changed gradient.grad to not introduce integer-valued gradients

theano/gradient.py

@@ -444,7 +444,7 @@ def grad(cost, wrt, g_cost=None, consider_constant=None, warn_type=False,
     grad_dict = {}
     # by default, the gradient of the cost is 1
     if g_cost is None:
-        g_cost = tensor.ones_like(cost)
+        g_cost = _float_ones_like(cost)
     grad_dict[cost] = g_cost
 
     # the gradient of the constants is 0
@@ -477,12 +477,18 @@ def grad(cost, wrt, g_cost=None, consider_constant=None, warn_type=False,
     if add_names:
         cost_name = cost.name
 
+    # Make sure we didn't initialize the grad_dict with any ints
+    for var in grad_dict:
+        g = grad_dict[var]
+        if hasattr(g.type,'dtype'):
+            assert g.type.dtype.find('float') != -1
+
     rval = _populate_grad_dict(var_to_node_to_idx,
             grad_dict, wrt, cost_name)
 
     for i in xrange(len(rval)):
         if isinstance(rval[i].type, DisconnectedType):
-            rval[i] = wrt[i].zeros_like()
+            rval[i] = _float_zeros_like(wrt[i])
 
     if using_tuple:
         rval = tuple(rval)
@@ -931,10 +937,31 @@ def grad_sources_inputs(sources, graph_inputs):
     for key in grad_dict:
         if isinstance(grad_dict[key].type, DisconnectedType):
             if hasattr(key, 'zeros_like'):
-                grad_dict[key] = key.zeros_like()
+                grad_dict[key] = _float_zeros_like(key)
 
     return grad_dict
 
+def _float_zeros_like(x):
+    """ Like zeros_like, but forces the object to have a
+    a floating point dtype """
+
+    rval = x.zeros_like()
+
+    if rval.type.dtype.find('float') != -1:
+        return rval
+
+    return rval.astype(theano.config.floatX)
+
+def _float_ones_like(x):
+    """ Like ones_like, but forces the object to have a
+    floating point dtype """
+
+    rval = tensor.ones_like(x)
+
+    if rval.type.dtype.find('float') != -1:
+        return rval
+
+    return rval.astype(theano.config.floatX)
 
 class numeric_grad(object):
     """

theano/tensor/basic.py

@@ -1910,6 +1910,7 @@ class TensorFromScalar(Op):
     def grad(self, inp, grads):
         s, = inp
         dt, = grads
+        assert dt.type.dtype.find('float') != -1
         return [scalar_from_tensor(dt)]
 
     def __str__(self):