From 635a69ba4be15c924541f8d222ba1183b793e4c2 Mon Sep 17 00:00:00 2001
From: ying <lcy.seso@gmail.com>
Date: Wed, 6 Dec 2017 17:50:38 +0800
Subject: [PATCH 01/68] add doc for how to use C-API.

---
 doc/howto/index_cn.rst                        |   1 +
 doc/howto/usage/capi/a_simple_example.md      | 211 ++++++++++++++++++
 doc/howto/usage/capi/compile_paddle_lib.md    |  68 ++++++
 doc/howto/usage/capi/core_concepts.md         |   0
 doc/howto/usage/capi/images/csr.png           | Bin 0 -> 174346 bytes
 .../usage/capi/organization_of_the_inputs.md  | 154 +++++++++++++
 doc/howto/usage/capi/overview.md              |   5 +
 .../examples/model_inference/dense/main.c     |  60 +++--
 .../model_inference/dense/merge_v2_model.py   |   8 +
 .../model_inference/dense/mnist_v2.py         | 117 ++++++++++
 .../model_inference/sparse_binary/main.c      |  11 +-
 python/paddle/utils/dump_v2_config.py         |  62 +++++
 python/paddle/utils/merge_model.py            |   3 +-
 13 files changed, 677 insertions(+), 23 deletions(-)
 create mode 100644 doc/howto/usage/capi/a_simple_example.md
 create mode 100644 doc/howto/usage/capi/compile_paddle_lib.md
 create mode 100644 doc/howto/usage/capi/core_concepts.md
 create mode 100644 doc/howto/usage/capi/images/csr.png
 create mode 100644 doc/howto/usage/capi/organization_of_the_inputs.md
 create mode 100644 doc/howto/usage/capi/overview.md
 create mode 100644 paddle/capi/examples/model_inference/dense/merge_v2_model.py
 create mode 100644 paddle/capi/examples/model_inference/dense/mnist_v2.py
 create mode 100644 python/paddle/utils/dump_v2_config.py

diff --git a/doc/howto/index_cn.rst b/doc/howto/index_cn.rst
index 991b9e2596..174fb7a074 100644
--- a/doc/howto/index_cn.rst
+++ b/doc/howto/index_cn.rst
@@ -12,6 +12,7 @@
   usage/k8s/k8s_basis_cn.md
   usage/k8s/k8s_cn.md
   usage/k8s/k8s_distributed_cn.md
+  usage/capi/overview.md
 
 开发标准
 --------
diff --git a/doc/howto/usage/capi/a_simple_example.md b/doc/howto/usage/capi/a_simple_example.md
new file mode 100644
index 0000000000..efcae3518e
--- /dev/null
+++ b/doc/howto/usage/capi/a_simple_example.md
@@ -0,0 +1,211 @@
+## 使用 C-API 开发预测程序
+
+这篇文档通过一个最简单的例子：手写数字识别，来介绍 CPU 下单线程使用 PaddlePaddle C-API 开发预测服务，完整代码见[此目录](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/examples/model_inference/dense/)。
+
+### 使用流程
+
+使用 C-API 分为：准备工作和预测程序开发两部分。
+- 准备
+    1. 将神经网络模型结构进行序列化。
+        - 调用C-API预测时，需要提供序列化之后的网络结构和训练好的模型参数文件。
+    1. 将PaddlePaddle训练出的模型参数文件（多个）合并成一个文件。
+        - 神经网络模型结构和训练好的模型将被序列化合并入一个文件。
+        - 预测时只需加载这一个文件，便于发布。
+    - **注意**：以上两种方式只需选择其一即可。
+- 调用 PaddlePaddle C-API 开发预测序
+    1. 初始化PaddlePaddle运行环境。
+    1. 创建神经网络的输入，组织输入数据。
+    1. 加载模型。
+    1. 进行前向计算，获得计算结果。
+    1. 清理。
+
+
+这里我们以手写数字识别任务为例，介绍如何使用 C-API 进行预测，完整代码请查看[此目录](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense)。
+
+运行目录下的 `python mnist_v2.py` 可以使用 PaddlePaddle 内置的 [MNIST 数据集](http://yann.lecun.com/exdb/mnist/)进行训练。脚本中的模型定义了一个简单的含有[两个隐层的全连接网络](https://github.com/PaddlePaddle/book/blob/develop/02.recognize_digits/README.cn.md#softmax回归softmax-regression)，网络接受一幅图片作为输入，将图片分类到 0 ~ 9 类别标签之一。训练好的模型默认保存在当前运行目录下的`models`目录中。下面，我们将调用 C-API 加载训练好的模型进行预测。
+
+
+### 外部准备
+
+1. 序列化神经网络模型配置
+
+    PaddlePaddle 使用 protobuf 来传输网络配置文件中定义的网络结构和相关参数，在使用 C-API 进行预测时，也需将网络结构使用 protobuf 进行序列化，写入文件中。
+
+    调用`paddle.utils.dump_v2_config`中的`dump_v2_config`函数能够将使用 PaddlePaddle V2 API 定义的神经网络结构 dump 到指定文件中。示例代码如下：
+
+    ```python
+    from paddle.utils.dump_v2_config import dump_v2_config
+    from mnist_v2 import network
+
+    predict = network(is_infer=True)
+    dump_v2_config(predict, "trainer_config.bin", True)
+    ```
+
+    对本例，或运行 `python mnist_v2.py --task dump_config`，会对示例中的网络结构进行序列化，并将结果写入当前目录下的`trainer_config.bin`文件中。
+
+    当选择使用这种方式调用 C-API 时，如果神经网络有多个可学习参数，请将它们全部放在同一文件夹内，C-API会从指定的目录寻找并加载训练好的模型。
+
+2. 合并模型文件(可选)
+
+    一些情况下为了便于发布，希望能够将序列化后的神经网络结构和训练好的模型参数打包进一个文件，这时可以使用`paddle.utils.merge_model`中的`merge_v2_model`接口对神经网络结构和训练好的参数进行序列化，将序列化结果写入一个文件内，调用C-API时直接只需加载这一个文件。
+
+    代码示例如下：
+
+    ```python
+    from paddle.utils.merge_model import merge_v2_model
+
+    from mnist_v2 import network
+
+    net = network(is_infer=True)
+    param_file = "models/params_pass_4.tar"
+    output_file = "output.paddle.model"
+    merge_v2_model(net, param_file, output_file)
+    ```
+    对本例，或者直接运行 `python merge_v2_model.py`，序列化结果将会写入当前目录下的`output.paddle.model`文件中，该文件在调用C-API时，可被直接加载。
+
+#### 注意事项
+1. C-API 需要序列化之后神经网络结构，在调用`dump_v2_config`时，参数`binary`必须指定为`True`。
+1. **预测使用的网络结构往往不同于训练**，通常需要去掉网络中的：（1）类别标签层；（2）损失函数层；（3）`evaluator`等，只留下核心计算层，请注意是否需要修改网络结构。
+1. 预测时，可以获取网络中定义的任意多个（大于等于一个）层前向计算的结果，需要哪些层的计算结果作为输出，就将这些层加入一个Python list中，作为调用`dump_v2_config`的第一个参数。
+
+### 编写预测代码
+
+#### step 1. 初始化及加载模型
+
+1. 初始化PaddlePaddle运行环境。
+    ```c
+    // Initalize the PaddlePaddle runtime environment.
+    char* argv[] = {"--use_gpu=False"};
+    CHECK(paddle_init(1, (char**)argv));
+    ```
+
+1. 加载训练好的模型。
+
+    这里需要介绍C-API使用中的一个重要概念：Gradient Machine。概念上，在 PaddlePaddle 内部，一个GradientMachine类的对象管理着一组计算层（PaddlePaddle Layers）来完成前向和反向计算，并处理与之相关的所有细节。特别的，在调用C-API预测时只需进行前向计算。这篇文档的之后部分我们会使用`gradient machine`来特指调用PaddlePaddle C-API创建的GradientMachine类的对象。
+
+    每一个 `gradient machine` 都会管理维护一份训练好的模型，模型可以通过以下两种方式获取：
+    1. 从磁盘加载；这时`gradient machine`会独立拥有一份训练好的模型；
+    1. 共享自其它`gradient machine`的模型；这种情况多出现在使用多线程预测时；
+
+    下面的代码片段创建 `gradient machine`，并从指定路径加载训练好的模型。
+
+    ```c
+    // Read the binary configuration file generated by `convert_protobin.sh`
+    long size;
+    void* buf = read_config(CONFIG_BIN, &size);
+
+    // Create the gradient machine for inference.
+    paddle_gradient_machine machine;
+    CHECK(paddle_gradient_machine_create_for_inference(&machine, buf, (int)size));
+
+    // Load the trained model. Modify the parameter MODEL_PATH to set the correct
+    // path of the trained model.
+    CHECK(paddle_gradient_machine_load_parameter_from_disk(machine, MODEL_PATH));
+    ```
+
+##### 注意事项
+1. 以上代码片段使用“仅序列化神经网络结构”的方式加载模型，需要同时指定模型参数存储的路径。
+    - 使用PaddlePaddle V2 API训练，模型中所有可学习参数会被存为一个压缩文件，需要手动进行解压，将它们放在同一目录中，C-API不会直接加载 V2 API 存储的压缩文件。
+1. 如果使用`merge model`方式将神经网络结构和训练好的参数序列化到一个文件，请参考此[示例](https://github.com/PaddlePaddle/Mobile/blob/develop/Demo/linux/paddle_image_recognizer.cpp#L59)。
+
+#### step 2. 创建神经网络输入，组织输入数据
+
+基本使用概念：
+- 在PaddlePaddle内部，神经网络中一个计算层的输入/输出被组织为一个 `Argument` 结构体，如果神经网络有多个输入或者多个输入，每一个输入/输入都会对应有自己的`Argument`。
+- `Argument` 并不真正“存储”数据，而是将输入/输出数据有机地组织在一起。
+- 在`Argument`内部由：1. `Matrix`（二维矩阵，存储浮点类型输入/输出）；2. `IVector`（一维数组，**仅用于存储整型值**，多用于自然语言处理任务）来实际存储数据。
+
+*注：这篇文档使用的示例任务手写数字识别不涉及一维整型序列输入/输出，因此不讨论一维整型输入/输出数据相关的内容。更多信息请参考：[输入数据组织](organization_of_the_inputs.md)。*
+
+在这篇文档的后面部分，我们会使用`argument`来**特指** PaddlePaddle C-API中神经网的一个输入/输出，使用`matrix`**特指**`argument`中用于存储数据的`Matrix`类的对象，用`ivector`特指`argument`中用于存储数据的`IVector`类的对象。
+
+于是，在组织神经网络输入，获取输出时，需要思考完成以下工作：
+1. 为每一个输入/输出创建`argument`；
+1. 为每一个`argument`创建`matrix`或者`ivector`来存储数据；
+
+与输入不同的是，输出`argument`的`matrix`变量并不需在使用C-API时为之要分配存储空间。PaddlePaddle内部，神经网络进行前向计算时会自己分配/管理每个计算层的存储空间；这些细节C-API会代为处理，只需在概念上理解，并按照约定调用相关的 C-API 接口即可。
+
+下面是示例代码片段。在这段代码中，生成了一条随机输入数据作为测试样本。
+```c
+// Inputs and outputs of the network are organized as paddle_arguments object
+// in C-API. In the comments below, "argument" specifically means one input of
+// the neural network in PaddlePaddle C-API.
+paddle_arguments in_args = paddle_arguments_create_none();
+
+// There is only one data layer in this demo MNIST network, invoke this
+// function to create one argument.
+CHECK(paddle_arguments_resize(in_args, 1));
+
+// Each argument needs one matrix or one ivector (integer vector, for sparse
+// index input, usually used in NLP task) to holds the real input data.
+// In the comments below, "matrix" specifically means the object needed by
+// argument to hold the data. Here we create the matrix for the above created
+// agument to store the testing samples.
+paddle_matrix mat =
+    paddle_matrix_create(/* height = batch size */ 1,
+                         /* width = dimensionality of the data layer */ 784,
+                         /* whether to use GPU */ false);
+
+paddle_real* array;
+// Get the pointer pointing to the start address of the first row of the
+// created matrix.
+CHECK(paddle_matrix_get_row(mat, 0, &array));
+
+// Fill the matrix with a randomly generated test sample.
+srand(time(0));
+for (int i = 0; i < 784; ++i) {
+  array[i] = rand() / ((float)RAND_MAX);
+}
+
+// Assign the matrix to the argument.
+CHECK(paddle_arguments_set_value(in_args, 0, mat));
+```
+
+#### step 3. 前向计算
+
+完成上述准备之后，通过调用 `paddle_gradient_machine_forward` 接口完成神经网络的前向计算。
+示例代码片段如下：
+
+```c
+// Create the output argument.
+paddle_arguments out_args = paddle_arguments_create_none();
+
+// Invoke the forward computation.
+CHECK(paddle_gradient_machine_forward(machine,
+                                      in_args,
+                                      out_args,
+                                      /* is train taks or not */ false));
+
+// Create the matrix to hold the forward result of the neural network.
+paddle_matrix prob = paddle_matrix_create_none();
+// Access the matrix of the output argument, the predicted result is stored in
+// which.
+CHECK(paddle_arguments_get_value(out_args, 0, prob));
+
+uint64_t height;
+uint64_t width;
+CHECK(paddle_matrix_get_shape(prob, &height, &width));
+CHECK(paddle_matrix_get_row(prob, 0, &array));
+
+printf("Prob: \n");
+for (int i = 0; i < height * width; ++i) {
+  printf("%.4f ", array[i]);
+  if ((i + 1) % width == 0) {
+    printf("\n");
+  }
+}
+printf("\n");
+```
+
+#### step 4. 清理
+
+结束预测之后，对使用的中间变量和资源进行清理和释放：
+
+```c
+// The cleaning up.
+CHECK(paddle_matrix_destroy(prob));
+CHECK(paddle_arguments_destroy(out_args));
+CHECK(paddle_matrix_destroy(mat));
+CHECK(paddle_arguments_destroy(in_args));
+CHECK(paddle_gradient_machine_destroy(machine));
+```
diff --git a/doc/howto/usage/capi/compile_paddle_lib.md b/doc/howto/usage/capi/compile_paddle_lib.md
new file mode 100644
index 0000000000..1ad5b90681
--- /dev/null
+++ b/doc/howto/usage/capi/compile_paddle_lib.md
@@ -0,0 +1,68 @@
+## 编译 PaddlePaddle 链接库
+
+使用 C-API 进行预测依赖于将 PaddlePaddle 核心代码编译成链接库，只需在编译时指定编译选项：`-DWITH_C_API=ON`。同时，**建议将：`DWITH_PYTHON`，`DWITH_SWIG_PY`，`DWITH_GOLANG`，均设置为`OFF`**，以避免链接不必要的库。其它编译选项按需进行设定。
+
+```shell
+INSTALL_PREFIX=/path/of/capi/
+PADDLE_ROOT=/path/of/paddle_source/
+cmake $PADDLE_ROOT -DCMAKE_INSTALL_PREFIX=$INSTALL_PREFIX \
+      -DCMAKE_BUILD_TYPE=Release \
+      -DWITH_C_API=ON \
+      -DWITH_SWIG_PY=OFF \
+      -DWITH_GOLANG=OFF \
+      -DWITH_PYTHON=OFF \
+      -DWITH_MKLML=OFF \
+      -DWITH_MKLDNN=OFF \
+      -DWITH_GPU=OFF \
+      ...
+```
+在上面的代码片段中，`PADDLE_ROOT` 表示 PaddlePaddle 源码所在目录，生成Makefile文件后执行：`make && make install`。成功执行后，使用CAPI所需的依赖（包括：（1）编译出的PaddlePaddle 链接和头文件；（2）第三方链接库和头文件）均会存放于`INSTALL_PREFIX`目录中。
+
+编译成功后在 `INSTALL_PREFIX` 下会看到如下目录结构（包括了编译出的PaddlePaddle头文件和链接库，以及第三方依赖链接库和头文件（如果需要，由链接方式决定））：
+
+```text
+├── include
+│   └── paddle
+│       ├── arguments.h
+│       ├── capi.h
+│       ├── capi_private.h
+│       ├── config.h
+│       ├── error.h
+│       ├── gradient_machine.h
+│       ├── main.h
+│       ├── matrix.h
+│       ├── paddle_capi.map
+│       └── vector.h
+├── lib
+│   ├── libpaddle_capi_engine.a
+│   ├── libpaddle_capi_layers.a
+│   ├── libpaddle_capi_shared.dylib
+│   └── libpaddle_capi_whole.a
+└── third_party
+    ├── ......
+```
+
+## 链接方式说明
+
+目前提供三种链接方式：
+
+1. 链接`libpaddle_capi_shared.so` 动态库
+    - 使用 PaddlePaddle C-API 开发预测程序链接`libpaddle_capi_shared.so`时，需注意：
+        1. 如果编译时指定编译CPU版本，且使用`OpenBLAS`矩阵库，在使用CAPI开发预测程序时，只需要链接`libpaddle_capi_shared.so`这一个库。
+        1. 如果是用编译时指定CPU版本，且使用`MKL`矩阵库，由于`MKL`库有自己独立的动态库文件，在使用PaddlePaddle CAPI开发预测程序时，需要自己链接MKL链接库。
+        1. 如果编译时指定编译GPU版本，CUDA相关库会在预测程序运行时动态装载，需要将CUDA相关的库设置到`LD_LIBRARY_PATH`环境变量中。
+    - 这种方式最为简便，链接相对容易，**在无特殊需求情况下，推荐使用此方式**。
+
+2. 链接静态库 `libpaddle_capi_whole.a`
+    - 使用PaddlePaddle C-API 开发预测程序链接`libpaddle_capi_whole.a`时，需注意：
+        1. 需要指定`-Wl,--whole-archive`链接选项。
+        1. 需要显式地链接 `gflags`、`glog`、`libz`、`protobuf` 等第三方库，可在`INSTALL_PREFIX\third_party`下找到。
+        1. 如果在编译 C-API 时使用OpenBLAS矩阵库，需要显示地链接`libopenblas.a`。
+        1. 如果在编译 C-API 是使用 MKL 矩阵库，需要显示地链接 MKL 的动态库。
+
+3. 链接静态库 `libpaddle_capi_layers.a`和`libpaddle_capi_engine.a`
+    - 使用PaddlePaddle C-API 开发预测程序链接`libpaddle_capi_whole.a`时，需注意：
+        1. 这种链接方式主要用于移动端预测。
+        1. 为了减少生成链接库的大小把`libpaddle_capi_whole.a`拆成以上两个静态链接库。
+        1. 需指定`-Wl,--whole-archive -lpaddle_capi_layers` 和 `-Wl,--no-whole-archive -lpaddle_capi_engine` 进行链接。
+        1. 第三方依赖库需要按照与方式2同样方法显示地进行链接。
diff --git a/doc/howto/usage/capi/core_concepts.md b/doc/howto/usage/capi/core_concepts.md
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diff --git a/doc/howto/usage/capi/organization_of_the_inputs.md b/doc/howto/usage/capi/organization_of_the_inputs.md
new file mode 100644
index 0000000000..b306c522e6
--- /dev/null
+++ b/doc/howto/usage/capi/organization_of_the_inputs.md
@@ -0,0 +1,154 @@
+## 输入/输出数据组织
+
+这篇文档介绍在使用 PaddlePaddle C-API 时如何组织输入数据，以及如何解析神经网络前向计算的输出数据。
+
+### 输入/输出数据类型
+在C-API中，按照基本数据类型在PaddlePaddle内部的定义和实现，输入数据可分为：
+1. 一维整型数组
+2. 二维浮点型矩阵
+    - 稠密矩阵
+    - 稀疏矩阵
+
+  - 说明：
+    1. 一维数组**仅支持整型值**；
+        - 常用于自然语言处理任务，例如：表示词语在词典中的序号；
+        - 分类任务中类别标签；
+    1. 逻辑上高于二维的数据（例如含有多个通道的图片，视频等）在程序实现中都会转化为二维矩阵，转化方法在相应的领域都有通用解决方案，需要使用者自己了解相关的转化表示方法；
+    1. 二维矩阵可以表示行向量和列向量，任何时候，如果需要浮点型数组（向量）时，都应使用C-API中的矩阵来表示，而不是C-API中的一维数组。
+
+不论是一维整型数组还是二维浮点数矩阵，**为它们附加上序列信息，将变成序列输入。PaddlePaddle 会通过判数据是否附带有序列信息来判断一个向量/矩阵是否是一个序列**。关于什么是“序列信息”，下文会进行详细地介绍。
+
+PaddlePaddle 支持两种序列类型：
+1. 单层序列
+    - 序列中的每一个元素是非序列，是进行计算的基本单位，不可再进行拆分。
+    - 例如：自然语言中的句子是一个序列，序列中的元素是词语；
+1. 双层序列
+    - 序列中的每一个元素又是一个序列。
+    - 例如：自然语言中的段落是一个双层序列；段落是有句子构成的序列；句子是由词语构成的序列。
+    - 双层序列在处理长序列的任务或是构建层级模型时会发挥作用。
+
+### 基本使用概念
+
+- 在PaddlePaddle内部，神经网络中一个计算层的输入/输出被组织为一个 `Argument` 结构体，如果神经网络有多个输入或者多个输入，每一个输入/输入都会对应有自己的`Argument`。
+- `Argument` 并不真正“存储”数据，而是将输入/输出信息有机地组织在一起。
+- 在`Argument`内部由`IVector`（对应着上文提到的一维整型数组）和`Matrix`（对应着上文提到的二维浮点型矩阵）来实际存储数据；由 `sequence start position` (下文详细解释) 来记录输入/输出的序列信息。
+
+**注意：这篇文档之后部分将会统一使用`argument`来特指PaddlePaddle中神经网络计算层一个输入/输出数据；使用`ivector`来特指PaddlePaddle中的一维整型数组；使用`matrix`来特指PaddlePaddle中的二维浮点型矩阵；使用`sequence_start_position`来特指PaddlePaddle中的序列信息。**
+
+于是，在组织神经网络输入时，需要思考完成以下工作：
+1. 为每一个输入/输出创建`argument`。
+    - C-API 中操作`argument`的接口请查看[argument.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/arguments.h)。
+1. 为每一个`argument`创建`matrix`或者`ivector`来存储数据。
+    - C-API 中操作`ivector`的接口请查看 [vector.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/vector.h)。
+    - C-API 中操作`matrix`的接口请查看[matrix.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/matrix.h)。
+1. 如果输入是序列数据，需要创建并填写`sequence_start_position`信息。
+    - 通过调用 [`paddle_arguments_set_sequence_start_pos`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/arguments.h#L137) 来为一个`argument`添加序列信息；
+    - 通过调用 [`paddle_arguments_get_sequence_start_pos`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/arguments.h#L150) 来读取一个`argument`添加序列信息；
+    - 接口说明请查看 [argument.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/arguments.h) 文件。
+
+### 组织非序列数据
+- 一维整型数组
+
+    概念上可以将`paddle_ivector`理解为一个一维的整型数组，通常用于表示离散的类别标签，或是在自然语言处理任务中表示词语在字典中的序号。下面的代码片段创建了含有三个元素`1`、`2`、`3`的`paddle_ivector`。
+    ```cpp
+    int ids[] = {1, 2, 3};
+     paddle_ivector ids_array =
+         paddle_ivector_create(ids, sizeof(ids) / sizeof(int), false, false);
+     CHECK(paddle_arguments_set_ids(in_args, 0, ids_array));
+    ```
+- **稠密矩阵**
+    - 一个$m×n$的稠密矩阵是一个由$m$行$n$列元素排列成的矩形阵列，矩阵里的元素是浮点数。对神经网络来说，矩阵的高度$m$是一次预测接受的样本数目，宽度$n$是神经网络定义时，`paddle.layer.data`的`size`。
+    - 下面的代码片段创建了一个高度为1，宽度为`layer_size`的稠密矩阵，矩阵中每个元素的值随机生成。
+
+    ```cpp
+    paddle_matrix mat =
+        paddle_matrix_create(/* height = batch size */ 1,
+                             /* width = dimensionality of the data layer */ layer_size,
+                             /* whether to use GPU */ false);
+
+    paddle_real* array;
+    // Get the pointer pointing to the start address of the first row of the
+    // created matrix.
+    CHECK(paddle_matrix_get_row(mat, 0, &array));
+
+    // Fill the matrix with a randomly generated test sample.
+    srand(time(0));
+    for (int i = 0; i < layer_size; ++i) {
+      array[i] = rand() / ((float)RAND_MAX);
+    }
+
+    // Assign the matrix to the argument.
+    CHECK(paddle_arguments_set_value(in_args, 0, mat));
+    ```
+
+- **稀疏矩阵**
+
+  PaddlePaddle C-API 中 稀疏矩阵使用[CSR（Compressed Sparse Row Format）](https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_row_(CSR,_CRS_or_Yale_format))格式存储。下图是CSR存储稀疏矩阵的示意图，在CSR表示方式中，通过（1）行偏移；（2）列号；（3）值；来决定矩阵的内容。
+
+  <p align="center">
+  <img src="images/csr.png" width=75%></br>图1. CSR存储示意图.
+  </p>
+
+  在PaddlePaddle C-API中通过以下接口创建稀疏矩阵：
+  ```cpp
+  PD_API paddle_matrix paddle_matrix_create_sparse(
+    uint64_t height, uint64_t width, uint64_t nnz, bool isBinary, bool useGpu);
+  ```
+  1. 创建稀疏矩阵时需要显示地指定矩阵的（1）高度（`height`，在神经网络中等于一次预测处理的样本数）（2）宽度（`width`，`paddle.layer.data`的`size`）以及（3）非零元个数（`nnz`）。
+  1. 当上述接口第4个参数`isBinary`指定为`true`时，**只需要设置行偏移（`row_offset`）和列号(`colum indices`)，不需要提供元素值（`values`）**，这时行偏移和列号指定的元素默认其值为1。
+
+  - 下面的代码片段创建了一个CPU上的二值稀疏矩阵：
+
+      ```cpp
+      paddle_matrix mat = paddle_matrix_create_sparse(1, layer_size, nnz, true, false);
+      int colIndices[] = {9, 93, 109};  // layer_size here is greater than 109.
+      int rowOffset[] = {0, sizeof(colIndices) / sizeof(int)};
+
+      CHECK(paddle_matrix_sparse_copy_from(mat,
+                                           rowOffset,
+                                           sizeof(rowOffset) / sizeof(int),
+                                           colIndices,
+                                           sizeof(colIndices) / sizeof(int),
+                                           NULL /*values array is NULL.*/,
+                                           0 /*size of the value arrary is 0.*/));
+      CHECK(paddle_arguments_set_value(in_args, 0, mat));
+      ```
+   - 下面的代码片段在创建了一个CPU上的带元素值的稀疏矩阵：
+      ```cpp
+      paddle_matrix mat = paddle_matrix_create_sparse(1, layer_size, nnz, false, false);
+      int colIndices[] = {9, 93, 109};  // layer_size here is greater than 109.
+      int rowOffset[] = {0, sizeof(colIndices) / sizeof(int)};
+      float values[] = {0.5, 0.5, 0.5};
+
+      CHECK(paddle_matrix_sparse_copy_from(mat,
+                                           rowOffset,
+                                           sizeof(rowOffset) / sizeof(int),
+                                           colIndices,
+                                           sizeof(colIndices) / sizeof(int),
+                                           values,
+                                           sizeof(values) / sizeof(float)));
+      ```
+
+### 组织序列数据
+
+
+
+### Python 端数据类型说明
+
+下表列出了Python端训练接口暴露的数据类型（`paddle.layer.data`函数`type`字段的取值）对应于调用C-API时需要创建的数据类型：
+
+
+Python 端数据类型  | C-API 输入数据类型|
+:-------------: | :-------------:
+`paddle.data_type.integer_value` |一维整型数组，无需附加序列信息|
+`paddle.data_type.dense_vector` |二维浮点型稠密矩阵，无需附加序列信息|
+`paddle.data_type.sparse_binary_vector` |二维浮点型稀疏矩阵，无需提供非零元的值，默认为1，无需附加序列信息|
+`paddle.data_type.sparse_vector` |二维浮点型稀疏矩阵，需提供非零元的值，无需附加序列信息|
+`paddle.data_type.integer_value_sequence` |一维整型数组，需附加序列信息|
+`paddle.data_type.dense_vector_sequence` |二维浮点型稠密矩阵，需附加序列信息|
+`paddle.data_type.sparse_binary_vector_sequence` |二维浮点型稀疏矩阵，无需提供非零元的值，默认为1，需附加序列信息|
+`paddle.data_type.sparse_vector_sequence` |二维浮点型稀疏矩阵，需提供非零元的值，需附加序列信息|
+`paddle.data_type.integer_value_sub_sequence` |一维整型数组，需附加双层序列信息|
+`paddle.data_type.dense_vector_sub_sequence` |二维浮点型稠密矩阵，需附加双层序列信息|
+`paddle.data_type.sparse_binary_vector_sub_sequence` |二维浮点型稀疏矩阵，无需提供非零元的值，默认为1，需附加双层序列信息|
+`paddle.data_type.sparse_vector_sub_sequence` |二维浮点型稀疏矩阵，需提供非零元的值，需附加双层序列信息|
diff --git a/doc/howto/usage/capi/overview.md b/doc/howto/usage/capi/overview.md
new file mode 100644
index 0000000000..5ec3bd0284
--- /dev/null
+++ b/doc/howto/usage/capi/overview.md
@@ -0,0 +1,5 @@
+- [编译 PaddlePaddle 链接库](compile_paddle_lib.md)
+- [C-API 使用示例](a_simple_example.md)
+- [输入数据组织](organize_input_data.md)
+- [核心概念介绍](core_concepts.md)
+- [F&Q]()
diff --git a/paddle/capi/examples/model_inference/dense/main.c b/paddle/capi/examples/model_inference/dense/main.c
index 5eeaf7e31f..376cd46fb0 100644
--- a/paddle/capi/examples/model_inference/dense/main.c
+++ b/paddle/capi/examples/model_inference/dense/main.c
@@ -3,59 +3,82 @@
 
 #include "../common/common.h"
 
+// Modify this path as needed.
 #define CONFIG_BIN "./trainer_config.bin"
+// Modify this path as needed.
+// This demo assumes that merged model is not used, then this path is the
+// directory storing all the trained parameters.
+// If the model is trained by PaddlePaddle V2 API, the model is saved as
+// a compressed file. You need to uncompress the compressed file first.
+#define MODEL_PATH "models/pass_4"
 
 int main() {
-  // Initalize Paddle
+  // Initalize the PaddlePaddle runtime environment.
   char* argv[] = {"--use_gpu=False"};
   CHECK(paddle_init(1, (char**)argv));
 
-  // Reading config binary file. It is generated by `convert_protobin.sh`
+  // Read the binary configuration file generated by `convert_protobin.sh`
   long size;
   void* buf = read_config(CONFIG_BIN, &size);
 
-  // Create a gradient machine for inference.
+  // Create the gradient machine for inference.
   paddle_gradient_machine machine;
   CHECK(paddle_gradient_machine_create_for_inference(&machine, buf, (int)size));
-  CHECK(paddle_gradient_machine_randomize_param(machine));
 
-  // Loading parameter. Uncomment the following line and change the directory.
-  // CHECK(paddle_gradient_machine_load_parameter_from_disk(machine,
-  //                                                "./some_where_to_params"));
+  // Load the trained model. Modify the parameter MODEL_PATH to set the correct
+  // path of the trained model.
+  CHECK(paddle_gradient_machine_load_parameter_from_disk(machine, MODEL_PATH));
+
+  // Inputs and outputs of the network are organized as paddle_arguments object
+  // in C-API. In the comments below, "argument" specifically means one input of
+  // the neural network in PaddlePaddle C-API.
   paddle_arguments in_args = paddle_arguments_create_none();
 
-  // There is only one input of this network.
+  // There is only one data layer in this demo MNIST network, invoke this
+  // function to create one argument.
   CHECK(paddle_arguments_resize(in_args, 1));
 
-  // Create input matrix.
-  paddle_matrix mat = paddle_matrix_create(/* sample_num */ 1,
-                                           /* size */ 784,
-                                           /* useGPU */ false);
-  srand(time(0));
+  // Each argument needs one matrix or one ivector (integer vector, for sparse
+  // index input, usually used in NLP task) to holds the real input data.
+  // In the comments below, "matrix" specifically means the object needed by
+  // argument to hold the data. Here we create the matrix for the above created
+  // agument to store the testing samples.
+  paddle_matrix mat =
+      paddle_matrix_create(/* height = batch size */ 1,
+                           /* width = dimensionality of the data layer */ 784,
+                           /* whether to use GPU */ false);
 
   paddle_real* array;
-
-  // Get First row.
+  // Get the pointer pointing to the start address of the first row of the
+  // created matrix.
   CHECK(paddle_matrix_get_row(mat, 0, &array));
 
+  // Fill the matrix with a randomly generated test sample.
+  srand(time(0));
   for (int i = 0; i < 784; ++i) {
     array[i] = rand() / ((float)RAND_MAX);
   }
 
+  // Assign the matrix to the argument.
   CHECK(paddle_arguments_set_value(in_args, 0, mat));
 
+  // Create the output argument.
   paddle_arguments out_args = paddle_arguments_create_none();
+
+  // Invoke the forward computation.
   CHECK(paddle_gradient_machine_forward(machine,
                                         in_args,
                                         out_args,
-                                        /* isTrain */ false));
-  paddle_matrix prob = paddle_matrix_create_none();
+                                        /* is train taks or not */ false));
 
+  // Create the matrix to hold the forward result of the neural network.
+  paddle_matrix prob = paddle_matrix_create_none();
+  // Access the matrix of the output argument, the predicted result is stored in
+  // which.
   CHECK(paddle_arguments_get_value(out_args, 0, prob));
 
   uint64_t height;
   uint64_t width;
-
   CHECK(paddle_matrix_get_shape(prob, &height, &width));
   CHECK(paddle_matrix_get_row(prob, 0, &array));
 
@@ -68,6 +91,7 @@ int main() {
   }
   printf("\n");
 
+  // The cleaning up.
   CHECK(paddle_matrix_destroy(prob));
   CHECK(paddle_arguments_destroy(out_args));
   CHECK(paddle_matrix_destroy(mat));
diff --git a/paddle/capi/examples/model_inference/dense/merge_v2_model.py b/paddle/capi/examples/model_inference/dense/merge_v2_model.py
new file mode 100644
index 0000000000..c030d572cb
--- /dev/null
+++ b/paddle/capi/examples/model_inference/dense/merge_v2_model.py
@@ -0,0 +1,8 @@
+from paddle.utils.merge_model import merge_v2_model
+
+from mnist_v2 import network
+
+net = network(is_infer=True)
+param_file = "models/params_pass_4.tar"
+output_file = "output.paddle.model"
+merge_v2_model(net, param_file, output_file)
diff --git a/paddle/capi/examples/model_inference/dense/mnist_v2.py b/paddle/capi/examples/model_inference/dense/mnist_v2.py
new file mode 100644
index 0000000000..ee28111153
--- /dev/null
+++ b/paddle/capi/examples/model_inference/dense/mnist_v2.py
@@ -0,0 +1,117 @@
+import os
+import sys
+import gzip
+import logging
+import argparse
+from PIL import Image
+import numpy as np
+
+import paddle.v2 as paddle
+from paddle.utils.dump_v2_config import dump_v2_config
+
+logger = logging.getLogger("paddle")
+logger.setLevel(logging.INFO)
+
+
+def multilayer_perceptron(img, layer_size, lbl_dim):
+    for idx, size in enumerate(layer_size):
+        hidden = paddle.layer.fc(input=(img if not idx else hidden),
+                                 size=size,
+                                 act=paddle.activation.Relu())
+    return paddle.layer.fc(input=hidden,
+                           size=lbl_dim,
+                           act=paddle.activation.Softmax())
+
+
+def network(input_dim=784, lbl_dim=10, is_infer=False):
+    images = paddle.layer.data(
+        name='pixel', type=paddle.data_type.dense_vector(input_dim))
+
+    predict = multilayer_perceptron(
+        images, layer_size=[128, 64], lbl_dim=lbl_dim)
+
+    if is_infer:
+        return predict
+    else:
+        label = paddle.layer.data(
+            name='label', type=paddle.data_type.integer_value(lbl_dim))
+        return paddle.layer.classification_cost(input=predict, label=label)
+
+
+def main(task="train", use_gpu=False, trainer_count=1, save_dir="models"):
+    if task == "train":
+        if not os.path.exists(save_dir):
+            os.mkdir(save_dir)
+
+        paddle.init(use_gpu=use_gpu, trainer_count=trainer_count)
+        cost = network()
+        parameters = paddle.parameters.create(cost)
+        optimizer = paddle.optimizer.Momentum(
+            learning_rate=0.1 / 128.0,
+            momentum=0.9,
+            regularization=paddle.optimizer.L2Regularization(rate=0.0005 * 128))
+
+        trainer = paddle.trainer.SGD(cost=cost,
+                                     parameters=parameters,
+                                     update_equation=optimizer)
+
+        def event_handler(event):
+            if isinstance(event, paddle.event.EndIteration):
+                if event.batch_id % 100 == 0:
+                    logger.info("Pass %d, Batch %d, Cost %f, %s" %
+                                (event.pass_id, event.batch_id, event.cost,
+                                 event.metrics))
+            if isinstance(event, paddle.event.EndPass):
+                with gzip.open(
+                        os.path.join(save_dir, "params_pass_%d.tar" %
+                                     event.pass_id), "w") as f:
+                    trainer.save_parameter_to_tar(f)
+
+        trainer.train(
+            reader=paddle.batch(
+                paddle.reader.shuffle(
+                    paddle.dataset.mnist.train(), buf_size=8192),
+                batch_size=128),
+            event_handler=event_handler,
+            num_passes=5)
+    elif task == "dump_config":
+        predict = network(is_infer=True)
+        dump_v2_config(predict, "trainer_config.bin", True)
+    else:
+        raise RuntimeError(("Error value for parameter task. "
+                            "Available options are: train and dump_config."))
+
+
+def parse_cmd():
+    parser = argparse.ArgumentParser(
+        description="PaddlePaddle MNIST demo for CAPI.")
+    parser.add_argument(
+        "--task",
+        type=str,
+        required=False,
+        help=("A string indicating the taks type. "
+              "Available options are: \"train\", \"dump_config\"."),
+        default="train")
+    parser.add_argument(
+        "--use_gpu",
+        type=bool,
+        help=("A bool flag indicating whether to use GPU device or not."),
+        default=False)
+    parser.add_argument(
+        "--trainer_count",
+        type=int,
+        help=("This parameter is only used in training task. It indicates "
+              "how many computing threads are created in training."),
+        default=1)
+    parser.add_argument(
+        "--save_dir",
+        type=str,
+        help=("This parameter is only used in training task. It indicates "
+              "path of the directory to save the trained models."),
+        default="models")
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = parse_cmd()
+    main(args.task, args.use_gpu, args.trainer_count, args.save_dir)
diff --git a/paddle/capi/examples/model_inference/sparse_binary/main.c b/paddle/capi/examples/model_inference/sparse_binary/main.c
index 8ba67aee56..029b94ee63 100644
--- a/paddle/capi/examples/model_inference/sparse_binary/main.c
+++ b/paddle/capi/examples/model_inference/sparse_binary/main.c
@@ -1,5 +1,6 @@
 #include <paddle/capi.h>
 #include <time.h>
+
 #include "../common/common.h"
 
 #define CONFIG_BIN "./trainer_config.bin"
@@ -9,16 +10,18 @@ int main() {
   char* argv[] = {"--use_gpu=False"};
   CHECK(paddle_init(1, (char**)argv));
 
-  // Reading config binary file. It is generated by `convert_protobin.sh`
+  // Read the binary configuration file which is generated by
+  // `convert_protobin.sh`
   long size;
   void* buf = read_config(CONFIG_BIN, &size);
 
-  // Create a gradient machine for inference.
+  // Create the gradient machine for inference.
   paddle_gradient_machine machine;
   CHECK(paddle_gradient_machine_create_for_inference(&machine, buf, (int)size));
   CHECK(paddle_gradient_machine_randomize_param(machine));
 
-  // Loading parameter. Uncomment the following line and change the directory.
+  // Load the trained parameters. Uncomment the following line and change the
+  // directory as needed.
   // CHECK(paddle_gradient_machine_load_parameter_from_disk(machine,
   //                                                "./some_where_to_params"));
   paddle_arguments in_args = paddle_arguments_create_none();
@@ -26,7 +29,7 @@ int main() {
   // There is only one input of this network.
   CHECK(paddle_arguments_resize(in_args, 1));
 
-  // Create input matrix.
+  // Create the input matrix.
   paddle_matrix mat = paddle_matrix_create_sparse(1, 784, 3, true, false);
   srand(time(0));
   paddle_real* array;
diff --git a/python/paddle/utils/dump_v2_config.py b/python/paddle/utils/dump_v2_config.py
new file mode 100644
index 0000000000..5dc2111e37
--- /dev/null
+++ b/python/paddle/utils/dump_v2_config.py
@@ -0,0 +1,62 @@
+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# 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.
+import collections
+
+from paddle.trainer_config_helpers.layers import LayerOutput
+from paddle.v2.layer import parse_network
+from paddle.proto import TrainerConfig_pb2
+
+__all__ = ["dump_v2_config"]
+
+
+def dump_v2_config(topology, save_path, binary=False):
+    """ Dump the network topology to a specified file.
+
+    This function is only used to dump network defined by using PaddlePaddle V2
+    APIs. This function will NOT dump configurations related to PaddlePaddle
+    optimizer.
+
+    :param topology: The output layers (can be more than one layers given in a
+                     Python List or Tuple) of the entire network. Using the
+                     specified layers (if more than one layer is given) as root,
+                     traversing back to the data layer(s), all the layers
+                     connected to the specified output layers will be dumped.
+                     Layers not connceted to the specified will not be dumped.
+    :type topology: LayerOutput|List|Tuple
+    :param save_path: The path to save the dumped network topology.
+    :type save_path: str
+    :param binary: Whether to dump the serialized network topology or not.
+                   The default value is false. NOTE that, if you call this
+                   function to generate network topology for PaddlePaddle C-API,
+                   a serialized version of network topology is required. When
+                   using PaddlePaddle C-API, this flag MUST be set to True.
+    :type binary: bool
+    """
+
+    if isinstance(topology, LayerOutput):
+        topology = [topology]
+    elif isinstance(topology, collections.Sequence):
+        for out_layer in topology:
+            assert isinstance(out_layer, LayerOutput), (
+                "The type of each element in the parameter topology "
+                "should be LayerOutput.")
+    else:
+        raise RuntimeError("Error input type for parameter topology.")
+
+    model_str = parse_network(topology)
+    with open(save_path, "w") as fout:
+        if binary:
+            fout.write(model_str.SerializeToString())
+        else:
+            fout.write(str(model_str))
diff --git a/python/paddle/utils/merge_model.py b/python/paddle/utils/merge_model.py
index 421e953d27..2b10020772 100644
--- a/python/paddle/utils/merge_model.py
+++ b/python/paddle/utils/merge_model.py
@@ -30,7 +30,8 @@ def merge_v2_model(net, param_file, output_file):
     which ends with .tar.gz.
 
