add config functions for kmax_sequence_score layer.

python/paddle/trainer/config_parser.py

@@ -3223,6 +3223,16 @@ class CTCLayer(LayerBase):
         config_assert(len(self.inputs) == 2, 'CTCLayer must have 2 inputs')
 
 
+@config_layer('kmax_seq_score')
+class KmaxSeqScoreLayer(LayerBase):
+    def __init__(self, name, inputs, beam_size, **xargs):
+        super(KmaxSeqScoreLayer, self).__init__(
+            name, 'kmax_seq_score', 0, inputs=inputs, **xargs)
+        config_assert(
+            len(self.inputs) == 1, 'KmaxSeqScoreLayer has only one input.')
+        self.config.beam_size = beam_size
+
+
 @config_layer('warp_ctc')
 class WarpCTCLayer(LayerBase):
     def __init__(self,

python/paddle/trainer_config_helpers/layers.py

@@ -131,6 +131,7 @@ __all__ = [
     'crop_layer',
     'clip_layer',
     'slice_projection',
+    'kmax_sequence_score_layer',
 ]
 
 
@@ -226,6 +227,8 @@ class LayerType(object):
     CROP_LAYER = 'crop'
     CLIP_LAYER = 'clip'
 
+    KMAX_SEQ_SCORE = 'kmax_seq_score'
+
     @staticmethod
     def is_layer_type(type_name):
         """
@@ -6119,3 +6122,22 @@ def clip_layer(input, min, max, name=None):
         max=max)
     return LayerOutput(
         name, LayerType.CLIP_LAYER, parents=[input], size=input.size)
+
+
+@wrap_name_default()
+@layer_support()
+def kmax_sequence_score_layer(input, name=None, beam_size=1):
+    assert isinstance(input, LayerOutput), ("kmax_sequence_score_layer "
+                                            "accept only one input.")
+    assert input.size == 1, (
+        "input of kmax_sequence_score_layer is a score"
+        "over a sequence or a nested sequence, so its width must be 1.")
+
+    Layer(
+        name=name,
+        type=LayerType.KMAX_SEQ_SCORE,
+        inputs=[input.name],
+        beam_size=beam_size)
+
+    return LayerOutput(
+        name, LayerType.KMAX_SEQ_SCORE, parents=[input], size=input.size)

python/paddle/trainer_config_helpers/tests/configs/file_list.sh

@@ -7,6 +7,7 @@ test_rnn_group shared_fc shared_lstm shared_gru test_cost_layers_with_weight
 test_spp_layer test_bilinear_interp test_maxout test_bi_grumemory math_ops
 test_seq_concat_reshape test_pad test_smooth_l1 test_multiplex_layer
 test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_layer
-test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer)
+test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer,
+test_kmax_seq_socre_layer)
 
 export whole_configs=(test_split_datasource)

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

@@ -0,0 +1,66 @@
+type: "nn"
+layers {
+  name: "input"
+  type: "data"
+  size: 300
+  active_type: ""
+}
+layers {
+  name: "data"
+  type: "data"
+  size: 128
+  active_type: ""
+}
+layers {
+  name: "__fc_layer_0__"
+  type: "fc"
+  size: 1
+  active_type: "exponential"
+  inputs {
+    input_layer_name: "data"
+    input_parameter_name: "___fc_layer_0__.w0"
+  }
+  bias_parameter_name: "___fc_layer_0__.wbias"
+}
+layers {
+  name: "__kmax_sequence_score_layer_0__"
+  type: "kmax_seq_score"
+  active_type: ""
+  inputs {
+    input_layer_name: "__fc_layer_0__"
+  }
+  beam_size: 5
+}
+parameters {
+  name: "___fc_layer_0__.w0"
+  size: 128
+  initial_mean: 0.0
+  initial_std: 0.0883883476483
+  dims: 128
+  dims: 1
+  initial_strategy: 0
+  initial_smart: true
+}
+parameters {
+  name: "___fc_layer_0__.wbias"
+  size: 1
+  initial_mean: 0.0
+  initial_std: 0.0
+  dims: 1
+  dims: 1
+  initial_strategy: 0
+  initial_smart: false
+}
+input_layer_names: "data"
+output_layer_names: "__kmax_sequence_score_layer_0__"
+sub_models {
+  name: "root"
+  layer_names: "input"
+  layer_names: "data"
+  layer_names: "__fc_layer_0__"
+  layer_names: "__kmax_sequence_score_layer_0__"
+  input_layer_names: "data"
+  output_layer_names: "__kmax_sequence_score_layer_0__"
+  is_recurrent_layer_group: false
+}
+