Merge remote-tracking branch 'upstream/develop' into factorization_machine_layer

cmake/generic.cmake

@@ -389,13 +389,60 @@ function(go_test TARGET_NAME)
     WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
 endfunction(go_test)
 
+# Modification of standard 'protobuf_generate_cpp()' with protobuf-lite support
+# Usage:
+#   paddle_protobuf_generate_cpp(<proto_srcs> <proto_hdrs> <proto_files>)
+
+function(paddle_protobuf_generate_cpp SRCS HDRS)
+  if(NOT ARGN)
+    message(SEND_ERROR "Error: paddle_protobuf_generate_cpp() called without any proto files")
+    return()
+  endif()
+
+  set(${SRCS})
+  set(${HDRS})
+
+  if (MOBILE_INFERENCE)
+      set(EXTRA_FLAG "lite:")  
+  else()
+      set(EXTRA_FLAG "") 
+  endif()
+
+  foreach(FIL ${ARGN})
+    get_filename_component(ABS_FIL ${FIL} ABSOLUTE)
+    get_filename_component(FIL_WE ${FIL} NAME_WE)
+    
+    set(_protobuf_protoc_src "${CMAKE_CURRENT_BINARY_DIR}/${FIL_WE}.pb.cc")
+    set(_protobuf_protoc_hdr "${CMAKE_CURRENT_BINARY_DIR}/${FIL_WE}.pb.h")
+    list(APPEND ${SRCS} "${_protobuf_protoc_src}")
+    list(APPEND ${HDRS} "${_protobuf_protoc_hdr}")
+    
+    add_custom_command(
+      OUTPUT "${_protobuf_protoc_src}"
+             "${_protobuf_protoc_hdr}"
+
+      COMMAND ${CMAKE_COMMAND} -E make_directory "${CMAKE_CURRENT_BINARY_DIR}"
+      COMMAND ${PROTOBUF_PROTOC_EXECUTABLE} 
+      -I${CMAKE_CURRENT_SOURCE_DIR}
+      --cpp_out "${EXTRA_FLAG}${CMAKE_CURRENT_BINARY_DIR}" ${ABS_FIL}
+      DEPENDS ${ABS_FIL} protoc
+      COMMENT "Running C++ protocol buffer compiler on ${FIL}"
+      VERBATIM )
+  endforeach()
+
+  set_source_files_properties(${${SRCS}} ${${HDRS}} PROPERTIES GENERATED TRUE)
+  set(${SRCS} ${${SRCS}} PARENT_SCOPE)
+  set(${HDRS} ${${HDRS}} PARENT_SCOPE)
+endfunction()
+
+
 function(proto_library TARGET_NAME)
   set(oneValueArgs "")
   set(multiValueArgs SRCS DEPS)
   cmake_parse_arguments(proto_library "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
   set(proto_srcs)
   set(proto_hdrs)
-  protobuf_generate_cpp(proto_srcs proto_hdrs ${proto_library_SRCS})
+  paddle_protobuf_generate_cpp(proto_srcs proto_hdrs ${proto_library_SRCS})
   cc_library(${TARGET_NAME} SRCS ${proto_srcs} DEPS ${proto_library_DEPS} protobuf)
 endfunction()
 

paddle/framework/CMakeLists.txt

@@ -43,11 +43,11 @@ cc_library(backward SRCS backward.cc DEPS net_op)
 cc_test(backward_test SRCS backward_test.cc DEPS backward recurrent_op device_context)
 
 cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto backward ${GLOB_OP_LIB})
-if(WITH_GPU)
-    nv_test(executor_test SRCS executor_test.cc DEPS executor)
-else()
-    cc_test(executor_test SRCS executor_test.cc DEPS executor)
-endif()
+#if(WITH_GPU)
+#    nv_test(executor_test SRCS executor_test.cc DEPS executor)
+#else()
+#    cc_test(executor_test SRCS executor_test.cc DEPS executor)
+#endif()
 
 cc_library(tensor_array SRCS tensor_array.cc DEPS lod_tensor)
 cc_test(tensor_array_test SRCS tensor_array_test.cc DEPS tensor_array place)

paddle/framework/executor_test.cc

@@ -25,16 +25,6 @@ limitations under the License. */
 #include "paddle/framework/op_registry.h"
 #include "paddle/framework/operator.h"
 
-USE_OP(elementwise_add);
-USE_OP(gaussian_random);
-USE_OP(feed);
-USE_OP(fetch);
-USE_OP(mul);
-USE_OP(sum);
-USE_OP(squared_l2_distance);
-USE_OP(fill_constant);
-USE_OP(sgd);
-
 using namespace paddle::platform;
 using namespace paddle::framework;
 

paddle/operators/CMakeLists.txt

@@ -112,7 +112,9 @@ set(DEPS_OPS
     cond_op
     cross_entropy_op
     softmax_with_cross_entropy_op
-    sum_op)
+    sum_op
+    pool_op
+    pool_with_index_op)
 
 
 op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
@@ -121,6 +123,8 @@ op_library(cond_op SRCS cond_op.cc DEPS framework_proto tensor operator net_op)
 op_library(cross_entropy_op DEPS cross_entropy)
 op_library(softmax_with_cross_entropy_op DEPS cross_entropy softmax)
 op_library(sum_op DEPS net_op)
+op_library(pool_op DEPS pooling)
+op_library(pool_with_index_op DEPS pooling)
 
 list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
 foreach(src ${GENERAL_OPS})

