Fix an opt being skipped due to wrong dtype exchange. This remove opt warning in a keras test.

theano/tensor/opt.py

@@ -5717,11 +5717,19 @@ def local_opt_alloc(node):
                                                     only_process_constants=True)
                     assert val.size == 1
                     # check which type of op
-                    casted = T.mul(*shapes).astype(str(input.dtype))
+                    size = T.mul(*shapes)
                     if isinstance(node.op, T.Sum):
-                        val = val.reshape(1)[0] * casted
+                        val = val.reshape(1)[0] * size
                     else:
-                        val = val.reshape(1)[0] ** casted
+                        val = val.reshape(1)[0] ** size
+                    # Sum can change the input dtype (upcast or bool
+                    # -> float32) by default or by user request.
+                    # We can ignore the acc_dtype, as there is only 1
+                    # elemwise we will do and not a sequence, so there is no
+                    # accumulation of errors.
+                    # So mostly, we just need to cast the output to the old
+                    # dtype.
+                    val = val.astype(node.outputs[0].dtype)
                     return [val]
 
                 except NotScalarConstantError:
@@ -5735,11 +5743,12 @@ def local_opt_alloc(node):
                     to_prod = [shapes[i] for i in xrange(len(shapes))
                                if i in node.op.axis]
                     if to_prod:
-                        casted = T.mul(*to_prod).astype(str(input.dtype))
+                        size = T.mul(*to_prod)
                         if isinstance(node.op, T.Sum):
-                            val *= casted
+                            val *= size
                         else:
-                            val = val ** casted
+                            val = val ** size
+                    val = val.astype(node.outputs[0].dtype)
                     return [T.alloc(val,
                                     *[shapes[i] for i in xrange(len(shapes))
                                       if i not in node.op.axis])]

theano/tensor/tests/test_opt.py

@@ -5580,6 +5580,19 @@ class T_local_opt_alloc(unittest.TestCase):
         finally:
             theano.config.warn_float64 = orig
 
+    @theano.configparser.change_flags(on_opt_error='raise')
+    def test_sum_bool_upcast(self):
+        s = theano.tensor.lscalar()
+        a = theano.tensor.alloc(np.asarray(True, dtype='bool'), s, s)
+        f = theano.function([s], a.sum())
+        f(5)
+        # test with user specified dtype
+        f = theano.function([s], a.sum(dtype='float32'))
+        f(5)
+        # test only 1 axis summed
+        f = theano.function([s], a.sum(axis=0, dtype='float32'))
+        f(5)
+
 
 class T_local_reduce(unittest.TestCase):
     def setUp(self):