Removed keep_wrt_type parameter of tensor.grad

theano/tensor/tensor_grad.py

@@ -234,7 +234,7 @@ def Lop(f, wrt, eval_points, consider_constant=None, warn_type=False,
 #########################
 
 def grad(cost, wrt, g_cost=None, consider_constant=None, warn_type=False,
-         disconnected_inputs='raise', keep_wrt_type=True):
+         disconnected_inputs='raise'):
     """
     :type cost: Scalar (0-dimensional) `Variable`
     :type wrt: `Variable` or list of `Variable`s.
@@ -254,14 +254,6 @@ def grad(cost, wrt, g_cost=None, consider_constant=None, warn_type=False,
         - 'warn': consider the gradient zero, and print a warning.
         - 'raise': raise an exception.
 
-    :param keep_wrt_type: When True, if `wrt` is a list or tuple, then the
-    returned output is of the same type. When False, if `wrt` is a one-element
-    list or tuple, then the returned value is a single `Variable` (and if
-    `wrt` is a list or tuple with at least two elements, then the returned
-    value is always a list -- never a tuple).
-    This option has no effect when `wrt` is a single `Variable` (in which case
-    the returned value is always a single `Variable`).
-
     :rtype: `Variable` or list/tuple of `Variable`s (depending upon `wrt`)
 
     :return: symbolic expression of gradient of `cost` with respect to `wrt`.
@@ -329,16 +321,16 @@ def grad(cost, wrt, g_cost=None, consider_constant=None, warn_type=False,
                         "'ignore', 'warn' and 'raise'.")
             ret.append(zeros_like(p))
 
-    if keep_wrt_type and using_tuple:
-        ret = tuple(ret)
-
-    if len(ret) == 1:
-        if keep_wrt_type and (using_list or using_tuple):
-            return ret
-        else:
+    if len(ret) == 1 and not (using_list or using_tuple):
+        # `wrt` was a single Variable, so we return a single Variable too.
             return ret[0]
     else:
-        return ret
+        # Ensure we preserve the original type of `wrt`.
+        if using_tuple:
+            return tuple(ret)
+        else:
+            assert using_list
+            return ret
 
 
 class numeric_grad:
@@ -614,8 +606,7 @@ def verify_grad(fun, pt, n_tests=2, rng=None, eps=None, abs_tol=None, rel_tol=No
         g_cost = cast(g_cost, o_output.dtype)
 
     symbolic_grad = grad(cost, tensor_pt, g_cost,
-                         disconnected_inputs='ignore',
-                         keep_wrt_type=True)
+                         disconnected_inputs='ignore')
     #if o_output.dtype in ['float32','float64']:
     #    assert all([x.dtype == o_output.dtype for x in symbolic_grad]),("Expected grad of type %s, got %s "%( symbolic_grad.dtype, o_output.dtyp))