paddle/operators/margin_rank_loss_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#include "paddle/operators/margin_rank_loss_op.h"
+
+namespace paddle {
+namespace operators {
+
+class MarginRankLossOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    // input check
+    PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) shouldn't be null.");
+    PADDLE_ENFORCE(ctx->HasInput("X1"), "Input(X1) shouldn't be null.");
+    PADDLE_ENFORCE(ctx->HasInput("X2"), "Input(X2) shouldn't be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) shouldn't be null.");
+    auto label_dims = ctx->GetInputDim("Label");
+    auto x1_dims = ctx->GetInputDim("X1");
+    auto x2_dims = ctx->GetInputDim("X2");
+    PADDLE_ENFORCE(
+        (label_dims == x1_dims) && (x1_dims == x2_dims) &&
+            (label_dims.size() == 2) && (label_dims[1] == 1),
+        "All inputs must be 2-D tensor with shape [batch_size x 1].");
+    ctx->SetOutputDim("Activated", label_dims);
+    ctx->SetOutputDim("Out", label_dims);
+  }
+};
+
+template <typename T>
+class MarginRankLossOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  MarginRankLossOpMaker(framework::OpProto *proto,
+                        framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X1",
+             "(2-D tensor with shape [batch_size x 1]) The score for "
+             "one item X1 to be ranked, from pairwise ranking model.");
+    AddInput("X2",
+             "(2-D tensor with shape [batch_size x 1]) The score for "
+             "another item X2 to be ranked, from pairwise ranking model.");
+    AddInput("Label",
+             "(2-D tensor with shape [batch_size x 1]) "
+             "The label indicating X1 ranked higher than X2 or not, "
+             "can only be +1 or -1.");
+    AddAttr<T>("margin", "(scalar, default 0) Margin for MarginRankLossOp.")
+        .SetDefault(static_cast<T>(0));
+    AddOutput("Activated",
+              "(2-D tensor with shape [batch_size x 1]) Intermediate tensor "
+              "to indicate whether each element of Output(Out) is activated.")
+        .AsIntermediate();
+    AddOutput("Out",
+              "(2-D tensor with shape [batch_size x 1]) "
+              "The output loss of MarginRankLoss operator.");
+    AddComment(R"DOC(
+
+MarginRankLoss operator measures the loss given a pair of training sample
+{`X1`, `X2`} and the `Label` with attribute `margin`, where `Label = +1` 
+indicating X1 is ranked higher than `X2`, otherwise `Label = -1`. The loss 
+turns out
+
+loss(X1, X2, Label) = max(0, -Label * (X1 - X2) + margin).
+
+The attribute `margin` involved here helps make the predictions more robust.
+Denote the item ranked higher as the positive sample, otherwise the negative 
+sample. If the score of the two samples satisfies 
+
+positive sample - negative sample < margin,
+
+the pair of samples will contribute to the final loss, which will backpropogate 
+and train the ranking model to enlarge the difference of the two score.
+
+For batch input with size `batch_size`, `X1`, `X2` and `Label`
+all have the same shape [batch_size x 1].
+
+)DOC");
+  }
+};
+
+class MarginRankLossGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) shouldn't be null.");
+    PADDLE_ENFORCE(ctx->HasInput("X1"), "Input(X1) shouldn't be null.");
+    PADDLE_ENFORCE(ctx->HasInput("X2"), "Input(X2) shouldn't be null.");
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
+                   "Input(Out@GRAD) shouldn't be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Activated"),
+                   "Intermediate(Activated) shouldn't be null.");
+    auto dims = ctx->GetInputDim("Label");
+    ctx->SetOutputDim(framework::GradVarName("X1"), dims);
+    ctx->SetOutputDim(framework::GradVarName("X2"), dims);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+namespace ops = paddle::operators;
+
+REGISTER_OP(margin_rank_loss, ops::MarginRankLossOp,
+            ops::MarginRankLossOpMaker<float>, margin_rank_loss_grad,
+            ops::MarginRankLossGradOp);
+REGISTER_OP_CPU_KERNEL(
+    margin_rank_loss,
+    ops::MarginRankLossKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    margin_rank_loss_grad,
+    ops::MarginRankLossGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/margin_rank_loss_op.cu

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#include "paddle/operators/margin_rank_loss_op.h"
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_GPU_KERNEL(
+    margin_rank_loss,
+    ops::MarginRankLossKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+    margin_rank_loss_grad,
+    ops::MarginRankLossGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/margin_rank_loss_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#pragma once
+
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+struct ReLU {
+  HOSTDEVICE T operator()(const T& val) const {
+    return val > 0 ? val : static_cast<T>(0);
+  }
+};
+
+template <typename T>
+struct Heaviside {
+  HOSTDEVICE T operator()(const T& val) const {
+    return static_cast<T>(val > 0 ? 1 : 0);
+  }
+};
+
+template <typename Place, typename T>
+class MarginRankLossKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const {
+    auto* out_t = ctx.Output<framework::Tensor>("Out");
+    auto* act_t = ctx.Output<framework::Tensor>("Activated");
+
+    auto* label_t = ctx.Input<framework::Tensor>("Label");
+    auto* x1_t = ctx.Input<framework::Tensor>("X1");
+    auto* x2_t = ctx.Input<framework::Tensor>("X2");
+
+    out_t->mutable_data<T>(ctx.GetPlace());
+    act_t->mutable_data<T>(ctx.GetPlace());
+
+    auto margin = static_cast<T>(ctx.Attr<T>("margin"));
+    auto out = framework::EigenVector<T>::Flatten(*out_t);
+    auto act = framework::EigenVector<T>::Flatten(*act_t);
+
+    auto label = framework::EigenVector<T>::Flatten(*label_t);
+    auto x1 = framework::EigenVector<T>::Flatten(*x1_t);
+    auto x2 = framework::EigenVector<T>::Flatten(*x2_t);
+
+    auto& dev = ctx.GetEigenDevice<Place>();
+    out.device(dev) = (-label * (x1 - x2) + margin).unaryExpr(ReLU<T>());
+    act.device(dev) = out.unaryExpr(Heaviside<T>());
+  }
+};
+
+template <typename Place, typename T>
+class MarginRankLossGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const {
+    auto* d_x1_t =
+        ctx.Output<framework::LoDTensor>(framework::GradVarName("X1"));
+    auto* d_x2_t =
+        ctx.Output<framework::LoDTensor>(framework::GradVarName("X2"));
+
+    auto* act_t = ctx.Input<framework::Tensor>("Activated");
+    auto* d_out_t = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
+    auto* label_t = ctx.Input<framework::Tensor>("Label");
+
+    auto d_out = framework::EigenVector<T>::Flatten(*d_out_t);
+    auto act = framework::EigenVector<T>::Flatten(*act_t);
+    auto label = framework::EigenVector<T>::Flatten(*label_t);
+    auto& dev = ctx.GetEigenDevice<Place>();
+
+    // compute d_x1
+    if (d_x1_t) {
+      d_x1_t->mutable_data<T>(ctx.GetPlace());
+      auto d_x1 = framework::EigenVector<T>::Flatten(*d_x1_t);
+      d_x1.device(dev) = -d_out * act * label;
+    }
+    // compute d_x2
+    if (d_x2_t) {
+      d_x2_t->mutable_data<T>(ctx.GetPlace());
+      auto d_x2 = framework::EigenVector<T>::Flatten(*d_x2_t);
+      d_x2.device(dev) = d_out * act * label;
+    }
+  }
+};
+}  // namespace operators
+}  // namespace paddle

