Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into detection_output

15b1e8e8 · sweetsky0901 · 65b641bf · 5926e9a2 · 15b1e8e8 · 15b1e8e8
32 changed file
--- a/RELEASE.cn.md
+++ b/RELEASE.cn.md
+# v0.11.0版本
+## PaddlePaddle Fluid
+- PaddlePaddle发布版本v0.11.0包含一个新的特性*PaddlePaddle Fluid*. Fluid 是设计用来让用户像Pytorch和Tensorflow Eager Execution一样执行程序。在这些系统中，不再有*模型*这个概念，应用也不再包含一个用于描述Operator图或者一系列层的符号描述，而是像通用程序那样描述训练或者预测的过程。而Fluid与PyTorch或Eager Execution的区别在于Fluid不依赖Python提供的控制流，例如 if-else-then或者for，而是提供了基于C++实现的控制流并暴露了对应的用with语法实现的Python接口。例如：
+  https://github.com/PaddlePaddle/Paddle/blob/3df78ed2a98d37f7ae6725894cc7514effd5664b/python/paddle/v2/fluid/tests/test_while_op.py#L36-L44
+- 在v0.11.0版本中，我们提供了一个C++类`Executor`用于运行一个Fluid程序。Executor类似一个解释器。在未来的版本中，我们将提升和优化Executor成为一个调试器，就像GDB。并可能提供一些编译器，这个编译器会读取一个上文所描述的应用然后编译成一个等价的
+源代码，这个源代码可以被nvcc编译成可以使用CUDA的二进制，或者被icc编译成可以充分利用Intel CPU的二进制。
+## 新特点
+* 发布 `PaddlePaddle Fluid`。
+* 增加了用于模型预测的C-API。
+* 用Fluid API实现了一个简单的GAN的例子。
+* 增加了关于性能调优的文档。
+* 为`paddle.v2.dataset`下载数据集提供了重试机制.
+* C++中使用protobuf-lite替换protobuf减少了二进制的大小。
+* 发布了新特性 [Elastic Deep Learning (EDL)](https://github.com/PaddlePaddle/cloud/tree/develop/doc/autoscale/experiment).
+* 基于Bazel API利用cmake实现了一个的新的构建系统函数库。
+* 当使用编译选项`WITH_MKL=ON`时自动下载和编译Intel® [MKLML](https://github.com/01org/mkl-dnn/releases/download/v0.11/mklml_lnx_2018.0.1.20171007.tgz) 函数库.
+* [Intel® MKL-DNN on PaddlePaddle](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/design/mkldnn):
+  - 完成了 11个 MKL-DNN 层: Convolution, Fully connectivity, Pooling, ReLU, Tanh, ELU, Softmax, BatchNorm, AddTo, Concat, LRN。
+  - 完成了 3个 MKL-DNN 网络: VGG-19, ResNet-50, GoogleNet
+  - 基于Intel Skylake 6148 CPU的[性能测试](https://github.com/PaddlePaddle/Paddle/blob/develop/benchmark/IntelOptimizedPaddle.md) : 相对于MKLML有2~3倍的训练加速。
+* 增加 [softsign activation](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/activation.html#softsign)
+* 增加 [dot product layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#dot-prod)
+* 增加 [L2 distance layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#l2-distance)
+* 增加 [sub-nested sequence layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#sub-nested-seq)
+* 增加 [kmax sequence score layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#kmax-sequence-score)
+* 增加 [sequence slice layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#seq-slice)
+* 增加 [row convolution layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#row-conv)
+* 增加移动端友好的网页
+## 改进
+* 使用一个Python`whl`包即可安装.
+* [V2 API可以实现用户定制化评估](https://github.com/PaddlePaddle/models/tree/develop/ltr#训练过程中输出自定义评估指标)。
+* 将 `PADDLE_ONLY_CPU` 改为 `PADDLE_WITH_GPU`, 因为我们会支持多种设备。
+* 删除了有一些bug的BarrierStat。
+* 清理和删除了paddle::Parameter中未使用的函数。
+* 删除了ProtoDataProvider。
+* Huber loss同时支持回归和分类。
+* 为sequence pooling 层增加`stride`参数。
+* v2 API自动使用cudnn batch normalization。
+* 可以使用一个固定的参数名共享BN层的参数。
+* 2D convolution operation支持variable-dimension input特性。
+* 重构cmake中关于CUDA的部分并实现自动检测GPU架构的功能。
+* 优化网页导航。
+## 错误修复
+* 修复ROI pooling的Bug. cc9a761
+* 修复当label是dense vector是AUC变成0的问题. #5274
+* 修复WarpCTC 层的Bug.
 # v0.10.0版本
 我们非常高兴发布了PaddlePaddle V0.10.0版，并开发了新的[Python API](http://research.baidu.com/paddlepaddles-new-api-simplifies-deep-learning-programs/)。

--- a/RELEASE.md
+++ b/RELEASE.md
+# Release v0.11.0
+## PaddlePaddle Fluid
+- Release 0.11.0 includes a new feature *PaddlePaddle Fluid*.  Fluid is
+  designed to allow users to program like PyTorch and TensorFlow Eager Execution.
+  In these systems, there is no longer the concept *model* and applications
+  do not include a symbolic description of a graph of operators nor a sequence
+  of layers. Instead, applications look exactly like a usual program that
+  describes a process of training or inference.  The difference between
+  Fluid and PyTorch or Eager Execution is that Fluid doesn't rely on Python's
+  control-flow, `if-then-else` nor `for`.  Instead, Fluid provides its
+  C++ implementations and their Python binding using the `with` statement.  For an example
+  https://github.com/PaddlePaddle/Paddle/blob/3df78ed2a98d37f7ae6725894cc7514effd5664b/python/paddle/v2/fluid/tests/test_while_op.py#L36-L44
+- In 0.11.0, we provides a C++ class `Executor` to run a Fluid program.
+Executor works like an interpreter. In future version, we will improve
+`Executor` into a debugger like GDB, and we might provide some compilers,
+which, for example, takes an application like the above one, and outputs
+an equivalent C++ source program, which can be compiled using
+[`nvcc`](http://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html)
+to generate binaries that use CUDA, or using
+[`icc`](https://software.intel.com/en-us/c-compilers) to generate binaries
+that make full use of Intel CPUs.
+## New Features
+* Release `PaddlePaddle Fluid`.
+* Add C-API for model inference
+* Use fluid API to create a simple GAN demo.
+* Add develop guide about performance tunning.
+* Add retry when download `paddle.v2.dataset`.
+* Linking protobuf-lite not protobuf in C++. Reduce the binary size.
+* Feature [Elastic Deep Learning (EDL)](https://github.com/PaddlePaddle/cloud/tree/develop/doc/autoscale/experiment) released.
+* A new style cmake functions for Paddle. It is based on Bazel API.
+* Automatically download and compile with Intel® [MKLML](https://github.com/01org/mkl-dnn/releases/download/v0.11/mklml_lnx_2018.0.1.20171007.tgz) library as CBLAS when build `WITH_MKL=ON`.
+* [Intel® MKL-DNN on PaddlePaddle](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/design/mkldnn):
+  - Complete 11 MKL-DNN layers: Convolution, Fully connectivity, Pooling, ReLU, Tanh, ELU, Softmax, BatchNorm, AddTo, Concat, LRN.
+  - Complete 3 MKL-DNN networks: VGG-19, ResNet-50, GoogleNet
+  - [Benchmark](https://github.com/PaddlePaddle/Paddle/blob/develop/benchmark/IntelOptimizedPaddle.md) on Intel Skylake 6148 CPU: 2~3x training speedup compared with MKLML.
+* Add the [`softsign` activation](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/activation.html#softsign).
+* Add the [dot product layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#dot-prod).
+* Add the [L2 distance layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#l2-distance).
+* Add the [sub-nested sequence layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#sub-nested-seq).
+* Add the [kmax sequence score layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#kmax-sequence-score).
+* Add the [sequence slice layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#seq-slice).
+* Add the [row convolution layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#row-conv)
+* Add mobile friendly webpages.
+## Improvements
+* Build and install using a single `whl` package.
+* [Custom evaluating in V2 API](https://github.com/PaddlePaddle/models/tree/develop/ltr#训练过程中输出自定义评估指标).
+* Change `PADDLE_ONLY_CPU` to `PADDLE_WITH_GPU`, since we will support many kinds of devices.
+* Remove buggy BarrierStat.
+* Clean and remove unused functions in paddle::Parameter.
+* Remove ProtoDataProvider.
+* Huber loss supports both regression and classification.
+* Add the `stride` parameter  for sequence pooling layers.
+* Enable v2 API use cudnn batch normalization automatically.
+* The BN layer's parameter can be shared by a fixed the parameter name.
+* Support variable-dimension input feature for 2D convolution operation.
+* Refine cmake about CUDA to automatically detect GPU architecture.
+* Improved website navigation.
+## Bug Fixes
+* Fix bug in ROI pooling. cc9a761
+* Fix AUC is zero when label is dense vector. #5274
+* Fix bug in WarpCTC layer.
 # Release v0.10.0
 We are glad to release version 0.10.0.  In this version, we are happy to release the new 

