on optdb to produce an
Optimizer:

.. code-block:: python
   
   from theano.compile import optdb

   # This is how the optimizer for the fast_run mode is defined
   fast_run = optdb.query(Query(include = ['fast_run']))

   # This is how the optimizer for the fast_compile mode is defined
   fast_compile = optdb.query(Query(include = ['fast_compile']))

   # This is the same as fast_run but no optimizations will replace
   # any operation by an inplace version. This assumes, of course,
   # that all inplace operations are tagged as 'inplace' (as they
   # should!)
   fast_run_no_inplace = optdb.query(Query(include = ['fast_run'], exclude = ['inplace']))
   fast_run_no_inplace = fast_run.excluding('inplace')


Registering an Optimizer
------------------------

Let's say we have a global optimizer called ``simplify``. We can add
it to ``optdb`` as follows:

.. code-block:: python

   # optdb.register(name, optimizer, order, *tags)
   optdb.register('simplify', simplify, 0.5, 'fast_run')

Once this is done, the FAST_RUN mode will automatically include your
optimization (since you gave it the 'fast_run' tag). Of course,
already-compiled functions will see no change. The 'order' parameter
(what it means and how to choose it) will be explained in
:ref:`optdb-structure` below.



Registering a LocalOptimizer
----------------------------

LocalOptimizers may be registered in two ways:

* Wrap them in a Navigator and insert them like a global optimizer
  (see previous section).
* Put them in an EquilibriumDB.

Theano defines two EquilibriumDBs where you can put local
optimizations:


.. function:: canonicalize

  This contains optimizations that aim to *simplify* the graph:

  * Replace rare or esoterical operations with their equivalents using
    elementary operations.

  * Order operations in a canonical way (any sequence of
    multiplications and divisions can be rewritten to contain at most
    one division, for example; ``x*x`` can be rewritten ``x**2``; etc.)

  * Fold constants (``Constant(2)*Constant(2)`` becomes ``Constant(4)``)


.. function:: specialize

  This contains optimizations that aim to *specialize* the graph:

  * Replace a combination of operations with a special operation that
    does the same thing (but better).


For each group, all optimizations of the group that are selected by
the Query will be applied on the graph over and over again until none
of them is applicable, so keep that in mind when designing it: check
carefully that your optimization leads to a fixpoint (a point where it
cannot apply anymore) at which point it returns ``False`` to indicate its
job is done. Also be careful not to undo the work of another local
optimizer in the group, because then the graph will oscillate between
two or more states and nothing will get done.


.. _optdb-structure:

optdb structure
---------------

optdb contains the following Optimizers and sub-DBs, with the given
priorities and tags:

+-------+---------------------+------------------------------+
| Order | Name                | Description                  |
+=======+=====================+==============================+
| 0     | merge1              | First merge operation        |
+-------+---------------------+------------------------------+
| 1     | canonicalize        | Simplify the graph           |
+-------+---------------------+------------------------------+
| 2     | specialize          | Add specialized operations   |
+-------+---------------------+------------------------------+
| 49    | merge2              | Second merge operation       |
+-------+---------------------+------------------------------+
| 49.5  | add_destroy_handler | Enable inplace optimizations |
+-------+---------------------+------------------------------+
| 100   | merge3              | Third merge operation        |
+-------+---------------------+------------------------------+

The merge operations are meant to put together parts of the graph that
represent the same computation. Since optimizations can modify the
graph in such a way that two previously different-looking parts of the
graph become similar, we merge at the beginning, in the middle and at
the very end. Technically, we only really need to do it at the end,
but doing it in previous steps reduces the size of the graph and
therefore increases the efficiency of the process.

See previous section for more information about the canonicalize and
specialize steps.

The ``add_destroy_handler`` step is not really an optimization. It is
a marker. Basically:

.. warning::

   Any optimization which inserts inplace operations in the
   computation graph must appear **after** the ``add_destroy_handler``
   "optimizer". In other words, the priority of any such optimization
   must be **>= 50**. Failure to comply by this restriction can lead
   to the creation of incorrect computation graphs.