paddle/operators/math/CMakeLists.txt

 if(WITH_GPU)
-    nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc im2col.cu pooling.cc pooling.cu DEPS cblas device_context operator)
+    nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc im2col.cu DEPS cblas device_context operator)
     nv_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)
     nv_library(softmax SRCS softmax.cc softmax.cu DEPS operator)
     nv_library(cross_entropy SRCS cross_entropy.cc cross_entropy.cu DEPS operator)
+    nv_library(pooling SRCS pooling.cc pooling.cu DEPS device_context)
     nv_library(vol2col SRCS vol2col.cc vol2col.cu DEPS device_context)
 else()
-    cc_library(math_function SRCS math_function.cc im2col.cc pooling.cc DEPS cblas device_context operator)
+    cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context operator)
     cc_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)
     cc_library(softmax SRCS softmax.cc DEPS operator)
     cc_library(cross_entropy SRCS cross_entropy.cc DEPS operator)
+    cc_library(pooling SRCS pooling.cc DEPS device_context)
     cc_library(vol2col SRCS vol2col.cc DEPS device_context)
 endif()
 

paddle/operators/pool_op.cc

@@ -22,157 +22,181 @@ int OutputSizePool(int input_size, int filter_size, int padding, int stride) {
   return output_size;
 }
 
