added test that the upgrade of connection_pattern works correctly

theano/tests/test_gradient.py

@@ -11,6 +11,8 @@ from theano import gradient
 from theano.tensor.nnet.Conv3D import conv3D
 from theano import config
 import numpy as np
+from theano.gradient import DisconnectedType
+from theano.gof.null_type import NullType
 
 one = theano.tensor.as_tensor_variable(1.)
 
@@ -316,5 +318,47 @@ def test_grad_disconnected():
     assert np.allclose(g,np.ones(x.shape,dtype=x.dtype))
 
 
+def test_disconnected_nan():
+
+    # test that connection_pattern can prevent getting NaN
+
+    # Op1 has two outputs, f and g
+    # x is connected to f but not to g
+    class Op1(theano.gof.Op):
+        def make_node(self, x):
+            return theano.Apply(self, inputs=[x],
+                    outputs = [ x.type(), theano.tensor.scalar() ])
+
+        def connection_pattern(self):
+            return [[True, False]]
+
+        def grad(self, inputs, output_grads):
+            return [ inputs[0].zeros_like()  ]
+
+    # Op2 has two inputs, f and g
+    # Its gradient with respect to g is not defined
+    class Op2(theano.gof.Op):
+        def make_node(self, f, g):
+            return theano.Apply(self, inputs=[f,g],
+                    outputs = [ theano.tensor.scalar() ])
+
+        def grad(self, inputs, output_grads):
+            return [ inputs[0].zeros_like(), NullType()() ]
+
+    x = theano.tensor.vector()
+    f, g = Op1()(x)
+    cost = Op2()(f,g)
+
+    # cost is differentiable wrt x
+    # but we can't tell that without using Op1's connection pattern
+    # looking at the theano graph alone, g is an ancestor of cost
+    # and has x as an ancestor, so we must compute its gradient
+
+    g = gradient.grad(cost, x)
+
+    # If we made it to here without an exception, then the
+    # connection_pattern functionality worked correctly
+
+
 if __name__ == '__main__':
     unittest.main()