Added sum-related canonicalizations.

theano/tensor/opt.py

@@ -1117,6 +1117,60 @@ def local_sum_mul_by_scalar(node):
         if thing_summed.owner and thing_summed.owner.op == T.neg:
             return [T.neg(node.op(thing_summed.owner.inputs[0]))]
 
+@register_canonicalize
+@gof.local_optimizer([])
+def local_sum_all_to_none(node):
+    """Sum{0,1,...N} -> Sum{}"""
+    if isinstance(node.op, T.Sum):
+        # if all the axes are named, then use None as a shorthand
+        # this permits more merging
+        if node.op.axis is None:
+            return
+        if set(node.op.axis) == set(range(node.inputs[0].type.ndim)):
+            return [T.Sum(axis=None)(node.inputs[0])]
+
+@register_canonicalize
+@gof.local_optimizer([])
+def local_sum_sum(node):
+    """Sum(Sum()) -> Sum"""
+    if isinstance(node.op, T.Sum):
+        summed, = node.inputs
+        if len(summed.clients) == 1:
+            if summed.owner and isinstance(summed.owner.op, T.Sum):
+                if summed.owner.op.axis is None:
+                    # special case of local_cut_useless_reduce
+                    return [T.Sum(None)(summed.owner.inputs[0])]
+                if node.op.axis is None:
+                    # we're summing up everything anyway so lets 
+                    # do it all at once
+                    return [T.Sum(None)(summed.owner.inputs[0])]
+
+                # figure out which dimensions of the original input are preserved
+                alldims = range(summed.owner.inputs[0].type.ndim)
+                
+                # trim out the dimensions that were removed by the first sum
+                alldims = [d for i,d in enumerate(alldims) if i in summed.owner.op.axis]
+
+                # trim out the dimensions removed by second sum
+                alldims = [d for i,d in enumerate(alldims) if i in node.op.axis]
+
+                # figure out an axis argument that combines the effect of both
+                newaxis = [i for i in xrange(summed.owner.inputs[0].type.ndim)
+                        if i not in alldims]
+
+                combined_sum = T.Sum(newaxis)
+                return [combined_sum(summed.owner.inputs[0])]
+
+@register_canonicalize
+@gof.local_optimizer([])
+def local_cut_useless_reduce(node):
+    """Sum(a, axis=[]) -> a  """
+    if isinstance(node.op, T.CAReduce):
+        summed, = node.inputs
+        # if reduce were doing anything, the output ndim would be reduced
+        if summed.type == node.outputs[0].type:
+            return [summed]
+
 @gof.local_optimizer([T.mul])
 def local_mul_to_neg(node):
     if node.op == T.mul and N.all(local_mul_canonizer.get_constant(node.inputs[0]) == -1.0):