The reason the destroy handler is not inserted at the beginning is
that it is costly to run. It is cheaper to run most optimizations
under the assumption there are no inplace operations.


.. _navigator:

Navigator
-------------------

WRITEME

.. _profiling_opt:

Profiling Theano function compilation
=====================================

You find that compiling a Theano function is taking too much time? You
can get profiling information about Theano optimization. The normal
:ref:`Theano profiler <tut_profiling>` will provide you with very
high-level information. The indentation shows the included in/subset
relationship between sections. The top of its output look like this:

.. code-block:: none

    Function profiling
    ==================
      Message: PATH_TO_A_FILE:23
      Time in 0 calls to Function.__call__: 0.000000e+00s
      Total compile time: 1.131874e+01s
        Number of Apply nodes: 50
        Theano Optimizer time: 1.152431e+00s
           Theano validate time: 2.790451e-02s
        Theano Linker time (includes C, CUDA code generation/compiling): 7.893991e-02s
           Import time 1.153541e-02s
      Time in all call to theano.grad() 4.732513e-02s

Explanations:

* ``Total compile time: 1.131874e+01s`` gives the total time spent inside `theano.function`.
* ``Number of Apply nodes: 50`` means that after optimization, there are 50 apply node in the graph.
* ``Theano Optimizer time: 1.152431e+00s`` means that we spend 1.15s in the ``theano.function`` phase where we optimize (modify) the graph to make it faster / more stable numerically / work on GPU /...
* ``Theano validate time: 2.790451e-02s`` means that we spent 2.8e-2s in the *validate* subset of the optimization phase.
* ``Theano Linker time (includes C, CUDA code generation/compiling): 7.893991e-02s`` means that we spent 7.9e-2s in *linker* phase of ``theano.function``.
* ``Import time 1.153541e-02s`` is a subset of the linker time where we import the compiled module.
* ``Time in all call to theano.grad() 4.732513e-02s`` tells that we spent a total of 4.7e-2s in all calls to ``theano.grad``. This is outside of the calls to ``theano.function``.

The *linker* phase includes the generation of the C code, the time spent
by g++ to compile and the time needed by Theano to build the object we
return. The C code generation and compilation is cached, so the first
time you compile a function and the following ones could take different
amount of execution time.

Detailed profiling of Theano optimizer
--------------------------------------

You can get more detailed profiling information about the Theano
optimizer phase by setting to `True` the Theano flags
:attr:`config.profile_optimizer`.

This will output something like this:

