Changed grad() to L_op in given elemwise (#5431)

theano/tensor/elemwise.py

@@ -634,11 +634,7 @@ second dimension
 
         return [[True for output in node.outputs] for ipt in node.inputs]
 
-    def grad(self, inputs, ograds):
-
-        outs = self(*inputs)
-        if not isinstance(outs, (list, tuple)):
-            outs = [outs]
+    def L_op(self, inputs, outs, ograds):
 
         # compute grad with respect to broadcasted input
         rval = self._bgrad(inputs, ograds)
@@ -1927,12 +1923,10 @@ class Sum(CAReduceDtype):
             str(self.acc_dtype)
         )
 
-    def grad(self, inp, grads):
+    def L_op(self, inp, out, grads):
         x, = inp
 
-        out = self(*inp)
-
-        if out.dtype not in theano.tensor.continuous_dtypes:
+        if out[0].dtype not in theano.tensor.continuous_dtypes:
             return [x.zeros_like(dtype=theano.config.floatX)]
 
         gz, = grads
@@ -1985,7 +1979,7 @@ class Prod(CAReduceDtype):
         if 'no_zeros_in_input' not in dct:
             self.no_zeros_in_input = False
 
-    def grad(self, inp, grads):
+    def L_op(self, inp, out, grads):
         """
         The grad of this Op could be very easy, if it is was not for the case
         where zeros are present in a given "group" (ie. elements reduced
@@ -2034,9 +2028,7 @@ class Prod(CAReduceDtype):
         prod_in, = inp
         gz, = grads
 
-        out = self(*inp)
-
-        if (out.dtype in theano.tensor.discrete_dtypes or
+        if (out[0].dtype in theano.tensor.discrete_dtypes or
                 self.acc_dtype in theano.tensor.discrete_dtypes):
             # There is an int conversion in the way
             return [prod_in.zeros_like(dtype=theano.config.floatX)]