Do some clean up and add some asserts to make sure we don't miss anything.

theano/scan_module/scan_op.py

@@ -217,10 +217,9 @@ class Scan(PureOp):
             if rval.ndim == as_var.ndim:
                 rval = as_var.type.filter_variable(rval)
             else:
-                tmp = as_var.type.__class__(
-                    broadcastable=tuple(var.broadcastable[:1])+\
-                                  tuple(as_var.broadcastable),
-                    dtype=as_var.dtype)
+                tmp = as_var.type.clone(
+                    broadcastable=(tuple(var.broadcastable[:1]) +
+                                   tuple(as_var.broadcastable)))
                 rval = tmp.filter_variable(rval)
             return rval
 
@@ -493,11 +492,11 @@ class Scan(PureOp):
         return aux_txt
 
     def __hash__(self):
-        return (hash(type(self)) ^
+        return hash((type(self),
                      # and a hash representing the inner graph using the
                      # CLinker.cmodule_key_
-                self._hash_inner_graph ^
-                scan_utils.hash_listsDictsTuples(self.info))
+                     self._hash_inner_graph,
+                     scan_utils.hash_listsDictsTuples(self.info)))
 
     def make_thunk(self, node, storage_map, compute_map, no_recycling):
         """

theano/scan_module/scan_utils.py

@@ -46,6 +46,7 @@ def safe_new(x, tag='', dtype=None):
         nw_name = x.name + tag
     else:
         nw_name = None
+
     if isinstance(x, theano.Constant):
         if dtype and x.dtype != dtype:
             casted_x = x.astype(dtype)
@@ -54,20 +55,14 @@ def safe_new(x, tag='', dtype=None):
             return nwx
         else:
             return x.clone()
-    # Note, as_tensor_variable will convert the Scalar into a
-    # TensorScalar that will require a ScalarFromTensor op,
-    # making the pushout optimization fail
-    elif isinstance(x, scalar.ScalarVariable):
-        if dtype:
-            nw_x = scalar.get_scalar_type(dtype=dtype)()
-        else:
-            nw_x = x.type()
-        nw_x.name = nw_name
-        return nw_x
+
+    # at this point we should only have Variables
+    assert isinstance(x, theano.Variable)
     nw_x = x.type()
     if dtype and nw_x.dtype != dtype:
         nw_x = nw_x.astype(dtype).type()
     nw_x.name = nw_name
+
     # Preserve test values so that the 'compute_test_value' option can be used.
     # The test value is deep-copied to ensure there can be no interactions
     # between test values, due to inplace operations for instance. This may
@@ -807,7 +802,7 @@ class scan_args(object):
     def __init__(self, outer_inputs, outer_outputs,
                  _inner_inputs, _inner_outputs, info):
         self.n_steps = outer_inputs[0]
-        rval = reconstruct_graph(_inner_inputs, _inner_outputs, '_merge')
+        rval = reconstruct_graph(_inner_inputs, _inner_outputs, '')
         if info['as_while']:
             self.cond = [rval[1][-1]]
             inner_outputs = rval[1][:-1]
@@ -911,6 +906,9 @@ class scan_args(object):
         p += n_shared_outs
         q += n_shared_outs
 
+        assert p == len(outer_outputs)
+        assert q == len(inner_outputs)
+
         self.other_info = OrderedDict()
         for k in ('truncate_gradient', 'name', 'mode', 'destroy_map',
                   'gpu', 'gpua', 'as_while', 'profile', 'allow_gc'):