fix grad to not expect same dtype of the grad as input

theano/scan_module/scan_op.py

@@ -1291,12 +1291,6 @@ class Scan(PureOp):
         offset = len(args) - len(other_args) - pos
         # 7.2. generate variables to represent previous steps of g_outs
         for idx, diff_in in enumerate(diff_inputs):
-            prev_gfn_out = safe_new(diff_in)
-            if hasattr(diff_in, 'name') and diff_in.name:
-                prev_gfn_out.name = 'g_prev_' + diff_in.name
-            else:
-                prev_gfn_out.name = 'g_prev_' + str(idx)
-            prev_inner_gfn_outs.append(prev_gfn_out)
             if idx < pos:
                 zeros_like_diff_ins.append(tensor.zeros_like(diff_in))
             else:
@@ -1328,10 +1322,7 @@ class Scan(PureOp):
             grad_outs = compute_gradient(out, _g_out)
             if not inner_gfn_outs:
                 for idx, gfn_out in enumerate(grad_outs):
-                    if idx >= self.n_seqs:
-                        inner_gfn_outs.append(prev_inner_gfn_outs[idx])
-                    else:
-                        inner_gfn_outs.append(None)
+                    inner_gfn_outs.append(None)
             # 7.4 Sum the gradients
             # safety check, some of this inputs might still not be
             # differentiable, for those we don't add them to the mix
@@ -1344,6 +1335,10 @@ class Scan(PureOp):
                 else:
                     inner_gfn_outs[i] = x
 
+        prev_inner_gfn_outs = [x.type() for x in inner_gfn_outs]
+        for dx in xrange(self.n_seqs, len(inner_gfn_outs)):
+            inner_gfn_outs[dx] = inner_gfn_outs[dx] + \
+                    prev_inner_gfn_outs[dx]
         ## 8. Mask the outputs that are not differentiable
         # backwards pass
         for i in xrange(len(inner_gfn_outs)):
@@ -1493,8 +1488,9 @@ class Scan(PureOp):
         n_sitsot_outs = len(prev_inner_gfn_outs[offset:])
         scan_sitsot_ins = prev_inner_gfn_outs[offset:]
         scan_sitsot_init = []
-        for x in zeros_like_diff_ins[offset:]:
-            shapes = [x.shape[i] for i in xrange(x.ndim)]
+        for x,y in zip(prev_inner_gfn_outs[offset:],
+                       zeros_like_diff_ins[offset:]):
+            shapes = [y.shape[i] for i in xrange(x.ndim)]
             empty = tensor.zeros([do_steps + 1] + shapes,
                                  dtype=x.dtype)
             scan_sitsot_init.append(empty)