pickle and unpickle done.

doc/library/config.txt

@@ -683,6 +683,16 @@ import theano and print the config variable, as in:
    optimization phase. Theano user's do not need to use this. This is
    to help debug shape error in Theano optimization.
 
+.. attribute:: config.reoptimize_unpickled_function
+
+    Bool value, default: True
+
+    Theano users can use the standard python pickle tools to save a compiled 
+    theano function. When pickling, both graph before and after the optimization 
+    are saved, including shared variables. When set to True, the graph is 
+    reoptimized when being unpickled. Otherwise, skip the graph optimization and 
+    use directly the optimized graph. 
+
 .. attribute:: config.exception_verbosity
 
     String Value: ``'low'``, ``'high'``.

theano/configdefaults.py

@@ -547,6 +547,7 @@ AddConfigVar('check_input',
              BoolParam(True))
 
 AddConfigVar('cache_optimizations',
+             "WARNING: work in progress, does not work yet."
              "Specify if the optimization cache should be used. This cache will"
              "any optimized graph and its optimization. Actually slow downs a lot"
              "the first optimization, and could possibly still contains some bugs."

theano/tests/test_graph_opt_caching.py

-import unittest
-import theano
-import theano.tensor as T
-from theano.gof.graph import is_same_graph
-from theano.gof import FunctionGraph
-from theano.scan_module.scan_utils import equal_computations
-
-def test_graph_equivalence():
-    # Test if equivalent graphs are in fact equivalent
-    # by using some functions in Theano
-
-    # graph g1
-    g1_a = T.fmatrix('inputs')
-    g1_b = T.fmatrix('inputs')
-    g1_y = T.sum(g1_a+g1_b)
-    g1_yy = T.sum(g1_a + g1_b)
-
-    g2_x = T.fmatrix('inputs')
-    g2_y = g2_x.sum()
-
-    g3_a = T.fmatrix('inputs')
-    g3_b = T.fmatrix('inputs')
-    g3_y = T.sum(g3_a+g3_b)
-
-    assert is_same_graph(g1_y, g2_y) is False
-    # This does not work.
-    assert is_same_graph(g1_y, g1_y)
-    assert is_same_graph(g1_y, g1_yy)
-    assert is_same_graph(g1_y, g3_y, givens={g1_a: g3_a, g1_b: g3_b})
-    l1 = theano.gof.graph.inputs([g1_y])
-    l2 = theano.gof.graph.inputs([g3_y])
-    assert len(l1) == len(l2)
-
-    #FunctionGraph([], g1_y)
-
-    #assert graphs_equal(g1_y, g3_y) == True
-    #assert graphs_equal(g1_y, g2_y) == False
-
-if __name__ == '__main__':
-    test_graph_equivalence()

theano/tests/test_pickle_unpickle_theano_fn.py

@@ -33,19 +33,19 @@ def test_pickle_unpickle_with_reoptimization():
     f = theano.function([x1,x2],y, updates=updates)
 
     # now pickle the compiled theano fn
-    pkl_path = open('thean_fn.pkl','wb')
-    cPickle.dump(f, pkl_path, -1)
+    string_pkl = cPickle.dumps(f, -1)
     
     in1 = numpy.ones((10, 10), dtype=floatX)
     in2 = numpy.ones((10, 10), dtype=floatX)
-    print 'the desired value is ',f(in1, in2)
     
     # test unpickle with optimization
-    theano.config.reoptimize_unpickled_function=True # the default is True
-    pkl_path = open('thean_fn.pkl','r')
-    f_ = cPickle.load(pkl_path)
-    print 'got value ', f_(in1, in2)
-    assert f(in1, in2) == f_(in1, in2)
+    default = theano.config.reoptimize_unpickled_function
+    try:
+        theano.config.reoptimize_unpickled_function=True # the default is True
+        f_ = cPickle.loads(string_pkl)
+        assert f(in1, in2) == f_(in1, in2)
+    finally:
+        theano.config.reoptimize_unpickled_function = default
     
 def test_pickle_unpickle_without_reoptimization():
     x1 = T.fmatrix('x1')
@@ -60,20 +60,20 @@ def test_pickle_unpickle_without_reoptimization():
     f = theano.function([x1,x2],y, updates=updates)
 
     # now pickle the compiled theano fn
-    pkl_path = open('thean_fn.pkl','wb')
-    cPickle.dump(f, pkl_path, -1)
+    string_pkl = cPickle.dumps(f, -1)
 
     # compute f value
     in1 = numpy.ones((10, 10), dtype=floatX)
     in2 = numpy.ones((10, 10), dtype=floatX)
-    print 'the desired value is ',f(in1, in2)
     
     # test unpickle without optimization
-    theano.config.reoptimize_unpickled_function=False # the default is True
-    pkl_path = open('thean_fn.pkl','r')
-    f_ = cPickle.load(pkl_path)
-    print 'got value ', f_(in1, in2)
-    assert f(in1, in2) == f_(in1, in2)
+    default = theano.config.reoptimize_unpickled_function
+    try:
+        theano.config.reoptimize_unpickled_function=False # the default is True
+        f_ = cPickle.loads(string_pkl)
+        assert f(in1, in2) == f_(in1, in2)
+    finally:
+        theano.config.reoptimize_unpickled_function = default
     
 if __name__ == '__main__':
     test_pickle_unpickle_with_reoptimization()