Merge pull request #9100 from pkuyym/fix-9049

Enhance sequence_expand operator

paddle/fluid/operators/math/math_function.cc

@@ -371,6 +371,8 @@ template struct RowwiseAdd<platform::CPUDeviceContext, double>;
 
 template struct ColwiseSum<platform::CPUDeviceContext, float>;
 template struct ColwiseSum<platform::CPUDeviceContext, double>;
+template struct ColwiseSum<platform::CPUDeviceContext, int>;
+template struct ColwiseSum<platform::CPUDeviceContext, int64_t>;
 
 template struct RowwiseSum<platform::CPUDeviceContext, float>;
 template struct RowwiseSum<platform::CPUDeviceContext, double>;

paddle/fluid/operators/math/math_function.cu

@@ -422,6 +422,8 @@ struct RowwiseAdd<platform::CUDADeviceContext, T> {
 template struct RowwiseAdd<platform::CUDADeviceContext, float>;
 template struct RowwiseAdd<platform::CUDADeviceContext, double>;
 template struct ColwiseSum<platform::CUDADeviceContext, float>;
+template struct ColwiseSum<platform::CUDADeviceContext, int>;
+template struct ColwiseSum<platform::CUDADeviceContext, int64_t>;
 // template struct ColwiseSum<platform::CUDADeviceContext, double>;
 // The ColwiseSum<platform::CUDADeviceContext, double> failed in debug mode,
 // and only failed for this case. So reimplemented it.

paddle/fluid/operators/sequence_expand_op.cc

@@ -17,7 +17,7 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
-using framework::Tensor;
+using framework::LoDTensor;
 
 class SequenceExpandOp : public framework::OperatorWithKernel {
  public:
@@ -25,15 +25,71 @@ class SequenceExpandOp : public framework::OperatorWithKernel {
 
  protected:
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"));
-    PADDLE_ENFORCE(ctx->HasOutput("Out"));
-    PADDLE_ENFORCE(ctx->HasInput("Y"));
-    framework::DDim out_dim;
-    auto y_dim = ctx->GetInputDim("Y");
-    out_dim = ctx->GetInputDim("X");
-    out_dim[0] = y_dim[0];
-    ctx->ShareLoD("Y", "Out");
-    ctx->SetOutputDim("Out", out_dim);
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of SequenceExpandOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Y"),
+                   "Input(Y) of SequenceExpandOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of SequenceExpandOp should not be null.");
+
+    auto x_dims = ctx->GetInputDim("X");
+    auto out_dims = x_dims;
+    int ref_level = ctx->Attrs().Get<int>("ref_level");
+
+    PADDLE_ENFORCE_GE(x_dims.size(), 2,
+                      "Dimension number of Input(X) should be at least 2.");
+
+    if (ctx->IsRuntime()) {
+      framework::Variable* x_var =
+          boost::get<framework::Variable*>(ctx->GetInputVarPtrs("X")[0]);
+      framework::Variable* y_var =
+          boost::get<framework::Variable*>(ctx->GetInputVarPtrs("Y")[0]);
+
+      auto& x_lod = x_var->Get<LoDTensor>().lod();
+      auto& y_lod = y_var->Get<LoDTensor>().lod();
+
+      PADDLE_ENFORCE_LE(x_lod.size(), 1,
+                        "Level number of Input(X)'s lod should not be "
+                        "greater than 1.");
+      PADDLE_ENFORCE_GT(y_lod.size(), 0,
+                        "Level number of Input(Y)'s lod should be "
+                        "greater than 0.");
+      PADDLE_ENFORCE(
+          ref_level == -1 ||
+              (ref_level >= 0 && ref_level < static_cast<int>(y_lod.size())),
+          "Invlid `ref_level`, which should be either equal to -1 "
+          "or in [0, %d)",
+          y_lod.size());
+
+      if (ref_level == -1) ref_level = y_lod.size() - 1;
+
+      if (x_lod.size() > 0) {
+        PADDLE_ENFORCE(x_lod[0].size() == y_lod[ref_level].size(),
+                       "Level number of Input(X)'s lod could be 0. Otherwise "
+                       "size of Input(X)'s first level lod should be equal to "
+                       "size of Input(Y)'s referred level lod.");
+      }
+
+      int64_t out_first_dim = 0;
+      if (y_lod[ref_level].size() <= 1) {
+        out_first_dim = x_dims[0];
+      } else {
+        for (size_t i = 1; i < y_lod[ref_level].size(); ++i) {
+          int x_seq_len = 1;
+          if (x_lod.size() == 1) {
+            x_seq_len = x_lod[0][i] - x_lod[0][i - 1];
+          }
+          out_first_dim +=
+              (y_lod[ref_level][i] - y_lod[ref_level][i - 1]) * x_seq_len;
+        }
+      }
+      out_dims[0] = out_first_dim;
+      ctx->SetOutputDim("Out", out_dims);
+    } else {
+      out_dims[0] = -1;
+      ctx->SetOutputDim("Out", out_dims);
+      ctx->ShareLoD("X", /*->*/ "Out");
+    }
   }
 };
 
