Merge pull request #1084 from goodfeli/test_grad_2

theano/gradient.py

@@ -365,7 +365,7 @@ def grad(cost, wrt, consider_constant=None,
         (or if all links are non-differentiable). The possible values are:
         - 'ignore': considers that the gradient on these parameters is zero.
         - 'warn': consider the gradient zero, and print a warning.
-        - 'raise': raise an exception.
+        - 'raise': raise DisconnectedInputError.
 
     :type add_names: bool
     :param add_names: If True, variables generated by grad will be named
@@ -482,28 +482,31 @@ def grad(cost, wrt, consider_constant=None,
         grad_dict[var] = g_var
 
 
-
-    # variables that do not influence the cost have zero gradient.
-    # if wrt is such a variable, populate the grad_dict with this info
-    # so that wrt not being in var_to_node_to_idx won't cause an error below
-    # according to the flag, possibly raise an error if wrt is disconnected
-    for elem in wrt:
-        if elem not in var_to_node_to_idx and elem is not cost \
-                and elem not in grad_dict:
+    def handle_disconnected(var):
             message = ("grad 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" % elem)
+                    "only by a non-differentiable operator: %s" % var)
             if disconnected_inputs == 'ignore':
                 pass
             elif disconnected_inputs == 'warn':
                 warnings.warn(message, stacklevel=2)
             elif disconnected_inputs == 'raise':
-                raise ValueError(message)
+                raise DisconnectedInputError(message)
             else:
                 raise ValueError("Invalid value for keyword "
                         "'disconnected_inputs', valid values are "
                         "'ignore', 'warn' and 'raise'.")
+
+
+    # variables that do not influence the cost have zero gradient.
+    # if wrt is such a variable, populate the grad_dict with this info
+    # so that wrt not being in var_to_node_to_idx won't cause an error below
+    # according to the flag, possibly raise an error if wrt is disconnected
+    for elem in wrt:
+        if elem not in var_to_node_to_idx and elem is not cost \
+                and elem not in grad_dict:
+            handle_disconnected(elem)
             grad_dict[elem] = DisconnectedType()()
 
     cost_name = None
@@ -523,6 +526,7 @@ def grad(cost, wrt, consider_constant=None,
 
     for i in xrange(len(rval)):
         if isinstance(rval[i].type, DisconnectedType):
+            handle_disconnected(rval[i])
             if return_disconnected == 'zero':
                 rval[i] = _float_zeros_like(wrt[i])
             elif return_disconnected == 'None':
@@ -719,7 +723,13 @@ class NullTypeGradError(TypeError):
     """
     Raised when grad encounters a NullType.
     """
-    pass
+
+class DisconnectedInputError(ValueError):
+    """
+    Raised when grad is asked to compute the gradient
+    with respect to a disconnected input and
+    disconnected_inputs='raise'.
+    """
 
 def _populate_grad_dict(var_to_node_to_idx,
         grad_dict, wrt, cost_name=None):

theano/tests/test_gradient.py

@@ -522,6 +522,8 @@ def test_undefined_cost_grad():
         # Tests that if we say the cost is not differentiable via the
         # known_grads mechanism, it is treated as such by the rest of the
         # system.
+        # This is so that Ops that are built around minigraphs like OpFromGraph
+        # and scan can implement Op.grad by passing ograds to known_grads
 
         x = theano.tensor.iscalar()
         y = theano.tensor.iscalar()
@@ -533,6 +535,24 @@ def test_undefined_cost_grad():
             return
         raise AssertionError("An undefined gradient has been ignored.")
 
+def test_disconnected_cost_grad():
+
+        # Tests that if we say the cost is disconnected via the
+        # known_grads mechanism, it is treated as such by the rest of the
+        # system.
+        # This is so that Ops that are built around minigraphs like OpFromGraph
+        # and scan can implement Op.grad by passing ograds to known_grads
+
+        x = theano.tensor.iscalar()
+        y = theano.tensor.iscalar()
+        cost = x + y
+        assert cost.dtype in theano.tensor.discrete_dtypes
+        try:
+            grads = theano.tensor.grad(cost, [x, y], known_grads = {cost: gradient.DisconnectedType()() },
+                    disconnected_inputs='raise')
+        except theano.gradient.DisconnectedInputError:
+            return
+        raise AssertionError("A disconnected gradient has been ignored.")
 
 if __name__ == '__main__':
     unittest.main()