.. code-block:: none

    Optimizer Profile
    -----------------
     SeqOptimizer  OPT_FAST_RUN  time 1.152s for 123/50 nodes before/after optimization
       0.028s for fgraph.validate()
       0.131s for callback
       time      - (name, class, index) - validate time
       0.751816s - ('canonicalize', 'EquilibriumOptimizer', 4) - 0.004s
         EquilibriumOptimizer      canonicalize
           time 0.751s for 14 passes
           nb nodes (start, end,  max) 108 81 117
           time io_toposort 0.029s
           time in local optimizers 0.687s
           time in global optimizers 0.010s
            0 - 0.050s 27 (0.000s in global opts, 0.002s io_toposort) - 108 nodes - ('local_dimshuffle_lift', 9) ('local_upcast_elemwise_constant_inputs', 5) ('local_shape_to_shape_i', 3) ('local_fill_sink', 3) ('local_fill_to_alloc', 2) ...
            1 - 0.288s 26 (0.002s in global opts, 0.002s io_toposort) - 117 nodes - ('local_dimshuffle_lift', 8) ('local_fill_sink', 4) ('constant_folding', 4) ('local_useless_elemwise', 3) ('local_subtensor_make_vector', 3) ...
            2 - 0.044s 13 (0.002s in global opts, 0.003s io_toposort) - 96 nodes - ('constant_folding', 4) ('local_dimshuffle_lift', 3) ('local_fill_sink', 3) ('local_useless_elemwise', 1) ('local_fill_to_alloc', 1) ...
            3 - 0.045s 11 (0.000s in global opts, 0.002s io_toposort) - 91 nodes - ('constant_folding', 3) ('local_fill_to_alloc', 2) ('local_dimshuffle_lift', 2) ('local_mul_canonizer', 2) ('MergeOptimizer', 1) ...
            4 - 0.035s 8 (0.002s in global opts, 0.002s io_toposort) - 93 nodes - ('local_fill_sink', 3) ('local_dimshuffle_lift', 2) ('local_fill_to_alloc', 1) ('MergeOptimizer', 1) ('constant_folding', 1)
            5 - 0.035s 6 (0.000s in global opts, 0.002s io_toposort) - 88 nodes - ('local_fill_sink', 2) ('local_dimshuffle_lift', 2) ('local_fill_to_alloc', 1) ('local_mul_canonizer', 1)
            6 - 0.038s 10 (0.001s in global opts, 0.002s io_toposort) - 95 nodes - ('local_fill_sink', 3) ('local_dimshuffle_lift', 3) ('constant_folding', 2) ('local_fill_to_alloc', 1) ('MergeOptimizer', 1)
            7 - 0.032s 5 (0.001s in global opts, 0.002s io_toposort) - 91 nodes - ('local_fill_sink', 3) ('MergeOptimizer', 1) ('local_dimshuffle_lift', 1)
            8 - 0.034s 5 (0.000s in global opts, 0.002s io_toposort) - 92 nodes - ('local_fill_sink', 3) ('MergeOptimizer', 1) ('local_greedy_distributor', 1)
            9 - 0.031s 6 (0.001s in global opts, 0.002s io_toposort) - 90 nodes - ('local_fill_sink', 2) ('local_fill_to_alloc', 1) ('MergeOptimizer', 1) ('local_dimshuffle_lift', 1) ('local_greedy_distributor', 1)
           10 - 0.032s 5 (0.000s in global opts, 0.002s io_toposort) - 89 nodes - ('local_dimshuffle_lift', 2) ('local_fill_to_alloc', 1) ('MergeOptimizer', 1) ('local_fill_sink', 1)
           11 - 0.030s 5 (0.000s in global opts, 0.002s io_toposort) - 88 nodes - ('local_dimshuffle_lift', 2) ('local_fill_to_alloc', 1) ('MergeOptimizer', 1) ('constant_folding', 1)
           12 - 0.026s 1 (0.000s in global opts, 0.003s io_toposort) - 81 nodes - ('MergeOptimizer', 1)
           13 - 0.031s 0 (0.000s in global opts, 0.003s io_toposort) - 81 nodes -
           times - times applied - nb node created - name:
           0.263s - 15 - 0 - constant_folding
           0.096s - 2 - 14 - local_greedy_distributor
           0.066s - 4 - 19 - local_mul_canonizer
           0.046s - 28 - 57 - local_fill_sink
           0.042s - 35 - 78 - local_dimshuffle_lift
           0.018s - 5 - 15 - local_upcast_elemwise_constant_inputs
           0.010s - 11 - 4 - MergeOptimizer
           0.009s - 4 - 0 - local_useless_elemwise
           0.005s - 11 - 2 - local_fill_to_alloc
           0.004s - 3 - 6 - local_neg_to_mul
           0.002s - 1 - 3 - local_lift_transpose_through_dot
           0.002s - 3 - 4 - local_shape_to_shape_i
           0.002s - 2 - 4 - local_subtensor_lift
           0.001s - 3 - 0 - local_subtensor_make_vector
           0.001s - 1 - 1 - local_sum_all_to_none
           0.131s - in 62 optimization that where not used (display only those with a runtime > 0)
             0.050s - local_add_canonizer
             0.018s - local_mul_zero
             0.016s - local_one_minus_erf
             0.010s - local_func_inv
             0.006s - local_0_dot_x
             0.005s - local_track_shape_i
             0.004s - local_mul_switch_sink
             0.004s - local_fill_cut
             0.004s - local_one_minus_erf2
             0.003s - local_remove_switch_const_cond
             0.003s - local_cast_cast
             0.002s - local_IncSubtensor_serialize
             0.001s - local_sum_div_dimshuffle
             0.001s - local_div_switch_sink
             0.001s - local_dimshuffle_no_inplace_at_canonicalize
             0.001s - local_cut_useless_reduce
             0.001s - local_reduce_join
             0.000s - local_sum_sum
             0.000s - local_useless_alloc
             0.000s - local_reshape_chain
             0.000s - local_useless_subtensor
             0.000s - local_reshape_lift
             0.000s - local_flatten_lift
             0.000s - local_useless_slice
             0.000s - local_subtensor_of_alloc
             0.000s - local_subtensor_of_dot
             0.000s - local_subtensor_merge
       0.101733s - ('elemwise_fusion', 'SeqOptimizer', 13) - 0.000s
         SeqOptimizer      elemwise_fusion  time 0.102s for 78/50 nodes before/after optimization
           0.000s for fgraph.validate()
           0.004s for callback
