Fixed a bug reported by cityhall consisting in not calling tensor.as_tensor on…

theano/scan.py

@@ -44,7 +44,7 @@ import tensor
 import misc.safe_asarray as safe_asarray
 from tensor import opt, TensorType
 import gof
-from gof import Optimizer, toolbox, Op, Apply
+from gof import Optimizer, toolbox, Op, Apply, Variable
 from compile import optdb, SharedVariable, function, Param
 import compile
 import gradient
@@ -1559,8 +1559,15 @@ class Scan(Op):
                             theano.config.floatX))
         inner_gfn_ins = inner_g_outs + self.inputs
 
-        g_args = [self.n_steps] + g_outs[:self.n_outs_not_shared] \
+
+        # Make sure you don't have numbers in here
+        if not isinstance(self.n_steps, Variable):
+            n_steps = tensor.as_tensor(self.n_steps)
+        else:
+            n_steps = self.n_steps
+        g_args = [n_steps] + g_outs[:self.n_outs_not_shared] \
                             + scan_outputs + args[1:]
+
         truncate_gradient = self.truncate_gradient
         for x in self.store_steps[:self.n_outs_not_shared]:
             if x>0 :
@@ -1571,6 +1578,7 @@ class Scan(Op):
                 self.n_seqs, self.n_outs, self.n_outs_not_shared,
                 self.go_backwards, self.seqs_taps, self.outs_taps,
                 truncate_gradient)
+
         g_scan_outs = g_scan(g_args)
         if not type(g_scan_outs) in (list, tuple):
             g_scan_outs = [ g_scan_outs ]

theano/tests/test_scan.py

@@ -1039,6 +1039,34 @@ class T_Scan(unittest.TestCase):
         assert updates[b].type.ndim == b.type.ndim
 
 
+    def test_scan_as_tensor_on_gradients(self):
+        """
+        Bug reported by cityhall on scan when computing the gradients
+        """
+        to_scan = theano.tensor.dvector('to_scan')
+        seq     = theano.tensor.dmatrix('seq')
+        f1      = theano.tensor.dscalar('f1')
+
+        def scanStep(prev, seq, f1):
+           return prev + f1 * seq
+
+        scanned, _ = theano.scan(fn = scanStep, \
+                                sequences    = [seq], \
+                                outputs_info = [to_scan], \
+                                non_sequences  = [f1])
+
+        f_scan = theano.function(inputs=[to_scan, seq, f1], outputs=scanned)
+        f_scan([1,2,3], numpy.arange(12).reshape([4,3]), 1.)
+
+        t_grad = theano.tensor.grad(scanned.sum(), wrt=[to_scan, f1],
+        consider_constant=[seq])
+        f_grad = theano.function(inputs=[to_scan, seq, f1], outputs=t_grad)
+
+        f_scan([1,2,3], numpy.arange(12).reshape([4,3]), 1.)
+        f_grad([1,2,3], numpy.arange(12).reshape([4,3]), 1.)
+
+
+
 if __name__ == '__main__':
     unittest.main()