     @param  net            The output layer of the network for inference.
-    @param  param_file     Path of the parameters (.tar.gz) which is stored by v2 api.
+    @param  param_file     Path of the parameters (.tar.gz) which is stored by
+                           v2 api.
     @param  output_file    Path of the merged file which will be generated.
 
     Usage:

From 878e6f7c54fd4448698bd38f7f658063189150b1 Mon Sep 17 00:00:00 2001
From: ying <lcy.seso@gmail.com>
Date: Tue, 19 Dec 2017 13:32:27 +0800
Subject: [PATCH 02/68] update the data organization part.

---
 doc/howto/usage/capi/a_simple_example.md      |   8 +-
 doc/howto/usage/capi/core_concepts.md         |   0
 doc/howto/usage/capi/images/csr.png           | Bin 174346 -> 370051 bytes
 doc/howto/usage/capi/images/sequence_data.png | Bin 0 -> 481460 bytes
 .../usage/capi/organization_of_the_inputs.md  | 174 +++++++++++++-----
 doc/howto/usage/capi/overview.md              |   4 +-
 6 files changed, 134 insertions(+), 52 deletions(-)
 delete mode 100644 doc/howto/usage/capi/core_concepts.md
 create mode 100644 doc/howto/usage/capi/images/sequence_data.png

diff --git a/doc/howto/usage/capi/a_simple_example.md b/doc/howto/usage/capi/a_simple_example.md
index efcae3518e..ae2eaa3ce2 100644
--- a/doc/howto/usage/capi/a_simple_example.md
+++ b/doc/howto/usage/capi/a_simple_example.md
@@ -115,15 +115,15 @@
 - `Argument` 并不真正“存储”数据，而是将输入/输出数据有机地组织在一起。
 - 在`Argument`内部由：1. `Matrix`（二维矩阵，存储浮点类型输入/输出）；2. `IVector`（一维数组，**仅用于存储整型值**，多用于自然语言处理任务）来实际存储数据。
 
-*注：这篇文档使用的示例任务手写数字识别不涉及一维整型序列输入/输出，因此不讨论一维整型输入/输出数据相关的内容。更多信息请参考：[输入数据组织](organization_of_the_inputs.md)。*
+*注：这篇文档使用的示例任务手写数字识别不涉及一维整型序列输入/输出，因此不讨论一维整型输入/输出数据相关的内容。更多信息请参考：[输入/输出数据组织](organization_of_the_inputs.md)。*
 
-在这篇文档的后面部分，我们会使用`argument`来**特指** PaddlePaddle C-API中神经网的一个输入/输出，使用`matrix`**特指**`argument`中用于存储数据的`Matrix`类的对象，用`ivector`特指`argument`中用于存储数据的`IVector`类的对象。
+这篇文档的之后部分会使用`argument`来**特指** PaddlePaddle C-API中神经网的一个输入/输出，使用`paddle_matrix`**特指**`argument`中用于存储数据的`Matrix`类的对象。
 
 于是，在组织神经网络输入，获取输出时，需要思考完成以下工作：
 1. 为每一个输入/输出创建`argument`；
-1. 为每一个`argument`创建`matrix`或者`ivector`来存储数据；
+1. 为每一个`argument`创建`paddle_matrix`来存储数据；
 
-与输入不同的是，输出`argument`的`matrix`变量并不需在使用C-API时为之要分配存储空间。PaddlePaddle内部，神经网络进行前向计算时会自己分配/管理每个计算层的存储空间；这些细节C-API会代为处理，只需在概念上理解，并按照约定调用相关的 C-API 接口即可。
+与输入不同的是，输出`argument`的`paddle_matrix`变量并不需在使用C-API时为之分配存储空间。PaddlePaddle内部，神经网络进行前向计算时会自己分配/管理每个计算层的存储空间；这些细节C-API会代为处理，只需在概念上理解，并按照约定调用相关的 C-API 接口即可。
 
 下面是示例代码片段。在这段代码中，生成了一条随机输入数据作为测试样本。
 ```c
diff --git a/doc/howto/usage/capi/core_concepts.md b/doc/howto/usage/capi/core_concepts.md
deleted file mode 100644
index e69de29bb2..0000000000
diff --git a/doc/howto/usage/capi/images/csr.png b/doc/howto/usage/capi/images/csr.png
index 16454e9261872e9ab337f2d95d1143b77b554ecb..3dc10b8de4f6d3f517624956b1694b689405a031 100644
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diff --git a/doc/howto/usage/capi/organization_of_the_inputs.md b/doc/howto/usage/capi/organization_of_the_inputs.md
index b306c522e6..1e573618a6 100644
--- a/doc/howto/usage/capi/organization_of_the_inputs.md
+++ b/doc/howto/usage/capi/organization_of_the_inputs.md
@@ -13,40 +13,22 @@
     1. 一维数组**仅支持整型值**；
         - 常用于自然语言处理任务，例如：表示词语在词典中的序号；
         - 分类任务中类别标签；
-    1. 逻辑上高于二维的数据（例如含有多个通道的图片，视频等）在程序实现中都会转化为二维矩阵，转化方法在相应的领域都有通用解决方案，需要使用者自己了解相关的转化表示方法；
-    1. 二维矩阵可以表示行向量和列向量，任何时候，如果需要浮点型数组（向量）时，都应使用C-API中的矩阵来表示，而不是C-API中的一维数组。
-
-不论是一维整型数组还是二维浮点数矩阵，**为它们附加上序列信息，将变成序列输入。PaddlePaddle 会通过判数据是否附带有序列信息来判断一个向量/矩阵是否是一个序列**。关于什么是“序列信息”，下文会进行详细地介绍。
-
-PaddlePaddle 支持两种序列类型：
-1. 单层序列
-    - 序列中的每一个元素是非序列，是进行计算的基本单位，不可再进行拆分。
-    - 例如：自然语言中的句子是一个序列，序列中的元素是词语；
-1. 双层序列
-    - 序列中的每一个元素又是一个序列。
-    - 例如：自然语言中的段落是一个双层序列；段落是有句子构成的序列；句子是由词语构成的序列。
-    - 双层序列在处理长序列的任务或是构建层级模型时会发挥作用。
+    1. 逻辑上高于二维的数据（例如含有多个通道的图片，视频等）在程序实现中都会转化为二维矩阵，转化方法在相应的领域都有通用解决方案，需要使用者自己了解并完成转化；
+    1. 二维矩阵可以表示行向量和列向量，任何时候如果需要浮点型数组（向量），都应使用C-API中的矩阵来表示，而不是C-API中的一维数组。
+    1. 不论是一维整型数组还是二维浮点数矩阵，**为它们附加上序列信息将变成序列输入。PaddlePaddle 会通过判数据是否附带有序列信息来判断一个向量/矩阵是否是一个序列**。当非序列输入时，无需关心和处理序列信息。关于什么是“序列信息”，下文会详细进行介绍。
 
 ### 基本使用概念
 
 - 在PaddlePaddle内部，神经网络中一个计算层的输入/输出被组织为一个 `Argument` 结构体，如果神经网络有多个输入或者多个输入，每一个输入/输入都会对应有自己的`Argument`。
 - `Argument` 并不真正“存储”数据，而是将输入/输出信息有机地组织在一起。
-- 在`Argument`内部由`IVector`（对应着上文提到的一维整型数组）和`Matrix`（对应着上文提到的二维浮点型矩阵）来实际存储数据；由 `sequence start position` (下文详细解释) 来记录输入/输出的序列信息。
+- 在`Argument`内部由`IVector`（对应着上文提到的一维整型数组）和`Matrix`（对应着上文提到的二维浮点型矩阵）来实际存储数据；由 `Sequence Start Positions` (下文详细解释) 来描述输入/输出的序列信息。
 
-**注意：这篇文档之后部分将会统一使用`argument`来特指PaddlePaddle中神经网络计算层一个输入/输出数据；使用`ivector`来特指PaddlePaddle中的一维整型数组；使用`matrix`来特指PaddlePaddle中的二维浮点型矩阵；使用`sequence_start_position`来特指PaddlePaddle中的序列信息。**
+- **注**：
+    1. 这篇文档之后部分将会统一使用`argument`来特指PaddlePaddle中神经网络计算层一个输入/输出数据。
+    2. 使用`paddle_ivector`来特指PaddlePaddle中的一维整型数组。
+    3. 使用`paddle_matrix`来特指PaddlePaddle中的二维浮点型矩阵。
 
-于是，在组织神经网络输入时，需要思考完成以下工作：
-1. 为每一个输入/输出创建`argument`。
-    - C-API 中操作`argument`的接口请查看[argument.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/arguments.h)。
-1. 为每一个`argument`创建`matrix`或者`ivector`来存储数据。
-    - C-API 中操作`ivector`的接口请查看 [vector.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/vector.h)。
-    - C-API 中操作`matrix`的接口请查看[matrix.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/matrix.h)。
-1. 如果输入是序列数据，需要创建并填写`sequence_start_position`信息。
-    - 通过调用 [`paddle_arguments_set_sequence_start_pos`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/arguments.h#L137) 来为一个`argument`添加序列信息；
-    - 通过调用 [`paddle_arguments_get_sequence_start_pos`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/arguments.h#L150) 来读取一个`argument`添加序列信息；
-    - 接口说明请查看 [argument.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/arguments.h) 文件。
-
-### 组织非序列数据
+### 组织输入数据
 - 一维整型数组
 
     概念上可以将`paddle_ivector`理解为一个一维的整型数组，通常用于表示离散的类别标签，或是在自然语言处理任务中表示词语在字典中的序号。下面的代码片段创建了含有三个元素`1`、`2`、`3`的`paddle_ivector`。
@@ -56,6 +38,7 @@ PaddlePaddle 支持两种序列类型：
          paddle_ivector_create(ids, sizeof(ids) / sizeof(int), false, false);
      CHECK(paddle_arguments_set_ids(in_args, 0, ids_array));
     ```
+
 - **稠密矩阵**
     - 一个$m×n$的稠密矩阵是一个由$m$行$n$列元素排列成的矩形阵列，矩阵里的元素是浮点数。对神经网络来说，矩阵的高度$m$是一次预测接受的样本数目，宽度$n$是神经网络定义时，`paddle.layer.data`的`size`。
     - 下面的代码片段创建了一个高度为1，宽度为`layer_size`的稠密矩阵，矩阵中每个元素的值随机生成。
@@ -83,17 +66,20 @@ PaddlePaddle 支持两种序列类型：
 
 - **稀疏矩阵**
 
-  PaddlePaddle C-API 中 稀疏矩阵使用[CSR（Compressed Sparse Row Format）](https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_row_(CSR,_CRS_or_Yale_format))格式存储。下图是CSR存储稀疏矩阵的示意图，在CSR表示方式中，通过（1）行偏移；（2）列号；（3）值；来决定矩阵的内容。
+  PaddlePaddle C-API 中 稀疏矩阵使用[CSR（Compressed Sparse Row Format）](https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_row_(CSR,_CRS_or_Yale_format))格式存储。下图是CSR存储稀疏矩阵的示意图。
 
   <p align="center">
-  <img src="images/csr.png" width=75%></br>图1. CSR存储示意图.
+  <img src="images/csr.png" width=70%></br>图1. CSR存储示意图.
   </p>
 
-  在PaddlePaddle C-API中通过以下接口创建稀疏矩阵：
+  CSR存储格式通过：（1）非零元素的值（上图中的`values`）；（2）行偏移(上图中的`row offsets`)：每一行元素在`values`中的起始偏移，`row offsets`中元素个数总是等于行数 + 1；（3）非零元素的列号（上图中的`column indices`）来确定稀疏矩阵的内容。
+
+  在PaddlePaddle C-API中，通过调用以下接口创建稀疏矩阵：
   ```cpp
   PD_API paddle_matrix paddle_matrix_create_sparse(
     uint64_t height, uint64_t width, uint64_t nnz, bool isBinary, bool useGpu);
   ```
+
   1. 创建稀疏矩阵时需要显示地指定矩阵的（1）高度（`height`，在神经网络中等于一次预测处理的样本数）（2）宽度（`width`，`paddle.layer.data`的`size`）以及（3）非零元个数（`nnz`）。
   1. 当上述接口第4个参数`isBinary`指定为`true`时，**只需要设置行偏移（`row_offset`）和列号(`colum indices`)，不需要提供元素值（`values`）**，这时行偏移和列号指定的元素默认其值为1。
 
@@ -129,26 +115,124 @@ PaddlePaddle 支持两种序列类型：
                                            sizeof(values) / sizeof(float)));
       ```
 
-### 组织序列数据
+- 注意事项：
+    1. 移动端预测**不支持**稀疏矩阵及相关的接口。
 
+### 组织序列信息
 
+多个排成一列的元素（可以是整型、浮点数、浮点数向量等）构成一个序列，元素之间的顺序是序列所携带的重要信息。不同序列可能会含有不同数目个元素。在 PaddlePaddle 中，序列输入/输出数据是在上文介绍的**数据输入（一维整型数组，二维浮点数矩阵）基础上，附加上序列信息**。下面详细解释什么是“序列信息”。
 
-### Python 端数据类型说明
+我们将神经网络一次计算接受的所有输入样本称之为一个`batch`（可以含有一条或多条样本），每一个序列在整个`batch`中的偏移，就是PaddlePaddle中所指的**序列信息**，称之为“sequence start positions”。PaddlePaddle 支持两种序列类型：
 
-下表列出了Python端训练接口暴露的数据类型（`paddle.layer.data`函数`type`字段的取值）对应于调用C-API时需要创建的数据类型：
+1. 单层序列
+    - 序列中的每一个元素是非序列，是进行计算的基本单位，不可再进行拆分。
+    - 例如：自然语言中的句子是一个序列，序列中的元素是词语；
+1. 双层序列
+    - 序列中的每一个元素又是一个序列。
+    - 例如：自然语言中的段落是一个双层序列；段落是由句子构成的序列；句子是由词语构成的序列。
+    - 双层序列在处理长序列的任务或是构建层级模型时会发挥作用。
 
+这篇文档之后部分会统一使用`sequence_start_positions`来特指：PaddlePaddle中神经网络计算层输入/输出所携带的序列信息。
+
+对双层序列来讲，不仅要提供每一个外层序列在整个`batch`中的偏移，每一个外层序列又含有若干个内层序列，需要同时提供每一个内层序列在整个`batch`中的偏移。也就是说：**双层序列需要设置分别为外层序列和内层序列分别设置`sequence_start_positions`信息**。
+
+**注：**
+1. 不论序列中的元素在内存中占用多少实际存储空间，`sequence_start_positions`表示的偏移是以“序列中的一个元素”作为统计的基本单位，而不是相对`batch`起始存储地址以数据的存储大小为单位的偏移。
+2. 非序列输入不携带`sequence_start_positions`，非序列输入无需构造`sequence_start_positions`。
+3. **不论是单层序列还是双层序列的序列信息，都使用`paddle_ivector`（也就是PaddlePaddle中的一维整型数组）来存储。**
+
+图2 是PaddlePaddle中单层序列和双层序列存储示意图。
+<p align="center">
+<img src="images/sequence_data.png" width=80%></br>图2. 序列输入示意图.
+</p>
+
+- 单层序列
+
+    图2 (a) 展示了一个含有4个序列的`batch`输入：
+    1. 4个序列的长度分别为：5、3、2、4；
+    2. 这时的`sequence_start_positions`为：`[0, 5, 8, 10, 14]`；
+    3. 不论数据域是`paddle_ivector`类型还是`paddle_matrix`类型，都可以通过调用下面的接口为原有的数据输入附加上序列信息，使之变为一个单层序列输入，代码片段如下：
+
+    ```cpp
+    int seq_pos_array[] = {0, 5, 8, 10, 14};
+    paddle_ivector seq_pos = paddle_ivector_create(
+        seq_pos_array, sizeof(seq_pos_array) / sizeof(int), false, false);
+    // Suppose the network only has one input data layer.
+    CHECK(paddle_arguments_set_sequence_start_pos(in_args, 0, 0, seq_pos));
+    ```
+
+- 双层序列
+
+    图2 (b) 展示了一个含有4个序列的`batch`输入；
+    1. 4个序列的长度分别为：5、3、2、4；这四个序列又分别含有3、2、1、2个子序列；
+    1. 这时的需要同时提供：
+        - 1. 外层序列在`batch`中的起始偏移`：[0, 5, 8, 10, 14]`；
+        - 2. 内层序列在`batch`中的起始偏移：`[0, 2, 3, 5, 7， 8， 10， 13， 14]`；
+
+    1. 不论数据域是`paddle_ivector`类型还是`paddle_matrix`类型，这时需要调用创建序列信息和为`argument`设置序列信息的接口**两次**，分别为数据输入添加外层序列和内层序列的序列信息，使之变为一个双层序列输入，代码片段如下：
+    ```cpp
+    // set the sequence start positions for the outter sequences.
+    int outter_seq_pos_array[] = {0, 5, 8, 10, 14};
+    paddle_ivector seq_pos =
+        paddle_ivector_create(outter_seq_pos_array,
+                              sizeof(outter_pos_array) / sizeof(int),
+                              false,
+                              false);
+    // The third parameter of this API indicates the sequence level.
+    // 0 for the outter sequence. 1 for the inner sequence.
+    // If the input is a sequence not the nested sequence, the third parameter is
+    // fixed to be 0.
+    CHECK(paddle_arguments_set_sequence_start_pos(in_args, 0, 0, seq_pos));
+
+    // set the sequence start positions for the outter sequences.
+    int inner_seq_pos_array[] = {0, 2, 3, 5, 7， 8， 10， 13， 14};
+    paddle_ivector seq_pos = paddle_ivector_create(
+        inner_pos_array, sizeof(inner_pos_array) / sizeof(int), false, false);
+    // The third parameter of this API indicates the sequence level.
+    // 0 for the outter sequence. 1 for the inner sequence.
+    CHECK(paddle_arguments_set_sequence_start_pos(in_args, 0, 1, seq_pos));
+    ```
+
+- 注意事项：
+  1. 当一个`batch`中含有多个序列，**不支持序列长度为`0`的序列（也就是空输入）** 作为输入。不同计算层对空输入的处理策略有可能不同，潜在会引起未定义行为，或者引起行时错误，请在输入时进行合法性检查。
+
+### Python 端数据类型说明
+
+下表列出了Python端训练接口暴露的数据类型（`paddle.layer.data`函数`type`字段的取值）对应于调用C-API需要创建的数据类型：
 
 Python 端数据类型  | C-API 输入数据类型|
 :-------------: | :-------------:
-`paddle.data_type.integer_value` |一维整型数组，无需附加序列信息|
-`paddle.data_type.dense_vector` |二维浮点型稠密矩阵，无需附加序列信息|
-`paddle.data_type.sparse_binary_vector` |二维浮点型稀疏矩阵，无需提供非零元的值，默认为1，无需附加序列信息|
-`paddle.data_type.sparse_vector` |二维浮点型稀疏矩阵，需提供非零元的值，无需附加序列信息|
-`paddle.data_type.integer_value_sequence` |一维整型数组，需附加序列信息|
-`paddle.data_type.dense_vector_sequence` |二维浮点型稠密矩阵，需附加序列信息|
-`paddle.data_type.sparse_binary_vector_sequence` |二维浮点型稀疏矩阵，无需提供非零元的值，默认为1，需附加序列信息|
-`paddle.data_type.sparse_vector_sequence` |二维浮点型稀疏矩阵，需提供非零元的值，需附加序列信息|
-`paddle.data_type.integer_value_sub_sequence` |一维整型数组，需附加双层序列信息|
-`paddle.data_type.dense_vector_sub_sequence` |二维浮点型稠密矩阵，需附加双层序列信息|
-`paddle.data_type.sparse_binary_vector_sub_sequence` |二维浮点型稀疏矩阵，无需提供非零元的值，默认为1，需附加双层序列信息|
-`paddle.data_type.sparse_vector_sub_sequence` |二维浮点型稀疏矩阵，需提供非零元的值，需附加双层序列信息|
+`paddle.data_type.integer_value` |整型数组，无需附加序列信息|
+`paddle.data_type.dense_vector` |浮点型稠密矩阵，无需附加序列信息|
+`paddle.data_type.sparse_binary_vector` |浮点型稀疏矩阵，无需提供非零元的值，默认为1，无需附加序列信息|
+`paddle.data_type.sparse_vector` |浮点型稀疏矩阵，需提供非零元的值，无需附加序列信息|
+`paddle.data_type.integer_value_sequence` |整型数组，需附加序列信息|
+`paddle.data_type.dense_vector_sequence` |浮点型稠密矩阵，需附加序列信息|
+`paddle.data_type.sparse_binary_vector_sequence` |浮点型稀疏矩阵，无需提供非零元的值，默认为1，需附加序列信息|
+`paddle.data_type.sparse_vector_sequence` |浮点型稀疏矩阵，需提供非零元的值，需附加序列信息|
+`paddle.data_type.integer_value_sub_sequence` |整型数组，需附加双层序列信息|
+`paddle.data_type.dense_vector_sub_sequence` |浮点型稠密矩阵，需附加双层序列信息|
+`paddle.data_type.sparse_binary_vector_sub_sequence` |浮点型稀疏矩阵，无需提供非零元的值，默认为1，需附加双层序列信息|
+`paddle.data_type.sparse_vector_sub_sequence` |浮点型稀疏矩阵，需提供非零元的值，需附加双层序列信息|
+
+### 输出数据
+
+PaddlePaddle中一个计算层的输出数据组织方式和输入数据组织方式完全相同。一个输出数据同样被组织为一个`argument`，`argument`通过`paddle_matrix`或`paddle_ivector`存数数据，如果输出是一个序列，那么会携带有`sequence_start_positions`信息。调用C-API相关接口，读取需要的结果即可。
+
+### 总结
+
+- 在PaddlePaddle内部，神经网络中一个计算层的输入/输出被组织为`argument`。
+- `argument`并不真正“存储”数据，而是将输入/输出信息有机地组织在一起。
+- 在`argument`内部由`paddle_ivector`（一维整型数组）和`paddle_matrix`（二维浮点型矩阵）来实际存储数据。
+如果是一个序列输入/输出由 `sequence start positions` 来记录输入/输出的序列信息。
+
+于是，在组织神经网络输入时，需要思考完成以下工作：
+1. 为每一个输入/输出创建`argument`。
+    - C-API 中操作`argument`的接口请查看[argument.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/arguments.h)。
+1. 为每一个`argument`创建`paddle_matrix`或者`paddle_ivector`来存储数据。
+    - C-API 中操作`paddle_ivector`的接口请查看 [vector.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/vector.h)。
+    - C-API 中操作`paddle_matrix`的接口请查看[matrix.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/matrix.h)。
+1. 如果输入是序列数据，需要创建并填写`sequence_start_positions`信息。
+    - 通过调用 [`paddle_arguments_set_sequence_start_pos`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/arguments.h#L137) 来为一个`argument`添加序列信息。
+    - 通过调用 [`paddle_arguments_get_sequence_start_pos`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/arguments.h#L150) 来读取一个`argument`添加序列信息。
+    - 接口说明请查看 [argument.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/arguments.h) 文件。
diff --git a/doc/howto/usage/capi/overview.md b/doc/howto/usage/capi/overview.md
index 5ec3bd0284..c55d39bac6 100644
--- a/doc/howto/usage/capi/overview.md
+++ b/doc/howto/usage/capi/overview.md
@@ -1,5 +1,3 @@
 - [编译 PaddlePaddle 链接库](compile_paddle_lib.md)
+- [输入/输出数据组织](organization_of_the_inputs.md)
 - [C-API 使用示例](a_simple_example.md)
-- [输入数据组织](organize_input_data.md)
-- [核心概念介绍](core_concepts.md)
-- [F&Q]()

From 93568cbec6932af15ef3a1750d5ac8419e227cc2 Mon Sep 17 00:00:00 2001
From: caoying03 <caoying03@baidu.com>
Date: Tue, 19 Dec 2017 20:37:17 +0800
Subject: [PATCH 03/68] fix the build of the doc.

---
 doc/howto/usage/capi/a_simple_example.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/howto/usage/capi/a_simple_example.md b/doc/howto/usage/capi/a_simple_example.md
index ae2eaa3ce2..847d8ef1e0 100644
--- a/doc/howto/usage/capi/a_simple_example.md
+++ b/doc/howto/usage/capi/a_simple_example.md
@@ -115,7 +115,7 @@
 - `Argument` 并不真正“存储”数据，而是将输入/输出数据有机地组织在一起。
 - 在`Argument`内部由：1. `Matrix`（二维矩阵，存储浮点类型输入/输出）；2. `IVector`（一维数组，**仅用于存储整型值**，多用于自然语言处理任务）来实际存储数据。
 
-*注：这篇文档使用的示例任务手写数字识别不涉及一维整型序列输入/输出，因此不讨论一维整型输入/输出数据相关的内容。更多信息请参考：[输入/输出数据组织](organization_of_the_inputs.md)。*
+注：本文使用的示例任务手写数字识别不涉及一维整型数组作为输入，因此，本文档仅限于讨论二维稠密矩阵作为输入的情形。更多输入数据格式请参考输入/输出数据一节的内容。
 
 这篇文档的之后部分会使用`argument`来**特指** PaddlePaddle C-API中神经网的一个输入/输出，使用`paddle_matrix`**特指**`argument`中用于存储数据的`Matrix`类的对象。
 

From e7d7d257467457a01f2f3d02590aa28fd3ce6d00 Mon Sep 17 00:00:00 2001
From: caoying03 <caoying03@baidu.com>
Date: Tue, 19 Dec 2017 20:37:17 +0800
Subject: [PATCH 04/68] fix the build of the doc.

---
 doc/howto/usage/capi/a_simple_example.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/howto/usage/capi/a_simple_example.md b/doc/howto/usage/capi/a_simple_example.md
index ae2eaa3ce2..847d8ef1e0 100644
--- a/doc/howto/usage/capi/a_simple_example.md
+++ b/doc/howto/usage/capi/a_simple_example.md
@@ -115,7 +115,7 @@
 - `Argument` 并不真正“存储”数据，而是将输入/输出数据有机地组织在一起。
 - 在`Argument`内部由：1. `Matrix`（二维矩阵，存储浮点类型输入/输出）；2. `IVector`（一维数组，**仅用于存储整型值**，多用于自然语言处理任务）来实际存储数据。
 
-*注：这篇文档使用的示例任务手写数字识别不涉及一维整型序列输入/输出，因此不讨论一维整型输入/输出数据相关的内容。更多信息请参考：[输入/输出数据组织](organization_of_the_inputs.md)。*
+注：本文使用的示例任务手写数字识别不涉及一维整型数组作为输入，因此，本文档仅限于讨论二维稠密矩阵作为输入的情形。更多输入数据格式请参考输入/输出数据一节的内容。
 
 这篇文档的之后部分会使用`argument`来**特指** PaddlePaddle C-API中神经网的一个输入/输出，使用`paddle_matrix`**特指**`argument`中用于存储数据的`Matrix`类的对象。
 

From 26d5a7aadf83b039cf82f24a8f0e006bfb44aea6 Mon Sep 17 00:00:00 2001
From: ying <lcy.seso@gmail.com>
Date: Tue, 19 Dec 2017 20:55:45 +0800
Subject: [PATCH 05/68] fix the build failure.

---
 doc/howto/index_cn.rst                     | 2 +-
 doc/howto/usage/capi/a_simple_example.md   | 4 ++--
 doc/howto/usage/capi/compile_paddle_lib.md | 4 +++-
 doc/howto/usage/capi/index_cn.rst          | 9 +++++++++
 doc/howto/usage/capi/overview.md           | 3 ---
 5 files changed, 15 insertions(+), 7 deletions(-)
 create mode 100644 doc/howto/usage/capi/index_cn.rst
 delete mode 100644 doc/howto/usage/capi/overview.md

diff --git a/doc/howto/index_cn.rst b/doc/howto/index_cn.rst
index 678a00c9d3..e0c69f7a6a 100644
--- a/doc/howto/index_cn.rst
+++ b/doc/howto/index_cn.rst
@@ -9,7 +9,7 @@
 
   usage/cmd_parameter/index_cn.rst
   usage/cluster/cluster_train_cn.md
-  usage/capi/overview.md
+  usage/capi/index_cn.rst
 
 开发标准
 --------
diff --git a/doc/howto/usage/capi/a_simple_example.md b/doc/howto/usage/capi/a_simple_example.md
index 847d8ef1e0..d9f0a1d128 100644
--- a/doc/howto/usage/capi/a_simple_example.md
+++ b/doc/howto/usage/capi/a_simple_example.md
@@ -1,4 +1,4 @@
-## 使用 C-API 开发预测程序
+## C-API CPU 单线程预测示例
 
 这篇文档通过一个最简单的例子：手写数字识别，来介绍 CPU 下单线程使用 PaddlePaddle C-API 开发预测服务，完整代码见[此目录](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/examples/model_inference/dense/)。
 
@@ -115,7 +115,7 @@
 - `Argument` 并不真正“存储”数据，而是将输入/输出数据有机地组织在一起。
 - 在`Argument`内部由：1. `Matrix`（二维矩阵，存储浮点类型输入/输出）；2. `IVector`（一维数组，**仅用于存储整型值**，多用于自然语言处理任务）来实际存储数据。
 
-注：本文使用的示例任务手写数字识别不涉及一维整型数组作为输入，因此，本文档仅限于讨论二维稠密矩阵作为输入的情形。更多输入数据格式请参考输入/输出数据一节的内容。
+*注：本文档使用的示例任务手写数字识别不涉及一维整型数组作为输入，因此，本文档仅讨论二维稠密矩阵作为输入的情形。更多输入数据格式请参考输入/输出数据一节的内容。*
 
 这篇文档的之后部分会使用`argument`来**特指** PaddlePaddle C-API中神经网的一个输入/输出，使用`paddle_matrix`**特指**`argument`中用于存储数据的`Matrix`类的对象。
 
diff --git a/doc/howto/usage/capi/compile_paddle_lib.md b/doc/howto/usage/capi/compile_paddle_lib.md
index 1ad5b90681..d4ac4f2771 100644
--- a/doc/howto/usage/capi/compile_paddle_lib.md
+++ b/doc/howto/usage/capi/compile_paddle_lib.md
@@ -1,5 +1,7 @@
 ## 编译 PaddlePaddle 链接库
 
+### 概述
+
 使用 C-API 进行预测依赖于将 PaddlePaddle 核心代码编译成链接库，只需在编译时指定编译选项：`-DWITH_C_API=ON`。同时，**建议将：`DWITH_PYTHON`，`DWITH_SWIG_PY`，`DWITH_GOLANG`，均设置为`OFF`**，以避免链接不必要的库。其它编译选项按需进行设定。
 
 ```shell
@@ -42,7 +44,7 @@ cmake $PADDLE_ROOT -DCMAKE_INSTALL_PREFIX=$INSTALL_PREFIX \
     ├── ......
 ```
 
-## 链接方式说明
+### 链接方式说明
 
 目前提供三种链接方式：
 
diff --git a/doc/howto/usage/capi/index_cn.rst b/doc/howto/usage/capi/index_cn.rst
new file mode 100644
index 0000000000..3b36f31bfd
--- /dev/null
+++ b/doc/howto/usage/capi/index_cn.rst
@@ -0,0 +1,9 @@
+PaddlePaddle C-API
+==================
+
+..  toctree::
+  :maxdepth: 1
+
+  compile_paddle_lib.md
+  organization_of_the_inputs.md
+  a_simple_example.md
diff --git a/doc/howto/usage/capi/overview.md b/doc/howto/usage/capi/overview.md
deleted file mode 100644
index c55d39bac6..0000000000
--- a/doc/howto/usage/capi/overview.md
+++ /dev/null
@@ -1,3 +0,0 @@
-- [编译 PaddlePaddle 链接库](compile_paddle_lib.md)
-- [输入/输出数据组织](organization_of_the_inputs.md)
-- [C-API 使用示例](a_simple_example.md)

From b915fde92a8a56f4011691411008f7795110769f Mon Sep 17 00:00:00 2001
From: ying <lcy.seso@gmail.com>
Date: Tue, 19 Dec 2017 21:14:36 +0800
Subject: [PATCH 06/68] use html table.

---
 doc/howto/usage/capi/a_simple_example.md      |  10 +-
 .../usage/capi/organization_of_the_inputs.md  | 195 +++++++++++-------
 2 files changed, 125 insertions(+), 80 deletions(-)

diff --git a/doc/howto/usage/capi/a_simple_example.md b/doc/howto/usage/capi/a_simple_example.md
index d9f0a1d128..b1eceea38b 100644
--- a/doc/howto/usage/capi/a_simple_example.md
+++ b/doc/howto/usage/capi/a_simple_example.md
@@ -143,8 +143,8 @@ CHECK(paddle_arguments_resize(in_args, 1));
 // agument to store the testing samples.
 paddle_matrix mat =
     paddle_matrix_create(/* height = batch size */ 1,
-                         /* width = dimensionality of the data layer */ 784,
-                         /* whether to use GPU */ false);
+                    /* width = dimensionality of the data layer */ 784,
+                    /* whether to use GPU */ false);
 
 paddle_real* array;
 // Get the pointer pointing to the start address of the first row of the
@@ -172,9 +172,9 @@ paddle_arguments out_args = paddle_arguments_create_none();
 
 // Invoke the forward computation.
 CHECK(paddle_gradient_machine_forward(machine,
-                                      in_args,
-                                      out_args,
-                                      /* is train taks or not */ false));
+                                in_args,
+                                out_args,
+                                s/* is train taks or not */ false));
 
 // Create the matrix to hold the forward result of the neural network.
 paddle_matrix prob = paddle_matrix_create_none();
diff --git a/doc/howto/usage/capi/organization_of_the_inputs.md b/doc/howto/usage/capi/organization_of_the_inputs.md
index 1e573618a6..7563e236da 100644
--- a/doc/howto/usage/capi/organization_of_the_inputs.md
+++ b/doc/howto/usage/capi/organization_of_the_inputs.md
@@ -9,13 +9,13 @@
     - 稠密矩阵
     - 稀疏矩阵
 
-  - 说明：
-    1. 一维数组**仅支持整型值**；
-        - 常用于自然语言处理任务，例如：表示词语在词典中的序号；
-        - 分类任务中类别标签；
-    1. 逻辑上高于二维的数据（例如含有多个通道的图片，视频等）在程序实现中都会转化为二维矩阵，转化方法在相应的领域都有通用解决方案，需要使用者自己了解并完成转化；
-    1. 二维矩阵可以表示行向量和列向量，任何时候如果需要浮点型数组（向量），都应使用C-API中的矩阵来表示，而不是C-API中的一维数组。
-    1. 不论是一维整型数组还是二维浮点数矩阵，**为它们附加上序列信息将变成序列输入。PaddlePaddle 会通过判数据是否附带有序列信息来判断一个向量/矩阵是否是一个序列**。当非序列输入时，无需关心和处理序列信息。关于什么是“序列信息”，下文会详细进行介绍。
+说明：
+1. 一维数组**仅支持整型值**；
+    - 常用于自然语言处理任务，例如：表示词语在词典中的序号；
+    - 分类任务中类别标签；
+1. 逻辑上高于二维的数据（例如含有多个通道的图片，视频等）在程序实现中都会转化为二维矩阵，转化方法在相应的领域都有通用解决方案，需要使用者自己了解并完成转化；
+1. 二维矩阵可以表示行向量和列向量，任何时候如果需要浮点型数组（向量），都应使用C-API中的矩阵来表示，而不是C-API中的一维数组。
+1. 不论是一维整型数组还是二维浮点数矩阵，**为它们附加上序列信息将变成序列输入。PaddlePaddle 会通过判数据是否附带有序列信息来判断一个向量/矩阵是否是一个序列**。当非序列输入时，无需关心和处理序列信息。关于什么是“序列信息”，下文会详细进行介绍。
 
 ### 基本使用概念
 
@@ -32,7 +32,7 @@
 - 一维整型数组
 
     概念上可以将`paddle_ivector`理解为一个一维的整型数组，通常用于表示离散的类别标签，或是在自然语言处理任务中表示词语在字典中的序号。下面的代码片段创建了含有三个元素`1`、`2`、`3`的`paddle_ivector`。
-    ```cpp
+    ```c
     int ids[] = {1, 2, 3};
      paddle_ivector ids_array =
          paddle_ivector_create(ids, sizeof(ids) / sizeof(int), false, false);
@@ -40,14 +40,14 @@
     ```
 
 - **稠密矩阵**
-    - 一个$m×n$的稠密矩阵是一个由$m$行$n$列元素排列成的矩形阵列，矩阵里的元素是浮点数。对神经网络来说，矩阵的高度$m$是一次预测接受的样本数目，宽度$n$是神经网络定义时，`paddle.layer.data`的`size`。
+    - 一个`m×n`的稠密矩阵是一个由`m`行`n`列元素排列成的矩形阵列，矩阵里的元素是浮点数。对神经网络来说，矩阵的高度`m`是一次预测接受的样本数目，宽度$n$是神经网络定义时，`paddle.layer.data`的`size`。
     - 下面的代码片段创建了一个高度为1，宽度为`layer_size`的稠密矩阵，矩阵中每个元素的值随机生成。
 
-    ```cpp
-    paddle_matrix mat =
-        paddle_matrix_create(/* height = batch size */ 1,
-                             /* width = dimensionality of the data layer */ layer_size,
-                             /* whether to use GPU */ false);
+    ```c
+    paddle_matrix mat = paddle_matrix_create(
+                            /* height = batch size */ 1,
+                            /* width = dimensionality of the data layer */ layer_size,
+                            /* whether to use GPU */ false);
 
     paddle_real* array;
     // Get the pointer pointing to the start address of the first row of the
@@ -67,56 +67,55 @@
 - **稀疏矩阵**
 
   PaddlePaddle C-API 中 稀疏矩阵使用[CSR（Compressed Sparse Row Format）](https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_row_(CSR,_CRS_or_Yale_format))格式存储。下图是CSR存储稀疏矩阵的示意图。
-
   <p align="center">
-  <img src="images/csr.png" width=70%></br>图1. CSR存储示意图.
+  <img src="https://user-images.githubusercontent.com/5842774/34159369-009fd328-e504-11e7-9e08-36bc6dc5e505.png" width=700><br> 图1. 稀疏矩阵存储示意图
   </p>
 
   CSR存储格式通过：（1）非零元素的值（上图中的`values`）；（2）行偏移(上图中的`row offsets`)：每一行元素在`values`中的起始偏移，`row offsets`中元素个数总是等于行数 + 1；（3）非零元素的列号（上图中的`column indices`）来确定稀疏矩阵的内容。
 
   在PaddlePaddle C-API中，通过调用以下接口创建稀疏矩阵：
-  ```cpp
+
+  ```c
   PD_API paddle_matrix paddle_matrix_create_sparse(
-    uint64_t height, uint64_t width, uint64_t nnz, bool isBinary, bool useGpu);
+      uint64_t height, uint64_t width, uint64_t nnz, bool isBinary, bool useGpu);
   ```
 