           0.095307s - ('composite_elemwise_fusion', 'FusionOptimizer', 1) - 0.000s
             FusionOptimizer
              nb_iter 3
              nb_replacement 10
              nb_inconsistency_replace 0
              validate_time 0.000249624252319
              callback_time 0.00316381454468
              time_toposort 0.00375390052795
           0.006412s - ('local_add_mul_fusion', 'FusionOptimizer', 0) - 0.000s
             FusionOptimizer
              nb_iter 2
              nb_replacement 3
              nb_inconsistency_replace 0
              validate_time 6.43730163574e-05
              callback_time 0.000783205032349
              time_toposort 0.0035240650177
       0.090089s - ('inplace_elemwise_optimizer', 'FromFunctionOptimizer', 30) - 0.019s
       0.048993s - ('BlasOpt', 'SeqOptimizer', 8) - 0.000s
         SeqOptimizer      BlasOpt  time 0.049s for 81/80 nodes before/after optimization
           0.000s for fgraph.validate()
           0.003s for callback
           0.035997s - ('gemm_optimizer', 'GemmOptimizer', 1) - 0.000s
             GemmOptimizer
              nb_iter 2
              nb_replacement 2
              nb_replacement_didn_t_remove 0
              nb_inconsistency_make 0
              nb_inconsistency_replace 0
              time_canonicalize 0.00720071792603
              time_factor_can 9.05990600586e-06
              time_factor_list 0.00128507614136
              time_toposort 0.00311398506165
              validate_time 4.60147857666e-05
              callback_time 0.00174236297607
           0.004569s - ('local_dot_to_dot22', 'TopoOptimizer', 0) - 0.000s
             TopoOptimizer
               nb_node (start, end, changed) (81, 81, 5)
               init io_toposort 0.00139284133911
               loop time 0.00312399864197
               callback_time 0.00172805786133
           0.002283s - ('local_dot22_to_dot22scalar', 'TopoOptimizer', 2) - 0.000s
             TopoOptimizer
               nb_node (start, end, changed) (80, 80, 0)
               init io_toposort 0.00171804428101
               loop time 0.000502109527588
               callback_time 0.0
           0.002257s - ('local_gemm_to_gemv', 'EquilibriumOptimizer', 3) - 0.000s
             EquilibriumOptimizer          local_gemm_to_gemv
               time 0.002s for 1 passes
               nb nodes (start, end,  max) 80 80 80
               time io_toposort 0.001s
               time in local optimizers 0.000s
               time in global optimizers 0.000s
                0 - 0.002s 0 (0.000s in global opts, 0.001s io_toposort) - 80 nodes -
           0.002227s - ('use_c_blas', 'TopoOptimizer', 4) - 0.000s
             TopoOptimizer
               nb_node (start, end, changed) (80, 80, 0)
               init io_toposort 0.0014750957489
               loop time 0.00068998336792
               callback_time 0.0
           0.001632s - ('use_scipy_ger', 'TopoOptimizer', 5) - 0.000s
             TopoOptimizer
               nb_node (start, end, changed) (80, 80, 0)
               init io_toposort 0.00138401985168
               loop time 0.000202178955078
               callback_time 0.0
       0.031740s - ('specialize', 'EquilibriumOptimizer', 9) - 0.000s
         EquilibriumOptimizer      specialize
           time 0.031s for 2 passes
           nb nodes (start, end,  max) 80 78 80
           time io_toposort 0.003s
           time in local optimizers 0.022s
           time in global optimizers 0.004s
            0 - 0.017s 6 (0.002s in global opts, 0.001s io_toposort) - 80 nodes - ('constant_folding', 2) ('local_mul_to_sqr', 1) ('local_elemwise_alloc', 1) ('local_div_to_inv', 1) ('local_mul_specialize', 1)
            1 - 0.014s 0 (0.002s in global opts, 0.001s io_toposort) - 78 nodes -
           times - times applied - nb node created - name:
           0.003s - 1 - 1 - local_mul_specialize
           0.002s - 1 - 2 - local_elemwise_alloc
           0.002s - 2 - 0 - constant_folding
           0.001s - 1 - 1 - local_div_to_inv
           0.001s - 1 - 1 - local_mul_to_sqr
           0.016s - in 69 optimization that where not used (display only those with a runtime > 0)
             0.004s - crossentropy_to_crossentropy_with_softmax_with_bias
             0.002s - local_one_minus_erf
             0.002s - Elemwise{sub,no_inplace}(z, Elemwise{mul,no_inplace}(alpha subject to <function <lambda> at 0x7f475e4da050>, SparseDot(x, y))) -> Usmm{no_inplace}(Elemwise{neg,no_inplace}(alpha), x, y, z)
             0.002s - local_add_specialize
             0.001s - local_func_inv
             0.001s - local_useless_elemwise
             0.001s - local_abs_merge
             0.001s - local_track_shape_i
             0.000s - local_one_minus_erf2
             0.000s - local_sum_mul_by_scalar
             0.000s - local_elemwise_sub_zeros
             0.000s - local_cast_cast
             0.000s - local_alloc_unary
             0.000s - Elemwise{log,no_inplace}(Softmax(x)) -> <function make_out_pattern at 0x7f47619a8410>(x)
             0.000s - local_sum_div_dimshuffle
             0.000s - local_sum_alloc
             0.000s - local_dimshuffle_lift
             0.000s - local_reduce_broadcastable
             0.000s - local_grad_log_erfc_neg
             0.000s - local_advanced_indexing_crossentropy_onehot
             0.000s - local_log_erfc
             0.000s - local_log1p
             0.000s - local_log_add
             0.000s - local_useless_alloc
             0.000s - local_neg_neg
             0.000s - local_neg_div_neg
    ...