-class PoolOp : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
- protected:
-  void InferShape(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"),
-                   "X(Input) of Pooling should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                   "Out(Output) of Pooling should not be null.");
-
-    auto in_x_dims = ctx->GetInputDim("X");
-
-    std::string pooling_type = ctx->Attrs().Get<std::string>("poolingType");
-    std::vector<int> ksize = ctx->Attrs().Get<std::vector<int>>("ksize");
-    std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
-    std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
-
-    PADDLE_ENFORCE(pooling_type == "max" || pooling_type == "avg",
-                   "pooling_type should be 'max' or 'avg'");
-    PADDLE_ENFORCE(in_x_dims.size() == 4 || in_x_dims.size() == 5,
-                   "Pooling intput should be 4-D or 5-D");
-
-    if (ctx->Attrs().Get<bool>("globalPooling")) {
-      ksize.resize(static_cast<size_t>(in_x_dims.size()) - 2);
-      for (size_t i = 0; i < ksize.size(); ++i)
-        ksize[i] = static_cast<int>(in_x_dims[i + 2]);
-    }
-
-    PADDLE_ENFORCE(in_x_dims.size() - ksize.size() == 2U,
-                   "Input size and Pooling size should be consistent.");
-    PADDLE_ENFORCE(ksize.size() == 2 || ksize.size() == 3,
-                   "Pooling size should be 2 elements. or 3 elements.");
-    PADDLE_ENFORCE_EQ(ksize.size(), strides.size(),
-                      "strides size and pooling size should be the same.");
-    PADDLE_ENFORCE_EQ(ksize.size(), paddings.size(),
-                      "paddings size and pooling size should be the same.");
-
-    std::vector<int64_t> output_shape({in_x_dims[0], in_x_dims[1]});
-    for (size_t i = 0; i < ksize.size(); ++i) {
-      output_shape.push_back(
-          OutputSizePool(in_x_dims[i + 2], ksize[i], paddings[i], strides[i]));
-    }
-    ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
+void PoolOp::InferShape(framework::InferShapeContext *ctx) const {
+  PADDLE_ENFORCE(ctx->HasInput("X"), "X(Input) of Pooling should not be null.");
+  PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                 "Out(Output) of Pooling should not be null.");
+
+  auto in_x_dims = ctx->GetInputDim("X");
+
+  std::string pooling_type = ctx->Attrs().Get<std::string>("poolingType");
+  std::vector<int> ksize = ctx->Attrs().Get<std::vector<int>>("ksize");
+  std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
+  std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
+
+  PADDLE_ENFORCE(in_x_dims.size() == 4 || in_x_dims.size() == 5,
+                 "Pooling intput should be 4-D or 5-D tensor.");
+
+  if (ctx->Attrs().Get<bool>("globalPooling")) {
+    ksize.resize(static_cast<size_t>(in_x_dims.size()) - 2);
+    for (size_t i = 0; i < ksize.size(); ++i)
+      ksize[i] = static_cast<int>(in_x_dims[i + 2]);
   }
-};
-
-class PoolOpGrad : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
- protected:
-  void InferShape(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"),
-                   "X(Input) of Pooling should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
-                   "Input@Grad of Pooling should not be null.");
-    ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
+
+  PADDLE_ENFORCE(in_x_dims.size() - ksize.size() == 2U,
+                 "Input size and pooling size should be consistent.");
+  PADDLE_ENFORCE_EQ(ksize.size(), strides.size(),
+                    "Strides size and pooling size should be the same.");
+  PADDLE_ENFORCE_EQ(ksize.size(), paddings.size(),
+                    "Paddings size and pooling size should be the same.");
+
+  std::vector<int64_t> output_shape({in_x_dims[0], in_x_dims[1]});
+  for (size_t i = 0; i < ksize.size(); ++i) {
+    output_shape.push_back(
+        OutputSizePool(in_x_dims[i + 2], ksize[i], paddings[i], strides[i]));
   }
-};
-
-class Pool2dOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  Pool2dOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
-      : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput(
-        "X",
-        "The input tensor of pooling operator. "
-        "The format of input tensor is NCHW. Where N is batch size, C is the "
-        "number of channels, H and W is the height and width of feature.");
-    AddOutput("Out",
-              "The output tensor of pooling operator."
-              "The format of output tensor is also NCHW.");
-
-    AddAttr<std::string>("poolingType",
-                         "PoolingType of pooling operator."
-                         "Str constant equal to 'max' or 'avg'.")
-        .InEnum({"max", "avg"});
-    AddAttr<std::vector<int>>(
-        "ksize",
-        "Pooling size(depth, height, width) of pooling operator."
-        "If globalPooling = true, ksize is ignored and need not be "
-        "specified.");  // TODO(Add checker)
-    AddAttr<bool>(
-        "globalPooling",
-        "Whether to use the globalPooling."
-        "Bool constant equal to false or true."
-        "Default false."
-        "If globalPooling = true, ksize is ignored and need not be specified.")
-        .SetDefault(false);
-    AddAttr<std::vector<int>>("strides",
-                              "Strides(height, width) of pooling operator."
-                              "Default {1,1}")
-        .SetDefault({1, 1});  // TODO(Add checker)
-    AddAttr<std::vector<int>>("paddings",
-                              "Paddings(height, width) of pooling operator."
-                              "Default {0,0}.")
-        .SetDefault({0, 0});  // TODO(Add checker)
-    AddComment(R"DOC(
+  ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
+}
+
+void PoolOpGrad::InferShape(framework::InferShapeContext *ctx) const {
+  PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
+  PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
+                 "Input(X@GRAD) should not be null.");
+  ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
+}
+
+Pool2dOpMaker::Pool2dOpMaker(framework::OpProto *proto,
+                             framework::OpAttrChecker *op_checker)
+    : OpProtoAndCheckerMaker(proto, op_checker) {
+  AddInput(
+      "X",
+      "(Tensor) The input tensor of pooling operator. "
+      "The format of input tensor is NCHW. Where N is batch size, C is the "
+      "number of channels, H and W is the height and width of feature.");
+  AddOutput("Out",
+            "(Tensor) The output tensor of pooling operator."
+            "The format of output tensor is also NCHW."
+            "Where N is batch size, C is "
+            "the number of channels, H and W is the height and "
+            "width of feature.");
+
+  AddAttr<std::string>("poolingType",
+                       "PoolingType of pooling operator."
+                       "Str constant equal to 'max' or 'avg'.")
+      .InEnum({"max", "avg"});
+
+  AddAttr<std::vector<int>>(
+      "ksize",
+      "The pooling window size(height, width) of pooling operator."
+      "If globalPooling = true, ksize is ignored and need not be "
+      "specified.");  // TODO(Chengduo): Add checker. (Currently,
+                      // TypedAttrChecker don't support vector type.)
+  AddAttr<bool>(
+      "globalPooling",
+      "Whether to use the globalPooling."
+      "Bool constant equal to false or true."
+      "Default false."
+      "If globalPooling = true, ksize is ignored and need not be specified.")
+      .SetDefault(false);
+  AddAttr<std::vector<int>>("strides",
+                            "The strides(height, width) of pooling window."
+                            "Default {1,1}.")
+      .SetDefault({1, 1});  // TODO(Chengduo): Add checker. (Currently,
+                            // TypedAttrChecker don't support vector type.)
+  AddAttr<std::vector<int>>("paddings",
+                            "The zero padding(height, width) size on both sides"
+                            "Default {0,0}.")
+      .SetDefault({0, 0});  // TODO(Chengduo): Add checker. (Currently,
+                            // TypedAttrChecker don't support vector type.)
+
+  AddComment(R"DOC(
 The pooling2d operation calculates the output based on
 the input, poolingType and ksize, strides, paddings parameters.
+Input(X) and output(Out) are in NCHW format. Where N is batch size, C is the
+number of channels, H and W is the height and width of feature.
+Parameters(ksize, strides, paddings) are two elements.
+These two elements represent height and width, respectively.
+The input(X) size and output(Out) size may be different.
+
+Example:
+  Input:
+       X shape: (N, C, H_in, W_in)
+  Output:
+       Out shape: (N, C, H_out, W_out)
+       Mask shape: (N, C, H_out, W_out)
+  where
+       H_out = (H_in - ksize[0] + 2 * paddings[0]) / strides[0] + 1;
+       W_out = (W_in - ksize[1] + 2 * paddings[1]) / strides[1] + 1;
 )DOC");
-  }
-};
-
-class Pool3dOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  Pool3dOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
-      : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X",
-             "The input tensor of pooling operator. "
-             "The format of input tensor is NCDHW. Where N is batch size, C is "
-             "the "
-             "number of channels, D, H and W is the depth, height and width of "
-             "feature.");
-    AddOutput("Out",
-              "The output tensor of pooling operator."
-              "The format of output tensor is also NCDHW.");
-
-    AddAttr<std::string>("poolingType",
-                         "PoolingType of pooling operator."
-                         "str constant equal to 'max' or 'avg'.")
-        .InEnum({"max", "avg"});
-    AddAttr<std::vector<int>>(
-        "ksize",
-        "Pooling size(depth, height, width) of pooling operator."
-        "If globalPooling = true, ksize is ignored and need not be "
-        "specified.");  // TODO(Add checker)
-    AddAttr<bool>(
-        "globalPooling",
-        "Whether to use the globalPooling."
-        "Bool constant equal to false or true."
-        "Default false."
-        "If globalPooling = true, ksize is ignored and need not be specified.")
-        .SetDefault(false);
-    AddAttr<std::vector<int>>(
-        "strides",
-        "Strides(depth, height, width) of pooling operator."
-        "Default {1,1,1}.")
-        .SetDefault({1, 1, 1});  // TODO(Add checker)
-    AddAttr<std::vector<int>>(
-        "paddings",
-        "Paddings(depth, height, width) of pooling operator."
-        "Default {0,0,0}.")
-        .SetDefault({0, 0, 0});  // TODO(Add checker)
-    AddComment(R"DOC(
+}
+
+Pool3dOpMaker::Pool3dOpMaker(framework::OpProto *proto,
+                             framework::OpAttrChecker *op_checker)
+    : OpProtoAndCheckerMaker(proto, op_checker) {
+  AddInput(
+      "X",
+      "(Tensor) The input tensor of pooling operator. "
+      "The format of input tensor is NCDHW. Where N is batch size, C is "
+      "the number of channels, D, H and W is the depth, height and width of "
+      "feature.");
+  AddOutput("Out",
+            "(Tensor) The output tensor of pooling operator."
+            "The format of output tensor is also NCDHW."
+            "Where N is batch size, C is "
+            "the number of channels, D, H and W is the depth, height and "
+            "width of feature.");
+
+  AddAttr<std::string>("poolingType",
+                       "PoolingType of pooling operator."
+                       "Str constant equal to 'max' or 'avg'.")
+      .InEnum({"max", "avg"});
+
+  AddAttr<std::vector<int>>(
+      "ksize",
+      "The pooling window size(depth, height, width) of pooling operator."
+      "If globalPooling = true, ksize is ignored and need not be "
+      "specified.");  // TODO(Chengduo): Add checker. (Currently,
+                      // TypedAttrChecker don't support vector type.)
+  AddAttr<bool>(
+      "globalPooling",
+      "Whether to use the globalPooling."
+      "Bool constant equal to false or true."
+      "Default false."
+      "If globalPooling = true, ksize is ignored and need not be specified.")
+      .SetDefault(false);
+  AddAttr<std::vector<int>>("strides",
+                            "Strides(depth, height, width) of pooling operator."
+                            "Default {1,1,1}.")
+      .SetDefault({1, 1, 1});  // TODO(Chengduo): Add checker. (Currently,
+                               // TypedAttrChecker don't support vector type.)
+  AddAttr<std::vector<int>>(
+      "paddings",
+      "Paddings(depth, height, width) of pooling operator."
+      "Default {0,0,0}.")
+      .SetDefault({0, 0, 0});  // TODO(Chengduo): Add checker. (Currently,
+                               // TypedAttrChecker don't support vector type.)
+
+  AddComment(R"DOC(
 The pooling3d operation calculates the output based on
 the input, poolingType and ksize, strides, paddings parameters.
+Input(X) and output(Out) are in NCDHW format. Where N is batch
+size, C is the number of channels, D, H and W is the depth, height and
+width of feature. Parameters(ksize, strides, paddings) are three elements.
+These three elements represent depth, height and width, respectively.
+The input(X) size and output(Out) size may be different.
+
+Example:
+  Input:
+       X shape: (N, C, D_in, H_in, W_in)
+  Output:
+       Out shape: (N, C, D_out, H_out, W_out)
+       Mask shape: (N, C, D_out, H_out, W_out)
+  where
+       D_out = (D_in - ksize[0] + 2 * paddings[0]) / strides[0] + 1;
+       H_out = (H_in - ksize[1] + 2 * paddings[1]) / strides[1] + 1;
+       W_out = (W_in - ksize[2] + 2 * paddings[2]) / strides[2] + 1;
 )DOC");
-  }
-};
+}
 }  // namespace operators
 }  // namespace paddle
 

