Incorporated reviewer comments.

theano/tensor/opt.py

@@ -1534,10 +1534,10 @@ def local_remove_useless_assert(node):
 def local_alloc_elemwise(node):
     """
     elemwise(alloc(x, shp), ..., y.TensorType(BROADCAST CONDITION))
-      -> elemwise(x, y.TensorType(no broadcast flag))
+      -> elemwise(x, y.TensorType(BROADCAST CONDITION))
 
     elemwise(dimshuffle(alloc(x, shp)),... ,y.TensorType(BROADCAST CONDITION))
-      -> elemwise(x.dimshuffle(...), y.TensorType(no broadcast flag))
+      -> elemwise(x.dimshuffle(...), y.TensorType(BROADCAST CONDITION))
 
     BROADCAST CONDITION: the condition is that the one input that are
     not to be optimized to have the same broadcast pattern as the
@@ -1596,20 +1596,6 @@ def local_alloc_elemwise(node):
             assert_op_idx = 0  # The first one is as good as any to use.
 
         else:
-            # When there is only one input then we can optimize if the
-            # broadcast patterns of the input and output match.
-            i =  node.inputs[0]
-            if i.type.broadcastable == node.outputs[0].type.broadcastable:
-                new_i = []
-                if isinstance(i.owner.op, T.Alloc):
-                    new_i.append(i.owner.inputs[0])
-                elif dimshuffled_alloc(i):
-                    new_i.append(i.owner.inputs[0].owner.inputs[0])
-
-                assert(len(new_i) > 0)
-                return node.op(*new_i,
-                               return_list=True)
-
             # Otherwise nothing can be done.
             return False
 
@@ -1626,7 +1612,7 @@ def local_alloc_elemwise(node):
 
             assert i.type.ndim == cmp_op.ndim
             if (theano.config.experimental.local_alloc_elemwise_assert
-                and node.fgraph.shape_feature.same_shape(i, cmp_op)):
+                and not node.fgraph.shape_feature.same_shape(i, cmp_op)):
                 assert_op = assert_(assert_op,
                                     *[T.eq(i.shape[idx], cmp_op.shape[idx])\
                                           for idx in xrange(i.type.ndim) \
@@ -1637,7 +1623,7 @@ def local_alloc_elemwise(node):
         elif i.owner and dimshuffled_alloc(i):
             assert i.type.ndim == cmp_op.type.ndim
             if (theano.config.experimental.local_alloc_elemwise_assert
-                and node.fgraph.shape_feature.same_shape(i, cmp_op)):
+                and not node.fgraph.shape_feature.same_shape(i, cmp_op)):
                 assert_op = assert_(assert_op,
                                     *[T.eq(i.shape[idx], cmp_op.shape[idx])
                                       for idx in xrange(i.type.ndim)
@@ -1655,10 +1641,14 @@ def local_alloc_elemwise(node):
 # an alloc all but one is optimized.
 
 theano.configparser.AddConfigVar('experimental.local_alloc_elemwise',
-        "If True enable the experimental optimization local_alloc_elemwise",
+                                 "DEPRECATED: If True, enable the experimental"
+                                 " optimization local_alloc_elemwise."
+                                 " Generates error if not True. Use"
+                                 " optimizer_excluding=local_alloc_elemwise"
+                                 " to dsiable.",
                                  theano.configparser.BoolParam(
-                                     False,
-                                     is_valid=lambda x: return not x
+                                     True,
+                                     is_valid=lambda x: x
                                  ),
                                  in_c_key=False)
 #This version if faster but not as safe.