--- a/benchmark/IntelOptimizedPaddle.md
+++ b/benchmark/IntelOptimizedPaddle.md
@@ -19,6 +19,8 @@ On each machine, we will test and compare the performance of training on single
 ## Benchmark Model
 ### Server
+#### Training
 Test on batch size 64, 128, 256 on Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
 Input image size - 3 * 224 * 224, Time: images/second
@@ -53,5 +55,33 @@ Input image size - 3 * 224 * 224, Time: images/second
 <img src="figs/googlenet-cpu-train.png" width="500">
+#### Inference
+Test on batch size 1, 2, 4, 8, 16 on Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
+- VGG-19
+| BatchSize | 1     | 2     | 4     | 8     | 16    |
+|-----------|-------|-------|-------|-------|-------|
+| OpenBLAS  | 1.07  | 1.08  | 1.06  | 0.88  | 0.65  |
+| MKLML     | 5.58  | 9.80  | 15.15 | 21.21 | 28.67 |
+| MKL-DNN   | 75.07 | 88.64 | 82.58 | 92.29 | 96.75 |
+- ResNet-50
+| BatchSize | 1     | 2      | 4      | 8      | 16     |
+|-----------|-------|--------|--------|--------|--------|
+| OpenBLAS  | 3.35  | 3.19   | 3.09   | 2.55   | 1.96   |
+| MKLML     | 6.33  | 12.02  | 22.88  | 40.53  | 63.09  |
+| MKL-DNN   | 107.83| 148.84 | 177.78 | 189.35 | 217.69 |
+- GoogLeNet
+| BatchSize | 1      | 2      | 4      | 8      | 16     |
+|-----------|--------|--------|--------|--------|--------|
+| OpenBLAS  | 12.04  | 11.31  | 10.00  | 9.07   | 4.34   |
+| MKLML     | 22.74  | 41.56  | 81.22  | 133.47 | 210.53 |
+| MKL-DNN   | 175.10 | 272.92 | 450.70 | 512.00 | 600.94 |
 ### Laptop
 TBD
--- a/cmake/cblas.cmake
+++ b/cmake/cblas.cmake
@@ -17,7 +17,7 @@ if(WITH_MKLML AND MKLML_INC_DIR AND MKLML_LIB)
  set(CBLAS_INC_DIR ${MKLML_INC_DIR})
  set(CBLAS_LIBRARIES ${MKLML_LIB})
-  add_definitions(-DPADDLE_USE_MKLML)
+  add_definitions(-DPADDLE_WITH_MKLML)
  add_definitions(-DLAPACK_FOUND)
  message(STATUS "Found cblas and lapack in MKLML "

--- a/cmake/external/mkldnn.cmake
+++ b/cmake/external/mkldnn.cmake
@@ -67,5 +67,5 @@ ADD_LIBRARY(mkldnn SHARED IMPORTED GLOBAL)
 SET_PROPERTY(TARGET mkldnn PROPERTY IMPORTED_LOCATION ${MKLDNN_LIB})
 ADD_DEPENDENCIES(mkldnn ${MKLDNN_PROJECT})
 MESSAGE(STATUS "MKLDNN library: ${MKLDNN_LIB}")
-add_definitions(-DPADDLE_USE_MKLDNN)
+add_definitions(-DPADDLE_WITH_MKLDNN)
 LIST(APPEND external_project_dependencies mkldnn)
--- a/paddle/api/CMakeLists.txt
+++ b/paddle/api/CMakeLists.txt
@@ -25,8 +25,18 @@ FILE(GLOB PY_PADDLE_PYTHON_FILES ${PADDLE_SOURCE_DIR}/paddle/py_paddle/*.py)
 SET_SOURCE_FILES_PROPERTIES(Paddle.i PROPERTIES CPLUSPLUS ON)
+SET(SWIG_NEED_FLAGS
+    -ftls-model=global-dynamic
+    -Wno-parentheses-equality
+    -Wno-self-assign
+    -Wno-maybe-uninitialized
+    -Wno-missing-field-initializers)
+  FOREACH(flag ${SWIG_NEED_FLAGS})
+  safe_set_cxxflag(SWIG_CXX_FLAGS ${flag})
+ENDFOREACH()
 SET(CMAKE_SWIG_OUTDIR ${CMAKE_CURRENT_BINARY_DIR})
-SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-parentheses-equality -Wno-missing-field-initializers -Wno-self-assign -ftls-model=global-dynamic")
+SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${SWIG_CXX_FLAGS}")
 SET(SWIG_MODULE_swig_paddle_EXTRA_DEPS
    paddle_parameter

--- a/paddle/framework/backward.cc
+++ b/paddle/framework/backward.cc
@@ -190,8 +190,9 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
      // collect all the offset for each alias,
      // insert a sum operator to add all aliases to output
      insert_position.push_back(
-          {dup_op.back(), OpRegistry::CreateOp("sum", {{"X", dup_outputs}},
+          {dup_op.back(),
-                                               {{"Out", {name}}}, {})});
+           OpRegistry::CreateOp("sum", {{"X", dup_outputs}}, {{"Out", {name}}},
+                                AttributeMap{})});
    }
    // make sure the inserted `sum` ops follow the BFS order.
@@ -216,7 +217,8 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
        // If part of input gradient of that operator is not calculated, fill
        // zero variables to that input gradient.
        net->AppendOp(OpRegistry::CreateOp("fill_zeros_like", {{"X", {prefix}}},
-                                           {{"Y", {grad_input}}}, {}));
+                                           {{"Y", {grad_input}}},
+                                           AttributeMap{}));
      }
      return false;
    });
@@ -392,8 +394,9 @@ std::vector<std::unique_ptr<OpDescBind>> MakeOpGrad(
            0, in_name.size() - sizeof(kGradVarSuffix) / sizeof(char) + 1);
        std::string new_name = prefix + kZeroVarSuffix;
        desc->Rename(in_name, new_name);
-        std::unique_ptr<OpDescBind> fill_zeros_op(new OpDescBind(
+        std::unique_ptr<OpDescBind> fill_zeros_op(
-            "fill_zeros_like", {{"X", {prefix}}}, {{"Y", {new_name}}}, {}));
+            new OpDescBind("fill_zeros_like", {{"X", {prefix}}},
+                           {{"Y", {new_name}}}, AttributeMap{}));
        pending_fill_zeros_ops.push_back(std::move(fill_zeros_op));
      }
    }
@@ -483,8 +486,9 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
        sum_op_inputs.emplace_back(new_name);
        next_g_name = sum_op_inputs.back();
      }
-      std::unique_ptr<OpDescBind> sum_op(new OpDescBind(
+      std::unique_ptr<OpDescBind> sum_op(
-          "sum", {{"X", sum_op_inputs}}, {{"Out", {out_name}}}, {}));
+          new OpDescBind("sum", {{"X", sum_op_inputs}}, {{"Out", {out_name}}},
+                         AttributeMap{}));
      pending_sum_ops.push_back({dup_op.back(), std::move(sum_op)});
    }
  }