paddle/operators/pool_op.h

@@ -24,6 +24,34 @@ namespace operators {
 
 using Tensor = framework::Tensor;
 
+class PoolOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override;
+};
+
+class PoolOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override;
+};
+
+class Pool2dOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  Pool2dOpMaker(framework::OpProto* proto,
+                framework::OpAttrChecker* op_checker);
+};
+
+class Pool3dOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  Pool3dOpMaker(framework::OpProto* proto,
+                framework::OpAttrChecker* op_checker);
+};
+
 template <typename Place, typename T>
 class PoolKernel : public framework::OpKernel<T> {
  public:

paddle/operators/pool_with_index_op.cc

@@ -43,7 +43,7 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
     std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
 
     PADDLE_ENFORCE(in_x_dims.size() == 4 || in_x_dims.size() == 5,
-                   "Pooling intput should be 4-D or 5-D");
+                   "Pooling intput should be 4-D or 5-D tensor.");
 
     if (ctx->Attrs().Get<bool>("globalPooling")) {
       ksize.resize(static_cast<size_t>(in_x_dims.size()) - 2);
@@ -52,7 +52,7 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
     }
 
     PADDLE_ENFORCE(in_x_dims.size() - ksize.size() == 2U,
-                   "Intput size and pooling size should be consistent.");
+                   "Input size and pooling size should be consistent.");
     PADDLE_ENFORCE_EQ(ksize.size(), strides.size(),
                       "Strides size and pooling size should be the same.");
     PADDLE_ENFORCE_EQ(ksize.size(), paddings.size(),
@@ -74,6 +74,7 @@ class MaxPoolWithIndexOpGrad : public framework::OperatorWithKernel {
 
  protected:
   void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Mask"), "Input(Mask) must not be null.");
     PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
     PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
                    "Input(X@GRAD) should not be null.");
@@ -88,17 +89,17 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput(
         "X",
-        "The input tensor of pooling operator. "
+        "(Tensor) The input tensor of pooling operator. "
         "The format of input tensor is NCHW. Where N is batch size, C is the "
         "number of channels, H and W is the height and width of image.");
     AddOutput("Out",
-              "The output tensor of pooling operator."
+              "(Tensor) The output tensor of pooling operator."
               "The format of output tensor is also NCHW."
               "Where N is batch size, C is "
               "the number of channels, H and W is the height and "
               "width of image.");
     AddOutput("Mask",
-              "The Mask tensor of pooling operator."
+              "(Tensor) The Mask tensor of pooling operator."
               "The format of output tensor is also NCHW."
               "Where N is batch size, C is the number of channels, H and W "
               "is the height and width of image."
@@ -106,7 +107,7 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
 
     AddAttr<std::vector<int>>(
         "ksize",
-        "The pooling size(height, width) of pooling operator."
+        "The pooling window size(height, width) of pooling operator."
         "If globalPooling = true, ksize is ignored and need not be "
         "specified.");  // TODO(Chengduo): Add checker. (Currently,
                         // TypedAttrChecker don't support vector type.)
@@ -118,13 +119,14 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
         "If globalPooling = true, ksize is ignored and need not be specified.")
         .SetDefault(false);
     AddAttr<std::vector<int>>("strides",
-                              "Strides(height, width) of pooling operator."
+                              "The strides(height, width) of pooling window."
                               "Default {1,1}.")
         .SetDefault({1, 1});  // TODO(Chengduo): Add checker. (Currently,
                               // TypedAttrChecker don't support vector type.)
-    AddAttr<std::vector<int>>("paddings",
-                              "Paddings(height, width) of pooling operator."
-                              "Default {0,0}.")
+    AddAttr<std::vector<int>>(
+        "paddings",
+        "The zero padding(height, width) size on both sides"
+        "Default {0,0}.")
         .SetDefault({0, 0});  // TODO(Chengduo): Add checker. (Currently,
                               // TypedAttrChecker don't support vector type.)
 
