Merge branch 'fix_bug_in_recordio' into dev_MultiEpochReader

doc/design/distributed_lookup_table_design.md → doc/fluid/design/dist_train/distributed_lookup_table_design.md

@@ -26,7 +26,7 @@ lookup of rows.
 The following figure illustrates the multiplication of x with two
 non-zero elements, or say, two symbols, and a lookup table W:
 
-![lookup table](./lookup_table.png)
+![lookup table](./src/lookup_table.png)
 
 ### The Backward Algorithm
 
@@ -42,7 +42,7 @@ or some more sophisticated algorithms that rely on both W' and W:
 $$W = f(W, W')$$
 
 The following figure illustrates the backward pass of the lookup
-operator: ![lookup table training](./lookup_table_training.png)
+operator: ![lookup table training](./src/lookup_table_training.png)
 
 ## Distributed Storage Service
 

doc/fluid/design/motivation/fluid.md

@@ -103,7 +103,7 @@ In computability theory, a system of data-manipulation rules, such as a programm
 
 There are two ways to execute a Fluid program.  When a program is executed, it creates a protobuf message [`ProgramDesc`](https://github.com/PaddlePaddle/Paddle/blob/a91efdde6910ce92a78e3aa7157412c4c88d9ee8/paddle/framework/framework.proto#L145) that describes the process and is conceptually like an [abstract syntax tree](https://en.wikipedia.org/wiki/Abstract_syntax_tree).
 
-There is a C++ class [`Executor`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/executor.h), which runs a `ProgramDesc`, similar to how an interpreter runs a Python program.
+There is a C++ class [`Executor`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/framework/executor.h), which runs a `ProgramDesc`, similar to how an interpreter runs a Python program.
 
 Fluid is moving towards the direction of a compiler, which is explain in [fluid_compiler.md](fluid_compiler.md).
 

doc/v2/api/fluid/optimizer.rst

@@ -47,3 +47,10 @@ DecayedAdagrad
     :members:
     :noindex:
 
+Adadelta
+--------------
+
+..  autoclass:: paddle.fluid.optimizer.AdadeltaOptimizer
+    :members:
+    :noindex:
+

paddle/fluid/framework/channel_test.cc

@@ -871,3 +871,67 @@ TEST(ChannelHolder, ChannelHolderDestroyUnblocksSendersTest) {
   ch->Reset<int>(0);
   ChannelHolderDestroyUnblockSenders(ch, false);
 }
+
+// This tests that closing a channelholder many times.
+void ChannelHolderManyTimesClose(ChannelHolder *ch) {
+  const int num_threads = 15;
+  std::thread t[num_threads];
+  bool thread_ended[num_threads];
+
+  // Launches threads that try to send data to channel.
+  for (size_t i = 0; i < num_threads / 3; i++) {
+    thread_ended[i] = false;
+    t[i] = std::thread(
+        [&](bool *ended) {
+          int data = 10;
+          ch->Send(&data);
+          *ended = true;
+        },
+        &thread_ended[i]);
+  }
+
+  // Launches threads that try to receive data to channel.
+  for (size_t i = num_threads / 3; i < 2 * num_threads / 3; i++) {
+    thread_ended[i] = false;
+    t[i] = std::thread(
+        [&](bool *p) {
+          int data;
+          if (ch->Receive(&data)) {
+            EXPECT_EQ(data, 10);
+          }
+          *p = true;
+        },
+        &thread_ended[i]);
+  }
+
+  // Launches threads that try to close the channel.
+  for (size_t i = 2 * num_threads / 3; i < num_threads; i++) {
+    thread_ended[i] = false;
+    t[i] = std::thread(
+        [&](bool *p) {
+          if (!ch->IsClosed()) {
+            ch->close();
+          }
+          *p = true;
+        },
+        &thread_ended[i]);
+  }
+
+  std::this_thread::sleep_for(std::chrono::milliseconds(100));  // wait
+
+  // Verify that all threads are unblocked
+  for (size_t i = 0; i < num_threads; i++) {
+    EXPECT_EQ(thread_ended[i], true);
+  }
+  EXPECT_TRUE(ch->IsClosed());
+  // delete the channel
+  delete ch;
+  for (size_t i = 0; i < num_threads; i++) t[i].join();
+}
+
+TEST(ChannelHolder, ChannelHolderManyTimesCloseTest) {
+  // Check for Buffered Channel
+  ChannelHolder *ch = new ChannelHolder();
+  ch->Reset<int>(10);
+  ChannelHolderManyTimesClose(ch);
+}

paddle/fluid/operators/dropout_op.cc

@@ -35,7 +35,6 @@ class DropoutOp : public framework::OperatorWithKernel {
   }
 };
 
-template <typename AttrType>
 class DropoutOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   DropoutOpMaker(OpProto* proto, OpAttrChecker* op_checker)
@@ -73,7 +72,6 @@ are set equal to their corresponding inputs.
   }
 };
 
-template <typename AttrType>
 class DropoutOpGrad : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -103,11 +101,10 @@ class DropoutOpGrad : public framework::OperatorWithKernel {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(dropout, ops::DropoutOp, ops::DropoutOpMaker<float>, dropout_grad,
-            ops::DropoutOpGrad<float>);
+REGISTER_OP(dropout, ops::DropoutOp, ops::DropoutOpMaker, dropout_grad,
+            ops::DropoutOpGrad);
 REGISTER_OP_CPU_KERNEL(
-    dropout,
-    ops::CPUDropoutKernel<paddle::platform::CPUDeviceContext, float, float>);
+    dropout, ops::CPUDropoutKernel<paddle::platform::CPUDeviceContext, float>);
 REGISTER_OP_CPU_KERNEL(
     dropout_grad,
     ops::DropoutGradKernel<paddle::platform::CPUDeviceContext, float>);

paddle/fluid/operators/dropout_op.cu

@@ -18,17 +18,18 @@ limitations under the License. */
 #include <thrust/random.h>
 #include <thrust/transform.h>
 #include "paddle/fluid/operators/dropout_op.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace paddle {
 namespace operators {
 
