Check for 'bool' added. Tests also slightly modified

theano/tensor/opt.py

@@ -2225,7 +2225,7 @@ def local_cast_cast(node):
         # We don't need to copy over any stack traces here
         return [x]
 
-    if(upcast(base.dtype, type1.dtype)):
+    if(is_an_upcast(base.dtype, type1.dtype)):
         # Checking for further redundancy. Eg: int8 -> int32 -> int8
         if(type2.dtype == base.dtype):
             return x.owner.inputs
@@ -2237,17 +2237,23 @@ def local_cast_cast(node):
             return [v]
 
 
-def upcast(type1, type2):
+def is_an_upcast(type1, type2):
     """Given two data types (as strings), check if converting to
     type2 from type1 constitutes an upcast.
+    Differs from theano.scalar.upcast
 
     """
     category = {
-        # Pair of numbers : the 'super-index' and 'sub-index'
-        'uint8': (0, 0), 'uint16': (0, 1), 'uint32': (0, 2), 'uint64': (0, 3),
-        'int8': (1, 0), 'int16': (1, 1), 'int32': (1, 2), 'int64': (1, 3),
-        'float16': (2, 0.5), 'float32': (2, 1.5), 'float64': (2, 2.5),
-        'complex64': (3, 2), 'complex128': (3, 3)
+        # The first number in the pair is the dtype (bool, uint, int, float,
+        # complex). Conversion from higher to lower is never an upcast.
+        # The second number roughly indicates the precision. Again, conversion
+        # from higher to lower is never an upcast.
+
+        'bool': (0, 0),
+        'uint8': (1, 1), 'uint16': (1, 2), 'uint32': (1, 3), 'uint64': (1, 4),
+        'int8': (2, 1), 'int16': (2, 2), 'int32': (2, 3), 'int64': (2, 4),
+        'float16': (3, 1.5), 'float32': (3, 2.5), 'float64': (3, 3.5),
+        'complex64': (4, 3), 'complex128': (4, 4)
     }
 
     cat1 = category[type1]

theano/tensor/tests/test_opt.py

@@ -4658,7 +4658,7 @@ class T_cast_cast(unittest.TestCase):
         f(dx)
         topo = f.maker.fgraph.toposort()
         assert len(topo) == 1
-        assert isinstance(topo[0].op, T.Elemwise)
+       assert isinstance(topo[0].op.scalar_op, scal.basic.Cast)
 
         x = T.dmatrix()
         o = T.Elemwise(scal.Cast(scal.Scalar("float32")))(x.astype("float32"))
@@ -4667,7 +4667,7 @@ class T_cast_cast(unittest.TestCase):
         f(dx)
         topo = f.maker.fgraph.toposort()
         assert len(topo) == 1
-        assert isinstance(topo[0].op, T.Elemwise)
+        assert isinstance(topo[0].op.scalar_op, scal.basic.Cast)
 
     def test_upcast(self):
         # Upcast followed by any other cast