   1. 创建稀疏矩阵时需要显示地指定矩阵的（1）高度（`height`，在神经网络中等于一次预测处理的样本数）（2）宽度（`width`，`paddle.layer.data`的`size`）以及（3）非零元个数（`nnz`）。
   1. 当上述接口第4个参数`isBinary`指定为`true`时，**只需要设置行偏移（`row_offset`）和列号(`colum indices`)，不需要提供元素值（`values`）**，这时行偏移和列号指定的元素默认其值为1。
 
-  - 下面的代码片段创建了一个CPU上的二值稀疏矩阵：
-
-      ```cpp
-      paddle_matrix mat = paddle_matrix_create_sparse(1, layer_size, nnz, true, false);
-      int colIndices[] = {9, 93, 109};  // layer_size here is greater than 109.
-      int rowOffset[] = {0, sizeof(colIndices) / sizeof(int)};
-
-      CHECK(paddle_matrix_sparse_copy_from(mat,
-                                           rowOffset,
-                                           sizeof(rowOffset) / sizeof(int),
-                                           colIndices,
-                                           sizeof(colIndices) / sizeof(int),
-                                           NULL /*values array is NULL.*/,
-                                           0 /*size of the value arrary is 0.*/));
-      CHECK(paddle_arguments_set_value(in_args, 0, mat));
-      ```
-   - 下面的代码片段在创建了一个CPU上的带元素值的稀疏矩阵：
-      ```cpp
-      paddle_matrix mat = paddle_matrix_create_sparse(1, layer_size, nnz, false, false);
-      int colIndices[] = {9, 93, 109};  // layer_size here is greater than 109.
-      int rowOffset[] = {0, sizeof(colIndices) / sizeof(int)};
-      float values[] = {0.5, 0.5, 0.5};
-
-      CHECK(paddle_matrix_sparse_copy_from(mat,
-                                           rowOffset,
-                                           sizeof(rowOffset) / sizeof(int),
-                                           colIndices,
-                                           sizeof(colIndices) / sizeof(int),
-                                           values,
-                                           sizeof(values) / sizeof(float)));
-      ```
-
-- 注意事项：
-    1. 移动端预测**不支持**稀疏矩阵及相关的接口。
+  下面的代码片段创建了一个CPU上的二值稀疏矩阵：
+
+  ```c
+  paddle_matrix mat = paddle_matrix_create_sparse(1, layer_size, nnz, true, false);
+  int colIndices[] = {9, 93, 109};  // layer_size here is greater than 109.
+  int rowOffset[] = {0, sizeof(colIndices) / sizeof(int)};
+
+  CHECK(paddle_matrix_sparse_copy_from(mat,
+                                 rowOffset,
+                                 sizeof(rowOffset) / sizeof(int),
+                                 colIndices,
+                                 (colIndices) / sizeof(int),
+                                 NULL /*values array is NULL.*/,
+                                 0 /*size of the value arrary is 0.*/));
+  CHECK(paddle_arguments_set_value(in_args, 0, mat));
+  ```
+  下面的代码片段在创建了一个CPU上的带元素值的稀疏矩阵：
+  ```c
+  paddle_matrix mat = paddle_matrix_create_sparse(1, layer_size, nnz, false, false);
+  int colIndices[] = {9, 93, 109};  // layer_size here is greater than 109.
+  int rowOffset[] = {0, sizeof(colIndices) / sizeof(int)};
+  float values[] = {0.5, 0.5, 0.5};
+
+  CHECK(paddle_matrix_sparse_copy_from(mat,
+                                 rowOffset,
+                                 sizeof(rowOffset) / sizeof(int),
+                                 colIndices,
+                                 sizeof(colIndices) / sizeof(int),
+                                 values,
+                                 sizeof(values) / sizeof(float)));
+  ```
+  注意事项：
+  1. 移动端预测**不支持**稀疏矩阵及相关的接口。
 
 ### 组织序列信息
 
@@ -143,7 +142,7 @@
 
 图2 是PaddlePaddle中单层序列和双层序列存储示意图。
 <p align="center">
-<img src="images/sequence_data.png" width=80%></br>图2. 序列输入示意图.
+<img src="https://user-images.githubusercontent.com/5842774/34159714-1f81a9be-e505-11e7-8a8a-4902146ec899.png" width=800><br>图2. 序列输入示意图
 </p>
 
 - 单层序列
@@ -153,7 +152,7 @@
     2. 这时的`sequence_start_positions`为：`[0, 5, 8, 10, 14]`；
     3. 不论数据域是`paddle_ivector`类型还是`paddle_matrix`类型，都可以通过调用下面的接口为原有的数据输入附加上序列信息，使之变为一个单层序列输入，代码片段如下：
 
-    ```cpp
+    ```c
     int seq_pos_array[] = {0, 5, 8, 10, 14};
     paddle_ivector seq_pos = paddle_ivector_create(
         seq_pos_array, sizeof(seq_pos_array) / sizeof(int), false, false);
@@ -166,11 +165,10 @@
     图2 (b) 展示了一个含有4个序列的`batch`输入；
     1. 4个序列的长度分别为：5、3、2、4；这四个序列又分别含有3、2、1、2个子序列；
     1. 这时的需要同时提供：
-        - 1. 外层序列在`batch`中的起始偏移`：[0, 5, 8, 10, 14]`；
-        - 2. 内层序列在`batch`中的起始偏移：`[0, 2, 3, 5, 7， 8， 10， 13， 14]`；
-
+        - 外层序列在`batch`中的起始偏移`：[0, 5, 8, 10, 14]`；
+        - 内层序列在`batch`中的起始偏移：`[0, 2, 3, 5, 7， 8， 10， 13， 14]`；
     1. 不论数据域是`paddle_ivector`类型还是`paddle_matrix`类型，这时需要调用创建序列信息和为`argument`设置序列信息的接口**两次**，分别为数据输入添加外层序列和内层序列的序列信息，使之变为一个双层序列输入，代码片段如下：
-    ```cpp
+    ```c
     // set the sequence start positions for the outter sequences.
     int outter_seq_pos_array[] = {0, 5, 8, 10, 14};
     paddle_ivector seq_pos =
@@ -193,28 +191,75 @@
     CHECK(paddle_arguments_set_sequence_start_pos(in_args, 0, 1, seq_pos));
     ```
 
-- 注意事项：
-  1. 当一个`batch`中含有多个序列，**不支持序列长度为`0`的序列（也就是空输入）** 作为输入。不同计算层对空输入的处理策略有可能不同，潜在会引起未定义行为，或者引起行时错误，请在输入时进行合法性检查。
+注意事项：
+1. 当一个`batch`中含有多个序列，**不支持序列长度为`0`的序列（也就是空输入）** 作为输入。不同计算层对空输入的处理策略有可能不同，潜在会引起未定义行为，或者引起行时错误，请在输入时进行合法性检查。
 
 ### Python 端数据类型说明
 
 下表列出了Python端训练接口暴露的数据类型（`paddle.layer.data`函数`type`字段的取值）对应于调用C-API需要创建的数据类型：
 
-Python 端数据类型  | C-API 输入数据类型|
-:-------------: | :-------------:
-`paddle.data_type.integer_value` |整型数组，无需附加序列信息|
-`paddle.data_type.dense_vector` |浮点型稠密矩阵，无需附加序列信息|
-`paddle.data_type.sparse_binary_vector` |浮点型稀疏矩阵，无需提供非零元的值，默认为1，无需附加序列信息|
-`paddle.data_type.sparse_vector` |浮点型稀疏矩阵，需提供非零元的值，无需附加序列信息|
-`paddle.data_type.integer_value_sequence` |整型数组，需附加序列信息|
-`paddle.data_type.dense_vector_sequence` |浮点型稠密矩阵，需附加序列信息|
-`paddle.data_type.sparse_binary_vector_sequence` |浮点型稀疏矩阵，无需提供非零元的值，默认为1，需附加序列信息|
-`paddle.data_type.sparse_vector_sequence` |浮点型稀疏矩阵，需提供非零元的值，需附加序列信息|
-`paddle.data_type.integer_value_sub_sequence` |整型数组，需附加双层序列信息|
-`paddle.data_type.dense_vector_sub_sequence` |浮点型稠密矩阵，需附加双层序列信息|
-`paddle.data_type.sparse_binary_vector_sub_sequence` |浮点型稀疏矩阵，无需提供非零元的值，默认为1，需附加双层序列信息|
-`paddle.data_type.sparse_vector_sub_sequence` |浮点型稀疏矩阵，需提供非零元的值，需附加双层序列信息|
-
+<html>
+<table border="2" frame="border">
+<table>
+<thead>
+<tr>
+<th style="text-align:left">Python 端数据类型</th>
+<th style="text-align:left">C-API 输入数据类型</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:left">paddle.data_type.integer_value</td>
+<td style="text-align:left">整型数组，无需附加序列信息</td>
+</tr>
+<tr>
+<td style="text-align:left">paddle.data_type.dense_vector</td>
+<td style="text-align:left">浮点型稠密矩阵，无需附加序列信息</td>
+</tr>
+<tr>
+<td style="text-align:left">paddle.data_type.sparse_binary_vector</td>
+<td style="text-align:left">浮点型稀疏矩阵，无需提供非零元的值，默认为1，无需附加序列信息</td>
+</tr>
+<tr>
+<td style="text-align:left">paddle.data_type.sparse_vector</td>
+<td style="text-align:left">浮点型稀疏矩阵，需提供非零元的值，无需附加序列信息</td>
+</tr>
+<tr>
+<td style="text-align:left">paddle.data_type.integer_value_sequence</td>
+<td style="text-align:left">整型数组，需附加序列信息</td>
+</tr>
+<tr>
+<td style="text-align:left">paddle.data_type.dense_vector_sequence</td>
+<td style="text-align:left">浮点型稠密矩阵，需附加序列信息</td>
+</tr>
+<tr>
+<td style="text-align:left">paddle.data_type.sparse_binary_vector_sequence</td>
+<td style="text-align:left">浮点型稀疏矩阵，无需提供非零元的值，默认为1，需附加序列信息</td>
+</tr>
+<tr>
+<td style="text-align:left">paddle.data_type.sparse_vector_sequence</td>
+<td style="text-align:left">浮点型稀疏矩阵，需提供非零元的值，需附加序列信息</td>
+</tr>
+<tr>
+<td style="text-align:left">paddle.data_type.integer_value_sub_sequence</td>
+<td style="text-align:left">整型数组，需附加双层序列信息</td>
+</tr>
+<tr>
+<td style="text-align:left">paddle.data_type.dense_vector_sub_sequence</td>
+<td style="text-align:left">浮点型稠密矩阵，需附加双层序列信息</td>
+</tr>
+<tr>
+<td style="text-align:left">paddle.data_type.sparse_binary_vector_sub_sequence</td>
+<td style="text-align:left">浮点型稀疏矩阵，无需提供非零元的值，默认为1，需附加双层序列信息</td>
+</tr>
+<tr>
+<td style="text-align:left">paddle.data_type.sparse_vector_sub_sequence</td>
+<td style="text-align:left">浮点型稀疏矩阵，需提供非零元的值，需附加双层序列信息</td>
+</tr>
+</tbody>
+</table>
+</html>
+<br>
 ### 输出数据
 
 PaddlePaddle中一个计算层的输出数据组织方式和输入数据组织方式完全相同。一个输出数据同样被组织为一个`argument`，`argument`通过`paddle_matrix`或`paddle_ivector`存数数据，如果输出是一个序列，那么会携带有`sequence_start_positions`信息。调用C-API相关接口，读取需要的结果即可。

From 079b82436dff0d2883ba0fb048bb1d1d405b6687 Mon Sep 17 00:00:00 2001
From: ying <lcy.seso@gmail.com>
Date: Tue, 26 Dec 2017 15:55:11 +0800
Subject: [PATCH 07/68] follow comments.

---
 ...mple_example.md => a_simple_example_cn.md} |  81 ++++++------
 doc/howto/usage/capi/compile_paddle_lib.md    |  70 ----------
 doc/howto/usage/capi/compile_paddle_lib_cn.md | 122 ++++++++++++++++++
 doc/howto/usage/capi/index_cn.rst             |   6 +-
 ...ts.md => organization_of_the_inputs_cn.md} |   2 +
 5 files changed, 172 insertions(+), 109 deletions(-)
 rename doc/howto/usage/capi/{a_simple_example.md => a_simple_example_cn.md} (68%)
 delete mode 100644 doc/howto/usage/capi/compile_paddle_lib.md
 create mode 100644 doc/howto/usage/capi/compile_paddle_lib_cn.md
 rename doc/howto/usage/capi/{organization_of_the_inputs.md => organization_of_the_inputs_cn.md} (99%)

diff --git a/doc/howto/usage/capi/a_simple_example.md b/doc/howto/usage/capi/a_simple_example_cn.md
similarity index 68%
rename from doc/howto/usage/capi/a_simple_example.md
rename to doc/howto/usage/capi/a_simple_example_cn.md
index b1eceea38b..abe07a41cf 100644
--- a/doc/howto/usage/capi/a_simple_example.md
+++ b/doc/howto/usage/capi/a_simple_example_cn.md
@@ -4,8 +4,8 @@
 
 ### 使用流程
 
-使用 C-API 分为：准备工作和预测程序开发两部分。
-- 准备
+使用 C-API 分为：准备预测模型和预测程序开发两部分。
+- 准备预测模型
     1. 将神经网络模型结构进行序列化。
         - 调用C-API预测时，需要提供序列化之后的网络结构和训练好的模型参数文件。
     1. 将PaddlePaddle训练出的模型参数文件（多个）合并成一个文件。
@@ -14,18 +14,17 @@
     - **注意**：以上两种方式只需选择其一即可。
 - 调用 PaddlePaddle C-API 开发预测序
     1. 初始化PaddlePaddle运行环境。
-    1. 创建神经网络的输入，组织输入数据。
     1. 加载模型。
+    1. 创建神经网络的输入，组织输入数据。
     1. 进行前向计算，获得计算结果。
     1. 清理。
 
+本文档以手写数字识别任务为例，介绍如何使用 C-API 进行预测，完整代码请查看[此目录](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense)。
 
-这里我们以手写数字识别任务为例，介绍如何使用 C-API 进行预测，完整代码请查看[此目录](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense)。
-
-运行目录下的 `python mnist_v2.py` 可以使用 PaddlePaddle 内置的 [MNIST 数据集](http://yann.lecun.com/exdb/mnist/)进行训练。脚本中的模型定义了一个简单的含有[两个隐层的全连接网络](https://github.com/PaddlePaddle/book/blob/develop/02.recognize_digits/README.cn.md#softmax回归softmax-regression)，网络接受一幅图片作为输入，将图片分类到 0 ~ 9 类别标签之一。训练好的模型默认保存在当前运行目录下的`models`目录中。下面，我们将调用 C-API 加载训练好的模型进行预测。
+### 准备预测模型
 
-
-### 外部准备
+通过在终端执行`python mnist_v2.py`
+运行[目录](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense)下的 `mnist_v2.py` s可以使用 PaddlePaddle 内置的 [MNIST 数据集](http://yann.lecun.com/exdb/mnist/)进行训练。脚本中的模型定义了一个简单的含有[两个隐层的全连接网络](https://github.com/PaddlePaddle/book/blob/develop/02.recognize_digits/README.cn.md#softmax回归softmax-regression)，网络接受一幅图片作为输入，将图片分类到 0 ~ 9 类别标签之一。训练好的模型默认保存在当前运行目录下的`models`目录中。下面，我们将调用 C-API 加载训练好的模型进行预测。
 
 1. 序列化神经网络模型配置
 
@@ -52,8 +51,7 @@
     代码示例如下：
 
     ```python
-    from paddle.utils.merge_model import merge_v2_model
-
+    from paddle.utils.merge_model import merge_v2_modelss
     from mnist_v2 import network
 
     net = network(is_infer=True)
@@ -70,54 +68,65 @@
 
 ### 编写预测代码
 
-#### step 1. 初始化及加载模型
+#### step 1. 初始化PaddlePaddle运行环境
+使用C-API第一步需首先调用[`paddle_init`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/main.h#L27) 初始化PaddlePaddle运行环境。接口接受两个参数：参数的个数和参数。
 
-1. 初始化PaddlePaddle运行环境。
-    ```c
-    // Initalize the PaddlePaddle runtime environment.
-    char* argv[] = {"--use_gpu=False"};
-    CHECK(paddle_init(1, (char**)argv));
-    ```
+下面的代码片段在初始化PaddlePaddle运行环境时指定不使用GPU：
 
-1. 加载训练好的模型。
+```c
+// Initalize the PaddlePaddle runtime environment.
+char* argv[] = {"--use_gpu=False"};
+CHECK(paddle_init(1, (char**)argv));
+```
 
-    这里需要介绍C-API使用中的一个重要概念：Gradient Machine。概念上，在 PaddlePaddle 内部，一个GradientMachine类的对象管理着一组计算层（PaddlePaddle Layers）来完成前向和反向计算，并处理与之相关的所有细节。特别的，在调用C-API预测时只需进行前向计算。这篇文档的之后部分我们会使用`gradient machine`来特指调用PaddlePaddle C-API创建的GradientMachine类的对象。
+下面的代码片段在初始化PaddlePaddle运行环境时指定了两个参数：不使用GPU和[使用MKLDNN](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/mkl/mkldnn.md)：
 
-    每一个 `gradient machine` 都会管理维护一份训练好的模型，模型可以通过以下两种方式获取：
-    1. 从磁盘加载；这时`gradient machine`会独立拥有一份训练好的模型；
-    1. 共享自其它`gradient machine`的模型；这种情况多出现在使用多线程预测时；
+```c
+char* argv[] = {"--use_gpu=False", "--use_mkldnn=True"};
+CHECK(paddle_init(2, (char**)argv));
+```
 
-    下面的代码片段创建 `gradient machine`，并从指定路径加载训练好的模型。
+#### step2. 加载模型
 
-    ```c
-    // Read the binary configuration file generated by `convert_protobin.sh`
-    long size;
-    void* buf = read_config(CONFIG_BIN, &size);
+这里介绍C-API使用中的一个重要概念：Gradient Machine。概念上，在 PaddlePaddle 内部一个GradientMachine类的对象管理着一组计算层（PaddlePaddle Layers）来完成前向和反向计算，并处理与之相关的所有细节。在调用C-API预测时，只需进行前向计算而无需调用反向计算。这篇文档的之后部分我们会使用`gradient machine`来特指调用PaddlePaddle C-API创建的GradientMachine类的对象。
 
-    // Create the gradient machine for inference.
-    paddle_gradient_machine machine;
-    CHECK(paddle_gradient_machine_create_for_inference(&machine, buf, (int)size));
+每一个 `gradient machine` 都会管理维护一份训练好的模型，下面是两种最常用的模型加载方式：
 
-    // Load the trained model. Modify the parameter MODEL_PATH to set the correct
-    // path of the trained model.
-    CHECK(paddle_gradient_machine_load_parameter_from_disk(machine, MODEL_PATH));
-    ```
+1. 从磁盘加载：这时`gradient machine`会独立拥有一份训练好的模型；
+1. 共享自其它`gradient machine`的模型：这种情况多出现在使用多线程预测时，通过多个线程共享同一个模型来减少内存开销。可参考[此示例](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/examples/model_inference/multi_thread/main.c)。
+
+下面的代码片段创建 `gradient machine`，并从指定路径加载训练好的模型。
+
+```c
+// Read the binary configuration file generated by `convert_protobin.sh`
+long size;
+void* buf = read_config(CONFIG_BIN, &size);
+
+// Create the gradient machine for inference.
+paddle_gradient_machine machine;
+CHECK(paddle_gradient_machine_create_for_inference(&machine, buf, (int)size));
+
+// Load the trained model. Modify the parameter MODEL_PATH to set the correct
+// path of the trained model.
+CHECK(paddle_gradient_machine_load_parameter_from_disk(machine, MODEL_PATH));
+```
 
 ##### 注意事项
 1. 以上代码片段使用“仅序列化神经网络结构”的方式加载模型，需要同时指定模型参数存储的路径。
     - 使用PaddlePaddle V2 API训练，模型中所有可学习参数会被存为一个压缩文件，需要手动进行解压，将它们放在同一目录中，C-API不会直接加载 V2 API 存储的压缩文件。
 1. 如果使用`merge model`方式将神经网络结构和训练好的参数序列化到一个文件，请参考此[示例](https://github.com/PaddlePaddle/Mobile/blob/develop/Demo/linux/paddle_image_recognizer.cpp#L59)。
+1. 加载模型有多种方式，也可以在程序运行过程中再加载另外一个模型。
 
 #### step 2. 创建神经网络输入，组织输入数据
 
 基本使用概念：
-- 在PaddlePaddle内部，神经网络中一个计算层的输入/输出被组织为一个 `Argument` 结构体，如果神经网络有多个输入或者多个输入，每一个输入/输入都会对应有自己的`Argument`。
+- 在PaddlePaddle内部，神经网络中一个计算层的输入输出被组织为一个 `Argument` 结构体，如果神经网络有多个输入或者多个输出，每一个输入/输出都会对应有自己的`Argument`。
 - `Argument` 并不真正“存储”数据，而是将输入/输出数据有机地组织在一起。
 - 在`Argument`内部由：1. `Matrix`（二维矩阵，存储浮点类型输入/输出）；2. `IVector`（一维数组，**仅用于存储整型值**，多用于自然语言处理任务）来实际存储数据。
 
 *注：本文档使用的示例任务手写数字识别不涉及一维整型数组作为输入，因此，本文档仅讨论二维稠密矩阵作为输入的情形。更多输入数据格式请参考输入/输出数据一节的内容。*
 
-这篇文档的之后部分会使用`argument`来**特指** PaddlePaddle C-API中神经网的一个输入/输出，使用`paddle_matrix`**特指**`argument`中用于存储数据的`Matrix`类的对象。
+这篇文档的之后部分会使用`argument`来**特指** PaddlePaddle C-API中神经网络的一个输入/输出，使用`paddle_matrix`**特指**`argument`中用于存储数据的`Matrix`类的对象。
 
 于是，在组织神经网络输入，获取输出时，需要思考完成以下工作：
 1. 为每一个输入/输出创建`argument`；
diff --git a/doc/howto/usage/capi/compile_paddle_lib.md b/doc/howto/usage/capi/compile_paddle_lib.md
deleted file mode 100644
index d4ac4f2771..0000000000
--- a/doc/howto/usage/capi/compile_paddle_lib.md
+++ /dev/null
@@ -1,70 +0,0 @@
-## 编译 PaddlePaddle 链接库
-
-### 概述
-
-使用 C-API 进行预测依赖于将 PaddlePaddle 核心代码编译成链接库，只需在编译时指定编译选项：`-DWITH_C_API=ON`。同时，**建议将：`DWITH_PYTHON`，`DWITH_SWIG_PY`，`DWITH_GOLANG`，均设置为`OFF`**，以避免链接不必要的库。其它编译选项按需进行设定。
-
-```shell
-INSTALL_PREFIX=/path/of/capi/
-PADDLE_ROOT=/path/of/paddle_source/
-cmake $PADDLE_ROOT -DCMAKE_INSTALL_PREFIX=$INSTALL_PREFIX \
-      -DCMAKE_BUILD_TYPE=Release \
-      -DWITH_C_API=ON \
-      -DWITH_SWIG_PY=OFF \
-      -DWITH_GOLANG=OFF \
-      -DWITH_PYTHON=OFF \
-      -DWITH_MKLML=OFF \
-      -DWITH_MKLDNN=OFF \
-      -DWITH_GPU=OFF \
-      ...
-```
-在上面的代码片段中，`PADDLE_ROOT` 表示 PaddlePaddle 源码所在目录，生成Makefile文件后执行：`make && make install`。成功执行后，使用CAPI所需的依赖（包括：（1）编译出的PaddlePaddle 链接和头文件；（2）第三方链接库和头文件）均会存放于`INSTALL_PREFIX`目录中。
-
-编译成功后在 `INSTALL_PREFIX` 下会看到如下目录结构（包括了编译出的PaddlePaddle头文件和链接库，以及第三方依赖链接库和头文件（如果需要，由链接方式决定））：
-
-```text
-├── include
-│   └── paddle
-│       ├── arguments.h
-│       ├── capi.h
-│       ├── capi_private.h
-│       ├── config.h
-│       ├── error.h
-│       ├── gradient_machine.h
-│       ├── main.h
-│       ├── matrix.h
-│       ├── paddle_capi.map
-│       └── vector.h
-├── lib
-│   ├── libpaddle_capi_engine.a
-│   ├── libpaddle_capi_layers.a
-│   ├── libpaddle_capi_shared.dylib
-│   └── libpaddle_capi_whole.a
-└── third_party
-    ├── ......
-```
-
-### 链接方式说明
-
-目前提供三种链接方式：
-
-1. 链接`libpaddle_capi_shared.so` 动态库
-    - 使用 PaddlePaddle C-API 开发预测程序链接`libpaddle_capi_shared.so`时，需注意：
-        1. 如果编译时指定编译CPU版本，且使用`OpenBLAS`矩阵库，在使用CAPI开发预测程序时，只需要链接`libpaddle_capi_shared.so`这一个库。
-        1. 如果是用编译时指定CPU版本，且使用`MKL`矩阵库，由于`MKL`库有自己独立的动态库文件，在使用PaddlePaddle CAPI开发预测程序时，需要自己链接MKL链接库。
-        1. 如果编译时指定编译GPU版本，CUDA相关库会在预测程序运行时动态装载，需要将CUDA相关的库设置到`LD_LIBRARY_PATH`环境变量中。
-    - 这种方式最为简便，链接相对容易，**在无特殊需求情况下，推荐使用此方式**。
-
-2. 链接静态库 `libpaddle_capi_whole.a`
-    - 使用PaddlePaddle C-API 开发预测程序链接`libpaddle_capi_whole.a`时，需注意：
-        1. 需要指定`-Wl,--whole-archive`链接选项。
-        1. 需要显式地链接 `gflags`、`glog`、`libz`、`protobuf` 等第三方库，可在`INSTALL_PREFIX\third_party`下找到。
-        1. 如果在编译 C-API 时使用OpenBLAS矩阵库，需要显示地链接`libopenblas.a`。
-        1. 如果在编译 C-API 是使用 MKL 矩阵库，需要显示地链接 MKL 的动态库。
-
-3. 链接静态库 `libpaddle_capi_layers.a`和`libpaddle_capi_engine.a`
-    - 使用PaddlePaddle C-API 开发预测程序链接`libpaddle_capi_whole.a`时，需注意：
-        1. 这种链接方式主要用于移动端预测。
-        1. 为了减少生成链接库的大小把`libpaddle_capi_whole.a`拆成以上两个静态链接库。
-        1. 需指定`-Wl,--whole-archive -lpaddle_capi_layers` 和 `-Wl,--no-whole-archive -lpaddle_capi_engine` 进行链接。
-        1. 第三方依赖库需要按照与方式2同样方法显示地进行链接。
diff --git a/doc/howto/usage/capi/compile_paddle_lib_cn.md b/doc/howto/usage/capi/compile_paddle_lib_cn.md
new file mode 100644
index 0000000000..ac5ecffe2e
--- /dev/null
+++ b/doc/howto/usage/capi/compile_paddle_lib_cn.md
@@ -0,0 +1,122 @@
+## 编译 PaddlePaddle 预测库
+
+### 概述
+
+使用 C-API 进行预测依赖于将 PaddlePaddle 核心代码编译成链接库，只需在编译时需配制下面这些编译选项：
+
+必须配置选项：
+- `WITH_C_API`，必须配置为`ON`。
+
+推荐配置选项：
+- `WITH_PYTHON`，推荐配置为`OFF`
+- `WITH_SWIG_PY`，推荐配置为`OFF`
+- `WITH_GOLANG`，推荐设置为`OFF`
+
+可选配置选项：
+- `WITH_GPU`，可配置为`ON/OFF`
+- `WITH_MKL`，可配置为`ON/OFF`
+
+对推荐配置中的选项建议按照设置，以避免链接不必要的库。其它可选编译选项按需进行设定。
+
+下面的代码片段从github拉取最新代码，配制编译选项（需要将PADDLE_ROOT替换为PaddlePaddle预测库的安装路径）：
+
+```shell
+PADDLE_ROOT=/path/of/capi
+git clone https://github.com/PaddlePaddle/Paddle.git
+cd Paddle
+mkdir build
+cd build
+cmake -DCMAKE_INSTALL_PREFIX=$PADDLE_ROOT \
+      -DCMAKE_BUILD_TYPE=Release \
+      -DWITH_C_API=ON \
+      -DWITH_SWIG_PY=OFF \
+      -DWITH_GOLANG=OFF \
+      -DWITH_PYTHON=OFF \
+      -DWITH_MKL=OFF \
+      -DWITH_GPU=OFF  \
+      ..
+```
+
+执行上述代码生成Makefile文件后，执行：`make && make install`。成功编译后，使用C-API所需的依赖（包括：（1）编译出的PaddlePaddle预测库和头文件；（2）第三方链接库和头文件）均会存放于`PADDLE_ROOT`目录中。
+
+编译成功后在 `PADDLE_ROOT` 下会看到如下目录结构（包括了编译出的PaddlePaddle头文件和链接库，以及第三方依赖链接库和头文件（如果需要，由链接方式决定））：
+
+```text
+├── include
+│   └── paddle
+│       ├── arguments.h
+│       ├── capi.h
+│       ├── capi_private.h
+│       ├── config.h
+│       ├── error.h
+│       ├── gradient_machine.h
+│       ├── main.h
+│       ├── matrix.h
+│       ├── paddle_capi.map
+│       └── vector.h
+├── lib
+│   ├── libpaddle_capi_engine.a
+│   ├── libpaddle_capi_layers.a
+│   ├── libpaddle_capi_shared.so
+│   └── libpaddle_capi_whole.a
+└── third_party
+    ├── gflags
+    │   ├── include
+    │   │   └── gflags
+    │   │       ├── gflags_completions.h
+    │   │       ├── gflags_declare.h
+    │   │       ...
+    │   └── lib
+    │       └── libgflags.a
+    ├── glog
+    │   ├── include
+    │   │   └── glog
+    │   │       ├── config.h
+    │   │       ...
+    │   └── lib
+    │       └── libglog.a
+    ├── openblas
+    │   ├── include
+    │   │   ├── cblas.h
+    │   │   ...
+    │   └── lib
+    │       ...
+    ├── protobuf
+    │   ├── include
+    │   │   └── google
+    │   │       └── protobuf
+    │   │           ...
+    │   └── lib
+    │       └── libprotobuf-lite.a
+    └── zlib
+        ├── include
+        │   ...
+        └── lib
+            ...
+
+```
+
+### 链接说明
+
+目前提供三种链接方式：
+
+1. 链接`libpaddle_capi_shared.so` 动态库
+    - 使用 PaddlePaddle C-API 开发预测程序链接`libpaddle_capi_shared.so`时，需注意：
+        1. 如果编译时指定编译CPU版本，且使用`OpenBLAS`数学库，在使用C-API开发预测程序时，只需要链接`libpaddle_capi_shared.so`这一个库。
+        1. 如果是用编译时指定CPU版本，且使用`MKL`数学库，由于`MKL`库有自己独立的动态库文件，在使用PaddlePaddle C-API开发预测程序时，需要自己链接MKL链接库。
+        1. 如果编译时指定编译GPU版本，CUDA相关库会在预测程序运行时动态装载，需要将CUDA相关的库设置到`LD_LIBRARY_PATH`环境变量中。
+    - 这种方式最为简便，链接相对容易，**在无特殊需求情况下，推荐使用此方式**。
+
+2. 链接静态库 `libpaddle_capi_whole.a`
+    - 使用PaddlePaddle C-API 开发预测程序链接`libpaddle_capi_whole.a`时，需注意：
+        1. 需要指定`-Wl,--whole-archive`链接选项。
+        1. 需要显式地链接 `gflags`、`glog`、`libz`、`protobuf` 等第三方库，可在`PADDLE_ROOT/third_party`下找到。
+        1. 如果在编译 C-API 时使用OpenBLAS数学库，需要显示地链接`libopenblas.a`。
+        1. 如果在编译 C-API 是使用MKL数学库，需要显示地链接MKL的动态库。
+
+3. 链接静态库 `libpaddle_capi_layers.a`和`libpaddle_capi_engine.a`
+    - 使用PaddlePaddle C-API 开发预测程序链接`libpaddle_capi_whole.a`时，需注意：
+        1. 这种链接方式主要用于移动端预测。
+        1. 为了减少生成链接库的大小把`libpaddle_capi_whole.a`拆成以上两个静态链接库。
+        1. 需指定`-Wl,--whole-archive -lpaddle_capi_layers` 和 `-Wl,--no-whole-archive -lpaddle_capi_engine` 进行链接。
+        1. 第三方依赖库需要按照与方式2同样方法显示地进行链接。
diff --git a/doc/howto/usage/capi/index_cn.rst b/doc/howto/usage/capi/index_cn.rst
index 3b36f31bfd..b2822fca2b 100644
--- a/doc/howto/usage/capi/index_cn.rst
+++ b/doc/howto/usage/capi/index_cn.rst
@@ -4,6 +4,6 @@ PaddlePaddle C-API
 ..  toctree::
   :maxdepth: 1
 
-  compile_paddle_lib.md
-  organization_of_the_inputs.md
-  a_simple_example.md
+  compile_paddle_lib_cn.md
+  organization_of_the_inputs_cn.md
+  a_simple_example_cn.md
diff --git a/doc/howto/usage/capi/organization_of_the_inputs.md b/doc/howto/usage/capi/organization_of_the_inputs_cn.md
similarity index 99%
rename from doc/howto/usage/capi/organization_of_the_inputs.md
rename to doc/howto/usage/capi/organization_of_the_inputs_cn.md
index 7563e236da..787af8b0a7 100644
--- a/doc/howto/usage/capi/organization_of_the_inputs.md
+++ b/doc/howto/usage/capi/organization_of_the_inputs_cn.md
@@ -260,6 +260,8 @@
 </table>
 </html>
 <br>
+
+
 ### 输出数据
 
 PaddlePaddle中一个计算层的输出数据组织方式和输入数据组织方式完全相同。一个输出数据同样被组织为一个`argument`，`argument`通过`paddle_matrix`或`paddle_ivector`存数数据，如果输出是一个序列，那么会携带有`sequence_start_positions`信息。调用C-API相关接口，读取需要的结果即可。

From ed55f1b9d49ed3f6609246159594da85810a9a2e Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Fri, 5 Jan 2018 19:25:30 +0800
Subject: [PATCH 08/68] transpiler_split_tensor

---
 .../paddle/v2/fluid/distribute_transpiler.py  | 241 +++++++++++-------
 1 file changed, 148 insertions(+), 93 deletions(-)

diff --git a/python/paddle/v2/fluid/distribute_transpiler.py b/python/paddle/v2/fluid/distribute_transpiler.py
index 49ece7b725..e5314cf272 100644
--- a/python/paddle/v2/fluid/distribute_transpiler.py
+++ b/python/paddle/v2/fluid/distribute_transpiler.py
@@ -1,51 +1,20 @@
+from __future__ import print_function
 import framework
 from framework import Program, default_main_program, Parameter, Variable
 import optimizer
 from layer_helper import LayerHelper
+from distributed_spliter import *
 
 
-def hash_name_to_server(params_grads, pserver_endpoints):
-    """
-    :param param_grads:
-    :return: a map of pserver endpoint -> 
-                    params -> [param list]
-                    grads  -> [grad list]
-    """
+class VarBlock:
+    def __init__(self, varname, offset, size):
+        self.varname = varname
+        # NOTE: real offset is offset * size
+        self.offset = offset
+        self.size = size
 
-    def _hash_param(param_name, total):
-        return hash(param_name) % total
-
-    param_grad_map = dict()
-    for param, grad in params_grads:
-        if param.trainable is True and grad is not None:
-            server_id = _hash_param(param.name, len(pserver_endpoints))
-            server_for_param = pserver_endpoints[server_id]
-            if not param_grad_map.has_key(server_for_param):
-                param_grad_map[server_for_param] = {"params": [], "grads": []}
-            param_grad_map[server_for_param]["params"].append(param)
-            param_grad_map[server_for_param]["grads"].append(grad)
-
-    return param_grad_map
-
-
-def round_robin(params_grads, pserver_endpoints):
-    assert (len(params_grads) > len(pserver_endpoints))
-
-    param_grad_map = dict()
-    pserver_idx = 0
-    for param, grad in params_grads:
-        if param.trainable is True:
-            server_for_param = pserver_endpoints[pserver_idx]
-            if not param_grad_map.has_key(server_for_param):
-                param_grad_map[server_for_param] = {"params": [], "grads": []}
-
-            param_grad_map[server_for_param]["params"].append(param)
-            param_grad_map[server_for_param]["grads"].append(grad)
-
-            pserver_idx += 1
-            if pserver_idx >= len(pserver_endpoints):
-                pserver_idx = 0
-    return param_grad_map
+    def __str__(self):
+        return "%s:%d:%d" % (self.varname, self.offset, self.size)
 
 
 class DistributeTranspiler:
@@ -58,7 +27,6 @@ class DistributeTranspiler:
                   split_method=round_robin):
         """
             Transpile the program to a distributed data-parallelism programs.
-
             The main_program will be transform to use a remote parameter server
             to do parameter optimization. And the optimization graph will be put
             in to a parameter server program.
@@ -66,45 +34,84 @@ class DistributeTranspiler:
             Use different methods to split trainable varialbles to different
             parameter servers.
 