-template <typename T, typename AttrType>
+template <typename T>
 __global__ void RandomGenerator(const size_t n, const int seed,
-                                const AttrType dropout_prob, const T* src,
+                                const float dropout_prob, const T* src,
                                 T* mask_data, T* dst) {
   thrust::minstd_rand rng;
   rng.seed(seed);
-  thrust::uniform_real_distribution<AttrType> dist(0, 1);
+  thrust::uniform_real_distribution<float> dist(0, 1);
 
   int idx = blockDim.x * blockIdx.x + threadIdx.x;
   for (; idx < n; idx += blockDim.x * gridDim.x) {
@@ -44,14 +45,14 @@ __global__ void RandomGenerator(const size_t n, const int seed,
 // It seems that Eigen::Tensor::setRandom in GPU will SEGFAULT.
 // Use std::random and thrust::random(thrust is a std library in CUDA) to
 // implement uniform random.
-template <typename Place, typename T, typename AttrType>
+template <typename Place, typename T>
 class GPUDropoutKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto* x = context.Input<Tensor>("X");
     auto* y = context.Output<Tensor>("Out");
     y->mutable_data<T>(context.GetPlace());
-    AttrType dropout_prob = context.Attr<AttrType>("dropout_prob");
+    float dropout_prob = context.Attr<float>("dropout_prob");
 
     auto X = EigenMatrix<T>::Reshape(*x, 1);
     auto Y = EigenMatrix<T>::Reshape(*y, 1);
@@ -70,11 +71,11 @@ class GPUDropoutKernel : public framework::OpKernel<T> {
 
       int threads = 512;
       int grid = (x->numel() + threads - 1) / threads;
-      RandomGenerator<T, AttrType><<<grid, threads, 0,
-                                     context.cuda_device_context().stream()>>>(
+      RandomGenerator<
+          T><<<grid, threads, 0, context.cuda_device_context().stream()>>>(
           size, seed, dropout_prob, x_data, mask_data, y_data);
     } else {
-      Y.device(place) = X * (1.0f - dropout_prob);
+      Y.device(place) = X * static_cast<T>(1.0f - dropout_prob);
     }
   }
 };
@@ -83,9 +84,9 @@ class GPUDropoutKernel : public framework::OpKernel<T> {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
+namespace plat = paddle::platform;
 REGISTER_OP_CUDA_KERNEL(
-    dropout,
-    ops::GPUDropoutKernel<paddle::platform::CUDADeviceContext, float, float>);
-REGISTER_OP_CUDA_KERNEL(
-    dropout_grad,
-    ops::DropoutGradKernel<paddle::platform::CUDADeviceContext, float>);
+    dropout, ops::GPUDropoutKernel<plat::CUDADeviceContext, float>,
+    ops::GPUDropoutKernel<plat::CUDADeviceContext, plat::float16>);
+REGISTER_OP_CUDA_KERNEL(dropout_grad,
+                        ops::DropoutGradKernel<plat::CUDADeviceContext, float>);

paddle/fluid/operators/dropout_op.h

@@ -25,7 +25,7 @@ template <typename T, int MajorType = Eigen::RowMajor,
           typename IndexType = Eigen::DenseIndex>
 using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
 
-template <typename DeviceContext, typename T, typename AttrType>
+template <typename DeviceContext, typename T>
 class CPUDropoutKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {

paddle/fluid/operators/lod_reset_op.cc

@@ -22,17 +22,16 @@ class LoDResetOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext *ctx) const override {
-    // input check
     PADDLE_ENFORCE(ctx->HasInput("X"),
                    "Input(X) of LoDResetOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
                    "Output(Out) of LoDResetOp should not be null.");
-    // If target LoD is not set form Input(), then it must be set from Attr().
-    if (!ctx->HasInput("TargetLoD")) {
+
+    if (!ctx->HasInput("Y")) {
       auto level0 = ctx->Attrs().Get<std::vector<int>>("target_lod");
-      PADDLE_ENFORCE(level0.size() > 1,
-                     "Target LoD is not found, should be set to be a valid one "
-                     "through Input() or Attr().");
+      PADDLE_ENFORCE_GT(level0.size(), 1,
+                        "If Input(Y) not provided, the target lod should be "
+                        "specified by attribute `target_lod`.");
     }
     ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
   }
@@ -50,36 +49,77 @@ class LoDResetOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   LoDResetOpMaker(OpProto *proto, OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "(LoDTensor) The input tensor of lod_reset operator.");
-    AddInput("TargetLoD",
-             "(Tensor, optional) The target level 0 LoD from Input().")
+    AddInput("X",
+             "(Tensor, LoDTensor) Input variable of LoDResetOp which "
+             "could be a Tensor or LoDTensor, where the data of output "
+             "variable inherits from.");
+    AddInput("Y",
+             "(Tensor, LoDTensor, optional) If provided and Y is LoDTensor, "
+             "lod of Input(Y) would be considered as the target lod first, "
+             "otherwise data of Input(Y) would be considered as the "
+             "target lod.")
         .AsDispensable();
-    AddOutput("Out", "(LoDTensor) The output tensor of lod_reset operator.");
+    AddOutput("Out",
+              "(LoDTensor) Output variable of LoDResetOp which should be a "
+              "LoDTensor.");
     AddAttr<std::vector<int>>("target_lod",
                               "The target level 0 LoD from Attr().")
         .SetDefault(std::vector<int>{});
     AddComment(R"DOC(LoDReset operator
 