To understand this profile here is some explanation of how optimizations work:

* Optimizations are organized in an hierarchy. At the top level, there
  is a ``SeqOptimizer`` (Sequence Optimizer). It contains other optimizers,
  and applies them in the order they were specified. Those sub-optimizers can be
  of other types, but are all *global* optimizers.

* Each Optimizer in the hierarchy will print some stats about
  itself. The information that it prints depends of the type of the
  optimizer.

* The SeqOptimizer will print some stats at the start:

    .. code-block:: none

        Optimizer Profile
        -----------------
         SeqOptimizer  OPT_FAST_RUN  time 1.152s for 123/50 nodes before/after optimization
           0.028s for fgraph.validate()
           0.131s for callback
           time      - (name, class, index) - validate time

      Then it will print, with some additional indentation, each sub-optimizer's profile
      information. These sub-profiles are ordered by the time they took to execute,
      not by their execution order.

  * ``OPT_FAST_RUN`` is the name of the optimizer
  * 1.152s is the total time spent in that optimizer
  * 123/50 means that before this optimization, there were 123 apply node in the function graph, and after only 50.
  * 0.028s means it spent that time calls to ``fgraph.validate()``
  * 0.131s means it spent that time for callbacks. This is a mechanism that can trigger other execution when there is a change to the FunctionGraph.
  * ``time      - (name, class, index) - validate time`` tells how the information for each sub-optimizer get printed.
  * All other instances of ``SeqOptimizer`` are described like this. In particular, some sub-optimizer from OPT_FAST_RUN that are also ``SeqOptimizer``s.