-            Example to run:
-
-            exe = fluid.Executor(place)
-            t = fluid.DistributeTranspiler()
-            t.transpile(optimize_ops, params_grads, pservers="127.0.0.1:6174", trainers=1)
-
-            pserver_endpoint = os.getenv("PSERVER")
-            if pserver_endpoint:
-                pserver_prog = t.get_pserver_program(pserver_endpoint, optimize_ops)
-                exe.run(fluid.default_startup_program())
-                exe.run(pserver_prog)
-            else:
-                feeder = fluid.DataFeeder(feed_list=[images, label], place=place)
-                exe.run(fluid.default_startup_program())
-
-                for pass_id in range(PASS_NUM):
-                    ...
-
             :param optimize_ops: op list of optimization, should be the
                                  return value of Optimizer.minimize
             :type optimize_ops: list
             :param program: program to optimize, default default_main_program
             :param pservers: parameter server endpoints like "m1:6174,m2:6174"
             :type pservers: string
-
             :return: return a list of programs
         """
+        assert (callable(split_method))
         if program is None:
             program = default_main_program()
         self.program = program
         self.trainers = trainers
         self.optimize_ops = optimize_ops
-        self._optimize_distributed(
-            optimize_ops,
-            program,
-            params_grads,
-            pservers=pservers,
-            trainers=trainers,
-            split_method=split_method)
+        # steps to transpile:
+        # 1. split variable to multiple blocks, align by product(dim[1:]) (width).
+        # 2. modify trainer program add split_op to each Grad.
+        # 3. append send_op to trainer.
+        # 4. append concat_op to trainer to update local weights.
+        # 5. create new program as parameter server.
+        # 5. create parameter server program by split_method generated endpoint->VarBlock
+        # 6. run compile time infershape for parameter server program
+
+        if kwargs.has_key("split_method"):
+            split_method = kwargs["split_method"]
+        else:
+            split_method = round_robin
+        pserver_endpoints = kwargs["pservers"].split(",")
+
+        grad2param = dict()
+        for param, grad in params_and_grads:
+            grad2param[grad.name()] = param.name()
+
+        # step1
+        param_list = [pg[0] for pg in params_and_grads]
+        grad_list = [pg[1] for pg in params_and_grads]
+        # TODO: add split selected rows support
+        grad_blocks = _split_dense_variable(grad_list, len(pserver_endpoints))
+        param_blocks = _split_dense_variable(param_list, len(pserver_endpoints))
+        ep2gradblock = split_method(grad_blocks, pserver_endpoints)
+        # self.param_grad_map
+        # step2
+        var2splited = self._split_trainer_vars(program, grad_blocks)
+
+        # step3
+        send_inputs = []
+        send_outputs = []
+        for _, splited in var2splited.iteritems():
+            send_inputs.extend(splited)
+        send_outputs = self._create_vars_from_blocklist(program, param_blocks)
+
+        send_op = program.global_block().append_op(
+            type="send",
+            inputs={"X": send_inputs},
+            outputs={"Out": send_outputs},
+            attrs={"endpoints": pserver_endpoints,
+                   "epmap": epmap})
+
+    def _create_vars_from_blocklist(self, program, block_list):
+        block_map = dict()
+        ret_vars = []
+        for block_str in block_list:
+            varname, offset, size = block_str.split(":")
+            if not block_map.has_key(varname):
+                block_map[varname] = []
+            block_map[varname].append((long(offset), long(size)))
+
+        for varname, splited in block_map.iteritems():
+            orig_var = program.global_block().vars[varname]
+            for block in splited:
+                size = block[1]
+                var = program.global_block().create_var(
+                    name="%s.block%d" % (varname, i),
+                    psersistable=False,
+                    dtype=orig_var.dtype,
+                    shape=[1, size])  # flattend splited var
+                ret_vars.append(var)
+        return ret_vars
 
     def _clone_param(self, block, v):
         assert isinstance(v, Parameter)
@@ -131,32 +138,80 @@ class DistributeTranspiler:
             lod_level=var.lod_level,
             persistable=var.persistable)
 
-    def _optimize_distributed(self, optimize_ops, program, params_and_grads,
-                              **kwargs):
-        if kwargs.has_key("split_method"):
-            split_method = kwargs["split_method"]
-        else:
-            split_method = round_robin
+    def _split_dense_variable(self,
+                              var_list,
+                              pserver_count,
+                              min_block_size=1024,
+                              max_block_size=1048576):
+        """
+            We may need to split dense tensor to one or several blocks and put
+            them equally onto parameter server. One block is a sub-tensor
+            aligned by dim[0] of the tensor.
+            
+            We need to have a minimal block size so that the calculations in
+            the parameter server side can gain better performance. By default
+            mininum block size is 1024. The max block size is used to prevent
+            too large block that may causing send error.
+        """
+        block_sizes = []
+        blocks = []
+        for grad in var_list:
+            dim1 = reduce(lambda x, y: x * y, grad.shape[1:])
+            grad_numel = reduce(lambda x, y: x * y, grad.shape)
+            if grad_numel < min_block_size:
+                block_sizes.append(grad_numel)
+            block_size = grad_numel / min_block_size
+            if block_size < min_block_size:
+                block_size = min_block_size
+            # align by dim1(width)
+            remains = block_size % dim1
+            if remains != 0:
+                block_size += dim1 - remains
+            block_sizes.append(block_size)
+            num_blocks = grad_numel / block_size
+            print("grad numel :%d, blocksize: %d" % grad_numel, block_size)
+            for block_id in xrange(num_blocks):
+                block = VarBlock(grad.name(), block_id, block_size)
+                blocks.append(str(block))
+        return blocks
 
-        assert (callable(split_method))
-        pserver_endpoints = kwargs["pservers"].split(",")
-        self.param_grad_map = split_method(params_and_grads, pserver_endpoints)
-
-        send_op_ordered_inputs = []
-        send_op_ordered_outputs = []
-        epmap = []
-        for ep, v in self.param_grad_map.iteritems():
-            send_op_ordered_inputs.extend(v["grads"])
-            send_op_ordered_outputs.extend(v["params"])
-            for i in v["grads"]:
-                epmap.append(ep)
-        send_op = program.global_block().append_op(
-            type="send",
-            inputs={"X": send_op_ordered_inputs
-                    },  # inputs is a list of tensors to be send
-            outputs={"Out": send_op_ordered_outputs},
-            attrs={"endpoints": pserver_endpoints,
-                   "epmap": epmap})
+    def _split_trainer_vars(self, program, gradblocks, params_and_grads):
+        var2blocks = dict()
+        splited = dict()
+        for block_str in gradblocks:
+            varname, offset, size = block_str.split(":")
+            if not var2blocks.has_key(varname):
+                var2blocks[varname] = []
+            var2blocks[varname].append((long(offset), long(size)))
+        for varname, blocks in var2blocks.iteritems():
+            orig_var = program.global_block().vars[varname]
+            split_outs = []
+            for i in xrange(len(blocks)):
+                size = blocks[i][1]
+                var = program.global_block().create_var(
+                    name="%s.block%d" % (varname, i),
+                    psersistable=False,
+                    dtype=orig_var.dtype,
+                    shape=[1, size])  # flattend splited var
+                split_outs.append(var)
+
+            splited[varname] = split_outs
+            program.global_block().append_op(
+                type="split",
+                inputs={"X": orig_var},
+                outputs={"Out": split_outs},
+                attrs={"num": len(blocks)}  # assume split evenly
+            )
+        return splited
+
+    def _concat_trainer_vars(self, program, splited):
+        for varname, to_merge_list in splited.iteritems():
+            orig_var = program.global_block().vars[varname]
+            program.global_block().append_op(
+                type="concat",
+                inputs={"X": to_merge_list},
+                outputs={"Out": orig_var},
+                attrs={})
 
     def get_trainer_program(self):
         # remove optimize ops and add a send op to main_program

From c70ea1cc30cfa54228d10e56934119e3a68e3e51 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Fri, 5 Jan 2018 19:35:52 +0800
Subject: [PATCH 09/68] add splitter

---
 .../paddle/v2/fluid/distribute_transpiler.py  |  1 -
 python/paddle/v2/fluid/distributed_spliter.py | 38 +++++++++++++++++++
 2 files changed, 38 insertions(+), 1 deletion(-)
 create mode 100644 python/paddle/v2/fluid/distributed_spliter.py

diff --git a/python/paddle/v2/fluid/distribute_transpiler.py b/python/paddle/v2/fluid/distribute_transpiler.py
index e5314cf272..4c90b4a853 100644
--- a/python/paddle/v2/fluid/distribute_transpiler.py
+++ b/python/paddle/v2/fluid/distribute_transpiler.py
@@ -80,7 +80,6 @@ class DistributeTranspiler:
 
         # step3
         send_inputs = []
-        send_outputs = []
         for _, splited in var2splited.iteritems():
             send_inputs.extend(splited)
         send_outputs = self._create_vars_from_blocklist(program, param_blocks)
diff --git a/python/paddle/v2/fluid/distributed_spliter.py b/python/paddle/v2/fluid/distributed_spliter.py
new file mode 100644
index 0000000000..e7ba53390d
--- /dev/null
+++ b/python/paddle/v2/fluid/distributed_spliter.py
@@ -0,0 +1,38 @@
+def hash_name(varblocks, pserver_endpoints):
+    """
+    :param varblocks: a list of VarBlock string indicating 
+                      sub blocks of variables
+    :return: a map of pserver endpoint -> varblock_str
+    """
+
+    def _hash_block(block_str, total):
+        return hash(block_str) % total
+
+    ep2block = dict()
+    for varblock_str in varblocks:
+        if param.trainable is True and grad is not None:
+            server_id = _hash_block(varblock_str, len(pserver_endpoints))
+            server_for_param = pserver_endpoints[server_id]
+            if not ep2block.has_key(server_for_param):
+                ep2block[server_for_param] = []
+            ep2block[server_for_param].append(varblock_str)
+
+    return ep2block
+
+
+def round_robin(varblocks, pserver_endpoints):
+    assert (len(varblocks) > len(pserver_endpoints))
+
+    ep2block = dict()
+    pserver_idx = 0
+    for varblock_str in varblocks:
+        if param.trainable is True:
+            server_for_param = pserver_endpoints[pserver_idx]
+            if not ep2block.has_key(server_for_param):
+                ep2block[server_for_param] = []
+            ep2block[server_for_param].append(varblock_str)
+
+            pserver_idx += 1
+            if pserver_idx >= len(pserver_endpoints):
+                pserver_idx = 0
+    return ep2block

From 4d4df084a70276ecb230fed056aaa1b454eaf203 Mon Sep 17 00:00:00 2001
From: ying <lcy.seso@gmail.com>
Date: Mon, 8 Jan 2018 11:55:28 +0800
Subject: [PATCH 10/68] follow comments.

---
 doc/howto/usage/capi/a_simple_example_cn.md   | 220 ------------------
 .../usage/capi/images/workflow_of_CAPI.png    | Bin 0 -> 458577 bytes
 doc/howto/usage/capi/index_cn.rst             |   2 +-
 doc/howto/usage/capi/workflow_of_capi.md      | 120 ++++++++++
 4 files changed, 121 insertions(+), 221 deletions(-)
 delete mode 100644 doc/howto/usage/capi/a_simple_example_cn.md
 create mode 100644 doc/howto/usage/capi/images/workflow_of_CAPI.png
 create mode 100644 doc/howto/usage/capi/workflow_of_capi.md

diff --git a/doc/howto/usage/capi/a_simple_example_cn.md b/doc/howto/usage/capi/a_simple_example_cn.md
deleted file mode 100644
index abe07a41cf..0000000000
--- a/doc/howto/usage/capi/a_simple_example_cn.md
+++ /dev/null
@@ -1,220 +0,0 @@
-## C-API CPU 单线程预测示例
-
-这篇文档通过一个最简单的例子：手写数字识别，来介绍 CPU 下单线程使用 PaddlePaddle C-API 开发预测服务，完整代码见[此目录](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/examples/model_inference/dense/)。
-
-### 使用流程
-
-使用 C-API 分为：准备预测模型和预测程序开发两部分。
-- 准备预测模型
-    1. 将神经网络模型结构进行序列化。
-        - 调用C-API预测时，需要提供序列化之后的网络结构和训练好的模型参数文件。
-    1. 将PaddlePaddle训练出的模型参数文件（多个）合并成一个文件。
-        - 神经网络模型结构和训练好的模型将被序列化合并入一个文件。
-        - 预测时只需加载这一个文件，便于发布。
-    - **注意**：以上两种方式只需选择其一即可。
-- 调用 PaddlePaddle C-API 开发预测序
-    1. 初始化PaddlePaddle运行环境。
-    1. 加载模型。
-    1. 创建神经网络的输入，组织输入数据。
-    1. 进行前向计算，获得计算结果。
-    1. 清理。
-
-本文档以手写数字识别任务为例，介绍如何使用 C-API 进行预测，完整代码请查看[此目录](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense)。
-
-### 准备预测模型
-
-通过在终端执行`python mnist_v2.py`
-运行[目录](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense)下的 `mnist_v2.py` s可以使用 PaddlePaddle 内置的 [MNIST 数据集](http://yann.lecun.com/exdb/mnist/)进行训练。脚本中的模型定义了一个简单的含有[两个隐层的全连接网络](https://github.com/PaddlePaddle/book/blob/develop/02.recognize_digits/README.cn.md#softmax回归softmax-regression)，网络接受一幅图片作为输入，将图片分类到 0 ~ 9 类别标签之一。训练好的模型默认保存在当前运行目录下的`models`目录中。下面，我们将调用 C-API 加载训练好的模型进行预测。
-
-1. 序列化神经网络模型配置
-
-    PaddlePaddle 使用 protobuf 来传输网络配置文件中定义的网络结构和相关参数，在使用 C-API 进行预测时，也需将网络结构使用 protobuf 进行序列化，写入文件中。
-
-    调用`paddle.utils.dump_v2_config`中的`dump_v2_config`函数能够将使用 PaddlePaddle V2 API 定义的神经网络结构 dump 到指定文件中。示例代码如下：
-
-    ```python
-    from paddle.utils.dump_v2_config import dump_v2_config
-    from mnist_v2 import network
-
-    predict = network(is_infer=True)
-    dump_v2_config(predict, "trainer_config.bin", True)
-    ```
-
-    对本例，或运行 `python mnist_v2.py --task dump_config`，会对示例中的网络结构进行序列化，并将结果写入当前目录下的`trainer_config.bin`文件中。
-
-    当选择使用这种方式调用 C-API 时，如果神经网络有多个可学习参数，请将它们全部放在同一文件夹内，C-API会从指定的目录寻找并加载训练好的模型。
-
-2. 合并模型文件(可选)
-
-    一些情况下为了便于发布，希望能够将序列化后的神经网络结构和训练好的模型参数打包进一个文件，这时可以使用`paddle.utils.merge_model`中的`merge_v2_model`接口对神经网络结构和训练好的参数进行序列化，将序列化结果写入一个文件内，调用C-API时直接只需加载这一个文件。
-
-    代码示例如下：
-
-    ```python
-    from paddle.utils.merge_model import merge_v2_modelss
-    from mnist_v2 import network
-
-    net = network(is_infer=True)
-    param_file = "models/params_pass_4.tar"
-    output_file = "output.paddle.model"
-    merge_v2_model(net, param_file, output_file)
-    ```
-    对本例，或者直接运行 `python merge_v2_model.py`，序列化结果将会写入当前目录下的`output.paddle.model`文件中，该文件在调用C-API时，可被直接加载。
-
-#### 注意事项
-1. C-API 需要序列化之后神经网络结构，在调用`dump_v2_config`时，参数`binary`必须指定为`True`。
-1. **预测使用的网络结构往往不同于训练**，通常需要去掉网络中的：（1）类别标签层；（2）损失函数层；（3）`evaluator`等，只留下核心计算层，请注意是否需要修改网络结构。
-1. 预测时，可以获取网络中定义的任意多个（大于等于一个）层前向计算的结果，需要哪些层的计算结果作为输出，就将这些层加入一个Python list中，作为调用`dump_v2_config`的第一个参数。
-
-### 编写预测代码
-
-#### step 1. 初始化PaddlePaddle运行环境
-使用C-API第一步需首先调用[`paddle_init`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/main.h#L27) 初始化PaddlePaddle运行环境。接口接受两个参数：参数的个数和参数。
-
-下面的代码片段在初始化PaddlePaddle运行环境时指定不使用GPU：
-
-```c
-// Initalize the PaddlePaddle runtime environment.
-char* argv[] = {"--use_gpu=False"};
-CHECK(paddle_init(1, (char**)argv));
-```
-
-下面的代码片段在初始化PaddlePaddle运行环境时指定了两个参数：不使用GPU和[使用MKLDNN](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/mkl/mkldnn.md)：
-
-```c
-char* argv[] = {"--use_gpu=False", "--use_mkldnn=True"};
-CHECK(paddle_init(2, (char**)argv));
-```
-
-#### step2. 加载模型
-
-这里介绍C-API使用中的一个重要概念：Gradient Machine。概念上，在 PaddlePaddle 内部一个GradientMachine类的对象管理着一组计算层（PaddlePaddle Layers）来完成前向和反向计算，并处理与之相关的所有细节。在调用C-API预测时，只需进行前向计算而无需调用反向计算。这篇文档的之后部分我们会使用`gradient machine`来特指调用PaddlePaddle C-API创建的GradientMachine类的对象。
-
-每一个 `gradient machine` 都会管理维护一份训练好的模型，下面是两种最常用的模型加载方式：
-
-1. 从磁盘加载：这时`gradient machine`会独立拥有一份训练好的模型；
-1. 共享自其它`gradient machine`的模型：这种情况多出现在使用多线程预测时，通过多个线程共享同一个模型来减少内存开销。可参考[此示例](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/examples/model_inference/multi_thread/main.c)。
-
-下面的代码片段创建 `gradient machine`，并从指定路径加载训练好的模型。
-
-```c
-// Read the binary configuration file generated by `convert_protobin.sh`
-long size;
-void* buf = read_config(CONFIG_BIN, &size);
-
-// Create the gradient machine for inference.
-paddle_gradient_machine machine;
-CHECK(paddle_gradient_machine_create_for_inference(&machine, buf, (int)size));
-
-// Load the trained model. Modify the parameter MODEL_PATH to set the correct
-// path of the trained model.
-CHECK(paddle_gradient_machine_load_parameter_from_disk(machine, MODEL_PATH));
-```
-
-##### 注意事项
-1. 以上代码片段使用“仅序列化神经网络结构”的方式加载模型，需要同时指定模型参数存储的路径。
-    - 使用PaddlePaddle V2 API训练，模型中所有可学习参数会被存为一个压缩文件，需要手动进行解压，将它们放在同一目录中，C-API不会直接加载 V2 API 存储的压缩文件。
-1. 如果使用`merge model`方式将神经网络结构和训练好的参数序列化到一个文件，请参考此[示例](https://github.com/PaddlePaddle/Mobile/blob/develop/Demo/linux/paddle_image_recognizer.cpp#L59)。
-1. 加载模型有多种方式，也可以在程序运行过程中再加载另外一个模型。
-
-#### step 2. 创建神经网络输入，组织输入数据
-
-基本使用概念：
-- 在PaddlePaddle内部，神经网络中一个计算层的输入输出被组织为一个 `Argument` 结构体，如果神经网络有多个输入或者多个输出，每一个输入/输出都会对应有自己的`Argument`。
-- `Argument` 并不真正“存储”数据，而是将输入/输出数据有机地组织在一起。
-- 在`Argument`内部由：1. `Matrix`（二维矩阵，存储浮点类型输入/输出）；2. `IVector`（一维数组，**仅用于存储整型值**，多用于自然语言处理任务）来实际存储数据。
-
-*注：本文档使用的示例任务手写数字识别不涉及一维整型数组作为输入，因此，本文档仅讨论二维稠密矩阵作为输入的情形。更多输入数据格式请参考输入/输出数据一节的内容。*
-
-这篇文档的之后部分会使用`argument`来**特指** PaddlePaddle C-API中神经网络的一个输入/输出，使用`paddle_matrix`**特指**`argument`中用于存储数据的`Matrix`类的对象。
-
-于是，在组织神经网络输入，获取输出时，需要思考完成以下工作：
-1. 为每一个输入/输出创建`argument`；
-1. 为每一个`argument`创建`paddle_matrix`来存储数据；
-
-与输入不同的是，输出`argument`的`paddle_matrix`变量并不需在使用C-API时为之分配存储空间。PaddlePaddle内部，神经网络进行前向计算时会自己分配/管理每个计算层的存储空间；这些细节C-API会代为处理，只需在概念上理解，并按照约定调用相关的 C-API 接口即可。
-
-下面是示例代码片段。在这段代码中，生成了一条随机输入数据作为测试样本。
-```c
-// Inputs and outputs of the network are organized as paddle_arguments object
-// in C-API. In the comments below, "argument" specifically means one input of
-// the neural network in PaddlePaddle C-API.
-paddle_arguments in_args = paddle_arguments_create_none();
-
-// There is only one data layer in this demo MNIST network, invoke this
-// function to create one argument.
-CHECK(paddle_arguments_resize(in_args, 1));
-
-// Each argument needs one matrix or one ivector (integer vector, for sparse
-// index input, usually used in NLP task) to holds the real input data.
-// In the comments below, "matrix" specifically means the object needed by
-// argument to hold the data. Here we create the matrix for the above created
-// agument to store the testing samples.
-paddle_matrix mat =
-    paddle_matrix_create(/* height = batch size */ 1,
-                    /* width = dimensionality of the data layer */ 784,
-                    /* whether to use GPU */ false);
-
-paddle_real* array;
-// Get the pointer pointing to the start address of the first row of the
-// created matrix.
-CHECK(paddle_matrix_get_row(mat, 0, &array));
-
-// Fill the matrix with a randomly generated test sample.
-srand(time(0));
-for (int i = 0; i < 784; ++i) {
-  array[i] = rand() / ((float)RAND_MAX);
-}
-
-// Assign the matrix to the argument.
-CHECK(paddle_arguments_set_value(in_args, 0, mat));
-```
-
-#### step 3. 前向计算
-
-完成上述准备之后，通过调用 `paddle_gradient_machine_forward` 接口完成神经网络的前向计算。
-示例代码片段如下：
-
-```c
-// Create the output argument.
-paddle_arguments out_args = paddle_arguments_create_none();
-
-// Invoke the forward computation.
-CHECK(paddle_gradient_machine_forward(machine,
-                                in_args,
-                                out_args,
-                                s/* is train taks or not */ false));
-
-// Create the matrix to hold the forward result of the neural network.
-paddle_matrix prob = paddle_matrix_create_none();
-// Access the matrix of the output argument, the predicted result is stored in
-// which.
-CHECK(paddle_arguments_get_value(out_args, 0, prob));
-
-uint64_t height;
-uint64_t width;
-CHECK(paddle_matrix_get_shape(prob, &height, &width));
-CHECK(paddle_matrix_get_row(prob, 0, &array));
-
-printf("Prob: \n");
-for (int i = 0; i < height * width; ++i) {
-  printf("%.4f ", array[i]);
-  if ((i + 1) % width == 0) {
-    printf("\n");
-  }
-}
-printf("\n");
-```
-
-#### step 4. 清理
-
-结束预测之后，对使用的中间变量和资源进行清理和释放：
-
-```c
-// The cleaning up.
-CHECK(paddle_matrix_destroy(prob));
-CHECK(paddle_arguments_destroy(out_args));
-CHECK(paddle_matrix_destroy(mat));
-CHECK(paddle_arguments_destroy(in_args));
-CHECK(paddle_gradient_machine_destroy(machine));
-```
diff --git a/doc/howto/usage/capi/images/workflow_of_CAPI.png b/doc/howto/usage/capi/images/workflow_of_CAPI.png
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diff --git a/doc/howto/usage/capi/index_cn.rst b/doc/howto/usage/capi/index_cn.rst
index b2822fca2b..dfd2009c32 100644
--- a/doc/howto/usage/capi/index_cn.rst
+++ b/doc/howto/usage/capi/index_cn.rst
@@ -6,4 +6,4 @@ PaddlePaddle C-API
 
   compile_paddle_lib_cn.md
   organization_of_the_inputs_cn.md
-  a_simple_example_cn.md
+  workflow_of_capi.md
diff --git a/doc/howto/usage/capi/workflow_of_capi.md b/doc/howto/usage/capi/workflow_of_capi.md
new file mode 100644
index 0000000000..56841bdcef
--- /dev/null
+++ b/doc/howto/usage/capi/workflow_of_capi.md
@@ -0,0 +1,120 @@
+## C-API 使用流程
+
+这篇文档介绍 PaddlePaddle C-API 开发预测服务的整体使用流程。
+
+### 使用流程
+
+使用 C-API 的整体工作流程分为准备预测模型和预测程序开发两部分， 如图1所示。
+
+<p align="center">
+<img src="https://user-images.githubusercontent.com/5842774/34658453-365f73ea-f46a-11e7-9b3f-0fd112b27bae.png" width=500><br> 图1. C-API使用流程示意图
+</p>
+
+- 准备预测模型
+    1. 将神经网络模型结构进行序列化。
+        - 调用C-API预测时，需要提供序列化之后的网络结构和训练好的模型参数文件。
+    1. 将PaddlePaddle训练出的模型参数文件（多个）合并成一个文件。
+        - 神经网络模型结构和训练好的模型将被序列化合并入一个文件。
+        - 预测时只需加载这一个文件，便于发布。
+    - **注意**：以上两种方式只需选择其一即可。
+- 调用 PaddlePaddle C-API 开发预测序
+    1. 初始化PaddlePaddle运行环境。
+    1. 加载模型。
+    1. 创建神经网络的输入，组织输入数据。
+    1. 进行前向计算，获得计算结果。
+    1. 清理。
+
+### 准备预测模型
+
+在准备预测模型部分，我们以手写数字识别任务为例，这个任务定义了一个含有[两个隐层的简单全连接网络](https://github.com/PaddlePaddle/book/blob/develop/02.recognize_digits/README.cn.md#softmax回归softmax-regression)，网络接受一幅图片作为输入，将图片分类到 0 ~ 9 类别标签之一。完整代码可以查看[此目录](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense) 中的相关脚本。
+
+调用C-API开发预测程序需要一个训练好的模型，在终端执行`python mnist_v2.py`
+运行[目录](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense) 会使用 PaddlePaddle 内置的 [MNIST 数据集](http://yann.lecun.com/exdb/mnist/)进行训练。训练好的模型默认保存在当前运行目录下的`models`目录中。
+
+下面，我们将训练好的模型转换成预测模型。
+
+1. 序列化神经网络模型配置
+
+    PaddlePaddle 使用 protobuf 来传输网络配置文件中定义的网络结构和相关参数，在使用 C-API 进行预测时，也需将网络结构使用 protobuf 进行序列化，写入文件中。
+
+    调用`paddle.utils.dump_v2_config`中的`dump_v2_config`函数能够将使用 PaddlePaddle V2 API 定义的神经网络结构 dump 到指定文件中。示例代码如下：
+
+    ```python
+    from paddle.utils.dump_v2_config import dump_v2_config
+    from mnist_v2 import network
+
+    predict = network(is_infer=True)
+    dump_v2_config(predict, "trainer_config.bin", True)
+    ```
+
+    对本例，或运行 `python mnist_v2.py --task dump_config`，会对示例中的网络结构进行序列化，并将结果写入当前目录下的`trainer_config.bin`文件中。
+
+    使用这种方式，需要**在运行时将神经网络的多个可学习参数放在同一个目录中**，C-API可以通过分别指定序列化后的网络结构文件和参数目录来加载训练好的模型。
+
+2. 合并模型文件(可选)
+
+    一些情况下为了便于发布，希望能够将序列化后的神经网络结构和训练好的模型参数打包进一个文件，这时可以使用`paddle.utils.merge_model`中的`merge_v2_model`接口对神经网络结构和训练好的参数进行序列化，将序列化结果写入一个文件内。
+
+    代码示例如下：
+
+    ```python
+    from paddle.utils.merge_model import merge_v2_modelss
+    from mnist_v2 import network
+
+    net = network(is_infer=True)
+    param_file = "models/params_pass_4.tar"
+    output_file = "output.paddle.model"
+    merge_v2_model(net, param_file, output_file)
+    ```
+    对本例，或者直接运行 `python merge_v2_model.py`，序列化结果将会写入当前目录下的`output.paddle.model`文件中。使用这种方式，运行时C-API可以通过指定output.paddle.model文件来加载模型。
+
+#### 注意事项
+1. 将训练模型转换成预测模型，需要序列化神经网络结构。在调用`dump_v2_config`时，参数`binary`必须指定为`True`。
+1. **预测使用的网络结构往往不同于训练**，通常需要去掉网络中的：（1）类别标签层；（2）损失函数层；（3）`evaluator`等，只留下核心计算层，请注意是否需要修改网络结构。
+1. 预测时，可以获取网络中定义的任意多个（大于等于一个）层前向计算的结果，需要哪些层的计算结果作为输出，就将这些层加入一个Python list中，作为调用`dump_v2_config`的第一个参数。
+
+### 编写预测代码
+
+预测代码更多详细示例代码请参考[C-API使用示例](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference) 目录下的代码示例。这一节对图1中预测代码编写的5个步骤进行介绍和说明。
+
+#### step 1. 初始化PaddlePaddle运行环境
+第一步需调用[`paddle_init`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/main.h#L27) 初始化PaddlePaddle运行环境。该接口接受两个参数：参数的个数和参数列表。
+
+#### step2. 加载模型
+
+这里介绍C-API使用中的一个重要概念：Gradient Machine。概念上，在 PaddlePaddle 内部一个GradientMachine类的对象管理着一组计算层（PaddlePaddle Layers）来完成前向和反向计算，并处理与之相关的所有细节。在调用C-API预测时，只需进行前向计算而无需调用反向计算。这篇文档的之后部分我们会使用`gradient machine`来特指调用PaddlePaddle C-API创建的GradientMachine类的对象。
+
+每一个 `gradient machine` 都会管理维护一份训练好的模型，下面是两种最常用的模型加载方式：
+
+1. 从磁盘加载：这时`gradient machine`会独立拥有一份训练好的模型；
+1. 共享自其它`gradient machine`的模型：这种情况多出现在使用多线程预测时，通过多个线程共享同一个模型来减少内存开销。可参考[此示例](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/examples/model_inference/multi_thread/main.c)。
+
+- 注意事项
+    1. 使用PaddlePaddle V2 API训练，模型中所有可学习参数会被存为一个压缩文件，需要手动进行解压，将它们放在同一目录中，C-API不会直接加载 V2 API 存储的压缩文件。
+    1. 如果使用`merge model`方式将神经网络结构和训练好的参数序列化到一个文件，请参考此[示例](https://github.com/PaddlePaddle/Mobile/blob/develop/Demo/linux/paddle_image_recognizer.cpp#L59)。
+    1. 加载模型有多种方式，也可以在程序运行过程中再加载另外一个模型。
+
+#### step 3. 创建神经网络输入，组织输入数据
+
+基本使用概念：
+- 在PaddlePaddle内部，神经网络中一个计算层的输入输出被组织为一个 `Argument` 结构体，如果神经网络有多个输入或者多个输出，每一个输入/输出都会对应有自己的`Argument`。
+- `Argument` 并不真正“存储”数据，而是将输入/输出数据有机地组织在一起。
+- 在`Argument`内部由：1. `Matrix`（二维矩阵，存储浮点类型输入/输出）；2. `IVector`（一维数组，**仅用于存储整型值**，多用于自然语言处理任务）来实际存储数据。
+
+*注：本文档使用的示例任务手写数字识别不涉及一维整型数组作为输入，因此，本文档仅讨论二维稠密矩阵作为输入的情形。更多输入数据格式请参考输入/输出数据一节的内容。*
+
+这篇文档的之后部分会使用`argument`来**特指** PaddlePaddle C-API中神经网络的一个输入/输出，使用`paddle_matrix`**特指**`argument`中用于存储数据的`Matrix`类的对象。
+
+于是，在组织神经网络输入，获取输出时，需要思考完成以下工作：
+1. 为每一个输入/输出创建`argument`；
+1. 为每一个`argument`创建`paddle_matrix`来存储数据；
+
+与输入不同的是，输出`argument`的`paddle_matrix`变量并不需在使用C-API时为之分配存储空间。PaddlePaddle内部，神经网络进行前向计算时会自己分配/管理每个计算层的存储空间；这些细节C-API会代为处理，只需在概念上理解，并按照约定调用相关的 C-API 接口即可。
+
+#### step 4. 前向计算
+
+完成上述准备之后，通过调用 `paddle_gradient_machine_forward` 接口完成神经网络的前向计算。
+
+#### step 5. 清理
+
+结束预测之后，对使用的中间变量和资源进行清理和释放。

From a79a5bddf0ab9ff1648a0e8954e370a47a04a718 Mon Sep 17 00:00:00 2001
From: ying <lcy.seso@gmail.com>
Date: Mon, 8 Jan 2018 14:03:32 +0800
Subject: [PATCH 11/68] update and follow comments.

---
 doc/howto/usage/capi/index_cn.rst             |   2 +-
 .../capi/organization_of_the_inputs_cn.md     |  16 +--
 doc/howto/usage/capi/workflow_of_capi.md      | 120 ------------------
 doc/howto/usage/capi/workflow_of_capi_cn.md   | 120 ++++++++++++++++++
 4 files changed, 129 insertions(+), 129 deletions(-)
 delete mode 100644 doc/howto/usage/capi/workflow_of_capi.md
 create mode 100644 doc/howto/usage/capi/workflow_of_capi_cn.md

diff --git a/doc/howto/usage/capi/index_cn.rst b/doc/howto/usage/capi/index_cn.rst
index dfd2009c32..fd774fbc74 100644
--- a/doc/howto/usage/capi/index_cn.rst
+++ b/doc/howto/usage/capi/index_cn.rst
@@ -6,4 +6,4 @@ PaddlePaddle C-API
 
   compile_paddle_lib_cn.md
   organization_of_the_inputs_cn.md
-  workflow_of_capi.md
+  workflow_of_capi_cn.md
diff --git a/doc/howto/usage/capi/organization_of_the_inputs_cn.md b/doc/howto/usage/capi/organization_of_the_inputs_cn.md
index 787af8b0a7..563ec5ca21 100644
--- a/doc/howto/usage/capi/organization_of_the_inputs_cn.md
+++ b/doc/howto/usage/capi/organization_of_the_inputs_cn.md
@@ -1,11 +1,11 @@
 ## 输入/输出数据组织
 
-这篇文档介绍在使用 PaddlePaddle C-API 时如何组织输入数据，以及如何解析神经网络前向计算的输出数据。
+这篇文档介绍在使用 PaddlePaddle C-API 时如何组织输入数据，以及如何解析神经网络前向计算的输出结果。
 
 ### 输入/输出数据类型
 在C-API中，按照基本数据类型在PaddlePaddle内部的定义和实现，输入数据可分为：
 1. 一维整型数组
-2. 二维浮点型矩阵
+1. 二维浮点型矩阵
     - 稠密矩阵
     - 稀疏矩阵
 
@@ -25,8 +25,8 @@
 
 - **注**：
     1. 这篇文档之后部分将会统一使用`argument`来特指PaddlePaddle中神经网络计算层一个输入/输出数据。
-    2. 使用`paddle_ivector`来特指PaddlePaddle中的一维整型数组。
-    3. 使用`paddle_matrix`来特指PaddlePaddle中的二维浮点型矩阵。
+    1. 使用`paddle_ivector`来特指PaddlePaddle中的一维整型数组。
+    1. 使用`paddle_matrix`来特指PaddlePaddle中的二维浮点型矩阵。
 
 ### 组织输入数据
 - 一维整型数组
@@ -137,8 +137,8 @@
 
 **注：**
 1. 不论序列中的元素在内存中占用多少实际存储空间，`sequence_start_positions`表示的偏移是以“序列中的一个元素”作为统计的基本单位，而不是相对`batch`起始存储地址以数据的存储大小为单位的偏移。
-2. 非序列输入不携带`sequence_start_positions`，非序列输入无需构造`sequence_start_positions`。
-3. **不论是单层序列还是双层序列的序列信息，都使用`paddle_ivector`（也就是PaddlePaddle中的一维整型数组）来存储。**
+1. 非序列输入不携带`sequence_start_positions`，非序列输入无需构造`sequence_start_positions`。
+1. **不论是单层序列还是双层序列的序列信息，都使用`paddle_ivector`（也就是PaddlePaddle中的一维整型数组）来存储。**
 
 图2 是PaddlePaddle中单层序列和双层序列存储示意图。
 <p align="center">
@@ -149,8 +149,8 @@
 
     图2 (a) 展示了一个含有4个序列的`batch`输入：
     1. 4个序列的长度分别为：5、3、2、4；
-    2. 这时的`sequence_start_positions`为：`[0, 5, 8, 10, 14]`；
-    3. 不论数据域是`paddle_ivector`类型还是`paddle_matrix`类型，都可以通过调用下面的接口为原有的数据输入附加上序列信息，使之变为一个单层序列输入，代码片段如下：
+    1. 这时的`sequence_start_positions`为：`[0, 5, 8, 10, 14]`；
+    1. 本地训练. 不论数据域是`paddle_ivector`类型还是`paddle_matrix`类型，都可以通过调用下面的接口为原有的数据输入附加上序列信息，使之变为一个单层序列输入，代码片段如下：
 