-Reset LoD of Input(X) into a new one specified by Input(TargetLoD) or
-Attr(target_lod), or set LoD for Input(X) if it doesn't have one.
-Currently the lod_reset operator only supports the reset of level 0 LoD.
-At least one of Input(TargetLoD) and Attr(target_lod) must be set,
-and if both of them are set, Input(TargetLoD) will be chosen as the
-target LoD.
+Set LoD of `X` to a new one specified by `Y` or attribute `target_lod`. When `Y`
+provided and `Y` is a LoDTensor, `Y.lod` would be considered as target LoD
+first, otherwise `Y.data` would be considered as target LoD. If `Y` is not
+provided, target LoD should be specified by attribute `target_lod`.
+If target LoD is specified by `Y.data` or `target_lod`, only one level LoD
+is supported.
+
+Example 1:
+
+Given a 1-level LoDTensor input(X):
+    X.lod =  [[ 0,     2,                   5      6 ]]
+    X.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
+    X.dims = [6, 1]
+
+attr(target_lod): [0, 4, 6]
+
+then we get a 1-level LoDTensor:
+    Out.lod =  [[ 0,                   4,            6 ]]
+    Out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
+    Out.dims = [6, 1]
+
+Example 2:
 
-An example:
-Given a float LoDTensor X with shape (6, 1), its transpose form represents
+Given a 1-level LoDTensor input(X):
+    X.lod =  [[ 0,     2,                   5      6 ]]
+    X.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
+    X.dims = [6, 1]
 
-    [1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
+input(Y) is a Tensor:
+    Y.data = [[0, 2, 6]]
+    Y.dims = [1, 3]
 
-with LoD = [[0, 2, 5, 6]] and the three (transposed) sequences look like
+then we get a 1-level LoDTensor:
+    Out.lod =  [[ 0,     2,                          6 ]]
+    Out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
+    Out.dims = [6, 1]
 
-    [1.0, 2.0], [3.0, 4.0, 5.0], [6.0].
+Example 3:
 
-If target LoD = [0, 4, 6], the lod_reset operator will reset the LoD and
-the sequences that the LoDTensor Output(Out) contains becomes:
+Given a 1-level LoDTensor input(X):
+    X.lod =  [[ 0,      2,                   5     6 ]]
+    X.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
+    X.dims = [6, 1]
 
-    [1.0, 2.0, 3.0, 4.0], [5.0, 6.0].
+input(Y) is a 2-level LoDTensor:
+    Y.lod =  [[0, 2, 4], [0, 2, 5, 6]]
+    Y.data = [[1.1], [2.1], [3.1], [4.1], [5.1], [6.1]]
+    Y.dims = [6, 1]
+
+then we get a 2-level LoDTensor:
+    Out.lod =  [[0, 2, 4], [0, 2, 5, 6]]
+    Out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
+    Out.dims = [6, 1]
 
 )DOC");
   }
@@ -90,10 +130,16 @@ class LoDResetGradOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) shouldn't be null.");
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of LoDResetGradOp should not be null.");
     PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
-                   "Input(Out@GRAD) shouldn't be null.");
-    ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
+                   "Input(Out@Grad) of LoDResetGradOp should not be null.");
+
+    auto x_grad_name = framework::GradVarName("X");
+    if (ctx->HasOutput(x_grad_name)) {
+      ctx->SetOutputDim(x_grad_name, ctx->GetInputDim("X"));
+      ctx->ShareLoD("X", /*->*/ x_grad_name);
+    }
   }
 
  protected:
@@ -111,9 +157,13 @@ class LoDResetGradOp : public framework::OperatorWithKernel {
 namespace ops = paddle::operators;
 REGISTER_OP(lod_reset, ops::LoDResetOp, ops::LoDResetOpMaker, lod_reset_grad,
             ops::LoDResetGradOp);
-REGISTER_OP_CPU_KERNEL(lod_reset,
-                       ops::LoDResetKernel<paddle::platform::CPUPlace, float>,
-                       ops::LoDResetKernel<paddle::platform::CPUPlace, double>);
+REGISTER_OP_CPU_KERNEL(
+    lod_reset, ops::LoDResetKernel<paddle::platform::CPUPlace, float>,
+    ops::LoDResetKernel<paddle::platform::CPUPlace, double>,
+    ops::LoDResetKernel<paddle::platform::CPUPlace, int>,
+    ops::LoDResetKernel<paddle::platform::CPUPlace, int64_t>);
 REGISTER_OP_CPU_KERNEL(
     lod_reset_grad, ops::LoDResetGradKernel<paddle::platform::CPUPlace, float>,
-    ops::LoDResetGradKernel<paddle::platform::CPUPlace, double>);
+    ops::LoDResetGradKernel<paddle::platform::CPUPlace, double>,
+    ops::LoDResetGradKernel<paddle::platform::CPUPlace, int>,
+    ops::LoDResetGradKernel<paddle::platform::CPUPlace, int64_t>);

paddle/fluid/operators/lod_reset_op.cu

@@ -18,8 +18,12 @@ namespace ops = paddle::operators;
 