* The ``SeqOptimizer`` will print some stats at the start:

    .. code-block:: none

       0.751816s - ('canonicalize', 'EquilibriumOptimizer', 4) - 0.004s
         EquilibriumOptimizer      canonicalize
           time 0.751s for 14 passes
           nb nodes (start, end,  max) 108 81 117
           time io_toposort 0.029s
           time in local optimizers 0.687s
           time in global optimizers 0.010s
            0 - 0.050s 27 (0.000s in global opts, 0.002s io_toposort) - 108 nodes - ('local_dimshuffle_lift', 9) ('local_upcast_elemwise_constant_inputs', 5) ('local_shape_to_shape_i', 3) ('local_fill_sink', 3) ('local_fill_to_alloc', 2) ...
            1 - 0.288s 26 (0.002s in global opts, 0.002s io_toposort) - 117 nodes - ('local_dimshuffle_lift', 8) ('local_fill_sink', 4) ('constant_folding', 4) ('local_useless_elemwise', 3) ('local_subtensor_make_vector', 3) ...
            2 - 0.044s 13 (0.002s in global opts, 0.003s io_toposort) - 96 nodes - ('constant_folding', 4) ('local_dimshuffle_lift', 3) ('local_fill_sink', 3) ('local_useless_elemwise', 1) ('local_fill_to_alloc', 1) ...
            3 - 0.045s 11 (0.000s in global opts, 0.002s io_toposort) - 91 nodes - ('constant_folding', 3) ('local_fill_to_alloc', 2) ('local_dimshuffle_lift', 2) ('local_mul_canonizer', 2) ('MergeOptimizer', 1) ...
            4 - 0.035s 8 (0.002s in global opts, 0.002s io_toposort) - 93 nodes - ('local_fill_sink', 3) ('local_dimshuffle_lift', 2) ('local_fill_to_alloc', 1) ('MergeOptimizer', 1) ('constant_folding', 1)
            5 - 0.035s 6 (0.000s in global opts, 0.002s io_toposort) - 88 nodes - ('local_fill_sink', 2) ('local_dimshuffle_lift', 2) ('local_fill_to_alloc', 1) ('local_mul_canonizer', 1)
            6 - 0.038s 10 (0.001s in global opts, 0.002s io_toposort) - 95 nodes - ('local_fill_sink', 3) ('local_dimshuffle_lift', 3) ('constant_folding', 2) ('local_fill_to_alloc', 1) ('MergeOptimizer', 1)
            7 - 0.032s 5 (0.001s in global opts, 0.002s io_toposort) - 91 nodes - ('local_fill_sink', 3) ('MergeOptimizer', 1) ('local_dimshuffle_lift', 1)
            8 - 0.034s 5 (0.000s in global opts, 0.002s io_toposort) - 92 nodes - ('local_fill_sink', 3) ('MergeOptimizer', 1) ('local_greedy_distributor', 1)
            9 - 0.031s 6 (0.001s in global opts, 0.002s io_toposort) - 90 nodes - ('local_fill_sink', 2) ('local_fill_to_alloc', 1) ('MergeOptimizer', 1) ('local_dimshuffle_lift', 1) ('local_greedy_distributor', 1)
           10 - 0.032s 5 (0.000s in global opts, 0.002s io_toposort) - 89 nodes - ('local_dimshuffle_lift', 2) ('local_fill_to_alloc', 1) ('MergeOptimizer', 1) ('local_fill_sink', 1)
           11 - 0.030s 5 (0.000s in global opts, 0.002s io_toposort) - 88 nodes - ('local_dimshuffle_lift', 2) ('local_fill_to_alloc', 1) ('MergeOptimizer', 1) ('constant_folding', 1)
           12 - 0.026s 1 (0.000s in global opts, 0.003s io_toposort) - 81 nodes - ('MergeOptimizer', 1)
           13 - 0.031s 0 (0.000s in global opts, 0.003s io_toposort) - 81 nodes -
           times - times applied - nb node created - name:
           0.263s - 15 - 0 - constant_folding
           0.096s - 2 - 14 - local_greedy_distributor
           0.066s - 4 - 19 - local_mul_canonizer
           0.046s - 28 - 57 - local_fill_sink
           0.042s - 35 - 78 - local_dimshuffle_lift
           0.018s - 5 - 15 - local_upcast_elemwise_constant_inputs
           0.010s - 11 - 4 - MergeOptimizer
           0.009s - 4 - 0 - local_useless_elemwise
           0.005s - 11 - 2 - local_fill_to_alloc
           0.004s - 3 - 6 - local_neg_to_mul
           0.002s - 1 - 3 - local_lift_transpose_through_dot
           0.002s - 3 - 4 - local_shape_to_shape_i
           0.002s - 2 - 4 - local_subtensor_lift
           0.001s - 3 - 0 - local_subtensor_make_vector
           0.001s - 1 - 1 - local_sum_all_to_none
           0.131s - in 62 optimization that where not used (display only those with a runtime > 0)
             0.050s - local_add_canonizer
             0.018s - local_mul_zero
             0.016s - local_one_minus_erf
             0.010s - local_func_inv
             0.006s - local_0_dot_x
             0.005s - local_track_shape_i
             0.004s - local_mul_switch_sink
             0.004s - local_fill_cut
             0.004s - local_one_minus_erf2
             0.003s - local_remove_switch_const_cond
             0.003s - local_cast_cast
             0.002s - local_IncSubtensor_serialize
             0.001s - local_sum_div_dimshuffle
             0.001s - local_div_switch_sink
             0.001s - local_dimshuffle_no_inplace_at_canonicalize
             0.001s - local_cut_useless_reduce
             0.001s - local_reduce_join
             0.000s - local_sum_sum
             0.000s - local_useless_alloc
             0.000s - local_reshape_chain
             0.000s - local_useless_subtensor
             0.000s - local_reshape_lift
             0.000s - local_flatten_lift
             0.000s - local_useless_slice
             0.000s - local_subtensor_of_alloc
             0.000s - local_subtensor_of_dot
             0.000s - local_subtensor_merge

