simplify handling of known_grads

theano/gradient.py

@@ -438,6 +438,9 @@ def grad(cost, wrt, consider_constant=None,
     # build a dict mapping var to the gradient of cost with respect to var
     grad_dict = {}
 
+    if known_grads is None:
+        known_grads = {}
+
     # The gradient of the cost is 1 unless specified otherwise by known_grads.
     if cost is not None:
         if cost in known_grads:
@@ -457,27 +460,26 @@ def grad(cost, wrt, consider_constant=None,
 
         grad_dict[cost] = g_cost
 
-    if known_grads is not None:
-        for var in known_grads:
-            g_var = known_grads[var]
-
-            if not hasattr(g_var, 'type'):
-                raise TypeError('output grads must be theano variables.'
-                    'Ambiguous whether %s should be made into tensor'
-                    ' or sparse theano variable' % str(type(g_var)))
-
-            if g_var.type not in [NullType, DisconnectedType] and 'float' \
-                not in str(g_var.type.dtype):
-                raise TypeError("Gradients must always be NullType, "
-                        "DisconnectedType, or continuous, but grad was "
-                        "given a known_grad of type "+str(g_var.type))
-
-            # DO NOT check that these gradients are equal to 0 if var is int
-            # The gradient is allowed to be non-zero on var in that case
-            # Ops outputing var should not backpropagate its gradient further
-            # but that is enforced elsewhere (grep for only_connected_to_int)
-
-            grad_dict[var] = g_var
+    for var in known_grads:
+        g_var = known_grads[var]
+
+        if not hasattr(g_var, 'type'):
+            raise TypeError('output grads must be theano variables.'
+                'Ambiguous whether %s should be made into tensor'
+                ' or sparse theano variable' % str(type(g_var)))
+
+        if g_var.type not in [NullType, DisconnectedType] and 'float' \
+            not in str(g_var.type.dtype):
+            raise TypeError("Gradients must always be NullType, "
+                    "DisconnectedType, or continuous, but grad was "
+                    "given a known_grad of type "+str(g_var.type))
+
+        # DO NOT check that these gradients are equal to 0 if var is int
+        # The gradient is allowed to be non-zero on var in that case
+        # Ops outputing var should not backpropagate its gradient further
+        # but that is enforced elsewhere (grep for only_connected_to_int)
+
+        grad_dict[var] = g_var