 REGISTER_OP_CUDA_KERNEL(
     lod_reset, ops::LoDResetKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::LoDResetKernel<paddle::platform::CUDADeviceContext, double>);
+    ops::LoDResetKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::LoDResetKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::LoDResetKernel<paddle::platform::CUDADeviceContext, int64_t>);
 REGISTER_OP_CUDA_KERNEL(
     lod_reset_grad,
     ops::LoDResetGradKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::LoDResetGradKernel<paddle::platform::CUDADeviceContext, double>);
+    ops::LoDResetGradKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::LoDResetGradKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::LoDResetGradKernel<paddle::platform::CUDADeviceContext, int64_t>);

paddle/fluid/operators/lod_reset_op.h

@@ -26,10 +26,21 @@ class LoDResetKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& ctx) const {
     auto* out = ctx.Output<framework::LoDTensor>("Out");
     auto* in = ctx.Input<framework::LoDTensor>("X");
-    auto* lod_t = ctx.Input<framework::Tensor>("TargetLoD");
+    auto* lod_t = ctx.Input<framework::LoDTensor>("Y");
+
+    out->ShareDataWith(*in);
 
     std::vector<int> level0;
     if (lod_t) {
+      if (lod_t->lod().size() > 0) {
+        auto y_lod = lod_t->lod();
+        auto last_level = y_lod[y_lod.size() - 1];
+        PADDLE_ENFORCE_EQ(last_level.back(), in->dims()[0],
+                          "Last value of `Y`'s last level LoD should be equal "
+                          "to the first dimension of `X`");
+        out->set_lod(y_lod);
+        return;  // early return, since lod already set
+      } else {
         auto* lod = lod_t->data<int>();
         if (platform::is_gpu_place(ctx.GetPlace())) {
           framework::Tensor lod_cpu;
@@ -38,15 +49,16 @@ class LoDResetKernel : public framework::OpKernel<T> {
           lod = lod_cpu.data<int>();
         }
         level0 = std::vector<int>(lod, lod + lod_t->numel());
+      }
     } else {
       level0 = ctx.Attr<std::vector<int>>("target_lod");
     }
 
-    PADDLE_ENFORCE(level0.size() > 1UL,
-                   "The size of target LoD should be greater than 1.");
-    PADDLE_ENFORCE(level0[0] == 0,
+    PADDLE_ENFORCE_GT(level0.size(), 1UL,
+                      "Size of target LoD should be greater than 1.");
+    PADDLE_ENFORCE_EQ(level0[0], 0,
                       "Target LoD should be a vector starting from 0.");
-    PADDLE_ENFORCE(level0.back() == in->dims()[0],
+    PADDLE_ENFORCE_EQ(level0.back(), in->dims()[0],
                       "Target LoD should be a vector end with the "
                       "first dimension of Input(X).");
     for (size_t i = 0; i < level0.size() - 1; ++i) {
@@ -54,7 +66,6 @@ class LoDResetKernel : public framework::OpKernel<T> {
                      "Target LoD should be an ascending vector.");
     }
 
-    out->ShareDataWith(*in);
     // cast level0 to size_t
     std::vector<size_t> ulevel0(level0.size(), 0);
     std::transform(level0.begin(), level0.end(), ulevel0.begin(),

paddle/fluid/operators/math/CMakeLists.txt

@@ -43,7 +43,7 @@ math_library(sequence2batch)
 math_library(sequence_padding)
 math_library(sequence_pooling DEPS math_function)
 math_library(sequence_scale)
-math_library(softmax)
+math_library(softmax DEPS math_function)
 math_library(unpooling)
 math_library(vol2col)
 

paddle/fluid/operators/math/concat.cc

@@ -44,7 +44,7 @@ class ConcatFunctor<platform::CPUDeviceContext, T> {
       out_cols += t_cols;
       input_cols[i] = t_cols;
     }
-    auto& cpu_place = boost::get<platform::CPUPlace>(context.GetPlace());
+    auto cpu_place = boost::get<platform::CPUPlace>(context.GetPlace());
 
     // computation
     for (int k = 0; k < out_rows; ++k) {
@@ -87,7 +87,7 @@ class ConcatGradFunctor<platform::CPUDeviceContext, T> {
       input_cols += t_cols;
       output_cols[i] = t_cols;
     }
-    auto& cpu_place = boost::get<platform::CPUPlace>(context.GetPlace());
+    auto cpu_place = boost::get<platform::CPUPlace>(context.GetPlace());
 
     // computation
     for (int k = 0; k < input_rows; ++k) {

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/reader/create_double_buffer_reader_op.cc

@@ -48,20 +48,24 @@ class DoubleBufferReader : public framework::DecoratedReader {
 
   void start_thread() {
     buffer_ = framework::MakeChannel<Item>(kDoubleBufferSize);
-    std::thread prefetch([this] { PrefetchThreadFunc(); });
-    prefetch.detach();
+    prefetcher_ = std::thread([this] { PrefetchThreadFunc(); });
   }
 
   void ReadNext(std::vector<framework::LoDTensor>* out) override;
   void ReInit() override;
 
-  ~DoubleBufferReader() { buffer_->Close(); }
+  ~DoubleBufferReader() {
+    buffer_->Close();
+    prefetcher_.join();
+    delete buffer_;
+  }
 
   bool HasNext() const override;
 
  private:
   void PrefetchThreadFunc();
 
+  std::thread prefetcher_;
   framework::Channel<Item>* buffer_;
   platform::Place place_;
   std::vector<std::unique_ptr<platform::DeviceContext>> ctxs_;
@@ -134,6 +138,8 @@ void DoubleBufferReader::ReadNext(std::vector<framework::LoDTensor>* out) {
 void DoubleBufferReader::ReInit() {
   reader_->ReInit();
   buffer_->Close();
+  prefetcher_.join();
+  delete buffer_;
   start_thread();
 }
 
