Profiling Theano function

Note

This method replace the old ProfileMode. Do not use ProfileMode anymore.

Besides checking for errors, another important task is to profile your code in terms of speed and/or memory usage.

You can profile your functions using either of the following two options:

1. Use Theano flag config.profile to enable profiling.

   • To enable the memory profiler use the Theano flag: config.profile_memory in addition to config.profile.
   • Moreover, to enable the profiling of Theano optimization phase, use the Theano flag: config.profile_optimizer in addition to config.profile.
   • You can also use the Theano flags profiling.n_apply, profiling.n_ops and profiling.min_memory_size to modify the quantify of information printed.

2. Pass the argument profile=True to the function theano.function. And then call f.profile.print_summary() for a single function.

   • Use this option when you want to profile not all the functions but one or more specific function(s).

   • You can also combine the profile of many functions:

     profile = theano.compile.ProfileStats()
     f = theano.function(..., profile=profile)
     g = theano.function(..., profile=profile)
     ...
     profile.print_summary()
     

The profiler will output one profile per Theano function and profile that is the sum of the printed profile. Each profile contains 4 sections: global info, class info, Ops info and Apply node info.

In the global section, the “Message” is the name of the Theano function. theano.function() has an optional parameter name that defaults to None. Change it to something else to help you profile many Theano functions. In that section, we also see the number of time the function was called (1) and the total time spent in all those calls. The time spent in Function.fn.__call__ and in thunks is useful to help understand Theano overhead.

Also, we see the time spent in the two parts of the compilation process: optimization(modify the graph to make it more stable/faster) and the linking (compile c code and make the Python callable returned by function).

The class, Ops and Apply nodes sections are the same information: information about the Apply node that ran. The Ops section takes the information from the Apply section and merge the Apply nodes that have exactly the same op. If two Apply nodes in the graph have two Ops that compare equal, they will be merged. Some Ops like Elemwise, will not compare equal, if their parameters differ (the scalar being executed). So the class section will merge more Apply nodes then the Ops section.

Here is an example output when we disable some Theano optimizations to give you a better idea of the difference between sections. With all optimizations enabled, there would be only one op left in the graph.

Note

To profile the peak memory usage on the GPU you need to do:

* In the file theano/sandbox/cuda/cuda_ndarray.cu, set the macro
  COMPUTE_GPU_MEM_USED to 1.
* Then call theano.sandbox.cuda.theano_allocated()
  It return a tuple with two ints. The first is the current GPU
  memory allocated by Theano. The second is the peak  GPU memory
  that was allocated by Theano.

Do not always enable this, as this slowdown memory allocation and free. As this slowdown the computation, this will affect speed profiling. So don’t use both at the same time.

to run the example:

THEANO_FLAGS=optimizer_excluding=fusion:inplace,profile=True python doc/tutorial/profiling_example.py

The output:

Function profiling
==================
  Message: None
  Time in 1 calls to Function.__call__: 5.698204e-05s
  Time in Function.fn.__call__: 1.192093e-05s (20.921%)
  Time in thunks: 6.198883e-06s (10.879%)
  Total compile time: 3.642474e+00s
    Theano Optimizer time: 7.326508e-02s
       Theano validate time: 3.712177e-04s
    Theano Linker time (includes C, CUDA code generation/compiling): 9.584920e-01s

Class
---
<% time> <sum %> <apply time> <time per call> <type> <#call> <#apply> <Class name>
  100.0%   100.0%       0.000s       2.07e-06s     C        3        3   <class 'theano.tensor.elemwise.Elemwise'>
   ... (remaining 0 Classes account for   0.00%(0.00s) of the runtime)

Ops
---
<% time> <sum %> <apply time> <time per call> <type> <#call> <#apply> <Op name>
  65.4%    65.4%       0.000s       2.03e-06s     C        2        2   Elemwise{add,no_inplace}
  34.6%   100.0%       0.000s       2.15e-06s     C        1        1   Elemwise{mul,no_inplace}
   ... (remaining 0 Ops account for   0.00%(0.00s) of the runtime)

Apply
------
<% time> <sum %> <apply time> <time per call> <#call> <id> <Apply name>
  50.0%    50.0%       0.000s       3.10e-06s      1     0   Elemwise{add,no_inplace}(x, y)
  34.6%    84.6%       0.000s       2.15e-06s      1     2   Elemwise{mul,no_inplace}(TensorConstant{(1,) of 2.0}, Elemwise{add,no_inplace}.0)
  15.4%   100.0%       0.000s       9.54e-07s      1     1   Elemwise{add,no_inplace}(Elemwise{add,no_inplace}.0, z)
   ... (remaining 0 Apply instances account for 0.00%(0.00s) of the runtime)