
.. _debug_faq:

=========================================
Debugging Theano: FAQ and Troubleshooting
=========================================

There are many kinds of bugs that might come up in a computer program.
This page is structured as a FAQ.  It provides recipes to tackle common
problems, and introduces some of the tools that we use to find problems in our
own Theano code, and even (it happens) in Theano's internals, in
:ref:`using_debugmode`.

Isolating the Problem/Testing Theano Compiler
---------------------------------------------

You can run your Theano function in a :ref:`DebugMode<using_debugmode>`.
This tests the Theano optimizations and helps to find where NaN, inf and other problems come from.

Interpreting Error Messages
---------------------------

Even in its default configuration, Theano tries to display useful error
messages. Consider the following faulty code.

.. testcode::

    import numpy as np
    import theano
    import theano.tensor as T

    x = T.vector()
    y = T.vector()
    z = x + x
    z = z + y
    f = theano.function([x, y], z)
    f(np.ones((2,)), np.ones((3,)))

Running the code above we see:

.. testoutput::

    Traceback (most recent call last):
      File "test0.py", line 10, in <module>
        f(np.ones((2,)), np.ones((3,)))
      File "/PATH_TO_THEANO/theano/compile/function_module.py", line 605, in __call__
        self.fn.thunks[self.fn.position_of_error])
      File "/PATH_TO_THEANO/theano/compile/function_module.py", line 595, in __call__
        outputs = self.fn()
    ValueError: Input dimension mis-match. (input[0].shape[0] = 3, input[1].shape[0] = 2)
    Apply node that caused the error: Elemwise{add,no_inplace}(<TensorType(float64, vector)>, <TensorType(float64, vector)>, <TensorType(float64, vector)>)
    Inputs types: [TensorType(float64, vector), TensorType(float64, vector), TensorType(float64, vector)]
    Inputs shapes: [(3,), (2,), (2,)]
    Inputs strides: [(8,), (8,), (8,)]
    Inputs scalar values: ['not scalar', 'not scalar', 'not scalar']

    HINT: Re-running with most Theano optimization disabled could give you a back-traces when this node was created. This can be done with by setting the Theano flags 'optimizer=fast_compile'. If that does not work, Theano optimization can be disabled with 'optimizer=None'.
    HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint of this apply node.

Arguably the most useful information is approximately half-way through
the error message, where the kind of error is displayed along with its
cause (`ValueError: Input dimension mis-match. (input[0].shape[0] = 3,
input[1].shape[0] = 2`).
Below it, some other information is given, such as the apply node that
caused the error, as well as the input types, shapes, strides and
scalar values.

The two hints can also be helpful when debugging. Using the theano flag
``optimizer=fast_compile`` or ``optimizer=None`` can often tell you
the faulty line, while ``exception_verbosity=high`` will display a
debugprint of the apply node. Using these hints, the end of the error
message becomes :

.. testoutput::

    Backtrace when the node is created:
      File "test0.py", line 8, in <module>
        z = z + y

    Debugprint of the apply node:
    Elemwise{add,no_inplace} [@A] <TensorType(float64, vector)> ''
     |Elemwise{add,no_inplace} [@B] <TensorType(float64, vector)> ''
     | |<TensorType(float64, vector)> [@C] <TensorType(float64, vector)>
     | |<TensorType(float64, vector)> [@C] <TensorType(float64, vector)>
     |<TensorType(float64, vector)> [@D] <TensorType(float64, vector)>

We can here see that the error can be traced back to the line ``z = z + y``.
For this example, using ``optimizer=fast_compile`` worked. If it did not,
you could set ``optimizer=None`` or use test values.

Using Test Values
-----------------

As of v.0.4.0, Theano has a new mechanism by which graphs are executed
on-the-fly, before a ``theano.function`` is ever compiled. Since optimizations
haven't been applied at this stage, it is easier for the user to locate the
source of some bug. This functionality is enabled through the config flag
``theano.config.compute_test_value``. Its use is best shown through the
following example. Here, we use ``exception_verbosity=high`` and
``optimizer=fast_compile``, which would not tell you the line at fault.
``optimizer=None`` would and it could therefore be used instead of test values.