     ```c
     int seq_pos_array[] = {0, 5, 8, 10, 14};
diff --git a/doc/howto/usage/capi/workflow_of_capi.md b/doc/howto/usage/capi/workflow_of_capi.md
deleted file mode 100644
index 56841bdcef..0000000000
--- a/doc/howto/usage/capi/workflow_of_capi.md
+++ /dev/null
@@ -1,120 +0,0 @@
-## C-API 使用流程
-
-这篇文档介绍 PaddlePaddle C-API 开发预测服务的整体使用流程。
-
-### 使用流程
-
-使用 C-API 的整体工作流程分为准备预测模型和预测程序开发两部分， 如图1所示。
-
-<p align="center">
-<img src="https://user-images.githubusercontent.com/5842774/34658453-365f73ea-f46a-11e7-9b3f-0fd112b27bae.png" width=500><br> 图1. C-API使用流程示意图
-</p>
-
-- 准备预测模型
-    1. 将神经网络模型结构进行序列化。
-        - 调用C-API预测时，需要提供序列化之后的网络结构和训练好的模型参数文件。
-    1. 将PaddlePaddle训练出的模型参数文件（多个）合并成一个文件。
-        - 神经网络模型结构和训练好的模型将被序列化合并入一个文件。
-        - 预测时只需加载这一个文件，便于发布。
-    - **注意**：以上两种方式只需选择其一即可。
-- 调用 PaddlePaddle C-API 开发预测序
-    1. 初始化PaddlePaddle运行环境。
-    1. 加载模型。
-    1. 创建神经网络的输入，组织输入数据。
-    1. 进行前向计算，获得计算结果。
-    1. 清理。
-
-### 准备预测模型
-
-在准备预测模型部分，我们以手写数字识别任务为例，这个任务定义了一个含有[两个隐层的简单全连接网络](https://github.com/PaddlePaddle/book/blob/develop/02.recognize_digits/README.cn.md#softmax回归softmax-regression)，网络接受一幅图片作为输入，将图片分类到 0 ~ 9 类别标签之一。完整代码可以查看[此目录](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense) 中的相关脚本。
-
-调用C-API开发预测程序需要一个训练好的模型，在终端执行`python mnist_v2.py`
-运行[目录](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense) 会使用 PaddlePaddle 内置的 [MNIST 数据集](http://yann.lecun.com/exdb/mnist/)进行训练。训练好的模型默认保存在当前运行目录下的`models`目录中。
-
-下面，我们将训练好的模型转换成预测模型。
-
-1. 序列化神经网络模型配置
-
-    PaddlePaddle 使用 protobuf 来传输网络配置文件中定义的网络结构和相关参数，在使用 C-API 进行预测时，也需将网络结构使用 protobuf 进行序列化，写入文件中。
-
-    调用`paddle.utils.dump_v2_config`中的`dump_v2_config`函数能够将使用 PaddlePaddle V2 API 定义的神经网络结构 dump 到指定文件中。示例代码如下：
-
-    ```python
-    from paddle.utils.dump_v2_config import dump_v2_config
-    from mnist_v2 import network
-
-    predict = network(is_infer=True)
-    dump_v2_config(predict, "trainer_config.bin", True)
-    ```
-
-    对本例，或运行 `python mnist_v2.py --task dump_config`，会对示例中的网络结构进行序列化，并将结果写入当前目录下的`trainer_config.bin`文件中。
-
-    使用这种方式，需要**在运行时将神经网络的多个可学习参数放在同一个目录中**，C-API可以通过分别指定序列化后的网络结构文件和参数目录来加载训练好的模型。
-
-2. 合并模型文件(可选)
-
-    一些情况下为了便于发布，希望能够将序列化后的神经网络结构和训练好的模型参数打包进一个文件，这时可以使用`paddle.utils.merge_model`中的`merge_v2_model`接口对神经网络结构和训练好的参数进行序列化，将序列化结果写入一个文件内。
-
-    代码示例如下：
-
-    ```python
-    from paddle.utils.merge_model import merge_v2_modelss
-    from mnist_v2 import network
-
-    net = network(is_infer=True)
-    param_file = "models/params_pass_4.tar"
-    output_file = "output.paddle.model"
-    merge_v2_model(net, param_file, output_file)
-    ```
-    对本例，或者直接运行 `python merge_v2_model.py`，序列化结果将会写入当前目录下的`output.paddle.model`文件中。使用这种方式，运行时C-API可以通过指定output.paddle.model文件来加载模型。
-
-#### 注意事项
-1. 将训练模型转换成预测模型，需要序列化神经网络结构。在调用`dump_v2_config`时，参数`binary`必须指定为`True`。
-1. **预测使用的网络结构往往不同于训练**，通常需要去掉网络中的：（1）类别标签层；（2）损失函数层；（3）`evaluator`等，只留下核心计算层，请注意是否需要修改网络结构。
-1. 预测时，可以获取网络中定义的任意多个（大于等于一个）层前向计算的结果，需要哪些层的计算结果作为输出，就将这些层加入一个Python list中，作为调用`dump_v2_config`的第一个参数。
-
-### 编写预测代码
-
-预测代码更多详细示例代码请参考[C-API使用示例](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference) 目录下的代码示例。这一节对图1中预测代码编写的5个步骤进行介绍和说明。
-
-#### step 1. 初始化PaddlePaddle运行环境
-第一步需调用[`paddle_init`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/main.h#L27) 初始化PaddlePaddle运行环境。该接口接受两个参数：参数的个数和参数列表。
-
-#### step2. 加载模型
-
-这里介绍C-API使用中的一个重要概念：Gradient Machine。概念上，在 PaddlePaddle 内部一个GradientMachine类的对象管理着一组计算层（PaddlePaddle Layers）来完成前向和反向计算，并处理与之相关的所有细节。在调用C-API预测时，只需进行前向计算而无需调用反向计算。这篇文档的之后部分我们会使用`gradient machine`来特指调用PaddlePaddle C-API创建的GradientMachine类的对象。
-
-每一个 `gradient machine` 都会管理维护一份训练好的模型，下面是两种最常用的模型加载方式：
-
-1. 从磁盘加载：这时`gradient machine`会独立拥有一份训练好的模型；
-1. 共享自其它`gradient machine`的模型：这种情况多出现在使用多线程预测时，通过多个线程共享同一个模型来减少内存开销。可参考[此示例](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/examples/model_inference/multi_thread/main.c)。
-
-- 注意事项
-    1. 使用PaddlePaddle V2 API训练，模型中所有可学习参数会被存为一个压缩文件，需要手动进行解压，将它们放在同一目录中，C-API不会直接加载 V2 API 存储的压缩文件。
-    1. 如果使用`merge model`方式将神经网络结构和训练好的参数序列化到一个文件，请参考此[示例](https://github.com/PaddlePaddle/Mobile/blob/develop/Demo/linux/paddle_image_recognizer.cpp#L59)。
-    1. 加载模型有多种方式，也可以在程序运行过程中再加载另外一个模型。
-
-#### step 3. 创建神经网络输入，组织输入数据
-
-基本使用概念：
-- 在PaddlePaddle内部，神经网络中一个计算层的输入输出被组织为一个 `Argument` 结构体，如果神经网络有多个输入或者多个输出，每一个输入/输出都会对应有自己的`Argument`。
-- `Argument` 并不真正“存储”数据，而是将输入/输出数据有机地组织在一起。
-- 在`Argument`内部由：1. `Matrix`（二维矩阵，存储浮点类型输入/输出）；2. `IVector`（一维数组，**仅用于存储整型值**，多用于自然语言处理任务）来实际存储数据。
-
-*注：本文档使用的示例任务手写数字识别不涉及一维整型数组作为输入，因此，本文档仅讨论二维稠密矩阵作为输入的情形。更多输入数据格式请参考输入/输出数据一节的内容。*
-
-这篇文档的之后部分会使用`argument`来**特指** PaddlePaddle C-API中神经网络的一个输入/输出，使用`paddle_matrix`**特指**`argument`中用于存储数据的`Matrix`类的对象。
-
-于是，在组织神经网络输入，获取输出时，需要思考完成以下工作：
-1. 为每一个输入/输出创建`argument`；
-1. 为每一个`argument`创建`paddle_matrix`来存储数据；
-
-与输入不同的是，输出`argument`的`paddle_matrix`变量并不需在使用C-API时为之分配存储空间。PaddlePaddle内部，神经网络进行前向计算时会自己分配/管理每个计算层的存储空间；这些细节C-API会代为处理，只需在概念上理解，并按照约定调用相关的 C-API 接口即可。
-
-#### step 4. 前向计算
-
-完成上述准备之后，通过调用 `paddle_gradient_machine_forward` 接口完成神经网络的前向计算。
-
-#### step 5. 清理
-
-结束预测之后，对使用的中间变量和资源进行清理和释放。
diff --git a/doc/howto/usage/capi/workflow_of_capi_cn.md b/doc/howto/usage/capi/workflow_of_capi_cn.md
new file mode 100644
index 0000000000..c1c2c86d0c
--- /dev/null
+++ b/doc/howto/usage/capi/workflow_of_capi_cn.md
@@ -0,0 +1,120 @@
+## C-API 使用流程
+
+这篇文档介绍 PaddlePaddle C-API 整体使用流程。
+
+### 使用流程
+
+使用 C-API 的工作流程如图1所示，分为（1）准备预测模型和（2）预测程序开发两大部分。
+
+<p align="center">
+<img src="https://user-images.githubusercontent.com/5842774/34658453-365f73ea-f46a-11e7-9b3f-0fd112b27bae.png" width=500><br> 图1. C-API使用流程示意图
+</p>
+
+- 准备预测模型
+    1. 只将神经网络结构进行序列化。
+        - 只对神经网络结构进行序列化，加载模型需同时指定：网络结构的序列化结果和模型参数存储目录。
+    1. 将网络结构定义和训练结束存储下来的模型参数文件（多个）合并入一个文件。
+        - 神经网络模型结构和训练好的模型将被序列化合并入一个文件。
+        - 预测时只需加载一个文件便于发布。
+    - **注意**：以上两种方式只需选择其一即可。
+- 调用 C-API 开发预测序
+    1. 初始化PaddlePaddle运行环境。
+    1. 加载预测模型。
+    1. 创建神经网络输入，组织输入数据。
+    1. 进行前向计算，获得计算结果。
+    1. 清理和结束。
+
+### 准备预测模型
+
+在准备预测模型部分的介绍，我们以手写数字识别任务为例。手写数字识别任务定义了一个含有[两个隐层的简单全连接网络](https://github.com/PaddlePaddle/book/blob/develop/02.recognize_digits/README.cn.md#softmax回归softmax-regression)，网络接受一幅图片作为输入，将图片分类到 0 ~ 9 类别标签之一。完整代码可以查看[此目录](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense) 中的相关脚本。
+
+调用C-API开发预测程序需要一个训练好的模型，在终端执行`python mnist_v2.py`
+运行[目录](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense) 会使用 PaddlePaddle 内置的 [MNIST 数据集](http://yann.lecun.com/exdb/mnist/)进行训练。训练好的模型默认保存在当前运行目录下的`models`目录中。
+
+下面，我们将训练结束后存储下来的模型转换成预测模型。
+
+1. 序列化神经网络模型配置
+
+    PaddlePaddle 使用 protobuf 来传输网络配置文件中定义的网络结构和相关参数，使用 C-API 进行预测时，需要将网络结构使用 protobuf 进行序列化，写入文件中。
+
+    调用[`paddle.utils.dump_v2_config`](https://github.com/PaddlePaddle/Paddle/tree/develop/python/paddle/utils/dump_v2_config.py)中的`dump_v2_config`函数能够将使用 PaddlePaddle V2 API 定义的神经网络结构 dump 到指定文件中，示例代码如下：
+
+    ```python
+    from paddle.utils.dump_v2_config import dump_v2_config
+    from mnist_v2 import network
+
+    predict = network(is_infer=True)
+    dump_v2_config(predict, "trainer_config.bin", True)
+    ```
+
+    对[手写数字识别](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense)这个示例，[`mnist_v2.py`](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense/mnist_v2.py)脚本集成了序列化神经网络结构的过程，可以直接运行 `python mnist_v2.py --task dump_config` 对神经网络结构进行序列化，结果会写入当前运行目录下的`trainer_config.bin`文件中。
+
+    使用这种方式，需要**在运行时将神经网络的多个可学习参数放在同一个目录中**，C-API可以通过分别指定序列化后的网络结构文件和参数目录来加载训练好的模型。
+
+2. 合并模型文件(可选)
+
+    一些情况为了便于发布，希望能够将序列化后的神经网络结构和训练好的模型参数打包进一个文件。对于这样的需求，可以使用`paddle.utils.merge_model`中的`merge_v2_model`接口对神经网络结构和训练好的参数进行序列化，将序列化结果写入一个文件内。
+
+    代码示例如下：
+
+    ```python
+    from paddle.utils.merge_model import merge_v2_modelss
+    from mnist_v2 import network
+
+    net = network(is_infer=True)
+    param_file = "models/params_pass_4.tar"
+    output_file = "output.paddle.model"
+    merge_v2_model(net, param_file, output_file)
+    ```
+    对[手写数字识别](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense)这个示例，可直接运行 `python` [merge_v2_model.py](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense/merge_v2_model.py)。序列化结果会写入当前运行目录下的`output.paddle.model`文件中。使用这种方式，运行时C-API可以通过指定`output.paddle.model`文件的路径来加载预测模型。
+
+#### 注意事项
+1. 为使用C-API，在调用`dump_v2_config`序列化神经网络结构时，参数`binary`必须指定为`True`。
+1. **预测使用的网络结构往往不同于训练**，通常需要去掉网络中的：（1）类别标签层；（2）损失函数层；（3）`evaluator`等，只留下核心计算层，请注意是否需要修改网络结构。
+1. 预测时，可以获取网络中定义的任意多个（大于等于一个）层前向计算的结果，需要哪些层的计算结果作为输出，就将这些层加入一个Python list中，作为调用`dump_v2_config`的第一个参数。
+
+### 编写预测代码
+
+预测代码更多详细示例代码请参考[C-API使用示例](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference) 目录下的代码示例。这一节对图1中预测代码编写的5个步骤进行介绍和说明。
+
+#### step 1. 初始化PaddlePaddle运行环境
+第一步需调用[`paddle_init`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/main.h#L27) 初始化PaddlePaddle运行环境，该接口接受两个参数：参数的个数和参数列表。
+
+#### step2. 加载模型
+
+这里介绍C-API使用中的一个重要概念：Gradient Machine。
+
+概念上，在 PaddlePaddle 内部，一个GradientMachine类的对象管理着一组计算层（PaddlePaddle Layers）来完成前向和反向计算，并处理与之相关的所有细节。在调用C-API预测时，只需进行前向计算而无需调用反向计算。这篇文档之后部分会使用`gradient machine`来特指调用PaddlePaddle C-API创建的GradientMachine类的对象。每一个 `gradient machine` 都会管理维护一份训练好的模型，下面是C-API提供的，两种常用的模型加载方式：
+
+1. 调用[`paddle_gradient_machine_load_parameter_from_disk`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/gradient_machine.h#L61)接口，从磁盘加载预测模型。这时`gradient machine`会独立拥有一份训练好的模型；
+1. 调用[`paddle_gradient_machine_create_shared_param`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/gradient_machine.h#L88)接口，与其它`gradient machine`的共享已经加载的预测模型。这种情况多出现在使用多线程预测时，通过多个线程共享同一个模型来减少内存开销。可参考[此示例](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/examples/model_inference/multi_thread/main.c)。
+
+- 注意事项
+    1. 使用PaddlePaddle V2 API训练，模型中所有可学习参数会被存为一个压缩文件，需要手动进行解压，将它们放在同一目录中，C-API不会直接加载 V2 API 存储的压缩文件。
+    1. 如果使用`merge model`方式将神经网络结构和训练好的参数序列化到一个文件，请参考此[示例](https://github.com/PaddlePaddle/Mobile/blob/develop/Demo/linux/paddle_image_recognizer.cpp#L59)。
+    1. 通过灵活使用以上两个接口，加载模型可其它多种方式，例如也可在程序运行过程中再加载另外一个模型。
+
+#### step 3. 创建神经网络输入，组织输入数据
+
+基本使用概念：
+- 在PaddlePaddle内部，神经网络中一个计算层的输入输出被组织为一个 `Argument` 结构体，如果神经网络有多个输入或者多个输出，每一个输入/输出都会对应有自己的`Argument`。
+- `Argument` 并不真正“存储”数据，而是将输入/输出数据有机地组织在一起。
+- 在`Argument`内部由：1. `Matrix`（二维矩阵，存储浮点类型输入/输出）；2. `IVector`（一维数组，**仅用于存储整型值**，多用于自然语言处理任务）来实际存储数据。
+
+C-API支持的所有输入数据类型和他们的组织方式，请参考“输入/输出数据组织”一节。
+
+这篇文档的之后部分会使用`argument`来特指PaddlePaddle C-API中神经网络的一个输入/输出，使用`paddle_matrix`**特指**`argument`中用于存储数据的`Matrix`类的对象。
+
+在组织神经网络输入，获取输出时，需要思考完成以下工作：
+1. 为每一个输入/输出创建`argument`；
+1. 为每一个`argument`创建`paddle_matrix`来存储数据；
+
+与输入不同的是，不需在使用C-API时为输出`argument`的`paddle_matrix`对象分配空间。前向计算之后PaddlePaddle内部已经分配/管理了每个计算层输出的存储空间。
+
+#### step 4. 前向计算
+
+完成上述准备之后，通过调用 `[paddle_gradient_machine_forward](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/gradient_machine.h#L73)` 接口完成神经网络的前向计算。
+
+#### step 5. 清理
+
+结束预测之后，对使用的中间变量和资源进行清理和释放。

From f35c56060c4af6a8b36ceb8ad9021c447d6dc2a0 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Mon, 8 Jan 2018 19:59:44 +0800
Subject: [PATCH 12/68] split tensor to pservers

---
 .../paddle/v2/fluid/distribute_transpiler.py  | 187 +++++++-------
 .../v2/fluid/distribute_transpiler_simple.py  | 242 ++++++++++++++++++
 python/paddle/v2/fluid/distributed_spliter.py |  51 ++--
 .../tests/book_distribute/test_split_var.py   |  38 +++
 4 files changed, 398 insertions(+), 120 deletions(-)
 create mode 100644 python/paddle/v2/fluid/distribute_transpiler_simple.py
 create mode 100644 python/paddle/v2/fluid/tests/book_distribute/test_split_var.py

diff --git a/python/paddle/v2/fluid/distribute_transpiler.py b/python/paddle/v2/fluid/distribute_transpiler.py
index 4c90b4a853..58d32bac12 100644
--- a/python/paddle/v2/fluid/distribute_transpiler.py
+++ b/python/paddle/v2/fluid/distribute_transpiler.py
@@ -4,6 +4,7 @@ from framework import Program, default_main_program, Parameter, Variable
 import optimizer
 from layer_helper import LayerHelper
 from distributed_spliter import *
+import math
 
 
 class VarBlock:
@@ -17,6 +18,47 @@ class VarBlock:
         return "%s:%d:%d" % (self.varname, self.offset, self.size)
 
 
+def split_dense_variable(var_list,
+                         pserver_count,
+                         min_block_size=1024,
+                         max_block_size=1048576):
+    """
+        We may need to split dense tensor to one or several blocks and put
+        them equally onto parameter server. One block is a sub-tensor
+        aligned by dim[0] of the tensor.
+        
+        We need to have a minimal block size so that the calculations in
+        the parameter server side can gain better performance. By default
+        mininum block size is 1024. The max block size is used to prevent
+        too large block that may causing send error.
+    """
+    blocks = []
+    for var in var_list:
+        split_count = pserver_count
+        var_numel = reduce(lambda x, y: x * y, var.shape)
+        max_pserver_count = int(math.floor(var_numel / float(min_block_size)))
+        if max_pserver_count == 0:
+            max_pserver_count = 1
+        if max_pserver_count < pserver_count:
+            split_count = max_pserver_count
+        block_size = int(math.ceil(var_numel / float(split_count)))
+
+        if len(var.shape) >= 2:
+            # align by dim1(width)
+            dim1 = reduce(lambda x, y: x * y, var.shape[1:])
+            remains = block_size % dim1
+            if remains != 0:
+                block_size += dim1 - remains
+        # update split_count after align
+        split_count = int(math.ceil(var_numel / float(block_size)))
+        for block_id in xrange(split_count):
+            curr_block_size = min(block_size, var_numel - (
+                (block_id) * block_size))
+            block = VarBlock(var.name, block_id, curr_block_size)
+            blocks.append(str(block))
+    return blocks
+
+
 class DistributeTranspiler:
     def transpile(self,
                   optimize_ops,
@@ -57,43 +99,49 @@ class DistributeTranspiler:
         # 5. create parameter server program by split_method generated endpoint->VarBlock
         # 6. run compile time infershape for parameter server program
 
-        if kwargs.has_key("split_method"):
-            split_method = kwargs["split_method"]
-        else:
-            split_method = round_robin
-        pserver_endpoints = kwargs["pservers"].split(",")
-
-        grad2param = dict()
-        for param, grad in params_and_grads:
-            grad2param[grad.name()] = param.name()
+        pserver_endpoints = pservers.split(",")
 
         # step1
-        param_list = [pg[0] for pg in params_and_grads]
-        grad_list = [pg[1] for pg in params_and_grads]
+        param_list = [pg[0] for pg in params_grads]
+        grad_list = [pg[1] for pg in params_grads]
         # TODO: add split selected rows support
-        grad_blocks = _split_dense_variable(grad_list, len(pserver_endpoints))
-        param_blocks = _split_dense_variable(param_list, len(pserver_endpoints))
-        ep2gradblock = split_method(grad_blocks, pserver_endpoints)
-        # self.param_grad_map
+        grad_blocks = split_dense_variable(grad_list, len(pserver_endpoints))
+        param_blocks = split_dense_variable(param_list, len(pserver_endpoints))
         # step2
-        var2splited = self._split_trainer_vars(program, grad_blocks)
+        grad_var_mapping = self._append_split_op(program, grad_blocks)
 
         # step3
         send_inputs = []
-        for _, splited in var2splited.iteritems():
+        send_outputs = []
+        for _, splited in grad_var_mapping.iteritems():
             send_inputs.extend(splited)
-        send_outputs = self._create_vars_from_blocklist(program, param_blocks)
+        param_var_mapping = self._create_vars_from_blocklist(program,
+                                                             param_blocks)
+        for _, splited in param_var_mapping.iteritems():
+            send_outputs.extend(splited)
+        # let send_op know which endpoint to send which var, eplist is of the same
+        # order of send_inputs.
+        eplist = split_method(send_inputs, pserver_endpoints)
 
         send_op = program.global_block().append_op(
             type="send",
             inputs={"X": send_inputs},
             outputs={"Out": send_outputs},
             attrs={"endpoints": pserver_endpoints,
-                   "epmap": epmap})
+                   "epmap": eplist})
+
+        # step4
+        for varname, splited_var in param_var_mapping.iteritems():
+            orig_param = program.global_block().vars[varname]
+            concat = program.global_block().append_op(
+                type="concat",
+                inputs={"X": send_outputs},
+                outputs={"Out": orig_param},
+                attrs={"axis": 0})
 
     def _create_vars_from_blocklist(self, program, block_list):
         block_map = dict()
-        ret_vars = []
+        var_mapping = dict()
         for block_str in block_list:
             varname, offset, size = block_str.split(":")
             if not block_map.has_key(varname):
@@ -102,15 +150,26 @@ class DistributeTranspiler:
 
         for varname, splited in block_map.iteritems():
             orig_var = program.global_block().vars[varname]
-            for block in splited:
+            orig_shape = orig_var.shape
+            orig_dim1_flatten = 1
+            if len(orig_shape) >= 2:
+                orig_dim1_flatten = reduce(lambda x, y: x * y, orig_shape[1:])
+            var_list = []
+            for i, block in enumerate(splited):
                 size = block[1]
+                rows = size / orig_dim1_flatten
+                splited_shape = [rows]
+                if len(orig_shape) >= 2:
+                    splited_shape.extend(orig_shape[1:])
+                print("block, splited shape:", block, splited_shape)
                 var = program.global_block().create_var(
                     name="%s.block%d" % (varname, i),
                     psersistable=False,
                     dtype=orig_var.dtype,
-                    shape=[1, size])  # flattend splited var
-                ret_vars.append(var)
-        return ret_vars
+                    shape=splited_shape)  # flattend splited var
+                var_list.append(var)
+            var_mapping[varname] = var_list
+        return var_mapping
 
     def _clone_param(self, block, v):
         assert isinstance(v, Parameter)
@@ -137,80 +196,22 @@ class DistributeTranspiler:
             lod_level=var.lod_level,
             persistable=var.persistable)
 
-    def _split_dense_variable(self,
-                              var_list,
-                              pserver_count,
-                              min_block_size=1024,
-                              max_block_size=1048576):
-        """
-            We may need to split dense tensor to one or several blocks and put
-            them equally onto parameter server. One block is a sub-tensor
-            aligned by dim[0] of the tensor.
-            
-            We need to have a minimal block size so that the calculations in
-            the parameter server side can gain better performance. By default
-            mininum block size is 1024. The max block size is used to prevent
-            too large block that may causing send error.
-        """
-        block_sizes = []
-        blocks = []
-        for grad in var_list:
-            dim1 = reduce(lambda x, y: x * y, grad.shape[1:])
-            grad_numel = reduce(lambda x, y: x * y, grad.shape)
-            if grad_numel < min_block_size:
-                block_sizes.append(grad_numel)
-            block_size = grad_numel / min_block_size
-            if block_size < min_block_size:
-                block_size = min_block_size
-            # align by dim1(width)
-            remains = block_size % dim1
-            if remains != 0:
-                block_size += dim1 - remains
-            block_sizes.append(block_size)
-            num_blocks = grad_numel / block_size
-            print("grad numel :%d, blocksize: %d" % grad_numel, block_size)
-            for block_id in xrange(num_blocks):
-                block = VarBlock(grad.name(), block_id, block_size)
-                blocks.append(str(block))
-        return blocks
-
-    def _split_trainer_vars(self, program, gradblocks, params_and_grads):
-        var2blocks = dict()
-        splited = dict()
-        for block_str in gradblocks:
-            varname, offset, size = block_str.split(":")
-            if not var2blocks.has_key(varname):
-                var2blocks[varname] = []
-            var2blocks[varname].append((long(offset), long(size)))
-        for varname, blocks in var2blocks.iteritems():
+    def _append_split_op(self, program, gradblocks):
+        var_mapping = self._create_vars_from_blocklist(program, gradblocks)
+        for varname, splited_vars in var_mapping.iteritems():
+            if len(splited_vars) == 1:
+                continue
             orig_var = program.global_block().vars[varname]
-            split_outs = []
-            for i in xrange(len(blocks)):
-                size = blocks[i][1]
-                var = program.global_block().create_var(
-                    name="%s.block%d" % (varname, i),
-                    psersistable=False,
-                    dtype=orig_var.dtype,
-                    shape=[1, size])  # flattend splited var
-                split_outs.append(var)
-
-            splited[varname] = split_outs
+            sections = []
+            for v in splited_vars:
+                sections.append(v.shape[0])
             program.global_block().append_op(
                 type="split",
                 inputs={"X": orig_var},
-                outputs={"Out": split_outs},
-                attrs={"num": len(blocks)}  # assume split evenly
+                outputs={"Out": splited_vars},
+                attrs={"sections": sections}  # assume split evenly
             )
-        return splited
-
-    def _concat_trainer_vars(self, program, splited):
-        for varname, to_merge_list in splited.iteritems():
-            orig_var = program.global_block().vars[varname]
-            program.global_block().append_op(
-                type="concat",
-                inputs={"X": to_merge_list},
-                outputs={"Out": orig_var},
-                attrs={})
+        return var_mapping
 
     def get_trainer_program(self):
         # remove optimize ops and add a send op to main_program
diff --git a/python/paddle/v2/fluid/distribute_transpiler_simple.py b/python/paddle/v2/fluid/distribute_transpiler_simple.py
new file mode 100644
index 0000000000..49ece7b725
--- /dev/null
+++ b/python/paddle/v2/fluid/distribute_transpiler_simple.py
@@ -0,0 +1,242 @@
+import framework
+from framework import Program, default_main_program, Parameter, Variable
+import optimizer
+from layer_helper import LayerHelper
+
+
+def hash_name_to_server(params_grads, pserver_endpoints):
+    """
+    :param param_grads:
+    :return: a map of pserver endpoint -> 
+                    params -> [param list]
+                    grads  -> [grad list]
+    """
+
+    def _hash_param(param_name, total):
+        return hash(param_name) % total
+
+    param_grad_map = dict()
+    for param, grad in params_grads:
+        if param.trainable is True and grad is not None:
+            server_id = _hash_param(param.name, len(pserver_endpoints))
+            server_for_param = pserver_endpoints[server_id]
+            if not param_grad_map.has_key(server_for_param):
+                param_grad_map[server_for_param] = {"params": [], "grads": []}
+            param_grad_map[server_for_param]["params"].append(param)
+            param_grad_map[server_for_param]["grads"].append(grad)
+
+    return param_grad_map
+
+
+def round_robin(params_grads, pserver_endpoints):
+    assert (len(params_grads) > len(pserver_endpoints))
+
+    param_grad_map = dict()
+    pserver_idx = 0
+    for param, grad in params_grads:
+        if param.trainable is True:
+            server_for_param = pserver_endpoints[pserver_idx]
+            if not param_grad_map.has_key(server_for_param):
+                param_grad_map[server_for_param] = {"params": [], "grads": []}
+
+            param_grad_map[server_for_param]["params"].append(param)
+            param_grad_map[server_for_param]["grads"].append(grad)
+
+            pserver_idx += 1
+            if pserver_idx >= len(pserver_endpoints):
+                pserver_idx = 0
+    return param_grad_map
+
+
+class DistributeTranspiler:
+    def transpile(self,
+                  optimize_ops,
+                  params_grads,
+                  program=None,
+                  pservers="127.0.0.1:6174",
+                  trainers=1,
+                  split_method=round_robin):
+        """
+            Transpile the program to a distributed data-parallelism programs.
+
+            The main_program will be transform to use a remote parameter server
+            to do parameter optimization. And the optimization graph will be put
+            in to a parameter server program.
+
+            Use different methods to split trainable varialbles to different
+            parameter servers.
+
+            Example to run:
+
+            exe = fluid.Executor(place)
+            t = fluid.DistributeTranspiler()
+            t.transpile(optimize_ops, params_grads, pservers="127.0.0.1:6174", trainers=1)
+
+            pserver_endpoint = os.getenv("PSERVER")
+            if pserver_endpoint:
+                pserver_prog = t.get_pserver_program(pserver_endpoint, optimize_ops)
+                exe.run(fluid.default_startup_program())
+                exe.run(pserver_prog)
+            else:
+                feeder = fluid.DataFeeder(feed_list=[images, label], place=place)
+                exe.run(fluid.default_startup_program())
+
+                for pass_id in range(PASS_NUM):
+                    ...
+
+            :param optimize_ops: op list of optimization, should be the
+                                 return value of Optimizer.minimize
+            :type optimize_ops: list
+            :param program: program to optimize, default default_main_program
+            :param pservers: parameter server endpoints like "m1:6174,m2:6174"
+            :type pservers: string
+
+            :return: return a list of programs
+        """
+        if program is None:
+            program = default_main_program()
+        self.program = program
+        self.trainers = trainers
+        self.optimize_ops = optimize_ops
+        self._optimize_distributed(
+            optimize_ops,
+            program,
+            params_grads,
+            pservers=pservers,
+            trainers=trainers,
+            split_method=split_method)
+
+    def _clone_param(self, block, v):
+        assert isinstance(v, Parameter)
+        new_p = Parameter(
+            block=block,
+            shape=v.shape,
+            dtype=v.dtype,
+            type=v.type,
+            lod_level=v.lod_level,
+            stop_gradient=v.stop_gradient,
+            trainable=v.trainable,
+            optimize_attr=v.optimize_attr,
+            regularizer=v.regularizer,
+            name=v.name)
+        block.vars[new_p.name] = new_p
+
+    def _clone_var(self, block, var):
+        assert isinstance(var, Variable)
+        return block.create_var(
+            name=var.name,
+            shape=var.shape,
+            dtype=var.dtype,
+            type=var.type,
+            lod_level=var.lod_level,
+            persistable=var.persistable)
+
+    def _optimize_distributed(self, optimize_ops, program, params_and_grads,
+                              **kwargs):
+        if kwargs.has_key("split_method"):
+            split_method = kwargs["split_method"]
+        else:
+            split_method = round_robin
+
+        assert (callable(split_method))
+        pserver_endpoints = kwargs["pservers"].split(",")
+        self.param_grad_map = split_method(params_and_grads, pserver_endpoints)
+
+        send_op_ordered_inputs = []
+        send_op_ordered_outputs = []
+        epmap = []
+        for ep, v in self.param_grad_map.iteritems():
+            send_op_ordered_inputs.extend(v["grads"])
+            send_op_ordered_outputs.extend(v["params"])
+            for i in v["grads"]:
+                epmap.append(ep)
+        send_op = program.global_block().append_op(
+            type="send",
+            inputs={"X": send_op_ordered_inputs
+                    },  # inputs is a list of tensors to be send
+            outputs={"Out": send_op_ordered_outputs},
+            attrs={"endpoints": pserver_endpoints,
+                   "epmap": epmap})
+
+    def get_trainer_program(self):
+        # remove optimize ops and add a send op to main_program
+        self.program.global_block().delete_ops(self.optimize_ops)
+        return self.program
+
+    def _create_var_for_trainers(self, block, var, trainers):
+        var_list = []
+        for i in xrange(trainers):
+            var_each = block.create_var(
+                name="%s.trainer_%d" % (var.name, i),
+                psersistable=var.persistable,
+                dtype=var.dtype,
+                shape=var.shape)
+            var_list.append(var_each)
+        return var_list
+
+    def get_pserver_program(self, endpoint, optimize_ops):
+        pserver_program = Program()
+        for v in self.param_grad_map[endpoint]["params"]:
+            self._clone_param(pserver_program.global_block(), v)
+
+        optimize_sub_program = Program()
+        grad_var_names = [
+            var.name for var in self.param_grad_map[endpoint]["grads"]
+        ]
+        for opt_op in optimize_ops:
+            for _, var in opt_op.inputs.iteritems():
+                # NOTE: append operators to merge gradients from multiple
+                # trainers. If trainers == 1, this is not needed.
+                if self.trainers > 1 and var.name in grad_var_names:
+                    vars2merge = self._create_var_for_trainers(
+                        optimize_sub_program.global_block(), var, self.trainers)
+                    merged_var = optimize_sub_program.global_block().create_var(
+                        name=var.name,
+                        persistable=var.persistable,
+                        dtype=var.dtype,
+                        shape=var.shape)
+                    optimize_sub_program.global_block().append_op(
+                        type="sum",
+                        inputs={"X": vars2merge},
+                        outputs={"Out": merged_var})
+                    optimize_sub_program.global_block().append_op(
+                        type="scale",
+                        inputs={"X": merged_var},
+                        outputs={"Out": merged_var},
+                        attrs={"scale": 1.0 / float(self.trainers)})
+                else:
+                    optimize_sub_program.global_block().create_var(
+                        name=var.name,
+                        persistable=var.persistable,
+                        dtype=var.dtype,
+                        shape=var.shape)
+
+            if opt_op.inputs.has_key("Grad"):
+                if opt_op.inputs["Grad"].name in grad_var_names:
+                    optimize_sub_program.global_block().append_op(
+                        type=opt_op.type,
+                        inputs=opt_op.inputs,
+                        outputs=opt_op.outputs,
+                        attrs=opt_op.attrs)
+            else:
+                optimize_sub_program.global_block().append_op(
+                    type=opt_op.type,
+                    inputs=opt_op.inputs,
+                    outputs=opt_op.outputs,
+                    attrs=opt_op.attrs)
+        pserver_program.global_block().append_op(
+            type="recv",
+            inputs={"RX":
+                    self.param_grad_map[endpoint]["grads"]},  # grads to recv
+            outputs={},
+            attrs={
+                "OptimizeProgram": optimize_sub_program.desc,
+                "endpoint": endpoint,
+                "ParamList":
+                [p.name for p in self.param_grad_map[endpoint]["params"]],
+                "GradList":
+                [p.name for p in self.param_grad_map[endpoint]["grads"]],
+                "Trainers": self.trainers
+            })
+        pserver_program.sync_with_cpp()
+        return pserver_program
diff --git a/python/paddle/v2/fluid/distributed_spliter.py b/python/paddle/v2/fluid/distributed_spliter.py
index e7ba53390d..eff30f7bb6 100644
--- a/python/paddle/v2/fluid/distributed_spliter.py
+++ b/python/paddle/v2/fluid/distributed_spliter.py
@@ -1,38 +1,35 @@
-def hash_name(varblocks, pserver_endpoints):
+def hash_name(varlist, pserver_endpoints):
     """
-    :param varblocks: a list of VarBlock string indicating 
-                      sub blocks of variables
-    :return: a map of pserver endpoint -> varblock_str
+    hash variable names to several endpoints.
+
+    :param varlist: a list of Variables
+    :return: a map of pserver endpoint -> varname
     """
 
     def _hash_block(block_str, total):
         return hash(block_str) % total
 
-    ep2block = dict()
-    for varblock_str in varblocks:
-        if param.trainable is True and grad is not None:
-            server_id = _hash_block(varblock_str, len(pserver_endpoints))
-            server_for_param = pserver_endpoints[server_id]
-            if not ep2block.has_key(server_for_param):
-                ep2block[server_for_param] = []
-            ep2block[server_for_param].append(varblock_str)
-
-    return ep2block
+    eplist = []
+    for var in varlist:
+        server_id = _hash_block(var.name(), len(pserver_endpoints))
+        server_for_param = pserver_endpoints[server_id]
+        eplist.append(server_for_param)
+    return eplist
 
 
-def round_robin(varblocks, pserver_endpoints):
-    assert (len(varblocks) > len(pserver_endpoints))
+def round_robin(varlist, pserver_endpoints):
+    """
+    distribute variables to several endpoints.
+    """
+    assert (len(varlist) > len(pserver_endpoints))
 
-    ep2block = dict()
+    eplist = []
     pserver_idx = 0
-    for varblock_str in varblocks:
-        if param.trainable is True:
-            server_for_param = pserver_endpoints[pserver_idx]
-            if not ep2block.has_key(server_for_param):
-                ep2block[server_for_param] = []
-            ep2block[server_for_param].append(varblock_str)
+    for var in varlist:
+        server_for_param = pserver_endpoints[pserver_idx]
+        eplist.append(server_for_param)
 
-            pserver_idx += 1
-            if pserver_idx >= len(pserver_endpoints):
-                pserver_idx = 0
-    return ep2block
+        pserver_idx += 1
+        if pserver_idx >= len(pserver_endpoints):
+            pserver_idx = 0
+    return eplist
diff --git a/python/paddle/v2/fluid/tests/book_distribute/test_split_var.py b/python/paddle/v2/fluid/tests/book_distribute/test_split_var.py
new file mode 100644
index 0000000000..1355e13e1c
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/book_distribute/test_split_var.py
@@ -0,0 +1,38 @@
+import math
+import unittest
+from paddle.v2.fluid.distribute_transpiler import split_dense_variable
+import paddle.v2.fluid as fluid
+import random
+
+
+class TestSplitVar(unittest.TestCase):
+    def test_check_output(self):
+        # split below shapes to 10 servers
+        shapes = [[3, 5], [1024], [28, 784], [8, 1020], [800, 10]]
+        expected_sizes = [
+            [15], [1024],
+            [2352, 2352, 2352, 2352, 2352, 2352, 2352, 2352, 2352, 784],
+            [2040, 2040, 2040, 2040],
+            [1150, 1150, 1150, 1150, 1150, 1150, 1100]
+        ]
+        var_list = []
+        program = fluid.Program()
+        for shape in shapes:
+            var = program.global_block().create_var(
+                name=str(random.randint(10000)),
+                persistable=True,
+                dtype=core.VarDesc.VarType.LOD_TENSOR,
+                shape=shape)
+            var_list.append(var)
+        blocks = split_dense_variable(var_list, 10)
+        all_sizes = []
+        for s in expected_sizes:
+            for s2 in s:
+                all_sizes.append(s2)
+        for i, block_str in enumerate(blocks):
+            varname, block_id, size = block_str.split(":")
+            self.assertEqual(int(size), all_sizes[i])
+
+
+if __name__ == '__main__':
+    unittest.main()

From 452fe1e1be8d947a213295f85425de6b7c127096 Mon Sep 17 00:00:00 2001
From: Xi Chen <putcncx@gmail.com>
Date: Mon, 8 Jan 2018 15:35:16 -0800
Subject: [PATCH 13/68] init checkin for distributed book chapter 6

---
 .../test_understand_sentiment_conv_dist.py    | 109 ++++++++++++++++++
 1 file changed, 109 insertions(+)
 create mode 100644 python/paddle/v2/fluid/tests/book_distribute/test_understand_sentiment_conv_dist.py

diff --git a/python/paddle/v2/fluid/tests/book_distribute/test_understand_sentiment_conv_dist.py b/python/paddle/v2/fluid/tests/book_distribute/test_understand_sentiment_conv_dist.py
new file mode 100644
index 0000000000..0f0d84be0a
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/book_distribute/test_understand_sentiment_conv_dist.py
@@ -0,0 +1,109 @@
+from __future__ import print_function
+import numpy as np
+import paddle.v2 as paddle
+import paddle.v2.fluid as fluid
+
+
+def convolution_net(data, label, input_dim, class_dim=2, emb_dim=32,
+                    hid_dim=32):
+    emb = fluid.layers.embedding(input=data, size=[input_dim, emb_dim])
+    conv_3 = fluid.nets.sequence_conv_pool(
+        input=emb,
+        num_filters=hid_dim,
+        filter_size=3,
+        act="tanh",
+        pool_type="sqrt")
+    conv_4 = fluid.nets.sequence_conv_pool(
+        input=emb,
+        num_filters=hid_dim,
+        filter_size=4,
+        act="tanh",
+        pool_type="sqrt")
+    prediction = fluid.layers.fc(input=[conv_3, conv_4],
+                                 size=class_dim,
+                                 act="softmax")
+    cost = fluid.layers.cross_entropy(input=prediction, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+    adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
+    optimize_ops, params_grads = adam_optimizer.minimize(avg_cost)
+    accuracy = fluid.evaluator.Accuracy(input=prediction, label=label)
+    return avg_cost, accuracy, accuracy.metrics[0], optimize_ops, params_grads
+
+
+def to_lodtensor(data, place):
+    seq_lens = [len(seq) for seq in data]
+    cur_len = 0
+    lod = [cur_len]
+    for l in seq_lens:
+        cur_len += l
+        lod.append(cur_len)
+    flattened_data = np.concatenate(data, axis=0).astype("int64")
+    flattened_data = flattened_data.reshape([len(flattened_data), 1])
+    res = fluid.LoDTensor()
+    res.set(flattened_data, place)
+    res.set_lod([lod])
+    return res
+
+
+def main():
+    BATCH_SIZE = 100
+    PASS_NUM = 5
+
+    word_dict = paddle.dataset.imdb.word_dict()
+    dict_dim = len(word_dict)
+    class_dim = 2
+
+    data = fluid.layers.data(
+        name="words", shape=[1], dtype="int64", lod_level=1)
+    label = fluid.layers.data(name="label", shape=[1], dtype="int64")
+    cost, accuracy, acc_out, optimize_ops, params_grads = convolution_net(
+        data, label, input_dim=dict_dim, class_dim=class_dim)
+
+    train_data = paddle.batch(
+        paddle.reader.shuffle(
+            paddle.dataset.imdb.train(word_dict), buf_size=1000),
+        batch_size=BATCH_SIZE)
+    place = fluid.CPUPlace()
+    exe = fluid.Executor(place)
+
+    t = fluid.DistributeTranspiler()
+
+    # all parameter server endpoints list for spliting parameters
+    pserver_endpoints = os.getenv("PSERVERS")
+    # server endpoint for current node
+    current_endpoint = os.getenv("SERVER_ENDPOINT")
+    # run as trainer or parameter server
+    training_role = os.getenv(
+        "TRAINING_ROLE", "TRAINER")  # get the training role: trainer/pserver
+    t.transpile(
+        optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)
+
+    exe.run(fluid.default_startup_program())
+
+    if training_role == "PSERVER":
+        if not current_endpoint:
+            print("need env SERVER_ENDPOINT")
+            exit(1)
+        pserver_prog = t.get_pserver_program(current_endpoint, optimize_ops)
+        exe.run(pserver_prog)
+    elif training_role == "TRAINER":
+        trainer_prog = t.get_trainer_program()
+        feeder = fluid.DataFeeder(feed_list=[data, label], place=place)
+
+        for pass_id in xrange(PASS_NUM):
+            accuracy.reset(exe)
+            for data in train_data():
+                cost_val, acc_val = exe.run(trainer_prog,
+                                            feed=feeder.feed(data),
+                                            fetch_list=[cost, acc_out])
+                pass_acc = accuracy.eval(exe)
+                print("cost=" + str(cost_val) + " acc=" + str(acc_val) +
+                      " pass_acc=" + str(pass_acc))
+                if cost_val < 1.0 and pass_acc > 0.8:
+                    exit(0)
+    else:
+        print("environment var TRAINER_ROLE should be TRAINER os PSERVER")
+
+
+if __name__ == '__main__':
+    main()

From 56e758fc10e2db646a82bc8a93c44d6427718c11 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Tue, 9 Jan 2018 16:23:41 +0800
Subject: [PATCH 14/68] trainer ok

---
 .../paddle/v2/fluid/distribute_transpiler.py  | 20 ++++++++++++-------
 1 file changed, 13 insertions(+), 7 deletions(-)

diff --git a/python/paddle/v2/fluid/distribute_transpiler.py b/python/paddle/v2/fluid/distribute_transpiler.py
index 58d32bac12..7f3da67463 100644
--- a/python/paddle/v2/fluid/distribute_transpiler.py
+++ b/python/paddle/v2/fluid/distribute_transpiler.py
@@ -56,6 +56,8 @@ def split_dense_variable(var_list,
                 (block_id) * block_size))
             block = VarBlock(var.name, block_id, curr_block_size)
             blocks.append(str(block))
+        print("$$ splited var: ", var.name, var.shape, split_count, len(blocks),
+              block_size)
     return blocks
 
 
@@ -132,10 +134,12 @@ class DistributeTranspiler:
 
         # step4
         for varname, splited_var in param_var_mapping.iteritems():
+            if len(splited_var) <= 1:
+                continue
             orig_param = program.global_block().vars[varname]
             concat = program.global_block().append_op(
                 type="concat",
-                inputs={"X": send_outputs},
+                inputs={"X": splited_var},
                 outputs={"Out": orig_param},
                 attrs={"axis": 0})
 
