Overview of the compilation pipeline¶
The purpose of this page is to explain each step of defining and compiling a Theano function.
Definition of the computation graph¶
By creating Theano Variables using
theano.tensor.lscalar
or theano.tensor.dmatrix
or by using
Theano functions such as theano.tensor.sin
or
theano.tensor.log
, the user builds a computation graph. The
structure of that graph and details about its components can be found
in the Graph Structures article.
Compilation of the computation graph¶
Once the user has built a computation graph, she can use
theano.function
in order to make one or more functions that
operate on real data. function takes a list of input Variables as well as a list of output Variables that define a
precise subgraph corresponding to the function(s) we want to define,
compile that subgraph and produce a callable.
Here is an overview of the various steps that are done with the computation graph in the compilation phase:
Step 1 - Create a FunctionGraph¶
The subgraph given by the end user is wrapped in a structure called FunctionGraph. That structure defines several hooks on adding and removing (pruning) nodes as well as on modifying links between nodes (for example, modifying an input of an Apply node) (see the article about fg – Graph Container [doc TODO] for more information).
FunctionGraph provides a method to change the input of an Apply node from one Variable to another and a more high-level method to replace a Variable with another. This is the structure that Optimizers work on.
Some relevant Features are typically added to the FunctionGraph, namely to prevent any optimization from operating inplace on inputs declared as immutable.
Step 2 - Execute main Optimizer¶
Once the FunctionGraph is made, an optimizer is produced by
the mode passed to function
(the Mode basically has two
important fields, linker
and optimizer
). That optimizer is
applied on the FunctionGraph using its optimize() method.
The optimizer is typically obtained through optdb
.
Step 3 - Execute linker to obtain a thunk¶
Once the computation graph is optimized, the linker is
extracted from the Mode. It is then called with the FunctionGraph as
argument to
produce a thunk
, which is a function with no arguments that
returns nothing. Along with the thunk, one list of input containers (a
theano.gof.Container is a sort of object that wraps another and does
type casting) and one list of output containers are produced,
corresponding to the input and output Variables as well as the updates
defined for the inputs when applicable. To perform the computations,
the inputs must be placed in the input containers, the thunk must be
called, and the outputs must be retrieved from the output containers
where the thunk put them.
Typically, the linker calls the toposort
method in order to obtain
a linear sequence of operations to perform. How they are linked
together depends on the Linker used. The CLinker produces a single
block of C code for the whole computation, whereas the OpWiseCLinker
produces one thunk for each individual operation and calls them in
sequence.
The linker is where some options take effect: the strict
flag of
an input makes the associated input container do type checking. The
borrow
flag of an output, if False, adds the output to a
no_recycling
list, meaning that when the thunk is called the
output containers will be cleared (if they stay there, as would be the
case if borrow
was True, the thunk would be allowed to reuse (or
“recycle”) the storage).
Note
Compiled libraries are stored within a specific compilation directory,
which by default is set to $HOME/.theano/compiledir_xxx
, where
xxx
identifies the platform (under Windows the default location
is instead $LOCALAPPDATA\Theano\compiledir_xxx
). It may be manually set
to a different location either by setting config.compiledir
or
config.base_compiledir
, either within your Python script or by
using one of the configuration mechanisms described in config
.
The compile cache is based upon the C++ code of the graph to be compiled.
So, if you change compilation configuration variables, such as
config.blas.ldflags
, you will need to manually remove your compile cache,
using Theano/bin/theano-cache clear
Theano also implements a lock mechanism that prevents
multiple compilations within the same compilation directory (to avoid
crashes with paralell execution of some scripts). This mechanism is
currently enabled by default, but if it causes any problem it may be
disabled using the function
theano.gof.compilelock.set_lock_status(..)
.
Step 4 - Wrap the thunk in a pretty package¶
The thunk returned by the linker along with input and output
containers is unwieldy. function
hides that complexity away so
that it can be used like a normal function with arguments and return
values.