theano scan optimization

selected from Theano Doc

Optimizing Scan performance

Minimizing Scan Usage

performan as much of the computation as possible outside of Scan. This may have the effect increasing memory usage but also reduce the overhead introduce by Scan.

Explicitly passing inputs of the inner function to scan

It's more efficient to explicitly pass parameter as non-sequence inputs.

Examples: Gibbs Sampling

Version One:

import theano
from theano import tensor as T

W = theano.shared(W_values) # we assume that ``W_values`` contains the
                            # initial values of your weight matrix

bvis = theano.shared(bvis_values)
bhid = theano.shared(bhid_values)

trng = T.shared_randomstreams.RandomStreams(1234)

def OneStep(vsample) :
    hmean = T.nnet.sigmoid(theano.dot(vsample, W) + bhid)
    hsample = trng.binomial(size=hmean.shape, n=1, p=hmean)
    vmean = T.nnet.sigmoid(theano.dot(hsample, W.T) + bvis)
    return trng.binomial(size=vsample.shape, n=1, p=vmean,
                         dtype=theano.config.floatX)

sample = theano.tensor.vector()
values, updates = theano.scan(OneStep, outputs_info=sample, n_steps=10)
gibbs10 = theano.function([sample], values[-1], updates=updates)

Version Two:

W = theano.shared(W_values) # we assume that ``W_values`` contains the
                            # initial values of your weight matrix

bvis = theano.shared(bvis_values)
bhid = theano.shared(bhid_values)

trng = T.shared_randomstreams.RandomStreams(1234)

# OneStep, with explicit use of the shared variables (W, bvis, bhid)
def OneStep(vsample, W, bvis, bhid):
    hmean = T.nnet.sigmoid(theano.dot(vsample, W) + bhid)
    hsample = trng.binomial(size=hmean.shape, n=1, p=hmean)
    vmean = T.nnet.sigmoid(theano.dot(hsample, W.T) + bvis)
    return trng.binomial(size=vsample.shape, n=1, p=vmean,
                     dtype=theano.config.floatX)

sample = theano.tensor.vector()

# The new scan, with the shared variables passed as non_sequences
values, updates = theano.scan(fn=OneStep,
                              outputs_info=sample,
                              non_sequences=[W, bvis, bhid],
                              n_steps=10)

gibbs10 = theano.function([sample], values[-1], updates=updates)

Deactivating garbage collecting in Scan

Deactivating garbage collecting in Scan can allow it to reuse memory between executins instead of always having to allocate new memory. Scan reuses memory between iterations of the same execution but frees the memory after the last iteration.

config.scan.allow_gc=False

Graph Optimizations

There are patterns that Theano can't optimize. the LSTM tutorial provides an example of optimization that theano can't perform. Instead of performing many matrix multiplications between matrix \(x_t\) and each of the shared msatrices \(W_i,W_c,W_f\) and \(W_o\), the matrixes \(W_{*}\) are merged into a single shared \(W\) and the graph performans a single larger matrix multiplication between \(W\) and \(x_t\). The resulting matrix is then sliced to obtain the results of that the small individial matrix multiplications by a single larger one and thus improves performance at the cost of a potentially higher memory usage.