@@ -135,6 +137,17 @@ output(Out, Mask) are in NCHW format. Where N is batch size, C is the
 number of channels, H and W is the height and width of feature.
 Parameters(ksize, strides, paddings) are two elements.
 These two elements represent height and width, respectively.
+The input(X) size and output(Out, Mask) size may be different.
+
+Example:
+  Input:
+       X shape: (N, C, H_in, W_in)
+  Output:
+       Out shape: (N, C, H_out, W_out)
+       Mask shape: (N, C, H_out, W_out)
+  where
+       H_out = (H_in - ksize[0] + 2 * paddings[0]) / strides[0] + 1;
+       W_out = (W_in - ksize[1] + 2 * paddings[1]) / strides[1] + 1;
 )DOC");
   }
 };
@@ -146,18 +159,18 @@ class MaxPool3dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput(
         "X",
-        "The input tensor of pooling operator. "
+        "(Tensor) The input tensor of pooling operator. "
         "The format of input tensor is NCDHW. Where N is batch size, C is "
         "the number of channels, D, H and W is the depth, height and width of "
         "image.");
     AddOutput("Out",
-              "The output tensor of pooling operator."
+              "(Tensor) The output tensor of pooling operator."
               "The format of output tensor is also NCDHW."
               "Where N is batch size, C is "
               "the number of channels, D, H and W is the depth, height and "
               "width of image.");
     AddOutput("Mask",
-              "The Mask tensor of pooling operator."
+              "(Tensor) The Mask tensor of pooling operator."
               "The format of output tensor is also NCDHW."
               "Where N is batch size, C is the number of channels, D, H and W "
               "is the depth, height and width of image."
@@ -165,7 +178,7 @@ class MaxPool3dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
 
     AddAttr<std::vector<int>>(
         "ksize",
-        "The pooling size(depth, height, width) of pooling operator."
+        "The pooling window size(depth, height, width) of pooling operator."
         "If globalPooling = true, ksize is ignored and need not be "
         "specified.");  // TODO(Chengduo): Add checker. (Currently,
                         // TypedAttrChecker don't support vector type.)
@@ -196,6 +209,18 @@ Input(X) and output(Out, Mask) are in NCDHW format. Where N is batch
 size, C is the number of channels, D, H and W is the depth, height and
 width of feature. Parameters(ksize, strides, paddings) are three elements.
 These three elements represent depth, height and width, respectively.
+The input(X) size and output(Out, Mask) size may be different.
+
+Example:
+  Input:
+       X shape: (N, C, D_in, H_in, W_in)
+  Output:
+       Out shape: (N, C, D_out, H_out, W_out)
+       Mask shape: (N, C, D_out, H_out, W_out)
+  where
+       D_out = (D_in - ksize[0] + 2 * paddings[0]) / strides[0] + 1;
+       H_out = (H_in - ksize[1] + 2 * paddings[1]) / strides[1] + 1;
+       W_out = (W_in - ksize[2] + 2 * paddings[2]) / strides[2] + 1;
 )DOC");
   }
 };

paddle/operators/sequence_concat_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/operators/sequence_concat_op.h"
+
+namespace paddle {
+namespace operators {
+
+class SequenceConcatOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInputs("X"),
+                   "Inputs(X) of SequenceConcatOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of SequenceConcatOp should not be null.");
+    const size_t level = static_cast<size_t>(ctx->Attrs().Get<int>("level"));
+    const size_t axis = static_cast<size_t>(ctx->Attrs().Get<int>("axis"));
+    PADDLE_ENFORCE(level == 0UL || level == 1UL,
+                   "The sequence_concat operator only accepts sequence "
+                   "or a nested sequence as its input.");
+    auto ins_dims = ctx->GetInputsDim("X");
+    framework::DDim out_dims = ins_dims[0];
+    const size_t n = ins_dims.size();
+    for (size_t i = 1; i < n; ++i) {
+      out_dims[axis] += ins_dims[i][axis];
+    }
+    ctx->SetOutputDim("Out", out_dims);
+  }
+};
+
+class SequenceConcatOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  SequenceConcatOpMaker(framework::OpProto* proto,
+                        framework::OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X",
+             "(A vector of LoDTensor), the input is a vector of LoDTensor, "
+             "each of which is a variable-length sequence or nested sequence.")
+        .AsDuplicable();
+    AddOutput("Out",
+              "(A LoDTensor), the variable-length output of "
+              "sequence_concat Op.");
+    AddAttr<int>("axis",
+                 "(int, default 0)"
+                 "The axis which the inputs will be joined with. "
+                 "If axis is 0, the inputs will be joined with LoD index.")
+        .SetDefault(0);
+    AddAttr<int>("level",
+                 "(int, default 0)"
+                 "The level at which the inputs will be joined. "
+                 "If the level is 0, the inputs will be joined at the nested "
+                 "sequence level. "
+                 "If the level is 1, the inputs will be joined at the "
+                 "sequence level. "
+                 "The level should be less than the level number of inputs.")
+        .SetDefault(0);
+    AddComment(R"DOC(
+    The sequence_concat operator concatenates multiple LoDTensors. 
+    It only supports sequence (LoD Tensor with level number is 1) 
+    or a nested sequence (LoD tensor with level number is 2) as its input.
+    - Case1:
+      If the axis is other than 0(here, axis is 1 and level is 1),
+      each input should have the same LoD information and the LoD 
+      information of the output keeps the same as the input.
+
+      LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)
+      LoD(x1) = {{0,2,4}, {0,1,2,3,4}}; Dims(x1) = (4,4,4)
+      LoD(Out) = {{0,2,4}, {0,1,2,3,4}}; Dims(Out) = (4,7,4)
+
+    - Case2:
+      If the axis is 0(here, leve is 0), the inputs are concatenated along 
+      time steps, the LoD information of the output need to re-compute.
+
+      LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)
+      LoD(x1) = {{0,3,5}, {0,1,2,3,5}}; Dims(x1) = (5,3,4)
+      LoD(Out) = {{0,5,9}, {0,1,2,3,4,5,6,7,9}}; Dims(Out) = (9,3,4)
+
+    - Case3:
+      If the axis is 0(here, level is 1).
+
+      LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)
+      LoD(x1) = {{0,3,5}, {0,1,3,4,5}}; Dims(x1) = (5,3,4)
+      LoD(Out) = {{0,5,9}, {0,2,5,7,9}}; Dims(Out) = (9,3,4)
+      
+    NOTE: The levels of all the inputs should be the same.
+    )DOC");
+  }
+};
+
+class SequenceConcatGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
+                   "The gradient of Out should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutputs(framework::GradVarName("X")),
+                   "The gradient of X should not be null.");
+    ctx->SetOutputsDim(framework::GradVarName("X"), ctx->GetInputsDim("X"));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(sequence_concat, ops::SequenceConcatOp, ops::SequenceConcatOpMaker,
+            sequence_concat_grad, ops::SequenceConcatGradOp);
+REGISTER_OP_CPU_KERNEL(
+    sequence_concat,
+    ops::SequenceConcatOpKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    sequence_concat_grad,
+    ops::SequenceConcatGradOpKernel<paddle::platform::CPUPlace, float>);

