
.. _advanced_theano:

***************
Advanced Theano
***************

Conditions
----------
**IfElse**

- Build condition over symbolic variables.
- IfElse Op takes a boolean condition and two variables to compute as input.
- While Switch Op evaluates both 'output' variables, IfElse Op is lazy and only
  evaluates one variable respect to the condition.

**IfElse Example: Comparison with Switch**

.. testcode::

   from theano import tensor as T
   from theano.ifelse import ifelse
   import theano, time, numpy

   a,b = T.scalars('a','b')
   x,y = T.matrices('x','y')

   z_switch = T.switch(T.lt(a,b), T.mean(x), T.mean(y))
   z_lazy = ifelse(T.lt(a,b), T.mean(x), T.mean(y))

   f_switch = theano.function([a,b,x,y], z_switch,
                              mode=theano.Mode(linker='vm'))
   f_lazyifelse = theano.function([a,b,x,y], z_lazy,
                                  mode=theano.Mode(linker='vm'))

   val1 = 0.
   val2 = 1.
   big_mat1 = numpy.ones((10000,1000))
   big_mat2 = numpy.ones((10000,1000))

   n_times = 10

   tic = time.clock()
   for i in xrange(n_times):
       f_switch(val1, val2, big_mat1, big_mat2)
   print 'time spent evaluating both values %f sec'%(time.clock()-tic)

   tic = time.clock()
   for i in xrange(n_times):
       f_lazyifelse(val1, val2, big_mat1, big_mat2)
   print 'time spent evaluating one value %f sec'%(time.clock()-tic)

.. testoutput::
   :hide:
   :options: +ELLIPSIS

   time spent evaluating both values ... sec
   time spent evaluating one value ... sec

IfElse Op spend less time (about an half) than Switch since it computes only
one variable instead of both.

.. code-block:: none

  $ python ifelse_switch.py
  time spent evaluating both values 0.6700 sec
  time spent evaluating one value 0.3500 sec

Note that IfElse condition is a boolean while Switch condition is a tensor, so
Switch is more general.

It is actually important to use  ``linker='vm'`` or ``linker='cvm'``,
otherwise IfElse will compute both variables and take the same computation
time as the Switch Op. The linker is not currently set by default to 'cvm' but
it will be in a near future.

Loops
-----

**Scan**

- General form of **recurrence**, which can be used for looping.
- **Reduction** and **map** (loop over the leading dimensions) are special cases of Scan
- You 'scan' a function along some input sequence, producing an output at each time-step
- The function can see the **previous K time-steps** of your function
- ``sum()`` could be computed by scanning the z + x(i) function over a list, given an initial state of ``z=0``.
- Often a for-loop can be expressed as a ``scan()`` operation, and ``scan`` is the closest that Theano comes to looping.
- The advantage of using ``scan`` over for loops

  - The number of iterations to be part of the symbolic graph
  - Minimizes GPU transfers if GPU is involved
  - Compute gradients through sequential steps
  - Slightly faster then using a for loop in Python with a compiled Theano function
  - Can lower the overall memory usage by detecting the actual amount of memory needed

**Scan Example: Computing pow(A,k)**

.. code-block:: python

  import theano
  import theano.tensor as T

  k = T.iscalar("k"); A = T.vector("A")

  def inner_fct(prior_result, A): return prior_result * A
  # Symbolic description of the result
  result, updates = theano.scan(fn=inner_fct,
                              outputs_info=T.ones_like(A),
                              non_sequences=A, n_steps=k)

  # Scan has provided us with A**1 through A**k.  Keep only the last
  # value. Scan notices this and does not waste memory saving them.
  final_result = result[-1]

  power = theano.function(inputs=[A,k], outputs=final_result,
                        updates=updates)

  print power(range(10),2)
  #[  0.   1.   4.   9.  16.  25.  36.  49.  64.  81.]


