fix opt crash when taking multiple consecutive sum.

theano/tensor/opt.py

@@ -3209,7 +3209,7 @@ def local_sum_sum(node):
                 for i in node.op.axis:
                     new_i = i
                     for ii in summed.owner.op.axis:
-                        if i >= ii:
+                        if new_i >= ii:
                             new_i += 1
                     assert new_i not in newaxis
                     newaxis.append(new_i)

theano/tensor/tests/test_opt.py

@@ -3493,17 +3493,32 @@ class T_local_sum(unittest.TestCase):
 
     def test_local_sum_sum(self):
         a = T.tensor3()
-        input = numpy.arange(3 * 3 * 3, dtype=config.floatX).reshape(3, 3, 3)
-        dims = [(0, 0), (1, 0), (2, 0), (0, 1), (1, 1), (2, 1)]
+        input = numpy.arange(3 * 4 * 5, dtype=config.floatX).reshape(3, 4, 5)
+        dims = [(0, 0), (1, 0), (2, 0), (0, 1), (1, 1), (2, 1),
+                ((0, 1), 0), ((1, 2), 0), (0, (0, 1)),
+                (1, (0, 1)), (2, (0, 1))]
 
         backup = config.warn.sum_sum_bug
         config.warn.sum_sum_bug = False
+
+        def my_sum(data, d, dd):
+            # This sum when d or dd is a tuple of 2 dimensions.
+            if not isinstance(d, tuple) and not isinstance(dd, tuple):
+                return data.sum(d).sum(dd)
+            if isinstance(d, tuple):
+                d = sorted(d)
+                return data.sum(d[1]).sum(d[0]).sum(dd)
+            else:
+                dd = sorted(dd)
+                return data.sum(d).sum(dd[1]).sum(dd[0])
+
         try:
             for d, dd in dims:
+                expected = my_sum(input, d, dd)
                 f = theano.function([a], a.sum(d).sum(dd), mode=self.mode)
-                assert numpy.allclose(f(input), input.sum(d).sum(dd))
+                assert numpy.allclose(f(input), expected)
                 assert len(f.maker.fgraph.apply_nodes) == 1
-            for d, dd in dims:
+            for d, dd in dims[:6]:
                 f = theano.function([a], a.sum(d).sum(dd).
                     sum(0), mode=self.mode)
                 assert numpy.allclose(f(input), input.sum(d).sum(dd).sum(0))