update the comments.

paddle/operators/reorder_lod_tensor_by_rank_op.cc

@@ -31,24 +31,40 @@ class ReorderLoDTensorByRankTableOpProtoMaker
              "Input(RankTable).");
     AddInput("RankTable",
              "(LoDRankTable), the rank table according to which Input(X) is "
-             "ordered.");
+             "reordered.");
     AddOutput("Out", "(LoDTensor), the reordered lod tensor.");
-    AddComment(R"DOC(ReorderLoDTensorByRankTable
+    AddComment(R"DOC(ReorderLoDTensorByRankTable operator.
 
-Reorder the Input(X) according to the information provided by Input(RankTable).
-For example, If the indices stored in the Input(RankTable) is [3, 0, 2, 1], the
+Input(X) is a batch of sequences. Input(RankTable) stores new orders of the
+input sequence batch. The reorder_lod_tensor_by_rank operator reorders the
+Input(X) according to the information provided by Input(RankTable).
+
+For example:
+
+If the indices stored in the Input(RankTable) is [3, 0, 2, 1], the
 Input(X) will be reordered that the forth sequence in Input(X) will become the
 first one, and then followed by the originally first, third, and the second one.
 
-NOTE: This operator sort Input(X) according to a given LoDRankTable which dose
+This is:
+X = [Seq0, Seq1, Seq2, Seq3]. The indices in RankTable are [3, 0, 2, 1].
+Out =  [Seq3, Seq0, Seq2, Seq1] with a new LoD information.
+
+If the LoD information of Input(X) is empty, this means Input(X) is not a
+sequcence. This is also identical to a batch of sequences, each sequence in
+which has a fixed length 1. In this case, the reorder_lod_tensor_by_rank operator
+reorders each slice of Input(X) along the first axis according to
+Input(RankTable).
+
+This is:
+X = [Slice0, Slice1, Slice2, Slice3] and its LoD information is empty. The
+indices in RankTable are [3, 0, 2, 1].
+Out = [Slice3, Slice0, Slice2, Slice1] with no LoD information is appended.
+
+NOTE: This operator sorts Input(X) according to a given LoDRankTable which dose
 not need to be calculated according to Input(X). It can be calculated according
-to any other different sequence, and then this operator sort Input(X) according
+to other different sequence, and then this operator sorts Input(X) according
 to the given LoDRankTable.
 
-For example:
-The X = [Seq0, Seq1, Seq2, Seq3]. The indices of RankTable are [3, 0, 2, 1].
-
-The Out =  [Seq3, Seq0, Seq2, Seq1] with new LoD information.
 )DOC");
   }
 };

paddle/operators/shrink_rnn_memory_op.cc

@@ -77,14 +77,15 @@ class ShrinkRNNMemoryOpProtoMaker : public framework::OpProtoAndCheckerMaker {
              "shrinked to match the size of the input of the index'th step.");
     AddOutput("Out", "(LoDTensor) The shrinked RNN step memory.");
     AddComment(R"DOC(
-This operator is used in dynamic RNN, which are able to handle variable-length
-sequences. In dynamic RNN, sequences in a mini-batch are sorted by its length
-first. After sorting, the longest sequence become the first sequence in the
-batch. Because of the multiple lengths, the size of each step input can be 
-different, which may lead to a mismatching between the input of
-the current step and the memory generated by the previous one. This 
-operator shrinks memory according to the size of the next step input, 
-to make sure that they can match each other.
+This operator is used to shrink output batch of memory defined in dynamic RNN.
+
+Dynamic RNN is able to handle variable-length sequences, in which, sequences in
+a mini-batch are sorted by its length first. After that, the longest sequence
+becomes the first one in the sorted batch, followed by the second longest, the
+third longest, and so on. Dynamic RNN then slices a batch input timestep by
+timestep from the sorted input. Once any sequence in the input batch reaches its
+end, memory defined in dynamicRNN has to shrink its outputs to adapt to the input
+batch size for the next time step.
 )DOC");
   }
 };

python/paddle/v2/fluid/layers/control_flow.py

@@ -687,11 +687,10 @@ def topk(input, k):
 
 
 def lod_tensor_to_array(x, table):
-    """This function performs the operation that converts an LOD_Tensor to
-       an array.
+    """ Convert a LOD_TENSOR_ARRAY to an TensorArray.
 
     Args:
-        x (Variable|list): The tensor that needs to be converted to an array.
+        x (Variable|list): The LoD tensor to be converted to a LoD tensor array.
         table (ParamAttr|list): The variable that stores the level of lod
                                 which is ordered by sequence length in
                                 descending order.
@@ -721,10 +720,10 @@ def lod_tensor_to_array(x, table):
 
 
 def array_to_lod_tensor(x, table):
-    """This function converts an LOD_TENSOR_ARRAY to an LODTensor.
+    """Convert a LoD_Tensor_Aarry to an LoDTensor.
 
     Args:
-        x (Variable|list): The array that needs to be converted to a tensor.
+        x (Variable|list): The LoD Tensor Array to be converted to a tensor.
         table (ParamAttr|list): The variable that stores the level of lod
                                 which is ordered by sequence length in
                                 descending order.
@@ -752,7 +751,8 @@ def array_to_lod_tensor(x, table):
 
 
 def increment(x, value=1.0, in_place=True):
-    """This function performs an operation that increments each value in the
+    """
+    This function performs an operation that increments each value in the
     input :math:`x` by an amount: :math:`value` as mentioned in the input
     parameter. This operation is performed in-place by default.
 
@@ -785,8 +785,9 @@ def increment(x, value=1.0, in_place=True):
 
 
 def array_write(x, i, array=None):
-    """This function writes the given input variable to the specifict position
-    which is indicated by the arrary index to an output LOD_TENSOR_ARRAY. If the
+    """
+    This function writes the given input variable to the specified position
+    indicating by the arrary index to an output LOD_TENSOR_ARRAY. If the
     output LOD_TENSOR_ARRAY is not given(None), a new one will be created and
     returned.
 

python/paddle/v2/fluid/layers/tensor.py

@@ -146,10 +146,10 @@ def fill_constant(shape, dtype, value, out=None):
     """
     **fill_constant**
 
-    This function creates a tensor with the specified *shape* and
-    *dtype*, and initializes it with the constant given by *value*.
+    This function creates a tensor with the specified `shape` and
+    `dtype`, and initializes it with the constant specifed by `value`.
 
-    The attribute *stop_gradient* of the created tensor is set to True.
+    The attribute `stop_gradient` of the created tensor is set to True.
 
     Args:
         shape(tuple|list|None): Shape of the output tensor.
@@ -166,6 +166,7 @@ def fill_constant(shape, dtype, value, out=None):
 
           data = fluid.layers.fill_constant(shape=[1], value=0, dtype='int64')
     """
+
     helper = LayerHelper("fill_constant", **locals())
     if out is None:
         out = helper.create_tmp_variable(dtype=dtype)