@@ -147,28 +151,29 @@ class DistributeTranspiler:
             if not block_map.has_key(varname):
                 block_map[varname] = []
             block_map[varname].append((long(offset), long(size)))
-
         for varname, splited in block_map.iteritems():
             orig_var = program.global_block().vars[varname]
+            var_mapping[varname] = []
+            if len(splited) == 1:
+                var_mapping[varname] = [orig_var]
+                continue
             orig_shape = orig_var.shape
             orig_dim1_flatten = 1
             if len(orig_shape) >= 2:
                 orig_dim1_flatten = reduce(lambda x, y: x * y, orig_shape[1:])
-            var_list = []
+
             for i, block in enumerate(splited):
                 size = block[1]
                 rows = size / orig_dim1_flatten
                 splited_shape = [rows]
                 if len(orig_shape) >= 2:
                     splited_shape.extend(orig_shape[1:])
-                print("block, splited shape:", block, splited_shape)
                 var = program.global_block().create_var(
                     name="%s.block%d" % (varname, i),
                     psersistable=False,
                     dtype=orig_var.dtype,
                     shape=splited_shape)  # flattend splited var
-                var_list.append(var)
-            var_mapping[varname] = var_list
+                var_mapping[varname].append(var)
         return var_mapping
 
     def _clone_param(self, block, v):
@@ -199,7 +204,8 @@ class DistributeTranspiler:
     def _append_split_op(self, program, gradblocks):
         var_mapping = self._create_vars_from_blocklist(program, gradblocks)
         for varname, splited_vars in var_mapping.iteritems():
-            if len(splited_vars) == 1:
+            # variable that don't need to split have empty splited_vars
+            if len(splited_vars) <= 1:
                 continue
             orig_var = program.global_block().vars[varname]
             sections = []

From 9c0b1cf1c2e28f7f6fb7b7a39c8bdffe07981bcd Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Tue, 9 Jan 2018 20:32:51 +0800
Subject: [PATCH 15/68] update wip pserver transpile

---
 .../paddle/v2/fluid/distribute_transpiler.py  | 152 ++++++++++++------
 1 file changed, 106 insertions(+), 46 deletions(-)

diff --git a/python/paddle/v2/fluid/distribute_transpiler.py b/python/paddle/v2/fluid/distribute_transpiler.py
index 7f3da67463..ac13a7cb60 100644
--- a/python/paddle/v2/fluid/distribute_transpiler.py
+++ b/python/paddle/v2/fluid/distribute_transpiler.py
@@ -98,8 +98,7 @@ class DistributeTranspiler:
         # 3. append send_op to trainer.
         # 4. append concat_op to trainer to update local weights.
         # 5. create new program as parameter server.
-        # 5. create parameter server program by split_method generated endpoint->VarBlock
-        # 6. run compile time infershape for parameter server program
+        # 6. create parameter server program by split_method generated endpoint->VarBlock
 
         pserver_endpoints = pservers.split(",")
 
@@ -124,6 +123,15 @@ class DistributeTranspiler:
         # let send_op know which endpoint to send which var, eplist is of the same
         # order of send_inputs.
         eplist = split_method(send_inputs, pserver_endpoints)
+        # create mapping of endpoint -> var to create pserver side program
+        self.param_grad_ep_mapping = dict()
+        for i, ep in enumerate(eplist):
+            param = send_outputs[i]
+            grad = send_inputs[i]
+            if not self.param_grad_ep_mapping.has_key(ep):
+                self.param_grad_ep_mapping[ep] = {"params": [], "grads": []}
+            self.param_grad_ep_mapping[ep]["params"].append(param)
+            self.param_grad_ep_mapping[ep]["grads"].append(grad)
 
         send_op = program.global_block().append_op(
             type="send",
@@ -235,27 +243,29 @@ class DistributeTranspiler:
             var_list.append(var_each)
         return var_list
 
-    def get_pserver_program(self, endpoint, optimize_ops):
-        pserver_program = Program()
-        for v in self.param_grad_map[endpoint]["params"]:
-            self._clone_param(pserver_program.global_block(), v)
-
-        optimize_sub_program = Program()
-        grad_var_names = [
-            var.name for var in self.param_grad_map[endpoint]["grads"]
-        ]
-        for opt_op in optimize_ops:
-            for _, var in opt_op.inputs.iteritems():
-                # NOTE: append operators to merge gradients from multiple
-                # trainers. If trainers == 1, this is not needed.
-                if self.trainers > 1 and var.name in grad_var_names:
+    def _append_pserver_ops(self, opt_op, endpoint):
+        new_inputs = dict()
+        for key, var in opt_op.inputs.iteritems():
+            if key == "Grad":
+                grad_block = None
+                for g in self.param_grad_ep_mapping[endpoint]["grads"]:
+                    if g.name.startswith(var.name):
+                        grad_block = g
+                        break
+                if not grad_block:
+                    # do not append this op if current endpoint
+                    # is not dealing with this grad block
+                    return
+                merged_var = optimize_sub_program.global_block().create_var(
+                    name=grad_block.name,
+                    persistable=grad_block.persistable,
+                    dtype=grad_block.dtype,
+                    shape=grad_block.shape)
+                # append merging ops if trainers > 1
+                if self.trainers > 1:
                     vars2merge = self._create_var_for_trainers(
-                        optimize_sub_program.global_block(), var, self.trainers)
-                    merged_var = optimize_sub_program.global_block().create_var(
-                        name=var.name,
-                        persistable=var.persistable,
-                        dtype=var.dtype,
-                        shape=var.shape)
+                        optimize_sub_program.global_block(), grad_block,
+                        self.trainers)
                     optimize_sub_program.global_block().append_op(
                         type="sum",
                         inputs={"X": vars2merge},
@@ -265,38 +275,88 @@ class DistributeTranspiler:
                         inputs={"X": merged_var},
                         outputs={"Out": merged_var},
                         attrs={"scale": 1.0 / float(self.trainers)})
-                else:
-                    optimize_sub_program.global_block().create_var(
-                        name=var.name,
-                        persistable=var.persistable,
-                        dtype=var.dtype,
-                        shape=var.shape)
+                new_inputs[key] = merged_var
+            elif key == "Param":
+                # param is already created on global program
+                param_block = None
+                for p in self.param_grad_ep_mapping[endpoint]["params"]:
+                    if p.name.startswith(var.name):
+                        param_block = p
+                        break
+                if not param_block:
+                    return
+                tmpvar = optimize_sub_program.global_block().create_var(
+                    name=param_block.name,
+                    persistable=param_block.persistable,
+                    dtype=param_block.dtype,
+                    shape=param_block.shape)
+                new_inputs[key] = tmpvar
+            else:
+                tmpvar = optimize_sub_program.global_block().create_var(
+                    name=var.name,
+                    persistable=var.persistable,
+                    dtype=var.dtype,
+                    shape=var.shape)
+                new_inputs[key] = tmpvar
 
-            if opt_op.inputs.has_key("Grad"):
-                if opt_op.inputs["Grad"].name in grad_var_names:
-                    optimize_sub_program.global_block().append_op(
-                        type=opt_op.type,
-                        inputs=opt_op.inputs,
-                        outputs=opt_op.outputs,
-                        attrs=opt_op.attrs)
+        # FIXME: change outputs ParamOut
+        optimize_sub_program.global_block().append_op(
+            type=opt_op.type,
+            inputs=new_inputs,
+            outputs=opt_op.outputs,
+            attrs=opt_op.attrs)
+
+    def _append_pserver_non_opt_ops(self, opt_op):
+        for _, var in opt_op.inputs.iteritems():
+            optimize_sub_program.global_block().create_var(
+                name=var.name,
+                persistable=var.persistable,
+                dtype=var.dtype,
+                shape=var.shape)
+        optimize_sub_program.global_block().append_op(
+            type=opt_op.type,
+            inputs=new_inputs,
+            outputs=opt_op.outputs,
+            attrs=opt_op.attrs)
+
+    def get_pserver_program(self, endpoint, optimize_ops):
+        """
+        get pserver side program by endpoint
+
+        NOTE: assume blocks of the same variable is not distributed
+        on the same pserver, only change param/grad varnames for
+        trainers to fetch. For each pserver endpoint, server side
+        program must be a sub-set of the original optimization program.
+        """
+        # step5
+        pserver_program = Program()
+        for v in self.param_grad_ep_mapping[endpoint]["params"]:
+            self._clone_param(pserver_program.global_block(), v)
+        # step6
+        optimize_sub_program = Program()
+        for opt_op in optimize_ops:
+            if opt_ops.inputs.has_key("Grad"):
+                # append optimize_op
+                self._append_pserver_ops(opt_op, endpoint)
             else:
-                optimize_sub_program.global_block().append_op(
-                    type=opt_op.type,
-                    inputs=opt_op.inputs,
-                    outputs=opt_op.outputs,
-                    attrs=opt_op.attrs)
+                self._append_pserver_non_opt_ops(opt_op)
+
         pserver_program.global_block().append_op(
             type="recv",
-            inputs={"RX":
-                    self.param_grad_map[endpoint]["grads"]},  # grads to recv
+            inputs={"RX": self.param_grad_ep_mapping[endpoint]["grads"]
+                    },  # grads to recv
             outputs={},
             attrs={
                 "OptimizeProgram": optimize_sub_program.desc,
                 "endpoint": endpoint,
-                "ParamList":
-                [p.name for p in self.param_grad_map[endpoint]["params"]],
-                "GradList":
-                [p.name for p in self.param_grad_map[endpoint]["grads"]],
+                "ParamList": [
+                    p.name
+                    for p in self.param_grad_ep_mapping[endpoint]["params"]
+                ],
+                "GradList": [
+                    p.name
+                    for p in self.param_grad_ep_mapping[endpoint]["grads"]
+                ],
                 "Trainers": self.trainers
             })
         pserver_program.sync_with_cpp()

From 4a3580ac751fdfdba52575d83ef151bed75b0396 Mon Sep 17 00:00:00 2001
From: Xi Chen <putcncx@gmail.com>
Date: Tue, 9 Jan 2018 14:49:08 -0800
Subject: [PATCH 16/68] add missing dependency

---
 .../tests/book_distribute/test_understand_sentiment_conv_dist.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/python/paddle/v2/fluid/tests/book_distribute/test_understand_sentiment_conv_dist.py b/python/paddle/v2/fluid/tests/book_distribute/test_understand_sentiment_conv_dist.py
index 0f0d84be0a..db419e23ab 100644
--- a/python/paddle/v2/fluid/tests/book_distribute/test_understand_sentiment_conv_dist.py
+++ b/python/paddle/v2/fluid/tests/book_distribute/test_understand_sentiment_conv_dist.py
@@ -1,4 +1,5 @@
 from __future__ import print_function
+import os
 import numpy as np
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid

From 50a02adf5e08abaa24f4c74d904be833c2f9e68a Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Wed, 10 Jan 2018 11:59:59 +0800
Subject: [PATCH 17/68] transpile program ok

---
 .../paddle/v2/fluid/distribute_transpiler.py  | 93 +++++++++++++------
 1 file changed, 66 insertions(+), 27 deletions(-)

diff --git a/python/paddle/v2/fluid/distribute_transpiler.py b/python/paddle/v2/fluid/distribute_transpiler.py
index ac13a7cb60..76e8734f13 100644
--- a/python/paddle/v2/fluid/distribute_transpiler.py
+++ b/python/paddle/v2/fluid/distribute_transpiler.py
@@ -114,12 +114,15 @@ class DistributeTranspiler:
         # step3
         send_inputs = []
         send_outputs = []
-        for _, splited in grad_var_mapping.iteritems():
-            send_inputs.extend(splited)
+        for b in grad_blocks:  # append by order
+            varname, block_id, _ = b.split(":")
+            send_inputs.append(grad_var_mapping[varname][int(block_id)])
+
         param_var_mapping = self._create_vars_from_blocklist(program,
                                                              param_blocks)
-        for _, splited in param_var_mapping.iteritems():
-            send_outputs.extend(splited)
+        for b in param_blocks:
+            varname, block_id, _ = b.split(":")
+            send_outputs.append(param_var_mapping[varname][int(block_id)])
         # let send_op know which endpoint to send which var, eplist is of the same
         # order of send_inputs.
         eplist = split_method(send_inputs, pserver_endpoints)
@@ -243,8 +246,37 @@ class DistributeTranspiler:
             var_list.append(var_each)
         return var_list
 
-    def _append_pserver_ops(self, opt_op, endpoint):
+    def _get_optimizer_input_shape(self, op_type, varkey, orig_shape,
+                                   param_shape):
+        """
+        Returns the shape for optimizer inputs that need to be reshaped when
+        Param and Grad is splited to multiple servers.
+        """
+        # HACK(typhoonzero): Should use functions of corresponding optimizer in
+        # optimizer.py to get the shape, do not  bind this in the transpiler.
+        if op_type == "adam":
+            if varkey in ["Moment1", "Moment2"]:
+                return param_shape
+        elif op_type == "adagrad":
+            if varkey == "Moment":
+                return param_shape
+        elif op_type == "adamax":
+            if varkey in ["Moment", "InfNorm"]:
+                return param_shape
+        elif op_type == "momentum":
+            if varkey == "Velocity":
+                return param_shape
+        elif op_type == "":
+            if varkey == "Moment":
+                return param_shape
+        elif op_type == "sgd":
+            pass
+        return orig_shape
+
+    def _append_pserver_ops(self, program, opt_op, endpoint):
         new_inputs = dict()
+        # update param/grad shape first, then other inputs like
+        # moment can use the updated shape
         for key, var in opt_op.inputs.iteritems():
             if key == "Grad":
                 grad_block = None
@@ -256,7 +288,7 @@ class DistributeTranspiler:
                     # do not append this op if current endpoint
                     # is not dealing with this grad block
                     return
-                merged_var = optimize_sub_program.global_block().create_var(
+                merged_var = program.global_block().create_var(
                     name=grad_block.name,
                     persistable=grad_block.persistable,
                     dtype=grad_block.dtype,
@@ -264,13 +296,12 @@ class DistributeTranspiler:
                 # append merging ops if trainers > 1
                 if self.trainers > 1:
                     vars2merge = self._create_var_for_trainers(
-                        optimize_sub_program.global_block(), grad_block,
-                        self.trainers)
-                    optimize_sub_program.global_block().append_op(
+                        program.global_block(), grad_block, self.trainers)
+                    program.global_block().append_op(
                         type="sum",
                         inputs={"X": vars2merge},
                         outputs={"Out": merged_var})
-                    optimize_sub_program.global_block().append_op(
+                    program.global_block().append_op(
                         type="scale",
                         inputs={"X": merged_var},
                         outputs={"Out": merged_var},
@@ -285,37 +316,45 @@ class DistributeTranspiler:
                         break
                 if not param_block:
                     return
-                tmpvar = optimize_sub_program.global_block().create_var(
+                tmpvar = program.global_block().create_var(
                     name=param_block.name,
                     persistable=param_block.persistable,
                     dtype=param_block.dtype,
                     shape=param_block.shape)
                 new_inputs[key] = tmpvar
-            else:
-                tmpvar = optimize_sub_program.global_block().create_var(
-                    name=var.name,
-                    persistable=var.persistable,
-                    dtype=var.dtype,
-                    shape=var.shape)
-                new_inputs[key] = tmpvar
+
+        for key, var in opt_op.inputs.iteritems():
+            if key in ["Param", "Grad"]:
+                continue
+            # update accumulator variable shape
+            param_shape = new_inputs["Param"].shape
+            new_shape = self._get_optimizer_input_shape(opt_op.type, key,
+                                                        var.shape, param_shape)
+            print("var, new shape", key, var.name, new_shape)
+            tmpvar = program.global_block().create_var(
+                name=var.name,
+                persistable=var.persistable,
+                dtype=var.dtype,
+                shape=new_shape)
+            new_inputs[key] = tmpvar
 
         # FIXME: change outputs ParamOut
-        optimize_sub_program.global_block().append_op(
+        program.global_block().append_op(
             type=opt_op.type,
             inputs=new_inputs,
             outputs=opt_op.outputs,
             attrs=opt_op.attrs)
 
-    def _append_pserver_non_opt_ops(self, opt_op):
+    def _append_pserver_non_opt_ops(self, program, opt_op):
         for _, var in opt_op.inputs.iteritems():
-            optimize_sub_program.global_block().create_var(
+            program.global_block().create_var(
                 name=var.name,
                 persistable=var.persistable,
                 dtype=var.dtype,
                 shape=var.shape)
-        optimize_sub_program.global_block().append_op(
+        program.global_block().append_op(
             type=opt_op.type,
-            inputs=new_inputs,
+            inputs=opt_op.inputs,
             outputs=opt_op.outputs,
             attrs=opt_op.attrs)
 
@@ -331,15 +370,15 @@ class DistributeTranspiler:
         # step5
         pserver_program = Program()
         for v in self.param_grad_ep_mapping[endpoint]["params"]:
-            self._clone_param(pserver_program.global_block(), v)
+            self._clone_var(pserver_program.global_block(), v)
         # step6
         optimize_sub_program = Program()
         for opt_op in optimize_ops:
-            if opt_ops.inputs.has_key("Grad"):
+            if opt_op.inputs.has_key("Grad"):
                 # append optimize_op
-                self._append_pserver_ops(opt_op, endpoint)
+                self._append_pserver_ops(optimize_sub_program, opt_op, endpoint)
             else:
-                self._append_pserver_non_opt_ops(opt_op)
+                self._append_pserver_non_opt_ops(optimize_sub_program, opt_op)
 
         pserver_program.global_block().append_op(
             type="recv",

From 42daf4c3020f358c6798cb90d87a320bc869737e Mon Sep 17 00:00:00 2001
From: dzhwinter <dzhwinter@gmail.com>
Date: Tue, 9 Jan 2018 23:12:26 -0800
Subject: [PATCH 18/68] "add sync op"

---
 python/paddle/v2/fluid/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/paddle/v2/fluid/__init__.py b/python/paddle/v2/fluid/__init__.py
index ccd5998e35..c163d9a92b 100644
--- a/python/paddle/v2/fluid/__init__.py
+++ b/python/paddle/v2/fluid/__init__.py
@@ -58,7 +58,7 @@ def __bootstrap__():
 
     read_env_flags = ['use_pinned_memory', 'check_nan_inf']
     if core.is_compile_gpu():
-        read_env_flags.append('fraction_of_gpu_memory_to_use')
+        read_env_flags.append(['fraction_of_gpu_memory_to_use', 'op_sync'])
     core.init_gflags([sys.argv[0]] +
                      ["--tryfromenv=" + ",".join(read_env_flags)])
     core.init_glog(sys.argv[0])

From f0316bdbbd351cff24b49b9376fab9b56f962e3d Mon Sep 17 00:00:00 2001
From: dzhwinter <dzhwinter@gmail.com>
Date: Tue, 9 Jan 2018 23:13:01 -0800
Subject: [PATCH 19/68] "add flags"

---
 paddle/framework/operator.cc | 10 ++++++++--
 paddle/platform/gpu_info.cc  |  4 ++++
 2 files changed, 12 insertions(+), 2 deletions(-)

diff --git a/paddle/framework/operator.cc b/paddle/framework/operator.cc
index 35ebe48ba6..c1a6d0221b 100644
--- a/paddle/framework/operator.cc
+++ b/paddle/framework/operator.cc
@@ -543,8 +543,14 @@ void OperatorWithKernel::Run(const Scope& scope,
 
   auto kernel_iter = kernels.find(expected_kernel_key);
 
-  kernel_iter->second->Compute(ExecutionContext(
-      *this, new_scope, *pool.Get(expected_kernel_key.place_)));
+  auto* new_dev_ctx = pool.Get(expected_kernel_key.place_);
+  kernel_iter->second->Compute(
+      ExecutionContext(*this, new_scope, *new_dev_ctx));
+
+  /*For profiling/benchmark only*/
+  if (FLAGS_op_sync) {
+    new_dev_ctx->Wait();
+  }
 }
 
 proto::DataType OperatorWithKernel::IndicateDataType(
diff --git a/paddle/platform/gpu_info.cc b/paddle/platform/gpu_info.cc
index 7037551d75..9d3147362a 100644
--- a/paddle/platform/gpu_info.cc
+++ b/paddle/platform/gpu_info.cc
@@ -22,6 +22,10 @@ DEFINE_double(fraction_of_gpu_memory_to_use, 0.92,
               "Default use 92% of GPU memory for PaddlePaddle,"
               "reserve the rest for page tables, etc");
 
+DEFINE_bool(op_sync, false,
+            "Default cuda is asynchronous device, set to True will"
+            "force op run in synchronous mode.");
+
 namespace paddle {
 namespace platform {
 

From a6edc0389e11787c57aa1881ae01bebf025715f2 Mon Sep 17 00:00:00 2001
From: dzhwinter <dzhwinter@gmail.com>
Date: Tue, 9 Jan 2018 23:19:22 -0800
Subject: [PATCH 20/68] "fix CI"

---
 paddle/framework/operator.cc | 5 +++++
 paddle/platform/gpu_info.cc  | 4 ----
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/paddle/framework/operator.cc b/paddle/framework/operator.cc
index c1a6d0221b..0f6071a69e 100644
--- a/paddle/framework/operator.cc
+++ b/paddle/framework/operator.cc
@@ -11,6 +11,7 @@ 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 <gflags/gflags.h>
 #include <glog/logging.h>
 
 #include <algorithm>
@@ -22,6 +23,10 @@ limitations under the License. */
 #include "paddle/framework/shape_inference.h"
 #include "paddle/framework/var_type.h"
 
+DEFINE_bool(op_sync, false,
+            "Default cuda is asynchronous device, set to True will"
+            "force op run in synchronous mode.");
+
 namespace paddle {
 namespace framework {
 
diff --git a/paddle/platform/gpu_info.cc b/paddle/platform/gpu_info.cc
index 9d3147362a..7037551d75 100644
--- a/paddle/platform/gpu_info.cc
+++ b/paddle/platform/gpu_info.cc
@@ -22,10 +22,6 @@ DEFINE_double(fraction_of_gpu_memory_to_use, 0.92,
               "Default use 92% of GPU memory for PaddlePaddle,"
               "reserve the rest for page tables, etc");
 
-DEFINE_bool(op_sync, false,
-            "Default cuda is asynchronous device, set to True will"
-            "force op run in synchronous mode.");
-
 namespace paddle {
 namespace platform {
 

From c7eb199b2e94292fc14b10cb75efd0b4bda47e56 Mon Sep 17 00:00:00 2001
From: Yang Yu <yuyang18@baidu.com>
Date: Wed, 10 Jan 2018 16:13:10 +0800
Subject: [PATCH 21/68] Init commit

---
 .../paddle/v2/fluid/tests/test_parallel_op.py | 108 +++++++++++-------
 1 file changed, 68 insertions(+), 40 deletions(-)

diff --git a/python/paddle/v2/fluid/tests/test_parallel_op.py b/python/paddle/v2/fluid/tests/test_parallel_op.py
index 59ed041e7f..3736d5ea5a 100644
--- a/python/paddle/v2/fluid/tests/test_parallel_op.py
+++ b/python/paddle/v2/fluid/tests/test_parallel_op.py
@@ -1,45 +1,73 @@
 import unittest
-
-import paddle.v2.fluid.layers as layers
 import paddle.v2.fluid as fluid
-from paddle.v2.fluid.framework import Program
-from paddle.v2.fluid.executor import Executor
-from paddle.v2.fluid.backward import append_backward
-import numpy as np
-import paddle.v2.fluid.core as core
-
-
-class ParallelOpTest(unittest.TestCase):
-    def setUp(self):
-        x = layers.data(
-            shape=[-1, 30, 40],
-            dtype='float32',
-            name='x',
-            append_batch_size=False,
-            stop_gradient=False)
-
-        places = layers.get_places(device_count=4)
-        pd = layers.ParallelDo(places=places)
-
-        with pd.do():
-            data = pd.read_input(x)
-            hidden = layers.fc(input=data, size=7)
-            pd.write_output(hidden)
-        data = pd()
-        loss = layers.mean(x=data)
-        sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
-        sgd_optimizer.minimize(loss)
-
-        exe = fluid.Executor(fluid.CPUPlace())
-        exe.run(fluid.default_startup_program())
-        exe.run(fluid.default_main_program(),
-                feed={
-                    x.name: np.random.uniform(0.1, 0.6,
-                                              (20, 30, 40)).astype("float32")
-                })
-
-    def test_forward(self):
-        pass
+import numpy
+
+
+class BaseParallelForTest(unittest.TestCase):
+    def main(self, callback, feed, fetch):
+        cpu = fluid.CPUPlace()
+        result_cpu = self._main_impl_(
+            callback=callback,
+            feed=feed,
+            fetch=fetch,
+            place=cpu,
+            use_parallel=False)
+        print result_cpu
+
+    def _main_impl_(self, callback, feed, fetch, place, use_parallel=False):
+        main = fluid.Program()
+        startup = fluid.Program()
+        # Fix seed
+        main.random_seed = 10
+        startup.random_seed = 10
+
+        with fluid.program_guard(main, startup):
+            generator = callback()
+            # Automatically insert parallel do if use_parallel = True
+            if use_parallel:
+                places = fluid.layers.get_places()
+                pd = fluid.layers.ParallelDo(places)
+                data = next(generator)
+
+                if isinstance(data, fluid.Variable):
+                    data = [data]
+                with pd.do():
+                    ins = map(pd.read_input, data)
+                    if len(ins) == 1:
+                        ins = ins[0]
+                    generator.send(ins)  # patch input
+                    loss = next(generator)
+                    pd.write_output(loss)
+
+                loss = pd()
+            else:
+                data = next(generator)
+                generator.send(data)
+                loss = next(generator)
+
+            avg_loss = fluid.layers.mean(x=loss)
+            fluid.backward.append_backward(loss=avg_loss)
+
+        exe = fluid.Executor(place)
+        exe.run(startup)
+        return exe.run(main, feed=feed, fetch_list=fetch)
+
+
+class ParallelOpTest(BaseParallelForTest):
+    def test_simple_fc(self):
+        def __network__():
+            x = fluid.layers.data(shape=[784], dtype='float32', name='img')
+            x = yield x
+            hidden = fluid.layers.fc(input=x, size=200, param_attr='fc1.w')
+            loss = fluid.layers.mean(x=hidden)
+            yield loss
+
+        self.main(
+            callback=__network__,
+            feed={
+                'img': numpy.random.random(size=(128, 784)).astype('float32')
+            },
+            fetch='fc1.w@GRAD')
 
 
 if __name__ == '__main__':

From 2f56995f7c97e0da8bc63406a1fc42f43815135f Mon Sep 17 00:00:00 2001
From: Yang Yu <yuyang18@baidu.com>
Date: Wed, 10 Jan 2018 16:15:54 +0800
Subject: [PATCH 22/68] Fix InitGLOG

glog will not hold ARGV[0] inside.
---
 paddle/framework/init.cc | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/paddle/framework/init.cc b/paddle/framework/init.cc
index e12bac1d78..4ef82a541e 100644
--- a/paddle/framework/init.cc
+++ b/paddle/framework/init.cc
@@ -11,6 +11,7 @@ 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 <string.h>  // for strdup
 #include <algorithm>
 #include <string>
 
@@ -60,7 +61,9 @@ void InitDevices() {
 }
 
 void InitGLOG(const std::string &prog_name) {
-  google::InitGoogleLogging(prog_name.c_str());
+  // glog will not hold the ARGV[0] inside.
+  // Use strdup to alloc a new string.
+  google::InitGoogleLogging(strdup(prog_name.c_str()));
   google::InstallFailureSignalHandler();
 }
 

From 2412f2f4124250ef0a0b8eb66efbfa08900c5d74 Mon Sep 17 00:00:00 2001
From: Yang Yu <yuyang18@baidu.com>
Date: Wed, 10 Jan 2018 16:37:42 +0800
Subject: [PATCH 23/68] Polish Unittest for ParallelFor

---
 .../paddle/v2/fluid/tests/test_parallel_op.py | 50 ++++++++++++++++---
 1 file changed, 44 insertions(+), 6 deletions(-)

diff --git a/python/paddle/v2/fluid/tests/test_parallel_op.py b/python/paddle/v2/fluid/tests/test_parallel_op.py
index 3736d5ea5a..049ae0fe28 100644
--- a/python/paddle/v2/fluid/tests/test_parallel_op.py
+++ b/python/paddle/v2/fluid/tests/test_parallel_op.py
@@ -12,9 +12,34 @@ class BaseParallelForTest(unittest.TestCase):
             fetch=fetch,
             place=cpu,
             use_parallel=False)
-        print result_cpu
+        result_cpu_parallel = self._main_impl_(
+            callback=callback,
+            feed=feed,
+            fetch=fetch,
+            place=cpu,
+            use_parallel=True)
+        if fluid.core.is_compile_gpu():
+            gpu = fluid.CUDAPlace(0)
+            result_gpu = self._main_impl_(
+                callback=callback,
+                feed=feed,
+                fetch=fetch,
+                place=gpu,
+                use_parallel=False)
+            result_gpu_parallel = self._main_impl_(
+                callback=callback,
+                feed=feed,
+                fetch=fetch,
+                place=gpu,
+                use_parallel=True)
+            self._assert_same_(fetch, result_cpu, result_cpu_parallel,
+                               result_gpu, result_gpu_parallel)
+        else:
+            self._assert_same_(fetch, result_cpu, result_cpu_parallel)
 
     def _main_impl_(self, callback, feed, fetch, place, use_parallel=False):
+        if isinstance(fetch, basestring):
+            fetch = [fetch]
         main = fluid.Program()
         startup = fluid.Program()
         # Fix seed
@@ -31,20 +56,19 @@ class BaseParallelForTest(unittest.TestCase):
 
                 if isinstance(data, fluid.Variable):
                     data = [data]
+
                 with pd.do():
                     ins = map(pd.read_input, data)
                     if len(ins) == 1:
                         ins = ins[0]
-                    generator.send(ins)  # patch input
-                    loss = next(generator)
+                    loss = generator.send(ins)  # patch input
                     pd.write_output(loss)
 
                 loss = pd()
             else:
                 data = next(generator)
-                generator.send(data)
-                loss = next(generator)
-
+                loss = generator.send(data)
+            self.assertIsNotNone(loss)
             avg_loss = fluid.layers.mean(x=loss)
             fluid.backward.append_backward(loss=avg_loss)
 
@@ -52,11 +76,25 @@ class BaseParallelForTest(unittest.TestCase):
         exe.run(startup)
         return exe.run(main, feed=feed, fetch_list=fetch)
 
+    def _assert_same_(self, fetch, *args):
+        def _impl_(a, b, fetch_id, item_id):
+            item_str = ['CPU', 'ParallelCPU', 'GPU', 'ParallelGPU']
+            flag = numpy.allclose(a, b, rtol=0.1)
+            self.assertTrue(flag, "The {0} are different in {1}".format(
+                fetch[fetch_id], item_str[item_id]))
+
+        for i, items in enumerate(zip(*args)):
+            self.assertGreater(len(items), 0)
+            for j in range(1, len(items)):
+                _impl_(items[0], items[j], fetch_id=i, item_id=j)
+
 
 class ParallelOpTest(BaseParallelForTest):
     def test_simple_fc(self):
         def __network__():
             x = fluid.layers.data(shape=[784], dtype='float32', name='img')
+            # FIXME: This is a bug of parallel.do
+            x.stop_gradient = False
             x = yield x
             hidden = fluid.layers.fc(input=x, size=200, param_attr='fc1.w')
             loss = fluid.layers.mean(x=hidden)

From 1f5f79cbf58615d1b3310164b3143eb5cf45aa46 Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Wed, 10 Jan 2018 18:03:27 +0800
Subject: [PATCH 24/68] fix error_clip errors and add unit test

---
 python/paddle/v2/fluid/clip.py            | 15 +++---
 python/paddle/v2/fluid/framework.py       |  3 ++
 python/paddle/v2/fluid/tests/test_clip.py | 59 +++++++++++++++++++++++
 3 files changed, 70 insertions(+), 7 deletions(-)
 create mode 100644 python/paddle/v2/fluid/tests/test_clip.py

diff --git a/python/paddle/v2/fluid/clip.py b/python/paddle/v2/fluid/clip.py
index b1fd1c2b65..6ed97cbe64 100644
--- a/python/paddle/v2/fluid/clip.py
+++ b/python/paddle/v2/fluid/clip.py
@@ -3,7 +3,8 @@ import layers
 from . import core
 
 __all__ = [
-    'GradientClipByValue', 'append_gradient_clip_ops', 'error_clip_callback'
+    'GradientClipByValue', 'ErrorClipByValue', 'append_gradient_clip_ops',
+    'error_clip_callback'
 ]
 
 
@@ -23,12 +24,12 @@ class ErrorClipByValue(BaseErrorClipAttr):
         self.min = min
 
     def append_clip_op(self, block, grad_name):
-        block.append_op(
-            type="clip",
-            inputs={"X": grad_name},
-            outputs={"Out": grad_name},
-            attrs={"min": self.min,
-                   "max": self.max})
+        clip_op_desc = block.desc.append_op()
+        clip_op_desc.set_type("clip")
+        clip_op_desc.set_input("X", [grad_name])
+        clip_op_desc.set_output("Out", [grad_name])
+        clip_op_desc.set_attr("min", self.min)
+        clip_op_desc.set_attr("max", self.max)
 
 
 def error_clip_callback(block, context):
diff --git a/python/paddle/v2/fluid/framework.py b/python/paddle/v2/fluid/framework.py
index 2fb388acfc..47506401f5 100644
--- a/python/paddle/v2/fluid/framework.py
+++ b/python/paddle/v2/fluid/framework.py
@@ -271,6 +271,9 @@ class Variable(object):
         uid = core.unique_integer(prefix)  # unique during whole process.
         return "_".join([prefix, str(uid)])
 
+    def set_error_clip(self, error_clip):
+        self.error_clip = error_clip
+
 
 def get_all_op_protos():
     """
diff --git a/python/paddle/v2/fluid/tests/test_clip.py b/python/paddle/v2/fluid/tests/test_clip.py
new file mode 100644
index 0000000000..7e72112a83
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/test_clip.py
@@ -0,0 +1,59 @@
+from __future__ import print_function
+import numpy as np
+import paddle.v2 as paddle
+import paddle.v2.fluid as fluid
+
+BATCH_SIZE = 128
+CLIP_MAX = 2e-6
+CLIP_MIN = -1e-6
+
+prog = fluid.framework.Program()
+
+with fluid.program_guard(main_program=prog):
+    image = fluid.layers.data(name='x', shape=[784], dtype='float32')
+
+    hidden1 = fluid.layers.fc(input=image, size=128, act='relu')
+    hidden2 = fluid.layers.fc(input=hidden1, size=64, act='relu')
+    predict = fluid.layers.fc(input=hidden2, size=10, act='softmax')
+
+    label = fluid.layers.data(name='y', shape=[1], dtype='int64')
+
+    cost = fluid.layers.cross_entropy(input=predict, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+
+prog_clip = prog.clone()
+prog_clip.block(0).var(hidden1.name).set_error_clip(
+    fluid.clip.ErrorClipByValue(
+        max=CLIP_MAX, min=CLIP_MIN))
+
+avg_cost_clip = prog_clip.block(0).var(avg_cost.name)
+fluid.backward.append_backward(loss=avg_cost)
+fluid.backward.append_backward(
+    loss=avg_cost_clip, callback=fluid.clip.error_clip_callback)
+
+hidden1_grad = prog.block(0).var(hidden1.name + "@GRAD")
+hidden1_grad_clip = prog_clip.block(0).var(hidden1.name + "@GRAD")
+
+train_reader = paddle.batch(
+    paddle.reader.shuffle(
+        paddle.dataset.mnist.train(), buf_size=8192),
+    batch_size=BATCH_SIZE)
+
+place = fluid.CPUPlace()
+exe = fluid.Executor(place)
+feeder = fluid.DataFeeder(feed_list=[image, label], place=place)
+exe.run(fluid.default_startup_program())
+
+count = 0
+for data in train_reader():
+    count += 1
+    if count > 5:
+        break
+    out = exe.run(prog, feed=feeder.feed(data), fetch_list=[hidden1_grad])
+    out_clip = exe.run(prog_clip,
+                       feed=feeder.feed(data),
+                       fetch_list=[hidden1_grad_clip])
+    if not (out[0].clip(min=CLIP_MIN, max=CLIP_MAX) == out_clip[0]).all():
+        exit(1)
+
+exit(0)

From 6fa56b9d014d1c82c1fe41d7395bce8484c4ba2e Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Wed, 10 Jan 2018 20:40:54 +0800
Subject: [PATCH 25/68] left startup program bug

---
 .../paddle/v2/fluid/distribute_transpiler.py  | 82 +++++++++++++------
 1 file changed, 59 insertions(+), 23 deletions(-)

diff --git a/python/paddle/v2/fluid/distribute_transpiler.py b/python/paddle/v2/fluid/distribute_transpiler.py
index 76e8734f13..009f079e83 100644
--- a/python/paddle/v2/fluid/distribute_transpiler.py
+++ b/python/paddle/v2/fluid/distribute_transpiler.py
@@ -56,8 +56,6 @@ def split_dense_variable(var_list,
                 (block_id) * block_size))
             block = VarBlock(var.name, block_id, curr_block_size)
             blocks.append(str(block))
-        print("$$ splited var: ", var.name, var.shape, split_count, len(blocks),
-              block_size)
     return blocks
 
 
@@ -126,7 +124,7 @@ class DistributeTranspiler:
         # let send_op know which endpoint to send which var, eplist is of the same
         # order of send_inputs.
         eplist = split_method(send_inputs, pserver_endpoints)
-        # create mapping of endpoint -> var to create pserver side program
+        # create mapping of endpoint -> splited var to create pserver side program
         self.param_grad_ep_mapping = dict()
         for i, ep in enumerate(eplist):
             param = send_outputs[i]
@@ -142,7 +140,6 @@ class DistributeTranspiler:
             outputs={"Out": send_outputs},
             attrs={"endpoints": pserver_endpoints,
                    "epmap": eplist})
-
         # step4
         for varname, splited_var in param_var_mapping.iteritems():
             if len(splited_var) <= 1:
@@ -187,21 +184,6 @@ class DistributeTranspiler:
                 var_mapping[varname].append(var)
         return var_mapping
 
-    def _clone_param(self, block, v):
-        assert isinstance(v, Parameter)
-        new_p = Parameter(
-            block=block,
-            shape=v.shape,
-            dtype=v.dtype,
-            type=v.type,
-            lod_level=v.lod_level,
-            stop_gradient=v.stop_gradient,
-            trainable=v.trainable,
-            optimize_attr=v.optimize_attr,
-            regularizer=v.regularizer,
-            name=v.name)
-        block.vars[new_p.name] = new_p
-
     def _clone_var(self, block, var):
         assert isinstance(var, Variable)
         return block.create_var(
@@ -210,7 +192,9 @@ class DistributeTranspiler:
             dtype=var.dtype,
             type=var.type,
             lod_level=var.lod_level,
-            persistable=var.persistable)
+            # HACK: let all param in pserver persistable so child
+            # program in recv can get them
+            persistable=True)
 
     def _append_split_op(self, program, gradblocks):
         var_mapping = self._create_vars_from_blocklist(program, gradblocks)
@@ -318,9 +302,10 @@ class DistributeTranspiler:
                     return
                 tmpvar = program.global_block().create_var(
                     name=param_block.name,
-                    persistable=param_block.persistable,
+                    persistable=True,
                     dtype=param_block.dtype,
                     shape=param_block.shape)
+
                 new_inputs[key] = tmpvar
 
         for key, var in opt_op.inputs.iteritems():
@@ -330,7 +315,6 @@ class DistributeTranspiler:
             param_shape = new_inputs["Param"].shape
             new_shape = self._get_optimizer_input_shape(opt_op.type, key,
                                                         var.shape, param_shape)
-            print("var, new shape", key, var.name, new_shape)
             tmpvar = program.global_block().create_var(
                 name=var.name,
                 persistable=var.persistable,
@@ -338,7 +322,8 @@ class DistributeTranspiler:
                 shape=new_shape)
             new_inputs[key] = tmpvar
 
-        # FIXME: change outputs ParamOut
+        # change outputs ParamOut variable
+        opt_op.outputs["ParamOut"] = new_inputs["Param"]
         program.global_block().append_op(
             type=opt_op.type,
             inputs=new_inputs,
@@ -380,6 +365,7 @@ class DistributeTranspiler:
             else:
                 self._append_pserver_non_opt_ops(optimize_sub_program, opt_op)
 
