added ability to put a comment on a bad grad exception

theano/gradient.py

@@ -194,10 +194,10 @@ def grad_sources_inputs(sources, graph_inputs, warn_type=True):
 
 
 class GradNotImplementedOp(gof.op.UncomputableOp):
-    """ A BadGradOp representing a gradient that hasn't been implemented yet.
+    """ An UncomputableOp representing a gradient that hasn't been implemented yet.
     """
 
-    def __init__(self, op, x_pos):
+    def __init__(self, op, x_pos, comment = ""):
         """
             op: A theano op  whose grad is not implemented for some input
             x_pos: An int, giving the index in the op's input list of
@@ -205,6 +205,8 @@ class GradNotImplementedOp(gof.op.UncomputableOp):
                 (if op has unimplemented gradients for several inputs,
                 it must still return a separate UnimplementedGradOp for
                 each)
+            comment: An optional comment explaining why the gradient isn't
+                implemented.
         """
 
         assert isinstance(op, gof.Op)
@@ -212,11 +214,11 @@ class GradNotImplementedOp(gof.op.UncomputableOp):
         assert x_pos >= 0
 
         super(GradNotImplementedOp,self).__init__(NotImplementedError,
-                "%s does not implement its gradient with respect to input %d" \
-                % (str(type(op)), x_pos))
+            "%s does not implement its gradient with respect to input %d. %s" \
+            % (str(type(op)), x_pos, comment))
 
 
-def grad_not_implemented(op, x_pos, x):
+def grad_not_implemented(op, x_pos, x, comment = ""):
     """
     Return an un-computable symbolic variable of type `x.type`.
 
@@ -225,9 +227,61 @@ def grad_not_implemented(op, x_pos, x):
     raised indicating that the gradient on the
     `x_pos`'th input of `op` has not been implemented. Likewise if
     any call to theano.function involves this variable.
+
+    Optionally adds a comment to the exception explaining why this
+    gradient is not implemented.
+    """
+
+    return GradNotImplementedOp(op, x_pos, comment)(x)
+
+class GradUndefinedError(Exception):
+    """ An exception raised upon attempts to use an undefined gradient.
+    """
+
+class GradUndefinedOp(gof.op.UncomputableOp):
+    """ An UncomputableOp representing a gradient that is mathematically
+        undefined.
+    """
+
+    def __init__(self, op, x_pos, comment = ""):
+        """
+            op: A theano op  whose grad is mathematically undefined for
+                some input
+            x_pos: An int, giving the index in the op's input list of
+                a variable for which the gradient is undefined
+                (if op has undefined gradients for several inputs,
+                it must still return a separate GradUndefinedOp for
+                each)
+            comment: An optional comment explaining why the gradient isn't
+                defined.
+        """
+
+        assert isinstance(op, gof.Op)
+        assert isinstance(x_pos, int)
+        assert x_pos >= 0
+
+        super(GradUndefinedOp,self).__init__(GradUndefinedError,
+            "%s does not implement its gradient with respect to input %d. %s" \
+            % (str(type(op)), x_pos, comment))
+
+
+def grad_undefined(op, x_pos, x, comment = ""):
+    """
+    Return an un-computable symbolic variable of type `x.type`.
+
+    If any call to tensor.grad results in an expression containing this
+    un-computable variable, an exception (GradUndefinedError) will be
+    raised indicating that the gradient on the
+    `x_pos`'th input of `op` is mathematically undefined. Likewise if
+    any call to theano.function involves this variable.
+
+    Optionally adds a comment to the exception explaining why this
+    gradient is not defined.
     """
 
-    return GradNotImplementedOp(op, x_pos)(x)
+    return GradUndefinedOp(op, x_pos, comment)(x)
+
+
 
 
 ########################