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).


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.