Fix optimization that is incorrect for non-bool

theano/tensor/opt.py

@@ -2087,16 +2087,16 @@ def local_subtensor_make_vector(node):
 @gof.local_optimizer([T.Elemwise])
 def local_useless_elemwise(node):
     """
-    eq(x,x) -> 1
-    neq(x,x) -> 0
+    eq(x, x) -> 1
+    neq(x, x) -> 0
     mul(x) -> x
     add(x) -> x
     identity(x) -> x
-    and(x,1) -> x
-    and(x,0) -> zeros_like(x)
-    or(x,0) -> x
-    or(x,1) -> ones_like(x)
-    xor(x,x) -> zeros_like(x)
+    and(x, 1) -> x  (if x.dtype == 'bool')
+    and(x, 0) -> zeros_like(x)
+    or(x, 0) -> x
+    or(x, 1) -> ones_like(x)  (if x.dtype == 'bool')
+    xor(x, x) -> zeros_like(x)
 
     """
     if isinstance(node.op, T.Elemwise):
@@ -2141,7 +2141,9 @@ def local_useless_elemwise(node):
                     if const_val == 0:
                         return [T.zeros_like(node.inputs[1], dtype=dtype,
                                              opt=True)]
-                    else:
+                    elif node.outputs[0].dtype == 'bool':
+                        # If the output is not Boolean, it is the bitwise AND,
+                        # and this optimization would be wrong
                         return [node.inputs[1].astype(node.outputs[0].dtype)]
 
             if isinstance(node.inputs[1], T.TensorConstant):
@@ -2150,7 +2152,9 @@ def local_useless_elemwise(node):
                     if const_val == 0:
                         return [T.zeros_like(node.inputs[0], dtype=dtype,
                                              opt=True)]
-                    else:
+                    elif node.outputs[0].dtype == 'bool':
+                        # If the output is not Boolean, it is the bitwise AND,
+                        # and this optimization would be wrong
                         return [node.inputs[0].astype(node.outputs[0].dtype)]
 
         elif (isinstance(node.op.scalar_op, scalar.OR) and
@@ -2161,7 +2165,9 @@ def local_useless_elemwise(node):
                 if not isinstance(const_val, Variable):
                     if const_val == 0:
                         return [node.inputs[1].astype(node.outputs[0].dtype)]
-                    else:
+                    elif node.outputs[0].dtype == 'bool':
+                        # If the output is not Boolean, it is the bitwise OR,
+                        # and this optimization would be wrong
                         return [T.ones_like(node.inputs[1], dtype=dtype,
                                             opt=True)]
 
@@ -2170,7 +2176,9 @@ def local_useless_elemwise(node):
                 if not isinstance(const_val, Variable):
                     if const_val == 0:
                         return [node.inputs[0].astype(node.outputs[0].dtype)]
-                    else:
+                    elif node.outputs[0].dtype == 'bool':
+                        # If the output is not Boolean, it is the bitwise OR,
+                        # and this optimization would be wrong
                         return [T.ones_like(node.inputs[0], dtype=dtype,
                                             opt=True)]