.. testcode:: testvalues

    import numpy
    import theano
    import theano.tensor as T

    # compute_test_value is 'off' by default, meaning this feature is inactive
    theano.config.compute_test_value = 'off' # Use 'warn' to activate this feature

    # configure shared variables
    W1val = numpy.random.rand(2, 10, 10).astype(theano.config.floatX)
    W1 = theano.shared(W1val, 'W1')
    W2val = numpy.random.rand(15, 20).astype(theano.config.floatX)
    W2 = theano.shared(W2val, 'W2')

    # input which will be of shape (5,10)
    x  = T.matrix('x')
    # provide Theano with a default test-value
    #x.tag.test_value = numpy.random.rand(5, 10)

    # transform the shared variable in some way. Theano does not
    # know off hand that the matrix func_of_W1 has shape (20, 10)
    func_of_W1 = W1.dimshuffle(2, 0, 1).flatten(2).T

    # source of error: dot product of 5x10 with 20x10
    h1 = T.dot(x, func_of_W1)

    # do more stuff
    h2 = T.dot(h1, W2.T)

    # compile and call the actual function
    f = theano.function([x], h2)
    f(numpy.random.rand(5, 10))

Running the above code generates the following error message:

.. testoutput:: testvalues

    Traceback (most recent call last):
      File "test1.py", line 31, in <module>
        f(numpy.random.rand(5, 10))
      File "PATH_TO_THEANO/theano/compile/function_module.py", line 605, in __call__
        self.fn.thunks[self.fn.position_of_error])
      File "PATH_TO_THEANO/theano/compile/function_module.py", line 595, in __call__
        outputs = self.fn()
    ValueError: Shape mismatch: x has 10 cols (and 5 rows) but y has 20 rows (and 10 cols)
    Apply node that caused the error: Dot22(x, DimShuffle{1,0}.0)
    Inputs types: [TensorType(float64, matrix), TensorType(float64, matrix)]
    Inputs shapes: [(5, 10), (20, 10)]
    Inputs strides: [(80, 8), (8, 160)]
    Inputs scalar values: ['not scalar', 'not scalar']

    Debugprint of the apply node:
    Dot22 [@A] <TensorType(float64, matrix)> ''
     |x [@B] <TensorType(float64, matrix)>
     |DimShuffle{1,0} [@C] <TensorType(float64, matrix)> ''
       |Flatten{2} [@D] <TensorType(float64, matrix)> ''
         |DimShuffle{2,0,1} [@E] <TensorType(float64, 3D)> ''
           |W1 [@F] <TensorType(float64, 3D)>

    HINT: Re-running with most Theano optimization disabled could give you a back-traces when this node was created. This can be done with by setting the Theano flags 'optimizer=fast_compile'. If that does not work, Theano optimization can be disabled with 'optimizer=None'.

If the above is not informative enough, by instrumenting the code ever
so slightly, we can get Theano to reveal the exact source of the error.

.. testcode:: testvalues

    # enable on-the-fly graph computations
    theano.config.compute_test_value = 'warn'

    ...

    # input which will be of shape (5, 10)
    x  = T.matrix('x')
    # provide Theano with a default test-value
    x.tag.test_value = numpy.random.rand(5, 10)

In the above, we are tagging the symbolic matrix *x* with a special test
value. This allows Theano to evaluate symbolic expressions on-the-fly (by
calling the ``perform`` method of each op), as they are being defined. Sources
of error can thus be identified with much more precision and much earlier in
the compilation pipeline. For example, running the above code yields the
following error message, which properly identifies *line 24* as the culprit.

.. testoutput:: testvalues

    Traceback (most recent call last):
      File "test2.py", line 24, in <module>
        h1 = T.dot(x, func_of_W1)
      File "PATH_TO_THEANO/theano/tensor/basic.py", line 4734, in dot
        return _dot(a, b)
      File "PATH_TO_THEANO/theano/gof/op.py", line 545, in __call__
        required = thunk()
      File "PATH_TO_THEANO/theano/gof/op.py", line 752, in rval
        r = p(n, [x[0] for x in i], o)
      File "PATH_TO_THEANO/theano/tensor/basic.py", line 4554, in perform
        z[0] = numpy.asarray(numpy.dot(x, y))
    ValueError: matrices are not aligned

The ``compute_test_value`` mechanism works as follows:

* Theano ``constants`` and ``shared`` variables are used as is. No need to instrument them.
* A Theano *variable* (i.e. ``dmatrix``, ``vector``, etc.) should be
  given a special test value through the attribute ``tag.test_value``.
* Theano automatically instruments intermediate results. As such, any quantity
  derived from *x* will be given a ``tag.test_value`` automatically.

