change the parameter position of gru_step_layer from 1 back to 0

paddle/gserver/layers/GruStepLayer.cpp

@@ -68,8 +68,8 @@ bool GruStepLayer::init(const LayerMap& layerMap,
   if (!Layer::init(layerMap, parameterMap)) return false;
   CHECK_EQ(2U, inputLayers_.size());
 
-  CHECK_EQ(getSize() * getSize() * 3, parameters_[1]->getSize());
-  weight_.reset(new Weight(getSize(), getSize() * 3, parameters_[1]));
+  CHECK_EQ(getSize() * getSize() * 3, parameters_[0]->getSize());
+  weight_.reset(new Weight(getSize(), getSize() * 3, parameters_[0]));
 
   if (biasParameter_.get() != NULL) {
     CHECK_EQ(getSize() * 3, biasParameter_->getSize());

paddle/gserver/tests/test_LayerGrad.cpp

@@ -1404,9 +1404,9 @@ TEST(Layer, GruStepLayer) {
   config.biasSize = 12;
 
   config.inputDefs.push_back(
-      {INPUT_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 0});
+      {INPUT_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48});
   config.inputDefs.push_back(
-      {INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 48});
+      {INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0});
   config.layerConfig.add_inputs();
   config.layerConfig.add_inputs();
 

python/paddle/trainer/config_parser.py

@@ -2996,7 +2996,7 @@ class GruStepLayer(LayerBase):
         config_assert(input_layer1.size == size,
                       'input_layer1.size != layer.size')
         self.config.active_gate_type = active_gate_type
-        self.create_input_parameter(1, size * size * 3, [size, size * 3])
+        self.create_input_parameter(0, size * size * 3, [size, size * 3])
         self.create_bias_parameter(bias, size * 3)
 
 

python/paddle/trainer_config_helpers/layers.py

@@ -2706,6 +2706,9 @@ def gru_step_layer(input,
     :param name:
     :param gate_act:
     :param bias_attr:
+    :param param_attr: the parameter_attribute for transforming the output_mem 
+                       from previous step. It is instead grouped with input due
+                       to backward model compatibility.
     :param layer_attr:
     :return: LayerOutput object.
     :rtype: LayerOutput
@@ -2716,7 +2719,7 @@ def gru_step_layer(input,
     Layer(
         name=name,
         type=LayerType.GRU_STEP_LAYER,
-        inputs=[input.name, Input(output_mem.name, **param_attr.attr)],
+        inputs=[Input(input.name, **param_attr.attr), output_mem.name],
         bias=ParamAttr.to_bias(bias_attr),
         size=size,
         active_type=act.name,

python/paddle/trainer_config_helpers/tests/configs/protostr/shared_gru.protostr

@@ -51,10 +51,10 @@ layers {
   active_type: "tanh"
   inputs {
     input_layer_name: "__simple_gru_0___transform@__simple_gru_0___recurrent_group"
+    input_parameter_name: "gru_param"
   }
   inputs {
     input_layer_name: "__simple_gru_0__+delay1@__simple_gru_0___recurrent_group"
-    input_parameter_name: "gru_param"
   }
   bias_parameter_name: "gru_bias"
   active_gate_type: "sigmoid"
@@ -105,10 +105,10 @@ layers {
   active_type: "tanh"
   inputs {
     input_layer_name: "__simple_gru_1___transform@__simple_gru_1___recurrent_group"
+    input_parameter_name: "gru_param"
   }
   inputs {
     input_layer_name: "__simple_gru_1__+delay1@__simple_gru_1___recurrent_group"
-    input_parameter_name: "gru_param"
   }
   bias_parameter_name: "gru_bias"
   active_gate_type: "sigmoid"

python/paddle/trainer_config_helpers/tests/configs/protostr/test_rnn_group.protostr

@@ -307,10 +307,10 @@ layers {
   active_type: "tanh"
   inputs {
     input_layer_name: "__mixed_1__@__gru_group_0___recurrent_group"
+    input_parameter_name: "___gru_group_0__@__gru_group_0___recurrent_group.w0"
   }
   inputs {
     input_layer_name: "__gru_group_0__+delay1@__gru_group_0___recurrent_group"
-    input_parameter_name: "___gru_group_0__@__gru_group_0___recurrent_group.w1"
   }
   bias_parameter_name: "___gru_group_0__@__gru_group_0___recurrent_group.wbias"
   active_gate_type: "sigmoid"
@@ -462,7 +462,7 @@ parameters {
   initial_smart: false
 }
 parameters {
-  name: "___gru_group_0__@__gru_group_0___recurrent_group.w1"
+  name: "___gru_group_0__@__gru_group_0___recurrent_group.w0"
   size: 30000
   initial_mean: 0.0
   initial_std: 0.1