rename opt local_sum_broadcast to local_reduce_broadcast.

theano/tensor/opt.py

@@ -3151,7 +3151,7 @@ def local_cut_useless_reduce(node):
 #@register_canonicalize
 @register_specialize
 @gof.local_optimizer([])
-def local_sum_broadcastable(node):
+def local_reduce_broadcastable(node):
     """Remove reduction over broadcastable dimensions"""
     if isinstance(node.op, T.CAReduce):
         reduced, = node.inputs

theano/tensor/tests/test_opt.py

@@ -3313,7 +3313,7 @@ class T_local_sum(unittest.TestCase):
         finally:
             config.on_opt_error = backup
 
-    def test_local_sum_broadcast_all_0(self):
+    def test_local_reduce_broadcast_all_0(self):
         optimizer = optdb.query(self.mode._optimizer)
 
         x = T.TensorType('int64', (True, True, True))()
@@ -3323,7 +3323,7 @@ class T_local_sum(unittest.TestCase):
             isinstance(node.op, T.CAReduce)
             for node in g.toposort()])
 
-    def test_local_sum_broadcast_all_1(self):
+    def test_local_reduce_broadcast_all_1(self):
         optimizer = optdb.query(self.mode._optimizer)
 
         x = T.TensorType('int64', (True, True))()
@@ -3333,7 +3333,7 @@ class T_local_sum(unittest.TestCase):
             isinstance(node.op, T.CAReduce)
             for node in g.toposort()])
 
-    def test_local_sum_broadcast_some_0(self):
+    def test_local_reduce_broadcast_some_0(self):
         optimizer = optdb.query(self.mode._optimizer)
 
         x = T.TensorType('int64', (True, False, True))()
@@ -3348,12 +3348,12 @@ class T_local_sum(unittest.TestCase):
         op = node.op
         assert isinstance(op, T.CAReduce)
         # -- the leading broadcastable dimension has been dropped
-        #   by the local_sum_broadcastable optimization
+        #   by the local_reduce_broadcastable optimization
         #   now summation is over the original x's dimension 1.
         assert node.inputs[0].ndim == 2, node
         assert op.axis == (0,), op.axis
 
-    def test_local_sum_broadcast_some_1(self):
+    def test_local_reduce_broadcast_some_1(self):
         optimizer = optdb.query(self.mode._optimizer)
 
         x = T.TensorType('int64', (True, False, True))()