Small fixes

theano/scan_module/scan_op.py

@@ -2201,6 +2201,7 @@ class Scan(PureOp):
         # Construct scan op
         # Seqs
         if self.as_while:
+            # equivalent to x[:n_steps][::-1]
             outer_inp_seqs = [x[n_steps - 1::-1] for x in inputs[1:1 + self.n_seqs]]
         else:
             outer_inp_seqs = [x[::-1] for x in inputs[1:1 + self.n_seqs]]
@@ -2222,6 +2223,7 @@ class Scan(PureOp):
         for x in self.outer_nitsot_outs(dC_douts):
             if not isinstance(x.type, DisconnectedType):
                 if self.as_while:
+                    # equivalent to x[:n_steps][::-1]
                     outer_inp_seqs.append(x[n_steps - 1::-1])
                 else:
                     outer_inp_seqs.append(x[::-1])
@@ -2232,20 +2234,25 @@ class Scan(PureOp):
             # fct add and we want to keep it for all Scan op.  This is
             # used in T_Scan.test_grad_multiple_outs_taps to test
             # that.
+            if self.as_while:
+                n = n_steps.tag.test_value
+            else:
+                n = inputs[0].tag.test_value
             for taps, x in zip(self.mitsot_taps(),
                                self.outer_mitsot_outs(outs)):
                 mintap = np.min(taps)
                 if hasattr(x[::-1][:mintap], 'test_value'):
-                    assert (x[::-1][:mintap].tag.test_value.shape[0] ==
-                            grad_steps.tag.test_value)
+                    assert (x[::-1][:mintap].tag.test_value.shape[0] == n)
             for x in self.outer_sitsot_outs(outs):
                 if hasattr(x[::-1][:-1].tag, 'test_value'):
-                    assert (x[::-1][:-1].tag.test_value.shape[0] ==
-                            grad_steps.tag.test_value)
+                    assert (x[::-1][:-1].tag.test_value.shape[0] == n)
             for x in self.outer_nitsot_outs(outs):
                 if hasattr(x[::-1].tag, 'test_value'):
-                    assert (x[grad_steps - 1::-1].tag.test_value.shape[0] ==
-                            grad_steps.tag.test_value)
+                    if self.as_while:
+                        assert (x[n_steps - 1::-1].tag.test_value.shape[0] ==
+                                n)
+                    else:
+                        assert (x[::-1].tag.test_value.shape[0] == n)
         outer_inp_seqs += [x[::-1][:np.min(taps)]
                            for taps, x in zip(self.mitsot_taps(),
                                               self.outer_mitsot_outs(outs))]

theano/scan_module/tests/test_scan.py

@@ -5535,7 +5535,7 @@ class TestGradUntil(unittest.TestCase):
 
     def setUp(self):
         self.x = tensor.vector(name='x')
-        self.until = tensor.scalar(name='until', dtype='int64')
+        self.threshold = tensor.scalar(name='threshold', dtype='int64')
         self.seq = np.arange(15, dtype=theano.config.floatX)
         self.numpy_output = self.seq[:7]**2
         z = np.zeros(8, dtype=theano.config.floatX)
@@ -5545,9 +5545,9 @@ class TestGradUntil(unittest.TestCase):
         r, _ = theano.scan(lambda x, u: (x * x,
                                          theano.scan_module.until(x > u)),
                            sequences=self.x,
-                           non_sequences=[self.until])
+                           non_sequences=[self.threshold])
         g = theano.grad(r.sum(), self.x)
-        f = theano.function([self.x, self.until], [r, g])
+        f = theano.function([self.x, self.threshold], [r, g])
         theano_output, theano_gradient = f(self.seq, 5)
 
         utt.assert_allclose(theano_output, self.numpy_output)
@@ -5558,10 +5558,10 @@ class TestGradUntil(unittest.TestCase):
         r, _ = theano.scan(lambda x, u: (x * x,
                                          theano.scan_module.until(x > u)),
                            sequences=self.x,
-                           non_sequences=[self.until],
+                           non_sequences=[self.threshold],
                            truncate_gradient=n)
         g = theano.grad(r.sum(), self.x)
-        f = theano.function([self.x, self.until], [r, g])
+        f = theano.function([self.x, self.threshold], [r, g])
         theano_output, theano_gradient = f(self.seq, 5)
 
         self.numpy_gradient[:7 - n] = 0