@@ -159,11 +165,12 @@ void DoubleBufferReader::PrefetchThreadFunc() {
 
     if (!buffer_->Send(&batch)) {
       VLOG(5) << "WARNING: The double buffer channel has been closed. The "
-                 "prefetch thread terminates.";
+                 "prefetch thread will terminate.";
       break;
     }
   }
   buffer_->Close();
+  VLOG(5) << "Prefetch thread terminates.";
 }
 
 bool DoubleBufferReader::HasNext() const {

paddle/fluid/operators/reader/create_shuffle_reader_op.cc

@@ -34,6 +34,9 @@ class ShuffleReader : public framework::DecoratedReader {
   }
 
   void ReadNext(std::vector<framework::LoDTensor>* out) override {
+    if (!HasNext()) {
+      PADDLE_THROW("There is no next data!");
+    }
     if (iteration_pos_ >= buffer_.size()) {
       VLOG(10) << "Resetting shuffle buffer";
       ReadIntoBuffers();
@@ -50,7 +53,6 @@ class ShuffleReader : public framework::DecoratedReader {
     buffer_.clear();
     buffer_.reserve(buffer_size_);
     iteration_pos_ = 0;
-    PADDLE_ENFORCE(reader_->HasNext());
     for (size_t i = 0; i < buffer_size_; ++i) {
       if (!reader_->HasNext()) {
         break;

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;
+      }
     }
   }
 };

paddle/fluid/platform/float16.h

@@ -483,8 +483,123 @@ DEVICE inline bool operator>=(const half& a, const half& b) {
 
 #endif  // PADDLE_CUDA_FP16
 
-// Arithmetic operators on ARMv8.2-A CPU
-#if defined(PADDLE_WITH_NATIVE_FP16)
+// Arithmetic operators for float16 on GPU
+#if defined(PADDLE_CUDA_FP16)
+HOSTDEVICE inline float16 operator+(const float16& a, const float16& b) {
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
+  return float16(__hadd(half(a), half(b)));
+#else
+  return float16(float(a) + float(b));
+#endif
+}
+
+HOSTDEVICE inline float16 operator-(const float16& a, const float16& b) {
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
+  return float16(__hsub(half(a), half(b)));
+#else
+  return float16(float(a) - float(b));
+#endif
+}
+
+HOSTDEVICE inline float16 operator*(const float16& a, const float16& b) {
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
+  return float16(__hmul(half(a), half(b)));
+#else
+  return float16(float(a) * float(b));
+#endif
+}
+
+HOSTDEVICE inline float16 operator/(const float16& a, const float16& b) {
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 300
+  // TODO(kexinzhao): check which cuda version starts to support __hdiv
+  float num = __half2float(half(a));
+  float denom = __half2float(half(b));
+  return float16(num / denom);
+#else
+  return float16(float(a) / float(b));
+#endif
+}
+
+HOSTDEVICE inline float16 operator-(const float16& a) {
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
+  return float16(__hneg(half(a)));
+#else
+  float16 res;
+  res.x = a.x ^ 0x8000;
+  return res;
+#endif
+}
+
+HOSTDEVICE inline float16& operator+=(float16& a, const float16& b) {
+  a = a + b;
+  return a;
+}
+
+HOSTDEVICE inline float16& operator-=(float16& a, const float16& b) {
+  a = a - b;
+  return a;
+}
+
+HOSTDEVICE inline float16& operator*=(float16& a, const float16& b) {
+  a = a * b;
+  return a;
+}
+
+HOSTDEVICE inline float16& operator/=(float16& a, const float16& b) {
+  a = a / b;
+  return a;
+}
+
+HOSTDEVICE inline bool operator==(const float16& a, const float16& b) {
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
+  return __heq(half(a), half(b));
+#else
+  return float(a) == float(b);
+#endif
+}
+
+HOSTDEVICE inline bool operator!=(const float16& a, const float16& b) {
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
+  return __hne(half(a), half(b));
+#else
+  return float(a) != float(b);
+#endif
+}
+
+HOSTDEVICE inline bool operator<(const float16& a, const float16& b) {
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
+  return __hlt(half(a), half(b));
+#else
+  return float(a) < float(b);
+#endif
+}
+
+HOSTDEVICE inline bool operator<=(const float16& a, const float16& b) {
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
+  return __hle(half(a), half(b));
+#else
+  return float(a) <= float(b);
+#endif
+}
+
+HOSTDEVICE inline bool operator>(const float16& a, const float16& b) {
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
+  return __hgt(half(a), half(b));
+#else
+  return float(a) > float(b);
+#endif
+}
+
+HOSTDEVICE inline bool operator>=(const float16& a, const float16& b) {
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
+  return __hge(half(a), half(b));
+#else
+  return float(a) >= float(b);
+#endif
+}
+
+// Arithmetic operators for float16 on ARMv8.2-A CPU
+#elif defined(PADDLE_WITH_NATIVE_FP16)
 HOST inline float16 operator+(const float16& a, const float16& b) {
   float16 res;
   asm volatile(
@@ -668,71 +783,71 @@ HOST inline bool operator>=(const float16& a, const float16& b) {
   return (res & 0xffff) != 0;
 }
 
-// Arithmetic operators, software emulated on other CPU
+// Arithmetic operators for float16, software emulated on other CPU
 #else
-HOSTDEVICE inline float16 operator+(const float16& a, const float16& b) {
+HOST inline float16 operator+(const float16& a, const float16& b) {
   return float16(float(a) + float(b));
 }
 
