Refine comments and function name

1. Add more comments and exmples
2. Rename repeat_lod to expand_lod
3. Remove unused head file

paddle/framework/lod_tensor.cc

@@ -103,28 +103,28 @@ void LoDTensor::ShrinkInLevel(size_t level, size_t elem_begin,
   lod_ = new_lod;
 }
 
-Vector<size_t> repeat_lod(Vector<size_t> data, Vector<size_t> starts,
+Vector<size_t> expand_lod(Vector<size_t> level, Vector<size_t> starts,
-                          Vector<size_t> times, bool is_first) {
+                          Vector<size_t> scales, bool repeat) {
   Vector<size_t> result;
-  result.push_back(data[0]);
+  result.push_back(level[0]);
   size_t p = 0, start = 0, end = 0;
-  if (is_first == true) {
+  if (!repeat) {
-    for (size_t i = 0; i < times.size(); ++i) {
+    for (size_t i = 0; i < scales.size(); ++i) {
-      result.push_back(result.back() + times[i] * (data[i + 1] - data[i]));
+      result.push_back(result.back() + scales[i] * (level[i + 1] - level[i]));
     }
   } else {
-    for (size_t i = 0; i < times.size(); ++i) {
+    for (size_t i = 0; i < scales.size(); ++i) {
-      while (starts[i] != data[p] && p < data.size()) {
+      while (starts[i] != level[p] && p < level.size()) {
         ++p;
       }
       start = p;
-      while (starts[i + 1] != data[p] && p < data.size()) {
+      while (starts[i + 1] != level[p] && p < level.size()) {
         ++p;
       }
       end = p + 1;
-      for (size_t j = 0; j < times[i]; ++j) {
+      for (size_t j = 0; j < scales[i]; ++j) {
         for (size_t index = start; index < end - 1; ++index) {
-          result.push_back(result.back() + data[index + 1] - data[index]);
+          result.push_back(result.back() + level[index + 1] - level[index]);
         }
       }
     }

paddle/framework/lod_tensor.h

@@ -15,9 +15,6 @@
 #pragma once
 
 #include <memory>
-#include "paddle/memory/memcpy.h"
-#include "paddle/platform/device_context.h"
-#include "paddle/platform/place.h"
 #ifdef PADDLE_WITH_CUDA
 #include <thrust/device_vector.h>
 #include <thrust/host_vector.h>
@@ -126,8 +123,8 @@ class LoDTensor : public Tensor {
   LoD lod_;
 };
 
-Vector<size_t> repeat_lod(Vector<size_t> data, Vector<size_t> starts,
+Vector<size_t> expand_lod(Vector<size_t> level, Vector<size_t> starts,
-                          Vector<size_t> times, bool is_first);
+                          Vector<size_t> scales, bool repeat);
 
 }  // namespace framework
 }  // namespace paddle

paddle/operators/seq_expand_op.cc

@@ -50,28 +50,68 @@ class SeqExpandOpMaker : public framework::OpProtoAndCheckerMaker {
   SeqExpandOpMaker(framework::OpProto* proto,
                    framework::OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    // TODO(wanghaoshuang): Add more comments
+    AddInput(
-    AddInput("X", "The input('X') of seq_expand op.");
+        "X",
-    AddInput("Y", "The reference input('Y') of seq_expand op.");
+        "The input('X') of seq_expand op. It can be LoDTensor or base Tensor.");
-    AddOutput("Out", "The output of seq_expand op.");
+    AddInput(
-    AddAttr<int>("repeat", "repeat times").SetDefault(0);
+        "Y",
+        "The reference input('Y') of seq_expand op."
+        "It must be a LoDTensor with k-level(k>0)."
+        "This reference input is essential if 'repeat' attribute is not "
+        "configured."
+        "Input(X) will be expanded by LoD of input(Y) while repeat ==  0.");
+    AddOutput("Out",
+              "The output of seq_expand op."
+              "The output is a (k+1)-level LoDTensor"
+              "while input(X) being k-level LoDTensor."
+              "(Given base tensor is 0-level LoDTensor.)");
+    AddAttr<int>("repeat",
+                 "(type:int; default value: 0)"
+                 "Repeatting times of each element while expanding input(X)."
+                 "It works while input(Y) is not configured.")
+        .SetDefault(0);
     AddComment(R"DOC(
-As an example:
+Expand k-level LoDTensor to (k+1)-level LoDTensor
+by lod of input(Y) or 'repeat' attribute.
 