--- a/paddle/framework/backward_test.cc
+++ b/paddle/framework/backward_test.cc
@@ -106,15 +106,15 @@ class FcOp : public operators::NetOp {
  FcOp(const std::string &type, const VariableNameMap &inputs,
       const VariableNameMap &outputs, const AttributeMap &attrs)
      : NetOp(type, inputs, outputs, attrs) {
-    AppendOp(OpRegistry::CreateOp("mul",
+    AppendOp(OpRegistry::CreateOp(
-                                  {{"X", {Input("X")}}, {"Y", {Input("W")}}},
+        "mul", {{"X", {Input("X")}}, {"Y", {Input("W")}}},
-                                  {{"Out", {Output("mul_result")}}}, {}));
+        {{"Out", {Output("mul_result")}}}, AttributeMap{}));
    auto input_b = Inputs("b");
    std::string before_act = "mul_result";
    if (input_b.size() != 0) {
      AppendOp(OpRegistry::CreateOp(
          "rowwise_add", {{"X", {Output("mul_result")}}, {"b", {input_b[0]}}},
-          {{"Out", {Output("add_result")}}}, {}));
+          {{"Out", {Output("add_result")}}}, AttributeMap{}));
      before_act = "add_result";
    } else {
      auto out_varname = Output("add_result");
@@ -124,7 +124,7 @@ class FcOp : public operators::NetOp {
    }
    AppendOp(OpRegistry::CreateOp("sigmoid", {{"X", {Output(before_act)}}},
-                                  {{"Out", {Output("Out")}}}, {}));
+                                  {{"Out", {Output("Out")}}}, AttributeMap{}));
    CompleteAddOp(false);
  }
 };
@@ -278,8 +278,9 @@ REGISTER_OPERATOR(scale, f::NoneOp);
 REGISTER_OP_CPU_KERNEL(scale, f::NoneKernel<paddle::platform::CPUPlace, float>);
 TEST(Backward, simple_op_not_need_grad) {
-  auto fwd = f::OpRegistry::CreateOp(
+  auto fwd =
-      "rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
+      f::OpRegistry::CreateOp("rowwise_add", {{"X", {"x"}}, {"b", {"b"}}},
+                              {{"Out", {"out"}}}, f::AttributeMap{});
  ASSERT_NE(fwd, nullptr);
  auto gop = f::Backward(*fwd, {"x"});
  ASSERT_EQ(gop->Output(f::GradVarName("X")), f::kEmptyVarName);
@@ -296,9 +297,10 @@ TEST(Backward, net_fc_backward_normal) {
                              {{"mul_result", {"mul_res"}},
                               {"add_result", {"add_re"}},
                               {"Out", {"out"}}},
-                              {});
+                              f::AttributeMap{});
  ASSERT_NE(fwd, nullptr);
-  std::shared_ptr<f::OperatorBase> gop = f::Backward(*fwd, {});
+  std::shared_ptr<f::OperatorBase> gop =
+      f::Backward(*fwd, std::unordered_set<std::string>{});
  ASSERT_TRUE(gop->IsNetOp());
  auto net = static_cast<ops::NetOp *>(gop.get());
@@ -322,9 +324,10 @@ TEST(Backward, net_fc_backward_not_have_b) {
                              {{"mul_result", {"mul_res"}},
                               {"add_result", {"add_res"}},
                               {"Out", {"tmp"}}},
-                              {});
+                              f::AttributeMap{});
  ASSERT_NE(fwd, nullptr);
-  std::shared_ptr<f::OperatorBase> gop = f::Backward(*fwd, {});
+  std::shared_ptr<f::OperatorBase> gop =
+      f::Backward(*fwd, std::unordered_set<std::string>{});
  ASSERT_TRUE(gop->IsNetOp());
  auto net = static_cast<ops::NetOp *>(gop.get());
@@ -346,13 +349,13 @@ TEST(Backward, net_input_of_network_not_need_grad) {
      {{"mul_result", {"mul_tmp_0"}},
       {"add_result", {"add_tmp_0"}},
       {"Out", {"hidden0"}}},
-      {}));
+      f::AttributeMap{}));
  net.AppendOp(f::OpRegistry::CreateOp(
      "fc", {{"X", {"hidden0"}}, {"W", {"W2"}}, {"b", {"b2"}}},
      {{"mul_result", {"mul_tmp_1"}},
       {"add_result", {"add_tmp_1"}},
       {"Out", {"hidden1"}}},
-      {}));
+      f::AttributeMap{}));
  net.CompleteAddOp();
  auto bwd = Backward(net, {"x"});  // x@GRAD is not need.
  ASSERT_TRUE(bwd->IsNetOp());
@@ -381,12 +384,13 @@ TEST(Backward, net_input_of_network_not_need_grad) {
 TEST(Backward, net_shared_weight) {
  ops::NetOp net;
  net.AppendOp(f::OpRegistry::CreateOp("mul", {{"X", {"x"}}, {"Y", {"w"}}},
-                                       {{"Out", {"out"}}}, {}));
+                                       {{"Out", {"out"}}}, f::AttributeMap{}));
  net.AppendOp(f::OpRegistry::CreateOp("mul", {{"X", {"out"}}, {"Y", {"w"}}},
-                                       {{"Out", {"FinalOut"}}}, {}));
+                                       {{"Out", {"FinalOut"}}},
+                                       f::AttributeMap{}));
  net.CompleteAddOp();
-  auto bwd = f::Backward(net, {});
+  auto bwd = f::Backward(net, std::unordered_set<std::string>{});
  ASSERT_TRUE(bwd->IsNetOp());
  auto bwd_net = static_cast<ops::NetOp *>(bwd.get());
  ASSERT_EQ(3UL, bwd_net->ops_.size());
@@ -394,8 +398,9 @@ TEST(Backward, net_shared_weight) {
 }
 TEST(Backward, op_all_input_are_not_need) {
-  auto fwd = f::OpRegistry::CreateOp(
+  auto fwd =
-      "rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
+      f::OpRegistry::CreateOp("rowwise_add", {{"X", {"x"}}, {"b", {"b"}}},
+                              {{"Out", {"out"}}}, f::AttributeMap{});
  auto backward = f::Backward(*fwd, {"x", "b"});
  ASSERT_TRUE(backward->IsNetOp());
  auto net = static_cast<ops::NetOp *>(backward.get());
@@ -403,8 +408,9 @@ TEST(Backward, op_all_input_are_not_need) {
 }
 TEST(Backward, op_all_output_are_not_need) {
-  auto fwd = f::OpRegistry::CreateOp(
+  auto fwd =
-      "rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
+      f::OpRegistry::CreateOp("rowwise_add", {{"X", {"x"}}, {"b", {"b"}}},
+                              {{"Out", {"out"}}}, f::AttributeMap{});
  auto backward = f::Backward(*fwd, {"out"});
  ASSERT_TRUE(backward->IsNetOp());
  auto net = static_cast<ops::NetOp *>(backward.get());
@@ -412,8 +418,9 @@ TEST(Backward, op_all_output_are_not_need) {
 }
 TEST(Backward, op_part_of_output_are_not_need) {
-  auto fwd = f::OpRegistry::CreateOp("many_output_op", {{"x", {"X"}}},
+  auto fwd =
-                                     {{"y", {"Y"}}, {"z", {"Z"}}}, {});
+      f::OpRegistry::CreateOp("many_output_op", {{"x", {"X"}}},
+                              {{"y", {"Y"}}, {"z", {"Z"}}}, f::AttributeMap{});
  auto backward = f::Backward(*fwd, {"Z"});
  ASSERT_TRUE(backward->IsNetOp());
  auto net = static_cast<ops::NetOp *>(backward.get());
@@ -437,7 +444,7 @@ TEST(Backward, op_part_of_output_are_not_need) {
 TEST(Backward, op_part_of_input_are_not_need) {
  auto fwd = f::OpRegistry::CreateOp("mul", {{"X", {"a"}}, {"Y", {"b"}}},
-                                     {{"Out", {"out"}}}, {});
+                                     {{"Out", {"out"}}}, f::AttributeMap{});
  auto backward = f::Backward(*fwd, {"a"});
  auto &grad_mul = *backward;
  ASSERT_EQ(grad_mul.Type(), "mul_grad");
@@ -458,19 +465,19 @@ TEST(Backward, linear_net_intermediate_variable_has_no_grad) {
      {{"mul_result", {"mul_out1"}},
       {"add_result", {"add_out1"}},
       {"Out", {"out1"}}},
-      {}));
+      f::AttributeMap{}));
  net.AppendOp(f::OpRegistry::CreateOp(
      "fc", {{"X", {"out1"}}, {"W", {"w2"}}, {"b", {"b2"}}},
      {{"mul_result", {"mul_out2"}},
       {"add_result", {"tmp_out2"}},
       {"Out", {"out2"}}},
-      {}));
+      f::AttributeMap{}));
  net.AppendOp(f::OpRegistry::CreateOp(
      "fc", {{"X", {"out2"}}, {"W", {"w3"}}, {"b", {"b3"}}},
      {{"mul_result", {"mul_out3"}},
       {"add_result", {"tmp_out3"}},
       {"Out", {"out3"}}},
-      {}));
+      f::AttributeMap{}));
  net.CompleteAddOp();
  auto backward = f::Backward(net, {"mul_out2", "tmp_out2", "out2"});
@@ -509,7 +516,8 @@ TEST(Backward, simple_single_op) {
  auto target = f::VarDescBind("out");
  target.SetShape({1});
-  auto var_to_grad = AppendBackward(program, target, {});
+  auto var_to_grad =
+      AppendBackward(program, target, std::unordered_set<std::string>{});
  ASSERT_EQ(block->AllOps().size(), 3UL);
  f::OpDescBind *fill_op = block->AllOps()[1];
@@ -546,7 +554,7 @@ TEST(Backward, default_attribute) {
  auto target = f::VarDescBind("out");
  target.SetShape({1});
-  AppendBackward(program, target, {});
+  AppendBackward(program, target, std::unordered_set<std::string>{});
  ASSERT_EQ(block->AllOps().size(), 3UL);
  EXPECT_EQ(boost::get<int>(op->GetAttr("x_num_col_dims")), 1);
@@ -585,7 +593,8 @@ TEST(Backward, simple_mult_op) {
  auto target = f::VarDescBind("out3");
  target.SetShape({1});
  size_t forward_len = block->AllOps().size();
-  auto var_to_grad = AppendBackward(program, target, {});
+  auto var_to_grad =
+      AppendBackward(program, target, std::unordered_set<std::string>{});
  ASSERT_EQ(block->AllOps().size(), 6UL + 1);
  f::OpDescBind *fill_op = block->AllOps()[forward_len];
@@ -817,7 +826,8 @@ TEST(Backward, shared_var) {
  auto target = f::VarDescBind("out3");
  target.SetShape({1});
  size_t forward_len = block->AllOps().size();
-  auto var_to_grad = AppendBackward(program, target, {});
+  auto var_to_grad =
+      AppendBackward(program, target, std::unordered_set<std::string>{});
  ASSERT_EQ(block->AllOps().size(), 8UL);
  f::OpDescBind *fill_op = block->AllOps()[forward_len];

