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提交 acdaf1d7 authored 作者: Frederic Bastien's avatar Frederic Bastien
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Remove the ProfileMode from doc and add NanGuardMode at a few places.

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doc/library/compile/mode.txt
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......@@ -22,6 +22,7 @@ Theano defines the following modes by name:
- ``'FAST_RUN'``: Apply all optimizations, and use C implementations where possible.
- ``'DebugMode'``: A mode for debugging. See :ref:`DebugMode <debugmode>` for details.
- ``'ProfileMode'``: Deprecated, use the Theano flag :attr:`config.profile`.
- ``'NanGuardMode``: :ref:`Nan detector <nanguardmode>`
- ``'DEBUG_MODE'``: Deprecated. Use the string DebugMode.
- ``'PROFILE_MODE'``: Deprecated, use the Theano flag :attr:`config.profile`.
......
doc/tutorial/modes.txt
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......@@ -139,10 +139,10 @@ 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
- ``'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'`` (deprecated): Same optimization as FAST_RUN, but print some profiling information.
- ``'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`,
......@@ -155,7 +155,6 @@ short name Full constructor
``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()`` Deprecated. C implementations where available, all available graph transformations, print profile information.
================= =============================================================== ===============================================================================
.. Note::
......@@ -169,8 +168,8 @@ 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.
like ``NanGuardMode`` and ``DebugMode``, add logic around the optimizer and
linker. ``NanGuardMode`` 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.
......@@ -184,7 +183,7 @@ c|py [#cpy1]_ yes yes "+++" Try C code. If none exis
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
ProfileMode no no "++++" (Deprecated) Compute some extra profiling info
NanGuardMode no no "++++" Check if nodes generate NaN
DebugMode no yes VERY HIGH Make many checks on what Theano computes
============= ========= ================= ========= ===
......@@ -259,123 +258,3 @@ ProfileMode
.. note::
ProfileMode is deprecated. Use :attr:`config.profile` instead.
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.
>>> v1, v2 = T.vectors(2)
>>> o = v1 + v2
>>> f = theano.function([v1,v2],[o], 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<profilemode>` in the library.
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