fix to fix to fill_local_alloc

theano/tensor/opt.py

@@ -81,7 +81,7 @@ def scalarconsts_rest(inputs):
             nonconsts.append(i)
     return consts, origconsts, nonconsts
 
-def broadcast_like(value, template, env):
+def broadcast_like(value, template, env, dtype=None):
     """Return a Variable with the same shape and dtype as the template,
     filled by broadcasting value through it. `value` will be casted as necessary.
 
@@ -89,12 +89,15 @@ def broadcast_like(value, template, env):
     shape_of = env.shape_feature.shape_of
     if template not in shape_of:
         raise NotImplementedError('broadcast_like currently requires the template Variable to be in the env already')
-    rval = T.alloc(T.cast(value, template.dtype), *shape_of[template])
+    if dtype is None:
+        dtype = template.dtype
+    rval = T.alloc(T.cast(value, dtype), *shape_of[template])
     # the template may have 1s in its shape without being broadcastable
     if rval.broadcastable != template.broadcastable:
         rval = T.unbroadcast(rval, *[i for i in xrange(rval.ndim) if rval.broadcastable[i]
             and not template.broadcastable[i]])
-    assert rval.type == template.type
+    assert rval.type.dtype == dtype
+    assert rval.type.broadcastable == template.broadcastable
     return rval
 
 
@@ -669,7 +672,7 @@ def local_fill_to_alloc(node):
             rval = [T.cast(v, node.outputs[0].type.dtype)]
         elif r.type.broadcastable == node.outputs[0].type.broadcastable:
             # we are broadcasting v somehow, but not r
-            rval = [broadcast_like(v, r, node.env)]
+            rval = [broadcast_like(v, r, node.env, dtype=v.dtype)]
         else:
             # we are broadcasting both v and r,
             # the output shape must be computed