  * ``0.751816s - ('canonicalize', 'EquilibriumOptimizer', 4) - 0.004s``
    This line is from ``SeqOptimizer``, and indicates information related
    to a sub-optimizer. It means that this sub-optimizer took
    a total of .7s. Its name is ``'canonicalize'``. It is an
    ``EquilibriumOptimizer``. It was executed at index 4 by the
    ``SeqOptimizer``. It spent 0.004s in the *validate* phase.
  * All other lines are from the profiler of the ``EquilibriumOptimizer`.

  * An ``EquilibriumOptimizer`` does multiple passes on the Apply nodes from
    the graph, trying to apply local and global optimizations.
    Conceptually, it tries to execute all global optimizations,
    and to apply all local optimizations on all
    nodes in the graph. If no optimization got applied during a pass, it
    stops. So it tries to find an equilibrium state where none of the
    optimizations get applied. This is useful when we do not know a fixed order for
    the execution of the optimization.
  * ``time 0.751s for 14 passes`` means that it took .7s and did 14 passes over the graph.

  * ``nb nodes (start, end, max) 108 81 117`` means that at the start,
    the graph had 108 node, at the end, it had 81 and the maximum size
    was 117.

  * Then it prints some global timing information: it spent 0.029s in
    ``io_toposort``, all local optimizers took 0.687s together for all
    passes, and global optimizers took a total of 0.010s.

  * Then we print the timing for each pass, the optimization that
    got applied, and the number of time they got applied. For example,
    in pass 0, the ``local_dimshuffle_lift`` optimizer changed the graph 9
    time.

  * Then we print the time spent in each optimizer, the number of times
    they changed the graph and the number of nodes they introduced in
    the graph.

  * Optimizations with that pattern `local_op_lift` means that a node
    with that op will be replaced by another node, with the same op,
    but will do computation closer to the inputs of the graph.
    For instance, ``local_op(f(x))`` getting replaced by ``f(local_op(x))``.

  * Optimization with that pattern `local_op_sink` is the opposite of
    `lift`. For instance ``f(local_op(x))`` getting replaced by ``local_op(f(x))``.

  * Local optimizers can replace any arbitrary node in the graph, not
    only the node it received as input. For this, it must return a
    dict. The keys being nodes to replace and the
    values being the corresponding replacement.

    This is useful to replace a client of the node received as
    parameter.
