Function to deal with the case when n_steps is 1

theano/sandbox/scan_module/scan.py

@@ -320,3 +320,43 @@ def scan(fn,
     # Note : see the internal documentation of the scan op for naming
     # conventions and all other details
 
+
+
+def one_step_scan(fn, us, xys_info, ws, T, truncate_gradient):
+    """
+    This function is evaluated if `n_steps` evaluates to either 1 or -1.
+    """
+    # 1. Grab slices of sequences
+    us_slices = [u[0] for u in us]
+
+    # 2. Grab slices of states
+    xs_slices = []
+    for n, x in enumerate(xys_info):
+        if x.get('taps', None) == [-1]:
+            xs_slices.append(x['initial'])
+        elif init_out.get('taps', None):
+            if numpy.any(numpy.array(init_out.get('taps', [])) > 0):
+                # Make sure we do not have requests for future values of a
+                # sequence we can not provide such values
+                raise ValueError('Can not use future taps of outputs',
+                                    init_out)
+            # go through the taps
+            mintap = abs(numpy.min(init_out['taps']))
+            xs_slices.append(x['initial'][k+mintap])
+
+    # Re-order args
+    args = (us_slices + xs_slices + non_seqs)
+    cond, xys_results, updates = scan_utils.get_updates_and_outputs(fn(*args))
+
+    # We do not need to use the scan op anymore, so we can just return
+    # the outputs and updates we have
+    if cond is not None:
+        _logger.warning(('When the number of steps is fixed and equal '
+                'to 1, the provided stopping condition, ',
+                str(cond), ' is ignored'))
+    xys_results = [tensor.unbroadcast(
+        tensor.shape_padleft(xy_results), 0) for xy in xys]
+    if len(xys) == 1:
+        xys_results = xys_results[0]
+
+    return (xys_results, updates)