paddle/operators/sequence_concat_op.cu

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#define EIGEN_USE_GPU
+
+#include "paddle/operators/sequence_concat_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(
+    sequence_concat,
+    ops::SequenceConcatOpKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+    sequence_concat_grad,
+    ops::SequenceConcatGradOpKernel<paddle::platform::GPUPlace, float>);

paddle/operators/sequence_concat_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+#include "paddle/framework/op_registry.h"
+#include "paddle/operators/strided_memcpy.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+using LoD = framework::LoD;
+
+template <typename T>
+LoD concatLoD(const std::vector<const T*> ins, const size_t axis,
+              const size_t level) {
+  auto out_lod = ins[0]->lod();
+  const size_t n = ins.size();
+  if (axis == 0UL) {
+    for (size_t i = 1; i < n; ++i) {
+      for (size_t j = 0; j < ins[i]->lod()[0].size(); ++j) {
+        out_lod[0][j] += ins[i]->lod()[0][j];
+      }
+
+      if (ins[0]->NumLevels() == 2) {
+        for (size_t j = 1; j < ins[i]->lod()[1].size(); ++j) {
+          if (level == 0UL) {
+            out_lod[1].push_back(out_lod[1].back() + ins[i]->lod()[1][j] -
+                                 ins[i]->lod()[1][j - 1]);
+          } else if (level == 1UL) {
+            out_lod[1][j] += ins[1]->lod()[1][j];
+          }
+        }
+      }
+    }
+  }
+  return out_lod;
+}
+
+template <typename Place, typename T>
+class SequenceConcatOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto ins = ctx.MultiInput<LoDTensor>("X");
+    auto* out = ctx.Output<LoDTensor>("Out");
+    const size_t axis = static_cast<size_t>(ctx.Attr<int>("axis"));
+    const size_t level = static_cast<size_t>(ctx.Attr<int>("level"));
+    const size_t n = ins.size();
+
+    for (size_t i = 1; i < n; ++i) {
+      PADDLE_ENFORCE_EQ(ins[0]->NumLevels(), ins[i]->NumLevels(),
+                        "The levels of all the input LoDTensors "
+                        "should be the same.");
+      PADDLE_ENFORCE_EQ(ins[0]->dims().size(), ins[i]->dims().size(),
+                        "The dimension size of all the input LoDTensors "
+                        "should be the same.");
+
+      const size_t dims_size = ins[i]->dims().size();
+      for (size_t j = 0; j < dims_size; ++j) {
+        if (j == axis) continue;
+        PADDLE_ENFORCE_EQ(ins[0]->dims()[j], ins[i]->dims()[j],
+                          "Except for the dimension of the specified "
+                          "axis along which all the inputs are concatenated, "
+                          "dimensions of all the other axises of the input "
+                          "LoDTensors should be the same.");
+      }
+    }
+    PADDLE_ENFORCE_GT(ins[0]->NumLevels(), level,
+                      "The levels of all the input LoDTensors "
+                      "should be greater than the specify level");
+
+    out->mutable_data<T>(ctx.GetPlace());
+    auto out_lod = concatLoD<LoDTensor>(ins, axis, level);
+    out->set_lod(out_lod);
+
+    auto out_lod_level = out_lod[level];
+    for (size_t i = 0; i < out_lod_level.size() - 1; ++i) {
+      Tensor out_t = out->Slice<T>(static_cast<int>(out_lod_level[i]),
+                                   static_cast<int>(out_lod_level[i + 1]));
+      auto out_stride = framework::stride(out_t.dims());
+      size_t offset = 0;
+
+      for (size_t j = 0; j < n; ++j) {
+        auto in_lod_level = ins[j]->lod()[level];
+        auto in_stride = framework::stride(ins[j]->dims());
+        Tensor in_t = ins[j]->Slice<T>(static_cast<int>(in_lod_level[i]),
+                                       static_cast<int>(in_lod_level[i + 1]));
+        size_t axis_dim = in_t.dims()[axis];
+        StridedMemcpy<T>(ctx.device_context(), in_t.data<T>(), in_stride,
+                         in_t.dims(), out_stride, out_t.data<T>() + offset);
+        offset += axis_dim * in_stride[axis];
+      }
+    }
+  }
+};
+
+template <typename Place, typename T>
+class SequenceConcatGradOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto ins = ctx.MultiInput<framework::LoDTensor>("X");
+    auto* out_grad =
+        ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"));
+    auto x_grads =
+        ctx.MultiOutput<framework::LoDTensor>(framework::GradVarName("X"));
+    size_t axis = static_cast<size_t>(ctx.Attr<int>("axis"));
+    size_t level = static_cast<size_t>(ctx.Attr<int>("level"));
+    const size_t n = x_grads.size();
+
+    // Set Grad(X) LoD as X
+    for (size_t i = 0; i < n; i++) {
+      x_grads[i]->set_lod(ins[i]->lod());
+      x_grads[i]->mutable_data<T>(ctx.GetPlace());
+    }
+
+    auto out_lod = concatLoD<LoDTensor>(ins, axis, level);
+    auto out_lod_level = out_lod[level];
+
+    for (size_t i = 0; i < out_lod_level.size() - 1; ++i) {
+      Tensor out_grad_t =
+          out_grad->Slice<T>(static_cast<int>(out_lod_level[i]),
+                             static_cast<int>(out_lod_level[i + 1]));
+      auto out_grad_stride = framework::stride(out_grad_t.dims());
+      size_t offset = 0;
+
+      for (size_t j = 0; j < n; ++j) {
+        auto x_grad_lod_level = x_grads[j]->lod()[level];
+        auto x_grad_stride = framework::stride(x_grads[j]->dims());
+        Tensor x_grad_t =
+            x_grads[j]->Slice<T>(static_cast<int>(x_grad_lod_level[i]),
+                                 static_cast<int>(x_grad_lod_level[i + 1]));
+        size_t axis_dim = x_grad_t.dims()[axis];
+        StridedMemcpy<T>(ctx.device_context(), out_grad_t.data<T>() + offset,
+                         out_grad_stride, out_grad_t.dims(), x_grad_stride,
+                         x_grad_t.data<T>());
+        offset += axis_dim * out_grad_stride[axis];
+      }
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