@@ -42,83 +98,81 @@ class SequenceExpandOpMaker : public framework::OpProtoAndCheckerMaker {
   SequenceExpandOpMaker(OpProto* proto, OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X",
-             "(Tensor or LoDTensor) The input(X) of this operator can be a "
-             "LoDTensor or a base Tensor.");
+             "(LoDTensor, default LoDTensor<float>) A 2-D LoDTensor whose lod "
+             "level is at most 1.");
     AddInput("Y",
-             "(LoDTensor)The reference input(Y) of sequence_expand op."
-             "It must be a LoDTensor with k-level(k>0)."
-             "The input(X) will be expanded according to LOD of input(Y)."
-             "The element numbers of last level in input(Y) "
-             "must be equal to dims[0] of input(X).");
+             "(LoDTensor, default LoDTensor<float>) Referred LoDTensor whose "
+             "lod (specified level) is referred by Input(X).");
     AddOutput("Out",
-              "(LodTensor)The output of sequence_expand op."
-              "The lod of output will be as same as input(Y)'s lod.");
+              "(LodTensor, default LoDTensor<float>) Output LoDTensor which is "
+              "generated from Input(X) by referring lod of Input(Y).");
+    AddAttr<int>("ref_level", "Specify lod level of Input(Y).").SetDefault(-1);
     AddComment(R"DOC(
 Sequence Expand Operator.
 
-This operator expands input(X) according to LOD of input(Y).
+This operator expands `X` according to specified level lod of `Y`. Current
+implementation constaints that lod level of `X` should be at most 1. Attribute
+`ref_level` is used to specify which level lod of `Y` is referred to expand `X`.
+If set `ref_level` to -1, then last level lod of `Y` would be referred.
+Please note, rank of `X` should be at least 2, when the rank exceeds 2, `X`
+would be viewed as a 2-D tensor.
+
 Following are cases to better explain how this works:
+
 Case 1:
 
-Given a 2-level LoDTensor input(X)
-    X.lod = [[0,       2, 3],
-             [0, 1,    3, 4]]
-    X.data = [a, b, c, d]
+Given a 1-level LoDTensor input(X)
+    X.lod =  [[0,   2,        4]]
+    X.data = [[a], [b], [c], [d]]
     X.dims = [4, 1]
 and input(Y)
     Y.lod = [[0,    2,    4],
              [0, 3, 6, 7, 8]]
-with condition len(Y.lod[-1]) -1 == X.dims[0]
-then we get 2-level LoDTensor
-    Out.lod = [[0,                2,    4],
-               [0,       3,       6, 7, 8]]
-    Out.data = [a, a, a, b, b, b, c, d]
+ref_level: 0
+then we get 1-level LoDTensor
+    Out.lod =  [[0,   2,        4,        6,        8]]
+    Out.data = [[a], [b], [a], [b], [c], [d], [c], [d]]
     Out.dims = [8, 1]
 
 Case 2:
 
+Given 1-level LoDTensor input(X)
+    X.lod =  [[0,   1,        4]]
+    X.data = [[a], [b], [c], [d]]
+    X.dims = [4, 1]
+and input(Y)
+    Y.lod = [[0,    2,    4],
+             [0, 3, 6, 6, 8]]
+ref_level: 0
+then we get 1-level LoDTensor
+    Out.lod =  [[0,   1,   2,        5,             8]]
+    Out.data = [[a], [a], [b], [c], [d], [b], [c], [d]]
+    Out.dims = [8, 1]
+
+Case 3:
+
 Given a common Tensor input(X)
-    X.data = [a, b, c]
+    X.data = [[a], [b], [c]]
     X.dims = [3, 1]
 and input(Y)
     Y.lod = [[0, 2, 3, 6]]
-with condition len(Y.lod[-1]) -1 == X.dims[0]
-then we get 1-level LoDTensor
-    Out.lod = [[0,    2, 3,      6]]
-    Out.data = [a, a, b, c, c, c]
+ref_level: -1
+then we get a common Tensor
+    Out.data = [[a], [a], [b], [c], [c], [c]]
     Out.dims = [6, 1]
 