**Scan Example: Calculating a Polynomial**

.. testcode::

  import theano
  import theano.tensor as T

  coefficients = theano.tensor.vector("coefficients")
  x = T.scalar("x"); max_coefficients_supported = 10000

  # Generate the components of the polynomial
  full_range=theano.tensor.arange(max_coefficients_supported)
  components, updates = theano.scan(fn=lambda coeff, power, free_var:
                                     coeff * (free_var ** power),
                                  outputs_info=None,
                                  sequences=[coefficients, full_range],
                                  non_sequences=x)
  polynomial = components.sum()
  calculate_polynomial = theano.function(inputs=[coefficients, x],
                                       outputs=polynomial)

  test_coeff = numpy.asarray([1, 0, 2], dtype=numpy.float32)
  print calculate_polynomial(test_coeff, 3)

.. testoutput::

  19.0



Exercise 4
-----------

- Run both examples
- Modify and execute the polynomial example to have the reduction done by scan



Compilation pipeline
--------------------

.. image:: ../hpcs2011_tutorial/pics/pipeline.png
   :width: 400 px

Inplace optimization
--------------------

- 2 type of inplace operations:

  - An op that return a view on its inputs (e.g. reshape, inplace transpose)
  - An op that write the output on the inputs memory space

- This allows some memory optimization
- The Op must tell Theano if they work inplace
- Inplace Op add constraints to the order of execution


Profiling
---------

- To replace the default mode with this mode, use the Theano flags ``mode=ProfileMode``

- To enable the memory profiling use the flags ``ProfileMode.profile_memory=True``

Theano output:

.. code-block:: python

    """
    Time since import 33.456s
    Theano compile time: 1.023s (3.1% since import)
      Optimization time: 0.789s
      Linker time: 0.221s
    Theano fct call 30.878s (92.3% since import)
     Theano Op time 29.411s 87.9%(since import) 95.3%(of fct call)
     Theano function overhead in ProfileMode 1.466s 4.4%(since import)
                                                  4.7%(of fct call)
    10001 Theano fct call, 0.003s per call
    Rest of the time since import 1.555s 4.6%

    Theano fct summary:
    <% total fct time> <total time> <time per call> <nb call> <fct name>
     100.0% 30.877s 3.09e-03s 10000 train
      0.0% 0.000s 4.06e-04s 1 predict

    Single Op-wise summary:
    <% of local_time spent on this kind of Op> <cumulative %>
        <self seconds> <cumulative seconds> <time per call> <nb_call>
        <nb_op> <nb_apply> <Op name>
       87.3%   87.3%  25.672s  25.672s  2.57e-03s   10000  1  1 <Gemv>
        9.7% s  97.0%  2.843s  28.515s  2.84e-04s   10001  1  2 <Dot>
        2.4%   99.3%  0.691s  29.206s  7.68e-06s * 90001 10 10 <Elemwise>
        0.4%   99.7%  0.127s  29.334s  1.27e-05s   10000  1  1 <Alloc>
        0.2%   99.9%  0.053s  29.386s  1.75e-06s * 30001  2  4 <DimShuffle>
        0.0%  100.0%  0.014s  29.400s  1.40e-06s * 10000  1  1 <Sum>
        0.0%  100.0%  0.011s  29.411s  1.10e-06s * 10000  1  1 <Shape_i>
    (*) Op is running a c implementation

    Op-wise summary:
    <% of local_time spent on this kind of Op> <cumulative %>
        <self seconds> <cumulative seconds> <time per call>
        <nb_call> <nb apply> <Op name>
       87.3%   87.3%  25.672s  25.672s  2.57e-03s   10000  1 Gemv{inplace}
        9.7%   97.0%  2.843s  28.515s  2.84e-04s   10001  2 dot
        1.3%   98.2%  0.378s  28.893s  3.78e-05s * 10000  1 Elemwise{Composite{scalar_softplus,{mul,scalar_softplus,{neg,mul,sub}}}}
        0.4%   98.7%  0.127s  29.021s  1.27e-05s   10000  1 Alloc
        0.3%   99.0%  0.092s  29.112s  9.16e-06s * 10000  1 Elemwise{Composite{exp,{mul,{true_div,neg,{add,mul}}}}}[(0, 0)]
        0.1%   99.3%  0.033s  29.265s  1.66e-06s * 20001  3 InplaceDimShuffle{x}
       ... (remaining 11 Apply account for 0.7%(0.00s) of the runtime)
    (*) Op is running a c implementation