``compute_test_value`` can take the following values:

* ``off``: Default behavior. This debugging mechanism is inactive.
* ``raise``: Compute test values on the fly. Any variable for which a test
  value is required, but not provided by the user, is treated as an error. An
  exception is raised accordingly.
* ``warn``: Idem, but a warning is issued instead of an *Exception*.
* ``ignore``: Silently ignore the computation of intermediate test values, if a
  variable is missing a test value.

.. note::
  This feature is currently incompatible with ``Scan`` and also with ops
  which do not implement a ``perform`` method.


"How do I Print an Intermediate Value in a Function?"
-----------------------------------------------------

Theano provides a 'Print' op to do this.

.. testcode::

    import numpy
    import theano

    x = theano.tensor.dvector('x')

    x_printed = theano.printing.Print('this is a very important value')(x)

    f = theano.function([x], x * 5)
    f_with_print = theano.function([x], x_printed * 5)

    #this runs the graph without any printing
    assert numpy.all( f([1, 2, 3]) == [5, 10, 15])

    #this runs the graph with the message, and value printed
    assert numpy.all( f_with_print([1, 2, 3]) == [5, 10, 15])

.. testoutput::

    this is a very important value __str__ = [ 1.  2.  3.]

Since Theano runs your program in a topological order, you won't have precise
control over the order in which multiple ``Print()`` ops are evaluted.  For a more
precise inspection of what's being computed where, when, and how, see the discussion
:ref:`faq_monitormode`.

.. warning::

    Using this ``Print`` Theano Op can prevent some Theano
    optimization from being applied. This can also happen with
    stability optimization. So if you use this Print and have nan, try
    to remove them to know if this is the cause or not.


"How do I Print a Graph?" (before or after compilation)
-------------------------------------------------------

.. TODO: dead links in the next paragraph

Theano provides two functions (:func:`theano.pp` and
:func:`theano.printing.debugprint`) to print a graph to the terminal before or after
compilation.  These two functions print expression graphs in different ways:
:func:`pp` is more compact and math-like, :func:`debugprint` is more verbose.
Theano also provides :func:`theano.printing.pydotprint` that creates a png image of the function.

You can read about them in :ref:`libdoc_printing`.



"The Function I Compiled is Too Slow, what's up?"
-------------------------------------------------

First, make sure you're running in ``FAST_RUN`` mode. Even though
``FAST_RUN`` is the default mode, insist by passing ``mode='FAST_RUN'``
to ``theano.function`` (or ``theano.make``) or by setting :attr:`config.mode`
to ``FAST_RUN``.

Second, try the Theano :ref:`using_profilemode`.  This will tell you which
``Apply`` nodes, and which ops are eating up your CPU cycles.

Tips:

* Use the flags ``floatX=float32`` to require type *float32* instead of *float64*;
  Use the Theano constructors matrix(),vector(),... instead of dmatrix(), dvector(),...
  since they respectively involve the default types *float32* and *float64*.
* Check in the ``profile`` mode that there is no ``Dot`` op in the post-compilation
  graph while you are multiplying two matrices of the same type. ``Dot`` should be
  optimized to ``dot22`` when the inputs are matrices and of the same type. This can
  still happen when using ``floatX=float32`` when one of the inputs of the graph is
  of type *float64*.


"Why does my GPU function seem to be slow?"
-------------------------------------------

When you compile a theano function, if you do not get the speedup that you expect over the 
CPU performance of the same code. It is oftentimes due to the fact that some Ops might be running
on CPU instead GPU. If that is the case, you can use assert_no_cpu_op to check if there 
is a CPU Op on your computational graph. assert_no_cpu_op can take the following one of the three 
options:

* ``warn``: Raise a warning
* ``pdb``: Stop with a pdb in the computational graph during the compilation
* ``raise``: Raise an error,
  if there is a CPU Op in the computational graph.

