simplified Lop's handling of consider_constant

theano/gradient.py

@@ -318,9 +318,6 @@ def Lop(f, wrt, eval_points, consider_constant=None,
         coordinates of the tensor element in the last
         If `f` is a list/tuple, then return a list/tuple with the results.
     """
-    if consider_constant is None:
-        consider_constant = []
-
     if type(eval_points) not in (list, tuple):
         eval_points = [eval_points]
 
@@ -334,48 +331,15 @@ def Lop(f, wrt, eval_points, consider_constant=None,
     f = list(f)
     grads = list(eval_points)
 
-    for elem in consider_constant:
-        assert elem not in f
-        f.append(elem)
-        grads.append(elem.zeros_like())
-
     if not isinstance(wrt, (list, tuple)):
         wrt = [wrt]
 
-    known = dict(zip(f, eval_points))
-
-    gmap = dict(zip(wrt, grad(cost=None, known_grads=known,
-            consider_constant=wrt, wrt=wrt)))
-
-    # Note : If p is not in gmap there can be several reasons, among which
-    # is the fact that p might not be part of the computational graph. A
-    # simple example is that for a+b for e.g. a[0] is not part of the graph,
-    # so Theano does not know how to compute TT.grad(TT.sum(a+b), a[0])
-    # such subtle cases can be fixed by a more careful implementation of the
-    # gradient, but for now Theano needs to throw an exception, and make the
-    # user aware that it does not know how to compute that gradient
-    ret = []
-    for p in wrt:
-        if p in gmap:
-            ret.append(gmap[p])
-        else:
-            message = (
-                "Lop method was asked to compute the gradient "
-                "with respect to a variable that is not part of "
-                "the computational graph of the cost, or is used "
-                "only by a non-differentiable operator: %s" % p)
-            if disconnected_inputs == 'ignore':
-                pass
-            elif disconnected_inputs == 'warn':
-                warnings.warn(message, stacklevel=1)
-            elif disconnected_inputs == 'raise':
-                raise ValueError(message)
-            else:
-                raise ValueError(
-                    "Invalid value for keyword "
-                    "'disconnected_inputs', valid values are "
-                    "'ignore', 'warn' and 'raise'.")
-            ret.append(p.zeros_like())
+    assert len(f) == len(grads)
+    known = dict(zip(f, grads))
+
+    ret = grad(cost=None, known_grads=known,
+            consider_constant=consider_constant, wrt=wrt,
+            disconnected_inputs=disconnected_inputs)
 
     return format_as(using_list, using_tuple, ret)