+        print("####", optimize_sub_program)
         pserver_program.global_block().append_op(
             type="recv",
             inputs={"RX": self.param_grad_ep_mapping[endpoint]["grads"]
@@ -400,3 +386,53 @@ class DistributeTranspiler:
             })
         pserver_program.sync_with_cpp()
         return pserver_program
+
+    def get_startup_program(self, endpoint):
+        """
+        Get startup program for current parameter server.
+        Modify operator input variables if there are variables that
+        was splited to several blocks.
+        """
+        s_prog = Program()
+        orig_s_prog = framework.default_startup_program()
+        params = self.param_grad_ep_mapping[endpoint]["params"]
+
+        def _get_splited_name_and_shape(varname):
+            for idx, splited_param in enumerate(params):
+                pname = splited_param.name
+                if pname.startswith(varname) and varname != pname:
+                    return pname, splited_param.shape
+            return "", []
+
+        # 1. create vars
+        created_var_map = dict()
+        for var in params:
+            print("%%%% append var", var.name, var.shape)
+            tmpvar = s_prog.global_block().create_var(
+                name=var.name,
+                persistable=True,
+                dtype=var.dtype,
+                shape=var.shape)
+            created_var_map[var.name] = tmpvar
+
+        # 2. rename op outputs
+        for op in orig_s_prog.global_block().ops:
+            new_outputs = dict()
+            for key, var in op.outputs.iteritems():
+                newname, _ = _get_splited_name_and_shape(var.name)
+                if newname:
+                    new_outputs[key] = created_var_map[newname]
+                else:
+                    new_outputs[key] = var
+            # do not append startup op if var is not on this pserver
+            var_on_pserver = False
+            for _, var in new_outputs.iteritems():
+                if var.name in created_var_map:
+                    var_on_pserver = True
+            if var_on_pserver:
+                s_prog.global_block().append_op(
+                    type=op.type,
+                    inputs=op.inputs,
+                    outputs=new_outputs,
+                    attrs=op.attrs)
+        return s_prog

From 2827607fa891328e31f84dc328301754b3c6ba1c Mon Sep 17 00:00:00 2001
From: "yi.wu" <yi.wu@baifendian.com>
Date: Wed, 10 Jan 2018 23:04:05 +0800
Subject: [PATCH 26/68] fix startup program shape

---
 python/paddle/v2/fluid/distribute_transpiler.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/paddle/v2/fluid/distribute_transpiler.py b/python/paddle/v2/fluid/distribute_transpiler.py
index 009f079e83..b064220ca2 100644
--- a/python/paddle/v2/fluid/distribute_transpiler.py
+++ b/python/paddle/v2/fluid/distribute_transpiler.py
@@ -365,7 +365,6 @@ class DistributeTranspiler:
             else:
                 self._append_pserver_non_opt_ops(optimize_sub_program, opt_op)
 
-        print("####", optimize_sub_program)
         pserver_program.global_block().append_op(
             type="recv",
             inputs={"RX": self.param_grad_ep_mapping[endpoint]["grads"]
@@ -407,7 +406,6 @@ class DistributeTranspiler:
         # 1. create vars
         created_var_map = dict()
         for var in params:
-            print("%%%% append var", var.name, var.shape)
             tmpvar = s_prog.global_block().create_var(
                 name=var.name,
                 persistable=True,
@@ -430,6 +428,8 @@ class DistributeTranspiler:
                 if var.name in created_var_map:
                     var_on_pserver = True
             if var_on_pserver:
+                # gaussian_random use attr to determine tensor shape
+                op.attrs["shape"] = new_outputs["Out"].shape
                 s_prog.global_block().append_op(
                     type=op.type,
                     inputs=op.inputs,

From 0064f6d9762668f37340d39f63017c55b93b97ee Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Thu, 11 Jan 2018 10:24:18 +0800
Subject: [PATCH 27/68] refine test

---
 python/paddle/v2/fluid/tests/test_clip.py | 18 +++++++++++++-----
 1 file changed, 13 insertions(+), 5 deletions(-)

diff --git a/python/paddle/v2/fluid/tests/test_clip.py b/python/paddle/v2/fluid/tests/test_clip.py
index 7e72112a83..a71823f7e8 100644
--- a/python/paddle/v2/fluid/tests/test_clip.py
+++ b/python/paddle/v2/fluid/tests/test_clip.py
@@ -34,6 +34,9 @@ fluid.backward.append_backward(
 hidden1_grad = prog.block(0).var(hidden1.name + "@GRAD")
 hidden1_grad_clip = prog_clip.block(0).var(hidden1.name + "@GRAD")
 
+hidden2_grad = prog.block(0).var(hidden2.name + "@GRAD")
+hidden2_grad_clip = prog_clip.block(0).var(hidden2.name + "@GRAD")
+
 train_reader = paddle.batch(
     paddle.reader.shuffle(
         paddle.dataset.mnist.train(), buf_size=8192),
@@ -49,11 +52,16 @@ for data in train_reader():
     count += 1
     if count > 5:
         break
-    out = exe.run(prog, feed=feeder.feed(data), fetch_list=[hidden1_grad])
-    out_clip = exe.run(prog_clip,
-                       feed=feeder.feed(data),
-                       fetch_list=[hidden1_grad_clip])
-    if not (out[0].clip(min=CLIP_MIN, max=CLIP_MAX) == out_clip[0]).all():
+    out1, out2 = exe.run(prog,
+                         feed=feeder.feed(data),
+                         fetch_list=[hidden1_grad, hidden2_grad])
+    out1_clip, out2_clip = exe.run(
+        prog_clip,
+        feed=feeder.feed(data),
+        fetch_list=[hidden1_grad_clip, hidden2_grad_clip])
+    if not ((out1.clip(
+            min=CLIP_MIN, max=CLIP_MAX) == out1_clip).all() and
+            (out2 == out2_clip).all()):
         exit(1)
 
 exit(0)

From 12aca860bf24ddc05a06722c1fc11ff3cfedc893 Mon Sep 17 00:00:00 2001
From: Yang Yu <yuyang18@baidu.com>
Date: Thu, 11 Jan 2018 11:03:54 +0800
Subject: [PATCH 28/68] Add comment

---
 .../paddle/v2/fluid/tests/test_parallel_op.py | 58 ++++++++++++++++---
 1 file changed, 51 insertions(+), 7 deletions(-)

diff --git a/python/paddle/v2/fluid/tests/test_parallel_op.py b/python/paddle/v2/fluid/tests/test_parallel_op.py
index 049ae0fe28..dde7206e4f 100644
--- a/python/paddle/v2/fluid/tests/test_parallel_op.py
+++ b/python/paddle/v2/fluid/tests/test_parallel_op.py
@@ -4,15 +4,35 @@ import numpy
 
 
 class BaseParallelForTest(unittest.TestCase):
-    def main(self, callback, feed, fetch):
+    def run_test(self, callback, feed, fetch):
+        """
+        Run the unittest for parallel.for
+        Args:
+            callback(callable): A callable function returns a generator. There 
+                are two yields in the generator function. The first yield 
+                returns the data layers, and the second yield returns the loss. 
+                The modified data variables will be sent back during the first 
+                yield.
+            
+            feed(dict): The executor feeding dictionary.
+            fetch(list|basestr): The fetch name lists. 
+
+        Returns:
+            None
+            
+        Raises:
+            AssertionError when the computation of cpu, parallel.for in cpu, 
+                gpu, parallel.for in gpu are different.
+
+        """
         cpu = fluid.CPUPlace()
-        result_cpu = self._main_impl_(
+        result_cpu = self._run_test_impl_(
             callback=callback,
             feed=feed,
             fetch=fetch,
             place=cpu,
             use_parallel=False)
-        result_cpu_parallel = self._main_impl_(
+        result_cpu_parallel = self._run_test_impl_(
             callback=callback,
             feed=feed,
             fetch=fetch,
@@ -20,13 +40,13 @@ class BaseParallelForTest(unittest.TestCase):
             use_parallel=True)
         if fluid.core.is_compile_gpu():
             gpu = fluid.CUDAPlace(0)
-            result_gpu = self._main_impl_(
+            result_gpu = self._run_test_impl_(
                 callback=callback,
                 feed=feed,
                 fetch=fetch,
                 place=gpu,
                 use_parallel=False)
-            result_gpu_parallel = self._main_impl_(
+            result_gpu_parallel = self._run_test_impl_(
                 callback=callback,
                 feed=feed,
                 fetch=fetch,
@@ -37,7 +57,17 @@ class BaseParallelForTest(unittest.TestCase):
         else:
             self._assert_same_(fetch, result_cpu, result_cpu_parallel)
 
-    def _main_impl_(self, callback, feed, fetch, place, use_parallel=False):
+    def _run_test_impl_(self, callback, feed, fetch, place, use_parallel=False):
+        """
+        Run a single test, returns the fetch values
+        Args:
+            place(Place): the computation place. 
+            use_parallel(bool): Whether use parallel.for or not. 
+
+        Returns:
+            Fetched numpy arrays.
+
+        """
         if isinstance(fetch, basestring):
             fetch = [fetch]
         main = fluid.Program()
@@ -77,6 +107,20 @@ class BaseParallelForTest(unittest.TestCase):
         return exe.run(main, feed=feed, fetch_list=fetch)
 
     def _assert_same_(self, fetch, *args):
+        """
+        Assert the return values of `run_test` are same.
+        Args:
+            fetch: Fetch list. Used for print error message
+            *args: The fetch result lists of each situations.
+
+        Returns:
+            None
+            
+        Raises:
+            AssertionError
+
+        """
+
         def _impl_(a, b, fetch_id, item_id):
             item_str = ['CPU', 'ParallelCPU', 'GPU', 'ParallelGPU']
             flag = numpy.allclose(a, b, rtol=0.1)
@@ -100,7 +144,7 @@ class ParallelOpTest(BaseParallelForTest):
             loss = fluid.layers.mean(x=hidden)
             yield loss
 
-        self.main(
+        self.run_test(
             callback=__network__,
             feed={
                 'img': numpy.random.random(size=(128, 784)).astype('float32')

From 92eb247f07a47a6ed6af61d39d305935fb2fcd76 Mon Sep 17 00:00:00 2001
From: dzhwinter <dzhwinter@gmail.com>
Date: Wed, 10 Jan 2018 19:13:29 -0800
Subject: [PATCH 29/68] "fix stupid error"

---
 python/paddle/v2/fluid/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/paddle/v2/fluid/__init__.py b/python/paddle/v2/fluid/__init__.py
index c163d9a92b..422aa0a5ba 100644
--- a/python/paddle/v2/fluid/__init__.py
+++ b/python/paddle/v2/fluid/__init__.py
@@ -58,7 +58,7 @@ def __bootstrap__():
 
     read_env_flags = ['use_pinned_memory', 'check_nan_inf']
     if core.is_compile_gpu():
-        read_env_flags.append(['fraction_of_gpu_memory_to_use', 'op_sync'])
+        read_env_flags += ['fraction_of_gpu_memory_to_use', 'op_sync']
     core.init_gflags([sys.argv[0]] +
                      ["--tryfromenv=" + ",".join(read_env_flags)])
     core.init_glog(sys.argv[0])

From 74c5e7c52d8910e79038e549872ba6a83d927deb Mon Sep 17 00:00:00 2001
From: tensor-tang <jian.j.tang@intel.com>
Date: Wed, 10 Jan 2018 22:08:00 +0800
Subject: [PATCH 30/68] enbale auto set env of V1 on Mac

---
 paddle/scripts/submit_local.sh.in | 29 ++++++++++++++++++++++-------
 1 file changed, 22 insertions(+), 7 deletions(-)

diff --git a/paddle/scripts/submit_local.sh.in b/paddle/scripts/submit_local.sh.in
index bb47ad614e..80fa0c72af 100755
--- a/paddle/scripts/submit_local.sh.in
+++ b/paddle/scripts/submit_local.sh.in
@@ -49,7 +49,18 @@ function cpu_config() {
   if [ "@WITH_MKL@" == "OFF" ]; then
     return 0
   fi
-  ht=`lscpu |grep "per core"|awk -F':' '{print $2}'|xargs`
+  platform="`uname -s`"
+  ht=0
+  if [ $platform == "Linux" ]; then
+    ht=`lscpu |grep "per core"|awk -F':' '{print $2}'|xargs`
+  elif [ $platform == "Darwin" ]; then
+    if [`sysctl -n hw.physicalcpu` -eq `sysctl -n hw.logicalcpu`]; then
+      # HT is OFF
+      ht=1
+    fi
+  else
+    return 0
+  fi
   if [ $ht -eq 1 ]; then # HT is OFF
     if [ -z "$KMP_AFFINITY" ]; then
       export KMP_AFFINITY="granularity=fine,compact,0,0"
@@ -72,7 +83,15 @@ function threads_config() {
   # according to trainer_count and total processors
   # only when MKL enabled
   # auto set OPENBLAS_NUM_THREADS when do not use MKL
-  processors=`grep "processor" /proc/cpuinfo|sort -u|wc -l`
+  platform="`uname -s`"
+  processors=0
+  if [ $platform == "Linux" ]; then
+    processors=`grep "processor" /proc/cpuinfo|sort -u|wc -l`
+  elif [ $platform == "Darwin" ]; then
+    processors=`sysctl -n hw.logicalcpu`
+  else
+    return 0
+  fi
   trainers=`grep -Eo 'trainer_count.[0-9]+' <<< "$@" |grep -Eo '[0-9]+'|xargs`
   if [ -z $trainers ]; then
     trainers=1
@@ -148,11 +167,7 @@ else:
   sys.exit(0)
 EOF
 
-if [ "`uname -s`" == "Linux" ]; then
-  # only support on linux yet, with mac can use v2
-  cpu_config
-fi
-
+cpu_config
 # echo $KMP_AFFINITY $OMP_DYNAMIC
 
 case "$1" in

From 83c72536e662b5e8be7eb1a3e69a4f02ac584b20 Mon Sep 17 00:00:00 2001
From: Yang Yu <yuyang18@baidu.com>
Date: Thu, 11 Jan 2018 12:35:18 +0800
Subject: [PATCH 31/68] Update batch_size

---
 python/paddle/v2/fluid/tests/test_parallel_op.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/python/paddle/v2/fluid/tests/test_parallel_op.py b/python/paddle/v2/fluid/tests/test_parallel_op.py
index dde7206e4f..2b51a1f504 100644
--- a/python/paddle/v2/fluid/tests/test_parallel_op.py
+++ b/python/paddle/v2/fluid/tests/test_parallel_op.py
@@ -147,7 +147,8 @@ class ParallelOpTest(BaseParallelForTest):
         self.run_test(
             callback=__network__,
             feed={
-                'img': numpy.random.random(size=(128, 784)).astype('float32')
+                'img':
+                numpy.random.random(size=(128 * 3, 784)).astype('float32')
             },
             fetch='fc1.w@GRAD')
 

From f784dae3da58788da3117f8ea825f16552f046db Mon Sep 17 00:00:00 2001
From: Abhinav Arora <aroraabhinav@baidu.com>
Date: Wed, 10 Jan 2018 21:28:26 -0800
Subject: [PATCH 32/68] Fix the documentation for elementwise op in fluid
 layers

---
 doc/api/v2/fluid/layers.rst            | 10 ++++++++++
 paddle/operators/elementwise_add_op.cc |  2 +-
 paddle/operators/elementwise_div_op.cc |  2 +-
 paddle/operators/elementwise_mul_op.cc |  2 +-
 paddle/operators/elementwise_op.h      | 20 +++++++++++---------
 paddle/operators/elementwise_sub_op.cc |  2 +-
 6 files changed, 25 insertions(+), 13 deletions(-)

diff --git a/doc/api/v2/fluid/layers.rst b/doc/api/v2/fluid/layers.rst
index a7c8670f66..696a8012aa 100644
--- a/doc/api/v2/fluid/layers.rst
+++ b/doc/api/v2/fluid/layers.rst
@@ -38,6 +38,16 @@ elementwise_add
 ..  autofunction:: paddle.v2.fluid.layers.elementwise_add
     :noindex:
 
+elementwise_sub
+---------------
+..  autofunction:: paddle.v2.fluid.layers.elementwise_sub
+    :noindex:
+
+elementwise_mul
+---------------
+..  autofunction:: paddle.v2.fluid.layers.elementwise_mul
+    :noindex:
+
 elementwise_div
 ---------------
 ..  autofunction:: paddle.v2.fluid.layers.elementwise_div
diff --git a/paddle/operators/elementwise_add_op.cc b/paddle/operators/elementwise_add_op.cc
index 70b7c9f2ec..37951fa758 100644
--- a/paddle/operators/elementwise_add_op.cc
+++ b/paddle/operators/elementwise_add_op.cc
@@ -21,7 +21,7 @@ class ElementwiseAddOpMaker : public ElementwiseOpMaker {
  public:
   ElementwiseAddOpMaker(OpProto* proto, OpAttrChecker* op_checker)
       : ElementwiseOpMaker(proto, op_checker) {
-    SetComment("Add", "$Out = X + Y$");
+    SetComment("Add", "Out = X + Y");
     AddComment(comment_);
   }
 };
diff --git a/paddle/operators/elementwise_div_op.cc b/paddle/operators/elementwise_div_op.cc
index 1fa960866f..6ebd58b1b3 100644
--- a/paddle/operators/elementwise_div_op.cc
+++ b/paddle/operators/elementwise_div_op.cc
@@ -21,7 +21,7 @@ class ElementwiseDivOpMaker : public ElementwiseOpMaker {
  public:
   ElementwiseDivOpMaker(OpProto* proto, OpAttrChecker* op_checker)
       : ElementwiseOpMaker(proto, op_checker) {
-    SetComment("Div", "$Out = X / Y$");
+    SetComment("Div", "Out = X / Y");
     AddComment(comment_);
   }
 };
diff --git a/paddle/operators/elementwise_mul_op.cc b/paddle/operators/elementwise_mul_op.cc
index a6d1173619..450dd05c79 100644
--- a/paddle/operators/elementwise_mul_op.cc
+++ b/paddle/operators/elementwise_mul_op.cc
@@ -22,7 +22,7 @@ class ElementwiseMulOpMaker : public ElementwiseOpMaker {
  public:
   ElementwiseMulOpMaker(OpProto* proto, OpAttrChecker* op_checker)
       : ElementwiseOpMaker(proto, op_checker) {
-    SetComment("Mul", "$Out = X \\odot\\ Y$");
+    SetComment("Mul", "Out = X \\odot\\ Y");
     AddComment(comment_);
   }
 };
diff --git a/paddle/operators/elementwise_op.h b/paddle/operators/elementwise_op.h
index f308ee05e1..a342595b54 100644
--- a/paddle/operators/elementwise_op.h
+++ b/paddle/operators/elementwise_op.h
@@ -58,7 +58,8 @@ Limited Elementwise {name} Operator.
 
 The equation is:
 
-{equation}
+.. math::
+  {equation}
 
 X is a tensor of any dimension and the dimensions of tensor Y must be smaller than
 or equal to the dimensions of X. 
@@ -71,15 +72,16 @@ For case 2:
 Y will be broadcasted to match the shape of X and axis should be 
 the starting dimension index for broadcasting Y onto X.
 
-example:
-  shape(X) = (2, 3, 4, 5), shape(Y) = (,)
-  shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
-  shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
-  shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
-  shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+For example
+  .. code-block:: python
 
-Both the input X and Y can carry the LoD (Level of Details) information,
-or not. But the output only shares the LoD information with input X.
+    shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+    shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+    shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
+    shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+    shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+
+Either of the inputs X and Y or none can carry the LoD (Level of Details) information. However, the output only shares the LoD information with input X.
 
 )DOC";
     AddComment(comment_);
diff --git a/paddle/operators/elementwise_sub_op.cc b/paddle/operators/elementwise_sub_op.cc
index 2a8d0845b1..d3c51f0a69 100644
--- a/paddle/operators/elementwise_sub_op.cc
+++ b/paddle/operators/elementwise_sub_op.cc
@@ -21,7 +21,7 @@ class ElementwiseSubOpMaker : public ElementwiseOpMaker {
  public:
   ElementwiseSubOpMaker(OpProto* proto, OpAttrChecker* op_checker)
       : ElementwiseOpMaker(proto, op_checker) {
-    SetComment("Sub", "$Out = X - Y$");
+    SetComment("Sub", "Out = X - Y");
     AddComment(comment_);
   }
 };

From 9867a3796e2460e0cee899926974ea7e8bac590c Mon Sep 17 00:00:00 2001
From: kexinzhao <kexin.zhao.paddle@gmail.com>
Date: Wed, 10 Jan 2018 23:39:25 -0800
Subject: [PATCH 33/68] refine op_kernel_type doc (#7448)

---
 doc/design/operator_kernel_type.md | 32 +++++++++++++++---------------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/doc/design/operator_kernel_type.md b/doc/design/operator_kernel_type.md
index aa82e96bf7..f86e6b7a56 100644
--- a/doc/design/operator_kernel_type.md
+++ b/doc/design/operator_kernel_type.md
@@ -1,6 +1,6 @@
 # Design Doc: The Keys of Operator Kernel Type
 ## Problem
-An operator can have different kernel implementations, and each operator will have a map to store the related kernels. Fluid uses `OpKernelType` as a key to identify a unique Kernel. Before an operator runs, an certain kernel must be chosen by a key of `OpKernelType`. Currently, `OpKernelType` is defined as follows:
+An operator can have different kernel implementations, and each operator will have a map to store the related kernels. Fluid uses `OpKernelType` as a key to identify a unique kernel. Before an operator runs, a certain type of kernel must be chosen via a key of `OpKernelType`. Currently, `OpKernelType` is defined as follows:
 
 ```cpp
 struct OpKernelType {
@@ -10,13 +10,13 @@ struct OpKernelType {
 ```
 For more details, please refer to [codes](https://github.com/PaddlePaddle/Paddle/blob/2d5ec16bc8a09fb8e0f62c89b116b0cd1d333907/paddle/framework/operator.h#L348-L374) in github.
 
-It contains two keys, `Place` and `DataType`. And these two keys will be hashed to a unique key to represent a certain type of kernel. However, these two keys are not enough. We need a more complete representation of `OpKernelType`. 
+It contains two keys, `Place` and `DataType`. And these two keys will be hashed to a unique key to represent a certain type of kernel. However, these two keys do not provide enough information. We need a more complete representation of `OpKernelType`.
 
-We often implement a kernel of an operator with some computing library in certain device(place). Please remind that computing library and device are not one-to-one corresponding. A device can have a lot of computing libraries and a computing library can also support several devices. 
+We often implement a kernel of an operator with some computing library on certain device(place). Please note that computing library and device do not have a one-to-one correspondence. A device can have a lot of computing libraries and a computing library can also support different devices.
 
-For example, Eigen library can support Nvidia GPU/AMD GPU/CPU. And MKLDNN library can support Intel CPU/Intel FPGA. Both `Place` and `Library` should be a key of `OpKernelType`.
+For example, Eigen library supports Nvidia GPU/AMD GPU/CPU and MKLDNN library supports Intel CPU/Intel FPGA. Both `Place` and `Library` should be a key of `OpKernelType`.
 
-It's obvious that different DataTypes, like fp64/fp32/int8 will have different kernels. But the data layout of a Tensor will also lead to different implementation. Please refer to the batch norm operator [kernels](https://github.com/PaddlePaddle/Paddle/blob/a948fac4d0ad7e0412d373b8aabeb711c2899563/paddle/operators/batch_norm_op.cc#L180-L209). Data Layout should also be taken into consideration.
+Different DataTypes, such as fp64/fp32/int8, will obviously have different kernels. But different data layout of a Tensor will also lead to different implementations. Please refer to the batch norm operator [kernels](https://github.com/PaddlePaddle/Paddle/blob/a948fac4d0ad7e0412d373b8aabeb711c2899563/paddle/operators/batch_norm_op.cc#L180-L209) as an example. Data layout should also be taken into consideration.
 
 ## Solution
 
@@ -31,17 +31,17 @@ struct OpKernelType {
 };
 ```
 
-Following is the details:
+The details are as follows:
 
 ### Place
 
-`Place` is defined as follows:
+`Place` is defined as:
 
 ```cpp
 typedef boost::variant<CUDAPlace, ROCmPlace, FPGAPlace, CPUPlace> Place;
 ```
 
-`Place` is to represent the device memory where data is locating.
+`Place` represents the device memory where data is located.
 
 
 ### Library
@@ -52,10 +52,10 @@ One operator kernel is usually implemented based on one library. `Library` is de
 enum Library { Plain, MKLDNN, CUDNN };
 ```
 
-We use `Plain` enumerator to represent default library. Since most operators in Fluid are implemented based on `Eigen` library, we take `Eigen` library as the `Plain` enumerator.
-A library usually has a corresponding `DeviceContext` which contains some handles needed by computation. Fluid now have two default DeviceContexts in CPU and CUDA, `CPUDeviceContext` and `CUDADeviceContext`. `CPUDeviceContext` contains a Eigen library handle and `CDUADeviceContext` contains a Eigen library handle and cuBLAS handle.
+We use `Plain` enumerator to represent default library. Since most operators in Fluid are implemented based on the `Eigen` library, we take `Eigen` library as the `Plain` enumerator.
+A library usually has a corresponding `DeviceContext` which contains some handles needed for computation. Fluid now has two default DeviceContexts for CPU and CUDA, namely, `CPUDeviceContext` and `CUDADeviceContext`. `CPUDeviceContext` contains an Eigen library handle and `CDUADeviceContext` contains an Eigen library handle and a cuBLAS handle.
 
-If we want to support new Library, a new enumerator need to be added to `Library` and a new corresponding `LibraryDeviceContext` will be created.
+If we want to support new library, a new enumerator need to be added to `Library` and a corresponding new `LibraryDeviceContext` need to be created.
 
 
 ### DataType
@@ -67,15 +67,15 @@ If we want to support new Library, a new enumerator need to be added to `Library
 
 Actually, a Tensor is a view of a block of memory. Besides a pointer to the memory, we also have to get some other descriptions of this block of memory, such as shape(ddim), stride, and layout.
 
-Different layout leads to different implementation of operator kernel. There are mainly 4 principles we have to follow to support layout in our fluid framework.
+Different layout leads to different implementation of the operator kernel. There are mainly 4 principles we have to follow to support layout in our Fluid framework.
 
-- We take layout as a data member of Tensor. Layout is actually a enum variable. If fluid is built with MKLDNN, then, the memory format in MKLDNN will be added into this enum variable too.
+- We take layout as a data member of Tensor. Layout is actually a enum variable. If Fluid is built with MKLDNN, then the memory format in MKLDNN will also be added into this enum variable.
 
-- Users have to set layout for input data. And some operators like fill_constant/random, also have to set layout of generating data. Of course, we can have some default layout, like NCHW.
+- Users have to set layout for input data. And some operators like fill_constant/random, also have to set layout for generating data. Of course, we can have some default layout, like NCHW.
 
-- The inference of Layout is at run-time, not compile-time.
+- The inference of Layout is at run-time, not at compile-time.
 
-- Every operator have to implement different kernels for different layouts. Let's take MKLDNN as an example, if we want to implement a MKLDNN convolution operator, we have to realize all the kernels for different layout, list at [here](http://01org.github.io/mkl-dnn/structmkldnn_1_1memory.html). And we will have a special macro to do registering kernels for MKLDNN operators.
+- Every operator has to implement different kernels for different layouts. Let's take MKLDNN as an example. If we want to implement an MKLDNN convolution operator, we have to implement all the kernels for different layouts, which are listed [here](http://01org.github.io/mkl-dnn/structmkldnn_1_1memory.html). And we will have a special macro to  register kernels for MKLDNN operators.
 
 `Layout` is also defined as a enum variable:
 

From 5325313e4cb1fb8c45cffcc223239cf5d85620af Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Thu, 11 Jan 2018 20:06:13 +0800
Subject: [PATCH 34/68] debugging shape match

---
 .../paddle/v2/fluid/distribute_transpiler.py  | 95 +++++++++++++++----
 1 file changed, 79 insertions(+), 16 deletions(-)

diff --git a/python/paddle/v2/fluid/distribute_transpiler.py b/python/paddle/v2/fluid/distribute_transpiler.py
index b064220ca2..75e103cb80 100644
--- a/python/paddle/v2/fluid/distribute_transpiler.py
+++ b/python/paddle/v2/fluid/distribute_transpiler.py
@@ -257,7 +257,45 @@ class DistributeTranspiler:
             pass
         return orig_shape
 
-    def _append_pserver_ops(self, program, opt_op, endpoint):
+    def _is_op_on_pserver(self, endpoint, all_ops, idx):
+        """
+        Recursively check if the op need to run on current server.
+        Assume that ops are in the execution order.
+        """
+        param_names = [
+            p.name for p in self.param_grad_ep_mapping[endpoint]["params"]
+        ]
+        op = all_ops[idx]
+        if op.inputs.has_key("Param"):
+            if op.inputs["Param"].name in param_names:
+                return True
+            else:
+                for n in param_names:
+                    if n.startswith(op.inputs["Param"].name+".block") and \
+                        n != op.inputs["Param"].name:
+                        return True
+                return False
+        else:
+            j = idx - 1
+            while j >= 0:
+                prev_op = all_ops[j]
+                prev_output_names = [o.name for o in prev_op.outputs.values()]
+                prev_input_names = [o.name for o in prev_op.inputs.values()]
+                found1 = False
+                found2 = False
+                for _, v in op.inputs.iteritems():
+                    if v.name in prev_output_names:
+                        found1 = self._is_op_on_pserver(endpoint, all_ops, j)
+                # later ops may produce output for prev op's next batch use.
+                for _, v in op.outputs.iteritems():
+                    if v.name in prev_input_names:
+                        found2 = self._is_op_on_pserver(endpoint, all_ops, j)
+                if found1 or found2:
+                    return True
+                j -= 1
+            return False
+
+    def _append_pserver_ops(self, program, pserver_program, opt_op, endpoint):
         new_inputs = dict()
         # update param/grad shape first, then other inputs like
         # moment can use the updated shape
@@ -321,6 +359,14 @@ class DistributeTranspiler:
                 dtype=var.dtype,
                 shape=new_shape)
             new_inputs[key] = tmpvar
+            # create var in pserver program global block.
+            # TODO(typhoonzero): put blocks in one program to avoid create two
+            # variables.
+            pserver_program.global_block().create_var(
+                name=var.name,
+                persistable=var.persistable,
+                dtype=var.dtype,
+                shape=new_shape)
 
         # change outputs ParamOut variable
         opt_op.outputs["ParamOut"] = new_inputs["Param"]
@@ -330,13 +376,18 @@ class DistributeTranspiler:
             outputs=opt_op.outputs,
             attrs=opt_op.attrs)
 
-    def _append_pserver_non_opt_ops(self, program, opt_op):
+    def _append_pserver_non_opt_ops(self, program, pserver_program, opt_op):
         for _, var in opt_op.inputs.iteritems():
             program.global_block().create_var(
                 name=var.name,
                 persistable=var.persistable,
                 dtype=var.dtype,
                 shape=var.shape)
+            pserver_program.global_block().create_var(
+                name=var.name,
+                persistable=var.persistable,
+                dtype=var.dtype,
+                shape=var.shape)
         program.global_block().append_op(
             type=opt_op.type,
             inputs=opt_op.inputs,
@@ -358,13 +409,18 @@ class DistributeTranspiler:
             self._clone_var(pserver_program.global_block(), v)
         # step6
         optimize_sub_program = Program()
-        for opt_op in optimize_ops:
+        for idx, opt_op in enumerate(optimize_ops):
+            is_op_on_pserver = self._is_op_on_pserver(endpoint, optimize_ops,
+                                                      idx)
+            if not is_op_on_pserver:
+                continue
             if opt_op.inputs.has_key("Grad"):
-                # append optimize_op
-                self._append_pserver_ops(optimize_sub_program, opt_op, endpoint)
+                self._append_pserver_ops(optimize_sub_program, pserver_program,
+                                         opt_op, endpoint)
             else:
-                self._append_pserver_non_opt_ops(optimize_sub_program, opt_op)
-
+                self._append_pserver_non_opt_ops(optimize_sub_program,
+                                                 pserver_program, opt_op)
+        print("****subprogram", optimize_sub_program)
         pserver_program.global_block().append_op(
             type="recv",
             inputs={"RX": self.param_grad_ep_mapping[endpoint]["grads"]
@@ -386,7 +442,7 @@ class DistributeTranspiler:
         pserver_program.sync_with_cpp()
         return pserver_program
 
-    def get_startup_program(self, endpoint):
+    def get_startup_program(self, endpoint, pserver_program):
         """
         Get startup program for current parameter server.
         Modify operator input variables if there are variables that
@@ -405,13 +461,17 @@ class DistributeTranspiler:
 
         # 1. create vars
         created_var_map = dict()
-        for var in params:
+        for _, var in pserver_program.global_block().vars.iteritems():
+            print("create var for startup", var.name, var.shape)
             tmpvar = s_prog.global_block().create_var(
                 name=var.name,
-                persistable=True,
+                persistable=var.persistable,
                 dtype=var.dtype,
                 shape=var.shape)
             created_var_map[var.name] = tmpvar
+        optimize_op_input_var_names = [
+            v.name for v in pserver_program.global_block().vars.values()
+        ]
 
         # 2. rename op outputs
         for op in orig_s_prog.global_block().ops:
@@ -423,13 +483,16 @@ class DistributeTranspiler:
                 else:
                     new_outputs[key] = var
             # do not append startup op if var is not on this pserver
-            var_on_pserver = False
-            for _, var in new_outputs.iteritems():
-                if var.name in created_var_map:
-                    var_on_pserver = True
-            if var_on_pserver:
+            op_on_pserver = False
+            for _, var in op.outputs.iteritems():
+                if var.name in optimize_op_input_var_names:
+                    op_on_pserver = True
+                    break
+
+            if op_on_pserver:
                 # gaussian_random use attr to determine tensor shape
-                op.attrs["shape"] = new_outputs["Out"].shape
+                if op.type in ["gaussian_random", "fill_constant"]:
+                    op.attrs["shape"] = new_outputs["Out"].shape
                 s_prog.global_block().append_op(
                     type=op.type,
                     inputs=op.inputs,

From 5d901d00bf9c93225548d707b1c3b79634b801b4 Mon Sep 17 00:00:00 2001
From: "yi.wu" <yi.wu@baifendian.com>
Date: Thu, 11 Jan 2018 22:41:24 +0800
Subject: [PATCH 35/68] update

---
 .../paddle/v2/fluid/distribute_transpiler.py  | 33 +++++++++++--------
 1 file changed, 20 insertions(+), 13 deletions(-)

diff --git a/python/paddle/v2/fluid/distribute_transpiler.py b/python/paddle/v2/fluid/distribute_transpiler.py
index 75e103cb80..59e74e0d6f 100644
--- a/python/paddle/v2/fluid/distribute_transpiler.py
+++ b/python/paddle/v2/fluid/distribute_transpiler.py
@@ -459,9 +459,10 @@ class DistributeTranspiler:
                     return pname, splited_param.shape
             return "", []
 
-        # 1. create vars
+        # 1. create vars in pserver program to startup program
+        pserver_vars = pserver_program.global_block().vars
         created_var_map = dict()
-        for _, var in pserver_program.global_block().vars.iteritems():
+        for _, var in pserver_vars.iteritems():
             print("create var for startup", var.name, var.shape)
             tmpvar = s_prog.global_block().create_var(
                 name=var.name,
@@ -469,30 +470,36 @@ class DistributeTranspiler:
                 dtype=var.dtype,
                 shape=var.shape)
             created_var_map[var.name] = tmpvar
-        optimize_op_input_var_names = [
-            v.name for v in pserver_program.global_block().vars.values()
-        ]
 
         # 2. rename op outputs
         for op in orig_s_prog.global_block().ops:
             new_outputs = dict()
+            # do not append startup op if var is not on this pserver
+            op_on_pserver = False
             for key, var in op.outputs.iteritems():
                 newname, _ = _get_splited_name_and_shape(var.name)
                 if newname:
+                    op_on_pserver = True
                     new_outputs[key] = created_var_map[newname]
-                else:
-                    new_outputs[key] = var
-            # do not append startup op if var is not on this pserver
-            op_on_pserver = False
-            for _, var in op.outputs.iteritems():
-                if var.name in optimize_op_input_var_names:
+                elif var.name in pserver_vars:
                     op_on_pserver = True
-                    break
+                    new_outputs[key] = pserver_vars[var.name]
+
+                # newname, _ = _get_splited_name_and_shape(var.name)
+                # if newname:
+                #     print("updating output", newname, created_var_map[newname])
+                #     new_outputs[key] = created_var_map[newname]
+                # else:
+                #     print("no update output", key, var)
+                #     new_outputs[key] = var
+                # if var.name in created_var_map or \
+                #     newname:
+                #     op_on_pserver = True
 
             if op_on_pserver:
-                # gaussian_random use attr to determine tensor shape
                 if op.type in ["gaussian_random", "fill_constant"]:
                     op.attrs["shape"] = new_outputs["Out"].shape
+                print("updated shape", op.attrs["shape"])
                 s_prog.global_block().append_op(
                     type=op.type,
                     inputs=op.inputs,

From ea782e38a66bfd494fe08610037e65093ef65489 Mon Sep 17 00:00:00 2001
From: Abhinav Arora <abhinavarora28@gmail.com>
Date: Thu, 11 Jan 2018 10:29:24 -0800
Subject: [PATCH 36/68] Fix typo in batch norm bias initialization (#7449)

---
 python/paddle/v2/fluid/layers/nn.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/paddle/v2/fluid/layers/nn.py b/python/paddle/v2/fluid/layers/nn.py
index 48a6bee558..1fb6523f55 100644
--- a/python/paddle/v2/fluid/layers/nn.py
+++ b/python/paddle/v2/fluid/layers/nn.py
@@ -983,7 +983,7 @@ def batch_norm(input,
         default_initializer=Constant(1.0))
 
     bias = helper.create_parameter(
-        attr=helper.param_attr, shape=param_shape, dtype=dtype, is_bias=True)
+        attr=helper.bias_attr, shape=param_shape, dtype=dtype, is_bias=True)
 
     mean = helper.create_global_variable(
         dtype=input.dtype,

From 6c0723661e7a3197f0589386cc985a08e3ea581f Mon Sep 17 00:00:00 2001
From: Helin Wang <ustc.harry@gmail.com>
Date: Thu, 11 Jan 2018 11:17:40 -0800
Subject: [PATCH 37/68] Add distributed label semantic role book chapter