-Given:
+Case 1:
-
-X.data = [1, 2 , 3, 4]
-X.lod = [[0, 3, 4], [0, 1, 3, 4]]
 
+Given a 2-level LoDTensor X:
+    X.data = [1, 2 , 3, 4]
+    X.lod = [[0, 3, 4], [0, 1, 3, 4]]
 and
-
+    repeat = 2
-repeat = 2
+then we get 3-level LoDTensor
-
+    Out.data = [1, 2, 3, 1, 2, 3, 4, 4]
-
+    Out.lod = [[0, 6, 8],
-then we get
+               [0, 3, 6, 7, 8],
-
+               [0, 1, 3, 4, 6, 7, 8]]
-Out.data = [1, 2, 3, 1, 2, 3, 4, 4]
+
-Out.lod = [[0, 6, 8], [0, 3, 6, 7, 8], [0, 1, 3, 4, 6, 7, 8]]
+Case 2:
+
+Given 2-level a LoDTensor X
+    X.data = [1, 2, 3, 4]
+    X.lod = [[0, 3, 4], [0, 1, 3, 4]]
+and
+    Y.lod = [[0, 6, 8],
+             [0, 3, 6, 7, 8],
+             [0,1,3,4,6,7,8]]
+then we get 3-level LoDTensor
+    Out.data = [1, 2, 3, 1, 2, 3, 4, 4]
+    Out.lod = [[0, 6, 8],
+               [0, 3, 6, 7, 8],
+               [0, 1, 3, 4, 6, 7, 8]]
+
+Case 3:
+
+Given a 0-level LoDTensor X
+    X.data = [1, 2, 3, 4]
+    X.lod = NULL
+and
+    repeat = 2
+then we get 1-level LoDTensor
+    Out.data = [1, 1, 2, 2, 3, 3, 4, 4]
+    Out.lod = [[0, 2, 4, 6, 8]]
 
 )DOC");
   }

paddle/operators/seq_expand_op.h

@@ -44,10 +44,10 @@ class SeqExpandKernel : public framework::OpKernel<T> {
     }
 
     size_t repeat = static_cast<size_t>(context.Attr<int>("repeat"));
-    framework::Vector<size_t> repeats;
+    framework::Vector<size_t> scales;
     if (repeat != 0) {
       for (int i = 0; i < x_lod[0].size() - 1; ++i) {
-        repeats.push_back(repeat);
+        scales.push_back(repeat);
       }
       std::vector<int64_t> dims = framework::vectorize(x->dims());
       dims[0] = dims[0] * repeat;
@@ -57,18 +57,18 @@ class SeqExpandKernel : public framework::OpKernel<T> {
       auto* y = context.Input<LoDTensor>("Y");
       auto y_lod = y->lod();
       for (int i = 0; i < y_lod[0].size() - 1; ++i) {
-        repeats.push_back((y_lod[0][i + 1] - y_lod[0][i]) /
+        scales.push_back((y_lod[0][i + 1] - y_lod[0][i]) /
-                          (x_lod[0][i + 1] - x_lod[0][i]));
+                         (x_lod[0][i + 1] - x_lod[0][i]));
       }
       out->Resize(y->dims());
     }
 
     framework::LoD out_lod;
-    auto level0 = framework::repeat_lod(x_lod[0], x_lod[0], repeats, true);
+    auto level0 = framework::expand_lod(x_lod[0], x_lod[0], scales, false);
     out_lod.push_back(level0);
     for (int i = 1; i < x_lod.size(); ++i) {
       out_lod.push_back(
-          framework::repeat_lod(x_lod[i], x_lod[0], repeats, false));
+          framework::expand_lod(x_lod[i], x_lod[0], scales, true));
     }
 
     size_t element_len = framework::product(x_dims) / x_dims[0];
@@ -77,9 +77,9 @@ class SeqExpandKernel : public framework::OpKernel<T> {
     // copy data
     Place place = boost::get<Place>(context.GetPlace());
     size_t count = 0;
-    for (size_t i = 0; i < repeats.size(); ++i) {
+    for (size_t i = 0; i < scales.size(); ++i) {
       count = element_len * (x_lod[0][i + 1] - x_lod[0][i]);
-      for (size_t j = 0; j < repeats[i]; ++j) {
+      for (size_t j = 0; j < scales[i]; ++j) {
         memory::Copy(place, out_data, place, x_data, sizeof(T) * count);
         out_data += count;
       }
@@ -95,9 +95,9 @@ class SeqExpandGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto* d_out = context.Input<LoDTensor>(framework::GradVarName("Out"));
-    auto* d_x = context.Output<LoDTensor>(framework::GradVarName("X"));
     auto* x = context.Input<LoDTensor>("X");
     auto* out = context.Input<LoDTensor>("Out");
+    auto* d_x = context.Output<LoDTensor>(framework::GradVarName("X"));
     auto out_lod = out->lod();
     d_x->set_lod(x->lod());
     const T* d_out_data = d_out->data<T>();