Postpone some code from Composite.__init__ to later in case we don't need them.…

theano/scalar/basic.py

@@ -3487,6 +3487,9 @@ class Composite(ScalarOp):
         Return the C code for this Composite Op.
 
         """
+        # It was already called
+        if hasattr(self, '_c_code'):
+            return
         subd = dict(chain(
             ((e, "%%(i%i)s" % i) for i, e in enumerate(self.fgraph.inputs)),
             ((e, "%%(o%i)s" % i) for i, e in enumerate(self.fgraph.outputs))))
@@ -3650,7 +3653,8 @@ class Composite(ScalarOp):
         # Postpone the creation in case it isn't needed.
         #  self.init_name()      # self.name
         self.name = None
-        self.init_c_code()    # self._c_code and self.nodenames
+
+    def prepare_node(self, node, storage_map, compute_map):
         self.init_py_impls()  # self._impls
 
     def output_types(self, input_types):
@@ -3695,6 +3699,9 @@ class Composite(ScalarOp):
         raise NotImplementedError("grad is not implemented for Composite")
 
     def c_code(self, node, nodename, inames, onames, sub):
+        if not hasattr(self, '_c_code'):
+            self.init_c_code()
+
         d = dict(chain(izip(("i%i" % i for i in xrange(len(inames))), inames),
                        izip(("o%i" % i for i in xrange(len(onames))),
                             onames)), **sub)
@@ -3752,9 +3759,13 @@ class Composite(ScalarOp):
             return False
         # see __hash__ for comment on why there is no mention of fgraph
         # or module cache key here.
+        if not hasattr(self, '_c_code'):
+            self.init_c_code()    # self._c_code and self.nodenames
         return (self._c_code == other._c_code)
 
     def __hash__(self):
+        if not hasattr(self, '_c_code'):
+            self.init_c_code()    # self._c_code and self.nodenames
         rval = hash((type(self),
                     self.nin,
                     self.nout,
@@ -3771,7 +3782,7 @@ class Composite(ScalarOp):
 
     def __getstate__(self):
         rval = dict(self.__dict__)
-        del rval['_impls']
+        rval.pop('_impls', None)
         del rval['fgraph']
         return rval
 

theano/tensor/elemwise.py

@@ -849,6 +849,13 @@ second dimension
             char = numpy.sctype2char(out_dtype)
             sig = char * node.nin + '->' + char * node.nout
             node.tag.sig = sig
+        node.tag.fake_node = Apply(self.scalar_op,
+                  [get_scalar_type(dtype=input.type.dtype).make_variable()
+                   for input in node.inputs],
+                  [get_scalar_type(dtype=output.type.dtype).make_variable()
+                   for output in node.outputs])
+
+        self.scalar_op.prepare_node(node.tag.fake_node, [], [])
 
     def perform(self, node, inputs, output_storage):
         if len(node.inputs) >= 32:
@@ -1109,11 +1116,7 @@ second dimension
 
         # We generate the C code of the inner loop using the scalar op
         task_code = self.scalar_op.c_code(
-            Apply(self.scalar_op,
-                  [get_scalar_type(dtype=input.type.dtype).make_variable()
-                   for input in node.inputs],
-                  [get_scalar_type(dtype=output.type.dtype).make_variable()
-                   for output in node.outputs]),
+            node.tag.fake_node,
             nodename + '_scalar_',
             ["%s_i" % s for s in _inames],
             ["%s_i" % s for s in onames],