-Case 3:
+Case 4:
 
 Given a common Tensor input(X)
     X.data = [[a, b], [c, d], [e, f]]
     X.dims = [3, 2]
 and input(Y)
     Y.lod = [[0, 2, 3, 6]]
-with condition len(Y.lod[-1]) -1 == X.dims[0]
-then we get 1-level LoDTensor
-    Out.lod = [[0,           2,     3,                     6]]
-    Out.data = [[a,b], [a,b] [c,d], [e, f], [e, f], [e, f]]
+ref_level: 0
+then we get a common LoDTensor
+    Out.data = [[a, b], [a, b] [c, d], [e, f], [e, f], [e, f]]
     Out.dims = [6, 2]
 
-Case 4:
-
-Given 2-level a LoDTensor input(X)
-    X.lod = [[0,       2, 3],
-             [0, 1,    3, 4]]
-    X.data = [a, b, c, d]
-    X.dims = [4, 1]
-and input(Y)
-    Y.lod = [[0,    2,    4],
-             [0, 3, 6, 6, 8]]
-with condition len(Y.lod[-1]) -1 == X.dims[0]
-then we get 2-level LoDTensor
-    Out.lod = [[0,                2,    4],
-               [0,       3,       6, 6, 8]]
-    Out.data = [a, a, a, b, b, b, d, d]
-    Out.dims = [8, 1]
-
-
 )DOC");
   }
 };
@@ -129,12 +183,14 @@ class SequenceExpandOpGrad : public framework::OperatorWithKernel {
 
  protected:
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"));
-    PADDLE_ENFORCE(ctx->HasInput("Out"));
+    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Out"), "Input(Out) should not be null.");
     PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
-                   "The input(Out@GRAD) should not be null");
+                   "Input(Out@GRAD) should not be null.");
+
     auto x_dims = ctx->GetInputDim("X");
     auto x_grad_name = framework::GradVarName("X");
+
     if (ctx->HasOutput(x_grad_name)) {
       ctx->SetOutputDim(x_grad_name, x_dims);
     }
@@ -149,7 +205,13 @@ REGISTER_OP(sequence_expand, ops::SequenceExpandOp, ops::SequenceExpandOpMaker,
             sequence_expand_grad, ops::SequenceExpandOpGrad);
 REGISTER_OP_CPU_KERNEL(
     sequence_expand,
-    ops::SequenceExpandKernel<paddle::platform::CPUDeviceContext, float>);
+    ops::SequenceExpandKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::SequenceExpandKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::SequenceExpandKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::SequenceExpandKernel<paddle::platform::CPUDeviceContext, int64_t>);
 REGISTER_OP_CPU_KERNEL(
     sequence_expand_grad,
-    ops::SequenceExpandGradKernel<paddle::platform::CPUDeviceContext, float>);
+    ops::SequenceExpandGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::SequenceExpandGradKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::SequenceExpandGradKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::SequenceExpandGradKernel<paddle::platform::CPUDeviceContext, int64_t>);

paddle/fluid/operators/sequence_expand_op.cu

@@ -18,7 +18,14 @@ limitations under the License. */
 namespace ops = paddle::operators;
 REGISTER_OP_CUDA_KERNEL(
     sequence_expand,
-    ops::SequenceExpandKernel<paddle::platform::CUDADeviceContext, float>);
+    ops::SequenceExpandKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::SequenceExpandKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::SequenceExpandKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::SequenceExpandKernel<paddle::platform::CUDADeviceContext, int64_t>);
 REGISTER_OP_CUDA_KERNEL(
     sequence_expand_grad,
-    ops::SequenceExpandGradKernel<paddle::platform::CUDADeviceContext, float>);
+    ops::SequenceExpandGradKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::SequenceExpandGradKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::SequenceExpandGradKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::SequenceExpandGradKernel<paddle::platform::CUDADeviceContext,
+                                  int64_t>);

