fix an error that make an optimization fail. Add test for that case.

theano/tensor/opt.py

@@ -1678,7 +1678,7 @@ def local_sum_div_dimshuffle(node):
                     new_new_order = list(ax for i,ax in enumerate(new_order) if i not in axis or ax != 'x')
                     #print 'new_new_order =', new_new_order
                     # Remove useless rebroadcast axes
-                    while new_new_order[0] == 'x':
+                    while len(new_new_order)>0 and new_new_order[0] == 'x':
                         del new_new_order[0]
                     #print 'new_new_order =', new_new_order
 

theano/tensor/tests/test_opt.py

@@ -1581,10 +1581,24 @@ class T_local_sum_dimshuffle(unittest.TestCase):
         a = T.matrix('a')
         b = T.vector('b')
         c = T.tensor3('c')
-
+        d = T.scalar('d')
         sums = [
+            sum(a/d),
+            sum(a/d.dimshuffle('x','x')),
+            sum(a/d.dimshuffle('x','x'), axis=0),
+            sum(a/d.dimshuffle('x','x'), axis=1),
+            sum(b/d),
+            sum(b/d.dimshuffle('x')),
+            sum(c/d),
+            sum(c/d.dimshuffle('x','x','x')),
+            sum(c/d.dimshuffle('x','x','x'),axis=0),
+            sum(c/d.dimshuffle('x','x','x'),axis=1),
+            sum(c/d.dimshuffle('x','x','x'),axis=2),
+
             sum(a / b, axis=0),
             sum(a / b.dimshuffle(0,'x'), axis=1),
+            sum(a.dimshuffle(0,1)/ b.dimshuffle(0,'x'), axis=1),
+            sum(a.dimshuffle(1,0)/ b.dimshuffle(0,'x'), axis=1),
             sum(c / a, axis=0),
             sum(c / a.dimshuffle(1, 0), axis=0),
             sum(c / a.dimshuffle(0,'x',1), axis=1),
@@ -1611,11 +1625,12 @@ class T_local_sum_dimshuffle(unittest.TestCase):
 
         for i,s in enumerate(sums):
             print i
-            f = theano.function([a,b,c], s, mode=self.mode)
+            f = theano.function([a,b,c,d], s, mode=self.mode)
             theano.printing.debugprint(f)
             g = f.maker.env.toposort()
             #print 'g =', g
             assert isinstance(g[-1].op.scalar_op, theano.scalar.basic.TrueDiv)
+            f([[1,2],[3,4]],[5,6],[[[7,8],[9,10]],[[11,12],[13,14]]],15)
 
     # TODO:
     # test_local_sum_prod_dimshuffle (a * b * c)