It is possible to use this mode by providing the flag in THEANO_FLAGS, such as:
``THEANO_FLAGS="float32,device=gpu,assert_no_cpu_op='raise'" python test.py``

But note that this optimization will not catch all the CPU Ops, it might miss some 
Ops.

.. _faq_monitormode:

"How do I Step through a Compiled Function?"
--------------------------------------------

You can use ``MonitorMode`` to inspect the inputs and outputs of each
node being executed when the function is called. The code snipped below
shows how to print all inputs and outputs:

.. testcode::

    import theano

    def inspect_inputs(i, node, fn):
        print i, node, "input(s) value(s):", [input[0] for input in fn.inputs],

    def inspect_outputs(i, node, fn):
        print "output(s) value(s):", [output[0] for output in fn.outputs]

    x = theano.tensor.dscalar('x')
    f = theano.function([x], [5 * x],
                        mode=theano.compile.MonitorMode(
                            pre_func=inspect_inputs,
                            post_func=inspect_outputs))
    f(3)

.. testoutput::

    0 Elemwise{mul,no_inplace}(TensorConstant{5.0}, x) input(s) value(s): [array(5.0), array(3.0)] output(s) value(s): [array(15.0)]

When using these ``inspect_inputs`` and ``inspect_outputs`` functions
with ``MonitorMode``, you should see [potentially a lot of] printed output.
Every ``Apply`` node will be printed out,
along with its position in the graph, the arguments to the functions ``perform`` or
``c_code`` and the output it computed.
Admittedly, this may be a huge amount of
output to read through if you are using big tensors... but you can choose to
add logic that would, for instance, print
something out only if a certain kind of op were used, at a certain program
position, or only if a particular value showed up in one of the inputs or outputs.
A typical example is to detect when NaN values are added into computations, which
can be achieved as follows:

.. testcode:: compiled

    import numpy

    import theano

    # This is the current suggested detect_nan implementation to
    # show you how it work.  That way, you can modify it for your
    # need.  If you want exactly this method, you can use
    # ``theano.compile.monitormode.detect_nan`` that will always
    # contain the current suggested version.

    def detect_nan(i, node, fn):
        for output in fn.outputs:
            if (not isinstance(output[0], numpy.random.RandomState) and
                numpy.isnan(output[0]).any()):
                print '*** NaN detected ***'
                theano.printing.debugprint(node)
                print 'Inputs : %s' % [input[0] for input in fn.inputs]
                print 'Outputs: %s' % [output[0] for output in fn.outputs]
                break

    x = theano.tensor.dscalar('x')
    f = theano.function([x], [theano.tensor.log(x) * x],
                        mode=theano.compile.MonitorMode(
                            post_func=detect_nan))
    f(0)  # log(0) * 0 = -inf * 0 = NaN

.. testoutput:: compiled

    *** NaN detected ***
    Elemwise{Composite{(log(i0) * i0)}} [@A] ''   
     |x [@B]
    Inputs : [array(0.0)]
    Outputs: [array(nan)]

To help understand what is happening in your graph, you can
disable the ``local_elemwise_fusion`` and all ``inplace``
optimizations. The first is a speed optimization that merges elemwise
operations together. This makes it harder to know which particular
elemwise causes the problem. The second optimization makes some ops'
outputs overwrite their inputs. So, if an op creates a bad output, you
will not be able to see the input that was overwriten in the ``post_func``
function. To disable those optimizations (with a Theano version after
0.6rc3), define the MonitorMode like this:

.. testcode:: compiled

   mode = theano.compile.MonitorMode(post_func=detect_nan).excluding(
       'local_elemwise_fusion', 'inplace')
   f = theano.function([x], [theano.tensor.log(x) * x],
                       mode=mode)

.. note::

    The Theano flags ``optimizer_including``, ``optimizer_excluding``
    and ``optimizer_requiring`` aren't used by the MonitorMode, they
    are used only by the ``default`` mode. You can't use the ``default``
    mode with MonitorMode, as you need to define what you monitor.

