Allow theano.clone() to specify or not to copy orphans. Default not changed.

theano/gof/graph.py

@@ -799,9 +799,8 @@ def orphans(i, o):
     return variables_and_orphans(i, o)[1]
 
 
-def clone(i, o, copy_inputs=True):
-    """
-    Copies the subgraph contained between i and o.
+def clone(i, o, copy_inputs=True, copy_orphans=None):
+    """Copies the subgraph contained between i and o.
 
     Parameters
     ----------
@@ -811,18 +810,32 @@ def clone(i, o, copy_inputs=True):
         Output Variables.
     copy_inputs : bool
         If True, the inputs will be copied (defaults to True).
+    copy_orphans:
+        When None, use the copy_inputs value,
+        When True, new orphans nodes are created.
+        When False, original orphans nodes are reused in the new graph.
 
     Returns
     -------
     object
         The inputs and outputs of that copy.
 
+    Note
+    ----
+
+    A constant, if in the ``i`` list is not an orpha. So it will be
+    copied depending of the ``copy_inputs`` parameter. Otherwise it
+    will be copied depending of the ``copy_orphans`` parameter.
+
     """
-    equiv = clone_get_equiv(i, o, copy_inputs)
+    if copy_orphans is None:
+        copy_orphans = copy_inputs
+    equiv = clone_get_equiv(i, o, copy_inputs, copy_orphans)
     return [equiv[input] for input in i], [equiv[output] for output in o]
 
 
-def clone_get_equiv(inputs, outputs, copy_inputs=True, copy_orphans=True, memo=None):
+def clone_get_equiv(inputs, outputs, copy_inputs=True, copy_orphans=True,
+                    memo=None):
     """
     Return a dictionary that maps from Variable and Apply nodes in the
     original graph to a new node (a clone) in a new graph.

theano/gof/tests/test_graph.py

@@ -156,6 +156,26 @@ class TestClone(X):
         assert self.str(inputs(new_node.outputs), new_node.outputs) == ["MyOp(R7, R8)"]
         assert self.str(inputs(node.outputs), node.outputs) == ["MyOp(MyOp(R1, R2), R5)"]
 
+    def test_constant(self):
+        r1, r2, r5 = MyVariable(1), MyVariable(2), MyVariable(5)
+        node = MyOp.make_node(MyOp.make_node(r1, r2).outputs[0], r5)
+        _, new = clone([r1, r2, r5], node.outputs, False)
+        new_node = new[0].owner
+        new_node.inputs = MyVariable(7), MyVariable(8)
+        c1 = tensor.constant(1.5)
+
+        i, o = clone([c1], [c1])
+        assert i[0] is not c1 and o[0] is not c1
+
+        i, o = clone([c1], [c1], False)
+        assert i[0] is c1 and o[0] is c1
+
+        i, o = clone([c1], [c1], True, False)
+        assert i[0] is not c1 and o[0] is not c1
+
+        i, o = clone([c1], [c1], False, True)
+        assert i[0] is c1 and o[0] is c1
+
 
 ############
 # toposort #