paddle/fluid/operators/sequence_expand_op.h

@@ -16,45 +16,75 @@ limitations under the License. */
 
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/memory/memcpy.h"
-#include "unsupported/Eigen/CXX11/Tensor"
+#include "paddle/fluid/operators/math/math_function.h"
 
 namespace paddle {
 namespace operators {
 
 using LoDTensor = framework::LoDTensor;
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
 
 template <typename DeviceContext, typename T>
 class SequenceExpandKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto* x = context.Input<LoDTensor>("X");
-    auto* out = context.Output<LoDTensor>("Out");
-    const T* x_data = x->data<T>();
-    auto x_dims = x->dims();
     auto* y = context.Input<LoDTensor>("Y");
-    PADDLE_ENFORCE(!y->lod().empty(), "y should have lod");
-    PADDLE_ENFORCE_EQ(static_cast<size_t>(x_dims[0]),
-                      y->lod().back().size() - 1,
-                      "The size of last lod level in Input(Y)"
-                      "must be equal to dims[0] of Input(X).");
-    out->set_lod(y->lod());
-    auto* place =
-        context.template device_context<DeviceContext>().eigen_device();
-    size_t element_len = framework::product(x_dims) / x_dims[0];
-    T* out_data = out->mutable_data<T>(context.GetPlace());
-    auto out_starts = out->lod().back();
-
-    for (size_t i = 0; i < out_starts.size() - 1; i++) {
-      int scale = out_starts[i + 1] - out_starts[i];
-      Eigen::TensorMap<
-          Eigen::Tensor<const T, 2, Eigen::RowMajor, Eigen::DenseIndex>>
-          x_t(x_data, 1, element_len);
-      Eigen::TensorMap<Eigen::Tensor<T, 2, Eigen::RowMajor, Eigen::DenseIndex>>
-          out_t(out_data, scale, element_len);
-      Eigen::array<int, 2> cast({{scale, 1}});
-      out_t.device(*place) = x_t.broadcast(cast);
-      x_data += element_len;
-      out_data += element_len * scale;
+    auto* out = context.Output<LoDTensor>("Out");
+
+    int ref_level = context.Attr<int>("ref_level");
+    auto& x_lod = x->lod();
+    auto& y_lod = y->lod();
+
+    if (ref_level == -1) ref_level = y_lod.size() - 1;
+
+    out->mutable_data<T>(context.GetPlace());
+
+    if (y_lod[ref_level].size() <= 1) {
+      framework::TensorCopy(*x, context.GetPlace(), out);
+      return;
+    }
+
+    auto& out_lod = *out->mutable_lod();
+    if (x_lod.size() == 1) {
+      out_lod.resize(1);
+      out_lod[0] = {0};
+    }
+
+    int out_offset = 0;
+    auto& eigen_place =
+        *context.template device_context<DeviceContext>().eigen_device();
+    for (size_t i = 1; i < y_lod[ref_level].size(); ++i) {
+      int repeat_num = y_lod[ref_level][i] - y_lod[ref_level][i - 1];
+      int x_start = i - 1;
+      int x_end = i;
+      if (x_lod.size() == 1) {
+        x_start = x_lod[0][i - 1];
+        x_end = x_lod[0][i];
+      }
+      int x_seq_len = x_end - x_start;
+      if (repeat_num > 0) {
+        auto x_sub_tensor = x->Slice(x_start, x_end);
+        x_sub_tensor.Resize({1, x_sub_tensor.numel()});
+        int out_start = out_offset;
+        if (x_lod.size() == 1) {
+          out_start = out_lod[0][out_offset];
+        }
+        auto out_sub_tensor =
+            out->Slice(out_start, out_start + x_seq_len * repeat_num);
+        out_sub_tensor.Resize({repeat_num, x_sub_tensor.dims()[1]});
