made consider_constant block gradient through constants but not set

theano/gradient.py

@@ -430,21 +430,6 @@ def grad(cost, wrt, g_cost=None, consider_constant=None,
     if cost.ndim != 0:
         raise TypeError("cost must be a scalar.")
 
-    if consider_constant is None:
-        consider_constant = []
-    else:
-        # error checking on consider_constant: verify that it is a collection
-        # of theano variables
-        # this is important, if someone accidentally passes a nested data
-        # structure with theano variables at the leaves, only the root will
-        # be properly considered constant
-        if not hasattr(consider_constant, '__iter__'):
-            raise TypeError('consider_constant must be an iterable collection,'
-                    ' got ' + str(type(consider_constant)))
-        for elem in consider_constant:
-            if not isinstance(elem, gof.Variable):
-                raise TypeError('Elements of consider_constant must be '
-                                'variables, but got ' + str(type(elem)))
 
     if isinstance(wrt, set):
         raise TypeError("wrt must not be a set. sets have no defined "
@@ -461,7 +446,7 @@ def grad(cost, wrt, g_cost=None, consider_constant=None,
             raise TypeError("Expected Variable, got " + str(elem) +
                     " of type "+str(type(elem)))
 
-    var_to_node_to_idx = _populate_var_to_node_to_idx([cost], wrt)
+    var_to_node_to_idx = _populate_var_to_node_to_idx([cost], wrt, consider_constant)
 
     # build a dict mapping var to the gradient of cost with respect to var
     grad_dict = {}
@@ -592,7 +577,7 @@ def _node_to_pattern(node):
     return connection_pattern
 
 
-def _populate_var_to_node_to_idx(outputs, wrt):
+def _populate_var_to_node_to_idx(outputs, wrt, consider_constant):
     """
     Common code shared between grad and grad_sources_inputs
 
@@ -601,6 +586,9 @@ def _populate_var_to_node_to_idx(outputs, wrt):
     wrt: a list of variables we want to take the gradient with
         respect to.
 
+    consider_constant: a list of variables not to backpropagate
+        through.
+
     returns:
 
      var_to_app_to_idx:
@@ -622,8 +610,28 @@ def _populate_var_to_node_to_idx(outputs, wrt):
       This set is exactly the set of variables that connect
       the variables in wrt to the cost being differentiated.
 
+      (A variable in consider_constant is not a function of
+      anything)
+
     """
 
+    # Validate and format consider_constant
+    if consider_constant is None:
+        consider_constant = []
+    else:
+        # error checking on consider_constant: verify that it is a collection
+        # of theano variables
+        # this is important, if someone accidentally passes a nested data
+        # structure with theano variables at the leaves, only the root will
+        # be properly considered constant
+        if not hasattr(consider_constant, '__iter__'):
+            raise TypeError('consider_constant must be an iterable collection,'
+                    ' got ' + str(type(consider_constant)))
+        for elem in consider_constant:
+            if not isinstance(elem, gof.Variable):
+                raise TypeError('Elements of consider_constant must be '
+                                'variables, but got ' + str(type(elem)))
+
     # var_to_app_to_idx[var][node] = [i,j] means node has
     # var as input at positions i and j
     var_to_app_to_idx = {}
@@ -638,9 +646,17 @@ def _populate_var_to_node_to_idx(outputs, wrt):
     accounted_for = set([])
 
     def account_for(var):
+        # Don't visit the same variable twice
         if var in accounted_for:
             return
         accounted_for.add(var)
+
+        # Constants are not a function of anything
+        if var in consider_constant:
+            return
+
+        # Recursively add the variables that this variable is
+        # a function of.
         if var.owner is not None:
             app = var.owner
 
@@ -1066,7 +1082,7 @@ def grad_sources_inputs(sources, graph_inputs):
 
     wrt = graph_inputs
 
-    var_to_node_to_idx = _populate_var_to_node_to_idx(outputs, wrt)
+    var_to_node_to_idx = _populate_var_to_node_to_idx(outputs, wrt, None)
 
     # build a dict mapping var to the gradient of cost with respect to var
     grad_dict = {}