    Apply-wise summary:
    <% of local_time spent at this position> <cumulative %%>
        <apply time> <cumulative seconds> <time per call>
        <nb_call> <Apply position> <Apply Op name>
       87.3%   87.3%  25.672s  25.672s 2.57e-03s  10000  15 Gemv{inplace}(w, TensorConstant{-0.01}, InplaceDimShuffle{1,0}.0, Elemwise{Composite{exp,{mul,{true_div,neg,{add,mul}}}}}[(0, 0)].0, TensorConstant{0.9998})
        9.7%   97.0%  2.843s  28.515s 2.84e-04s  10000   1 dot(x, w)
        1.3%   98.2%  0.378s  28.893s 3.78e-05s  10000   9 Elemwise{Composite{scalar_softplus,{mul,scalar_softplus,{neg,mul,sub}}}}(y, Elemwise{Composite{neg,sub}}[(0, 0)].0, Elemwise{sub,no_inplace}.0, Elemwise{neg,no_inplace}.0)
        0.4%   98.7%  0.127s  29.020s 1.27e-05s  10000  10 Alloc(Elemwise{inv,no_inplace}.0, Shape_i{0}.0)
        0.3%   99.0%  0.092s  29.112s 9.16e-06s  10000  13 Elemwise{Composite{exp,{mul,{true_div,neg,{add,mul}}}}}[(0,0)](Elemwise{ScalarSigmoid{output_types_preference=transfer_type{0}, _op_use_c_code=True}}[(0, 0)].0, Alloc.0, y, Elemwise{Composite{neg,sub}}[(0,0)].0, Elemwise{sub,no_inplace}.0, InplaceDimShuffle{x}.0)
        0.3%   99.3%  0.080s  29.192s 7.99e-06s  10000  11 Elemwise{ScalarSigmoid{output_types_preference=transfer_type{0}, _op_use_c_code=True}}[(0, 0)](Elemwise{neg,no_inplace}.0)
       ... (remaining 14 Apply instances account for
           0.7%(0.00s) of the runtime)

    Profile of Theano functions memory:
    (This check only the output of each apply node. It don't check the temporary memory used by the op in the apply node.)
    Theano fct: train
        Max without gc, inplace and view (KB) 2481
        Max FAST_RUN_NO_GC (KB) 16
        Max FAST_RUN (KB) 16
        Memory saved by view (KB) 2450
        Memory saved by inplace (KB) 15
        Memory saved by GC (KB) 0
        <Sum apply outputs (bytes)> <Apply outputs memory size(bytes)>
            <created/inplace/view> <Apply node>
        <created/inplace/view> is taked from the op declaration, not ...
             2508800B  [2508800] v InplaceDimShuffle{1,0}(x)
                6272B  [6272] i Gemv{inplace}(w, ...)
                3200B  [3200] c Elemwise{Composite{...}}(y, ...)

    Here are tips to potentially make your code run faster (if you think of new ones, suggest them on the mailing list).
    Test them first, as they are not guaranteed to always provide a speedup.
      - Try the Theano flag floatX=float32
    """

Exercise 5
-----------

- In the last exercises, do you see a speed up with the GPU?
- Where does it come from? (Use ProfileMode)
- Is there something we can do to speed up the GPU version?


Printing/Drawing Theano graphs
------------------------------

Consider the following logistic regression model:

>>> import numpy
>>> import theano
>>> import theano.tensor as T
>>> rng = numpy.random
>>> # Training data
>>> N = 400
>>> feats = 784
>>> D = (rng.randn(N, feats).astype(theano.config.floatX), rng.randint(size=N,low=0, high=2).astype(theano.config.floatX))
>>> training_steps = 10000
>>> # Declare Theano symbolic variables
>>> x = T.matrix("x")
>>> y = T.vector("y")
>>> w = theano.shared(rng.randn(feats).astype(theano.config.floatX), name="w")
>>> b = theano.shared(numpy.asarray(0., dtype=theano.config.floatX), name="b")
>>> x.tag.test_value = D[0]
>>> y.tag.test_value = D[1]
>>> # Construct Theano expression graph
>>> p_1 = 1 / (1 + T.exp(-T.dot(x, w)-b)) # Probability of having a one
>>> prediction = p_1 > 0.5 # The prediction that is done: 0 or 1
>>> # Compute gradients
>>> xent = -y*T.log(p_1) - (1-y)*T.log(1-p_1) # Cross-entropy
>>> cost = xent.mean() + 0.01*(w**2).sum() # The cost to optimize
>>> gw,gb = T.grad(cost, [w,b])
>>> # Training and prediction function
>>> train = theano.function(inputs=[x,y], outputs=[prediction, xent], updates=[[w, w-0.01*gw], [b, b-0.01*gb]], name = "train")
>>> predict = theano.function(inputs=[x], outputs=prediction, name = "predict")

We will now make use of Theano's printing features to compare the unoptimized
graph (``prediction``) to the optimized graph (``predict``).