-HOSTDEVICE inline float16 operator-(const float16& a, const float16& b) {
+HOST inline float16 operator-(const float16& a, const float16& b) {
   return float16(float(a) - float(b));
 }
 
-HOSTDEVICE inline float16 operator*(const float16& a, const float16& b) {
+HOST inline float16 operator*(const float16& a, const float16& b) {
   return float16(float(a) * float(b));
 }
 
-HOSTDEVICE inline float16 operator/(const float16& a, const float16& b) {
+HOST inline float16 operator/(const float16& a, const float16& b) {
   return float16(float(a) / float(b));
 }
 
-HOSTDEVICE inline float16 operator-(const float16& a) {
+HOST inline float16 operator-(const float16& a) {
   float16 res;
   res.x = a.x ^ 0x8000;
   return res;
 }
 
-HOSTDEVICE inline float16& operator+=(float16& a, const float16& b) {
+HOST inline float16& operator+=(float16& a, const float16& b) {
   a = float16(float(a) + float(b));
   return a;
 }
 
-HOSTDEVICE inline float16& operator-=(float16& a, const float16& b) {
+HOST inline float16& operator-=(float16& a, const float16& b) {
   a = float16(float(a) - float(b));
   return a;
 }
 
-HOSTDEVICE inline float16& operator*=(float16& a, const float16& b) {
+HOST inline float16& operator*=(float16& a, const float16& b) {
   a = float16(float(a) * float(b));
   return a;
 }
 
-HOSTDEVICE inline float16& operator/=(float16& a, const float16& b) {
+HOST inline float16& operator/=(float16& a, const float16& b) {
   a = float16(float(a) / float(b));
   return a;
 }
 
-HOSTDEVICE inline bool operator==(const float16& a, const float16& b) {
+HOST inline bool operator==(const float16& a, const float16& b) {
   return float(a) == float(b);
 }
 
-HOSTDEVICE inline bool operator!=(const float16& a, const float16& b) {
+HOST inline bool operator!=(const float16& a, const float16& b) {
   return float(a) != float(b);
 }
 
-HOSTDEVICE inline bool operator<(const float16& a, const float16& b) {
+HOST inline bool operator<(const float16& a, const float16& b) {
   return float(a) < float(b);
 }
 
-HOSTDEVICE inline bool operator<=(const float16& a, const float16& b) {
+HOST inline bool operator<=(const float16& a, const float16& b) {
   return float(a) <= float(b);
 }
 
-HOSTDEVICE inline bool operator>(const float16& a, const float16& b) {
+HOST inline bool operator>(const float16& a, const float16& b) {
   return float(a) > float(b);
 }
 
-HOSTDEVICE inline bool operator>=(const float16& a, const float16& b) {
+HOST inline bool operator>=(const float16& a, const float16& b) {
   return float(a) >= float(b);
 }
 #endif

paddle/fluid/recordio/header.cc

@@ -29,8 +29,8 @@ Header::Header(uint32_t num, uint32_t sum, Compressor c, uint32_t cs)
 
 bool Header::Parse(std::istream& is) {
   uint32_t magic;
-  size_t read_size =
-      is.readsome(reinterpret_cast<char*>(&magic), sizeof(uint32_t));
+  is.read(reinterpret_cast<char*>(&magic), sizeof(uint32_t));
+  size_t read_size = is.gcount();
   if (read_size < sizeof(uint32_t)) {
     return false;
   }

python/paddle/fluid/concurrency.py

@@ -131,7 +131,7 @@ def make_channel(dtype, capacity=0):
     return channel
 
 
-def channel_send(channel, value):
+def channel_send(channel, value, copy=False):
     """
     Sends a value through a channel variable. Used by an unbuffered or buffered
     channel to pass data from within or to a concurrent Go block, where
@@ -141,6 +141,8 @@ def channel_send(channel, value):
         channel (Variable|Channel): Channel variable created using
         `make_channel`.
         value (Variable): Value to send to channel
+        copy (bool): Copy data while channel send. If False, then data
+        is moved. The input cannot be used after move.
     Returns:
         Variable: The boolean status on whether or not the channel
                   successfully sent the passed value.
@@ -162,11 +164,26 @@ def channel_send(channel, value):
         type=core.VarDesc.VarType.LOD_TENSOR,
         dtype=core.VarDesc.VarType.BOOL)
 
+    X = value
+
+    if copy is True:
+        copied_X = helper.create_variable(
+            name=unique_name.generate(value.name + '_copy'),
+            type=value.type,
+            dtype=value.dtype,
+            shape=value.shape,
+            lod_level=value.lod_level,
+            capacity=value.capacity)
+
+        assign_op = channel_send_block.append_op(
+            type="assign_op", inputs={"X": value}, outputs={"Out": copied_X})
+        X = copied_X
+
     channel_send_op = channel_send_block.append_op(
         type="channel_send",
         inputs={
             "Channel": channel,
-            "X": value,
+            "X": X,
         },
         outputs={"Status": status})
 

python/paddle/fluid/layers/nn.py

@@ -73,6 +73,7 @@ __all__ = [
     'smooth_l1',
     'one_hot',
     'autoincreased_step_counter',
+    'lod_reset',
 ]
 
 
@@ -1808,50 +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.
 
