made gradient computation raise an explicit ValueError instead of an

theano/gradient.py

@@ -25,8 +25,6 @@ from theano.gof.nan_type import NaNType
 from theano.printing import min_informative_str
 
 _msg_retType = 'op.grad(...) returned a non-list'
-_msg_badlen = 'op.grad(...) returned wrong number of gradients'
-
 
 def format_as(use_list, use_tuple, outputs):
     """
@@ -547,9 +545,14 @@ def grad(cost, wrt, g_cost = None, consider_constant = None, warn_type = 'ignore
             output_grads = [ access_grad_cache(var) for var in node.outputs ]
             input_grads = node.op.grad(inputs, output_grads)
 
+            if len(input_grads) != len(inputs):
+                raise ValueError(("%s returned the wrong number of gradient"+\
+                        "terms.") %  str(node.op))
+
             #must convert to list in case the op returns a tuple
             #we won't be able to post-process out the Nones if it does that
             term_dict[node] = list(input_grads)
+
             for i in xrange(len(term_dict[node])):
                 if term_dict[node][i] is None:
                     term_dict[node][i] = node.inputs[i].zeros_like()
@@ -661,10 +664,18 @@ def grad_sources_inputs(sources, graph_inputs, warn_type = 'ignored'):
     #populate term_dict[node] and return it
     def access_term_cache(node):
         if node not in term_dict:
+
+            inputs = node.inputs
+            input_grads = node.op.grad(node.inputs,
+                    [access_grad_cache(var) for var in node.outputs])
+
+            if len(input_grads) != len(inputs):
+                raise ValueError(("%s returned the wrong number of gradient"+\
+                        "terms.") %  str(node.op))
+
             #must convert to list in case the op returns a tuple
             #we won't be able to post-process out the Nones if it does that
-            term_dict[node] = list(node.op.grad(node.inputs,
-                    [access_grad_cache(var) for var in node.outputs]))
+            term_dict[node] = list(input_grads)
             for i in xrange(len(term_dict[node])):
                 if term_dict[node][i] is None:
                     term_dict[node][i] = node.inputs[i].zeros_like()