--- a/paddle/framework/op_desc.cc
+++ b/paddle/framework/op_desc.cc
@@ -316,8 +316,8 @@ static void InitInferShapeFuncs() {
    for (auto &kern_pair : OperatorWithKernel::AllOpKernels()) {
      auto op_type = kern_pair.first;
      auto &op_info = info_map.at(op_type);
-      auto op =
+      auto op = static_cast<OperatorWithKernel *>(op_info.Creator()(
-          static_cast<OperatorWithKernel *>(op_info.Creator()("", {}, {}, {}));
+          "", VariableNameMap{}, VariableNameMap{}, AttributeMap{}));
      if (op_info.infer_shape_) {  // infer_shape has been registered.
        continue;
      }

--- a/paddle/framework/operator_test.cc
+++ b/paddle/framework/operator_test.cc
@@ -261,7 +261,9 @@ class OperatorClone : public paddle::framework::OperatorBase {
 };
 TEST(Operator, Clone) {
-  OperatorClone a("ABC", {}, {}, {});
+  OperatorClone a("ABC", paddle::framework::VariableNameMap{},
+                  paddle::framework::VariableNameMap{},
+                  paddle::framework::AttributeMap{});
  auto b = a.Clone();
  ASSERT_EQ(a.Type(), b->Type());
 }
--- a/paddle/framework/prune_test.cc
+++ b/paddle/framework/prune_test.cc
@@ -54,7 +54,8 @@ TEST(Prune, one_operator) {
  f::ProgramDescBind program;
  f::BlockDescBind *block = program.MutableBlock(0);
-  AddOp("one_one", {{"input", {"a"}}}, {{"output", {"b"}}}, {}, block);
+  AddOp("one_one", {{"input", {"a"}}}, {{"output", {"b"}}}, f::AttributeMap{},
+        block);
  f::ProgramDesc *pdesc = program.Proto();
  f::ProgramDesc pruned;
@@ -71,10 +72,14 @@ TEST(Prune, forward) {
  f::ProgramDescBind program;
  f::BlockDescBind *block = program.MutableBlock(0);
-  AddOp("one_one", {{"input", {"a"}}}, {{"output", {"b"}}}, {}, block);
+  AddOp("one_one", {{"input", {"a"}}}, {{"output", {"b"}}}, f::AttributeMap{},
-  AddOp("one_one", {{"input", {"b"}}}, {{"output", {"c"}}}, {}, block);
+        block);
-  AddOp("one_one", {{"input", {"c"}}}, {{"output", {"d"}}}, {}, block);
+  AddOp("one_one", {{"input", {"b"}}}, {{"output", {"c"}}}, f::AttributeMap{},
-  AddOp("one_one", {{"input", {"d"}}}, {{"output", {"e"}}}, {}, block);
+        block);
+  AddOp("one_one", {{"input", {"c"}}}, {{"output", {"d"}}}, f::AttributeMap{},
+        block);
+  AddOp("one_one", {{"input", {"d"}}}, {{"output", {"e"}}}, f::AttributeMap{},
+        block);
  f::ProgramDesc *pdesc = program.Proto();
@@ -90,11 +95,14 @@ TEST(Prune, multi_input_op) {
  f::ProgramDescBind program;
  f::BlockDescBind *block = program.MutableBlock(0);
-  AddOp("one_one", {{"input", {"a0"}}}, {{"output", {"b0"}}}, {}, block);
+  AddOp("one_one", {{"input", {"a0"}}}, {{"output", {"b0"}}}, f::AttributeMap{},
-  AddOp("one_one", {{"input", {"a1"}}}, {{"output", {"b1"}}}, {}, block);
+        block);
-  AddOp("one_one", {{"input", {"a2"}}}, {{"output", {"b2"}}}, {}, block);
+  AddOp("one_one", {{"input", {"a1"}}}, {{"output", {"b1"}}}, f::AttributeMap{},
-  AddOp("three_one", {{"input", {"b0", "b1", "b2"}}}, {{"output", {"c"}}}, {},
        block);
+  AddOp("one_one", {{"input", {"a2"}}}, {{"output", {"b2"}}}, f::AttributeMap{},
+        block);
+  AddOp("three_one", {{"input", {"b0", "b1", "b2"}}}, {{"output", {"c"}}},
+        f::AttributeMap{}, block);
  f::ProgramDesc *pdesc = program.Proto();
  pdesc->mutable_blocks(0)->mutable_ops(3)->set_is_target(true);
@@ -108,9 +116,12 @@ TEST(Prune, multi_output_op) {
  f::ProgramDescBind program;
  f::BlockDescBind *block = program.MutableBlock(0);
-  AddOp("one_two", {{"input", {"a"}}}, {{"output", {"b", "c"}}}, {}, block);
+  AddOp("one_two", {{"input", {"a"}}}, {{"output", {"b", "c"}}},
-  AddOp("one_one", {{"input", {"b"}}}, {{"output", {"b1"}}}, {}, block);
+        f::AttributeMap{}, block);
-  AddOp("one_one", {{"input", {"c"}}}, {{"output", {"c1"}}}, {}, block);
+  AddOp("one_one", {{"input", {"b"}}}, {{"output", {"b1"}}}, f::AttributeMap{},
+        block);
+  AddOp("one_one", {{"input", {"c"}}}, {{"output", {"c1"}}}, f::AttributeMap{},
+        block);
  f::ProgramDesc *pdesc = program.Proto();
  pdesc->mutable_blocks(0)->mutable_ops(2)->set_is_target(true);
@@ -124,9 +135,12 @@ TEST(Prune, multi_target) {
  f::ProgramDescBind program;
  f::BlockDescBind *block = program.MutableBlock(0);
-  AddOp("one_two", {{"input", {"a"}}}, {{"output", {"b", "c"}}}, {}, block);
+  AddOp("one_two", {{"input", {"a"}}}, {{"output", {"b", "c"}}},
-  AddOp("one_one", {{"input", {"b"}}}, {{"output", {"b1"}}}, {}, block);
+        f::AttributeMap{}, block);
-  AddOp("one_one", {{"input", {"c"}}}, {{"output", {"c1"}}}, {}, block);
+  AddOp("one_one", {{"input", {"b"}}}, {{"output", {"b1"}}}, f::AttributeMap{},
+        block);
+  AddOp("one_one", {{"input", {"c"}}}, {{"output", {"c1"}}}, f::AttributeMap{},
+        block);
  f::ProgramDesc *pdesc = program.Proto();
  pdesc->mutable_blocks(0)->mutable_ops(1)->set_is_target(true);