proto/CMakeLists.txt

-file(GLOB proto_filenames . *.proto)
+if (MOBILE_INFERENCE)
+    file(GLOB proto_filenames . ModelConfig.proto ParameterConfig.proto
+         TrainerConfig.proto DataConfig.proto)
+else()
+    file(GLOB proto_filenames . *.proto)
+endif()
+
 include_directories(${CMAKE_CURRENT_BINARY_DIR})
 proto_library(paddle_proto SRCS ${proto_filenames})
 

python/paddle/v2/framework/tests/test_margin_rank_loss_op.py

+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestMarginRankLossOp(OpTest):
+    def setUp(self):
+        self.op_type = "margin_rank_loss"
+        batch_size = 5
+        margin = 0.5
+        # labels_{i} = {-1, 1}
+        label = 2 * np.random.randint(
+            0, 2, size=(batch_size, 1)).astype("float32") - 1
+        x1 = np.random.random((batch_size, 1)).astype("float32")
+        x2 = np.random.random((batch_size, 1)).astype("float32")
+        # loss = max(0, -label * (x1 - x2) + margin)
+        loss = -label * (x1 - x2) + margin
+        loss = np.where(loss > 0, loss, 0)
+        act = np.where(loss > 0, 1., 0.)
+
+        self.attrs = {'margin': margin}
+        self.inputs = {'Label': label, 'X1': x1, 'X2': x2}
+        self.outputs = {'Activated': act, 'Out': loss}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(["X1", "X2"], "Out")
+
+    def test_check_grad_ignore_x1(self):
+        self.check_grad(["X2"], "Out", no_grad_set=set('X1'))
+
+    def test_check_grad_ignore_x2(self):
+        self.check_grad(["X1"], "Out", no_grad_set=set('X2'))
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/v2/framework/tests/test_seq_concat_op.py

+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestConcatOp(OpTest):
+    def set_data(self):
+        # two level, batch size is 3
+        x0 = np.random.random((4, 6, 3)).astype('float32')
+        lod0 = [[0, 2, 4], [0, 1, 2, 3, 4]]
+        x1 = np.random.random((4, 8, 3)).astype('float32')
+        lod1 = [[0, 2, 4], [0, 1, 2, 3, 4]]
+        axis = 1
+        level = 1
+        self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
+        self.attrs = {'axis': axis, 'level': level}
+        outs = []
+        for i in range(4):
+            sub_x0 = x0[lod0[level][i]:lod0[level][i + 1], :]
+            sub_x1 = x1[lod1[level][i]:lod1[level][i + 1], :]
+            outs.append(np.concatenate((sub_x0, sub_x1), axis=axis))
+
+        self.outputs = {'Out': np.concatenate(outs, axis=0)}
+
+    def setUp(self):
+        self.op_type = "sequence_concat"
+        self.set_data()
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['x0'], 'Out')
+
+
+class TestConcatOpDiffLod(TestConcatOp):
+    def set_data(self):
+        # two level, batch size is 3
+        x0 = np.random.random((4, 6, 3)).astype('float32')
+        lod0 = [[0, 2, 4], [0, 1, 2, 3, 4]]
+        x1 = np.random.random((5, 6, 3)).astype('float32')
+        lod1 = [[0, 3, 5], [0, 1, 2, 3, 5]]
+        axis = 0
+        level = 1
+        self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
+        self.attrs = {'axis': axis, 'level': level}
+        outs = []
+        for i in range(4):
+            sub_x0 = x0[lod0[level][i]:lod0[level][i + 1], :]
+            sub_x1 = x1[lod1[level][i]:lod1[level][i + 1], :]
+            outs.append(np.concatenate((sub_x0, sub_x1), axis=axis))
+
+        self.outputs = {'Out': np.concatenate(outs, axis=0)}
+
+
+class TestConcatOpLevelZero(TestConcatOp):
+    def set_data(self):
+        # two level, batch size is 3
+        x0 = np.random.random((4, 3, 4)).astype('float32')
+        lod0 = [[0, 2, 4], [0, 1, 2, 3, 4]]
+        x1 = np.random.random((5, 3, 4)).astype('float32')
+        lod1 = [[0, 3, 5], [0, 1, 3, 4, 5]]
+        axis = 0
+        level = 0
+        self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
+        self.attrs = {'axis': axis, 'level': level}
+        outs = []
+        for i in range(2):
+            sub_x0 = x0[lod0[level][i]:lod0[level][i + 1], :]
+            sub_x1 = x1[lod1[level][i]:lod1[level][i + 1], :]
+            outs.append(np.concatenate((sub_x0, sub_x1), axis=axis))
+
+        self.outputs = {'Out': np.concatenate(outs, axis=0)}
+
+
+if __name__ == '__main__':
+    unittest.main()