refine doc

paddle/fluid/operators/lookup_table_op.cc

@@ -34,8 +34,11 @@ class LookupTableOp : public framework::OperatorWithKernel {
     auto ids_dims = ctx->GetInputDim("Ids");
 
     auto ids_var_type = ctx->GetInputsVarType("Ids").front();
-    // ids_var_types also can be LOD_TENSOR_ARRAY, it's used as concat_rows.
-    // Maybe near future we will add concat_rows op.
+    // The type of Ids(Input) is SelectedRows or LoDTensor, when Ids's type
+    // is LoDTensor, this tensor contains the ids to be looked up in W
+    // and it must be a column vector with rank = 2 while the 2nd dimension
+    // size must be 1, when Ids's type is SelectedRows, the rows of Ids
+    // contains the ids to be looked up in W;
     if (ids_var_type == framework::proto::VarType::LOD_TENSOR) {
       PADDLE_ENFORCE_EQ(ids_dims.size(), 2);
       PADDLE_ENFORCE_EQ(ids_dims[1], 1);
@@ -59,17 +62,22 @@ class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
   LookupTableOpMaker(OpProto* proto, OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("W",
-             "An input represents embedding tensors, "
+             "(Tensor) The input represents embedding tensors, "
              "which is a learnable parameter.");
-    AddInput("Ids",
-             "An input with type int32 or int64 "
-             "contains the ids to be looked up in W. "
-             "Ids must be a column vector with rank = 2. "
-             "The 2nd dimension size must be 1.");
-    AddOutput("Out", "The lookup results, which have the same type as W.");
+    AddInput(
+        "Ids",
+        "(Tensor or SelectedRows) Ids's type can be Tensor or "
+        "SelectedRows, when Ids's type is Tensor, this tensor contains "
+        "the ids to be looked up in W and it must be a column vector with "
+        "rank = 2 while the 2nd dimension size must be 1; when Ids's type is "
+        "SelectedRows, the rows of Ids contains the ids to be looked up "
+        "in W.");
+    AddOutput("Out",
+              "(Tensor or SelectedRows) The lookup results, which have the "
+              "same type as W.");
     AddAttr<bool>("is_sparse",
                   "(boolean, default false) "
-                  "Sparse update")
+                  "Sparse update.")
         .SetDefault(false);
     AddAttr<int64_t>("padding_idx",
                      "(int64, default -1) "
@@ -81,10 +89,15 @@ class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
 Lookup Table Operator.
 
 This operator is used to perform lookups on the parameter W,
-then concatenated into a dense tensor.
+then concatenated into a dense or sparse tensor.
+
+The type of Ids(Input) is SelectedRows, Tensor or LoDTensor, when Ids's
+type is SelectedRows, the rows of Ids contains the ids to be looked up in W;
+when Ids's type is Tensor, this tensor contains the ids to be looked up in W
+and it must be a column vector with rank = 2 while the 2nd dimension size must be 1,
+at this time, Ids can carry the LoD (Level of Details) information, or not, and
+the output only shares the LoD information with input Ids.
 
-The input Ids can carry the LoD (Level of Details) information,
-or not. And the output only shares the LoD information with input Ids.
 
 )DOC");
   }

paddle/fluid/operators/lookup_table_op.cu

@@ -75,22 +75,22 @@ class LookupTableCUDAKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& context) const override {
     auto* table_t = context.Input<LoDTensor>("W");
     int64_t padding_idx = context.Attr<int64_t>("padding_idx");
-    auto* ids_var = context.InputVar("Ids");  // int tensor
+    auto* ids_var = context.InputVar("Ids");
+    Tensor* output_t = context.Output<Tensor>("Out");
 
     int64_t* ids;
     int64_t K;
-    framework::Tensor* output_t;
 
-    // ids_var_types also can be LOD_TENSOR_ARRAY, it's used as concat_rows.
-    // Maybe near future we will add concat_rows op.
+    // The type of Ids(Input) is SelectedRows or LoDTensor, when Ids's type
+    // is LoDTensor, this tensor contains the ids to be looked up in W;
+    // when Ids's type is SelectedRows, the rows of Ids contains the
+    // ids to be looked up in W.
     if (ids_var->IsType<framework::LoDTensor>()) {
       auto* ids_t = context.Input<LoDTensor>("Ids");
-      output_t = context.Output<LoDTensor>("Out");  // float tensor
       ids = const_cast<int64_t*>(ids_t->data<int64_t>());
       K = ids_t->numel();
     } else if (ids_var->IsType<framework::SelectedRows>()) {
       auto* ids_t = context.Input<framework::SelectedRows>("Ids");
-      output_t = context.Output<SelectedRows>("Out")->mutable_value();
       ids = const_cast<int64_t*>(ids_t->rows().CUDAData(context.GetPlace()));
       K = ids_t->rows().size();
       output_t->Resize({K, table_t->dims()[1]});

paddle/fluid/operators/lookup_table_op.h

@@ -30,23 +30,23 @@ template <typename T>
 class LookupTableKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    auto* table_t = context.Input<LoDTensor>("W");  // float tensor
-    auto* ids_var = context.InputVar("Ids");        // int tensor
+    auto* table_t = context.Input<LoDTensor>("W");
+    auto* ids_var = context.InputVar("Ids");
+    Tensor* output_t = context.Output<Tensor>("Out");
 
     int64_t* ids;
     int64_t ids_numel;
-    Tensor* output_t;
 
-    // ids_var_types also can be LOD_TENSOR_ARRAY, it's used as concat_rows.
-    // Maybe near future we will add concat_rows op.
+    // The type of Ids(Input) is SelectedRows or LoDTensor, when Ids's type
+    // is LoDTensor, this tensor contains the ids to be looked up in W;
+    // when Ids's type is SelectedRows, the rows of Ids contains the
+    // ids to be looked up in W.
     if (ids_var->IsType<LoDTensor>()) {
       auto* ids_t = context.Input<LoDTensor>("Ids");
-      output_t = context.Output<LoDTensor>("Out");
       ids = const_cast<int64_t*>(ids_t->data<int64_t>());
       ids_numel = ids_t->numel();
     } else if (ids_var->IsType<SelectedRows>()) {
       auto* ids_t = context.Input<SelectedRows>("Ids");
-      output_t = context.Output<SelectedRows>("Out")->mutable_value();
       ids = const_cast<int64_t*>(ids_t->rows().data());
       ids_numel = ids_t->rows().size();
       output_t->Resize({ids_numel, table_t->dims()[1]});