--- a/paddle/gserver/activations/ActivationFunction.cpp
+++ b/paddle/gserver/activations/ActivationFunction.cpp
@@ -24,7 +24,7 @@ limitations under the License. */
 #include "paddle/utils/ClassRegistrar.h"
 #include "paddle/utils/Logging.h"
-#ifdef PADDLE_USE_MKLDNN
+#ifdef PADDLE_WITH_MKLDNN
 #include "MKLDNNActivation.h"
 #endif
@@ -490,7 +490,7 @@ Error __must_check backward(Argument& act) {
 END_DEFINE_ACTIVATION(log)
 ActivationFunction* ActivationFunction::create(const std::string& type) {
-#ifdef PADDLE_USE_MKLDNN
+#ifdef PADDLE_WITH_MKLDNN
  if (!type.empty() && type.compare(0, 7, "mkldnn_") == 0) {
    return MKLDNNActivation::create(type);
  }

--- a/paddle/gserver/gradientmachines/NeuralNetwork.cpp
+++ b/paddle/gserver/gradientmachines/NeuralNetwork.cpp
@@ -20,7 +20,7 @@ limitations under the License. */
 #include "paddle/utils/Logging.h"
 #include "paddle/utils/Stat.h"
-#ifdef PADDLE_USE_MKLDNN
+#ifdef PADDLE_WITH_MKLDNN
 #include "paddle/gserver/layers/MKLDNNLayer.h"
 #endif
@@ -307,7 +307,7 @@ void NeuralNetwork::backward(const UpdateCallback& callback) {
 }
 void NeuralNetwork::finish() {
-#ifdef PADDLE_USE_MKLDNN
+#ifdef PADDLE_WITH_MKLDNN
  FOR_EACH_R(layer, layers_) {
    MKLDNNLayerPtr dnnLayer = std::dynamic_pointer_cast<MKLDNNLayer>(*layer);
    if (dnnLayer) {

--- a/paddle/math/Allocator.h
+++ b/paddle/math/Allocator.h
@@ -48,7 +48,7 @@ public:
   */
  virtual void* alloc(size_t size) {
    void* ptr;
-#ifdef PADDLE_USE_MKLDNN
+#ifdef PADDLE_WITH_MKLDNN
    // refer to https://github.com/01org/mkl-dnn/blob/master/include/mkldnn.hpp
    // memory alignment
    CHECK_EQ(posix_memalign(&ptr, 4096ul, size), 0);

--- a/paddle/math/MathFunctions.cpp
+++ b/paddle/math/MathFunctions.cpp
@@ -206,7 +206,7 @@ double dotProduct<double>(const int n, const double* x, const double* y) {
 }
 #endif
-#if defined(PADDLE_USE_MKLML)
+#if defined(PADDLE_WITH_MKLML)
 template <>
 void vExp<float>(const int n, const float* a, float* r) {

--- a/paddle/math/MathFunctions.h
+++ b/paddle/math/MathFunctions.h
@@ -15,7 +15,7 @@ limitations under the License. */
 #ifndef MATHFUNCTIONS_H_
 #define MATHFUNCTIONS_H_
-#ifdef PADDLE_USE_MKLML
+#ifdef PADDLE_WITH_MKLML
 #include <mkl_cblas.h>
 #include <mkl_lapacke.h>
 #include <mkl_vml_functions.h>

--- a/paddle/math/tests/CMakeLists.txt
+++ b/paddle/math/tests/CMakeLists.txt
@@ -34,4 +34,4 @@ add_simple_unittest(test_FPException)
 add_simple_unittest(test_GpuProfiler)
 add_simple_unittest(test_BaseMatrix)
 add_simple_unittest(test_Matrix)
-cc_test(test_float16 SRCS test_float16.cpp)
+add_simple_unittest(test_float16)
--- a/paddle/memory/detail/system_allocator.cc
+++ b/paddle/memory/detail/system_allocator.cc
@@ -43,7 +43,7 @@ void* CPUAllocator::Alloc(size_t& index, size_t size) {
  void* p;
-#ifdef PADDLE_USE_MKLDNN
+#ifdef PADDLE_WITH_MKLDNN
  // refer to https://github.com/01org/mkl-dnn/blob/master/include/mkldnn.hpp
  // memory alignment
  PADDLE_ENFORCE_EQ(posix_memalign(&p, 4096ul, size), 0);

--- a/paddle/operators/conditional_block_op.cc
+++ b/paddle/operators/conditional_block_op.cc
@@ -142,9 +142,9 @@ class ConditionalBlockGradOp : public ConditionalOp {
        continue;
      }
      auto new_in_grad_name = cur_scope.Rename(in_grad_name);
-      auto assign =
+      auto assign = framework::OpRegistry::CreateOp(
-          framework::OpRegistry::CreateOp("assign", {{"X", {new_in_grad_name}}},
+          "assign", {{"X", {new_in_grad_name}}}, {{"Out", {out_grad_name}}},
-                                          {{"Out", {out_grad_name}}}, {});
+          framework::AttributeMap{});
      assign->Run(cur_scope, dev_ctx);
      cur_scope.Rename(new_in_grad_name, in_grad_name);
    }

