Make it easier to use test values with Rop and optimizations

theano/tensor/basic.py

@@ -2579,7 +2579,7 @@ class Alloc(gof.Op):
     def R_op(self, inputs, eval_points):
         if eval_points[0] is None:
             return [None]
-        return self.make_node(eval_points[0], *inputs[1:]).outputs
+        return self(eval_points[0], *inputs[1:], **dict(return_list=True))
 
     def do_constant_folding(self, node):
         if not getattr(node.outputs[0], 'clients', []):
@@ -3275,7 +3275,7 @@ class Rebroadcast(Op):
     def R_op(self, inputs, eval_points):
         if eval_points[0] is None:
             return [None]
-        return self.make_node(*eval_points).outputs
+        return self(*eval_points, **dict(return_list=True))
 
 
 def addbroadcast(x, *axes):
@@ -3805,7 +3805,7 @@ class Reshape(Op):
     def R_op(self, inputs, eval_points):
         if eval_points[0] is None:
             return [None]
-        return self.make_node(eval_points[0], *inputs[1:]).outputs
+        return self(eval_points[0], *inputs[1:], **dict(return_list=True))
 
     def infer_shape(self, node, ishapes):
         # inputs[1] can contain at most one value of '-1', meaning the actual
@@ -4600,7 +4600,7 @@ class Dot(Op):
             eval_point_values = [ev0, ev1]
 
             for i in xrange(2):
-                if eval_point_values[i] and \
+                if eval_point_values[i] is not None and \
                    input_values[i].shape != eval_point_values[i].shape:
                     raise ValueError('input ' + str(i) + ' and eval_point ' +
                                      str(i) + ' to Dot.R_op '

theano/tensor/elemwise.py

@@ -273,7 +273,7 @@ class DimShuffle(Op):
     def R_op(self, inputs, eval_points):
         if None in eval_points:
             return [None]
-        return self.make_node(*eval_points).outputs
+        return self(*eval_points, **dict(return_list=True))
 
     def c_code(self, node, name, inp, out, sub):
         input, = inp
@@ -616,7 +616,7 @@ class Elemwise(Op):
             return self.name
 
     def R_op(self, inputs, eval_points):
-        outs = self.make_node(*inputs).outputs
+        outs = self(*inputs, **dict(return_list=True))
         rval = [None for x in outs]
         # For each output
         for idx, out in enumerate(outs):
@@ -1882,7 +1882,7 @@ class Sum(CAReduceDtype):
         # part of self
         if None in eval_points:
             return [None]
-        return self.make_node(*eval_points).outputs
+        return self(*eval_points, **dict(return_list=True))
 
     def __str__(self):
         if self.axis is None:

theano/tensor/opt.py

@@ -263,10 +263,11 @@ def inplace_elemwise_optimizer_op(OP):
                                 scalar.transfer_type(
                                     *[inplace_pattern.get(i, None) \
                                           for i in xrange(len(node.outputs))]))
-                        new = OP(new_scal, inplace_pattern).make_node(
-                            *node.inputs)
+                        new_outputs = OP(new_scal, inplace_pattern)(
+                                *node.inputs, **dict(return_list=True))
+                        new_node = new_outputs[0].owner
 
-                        for r, new_r in zip(node.outputs, new.outputs):
+                        for r, new_r in zip(node.outputs, new_outputs):
                             fgraph.replace(r, new_r,
                                         reason="inplace_elemwise_optimizer")
                         nb_change_no_validate += 1
@@ -284,7 +285,7 @@ def inplace_elemwise_optimizer_op(OP):
                         fgraph.revert(chk)
                         continue
                     candidate_inputs.remove(candidate_input)
-                    node = new
+                    node = new_node
                     baseline = inplace_pattern
                     break
 

theano/tensor/subtensor.py

@@ -883,7 +883,7 @@ class Subtensor(Op):
         # (they should be defaulted to zeros_like by the global R_op)
         if eval_points[0] is None:
             return [None]
-        return self.make_node(eval_points[0], *inputs[1:]).outputs
+        return self(eval_points[0], *inputs[1:], **dict(return_list=True))
 
 
 class SubtensorPrinter:
@@ -1414,8 +1414,8 @@ class IncSubtensor(Op):
             return [None]
         # Again we ignore eval points for indices because incsubtensor is
         # not differentiable wrt to those
-        return self.make_node(eval_points[0], eval_points[1],
-                            *inputs[2:]).outputs
+        return self(eval_points[0], eval_points[1], *inputs[2:],
+                    **dict(return_list=True))
 
     def connection_pattern(self, node):