Use safe_new(x) instead of x.type() in scan grad.

theano/scan_module/scan_op.py

@@ -1446,7 +1446,7 @@ class Scan(PureOp):
 
             # We are looking for x[t-1] for a given x[t]
             if idx >= self.n_mit_mot_outs:
-                Xt_placeholder = Xt.type()
+                Xt_placeholder = safe_new(Xt)
                 Xts.append(Xt_placeholder)
             if Xt not in self.inner_nitsot_outs(self_outputs):
                 # What we do here is loop through dC_douts and collect all
@@ -1502,12 +1502,12 @@ class Scan(PureOp):
         for pos, x in enumerate(dC_dinps_t[self.n_seqs:]):
             opos = self.get_output_pos(pos)
             if opos >= 0:
-                dC_dXtm1s.append(dC_dXts[opos].type())
+                dC_dXtm1s.append(safe_new(dC_dXts[opos]))
                 if x.dtype != dC_dXts[opos].dtype:
                     dC_dinps_t[pos + self.n_seqs] = \
                             x.astype(dC_dXts[opos].dtype)
             else:
-                dC_dXtm1s.append(x.type())
+                dC_dXtm1s.append(safe_new(x))
         for dx, dC_dXtm1 in enumerate(dC_dXtm1s):
             dC_dinps_t[dx + self.n_seqs] += dC_dXtm1
         # Construct scan op

theano/scan_module/tests/test_scan.py

@@ -3809,10 +3809,35 @@ def test_compute_test_value():
         x.tag.test_value = xv
         y = theano.shared(numpy.arange(3, dtype=theano.config.floatX),
                           name='y')
-        z, _ = theano.scan(
+        z, updates = theano.scan(
                 fn=lambda u, v: u + v,
                 sequences=[x, y])
-        assert not _
+        assert not updates
+        z.name = 'z'
+        # The gradient computation used to crash before 6af465e.
+        g = tensor.grad(z.sum(), x)
+        #f = theano.function([x], g)
+        #print f(xv)
+    finally:
+        theano.config.compute_test_value = backup
+
+
+def test_compute_test_value_nonseq():
+    # Verify that test values can be used for non_sequences with scan.
+    backup = theano.config.compute_test_value
+    theano.config.compute_test_value = 'raise'
+    try:
+        x = tensor.vector('x')
+        xv = numpy.ones(3, dtype=theano.config.floatX)
+        x.tag.test_value = xv
+        y = theano.shared(
+                numpy.arange(9, dtype=theano.config.floatX).reshape(3, 3),
+                name='y')
+        z, updates = theano.scan(
+                fn=lambda u, v: u + v,
+                sequences=[x],
+                non_sequences=[y])
+        assert not updates
         z.name = 'z'
         # The gradient computation used to crash before 6af465e.
         g = tensor.grad(z.sum(), x)