scan now makes copies of the other arguments

theano/scan.py

@@ -629,10 +629,24 @@ def scan(fn, sequences=[], outputs_info=[], non_sequences=[],
     # remove shared variables from the non sequences list
     # such that we can compile the function ( the user has the option to add them when writing
     # scan, because in some situations this might make the code more readable)
+    # Also duplicate the list of non sequences arguments to contain copies of the non-shared 
+    # inputs ( this fixes the case when one of this inputs has a default update attached to it 
+    # that belongs to some shared random stream ). 
+    #
+    # Note : In that case, scan assumes that you do not want to draw new numbers at every call ( you
+    #        would have made the internal function do that explicitly if you wanted to) but rather to 
+    #        use that initial draw as a matrix of values
+    new_non_seqs = []
     notshared_other_args = []
+    notshared_other_args_copies = []
     for non_seq in non_seqs:
         if not isinstance(non_seq, SharedVariable):
+            non_seq_copy = non_seq.type()
             notshared_other_args += [non_seq]
+            notshared_other_args_copies += [non_seq_copy]
+            new_non_seqs += [non_seq_copy]
+        else:
+            new_non_seqs += [non_seq]
 
     # add only the not shared variables to the arguments of the dummy
     # function [ a function should not get shared variables as input ]
@@ -640,10 +654,10 @@ def scan(fn, sequences=[], outputs_info=[], non_sequences=[],
     for arg in args:
         if not isinstance(arg, SharedVariable):
             dummy_args += [arg]
-    dummy_args += notshared_other_args
+    dummy_args += notshared_other_args_copies
     # arguments for the lambda expression that gives us the output
     # of the inner function
-    args += non_seqs
+    args += new_non_seqs
 
     # when we apply the lambda expression we get a mixture of update rules
     # and outputs that needs to be separated
@@ -726,6 +740,7 @@ def scan(fn, sequences=[], outputs_info=[], non_sequences=[],
             not isinstance(x,SharedVariable) and not isinstance(x,gof.Constant), \
             gof.graph.inputs(dummy_args)), outputs, updates = updates, mode = compile.mode.Mode(linker='py',optimizer=None))
     else:
+        print [printing.pp(x) for x in dummy_args]
         dummy_f = function(filter(lambda x: isinstance(x, gof.Variable) and \
             not isinstance(x,SharedVariable) and not isinstance(x,gof.Constant), \
             dummy_args), outputs, updates = updates, mode = compile.mode.Mode(linker='py',optimizer=None))

theano/tests/test_scan.py

@@ -922,7 +922,24 @@ class T_Scan(unittest.TestCase):
         assert len(analytic_grad[0]) == 3
 
 
+    def test_draw_as_input_to_scan(self):
+        trng = theano.tensor.shared_randomstreams.RandomStreams(123)
 
+        x = theano.tensor.matrix('x')
+        y = trng.binomial(size = x.shape, p = x)
+        z,updates = theano.scan(lambda a:a, non_sequences=y, n_steps=2)
+
+        f = theano.function([x],[y,z], updates = updates)
+
+        rng = numpy.random.RandomState(utt.fetch_seed())
+        nx = rng.uniform( size = (10,10) )
+        ny1,nz1 = f(nx)
+        ny2,nz2 = f(nx)
+
+
+        assert numpy.allclose([ny1,ny1], nz1)
+        assert numpy.allclose([ny2,ny2], nz2)
+        assert not numpy.allclose(ny1,ny2)
 
 if __name__ == '__main__':
     unittest.main()