To be sure all inputs of the node are available during the call to
``post_func``, you must also disable the garbage collector. Otherwise,
the execution of the node can garbage collect its inputs that aren't
needed anymore by the Theano function. This can be done with the Theano
flag:

.. testcode:: compiled

   allow_gc=False



.. TODO: documentation for link.WrapLinkerMany


How to Use pdb
--------------

In the majority of cases, you won't be executing from the interactive shell
but from a set of Python scripts. In such cases, the use of the Python
debugger can come in handy, especially as your models become more complex.
Intermediate results don't necessarily have a clear name and you can get
exceptions which are hard to decipher, due to the "compiled" nature of the
functions.

Consider this example script ("ex.py"):

.. testcode::

        import theano
        import numpy
        import theano.tensor as T

        a = T.dmatrix('a')
        b = T.dmatrix('b')

        f = theano.function([a, b], [a * b])

        # matrices chosen so dimensions are unsuitable for multiplication
        mat1 = numpy.arange(12).reshape((3, 4))
        mat2 = numpy.arange(25).reshape((5, 5))

        f(mat1, mat2)

This is actually so simple the debugging could be done easily, but it's for
illustrative purposes. As the matrices can't be multiplied element-wise
(unsuitable shapes), we get the following exception:

.. testoutput::

    File "ex.py", line 14, in <module>
      f(mat1, mat2)
    File "/u/username/Theano/theano/compile/function_module.py", line 451, in __call__
    File "/u/username/Theano/theano/gof/link.py", line 271, in streamline_default_f
    File "/u/username/Theano/theano/gof/link.py", line 267, in streamline_default_f
    File "/u/username/Theano/theano/gof/cc.py", line 1049, in execute ValueError: ('Input dimension mis-match. (input[0].shape[0] = 3, input[1].shape[0] = 5)', Elemwise{mul,no_inplace}(a, b), Elemwise{mul,no_inplace}(a, b))

The call stack contains some useful information to trace back the source
of the error. There's the script where the compiled function was called --
but if you're using (improperly parameterized) prebuilt modules, the error
might originate from ops in these modules, not this script. The last line
tells us about the op that caused the exception. In this case it's a "mul"
involving variables with names "a" and "b". But suppose we instead had an
intermediate result to which we hadn't given a name.

After learning a few things about the graph structure in Theano, we can use
the Python debugger to explore the graph, and then we can get runtime
information about the error. Matrix dimensions, especially, are useful to
pinpoint the source of the error. In the printout, there are also 2 of the 4
dimensions of the matrices involved, but for the sake of example say we'd
need the other dimensions to pinpoint the error. First, we re-launch with
the debugger module and run the program with "c":

.. code-block:: text

    python -m pdb ex.py
    > /u/username/experiments/doctmp1/ex.py(1)<module>()
    -> import theano
    (Pdb) c

Then we get back the above error printout, but the interpreter breaks in
that state. Useful commands here are

* "up" and "down" (to move up and down the call stack),
* "l" (to print code around the line in the current stack position),
* "p variable_name" (to print the string representation of 'variable_name'),
* "p dir(object_name)", using the Python dir() function to print the list of an object's members

Here, for example, I do "up", and a simple "l" shows me there's a local
variable "node". This is the "node" from the computation graph, so by
following the "node.inputs", "node.owner" and "node.outputs" links I can
explore around the graph.

That graph is purely symbolic (no data, just symbols to manipulate it
abstractly). To get information about the actual parameters, you explore the
"thunk" objects, which bind the storage for the inputs (and outputs) with
the function itself (a "thunk" is a concept related to closures). Here, to
get the current node's first input's shape, you'd therefore do "p
thunk.inputs[0][0].shape", which prints out "(3, 4)".

.. _faq_dump_fct:

Dumping a Function to help debug
--------------------------------

If you are reading this, there is high chance that you emailed our
mailing list and we asked you to read this section. This section
explain how to dump all the parameter passed to
``theano.function()``. This is useful to help us reproduce a problem
during compilation and it don't request you to make a self contained
example.

For this to work, we need to be able to import the code for all Op in
the graph. So if you create your own Op, we will need this
code. Otherwise, we won't be able to unpickle it. We already have all
the Ops from Theano and Pylearn2.

.. code-block:: python

    # Replace this line:
    theano.function(...)
    # with
    theano.function_dump(filename, ...)
    # Where filename is a string to a file that we will write to.

Then send us filename.
