Fix stability opt not being applied

theano/tensor/opt.py

@@ -2978,8 +2978,8 @@ def local_mul_switch_sink(node):
         if i.owner and i.owner.op == T.switch:
             switch = i.owner
             try:
-                if (isinstance(switch.inputs[0], Constant) and
-                    get_scalar_constant_value(switch.inputs[1]) == 0.):
+                if (get_scalar_constant_value(
+                        switch.inputs[1], only_process_constants=True) == 0.):
                     listmul = node.inputs[:idx] + node.inputs[idx + 1:]
                     fct = [T.switch(switch.inputs[0], 0,
                                     T.mul(*(listmul + [switch.inputs[2]])))]
@@ -2988,8 +2988,8 @@ def local_mul_switch_sink(node):
             except NotScalarConstantError:
                 pass
             try:
-                if (isinstance(switch.inputs[2], Constant) and
-                    get_scalar_constant_value(switch.inputs[2]) == 0.):
+                if (get_scalar_constant_value(
+                        switch.inputs[2], only_process_constants=True) == 0.):
                     listmul = node.inputs[:idx] + node.inputs[idx + 1:]
                     fct = [T.switch(switch.inputs[0],
                                     T.mul(*(listmul + [switch.inputs[1]])), 0)]

theano/tensor/tests/test_opt.py

@@ -3983,6 +3983,12 @@ class T_local_switch_sink(unittest.TestCase):
                         resm[idx])).sum() == self.resm[idx].size
                 idx += 1
 
+    # This case caused a missed optimization in the past.
+    x = T.dscalar('x')
+    y = T.switch(x < 7, x, T.sqrt(x - 7))
+    f = theano.function([x], T.grad(y, x))
+    assert f(5) == 1
+
     @attr('slow')
     def test_local_div_switch_sink(self):
         c = T.dscalar()