Use a cast instead of an identify that modify the dtype. This allow more optimization to work.

theano/tensor/opt.py

@@ -4998,10 +4998,7 @@ class Canonizer(gof.LocalOptimizer):
 
         new = self.merge_num_denum(num, denum)
         if new.type.dtype != out.type.dtype:
-            # new = T.fill(out, new)
-            elem_op = T.Elemwise(scalar.Identity(scalar.specific_out(
-                getattr(scalar, out.type.dtype))))
-            new = elem_op(new)
+            new = T.cast(new, out.type.dtype)
 
         assert (new.type == out.type) == (not (new.type != out.type))
 

theano/tensor/tests/test_opt.py

@@ -898,6 +898,25 @@ def test_const_type_in_mul_canonizer():
         f1(ival, wval, visbval, hidbval, betaval, aval))
 
 
+def test_cast_in_mul_canonizer():
+    x, y = tensor.vectors('xy')
+    m = tensor.minimum(x, y)
+    o = m.sum()
+    go = tensor.fill(o, 1)
+    e = tensor.eq(go, x)
+    o1 = (1 - e) * go
+    o2 = e * go
+    mode = theano.compile.get_default_mode().excluding('fusion').including('fast_run')
+    f = theano.function([x, y], [o1, o2], mode=mode)
+    theano.printing.debugprint(f, print_type=True)
+    nodes = f.maker.fgraph.apply_nodes
+    assert len([n for n in nodes if isinstance(getattr(n.op, 'scalar_op', None),
+                                               theano.scalar.Identity)]) == 0
+    assert len([n for n in nodes if isinstance(getattr(n.op, 'scalar_op'),
+                                               theano.scalar.Cast)]) == 1
+    f([1], [1])
+
+
 class test_fusion(unittest.TestCase):
     mode = copy.copy(compile.mode.get_default_mode())
     _shared = staticmethod(shared)