Implement input preference when making elemwise ops inplace

theano/tensor/opt.py

@@ -320,7 +320,50 @@ def inplace_elemwise_optimizer_op(OP):
             raised_warning = not verbose
 
             for candidate_output in candidate_outputs:
-                for candidate_input in candidate_inputs:
+
+                # If the output of the node can be established as an update
+                # output of the fgraph, visit the candidate_inputs in an order
+                # that will improve the chances of making the node operate
+                # inplace on the input it's meant to update
+                candidate_out_var = node.outputs[candidate_output]
+                sorted_candidate_inputs = candidate_inputs
+                if fgraph.update_mapping:
+
+                    update_outs = [fgraph.outputs[i]
+                                   for i in fgraph.update_mapping]
+
+                    if candidate_out_var in update_outs:
+
+                        # The candidate output is an update. Sort the
+                        # variables in candidate_inputs in the following order:
+                        # - Vars corresponding to the actual updated input
+                        # - Vars computed inplace on the updates input
+                        # - Remaining variables
+                        fgraph_out_idx = fgraph.outputs.index(candidate_out_var)
+                        updated_inp_idx = fgraph.update_mapping[fgraph_out_idx]
+                        updated_inp = fgraph.inputs[updated_inp_idx]
+
+                        updated_vars = []
+                        vars_from_inplace = []
+                        other_vars = []
+                        for inp_idx in candidate_inputs:
+                            inp = node.inputs[inp_idx]
+                            if inp is updated_inp:
+                                updated_vars.append(inp_idx)
+                            elif (hasattr(fgraph, 'destroy_handler') and
+                                  inp.owner and
+                                  updated_inp in fgraph.destroy_handler.root_destroyer and
+                                  fgraph.destroy_handler.root_destroyer[updated_inp] == inp.owner):
+
+                                vars_from_inplace.append(inp_idx)
+                            else:
+                                other_vars.append(inp_idx)
+
+                        sorted_candidate_inputs = (updated_vars +
+                                                   vars_from_inplace +
+                                                   other_vars)
+
+                for candidate_input in sorted_candidate_inputs:
                     # remove inputs that don't have the same dtype as the output
                     if node.inputs[candidate_input].type != node.outputs[
                             candidate_output].type: