follow comments.

paddle/gserver/layers/KmaxSeqScoreLayer.cpp

@@ -97,19 +97,13 @@ void KmaxSeqScoreLayer::forward(PassType passType) {
     scores_ = inputScore;
   }
 
-  Matrix::resizeOrCreate(
-      output_.value,
-      input.hasSubseq() ? input.getNumSubSequences() : input.getNumSequences(),
-      beamSize_,
-      false,
-      false);
+  int seqNum =
+      input.hasSubseq() ? input.getNumSubSequences() : input.getNumSequences();
+  Matrix::resizeOrCreate(output_.value, seqNum, beamSize_, false, false);
   output_.value->one();
   output_.value->mulScalar(-1.);
 
-  kmaxScorePerSeq(scores_->getData(),
-                  output_.value->getData(),
-                  input.hasSubseq() ? input.subSequenceStartPositions
-                                    : input.sequenceStartPositions);
+  kmaxScorePerSeq(scores_->getData(), output_.value->getData(), seqNum);
 }
 
 void KmaxSeqScoreLayer::backward(const UpdateCallback& callback) {}

python/paddle/trainer_config_helpers/layers.py

@@ -6129,7 +6129,7 @@ def clip_layer(input, min, max, name=None):
 @layer_support()
 def kmax_sequence_score_layer(input, name=None, beam_size=1):
     """
-    This layer accepts one input which is scores over a sequence or a nested
+    This layer accepts one input which are scores over a sequence or a nested
     sequence, and returns indices of beam_size sequences with highest scores.
 
     .. code-block:: python
@@ -6139,7 +6139,7 @@ def kmax_sequence_score_layer(input, name=None, beam_size=1):
 
     :param name: The Layer Name.
     :type name: basestring
-    :param input: The input layer. It is scores over a sequence or a nested
+    :param input: The input layer. It stores scores over a sequence or a nested
         sequence and its size must be 1.
     :type input: LayerOutput.
     :param beam_size: squence indices with top beam_size scores are returned.