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paddle/operators/reorder_lod_tensor_by_rank_op.cc

@@ -19,21 +19,28 @@
 namespace paddle {
 namespace operators {
 
-class ReorderLoDTensorProtoMaker : public framework::OpProtoAndCheckerMaker {
+class ReorderLoDTensorByRankTableOpProtoMaker
+    : public framework::OpProtoAndCheckerMaker {
  public:
-  ReorderLoDTensorProtoMaker(OpProto *proto, OpAttrChecker *op_checker)
+  ReorderLoDTensorByRankTableOpProtoMaker(OpProto *proto,
+                                          OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "(LoDTensor) the input lod tensor need to be reordered.");
     AddInput("RankTable",
              "(LoDRankTable) the rank table that input need follow");
     AddOutput("Out", "(LoDTensor) reordered lod tensor");
-    AddComment(R"DOC(ReorderLoDTensorLoDRankTable
+    AddComment(R"DOC(ReorderLoDTensorByRankTable
 
 Reorder the input X by the rank of `RankTable`. If `RankTable` is ordered by
 index [3, 0, 2, 1]. Input X will reorder its sequence, the third sequence of
 X will be the first sequence of Output.
 
 NOTE: The RankTable does not need to be calculated by X.
+
+For example:
+The X = [Seq0, Seq1, Seq2, Seq3]. The indices of RankTable are [3, 0, 2, 1].
+
+The Out =  [Seq3, Seq0, Seq2, Seq1] with correct LoD information.
 )DOC");
   }
 };
@@ -146,8 +153,9 @@ class ReorderLoDTensorByRankTableOp : public ReorderLoDTensorByRankTableBase {
     size_t out_offset = 0;
     out->mutable_lod()->clear();
     for (auto &item : rank_table.items()) {
-      out_offset = this->CopyTensorAndLod(dev_ctx, absolute_table[item.index],
-                                          x, out, out_offset);
+      PADDLE_ENFORCE_LT(item.index, absolute_table.size());
+      out_offset = CopyTensorAndLod(dev_ctx, absolute_table[item.index], x, out,
+                                    out_offset);
     }
   }
 };
@@ -220,6 +228,7 @@ namespace ops = paddle::operators;
 REGISTER_OPERATOR(reorder_lod_tensor_by_rank,
                   ops::ReorderLoDTensorByRankTableOp,
                   ops::ReorderLodTensorByRankGradOpMaker,
-                  ops::ReorderLoDTensorProtoMaker, ops::IdentityInferShape);
+                  ops::ReorderLoDTensorByRankTableOpProtoMaker,
+                  ops::IdentityInferShape);
 REGISTER_OPERATOR(reorder_lod_tensor_by_rank_grad,
                   ops::ReorderLoDTensorByRankGradOp, ops::IdentityInferShape);

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

@@ -3,6 +3,7 @@ from ..framework import Program, Variable, Operator
 from .. import core
 from tensor import assign, fill_constant
 import contextlib
+from ..registry import autodoc
 
 __all__ = [
     'split_lod_tensor', 'merge_lod_tensor', 'BlockGuard', 'StaticRNNGuard',
@@ -983,16 +984,8 @@ class DynamicRNN(object):
                 method))
 
 
+@autodoc
 def reorder_lod_tensor_by_rank(x, rank_table):
-    """
-    
-    Args:
-        x(Variable): 
-        rank_table(Variable): 
-
-    Returns:
-
-    """
     helper = LayerHelper('reorder_lod_tensor_by_rank', **locals())
     helper.is_instance('x', Variable)
     helper.is_instance('rank_table', Variable)

python/paddle/v2/fluid/registry.py

@@ -8,7 +8,7 @@ import proto.framework_pb2 as framework_pb2
 from framework import OpProtoHolder, Variable, Program, Operator
 from paddle.v2.fluid.layer_helper import LayerHelper, unique_name
 
-__all__ = ['deprecated', 'register_layer']
+__all__ = ['deprecated', 'register_layer', 'autodoc']
 
 
 def _convert_(name):
@@ -175,12 +175,18 @@ def deprecated(func_or_class):
         """
         Wrap func with deprecated warning
         """
-        warnings.simplefilter('always', DeprecationWarning)  #turn off filter
+        warnings.simplefilter('always', DeprecationWarning)  # turn off filter
         warnings.warn(
             "Call to deprecated function {}.".format(func.__name__),
             category=DeprecationWarning,
             stacklevel=2)
-        warnings.simplefilter('default', DeprecationWarning)  #reset filter
+        warnings.simplefilter('default', DeprecationWarning)  # reset filter
         return func(*args, **kwargs)
 
     return func_wrapper
+
+
+def autodoc(func):
+    func.__doc__ = _generate_doc_string_(OpProtoHolder.instance().get_op_proto(
+        func.__name__))
+    return func