+        EigenMatrix<T>::From(out_sub_tensor).device(eigen_place) =
+            EigenMatrix<T>::From(x_sub_tensor)
+                .broadcast(Eigen::array<int, 2>({{repeat_num, 1}}));
+      }
+      for (int j = 0; j < repeat_num; ++j) {
+        if (x_lod.size() == 1) {
+          out_lod[0].push_back(out_lod[0].back() + x_seq_len);
+        }
+        out_offset++;
+      }
     }
   }
 };
@@ -75,27 +105,51 @@ template <typename DeviceContext, typename T>
 class SequenceExpandGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    auto* d_out = context.Input<LoDTensor>(framework::GradVarName("Out"));
+    auto* g_out = context.Input<LoDTensor>(framework::GradVarName("Out"));
     auto* x = context.Input<LoDTensor>("X");
-    auto* out = context.Input<LoDTensor>("Out");
-    auto* d_x = context.Output<LoDTensor>(framework::GradVarName("X"));
-    auto out_last_level = out->lod().back();
-    d_x->set_lod(x->lod());
-    const T* d_out_data = d_out->data<T>();
-    T* d_x_data = d_x->mutable_data<T>(context.GetPlace());
-    size_t element_len = d_out->numel() / d_out->dims()[0];
-    for (size_t i = 0; i < out_last_level.size() - 1; ++i) {
-      size_t repeat = out_last_level[i + 1] - out_last_level[i];
-      Eigen::TensorMap<
-          Eigen::Tensor<const T, 2, Eigen::RowMajor, Eigen::DenseIndex>>
-      d_out_t(d_out_data, static_cast<int>(repeat), element_len);
-      Eigen::TensorMap<Eigen::Tensor<T, 1, Eigen::RowMajor, Eigen::DenseIndex>>
-      d_x_t(d_x_data, static_cast<int>(element_len));
-      auto place =
-          context.template device_context<DeviceContext>().eigen_device();
-      d_x_t.device(*place) = d_out_t.sum(Eigen::array<int, 1>({{0}}));
-      d_out_data += (repeat * element_len);
-      d_x_data += element_len;
+    auto* y = context.Input<LoDTensor>("Y");
+    auto* g_x = context.Output<LoDTensor>(framework::GradVarName("X"));
+    int ref_level = context.Attr<int>("ref_level");
+
+    g_x->mutable_data<T>(context.GetPlace());
+    g_x->set_lod(x->lod());
+
+    auto& x_lod = x->lod();
+    auto& y_lod = y->lod();
+
+    if (ref_level == -1) ref_level = y_lod.size() - 1;
+
+    // just copy the gradient
+    if (y_lod[ref_level].size() <= 1) {
+      framework::TensorCopy(*g_out, context.GetPlace(), g_x);
+      return;
+    }
+
+    auto& dev_ctx = context.template device_context<DeviceContext>();
+
+    math::SetConstant<DeviceContext, T> set_zero;
+    set_zero(dev_ctx, g_x, static_cast<T>(0));
+
+    int g_out_offset = 0;
+    for (size_t i = 1; i < y_lod[ref_level].size(); ++i) {
+      int repeat_num = y_lod[ref_level][i] - y_lod[ref_level][i - 1];
+      if (repeat_num > 0) {
+        int x_start = i - 1;
+        int x_end = i;
+        if (x_lod.size() == 1) {
+          x_start = x_lod[0][i - 1];
+          x_end = x_lod[0][i];
+        }
+        int x_seq_len = x_end - x_start;
+        auto g_x_sub = g_x->Slice(x_start, x_end);
+        g_x_sub.Resize(flatten_to_1d(g_x_sub.dims()));
+        int g_out_end = g_out_offset + repeat_num * x_seq_len;
+        auto g_out_sub = g_out->Slice(g_out_offset, g_out_end);
+        g_out_sub.Resize({repeat_num, g_x_sub.dims()[0]});
+        math::ColwiseSum<DeviceContext, T> col_sum;
+        col_sum(dev_ctx, g_out_sub, &g_x_sub);
+        g_out_offset += repeat_num * x_seq_len;
+      }
     }
   }
 };