@@ -1864,14 +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
 
 
@@ -3292,3 +3296,98 @@ def autoincreased_step_counter(counter_name=None, begin=1, step=1):
         counter.stop_gradient = True
 
     return counter
+
+
+def lod_reset(x, y=None, target_lod=None):
+    """
+    LoD Reset Operator. Set LoD of **x** to a new one specified by **y** or
+    **target_lod**. When **y** provided, **y.lod** would be considered as target
+    LoD first, otherwise **y.data** would be considered as target LoD. If **y**
+    is not provided, target LoD should be specified by **target_lod**.
+    If target LoD is specified by **Y.data** or **target_lod**, only one level
+    LoD is supported.
+
+    .. code-block:: text
+
+        * Example 1:
+
+            Given a 1-level LoDTensor x:
+                x.lod =  [[ 0,     2,                   5      6 ]]
+                x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
+                x.dims = [6, 1]
+
+            target_lod: [0, 4, 6]
+
+            then we get a 1-level LoDTensor:
+                out.lod =  [[ 0,                   4,            6 ]]
+                out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
+                out.dims = [6, 1]
+
+        * Example 2:
+
+            Given a 1-level LoDTensor x:
+                x.lod =  [[ 0,     2,                   5      6 ]]
+                x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
+                x.dims = [6, 1]
+
+            y is a Tensor:
+                y.data = [[0, 2, 6]]
+                y.dims = [1, 3]
+
+            then we get a 1-level LoDTensor:
+                out.lod =  [[ 0,     2,                          6 ]]
+                out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
+                out.dims = [6, 1]
+
+        * Example 3:
+
+            Given a 1-level LoDTensor x:
+                x.lod =  [[ 0,      2,                   5     6 ]]
+                x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
+                x.dims = [6, 1]
+
+            y is a 2-level LoDTensor:
+                y.lod =  [[0, 2, 4], [0, 2, 5, 6]]
+                y.data = [[1.1], [2.1], [3.1], [4.1], [5.1], [6.1]]
+                y.dims = [6, 1]
+
+            then we get a 2-level LoDTensor:
+                out.lod =  [[0, 2, 4], [0, 2, 5, 6]]
+                out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
+                out.dims = [6, 1]
+
+    Args:
+        x (Variable): Input variable which could be a Tensor or LodTensor.
+        y (Variable|None): If provided, output's LoD would be derived from y.
+        target_lod (list|tuple|None): One level LoD which should be considered
+                                      as target LoD when y not provided.
+
+    Returns:
+        Variable: Output variable with LoD specified by this operator.
+
+    Raises:
+        ValueError: If y and target_lod are both None.
+
+    Examples:
+        .. code-block:: python
+
+            x = layers.data(name='x', shape=[10])
+            y = layers.data(name='y', shape=[10, 20], lod_level=2)
+            out = layers.lod_reset(x=x, y=y)
+    """
+    helper = LayerHelper("lod_reset", **locals())
+    out = helper.create_tmp_variable(dtype=x.dtype)
+    if y is not None:
+        helper.append_op(
+            type="lod_reset", inputs={'X': x,
+                                      'Y': y}, outputs={'Out': out})
+    elif target_lod is not None:
+        helper.append_op(
+            type="lod_reset",
+            inputs={'X': x},
+            attrs={'target_lod': target_lod},
+            outputs={'Out': out})
+    else:
+        raise ValueError("y and target_lod should not be both None.")
+
+    return out

python/paddle/fluid/optimizer.py

@@ -24,7 +24,9 @@ from layer_helper import LayerHelper
 from regularizer import append_regularization_ops
 from clip import append_gradient_clip_ops, error_clip_callback
 
-__all__ = ['SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad']
+__all__ = [
+    'SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad', 'Adadelta'
+]
 
 
 class Optimizer(object):
@@ -580,6 +582,88 @@ class DecayedAdagradOptimizer(Optimizer):
         return decayed_adagrad_op
 
 
+class AdadeltaOptimizer(Optimizer):
+    """
+    **Adadelta Optimizer**
+    Simple Adadelta optimizer with average squared grad state and
+    average squared update state.
+    The details of adadelta please refer to this
+    `ADADELTA: AN ADAPTIVE LEARNING RATE METHOD
+    <http://www.matthewzeiler.com/pubs/googleTR2012/googleTR2012.pdf>`_.
+
+    ..  math::
+
+        E(g_t^2) &= \\rho * E(g_{t-1}^2) + (1-\\rho) * g^2 \\\\
+        learning\\_rate &= sqrt( ( E(dx_{t-1}^2) + \\epsilon ) / ( \\
+                          E(g_t^2) + \\epsilon ) ) \\\\
+        E(dx_t^2) &= \\rho * E(dx_{t-1}^2) + (1-\\rho) * (-g*learning\\_rate)^2
+
+    Args:
+        learning_rate(float): global leraning rate
+        rho(float): rho in equation
+        epsilon(float): epsilon in equation
+
+    Examples:
+        .. code-block:: python
+
+            optimizer = fluid.optimizer.Adadelta(
+                learning_rate=0.0003, epsilon=1.0e-6, rho=0.95)
+            _, params_grads = optimizer.minimize(cost)
+    """
+
+    _avg_squared_grad_acc_str = "_avg_squared_grad"
+    _avg_squared_update_acc_str = "_avg_squared_update"
+
+    def __init__(self, learning_rate, epsilon=1.0e-6, rho=0.95, **kwargs):
+        if learning_rate is None:
+            raise ValueError("learning_rate is not set.")
+        if epsilon is None:
+            raise ValueError("epsilon is not set.")
+        if rho is None:
+            raise ValueError("rho is not set.")
+        super(AdadeltaOptimizer, self).__init__(
+            learning_rate=learning_rate, **kwargs)
+        self.type = "adadelta"
+        self._epsilon = epsilon
+        self._rho = rho
+
+    def _create_accumulators(self, block, parameters):
+        if not isinstance(block, framework.Block):
+            raise TypeError("block is not instance of framework.Block.")
+
+        for p in parameters:
+            self._add_accumulator(self._avg_squared_grad_acc_str, p)
+            self._add_accumulator(self._avg_squared_update_acc_str, p)
+
+    def _append_optimize_op(self, block, param_and_grad):
+        if not isinstance(block, framework.Block):
+            raise TypeError("block is not instance of framework.Block.")
+
+        avg_squared_grad_acc = self._get_accumulator(
+            self._avg_squared_grad_acc_str, param_and_grad[0])
+        avg_squared_update_acc = self._get_accumulator(
+            self._avg_squared_update_acc_str, param_and_grad[0])
+
+        # Create the adadelta optimizer op
+        adadelta_op = block.append_op(
+            type=self.type,
+            inputs={
+                "Param": param_and_grad[0],
+                "Grad": param_and_grad[1],
+                "AvgSquaredGrad": avg_squared_grad_acc,
+                "AvgSquaredUpdate": avg_squared_update_acc
+            },
+            outputs={
+                "ParamOut": param_and_grad[0],
+                "AvgSquaredGradOut": avg_squared_grad_acc,
+                "AvgSquaredUpdateOut": avg_squared_update_acc
+            },
+            attrs={"epsilon": self._epsilon,
+                   "rho": self._rho})
+
+        return adadelta_op
+
+
 # We short the class name, since users will use the optimizer with the package
 # name. The sample code:
 #
@@ -594,3 +678,4 @@ Adagrad = AdagradOptimizer
 Adam = AdamOptimizer
 Adamax = AdamaxOptimizer
 DecayedAdagrad = DecayedAdagradOptimizer
+Adadelta = AdadeltaOptimizer

