new test for the bug Justin found

theano/tests/test_scan.py

@@ -1065,6 +1065,52 @@ class T_Scan(unittest.TestCase):
         f_scan([1,2,3], numpy.arange(12).reshape([4,3]), 1.)
         f_grad([1,2,3], numpy.arange(12).reshape([4,3]), 1.)
 
+    def caching_nsteps_by_scan_op(self):
+
+        import theano
+        import theano.tensor as T
+        import scipy
+
+
+        W = T.matrix('weights')
+        initial = T.vector('initial')
+        inpt = T.matrix('inpt')
+
+        def one_step(x_t, h_tm1, W):
+          expr = T.dot(h_tm1, W) + x_t
+          return expr
+
+        expr, _ = theano.scan(
+          fn=one_step,
+          sequences=[inpt],
+          outputs_info=[initial],
+          non_sequences=[W])
+
+        sh = expr.shape[0]
+
+        shapef = theano.function([W], expr,
+                                 givens={initial: theano.shared(
+                                     scipy.ones(5,
+                                                dtype=theano.config.floatX)),
+                                         inpt: theano.shared(
+                                             scipy.ones((5, 5),
+                                                       dtype=theano.config.floatX))})
+        # First execution to cache n_steps
+        shapef(scipy.ones((5, 5), dtype=theano.config.floatX))
+
+
+        cost = expr.sum()
+        d_cost_wrt_W = T.grad(cost, [W])
+        f = theano.function([W, inpt], d_cost_wrt_W,
+                             givens={initial: theano.shared(scipy.zeros(5))})
+
+        rval = numpy.asarray([[5187989]*5]*5, dtype = theano.config.floatX)
+        assert numpy.allclose( f(scipy.ones((5, 5),
+                                            dtype=theano.config.floatX)
+                                 , scipy.ones((10, 5),
+                                              dtype=theano.config.floatX))
+                              ,rval)
+
 
 
 if __name__ == '__main__':