Merge pull request #427 from nouiz/fix_reduce

theano/tensor/elemwise.py

@@ -1026,6 +1026,9 @@ class CAReduce(Op):
             self.axis.sort()
             self.axis = tuple(self.axis)
 
+        self.set_ufunc(scalar_op)
+
+    def set_ufunc(self, scalar_op):
         # This is probably a speed up of the implementation
         if isinstance(scalar_op, theano.scalar.basic.Add):
             self.ufunc = numpy.add
@@ -1078,12 +1081,12 @@ class CAReduce(Op):
 
     def __getstate__(self):
         d = copy(self.__dict__)
-        d.pop('ufunc')
+        d.pop('ufunc', None)
         return d
 
     def __setstate__(self, d):
         self.__dict__.update(d)
-        self.ufunc = numpy.frompyfunc(self.scalar_op.impl, 2, 1)
+        self.set_ufunc(self.scalar_op)
 
     def __eq__(self, other):
         return type(self) == type(other) and self.scalar_op == other.scalar_op and self.axis == other.axis
@@ -1357,8 +1360,25 @@ class CAReduceDtype(CAReduce):
     def __hash__(self):
         return CAReduce.__hash__(self) ^ hash(self.dtype)
 
+    def __setstate__(self, d):
+        self.__dict__.update(d)
+        if not hasattr(self, "dtype"):
+            # This is needed as old pickled will crash otherwise.
+            # We need to keep the old dtype behavior as the op
+            # could be in an apply node with a specified dtype.
+            self.dtype = "OLD"
+
     def _output_dtype(self, idtype):
         dtype = self.dtype
+        if dtype == "OLD":
+            return dict(
+                int8='int32',
+                int16='int32',
+                int32='int64',
+                uint8='uint32',
+                uint16='uint32',
+                uint32='uint64',
+                ).get(idtype, idtype)
         if dtype is None:
             # If input has a discrete dtype, upcast it to 64
             return dict(