python/paddle/fluid/layers/nn.py

@@ -1809,50 +1809,50 @@ def conv2d_transpose(input,
     return out
 
 
-def sequence_expand(x, y, name=None):
+def sequence_expand(x, y, ref_level=-1, name=None):
     """Sequence Expand Layer. This layer will expand the input variable **x**
-    according to LoD information of **y**. And the following examples will
-    explain how sequence_expand works:
+    according to specified level lod of **y**. Please note that lod level of
+    **x** is at most 1 and rank of **x** is at least 2. When rank of **x**
+    is greater than 2, then it would be viewed as a 2-D tensor.
+    Following examples will explain how sequence_expand works:
 
     .. code-block:: text
 
         * Case 1
             x is a LoDTensor:
-                x.lod = [[0,       2, 3],
-                         [0, 1,    3, 4]]
-                x.data = [a, b, c, d]
+                x.lod  = [[0,   2,        4]]
+                x.data = [[a], [b], [c], [d]]
                 x.dims = [4, 1]
 
             y is a LoDTensor:
                 y.lod = [[0,    2,    4],
                          [0, 3, 6, 7, 8]]
 
-            with condition len(y.lod[-1]) - 1 == x.dims[0]
+            ref_level: 0
 
-            then output is a 2-level LoDTensor:
-                out.lod = [[0,                2,    4],
-                           [0,       3,       6, 7, 8]]
-                out.data = [a, a, a, b, b, b, c, d]
+            then output is a 1-level LoDTensor:
+                out.lod =  [[0,   2,        4,        6,        8]]
+                out.data = [[a], [b], [a], [b], [c], [d], [c], [d]]
                 out.dims = [8, 1]
 
         * Case 2
             x is a Tensor:
-                x.data = [a, b, c]
+                x.data = [[a], [b], [c]]
                 x.dims = [3, 1]
 
             y is a LoDTensor:
-                y.lod = [[0, 2, 3, 6]]
-
-            with condition len(y.lod[-1]) - 1 == x.dims[0]
+                y.lod = [[0, 2, 2, 5]]
 
-            then output is a 1-level LoDTensor:
-                out.lod = [[0,    2, 3,      6]]
-                out.data = [a, a, b, c, c, c]
-                out.dims = [6, 1]
+            ref_level: -1
 
+            then output is a Tensor:
+                out.data = [[a], [a], [c], [c], [c]]
+                out.dims = [5, 1]
     Args:
         x (Variable): The input variable which is a Tensor or LoDTensor.
         y (Variable): The input variable which is a LoDTensor.
+        ref_level (int): Lod level of `y` to be referred by `x`. If set to -1,
+                         refer the last level of lod.
         name(str|None): A name for this layer(optional). If set None, the layer
                         will be named automatically.
 
@@ -1865,14 +1865,17 @@ def sequence_expand(x, y, name=None):
             x = fluid.layers.data(name='x', shape=[10], dtype='float32')
             y = fluid.layers.data(name='y', shape=[10, 20],
                              dtype='float32', lod_level=1)
-            out = layers.sequence_expand(x=x, y=y)
+            out = layers.sequence_expand(x=x, y=y, ref_level=0)
     """
     helper = LayerHelper('sequence_expand', input=x, **locals())
     dtype = helper.input_dtype()
     tmp = helper.create_tmp_variable(dtype)
     helper.append_op(
-        type='sequence_expand', inputs={'X': x,
-                                        'Y': y}, outputs={'Out': tmp})
+        type='sequence_expand',
+        inputs={'X': x,
+                'Y': y},
+        outputs={'Out': tmp},
+        attrs={'ref_level': ref_level})
     return tmp
 
 

python/paddle/fluid/tests/book/test_machine_translation.py

@@ -118,12 +118,12 @@ def decoder_decode(context, is_sparse):
             is_sparse=is_sparse)
 
         # use rnn unit to update rnn
-        current_state = pd.fc(input=[pre_ids_emb, pre_state_expanded],
+        current_state = pd.fc(input=[pre_state_expanded, pre_ids_emb],
                               size=decoder_size,
                               act='tanh')
-
+        current_state_with_lod = pd.lod_reset(x=current_state, y=pre_score)
         # use score to do beam search
-        current_score = pd.fc(input=current_state,
+        current_score = pd.fc(input=current_state_with_lod,
                               size=target_dict_dim,
                               act='softmax')
         topk_scores, topk_indices = pd.topk(current_score, k=50)

python/paddle/fluid/tests/unittests/test_layers.py

@@ -181,8 +181,8 @@ class TestBook(unittest.TestCase):
         with program_guard(program):
             x = layers.data(name='x', shape=[10], dtype='float32')
             y = layers.data(
-                name='y', shape=[10, 20], dtype='float32', lod_level=1)
-            self.assertIsNotNone(layers.sequence_expand(x=x, y=y))
+                name='y', shape=[10, 20], dtype='float32', lod_level=2)
+            self.assertIsNotNone(layers.sequence_expand(x=x, y=y, ref_level=1))
         print(str(program))
 
