made node_to_pattern a module level private function so it can be used

theano/gradient.py

@@ -491,40 +491,13 @@ def grad(cost, wrt, g_cost=None, consider_constant=None, warn_type=False,
         rval, = rval
     return rval
 
-
-def _populate_var_to_node_to_idx(outputs, wrt):
-    """
-    Common code shared between grad and grad_sources_inputs
-
-    outputs: a list of variables we want to take gradients of
-
-    wrt: a list of variables we want to take the gradient with
-        respect to.
-
-    returns:
-        var_to_node_to_idx: a dictionary mapping a variable to
-            a second dictionary.
-            the second dictionary maps apply nodes acting on
-            this variable to the variable's index in the apply
-            node's input list
-            This dictionary will only contain variables that
-            meet two criteria:
-                1) The elements of at least one output are a
-                   function of the elements of the variable
-                2) The elements of the variable are a function
-                   of the elements of at least one member of
-                   wrt
-            This set is exactly the set of variables that
-            connect the variables in wrt to the cost being
-            differentiated.
-
+def _node_to_pattern(node):
+    """ given an apply node, obtain its connection pattern
+     this is just a wrapper around Op.connection_pattern
+     that does type checking and supplies the default value
+     if the method is not implemented
     """
 
-    def node_to_pattern(node):
-        # given an apply node, obtain its connection pattern
-        # this is just a wrapper around Op.connection_pattern
-        # that does type checking and supplies the default value
-        # if the method is not implemented
     if hasattr(node.op,'connection_pattern'):
         connection_pattern = node.op.connection_pattern(node)
 
@@ -554,6 +527,35 @@ def _populate_var_to_node_to_idx(outputs, wrt):
         assert len(connection_pattern[ii]) == \
                 len(node.outputs)
     return connection_pattern
+
+def _populate_var_to_node_to_idx(outputs, wrt):
+    """
+    Common code shared between grad and grad_sources_inputs
+
+    outputs: a list of variables we want to take gradients of
+
+    wrt: a list of variables we want to take the gradient with
+        respect to.
+
+    returns:
+        var_to_node_to_idx: a dictionary mapping a variable to
+            a second dictionary.
+            the second dictionary maps apply nodes acting on
+            this variable to the variable's index in the apply
+            node's input list
+            This dictionary will only contain variables that
+            meet two criteria:
+                1) The elements of at least one output are a
+                   function of the elements of the variable
+                2) The elements of the variable are a function
+                   of the elements of at least one member of
+                   wrt
+            This set is exactly the set of variables that
+            connect the variables in wrt to the cost being
+            differentiated.
+
+    """
+
     # var_to_node_to_idx[var][node] = [i,j] means node has
     # var as input at positions i and j
     var_to_node_to_idx = {}
@@ -573,7 +575,7 @@ def _populate_var_to_node_to_idx(outputs, wrt):
             if node not in accounted_for:
                 accounted_for.add(node)
 
-                connection_pattern = node_to_pattern(node)
+                connection_pattern = _node_to_pattern(node)
 
                 var_idx = node.outputs.index(var)
 
@@ -611,7 +613,7 @@ def _populate_var_to_node_to_idx(outputs, wrt):
         visited.add(var)
         nodes = var_to_node_to_idx[var]
         for node in nodes:
-            connection_pattern = node_to_pattern(node)
+            connection_pattern = _node_to_pattern(node)
             for idx in nodes[node]:
                 for ii, output in enumerate(node.outputs):
                     if connection_pattern[idx][ii]: