cache OpFromGrad sub grad computation. This remove duplicate op creation.

theano/compile/builders.py

@@ -84,18 +84,24 @@ class OpFromGraph(gof.Op):
         # compute the right numerical value for the gradients but
         # could fail to raise the disconnected inputs error in some
         # cases.
-        gs = G.grad(cost=None, known_grads=dict(zip(self.outputs, output_grads)),
-                    wrt=self.inputs, disconnected_inputs='ignore')
-        grad_ops = []
-        for g in gs:
-            if g is None:
-                grad_ops.append(lambda *args: None)
-            else:
-                # It is normal if some inputs are not needed in order
-                # to compute the gradient, so we ignore them.
-                grad_ops.append(OpFromGraph(self.inputs + output_grads,
-                                            [g],
-                                            on_unused_input='ignore'))
+        if hasattr(self, "grad_ops"):
+            grad_ops = self.grad_ops
+        else:
+            gs = G.grad(cost=None,
+                        known_grads=dict(zip(self.outputs, output_grads)),
+                        wrt=self.inputs, disconnected_inputs='ignore')
+
+            grad_ops = []
+            for g in gs:
+                if g is None:
+                    grad_ops.append(lambda *args: None)
+                else:
+                    # It is normal if some inputs are not needed in order
+                    # to compute the gradient, so we ignore them.
+                    grad_ops.append(OpFromGraph(self.inputs + output_grads,
+                                                [g],
+                                                on_unused_input='ignore'))
+            self.grad_ops = grad_ops
 
         return [go(*(inputs + output_grads)) for go in grad_ops]