     def test_lstm_unit(self):

python/paddle/fluid/tests/unittests/test_sequence_expand.py

@@ -27,12 +27,36 @@ class TestSequenceExpand(OpTest):
     def compute(self):
         x = self.inputs['X']
         x_data, x_lod = x if type(x) == tuple else (x, None)
-        n = 1 + x_data.shape[0] if not x_lod else len(x_lod[0])
         y_data, y_lod = self.inputs['Y']
-        repeats = [((y_lod[-1][i + 1] - y_lod[-1][i]))
-                   for i in range(len(y_lod[-1]) - 1)]
-        out = x_data.repeat(repeats, axis=0)
+
+        if hasattr(self, 'attrs'):
+            ref_level = self.attrs['ref_level']
+        else:
+            ref_level = len(y_lod) - 1
+
+        out = np.zeros(shape=((0, ) + x_data.shape[1:]), dtype=x_data.dtype)
+
+        if x_lod is None:
+            x_idx = [i for i in xrange(x_data.shape[0] + 1)]
+        else:
+            x_idx = x_lod[0]
+            out_lod = [[0]]
+
+        for i in xrange(1, len(y_lod[ref_level])):
+            repeat_num = y_lod[ref_level][i] - y_lod[ref_level][i - 1]
+            x_len = x_idx[i] - x_idx[i - 1]
+            if repeat_num > 0:
+                x_sub = x_data[x_idx[i - 1]:x_idx[i], :]
+                x_sub = np.repeat(x_sub, repeat_num, axis=0)
+                out = np.vstack((out, x_sub))
+                if x_lod is not None:
+                    for j in xrange(repeat_num):
+                        out_lod[0].append(out_lod[0][-1] + x_len)
+
+        if x_lod is None:
             self.outputs = {'Out': out}
+        else:
+            self.outputs = {'Out': (out, out_lod)}
 
     def setUp(self):
         self.op_type = 'sequence_expand'
@@ -52,7 +76,8 @@ class TestSequenceExpandCase1(TestSequenceExpand):
         x_lod = [[0, 2, 5]]
         y_data = np.random.uniform(0.1, 1, [13, 1]).astype('float32')
         y_lod = [[0, 2, 5], [0, 2, 4, 7, 10, 13]]
-        self.inputs = {'X': (x_data, x_lod), 'Y': (y_data, y_lod)}
+        self.inputs = {'X': x_data, 'Y': (y_data, y_lod)}
+        self.attrs = {'ref_level': 0}
 
 
 class TestSequenceExpandCase2(TestSequenceExpand):
@@ -60,8 +85,9 @@ class TestSequenceExpandCase2(TestSequenceExpand):
         x_data = np.random.uniform(0.1, 1, [1, 2, 2]).astype('float32')
         x_lod = [[0, 1]]
         y_data = np.random.uniform(0.1, 1, [2, 2, 2]).astype('float32')
-        y_lod = [[0, 2]]
+        y_lod = [[0, 2], [0, 2]]
         self.inputs = {'X': (x_data, x_lod), 'Y': (y_data, y_lod)}
+        self.attrs = {'ref_level': 0}
 
 
 class TestSequenceExpandCase3(TestSequenceExpand):
@@ -75,14 +101,9 @@ class TestSequenceExpandCase3(TestSequenceExpand):
 
 class TestSequenceExpandCase4(TestSequenceExpand):
     def set_data(self):
-        x_data = np.array(
-            [0.1, 0.3, 0.2, 0.15, 0.25, 0.2, 0.15, 0.25, 0.1, 0.3]).reshape(
-                [2, 5]).astype('float32')
-        x_lod = [[
-            0,
-            1,
-            2,
-        ]]
+        data = [0.1, 0.3, 0.2, 0.15, 0.25, 0.2, 0.15, 0.25, 0.1, 0.3]
+        x_data = np.array(data).reshape([5, 2]).astype('float32')
+        x_lod = [[0, 2, 5]]
         y_data = np.random.uniform(0.1, 1, [2, 1]).astype('float32')
         y_lod = [[0, 1, 2], [0, 1, 2]]
         self.inputs = {'X': (x_data, x_lod), 'Y': (y_data, y_lod)}