- Pretty Printing

>>> theano.printing.pprint(prediction) # doctest: +NORMALIZE_WHITESPACE
'gt((TensorConstant{1} / (TensorConstant{1} + exp(((-(x \\dot w)) - b)))),
TensorConstant{0.5})'


- Debug Print

The graph before optimization:

>>> theano.printing.debugprint(prediction) # doctest: +NORMALIZE_WHITESPACE
    Elemwise{gt,no_inplace} [@A] ''
    |Elemwise{true_div,no_inplace} [@B] ''
    | |DimShuffle{x} [@C] ''
    | | |TensorConstant{1} [@D]
    | |Elemwise{add,no_inplace} [@E] ''
    |   |DimShuffle{x} [@F] ''
    |   | |TensorConstant{1} [@D]
    |   |Elemwise{exp,no_inplace} [@G] ''
    |     |Elemwise{sub,no_inplace} [@H] ''
    |       |Elemwise{neg,no_inplace} [@I] ''
    |       | |dot [@J] ''
    |       |   |x [@K]
    |       |   |w [@L]
    |       |DimShuffle{x} [@M] ''
    |         |b [@N]
    |DimShuffle{x} [@O] ''
      |TensorConstant{0.5} [@P]

The graph after optimization:

>>> theano.printing.debugprint(predict) # doctest: +NORMALIZE_WHITESPACE
    Elemwise{Composite{GT(scalar_sigmoid((-((-i0) - i1))), i2)}} [@A] ''   4
     |CGemv{inplace} [@B] ''   3
     | |Alloc [@C] ''   2
     | | |TensorConstant{0.0} [@D]
     | | |Shape_i{0} [@E] ''   1
     | |   |x [@F]
     | |TensorConstant{1.0} [@G]
     | |x [@F]
     | |w [@H]
     | |TensorConstant{0.0} [@D]
     |InplaceDimShuffle{x} [@I] ''   0
     | |b [@J]
     |TensorConstant{(1,) of 0.5} [@K]


- Picture Printing of Graphs
``pydotprint`` requires graphviz and pydot.

The graph before optimization:

>>> theano.printing.pydotprint(prediction, outfile="pics/logreg_pydotprint_prediction.png", var_with_name_simple=True)
The output file is available at pics/logreg_pydotprint_prediction.png

.. image:: ./pics/logreg_pydotprint_prediction.png
   :width: 800 px

The graph after optimization:

>>> theano.printing.pydotprint(predict, outfile="pics/logreg_pydotprint_predict.png", var_with_name_simple=True)
The output file is available at pics/logreg_pydotprint_predict.png

.. image:: ./pics/logreg_pydotprint_predict.png
   :width: 800 px

The optimized training graph:

>>> theano.printing.pydotprint(train, outfile="pics/logreg_pydotprint_train.png", var_with_name_simple=True)
The output file is available at pics/logreg_pydotprint_train.png

.. image:: ./pics/logreg_pydotprint_train.png
   :width: 1500 px


Debugging
---------

- Run with the flag ``mode=DebugMode``

  - 100-1000x slower
  - Test all optimization steps from the original graph to the final graph
  - Checks many things that Op should/shouldn't do
  - Executes both the Python and C code versions

- Run with the Theano flag ``compute_test_value = {``off'',``ignore'', ``warn'', ``raise''}``

  - Run the code as we create the graph
  - Allows you to find the bug earlier (ex: shape mismatch)
  - Makes it easier to identify where the problem is in *your* code
  - Use the value of constants and shared variables directly
  - For pure symbolic variables uses ``x.tag.test_value = numpy.random.rand(5,10)``

- Run with the flag ``mode=FAST_COMPILE``

  - Few optimizations
  - Run Python code (better error messages and can be debugged interactively in the Python debugger)

Known limitations
-----------------

- Compilation phase distinct from execution phase
- Compilation time can be significant

  - Amortize it with functions over big input or reuse functions

- Execution overhead

  - Needs a certain number of operations to be useful
  - We have started working on this in a branch

- Compilation time superlinear in the size of the graph.

  - A few hundreds nodes is fine
  - Disabling a few optimizations can speed up compilation
  - Usually too many nodes indicates a problem with the graph