--- a/paddle/operators/math/math_function.cc
+++ b/paddle/operators/math/math_function.cc
@@ -132,7 +132,7 @@ void matmul<platform::CPUPlace, double>(
      matrix_b.data<double>(), beta, matrix_out->data<double>());
 }
-#ifdef PADDLE_USE_MKLML
+#ifdef PADDLE_WITH_MKLML
 // Use cblas_{s,d}gemm_batched if available: Run with 1 group of size batchSize.
 template <>
 void batched_gemm<platform::CPUPlace, float>(

--- a/paddle/operators/math/math_function.h
+++ b/paddle/operators/math/math_function.h
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 #pragma once
-#ifdef PADDLE_USE_MKLML
+#ifdef PADDLE_WITH_MKLML
 #include <mkl_cblas.h>
 #include <mkl_lapacke.h>
 #include <mkl_vml_functions.h>

--- a/paddle/operators/net_op.h
+++ b/paddle/operators/net_op.h
@@ -38,7 +38,10 @@ namespace operators {
 class NetOp : public framework::OperatorBase {
 public:
  static const char kAll[];
-  NetOp() : framework::OperatorBase("plain_net", {}, {}, {}) {}
+  NetOp()
+      : framework::OperatorBase("plain_net", framework::VariableNameMap{},
+                                framework::VariableNameMap{},
+                                framework::AttributeMap{}) {}
  NetOp(const std::string& type, const framework::VariableNameMap& inputs,
        const framework::VariableNameMap& outputs,

--- a/paddle/operators/net_op_test.cc
+++ b/paddle/operators/net_op_test.cc
@@ -38,10 +38,10 @@ TEST(OpKernel, all) {
  net->AppendOp(std::unique_ptr<TestOp>(
      new TestOp("test", {{"X", {"x"}}, {"W", {"w1"}}, {"b", {"b1"}}},
-                 {{"Out", {"y"}}}, {})));
+                 {{"Out", {"y"}}}, framework::AttributeMap{})));
  net->AppendOp(std::unique_ptr<TestOp>(
      new TestOp("test", {{"X", {"y"}}, {"W", {"w2"}}, {"b", {"b2"}}},
-                 {{"Out", {"z"}}}, {})));
+                 {{"Out", {"z"}}}, framework::AttributeMap{})));
  net->CompleteAddOp();
  AssertSameVectorWithoutOrder({"x", "w1", "b1", "w2", "b2"},
@@ -58,7 +58,7 @@ TEST(NetOp, insert_op) {
  NetOp net;
  auto op1 = std::unique_ptr<framework::NOP>(
      new framework::NOP("empty", {{"X", {"x"}}, {"W", {"w1"}}, {"b", {"b1"}}},
-                         {{"Out", {"y"}}}, {}));
+                         {{"Out", {"y"}}}, framework::AttributeMap{}));
  net.AppendOp(*op1);
  net.InsertOp(0, *op1);
  ASSERT_EQ(2UL, net.ops_.size());
@@ -68,10 +68,12 @@ TEST(NetOp, insert_op) {
 TEST(NetOp, Clone) {
  NetOp net;
-  net.AppendOp(
+  net.AppendOp(std::unique_ptr<framework::NOP>(new framework::NOP{
-      std::unique_ptr<framework::NOP>(new framework::NOP{"empty", {}, {}, {}}));
+      "empty", framework::VariableNameMap{}, framework::VariableNameMap{},
-  net.AppendOp(std::unique_ptr<framework::NOP>(
+      framework::AttributeMap{}}));
-      new framework::NOP{"empty2", {}, {}, {}}));
+  net.AppendOp(std::unique_ptr<framework::NOP>(new framework::NOP{
+      "empty2", framework::VariableNameMap{}, framework::VariableNameMap{},
+      framework::AttributeMap{}}));
  net.CompleteAddOp(true);
  auto new_net_op = net.Clone();
  ASSERT_NE(new_net_op, nullptr);

--- a/paddle/operators/recurrent_op.cc
+++ b/paddle/operators/recurrent_op.cc
@@ -408,7 +408,8 @@ class RecurrentGradOp : public RecurrentBase {
            attrs["value"] = 0.0f;
            auto zero_op = framework::OpRegistry::CreateOp(
-                "fill_constant", {}, {{"Out", {pg_names[param_id]}}}, attrs);
+                "fill_constant", framework::VariableNameMap{},
+                {{"Out", {pg_names[param_id]}}}, attrs);
            zero_op->Run(scope, dev_ctx);
          }
@@ -417,7 +418,7 @@ class RecurrentGradOp : public RecurrentBase {
          auto sum_op = framework::OpRegistry::CreateOp(
              "sum", {{"X", {pg_names[param_id], new_inside_name}}},
-              {{"Out", {pg_names[param_id]}}}, {});
+              {{"Out", {pg_names[param_id]}}}, framework::AttributeMap{});
          sum_op->Run(cur_scope, dev_ctx);
          cur_scope.Rename(new_inside_name, inside_grad_name);

--- a/paddle/operators/recv_op.cc
+++ b/paddle/operators/recv_op.cc
@@ -72,11 +72,13 @@ class RecvOp : public framework::OperatorBase {
    // FIXME(typhoonzero): do not copy
    framework::CopyFrom(t, dev_ctx.GetPlace(), dev_ctx, tensor);
-    auto *block = Attr<framework::BlockDescBind *>("OptimizeBlock");
+    std::string program_str = Attr<std::string>("OptimizeProgram");
-    auto *program = block->Program();
+    framework::ProgramDesc program_desc;
+    program_desc.ParseFromString(program_str);
+    framework::ProgramDescBind program(program_desc);
    framework::Executor executor(dev_ctx);
    // Run sub graph to get optimized tensor
-    executor.Run(*program, &recv_scope, block->ID(),
+    executor.Run(program, &recv_scope, 0, /*global_block*/
                 false /*create_local_scope*/);
    auto *out_var = recv_scope.FindVar("Out");
@@ -108,8 +110,8 @@ This operator will recv tensor from send_op
                         "IP address to listen on.")
        .SetDefault("127.0.0.1:6164")
        .AddCustomChecker([](const std::string &ip) { return !ip.empty(); });
-    AddAttr<framework::BlockDescBind *>("OptimizeBlock", "type BlockDescBind*",
+    AddAttr<std::string>("OptimizeProgram", "type string",
-                                        "optimize network run in server");
+                         "Serialized ProgramDesc string for recv to run.");
  }
 };

--- a/paddle/operators/send_recv_op_test.cc
+++ b/paddle/operators/send_recv_op_test.cc
@@ -85,7 +85,10 @@ void StartServerNet() {
  paddle::framework::AttributeMap attrs;
  attrs.insert({"endpoint", std::string("127.0.0.1:6174")});
-  attrs.insert({"OptimizeBlock", block});
+  std::string program_proto;
+  PADDLE_ENFORCE(program.Proto()->SerializeToString(&program_proto));
+  attrs.insert({"OptimizeProgram", program_proto});
  recv_op = paddle::framework::OpRegistry::CreateOp("recv", {{"RX", {"RX"}}},
                                                    {{"Out", {"Out"}}}, attrs);
  paddle::platform::CPUDeviceContext ctx(place);

--- a/paddle/operators/while_op.cc
+++ b/paddle/operators/while_op.cc
@@ -187,7 +187,8 @@ class WhileGradOp : public framework::OperatorBase {
            attrs["value"] = 0.0f;
            auto zero_op = framework::OpRegistry::CreateOp(
-                "fill_constant", {}, {{"Out", {pg_names[param_id]}}}, attrs);
+                "fill_constant", framework::VariableNameMap{},
+                {{"Out", {pg_names[param_id]}}}, attrs);
            zero_op->Run(scope, dev_ctx);
          }
        }
@@ -195,7 +196,7 @@ class WhileGradOp : public framework::OperatorBase {
        auto new_inside_name = cur_scope.Rename(inside_grad_name);
        auto sum_op = framework::OpRegistry::CreateOp(
            "sum", {{"X", {pg_names[param_id], new_inside_name}}},
-            {{"Out", {pg_names[param_id]}}}, {});
+            {{"Out", {pg_names[param_id]}}}, framework::AttributeMap{});
        sum_op->Run(cur_scope, dev_ctx);
        cur_scope.Rename(new_inside_name, inside_grad_name);
      }

