From 0752b3b710a5f29fba7f7d5e595aa9da3a76fdda Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Mon, 14 Nov 2016 14:08:29 +0800
Subject: [PATCH 1/2] add layer check for recurrent_group

---
 .../paddle/trainer_config_helpers/layers.py   | 23 +++++++++++++++++--
 1 file changed, 21 insertions(+), 2 deletions(-)

diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py
index 796121a64136ee..952b1f097133c6 100644
--- a/python/paddle/trainer_config_helpers/layers.py
+++ b/python/paddle/trainer_config_helpers/layers.py
@@ -2754,7 +2754,12 @@ def __init__(self, input):
 
 
 @wrap_name_default("recurrent_group")
-def recurrent_group(step, input, reverse=False, name=None, targetInlink=None):
+def recurrent_group(step,
+                    input,
+                    reverse=False,
+                    name=None,
+                    targetInlink=None,
+                    is_train=True):
     """
     Recurrent layer group is an extremely flexible recurrent unit in
     PaddlePaddle. As long as the user defines the calculation done within a
@@ -2819,6 +2824,12 @@ def step(input):
 
     :type targetInlink: LayerOutput|SubsequenceInput
 
+    :param is_train: recurrent_group is used for training (True) or generating (False).
+                     If is training, one of the input type must be LayerOutput; else, 
+                     none of input type should be LayerOutput.
+
+    : type is_train: bool                 
+    
     :return: LayerOutput object.
     :rtype: LayerOutput
     """
@@ -2866,6 +2877,7 @@ def map_in_links(x):
         seq_reversed=reverse,
         target_inlinkname=targetInlinkName)
     in_args = []
+    has_LayerOutput = True
     for each_input in input:
         assert is_single_input(each_input)
         if isinstance(each_input, LayerOutput):
@@ -2873,6 +2885,7 @@ def map_in_links(x):
         elif isinstance(each_input, SubsequenceInput):
             in_args.append(each_input.input)
         else:
+            has_LayerOutput = False
             mem_name = "__%s_memory__" % each_input.input.name
             mem = memory(
                 name=mem_name,
@@ -2886,6 +2899,8 @@ def map_in_links(x):
                 mix += identity_projection(mem)
             in_args.append(mem)
 
+    assert (is_train == has_LayerOutput)
+
     layer_outs = step(*in_args)
 
     if isinstance(layer_outs, LayerOutput):
@@ -3177,7 +3192,11 @@ def __real_step__(*args):
         return predict
 
     tmp = recurrent_group(
-        step=__real_step__, input=real_input, reverse=False, name=name)
+        step=__real_step__,
+        input=real_input,
+        reverse=False,
+        name=name,
+        is_train=False)
 
     return tmp
 

From 8c59437612280738f59df859d9f512b318153d05 Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Tue, 15 Nov 2016 12:35:36 +0800
Subject: [PATCH 2/2] change is_train to is_generating

---
 python/paddle/trainer_config_helpers/layers.py | 17 ++++++++---------
 1 file changed, 8 insertions(+), 9 deletions(-)

diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py
index f0757b9ce21fc1..7cd290023aba10 100644
--- a/python/paddle/trainer_config_helpers/layers.py
+++ b/python/paddle/trainer_config_helpers/layers.py
@@ -494,8 +494,7 @@ def scaling_projection(input, param_attr=None):
     :return: A ScalingProjection object
     :rtype: ScalingProjection
     """
-    proj = ScalingProjection(input_layer_name=input.name,
-                             **param_attr.attr)
+    proj = ScalingProjection(input_layer_name=input.name, **param_attr.attr)
     proj.origin = input
     return proj
 
@@ -2788,7 +2787,7 @@ def recurrent_group(step,
                     reverse=False,
                     name=None,
                     targetInlink=None,
-                    is_train=True):
+                    is_generating=False):
     """
     Recurrent layer group is an extremely flexible recurrent unit in
     PaddlePaddle. As long as the user defines the calculation done within a
@@ -2853,11 +2852,11 @@ def step(input):
 
     :type targetInlink: LayerOutput|SubsequenceInput
 
-    :param is_train: recurrent_group is used for training (True) or generating (False).
-                     If is training, one of the input type must be LayerOutput; else, 
-                     none of input type should be LayerOutput.
+    :param is_generating: If is generating, none of input type should be LayerOutput;
+                          else, for training or testing, one of the input type must 
+                          be LayerOutput.
 
-    : type is_train: bool                 
+    : type is_generating: bool
     
     :return: LayerOutput object.
     :rtype: LayerOutput
@@ -2928,7 +2927,7 @@ def map_in_links(x):
                 mix += identity_projection(mem)
             in_args.append(mem)
 
-    assert (is_train == has_LayerOutput)
+    assert (is_generating != has_LayerOutput)
 
     layer_outs = step(*in_args)
 
@@ -3225,7 +3224,7 @@ def __real_step__(*args):
         input=real_input,
         reverse=False,
         name=name,
-        is_train=False)
+        is_generating=True)
 
     return tmp