code cleanup.

theano/tensor/blas.py

@@ -1906,8 +1906,9 @@ def local_dot22_to_dot22scalar(node):
     d = node.inputs[dot22_idx]
     i_scalar = [_as_scalar(x, dtype=d.dtype) for x in node.inputs]
     if not any(i_scalar):
-        # Check witch input of node is a mul with scalar inputs.
-        # We could reuse this scalar value.
+        # Check if we can reorder the graph as this mul have a mul in inputs.
+        # We support only 1 additional level of mul.
+        # The canonizer should have merged those mul together.
         i_mul = [x.owner and x.owner.op == T.mul and
                  any([_as_scalar(x_i, dtype=d.dtype)
                    for x_i in x.owner.inputs])
@@ -1918,14 +1919,9 @@ def local_dot22_to_dot22scalar(node):
             #by the associativity of the graph.
             return False
 
-        #maybe we can reorder the graph as this mul have a mul in input.
-        #The canonizer should have merged those mul together.
-        #We support only 1 additional level of mul.
         mul_idx = i_mul.index(True)  # The first one should always work
         m = node.inputs[mul_idx]
 
-        if any([_as_scalar(x, dtype=d.dtype)
-                for x in m.owner.inputs]):  # This should be always True
         scalar_idx = -1
         for i, x in enumerate(m.owner.inputs):
             if _as_scalar(x, dtype=d.dtype) and (theano.scalar.upcast(
@@ -1957,13 +1953,6 @@ def local_dot22_to_dot22scalar(node):
                           if i != scalar_idx]
 
         return [T.mul(dot, *(other_factors + other_m_inputs))]
-        elif m.owner and m.owner.op == T.mul:
-            _logger.info('Not optimizing dot22 with inputs %s %s %s %s. '
-                    'we need to check in a recursive way in the mul if we can '
-                    'reorder the graph. The canonizer should have done this.',
-                    d, m, d.type, m.type)
-        else:
-            return False
 
     scalar_idx = -1
     for i, x in enumerate(node.inputs):