--- a/paddle/parameter/FirstOrderOptimizer.h
+++ b/paddle/parameter/FirstOrderOptimizer.h
@@ -38,7 +38,7 @@ public:
    real torch_learningRate = optConfig_.learning_method() == "torch_momentum"
                                  ? 1.0 - paraConfig.momentum()
                                  : 1.0;
-#ifdef PADDLE_USE_MKLDNN
+#ifdef PADDLE_WITH_MKLDNN
    sgdUpdate(learningRate_ * paraConfig.learning_rate() *
                  (firstTime_ ? 1.0 : torch_learningRate),
              paraConfig.momentum(),

--- a/paddle/parameter/ParameterUpdateFunctions.cpp
+++ b/paddle/parameter/ParameterUpdateFunctions.cpp
@@ -30,7 +30,7 @@ void sgdUpdateCpu(real learningRate,
                  const real* grad,
                  real* momentumVec) {
  decayRate *= learningRate;
-#ifdef PADDLE_USE_MKLML
+#ifdef PADDLE_WITH_MKLML
 #pragma omp parallel for
 #endif
  for (size_t i = 0; i < size; ++i) {

--- a/paddle/utils/Flags.cpp
+++ b/paddle/utils/Flags.cpp
@@ -20,7 +20,7 @@ DEFINE_bool(use_gpu, false, "Only support CPU training");
 DEFINE_bool(use_gpu, true, "Whether to use GPU for training");
 #endif
-#ifdef PADDLE_USE_MKLDNN
+#ifdef PADDLE_WITH_MKLDNN
 // TODO(TJ): change to true when MKLDNN layers support multi-inputs
 DEFINE_bool(use_mkldnn, false, "Default still keep use CPU training");
 #else

--- a/python/paddle/trainer_config_helpers/layers.py
+++ b/python/paddle/trainer_config_helpers/layers.py
@@ -1519,34 +1519,33 @@ def lstmemory(input,
    NOTE: This is a low level user interface. You can use network.simple_lstm
    to config a simple plain lstm layer.
-    Please refer to **Generating Sequences With Recurrent Neural Networks** for
+    Reference:
-    more details about LSTM.
+        `Generating Sequences With Recurrent Neural Networks
+        <https://arxiv.org/pdf/1308.0850.pdf>`_
-    Link_ goes as below.
-    .. _Link: http://arxiv.org/abs/1308.0850
-    :param name: The lstmemory layer name.
+    :param name: The name of this layer. It is optional.
    :type name: basestring
-    :param size: DEPRECATED. size of the lstm cell
+    :param size: DEPRECATED. The dimension of the lstm cell.
    :type size: int
    :param input: The input of this layer.
    :type input: LayerOutput
-    :param reverse: is sequence process reversed or not.
+    :param reverse: Whether the input sequence is processed in a reverse order.
    :type reverse: bool
    :param act: Activation type. TanhActivation is the default activation.
    :type act: BaseActivation
-    :param gate_act: gate activation type, SigmoidActivation by default.
+    :param gate_act: Activation type of this layer's gates. SigmoidActivation is the
+                     default activation.
    :type gate_act: BaseActivation
-    :param state_act: state activation type, TanhActivation by default.
+    :param state_act: Activation type of the state. TanhActivation is the default activation.
    :type state_act: BaseActivation
    :param bias_attr: The bias attribute. If the parameter is set to False or an object
                      whose type is not ParameterAttribute, no bias is defined. If the
                      parameter is set to True, the bias is initialized to zero.
    :type bias_attr: ParameterAttribute | None | bool | Any
-    :param param_attr: Parameter Attribute.
+    :param param_attr: The parameter attribute. See ParameterAttribute for details.
-    :type param_attr: ParameterAttribute | None | False
+    :type param_attr: ParameterAttribute
-    :param layer_attr: Extra Layer attribute
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
    :type layer_attr: ExtraLayerAttribute | None
    :return: LayerOutput object.
    :rtype: LayerOutput
@@ -1635,13 +1634,13 @@ def grumemory(input,
        h_t = (1 - z_t) h_{t-1} + z_t {\\tilde{h_t}}
    NOTE: In PaddlePaddle's implementation, the multiplication operations
-    :math:`W_{r}x_{t}`, :math:`W_{z}x_{t}` and :math:`W x_t` are not computed in
+    :math:`W_{r}x_{t}`, :math:`W_{z}x_{t}` and :math:`W x_t` are not performed
-    gate_recurrent layer. Consequently, an additional mixed_layer with
+    in gate_recurrent layer. Consequently, an additional mixed_layer with
    full_matrix_projection or a fc_layer must be included before grumemory
    is called.
-    More details can be found by referring to `Empirical Evaluation of Gated
+    Reference:
-    Recurrent Neural Networks on Sequence Modeling.
+        `Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
        <https://arxiv.org/abs/1412.3555>`_
    The simple usage is:
@@ -1650,28 +1649,29 @@ def grumemory(input,
       gru = grumemory(input)
-    :param name: The gru layer name.
+    :param name: The name of this layer. It is optional.
-    :type name: None | basestring
+    :type name: basestring
    :param input: The input of this layer.
    :type input: LayerOutput.
-    :param size: DEPRECATED. size of the gru cell
+    :param size: DEPRECATED. The dimension of the gru cell.
    :type size: int
-    :param reverse: Whether sequence process is reversed or not.
+    :param reverse: Whether the input sequence is processed in a reverse order.
    :type reverse: bool
    :param act: Activation type, TanhActivation is the default. This activation
                affects the :math:`{\\tilde{h_t}}`.
    :type act: BaseActivation
-    :param gate_act: gate activation type, SigmoidActivation by default.
+    :param gate_act: Activation type of this layer's two gates. SigmoidActivation is
-                     This activation affects the :math:`z_t` and :math:`r_t`. It is the
+                     the default activation. This activation affects the :math:`z_t`
-                     :math:`\\sigma` in the above formula.
+                     and :math:`r_t`. It is the :math:`\\sigma` in the above formula.
    :type gate_act: BaseActivation
    :param bias_attr: The bias attribute. If the parameter is set to False or an object
                      whose type is not ParameterAttribute, no bias is defined. If the
                      parameter is set to True, the bias is initialized to zero.
    :type bias_attr: ParameterAttribute | None | bool | Any
-    :param param_attr: Parameter Attribute.
+    :param param_attr: The parameter attribute. See ParameterAttribute for details.
-    :type param_attr: ParameterAttribute | None | False
+    :type param_attr: ParameterAttribute
-    :param layer_attr: Extra Layer attribute
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
    :type layer_attr: ExtraLayerAttribute | None
    :return: LayerOutput object.
    :rtype: LayerOutput
@@ -1715,10 +1715,10 @@ def last_seq(input,
    """
    Get Last Timestamp Activation of a sequence.
-    If stride > 0, this layer slides a window whose size is determined by stride,
+    If stride > 0, this layer will slide a window whose size is determined by stride,
-    and return the last value of the window as the output. Thus, a long sequence
+    and return the last value of the sequence in the window as the output. Thus, a
-    will be shorten. Note that for sequence with sub-sequence, the default value
+    long sequence will be shortened. Note that for sequence with sub-sequence, the
-    of stride is -1.
+    default value of stride is -1.
    The simple usage is:
@@ -1727,14 +1727,16 @@ def last_seq(input,
       seq = last_seq(input=layer)
    :param agg_level: Aggregated level
+    :type agg_level: AggregateLevel
    :param name: The name of this layer. It is optional.
    :type name: basestring
    :param input: The input of this layer.
    :type input: LayerOutput
    :param stride: The step size between successive pooling regions.
-    :type stride: Int
+    :type stride: int
-    :param layer_attr: extra layer attributes.
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
-    :type layer_attr: ExtraLayerAttribute.
+                       details.
+    :type layer_attr: ExtraLayerAttribute
    :return: LayerOutput object.
    :rtype: LayerOutput
    """
@@ -1771,10 +1773,10 @@ def first_seq(input,
    """
    Get First Timestamp Activation of a sequence.
-    If stride > 0, this layer slides a window whose size is determined by stride,
+    If stride > 0, this layer will slide a window whose size is determined by stride,
-    and return the first value of the window as the output. Thus, a long sequence
+    and return the first value of the sequence in the window as the output. Thus, a
-    will be shorten. Note that for sequence with sub-sequence, the default value
+    long sequence will be shortened. Note that for sequence with sub-sequence, the
-    of stride is -1.
+    default value of stride is -1.
    The simple usage is:
@@ -1783,13 +1785,15 @@ def first_seq(input,
       seq = first_seq(input=layer)
    :param agg_level: aggregation level
+    :type agg_level: AggregateLevel
    :param name: The name of this layer. It is optional.
    :type name: basestring
    :param input: The input of this layer.
    :type input: LayerOutput
    :param stride: The step size between successive pooling regions.
-    :type stride: Int
+    :type stride: int
-    :param layer_attr: extra layer attributes.
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
    :type layer_attr: ExtraLayerAttribute.
    :return: LayerOutput object.
    :rtype: LayerOutput
@@ -1847,8 +1851,8 @@ def expand_layer(input,
                 expand_level=ExpandLevel.FROM_NO_SEQUENCE,
                 layer_attr=None):
    """
-    A layer for "Expand Dense data or (sequence data where the length of each
+    A layer for expanding dense data or (sequence data where the length of each
-    sequence is one) to sequence data."
+    sequence is one) to sequence data.
    The example usage is:
@@ -1860,7 +1864,9 @@ def expand_layer(input,
    :param input: The input of this layer.
    :type input: LayerOutput
-    :param expand_as: Expand as this layer's sequence info.
+    :param expand_as: Expand the input according to this layer's sequence infomation. And
+                      after the operation, the input expanded will have the same number of
+                      elememts as this layer.
    :type expand_as: LayerOutput
    :param name: The name of this layer. It is optional.
    :type name: basestring
@@ -1868,9 +1874,10 @@ def expand_layer(input,
                      whose type is not ParameterAttribute, no bias is defined. If the
                      parameter is set to True, the bias is initialized to zero.
    :type bias_attr: ParameterAttribute | None | bool | Any
-    :param expand_level: whether input layer is timestep(default) or sequence.
+    :param expand_level: Whether the input layer is a sequence or the element of a sequence.
    :type expand_level: ExpandLevel
-    :param layer_attr: extra layer attributes.
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
    :type layer_attr: ExtraLayerAttribute.
    :return: LayerOutput object.
    :rtype: LayerOutput
@@ -3304,7 +3311,7 @@ def row_l2_norm_layer(input, name=None, layer_attr=None):
    A layer for L2-normalization in each row.
    .. math::
-       out[i] = \frac{in[i]}{\sqrt{\sum_{k=1}^N in[k]^{2}}}
+       out[i] = \\frac{in[i]} {\\sqrt{\\sum_{k=1}^N in[k]^{2}}}
    where the size of :math:`in` is (batchSize x dataDim) ,
    and the size of :math:`out` is a (batchSize x dataDim) .
@@ -6173,9 +6180,11 @@ def huber_regression_cost(input,
    Given a prediction f(x), a label y and :math:`\delta`, the loss function
    is defined as:
-    .. math:
+    .. math::
-       loss = 0.5*\left ( y-f(x) \right )^2, \left | y-f(x) \right |\leq \delta
-       loss = \delta \left | y-f(x) \right |-0.5\delta ^2, otherwise
+       loss = 0.5*(y-f(x))^{2}, | y-f(x) | < \delta
+       loss = \delta | y-f(x) | - 0.5 \delta ^2, otherwise
    The example usage is:
@@ -6222,12 +6231,14 @@ def huber_classification_cost(input,
    """
    For classification purposes, a variant of the Huber loss called modified Huber
    is sometimes used. Given a prediction f(x) (a real-valued classifier score) and
-    a true binary class label :math:`y\in \left \{-1, 1 \right \}`, the modified Huber
+    a true binary class label :math:`y\in \{-1, 1 \}`, the modified Huber
    loss is defined as:
    .. math:
-       loss = \max \left ( 0, 1-yf(x) \right )^2, yf(x)\geq 1
-       loss = -4yf(x), \text{otherwise}
+       loss = \max ( 0, 1-yf(x) )^2, yf(x) \geq -1
+       loss = -4yf(x), otherwise
    The example usage is:
@@ -6972,7 +6983,7 @@ def clip_layer(input, min, max, name=None):
    .. math::
-        out[i] = \min\left(\max\left(in[i],p_{1}\right),p_{2}\right)
+        out[i] = \min (\max (in[i],p_{1} ),p_{2} )
    .. code-block:: python

