Configuration Settings and Compiling Modes

Configuration

The config module contains several attributes that modify Theano’s behavior. Many of these attributes are examined during the import of the theano module and several are assumed to be read-only.

As a rule, the attributes in the config module should not be modified inside the user code.

Theano’s code comes with default values for these attributes, but you can override them from your .theanorc file, and override those values in turn by the THEANO_FLAGS environment variable.

The order of precedence is:

  1. an assignment to theano.config.<property>
  2. an assignment in THEANO_FLAGS
  3. an assignment in the .theanorc file (or the file indicated in THEANORC)

You can display the current/effective configuration at any time by printing theano.config. For example, to see a list of all active configuration variables, type this from the command-line:

python -c 'import theano; print(theano.config)' | less

For more detail, see Configuration in the library.

Exercise

Consider the logistic regression:

import numpy
import theano
import theano.tensor as T
rng = numpy.random

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
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])

# Compile expressions to functions
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")

if any([x.op.__class__.__name__ in ['Gemv', 'CGemv', 'Gemm', 'CGemm'] for x in
        train.maker.fgraph.toposort()]):
    print('Used the cpu')
elif any([x.op.__class__.__name__ in ['GpuGemm', 'GpuGemv'] for x in
          train.maker.fgraph.toposort()]):
    print('Used the gpu')
else:
    print('ERROR, not able to tell if theano used the cpu or the gpu')
    print(train.maker.fgraph.toposort())

for i in range(training_steps):
    pred, err = train(D[0], D[1])

print("target values for D")
print(D[1])

print("prediction on D")
print(predict(D[0]))

Modify and execute this example to run on CPU (the default) with floatX=float32 and time the execution using the command line time python file.py. Save your code as it will be useful later on.

Note

  • Apply the Theano flag floatX=float32 (through theano.config.floatX) in your code.
  • Cast inputs before storing them into a shared variable.
  • Circumvent the automatic cast of int32 with float32 to float64:
    • Insert manual cast in your code or use [u]int{8,16}.
    • Insert manual cast around the mean operator (this involves division by length, which is an int64).
    • Note that a new casting mechanism is being developed.

Solution

Mode

Every time theano.function is called, the symbolic relationships between the input and output Theano variables are optimized and compiled. The way this compilation occurs is controlled by the value of the mode parameter.

Theano defines the following modes by name:

  • 'FAST_COMPILE': Apply just a few graph optimizations and only use Python implementations. So GPU is disabled.

  • 'FAST_RUN': Apply all optimizations and use C implementations where possible.

  • 'DebugMode': Verify the correctness of all optimizations, and compare C and Python

    implementations. This mode can take much longer than the other modes, but can identify several kinds of problems.

  • 'NanGuardMode': Same optimization as FAST_RUN, but check if a node generate nans.

The default mode is typically FAST_RUN, but it can be controlled via the configuration variable config.mode, which can be overridden by passing the keyword argument to theano.function.

short name Full constructor What does it do?
FAST_COMPILE compile.mode.Mode(linker='py', optimizer='fast_compile') Python implementations only, quick and cheap graph transformations
FAST_RUN compile.mode.Mode(linker='cvm', optimizer='fast_run') C implementations where available, all available graph transformations.
DebugMode compile.debugmode.DebugMode() Both implementations where available, all available graph transformations.

Note

For debugging purpose, there also exists a MonitorMode (which has no short name). It can be used to step through the execution of a function: see the debugging FAQ for details.

Linkers

A mode is composed of 2 things: an optimizer and a linker. Some modes, like NanGuardMode and DebugMode, add logic around the optimizer and linker. NanGuardMode and DebugMode use their own linker.

You can select which linker to use with the Theano flag config.linker. Here is a table to compare the different linkers.

linker gc [1] Raise error by op Overhead Definition
cvm yes yes “++” As c|py, but the runtime algo to execute the code is in c
cvm_nogc no yes “+” As cvm, but without gc
c|py [2] yes yes “+++” Try C code. If none exists for an op, use Python
c|py_nogc no yes “++” As c|py, but without gc
c no yes “+” Use only C code (if none available for an op, raise an error)
py yes yes “+++” Use only Python code
NanGuardMode no no “++++” Check if nodes generate NaN
DebugMode no yes VERY HIGH Make many checks on what Theano computes
[1]Garbage collection of intermediate results during computation. Otherwise, their memory space used by the ops is kept between Theano function calls, in order not to reallocate memory, and lower the overhead (make it faster...).
[2]Default

For more detail, see Mode in the library.

Using DebugMode

While normally you should use the FAST_RUN or FAST_COMPILE mode, it is useful at first (especially when you are defining new kinds of expressions or new optimizations) to run your code using the DebugMode (available via mode='DebugMode). The DebugMode is designed to run several self-checks and assertions that can help diagnose possible programming errors leading to incorrect output. Note that DebugMode is much slower than FAST_RUN or FAST_COMPILE so use it only during development (not when you launch 1000 processes on a cluster!).

DebugMode is used as follows:

x = T.dvector('x')

f = theano.function([x], 10 * x, mode='DebugMode')

f([5])
f([0])
f([7])

If any problem is detected, DebugMode will raise an exception according to what went wrong, either at call time (f(5)) or compile time ( f = theano.function(x, 10 * x, mode='DebugMode')). These exceptions should not be ignored; talk to your local Theano guru or email the users list if you cannot make the exception go away.

Some kinds of errors can only be detected for certain input value combinations. In the example above, there is no way to guarantee that a future call to, say f(-1), won’t cause a problem. DebugMode is not a silver bullet.

If you instantiate DebugMode using the constructor (see DebugMode) rather than the keyword DebugMode you can configure its behaviour via constructor arguments. The keyword version of DebugMode (which you get by using mode='DebugMode') is quite strict.

For more detail, see DebugMode in the library.

ProfileMode

Note

ProfileMode is deprecated. Use config.profile instead.