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_dropout_op.py

@@ -14,6 +14,7 @@
 
 import unittest
 import numpy as np
+import paddle.fluid.core as core
 from op_test import OpTest
 
 
@@ -82,5 +83,37 @@ class TestDropoutOp5(OpTest):
         self.check_output()
 
 
+class TestFP16DropoutOp(OpTest):
+    def setUp(self):
+        self.op_type = "dropout"
+        self.init_test_case()
+
+        x = np.random.random(self.input_size).astype("float16")
+        out = x * (1.0 - self.prob)
+        self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
+        self.attrs = {
+            'dropout_prob': self.prob,
+            'fix_seed': self.fix_seed,
+            'is_test': True
+        }
+        self.outputs = {'Out': out}
+
+    def init_test_case(self):
+        self.input_size = [32, 64]
+        self.prob = 0.35
+        self.fix_seed = True
+
+    def test_check_output(self):
+        if core.is_compiled_with_cuda() and core.op_support_gpu("dropout"):
+            self.check_output_with_place(core.CUDAPlace(0), atol=1e-3)
+
+
+class TestFP16DropoutOp2(TestFP16DropoutOp):
+    def init_test_case(self):
+        self.input_size = [32, 64, 3]
+        self.prob = 0.75
+        self.fix_seed = False
+
+
 if __name__ == '__main__':
     unittest.main()

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):
@@ -327,6 +327,15 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(loss)
         print(str(program))
 
+    def test_lod_reset(self):
+        program = Program()
+        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=2)
+            print(layers.lod_reset(x=x, y=y))
+        print(str(program))
+
 
 if __name__ == '__main__':
     unittest.main()

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

@@ -42,7 +42,7 @@ class TestLodResetOpByInput(OpTest):
         target_lod_0 = [0, 4, 7, 10]
         self.inputs = {
             'X': (x, lod),
-            'TargetLoD': np.array([target_lod_0]).astype('int32')
+            'Y': np.array([target_lod_0]).astype('int32')
         }
         self.outputs = {'Out': (x, [target_lod_0])}
 
@@ -50,7 +50,7 @@ class TestLodResetOpByInput(OpTest):
         self.check_output()
 
     def test_check_grad(self):
-        self.check_grad(["X"], "Out", no_grad_set=set("TargetLoD"))
+        self.check_grad(["X"], "Out", no_grad_set=set("Y"))
 
 
 class TestLodResetOpBoth(OpTest):
@@ -62,7 +62,7 @@ class TestLodResetOpBoth(OpTest):
         target_lod_0_in = [0, 4, 7, 10]
         self.inputs = {
             'X': (x, lod),
-            'TargetLoD': np.array(target_lod_0_in).astype('int32')
+            'Y': np.array(target_lod_0_in).astype('int32')
         }
         self.attrs = {'target_lod': target_lod_0_attr}
         self.outputs = {'Out': (x, [target_lod_0_in])}
@@ -71,7 +71,24 @@ class TestLodResetOpBoth(OpTest):
         self.check_output()
 
     def test_check_grad(self):
-        self.check_grad(["X"], "Out", no_grad_set=set("TargetLoD"))
+        self.check_grad(["X"], "Out", no_grad_set=set("Y"))
+
+
+class TestLodResetOpYIsLoDTensor(OpTest):
+    def setUp(self):
+        self.op_type = "lod_reset"
+        x = np.random.random((10, 20)).astype("float32")
+        lod = [[0, 3, 5, 10]]
+        y = np.random.random((10, 10)).astype("float32")
+        target_lod_0 = [[0, 4, 7, 10]]
+        self.inputs = {'X': (x, lod), 'Y': (y, target_lod_0)}
+        self.outputs = {'Out': (x, target_lod_0)}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(["X"], "Out", no_grad_set=set("Y"))
 
 
 if __name__ == '__main__':

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)}