--- a/python/paddle/v2/fluid/layers.py
+++ b/python/paddle/v2/fluid/layers.py
@@ -762,7 +762,7 @@ def sequence_conv(input,
    helper = LayerHelper('sequence_conv', **locals())
    dtype = helper.input_dtype()
    filter_shape = [filter_size * input.shape[1], num_filters]
-    filter = helper.create_parameter(
+    filter_param = helper.create_parameter(
        attr=helper.param_attr, shape=filter_shape, dtype=dtype)
    pre_bias = helper.create_tmp_variable(dtype)
@@ -770,7 +770,7 @@ def sequence_conv(input,
        type='sequence_conv',
        inputs={
            'X': [input],
-            'Filter': [filter],
+            'Filter': [filter_param],
        },
        outputs={"Out": pre_bias},
        attrs={
@@ -785,7 +785,7 @@ def sequence_conv(input,
 def conv2d(input,
           num_filters,
           filter_size,
-           stride=[1, 1],
+           stride=None,
           padding=None,
           groups=None,
           param_attr=None,
@@ -802,6 +802,8 @@ def conv2d(input,
    conv-2d output, if mentioned in the input parameters.
    """
+    if stride is None:
+        stride = [1, 1]
    helper = LayerHelper('conv2d', **locals())
    dtype = helper.input_dtype()
@@ -827,7 +829,7 @@ def conv2d(input,
        std = (2.0 / (filter_size[0]**2 * num_channels))**0.5
        return Normal(0.0, std, 0)
-    filter = helper.create_parameter(
+    filter_param = helper.create_parameter(
        attr=helper.param_attr,
        shape=filter_shape,
        dtype=dtype,
@@ -839,7 +841,7 @@ def conv2d(input,
        type='conv2d_cudnn',
        inputs={
            'Input': input,
-            'Filter': filter,
+            'Filter': filter_param,
        },
        outputs={"Output": pre_bias},
        attrs={'strides': stride,
@@ -875,8 +877,8 @@ def sequence_pool(input, pool_type, **kwargs):
 def pool2d(input,
           pool_size,
           pool_type,
-           pool_stride=[1, 1],
+           pool_stride=None,
-           pool_padding=[0, 0],
+           pool_padding=None,
           global_pooling=False,
           main_program=None,
           startup_program=None):
@@ -884,6 +886,10 @@ def pool2d(input,
    This function adds the operator for pooling in 2 dimensions, using the
    pooling configurations mentioned in input parameters.
    """
+    if pool_padding is None:
+        pool_padding = [0, 0]
+    if pool_stride is None:
+        pool_stride = [1, 1]
    if pool_type not in ["max", "avg"]:
        raise ValueError(
            "Unknown pool_type: '%s'. It can only be 'max' or 'avg'.",