---
 .../test_dist_label_semantic_roles.py         | 225 ++++++++++++++++++
 1 file changed, 225 insertions(+)
 create mode 100644 python/paddle/v2/fluid/tests/book_distribute/test_dist_label_semantic_roles.py

diff --git a/python/paddle/v2/fluid/tests/book_distribute/test_dist_label_semantic_roles.py b/python/paddle/v2/fluid/tests/book_distribute/test_dist_label_semantic_roles.py
new file mode 100644
index 0000000000..5fa5e0e5f3
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/book_distribute/test_dist_label_semantic_roles.py
@@ -0,0 +1,225 @@
+import math
+
+import numpy as np
+import paddle.v2 as paddle
+import paddle.v2.dataset.conll05 as conll05
+import paddle.v2.fluid as fluid
+import time
+import os
+
+word_dict, verb_dict, label_dict = conll05.get_dict()
+word_dict_len = len(word_dict)
+label_dict_len = len(label_dict)
+pred_len = len(verb_dict)
+
+mark_dict_len = 2
+word_dim = 32
+mark_dim = 5
+hidden_dim = 512
+depth = 8
+mix_hidden_lr = 1e-3
+
+IS_SPARSE = True
+PASS_NUM = 10
+BATCH_SIZE = 20
+
+embedding_name = 'emb'
+
+
+def load_parameter(file_name, h, w):
+    with open(file_name, 'rb') as f:
+        f.read(16)  # skip header.
+        return np.fromfile(f, dtype=np.float32).reshape(h, w)
+
+
+def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
+            **ignored):
+    # 8 features
+    predicate_embedding = fluid.layers.embedding(
+        input=predicate,
+        size=[pred_len, word_dim],
+        dtype='float32',
+        is_sparse=IS_SPARSE,
+        param_attr='vemb')
+
+    mark_embedding = fluid.layers.embedding(
+        input=mark,
+        size=[mark_dict_len, mark_dim],
+        dtype='float32',
+        is_sparse=IS_SPARSE)
+
+    word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
+    emb_layers = [
+        fluid.layers.embedding(
+            size=[word_dict_len, word_dim],
+            input=x,
+            param_attr=fluid.ParamAttr(
+                name=embedding_name, trainable=False)) for x in word_input
+    ]
+    emb_layers.append(predicate_embedding)
+    emb_layers.append(mark_embedding)
+
+    hidden_0_layers = [
+        fluid.layers.fc(input=emb, size=hidden_dim) for emb in emb_layers
+    ]
+
+    hidden_0 = fluid.layers.sums(input=hidden_0_layers)
+
+    lstm_0 = fluid.layers.dynamic_lstm(
+        input=hidden_0,
+        size=hidden_dim,
+        candidate_activation='relu',
+        gate_activation='sigmoid',
+        cell_activation='sigmoid')
+
+    # stack L-LSTM and R-LSTM with direct edges
+    input_tmp = [hidden_0, lstm_0]
+
+    for i in range(1, depth):
+        mix_hidden = fluid.layers.sums(input=[
+            fluid.layers.fc(input=input_tmp[0], size=hidden_dim),
+            fluid.layers.fc(input=input_tmp[1], size=hidden_dim)
+        ])
+
+        lstm = fluid.layers.dynamic_lstm(
+            input=mix_hidden,
+            size=hidden_dim,
+            candidate_activation='relu',
+            gate_activation='sigmoid',
+            cell_activation='sigmoid',
+            is_reverse=((i % 2) == 1))
+
+        input_tmp = [mix_hidden, lstm]
+
+    feature_out = fluid.layers.sums(input=[
+        fluid.layers.fc(input=input_tmp[0], size=label_dict_len),
+        fluid.layers.fc(input=input_tmp[1], size=label_dict_len)
+    ])
+
+    return feature_out
+
+
+def to_lodtensor(data, place):
+    seq_lens = [len(seq) for seq in data]
+    cur_len = 0
+    lod = [cur_len]
+    for l in seq_lens:
+        cur_len += l
+        lod.append(cur_len)
+    flattened_data = np.concatenate(data, axis=0).astype("int64")
+    flattened_data = flattened_data.reshape([len(flattened_data), 1])
+    res = fluid.LoDTensor()
+    res.set(flattened_data, place)
+    res.set_lod([lod])
+    return res
+
+
+def main():
+    # define network topology
+    word = fluid.layers.data(
+        name='word_data', shape=[1], dtype='int64', lod_level=1)
+    predicate = fluid.layers.data(
+        name='verb_data', shape=[1], dtype='int64', lod_level=1)
+    ctx_n2 = fluid.layers.data(
+        name='ctx_n2_data', shape=[1], dtype='int64', lod_level=1)
+    ctx_n1 = fluid.layers.data(
+        name='ctx_n1_data', shape=[1], dtype='int64', lod_level=1)
+    ctx_0 = fluid.layers.data(
+        name='ctx_0_data', shape=[1], dtype='int64', lod_level=1)
+    ctx_p1 = fluid.layers.data(
+        name='ctx_p1_data', shape=[1], dtype='int64', lod_level=1)
+    ctx_p2 = fluid.layers.data(
+        name='ctx_p2_data', shape=[1], dtype='int64', lod_level=1)
+    mark = fluid.layers.data(
+        name='mark_data', shape=[1], dtype='int64', lod_level=1)
+    feature_out = db_lstm(**locals())
+    target = fluid.layers.data(
+        name='target', shape=[1], dtype='int64', lod_level=1)
+    crf_cost = fluid.layers.linear_chain_crf(
+        input=feature_out,
+        label=target,
+        param_attr=fluid.ParamAttr(
+            name='crfw', learning_rate=mix_hidden_lr))
+    avg_cost = fluid.layers.mean(x=crf_cost)
+
+    # TODO(qiao)
+    # check other optimizers and check why out will be NAN
+    sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.0001)
+    optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
+
+    # TODO(qiao)
+    # add dependency track and move this config before optimizer
+    crf_decode = fluid.layers.crf_decoding(
+        input=feature_out, param_attr=fluid.ParamAttr(name='crfw'))
+
+    chunk_evaluator = fluid.evaluator.ChunkEvaluator(
+        input=crf_decode,
+        label=target,
+        chunk_scheme="IOB",
+        num_chunk_types=int(math.ceil((label_dict_len - 1) / 2.0)))
+
+    train_data = paddle.batch(
+        paddle.reader.shuffle(
+            paddle.dataset.conll05.test(), buf_size=8192),
+        batch_size=BATCH_SIZE)
+    place = fluid.CPUPlace()
+    feeder = fluid.DataFeeder(
+        feed_list=[
+            word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, predicate, mark, target
+        ],
+        place=place)
+    exe = fluid.Executor(place)
+
+    t = fluid.DistributeTranspiler()
+    pserver_endpoints = os.getenv("PSERVERS")
+    # server endpoint for current node
+    current_endpoint = os.getenv("SERVER_ENDPOINT")
+    # run as trainer or parameter server
+    training_role = os.getenv(
+        "TRAINING_ROLE", "TRAINER")  # get the training role: trainer/pserver
+    t.transpile(
+        optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)
+
+    if training_role == "PSERVER":
+        if not current_endpoint:
+            print("need env SERVER_ENDPOINT")
+            exit(1)
+        pserver_prog = t.get_pserver_program(current_endpoint, optimize_ops)
+        exe.run(fluid.default_startup_program())
+        exe.run(pserver_prog)
+    elif training_role == "TRAINER":
+        trainer_prog = t.get_trainer_program()
+        start_time = time.time()
+        batch_id = 0
+        exe.run(fluid.default_startup_program())
+        embedding_param = fluid.global_scope().find_var(
+            embedding_name).get_tensor()
+        embedding_param.set(
+            load_parameter(conll05.get_embedding(), word_dict_len, word_dim),
+            place)
+        for pass_id in xrange(PASS_NUM):
+            chunk_evaluator.reset(exe)
+            for data in train_data():
+                cost, precision, recall, f1_score = exe.run(
+                    trainer_prog,
+                    feed=feeder.feed(data),
+                    fetch_list=[avg_cost] + chunk_evaluator.metrics)
+                pass_precision, pass_recall, pass_f1_score = chunk_evaluator.eval(
+                    exe)
+
+                if batch_id % 10 == 0:
+                    print("avg_cost:" + str(cost) + " precision:" + str(
+                        precision) + " recall:" + str(recall) + " f1_score:" +
+                          str(f1_score) + " pass_precision:" + str(
+                              pass_precision) + " pass_recall:" + str(
+                                  pass_recall) + " pass_f1_score:" + str(
+                                      pass_f1_score))
+                    if batch_id != 0:
+                        print("second per batch: " + str((time.time(
+                        ) - start_time) / batch_id))
+
+                batch_id = batch_id + 1
+
+
+if __name__ == '__main__':
+    main()

From ce233796ea33e029304b710c98ed933930c76efb Mon Sep 17 00:00:00 2001
From: xuwei06 <xuwei06@baidu.com>
Date: Mon, 8 Jan 2018 17:37:37 -0800
Subject: [PATCH 38/68] assign_value operator

We need this operator to assign value to a tensor and the values are stored in the program so that they can be used independent of python.
---
 paddle/operators/assign_value_op.cc           | 82 +++++++++++++++++++
 paddle/operators/assign_value_op.cu.cc        | 36 ++++++++
 paddle/operators/assign_value_op.h            | 55 +++++++++++++
 python/paddle/v2/fluid/layers/tensor.py       | 37 +++++++--
 .../v2/fluid/tests/test_assign_value_op.py    | 38 +++++++++
 5 files changed, 242 insertions(+), 6 deletions(-)
 create mode 100644 paddle/operators/assign_value_op.cc
 create mode 100644 paddle/operators/assign_value_op.cu.cc
 create mode 100644 paddle/operators/assign_value_op.h
 create mode 100644 python/paddle/v2/fluid/tests/test_assign_value_op.py

diff --git a/paddle/operators/assign_value_op.cc b/paddle/operators/assign_value_op.cc
new file mode 100644
index 0000000000..a0bce99ff3
--- /dev/null
+++ b/paddle/operators/assign_value_op.cc
@@ -0,0 +1,82 @@
+/* Copyright (c) 2018 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/assign_value_op.h"
+
+namespace paddle {
+namespace operators {
+
+class AssignValueOp : public framework::OperatorWithKernel {
+ public:
+  AssignValueOp(const std::string &type,
+                const framework::VariableNameMap &inputs,
+                const framework::VariableNameMap &outputs,
+                const framework::AttributeMap &attrs)
+      : OperatorWithKernel(type, inputs, outputs, attrs) {}
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of AssignValueOp should not be null.");
+    auto shape = ctx->Attrs().Get<std::vector<int>>("shape");
+    ctx->SetOutputDim("Out", framework::make_ddim(shape));
+  }
+
+ protected:
+  framework::OpKernelType GetActualKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    return framework::OpKernelType(
+        framework::proto::DataType(ctx.Attr<int>("dtype")), ctx.GetPlace());
+  }
+};
+
+class AssignValueOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  AssignValueOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddOutput("Out", "(Tensor) Output tensor of assign_value operator.");
+    AddAttr<std::vector<int>>("shape",
+                              "(vector<int>) "
+                              "Shape of values.");
+    AddAttr<int>("dtype", "data type of values")
+        .InEnum({framework::proto::DataType::INT32,
+                 framework::proto::DataType::FP32});
+    AddAttr<std::vector<float>>("fp32_values", "store the float values")
+        .SetDefault({});
+    AddAttr<std::vector<int>>("int32_values", "store the int values")
+        .SetDefault({});
+    AddComment(R"DOC(
+AssignValue operator
+
+$$Out = values$$
+)DOC");
+  }
+};
+
+template <typename T>
+class AssignValueCPUKernel : public AssignValueKernel<T> {
+ protected:
+  virtual void Copy(void *dst, const void *src, size_t size,
+                    const framework::ExecutionContext &ctx) const {
+    std::memcpy(dst, src, size);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OPERATOR(assign_value, ops::AssignValueOp, ops::AssignValueOpMaker);
+REGISTER_OP_CPU_KERNEL(assign_value, ops::AssignValueCPUKernel<int>,
+                       ops::AssignValueCPUKernel<float>)
diff --git a/paddle/operators/assign_value_op.cu.cc b/paddle/operators/assign_value_op.cu.cc
new file mode 100644
index 0000000000..8afb032037
--- /dev/null
+++ b/paddle/operators/assign_value_op.cu.cc
@@ -0,0 +1,36 @@
+/* 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.
+Indicesou 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/assign_value_op.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+class AssignValueGPUKernel : public AssignValueKernel<T> {
+ protected:
+  virtual void Copy(void* dst, const void* src, size_t size,
+                    const framework::ExecutionContext& ctx) const {
+    auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
+    paddle::platform::GpuMemcpyAsync(dst, src, size, cudaMemcpyHostToDevice,
+                                     dev_ctx.stream());
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(assign_value, ops::AssignValueGPUKernel<int>,
+                        ops::AssignValueGPUKernel<float>);
diff --git a/paddle/operators/assign_value_op.h b/paddle/operators/assign_value_op.h
new file mode 100644
index 0000000000..bdb5bce272
--- /dev/null
+++ b/paddle/operators/assign_value_op.h
@@ -0,0 +1,55 @@
+/* Copyright (c) 2018 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"
+#include "paddle/platform/enforce.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+class AssignValueKernel : public framework::OpKernel<T> {
+ public:
+  virtual void Compute(const framework::ExecutionContext& ctx) const {
+    auto shape = ctx.Attr<std::vector<int>>("shape");
+    auto* out = ctx.Output<framework::Tensor>("Out");
+    out->Resize(framework::make_ddim(shape));
+    auto* dst = out->mutable_data<T>(ctx.GetPlace());
+    int dtype = ctx.Attr<int>("dtype");
+    const char* value_name = nullptr;
+    switch (dtype) {
+      case framework::proto::DataType::INT32:
+        value_name = "int32_values";
+        break;
+      case framework::proto::DataType::FP32:
+        value_name = "fp32_values";
+        break;
+      default:
+        PADDLE_THROW("Unsupported dtype for assign_value_op: %d", dtype);
+        break;
+    }
+    auto values = ctx.Attr<std::vector<T>>(value_name);
+    Copy(dst, values.data(), sizeof(T) * values.size(), ctx);
+  }
+
+ protected:
+  virtual void Copy(void* dst, const void* src, size_t size,
+                    const framework::ExecutionContext& ctx) const = 0;
+};
+
+}  // namespace operators
+}  // namespace paddle
diff --git a/python/paddle/v2/fluid/layers/tensor.py b/python/paddle/v2/fluid/layers/tensor.py
index 5f12ecfc14..639f8b03ed 100644
--- a/python/paddle/v2/fluid/layers/tensor.py
+++ b/python/paddle/v2/fluid/layers/tensor.py
@@ -1,5 +1,9 @@
 from ..layer_helper import LayerHelper
 from ..param_attr import ParamAttr
+from ..framework import convert_np_dtype_to_dtype_
+from ..framework import Variable
+from ..core import DataType
+import numpy
 
 __all__ = [
     'create_tensor', 'create_parameter', 'cast', 'concat', 'sums', 'assign',
@@ -121,7 +125,7 @@ def assign(input, output):
     This function copies the *input* Variable to the *output* Variable.
 
     Args:
-        input(Variable): The source variable
+        input(Variable|numpy.ndarray): The source variable
         output(Variable): The destination variable
 
     Returns:
@@ -134,11 +138,32 @@ def assign(input, output):
           fluid.layers.assign(hidden, out)
     """
     helper = LayerHelper('assign', **locals())
-    helper.append_op(
-        type='scale',
-        inputs={'X': [input]},
-        outputs={'Out': [output]},
-        attrs={'scale': 1.0})
+    if isinstance(input, Variable):
+        helper.append_op(
+            type='scale',
+            inputs={'X': [input]},
+            outputs={'Out': [output]},
+            attrs={'scale': 1.0})
+    elif isinstance(input, numpy.ndarray):
+        dtype = convert_np_dtype_to_dtype_(input.dtype)
+        if dtype == DataType.FP32:
+            value_name = "fp32_values"
+        elif dtype == DataType.INT32:
+            value_name = "int32_values"
+        else:
+            raise ValueError("Unsupported dtype %s", input.dtype)
+
+        helper.append_op(
+            type='assign_value',
+            outputs={'Out': [output]},
+            attrs={
+                'dtype': dtype,
+                'shape': list(input.shape),
+                value_name: [float(v) for v in input.flat]
+            })
+    else:
+        raise ValueError("Wrong type for assign input: %s" % type(input))
+
     return output
 
 
diff --git a/python/paddle/v2/fluid/tests/test_assign_value_op.py b/python/paddle/v2/fluid/tests/test_assign_value_op.py
new file mode 100644
index 0000000000..c3f3f87839
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/test_assign_value_op.py
@@ -0,0 +1,38 @@
+import paddle.v2.fluid as fluid
+import paddle.v2.fluid.layers as layers
+import op_test
+import numpy
+import unittest
+import paddle.v2.fluid.framework as framework
+
+
+class TestAssignValueOp(op_test.OpTest):
+    def setUp(self):
+        self.op_type = "assign_value"
+        x = numpy.random.random(size=(2, 5)).astype(numpy.float32)
+        self.inputs = {}
+        self.outputs = {'Out': x}
+        self.attrs = {
+            'shape': x.shape,
+            'dtype': framework.convert_np_dtype_to_dtype_(x.dtype),
+            'fp32_values': [float(v) for v in x.flat]
+        }
+
+    def test_forward(self):
+        self.check_output()
+
+    def test_assign(self):
+        val = numpy.random.random(size=(2, 5)).astype(numpy.float32)
+        x = layers.create_tensor(dtype="float32")
+        layers.assign(input=val, output=x)
+        exe = fluid.Executor(fluid.CPUPlace())
+        fetched_x = exe.run(fluid.default_main_program(),
+                            feed={},
+                            fetch_list=[x])
+        self.assertTrue(
+            numpy.allclose(fetched_x, val),
+            "fetch_x=%s val=%s" % (fetched_x, val))
+
+
+if __name__ == '__main__':
+    unittest.main()

From 7306aab61d9ca6c85b8a707d520059a325a97d5c Mon Sep 17 00:00:00 2001
From: xuwei06 <xuwei06@baidu.com>
Date: Wed, 10 Jan 2018 10:00:49 -0800
Subject: [PATCH 39/68] GetActualKernelType => GetExpectedKernelType

---
 paddle/operators/assign_value_op.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/paddle/operators/assign_value_op.cc b/paddle/operators/assign_value_op.cc
index a0bce99ff3..7f6d351f50 100644
--- a/paddle/operators/assign_value_op.cc
+++ b/paddle/operators/assign_value_op.cc
@@ -33,7 +33,7 @@ class AssignValueOp : public framework::OperatorWithKernel {
   }
 
  protected:
-  framework::OpKernelType GetActualKernelType(
+  framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext &ctx) const override {
     return framework::OpKernelType(
         framework::proto::DataType(ctx.Attr<int>("dtype")), ctx.GetPlace());

From 237385cf414bf2e176a52c46e650e37a2cfc40a7 Mon Sep 17 00:00:00 2001
From: xuwei06 <xuwei06@baidu.com>
Date: Wed, 10 Jan 2018 11:39:03 -0800
Subject: [PATCH 40/68] Correctly handle int values for assign_value_op

---
 python/paddle/v2/fluid/layers/tensor.py              | 7 ++++++-
 python/paddle/v2/fluid/tests/test_assign_value_op.py | 8 +++++---
 2 files changed, 11 insertions(+), 4 deletions(-)

diff --git a/python/paddle/v2/fluid/layers/tensor.py b/python/paddle/v2/fluid/layers/tensor.py
index 639f8b03ed..57668a7983 100644
--- a/python/paddle/v2/fluid/layers/tensor.py
+++ b/python/paddle/v2/fluid/layers/tensor.py
@@ -148,10 +148,15 @@ def assign(input, output):
         dtype = convert_np_dtype_to_dtype_(input.dtype)
         if dtype == DataType.FP32:
             value_name = "fp32_values"
+            values = [float(v) for v in input.flat]
         elif dtype == DataType.INT32:
             value_name = "int32_values"
+            values = [int(v) for v in input.flat]
         else:
             raise ValueError("Unsupported dtype %s", input.dtype)
+        if input.size > 1024 * 1024:
+            raise ValueError("The size of input is too big. Please consider "
+                             "saving it to file and 'load_op' to load it")
 
         helper.append_op(
             type='assign_value',
@@ -159,7 +164,7 @@ def assign(input, output):
             attrs={
                 'dtype': dtype,
                 'shape': list(input.shape),
-                value_name: [float(v) for v in input.flat]
+                value_name: values
             })
     else:
         raise ValueError("Wrong type for assign input: %s" % type(input))
diff --git a/python/paddle/v2/fluid/tests/test_assign_value_op.py b/python/paddle/v2/fluid/tests/test_assign_value_op.py
index c3f3f87839..51b99d0918 100644
--- a/python/paddle/v2/fluid/tests/test_assign_value_op.py
+++ b/python/paddle/v2/fluid/tests/test_assign_value_op.py
@@ -22,16 +22,18 @@ class TestAssignValueOp(op_test.OpTest):
         self.check_output()
 
     def test_assign(self):
-        val = numpy.random.random(size=(2, 5)).astype(numpy.float32)
+        val = (
+            -100 + 200 * numpy.random.random(size=(2, 5))).astype(numpy.int32)
         x = layers.create_tensor(dtype="float32")
         layers.assign(input=val, output=x)
         exe = fluid.Executor(fluid.CPUPlace())
         fetched_x = exe.run(fluid.default_main_program(),
                             feed={},
-                            fetch_list=[x])
+                            fetch_list=[x])[0]
         self.assertTrue(
-            numpy.allclose(fetched_x, val),
+            numpy.array_equal(fetched_x, val),
             "fetch_x=%s val=%s" % (fetched_x, val))
+        self.assertEqual(fetched_x.dtype, val.dtype)
 
 
 if __name__ == '__main__':

From 25ecd2061ae13656653fbd89c2216375e0cd9e55 Mon Sep 17 00:00:00 2001
From: xuwei06 <xuwei06@baidu.com>
Date: Thu, 11 Jan 2018 12:52:54 -0800
Subject: [PATCH 41/68] Use CopyFromVector for assign_value_op

---
 paddle/framework/tensor_util.h         |  4 ++--
 paddle/operators/assign_value_op.cc    | 13 ++-----------
 paddle/operators/assign_value_op.cu.cc | 21 ++-------------------
 paddle/operators/assign_value_op.h     |  9 ++-------
 4 files changed, 8 insertions(+), 39 deletions(-)

diff --git a/paddle/framework/tensor_util.h b/paddle/framework/tensor_util.h
index f541d2ba69..091b63bf0f 100644
--- a/paddle/framework/tensor_util.h
+++ b/paddle/framework/tensor_util.h
@@ -116,8 +116,8 @@ inline void Copy(const Tensor& src, const platform::Place& dst_place,
  * @param[in] src        The external tensor.
  * @param[in] ctx        The device context contains device resources.
  *
- * * @note    CopyFromVector assumes that the tensor has been resized
- *            before invoking.
+ * * @note    CopyFromVector will resize dst to an 1D tensor with the same
+ *            size as src.
  */
 template <typename T>
 inline void CopyFromVector(const std::vector<T>& src,
diff --git a/paddle/operators/assign_value_op.cc b/paddle/operators/assign_value_op.cc
index 7f6d351f50..d5671c1183 100644
--- a/paddle/operators/assign_value_op.cc
+++ b/paddle/operators/assign_value_op.cc
@@ -63,20 +63,11 @@ $$Out = values$$
   }
 };
 
-template <typename T>
-class AssignValueCPUKernel : public AssignValueKernel<T> {
- protected:
-  virtual void Copy(void *dst, const void *src, size_t size,
-                    const framework::ExecutionContext &ctx) const {
-    std::memcpy(dst, src, size);
-  }
-};
-
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 
 REGISTER_OPERATOR(assign_value, ops::AssignValueOp, ops::AssignValueOpMaker);
-REGISTER_OP_CPU_KERNEL(assign_value, ops::AssignValueCPUKernel<int>,
-                       ops::AssignValueCPUKernel<float>)
+REGISTER_OP_CPU_KERNEL(assign_value, ops::AssignValueKernel<int>,
+                       ops::AssignValueKernel<float>);
diff --git a/paddle/operators/assign_value_op.cu.cc b/paddle/operators/assign_value_op.cu.cc
index 8afb032037..b17e201500 100644
--- a/paddle/operators/assign_value_op.cu.cc
+++ b/paddle/operators/assign_value_op.cu.cc
@@ -14,23 +14,6 @@ limitations under the License. */
 
 #include "paddle/operators/assign_value_op.h"
 
-namespace paddle {
-namespace operators {
-
-template <typename T>
-class AssignValueGPUKernel : public AssignValueKernel<T> {
- protected:
-  virtual void Copy(void* dst, const void* src, size_t size,
-                    const framework::ExecutionContext& ctx) const {
-    auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
-    paddle::platform::GpuMemcpyAsync(dst, src, size, cudaMemcpyHostToDevice,
-                                     dev_ctx.stream());
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
 namespace ops = paddle::operators;
-REGISTER_OP_CUDA_KERNEL(assign_value, ops::AssignValueGPUKernel<int>,
-                        ops::AssignValueGPUKernel<float>);
+REGISTER_OP_CUDA_KERNEL(assign_value, ops::AssignValueKernel<int>,
+                        ops::AssignValueKernel<float>);
diff --git a/paddle/operators/assign_value_op.h b/paddle/operators/assign_value_op.h
index bdb5bce272..db2e430779 100644
--- a/paddle/operators/assign_value_op.h
+++ b/paddle/operators/assign_value_op.h
@@ -27,8 +27,6 @@ class AssignValueKernel : public framework::OpKernel<T> {
   virtual void Compute(const framework::ExecutionContext& ctx) const {
     auto shape = ctx.Attr<std::vector<int>>("shape");
     auto* out = ctx.Output<framework::Tensor>("Out");
-    out->Resize(framework::make_ddim(shape));
-    auto* dst = out->mutable_data<T>(ctx.GetPlace());
     int dtype = ctx.Attr<int>("dtype");
     const char* value_name = nullptr;
     switch (dtype) {
@@ -43,12 +41,9 @@ class AssignValueKernel : public framework::OpKernel<T> {
         break;
     }
     auto values = ctx.Attr<std::vector<T>>(value_name);
-    Copy(dst, values.data(), sizeof(T) * values.size(), ctx);
+    framework::CopyFromVector(values, ctx.device_context(), out);
+    out->Resize(framework::make_ddim(shape));
   }
-
- protected:
-  virtual void Copy(void* dst, const void* src, size_t size,
-                    const framework::ExecutionContext& ctx) const = 0;
 };
 
 }  // namespace operators

From 1baca7ffba7c603aaac9220f5aec6caf4299c2a4 Mon Sep 17 00:00:00 2001
From: Yi Wang <yi.wang.2005@gmail.com>
Date: Thu, 11 Jan 2018 13:11:27 -0800
Subject: [PATCH 42/68] Move content in the buildtools repo into paddle repo
 (#7326)

* Import content from the buildtools repo

* Renmae Dockerfile-x86_64 into Dockerfile.x64

* yapf

* Remove subdirectory root
---
 tools/manylinux1/Dockerfile.android           |  55 ++++++
 tools/manylinux1/Dockerfile.x64               |  54 ++++++
 tools/manylinux1/README.md                    |  30 +++
 tools/manylinux1/build_all.sh                 |  26 +++
 tools/manylinux1/build_scripts/build.sh       | 152 +++++++++++++++
 tools/manylinux1/build_scripts/build_utils.sh | 173 ++++++++++++++++++
 .../build_scripts/manylinux1-check.py         |  56 ++++++
 .../build_scripts/python-tag-abi-tag.py       |   7 +
 tools/manylinux1/build_scripts/ssl-check.py   |  32 ++++
 9 files changed, 585 insertions(+)
 create mode 100644 tools/manylinux1/Dockerfile.android
 create mode 100644 tools/manylinux1/Dockerfile.x64
 create mode 100644 tools/manylinux1/README.md
 create mode 100755 tools/manylinux1/build_all.sh
 create mode 100644 tools/manylinux1/build_scripts/build.sh
 create mode 100755 tools/manylinux1/build_scripts/build_utils.sh
 create mode 100644 tools/manylinux1/build_scripts/manylinux1-check.py
 create mode 100644 tools/manylinux1/build_scripts/python-tag-abi-tag.py
 create mode 100644 tools/manylinux1/build_scripts/ssl-check.py

diff --git a/tools/manylinux1/Dockerfile.android b/tools/manylinux1/Dockerfile.android
new file mode 100644
index 0000000000..b6cae228a0
--- /dev/null
+++ b/tools/manylinux1/Dockerfile.android
@@ -0,0 +1,55 @@
+FROM ubuntu:16.04
+MAINTAINER PaddlePaddle Authors <paddle-dev@baidu.com>
+
+ARG UBUNTU_MIRROR
+RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ubuntu.com/ubuntu#${UBUNTU_MIRROR}#g' /etc/apt/sources.list; fi'
+
+# ENV variables
+ARG ANDROID_ABI
+ARG ANDROID_API
+
+ENV ANDROID_ABI=${ANDROID_ABI:-"armeabi-v7a"}
+ENV ANDROID_API=${ANDROID_API:-21}
+
+ENV HOME=/root \
+    ANDROID_NDK_HOME=/opt/android-ndk-linux \
+    ANDROID_TOOLCHAINS_DIR=/opt/toolchains
+
+RUN apt-get update && \
+    apt-get install -y \
+    git python-dev python-pip python-numpy \
+    wget curl tar unzip gcc g++ locales clang-format-3.8 swig cmake && \
+    apt-get clean -y
+
+# Install Go and glide
+RUN wget -qO- go.tgz https://storage.googleapis.com/golang/go1.8.1.linux-amd64.tar.gz | \
+    tar -xz -C /usr/local && \
+    mkdir /root/gopath && \
+    mkdir /root/gopath/bin && \
+    mkdir /root/gopath/src
+ENV GOROOT=/usr/local/go GOPATH=/root/gopath
+# should not be in the same line with GOROOT definition, otherwise docker build could not find GOROOT.
+ENV PATH=${PATH}:${GOROOT}/bin:${GOPATH}/bin
+
+# git credential to skip password typing
+RUN git config --global credential.helper store
+
+# Fix locales to en_US.UTF-8
+RUN localedef -i en_US -f UTF-8 en_US.UTF-8
+
+RUN pip install --upgrade pip && \
+    pip install -U 'protobuf==3.1.0' && \
+    pip install -U wheel sphinx && \
+    pip install pre-commit
+
+# Android NDK
+RUN mkdir -p ${ANDROID_TOOLCHAINS_DIR} && \
+    mkdir -p /opt/android-ndk-tmp && \
+    cd /opt/android-ndk-tmp && \
+    wget -q https://dl.google.com/android/repository/android-ndk-r14b-linux-x86_64.zip && \
+    unzip -q android-ndk-r14b-linux-x86_64.zip && \
+    mv android-ndk-r14b ${ANDROID_NDK_HOME} && \
+    rm -rf /opt/android-ndk-tmp
+
+CMD ["bash", "/paddle/paddle/scripts/docker/build_android.sh"]
+
diff --git a/tools/manylinux1/Dockerfile.x64 b/tools/manylinux1/Dockerfile.x64
new file mode 100644
index 0000000000..2c6ba650a5
--- /dev/null
+++ b/tools/manylinux1/Dockerfile.x64
@@ -0,0 +1,54 @@
+# NOTE The manylinux1 policy mandates CentOS-5. We replace it with CentOS-6 in
+# order to satisfy the build of capnproto library (a nupic.core dependency),
+# which requires some headers and symbols not present on CentOS-5 (e.g.,
+# signalfd.h, pipe2, O_NONBLOCK, SOCK_NONBLOCK, etc.). See
+# https://github.com/sandstorm-io/capnproto/issues/350.
+FROM nvidia/cuda:<baseimg>
+MAINTAINER Numenta, based on the ManyLinux project
+
+ENV LC_ALL en_US.UTF-8
+ENV LANG en_US.UTF-8
+ENV LANGUAGE en_US.UTF-8
+ENV PATH /opt/rh/devtoolset-2/root/usr/bin:$PATH
+ENV LD_LIBRARY_PATH /opt/rh/devtoolset-2/root/usr/lib64:/opt/rh/devtoolset-2/root/usr/lib:/usr/local/lib64:/usr/local/lib:${LD_LIBRARY_PATH}
+ENV PKG_CONFIG_PATH=/usr/local/lib/pkgconfig
+
+COPY build_scripts /build_scripts
+RUN bash build_scripts/build.sh && rm -r build_scripts
+
+ENV SSL_CERT_FILE=/opt/_internal/certs.pem
+
+# for paddle
+RUN wget --no-check-certificate -qO- https://storage.googleapis.com/golang/go1.8.1.linux-amd64.tar.gz | \
+    tar -xz -C /usr/local && \
+    mkdir /root/gopath && \
+    mkdir /root/gopath/bin && \
+    mkdir /root/gopath/src
+
+
+ENV GOROOT=/usr/local/go GOPATH=/root/gopath
+ENV PATH=${GOROOT}/bin:${GOPATH}/bin:${PATH}
+
+# protobuf 3.1.0
+RUN cd /opt && wget -q --no-check-certificate https://github.com/google/protobuf/releases/download/v3.1.0/protobuf-cpp-3.1.0.tar.gz && \
+    tar xzf protobuf-cpp-3.1.0.tar.gz && \
+    cd protobuf-3.1.0 && ./configure && make -j4 && make install && cd .. && rm -f protobuf-cpp-3.1.0.tar.gz
+
+
+RUN yum install -y sqlite-devel zlib-devel openssl-devel boost boost-devel pcre-devel vim tk-devel tkinter libtool
+
+RUN wget -O /root/requirements.txt https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/python/requirements.txt
+
+RUN LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs4/lib:${LD_LIBRARY_PATH} /opt/python/cp27-cp27mu/bin/pip install -r /root/requirements.txt && \
+    LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs2/lib:${LD_LIBRARY_PATH} /opt/python/cp27-cp27m/bin/pip install -r /root/requirements.txt && \
+    go get github.com/Masterminds/glide && \
+    rm -rf /root/requirements.txt
+
+RUN LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs4/lib:${LD_LIBRARY_PATH} /opt/python/cp27-cp27mu/bin/pip install pre-commit 'ipython==5.3.0' opencv-python && \
+    LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs2/lib:${LD_LIBRARY_PATH} /opt/python/cp27-cp27m/bin/pip install pre-commit 'ipython==5.3.0' opencv-python
+
+RUN wget -O /opt/swig-2.0.12.tar.gz https://sourceforge.net/projects/swig/files/swig/swig-2.0.12/swig-2.0.12.tar.gz/download && \
+    cd /opt && tar xzf swig-2.0.12.tar.gz && cd /opt/swig-2.0.12 && ./configure && make && make install && cd /opt && rm swig-2.0.12.tar.gz
+
+RUN mkdir -p /src && cd /src && git clone https://github.com/NVIDIA/nccl.git nccl && cd nccl &&\
+    make -j `nproc` install <NCCL_MAKE_OPTS>  && cd .. && rm -rf nccl
diff --git a/tools/manylinux1/README.md b/tools/manylinux1/README.md
new file mode 100644
index 0000000000..cb0a9ac22c
--- /dev/null
+++ b/tools/manylinux1/README.md
@@ -0,0 +1,30 @@
+# buildtools
+
+We release PaddlePaddle and PaddlePaddle Fluid as shared libraries,
+which, we hope could be released as wheel packages on PyPI, so we need
+to make sure that the build follows the
+[manulinux1](https://www.python.org/dev/peps/pep-0513/) standard.
+
+The manylinux standard suggests building Python modules on an old
+system, because that a module would anyway depend on some shared
+libraries, and Linux's shared library standard states that those built
+with newer version compilers cannot work with those with older
+versions.  The suggested building environment is as old as CentOS 5.
+However, PaddlePaddle relies on CUDA, and the earlies version of
+[CentOS works with CUDA is 6](https://hub.docker.com/r/nvidia/cuda/).
+So, here we provide a Docker image basing on CentOS 6 and CUDA for
+building PaddlePaddle and making the release supports "as-manylinux as
+possible."  or "sufficiently many Linux" according to [this
+discussion](https://mail.python.org/pipermail/wheel-builders/2016-July/000175.html).
+
+The build output of our Docker image includes multiple wheel files --
+some contain the CPU-only binary, some others support CUDA; some are
+compatible with the cp27m Python ABI, some others with cp27.
+
+To build these wheels, please run the following commands:
+
+```bash
+git clone https://github.com/paddlepaddle/paddle
+cd paddle/tools/manylinux1
+REPO=[yourrepo] ./build_all.sh
+```
diff --git a/tools/manylinux1/build_all.sh b/tools/manylinux1/build_all.sh
new file mode 100755
index 0000000000..097bedb526
--- /dev/null
+++ b/tools/manylinux1/build_all.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+set -xe
+
+REPO="${REPO:-typhoon1986}"
+
+# NOTE: version matches are determined!
+sed 's/<baseimg>/7.5-cudnn5-devel-centos6/g' Dockerfile.x64 | \
+sed 's/<NCCL_MAKE_OPTS>/NVCC_GENCODE="-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_52,code=sm_52"/g'> Dockerfile.tmp
+docker build -t ${REPO}/paddle_manylinux_devel:cuda7.5_cudnn5 -f Dockerfile.tmp .
+docker push ${REPO}/paddle_manylinux_devel:cuda7.5_cudnn5
+
+sed 's/<baseimg>/8.0-cudnn5-devel-centos6/g' Dockerfile.x64 | \
+sed 's/<NCCL_MAKE_OPTS>/NVCC_GENCODE="-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_52,code=sm_52 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_60,code=compute_60 -gencode=arch=compute_61,code=sm_61 -gencode=arch=compute_62,code=sm_62"/g'> Dockerfile.tmp
+docker build -t ${REPO}/paddle_manylinux_devel:cuda8.0_cudnn5 -f Dockerfile.tmp .
+docker push ${REPO}/paddle_manylinux_devel:cuda8.0_cudnn5
+
+sed 's/<baseimg>/8.0-cudnn7-devel-centos6/g' Dockerfile.x64 | \
+sed 's/<NCCL_MAKE_OPTS>/NVCC_GENCODE="-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_52,code=sm_52 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_60,code=compute_60 -gencode=arch=compute_61,code=sm_61 -gencode=arch=compute_62,code=sm_62"/g'> Dockerfile.tmp
+
+docker build -t ${REPO}/paddle_manylinux_devel:cuda8.0_cudnn7 -f Dockerfile.tmp .
+docker push ${REPO}/paddle_manylinux_devel:cuda8.0_cudnn7
+
+sed 's/<baseimg>/9.0-cudnn7-devel-centos6/g' Dockerfile.x64 | \
+sed 's/<NCCL_MAKE_OPTS>/NVCC_GENCODE="-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_52,code=sm_52 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_60,code=compute_60 -gencode=arch=compute_61,code=sm_61 -gencode=arch=compute_62,code=sm_62 -gencode=arch=compute_70,code=sm_70"/g'> Dockerfile.tmp
+docker build -t ${REPO}/paddle_manylinux_devel:cuda9.0_cudnn7 -f Dockerfile.tmp .
+docker push ${REPO}/paddle_manylinux_devel:cuda9.0_cudnn7
diff --git a/tools/manylinux1/build_scripts/build.sh b/tools/manylinux1/build_scripts/build.sh
new file mode 100644
index 0000000000..93591fa9dd
--- /dev/null
+++ b/tools/manylinux1/build_scripts/build.sh
@@ -0,0 +1,152 @@
+#!/bin/bash
+# Top-level build script called from Dockerfile
+
+# Stop at any error, show all commands
+set -ex
+
+# Python versions to be installed in /opt/$VERSION_NO
+# NOTE Only need python 2.7.11 for nupic.core/nupic.bindings at this time, so
+# remove others to expedite build and reduce docker image size. The original
+# manylinux docker image project builds many python versions.
+# NOTE We added back 3.5.1, since auditwheel requires python 3.3+
+CPYTHON_VERSIONS="2.7.11 3.5.1"
+
+# openssl version to build, with expected sha256 hash of .tar.gz
+# archive
+OPENSSL_ROOT=openssl-1.0.2l
+OPENSSL_HASH=ce07195b659e75f4e1db43552860070061f156a98bb37b672b101ba6e3ddf30c
+EPEL_RPM_HASH=e5ed9ecf22d0c4279e92075a64c757ad2b38049bcf5c16c4f2b75d5f6860dc0d
+DEVTOOLS_HASH=a8ebeb4bed624700f727179e6ef771dafe47651131a00a78b342251415646acc
+PATCHELF_HASH=d9afdff4baeacfbc64861454f368b7f2c15c44d245293f7587bbf726bfe722fb
+CURL_ROOT=curl-7.49.1
+CURL_HASH=eb63cec4bef692eab9db459033f409533e6d10e20942f4b060b32819e81885f1
+AUTOCONF_ROOT=autoconf-2.69
+AUTOCONF_HASH=954bd69b391edc12d6a4a51a2dd1476543da5c6bbf05a95b59dc0dd6fd4c2969
+
+# Dependencies for compiling Python that we want to remove from
+# the final image after compiling Python
+PYTHON_COMPILE_DEPS="zlib-devel bzip2-devel ncurses-devel sqlite-devel readline-devel tk-devel gdbm-devel db4-devel libpcap-devel xz-devel"
+
+# Libraries that are allowed as part of the manylinux1 profile
+MANYLINUX1_DEPS="glibc-devel libstdc++-devel glib2-devel libX11-devel libXext-devel libXrender-devel  mesa-libGL-devel libICE-devel libSM-devel ncurses-devel"
+
+# Get build utilities
+MY_DIR=$(dirname "${BASH_SOURCE[0]}")
+source $MY_DIR/build_utils.sh
+
+# EPEL support
+yum -y install wget curl
+curl -sLO https://dl.fedoraproject.org/pub/epel/6/x86_64/epel-release-6-8.noarch.rpm
+check_sha256sum epel-release-6-8.noarch.rpm $EPEL_RPM_HASH
+
+# Dev toolset (for LLVM and other projects requiring C++11 support)
+curl -sLO http://people.centos.org/tru/devtools-2/devtools-2.repo
+check_sha256sum devtools-2.repo $DEVTOOLS_HASH
+mv devtools-2.repo /etc/yum.repos.d/devtools-2.repo
+rpm -Uvh --replacepkgs epel-release-6*.rpm
+rm -f epel-release-6*.rpm
+
+# Development tools and libraries
+yum -y install bzip2 make git patch unzip bison yasm diffutils \
+    automake which file \
+    kernel-devel-`uname -r` \
+    devtoolset-2-binutils devtoolset-2-gcc \
+    devtoolset-2-gcc-c++ devtoolset-2-gcc-gfortran \
+    ${PYTHON_COMPILE_DEPS}
+
+# Install more recent version of cmake
+# curl -O https://cmake.org/files/v3.8/cmake-3.8.1-Linux-x86_64.sh
+# /bin/sh cmake-3.8.1-Linux-x86_64.sh --prefix=/usr/local --skip-license
+# rm cmake-3.8.1-Linux-x86_64.sh
+
+wget -q https://cmake.org/files/v3.5/cmake-3.5.2.tar.gz && tar xzf cmake-3.5.2.tar.gz && \
+cd cmake-3.5.2 && ./bootstrap && \
+make -j4 && make install && cd .. && rm cmake-3.5.2.tar.gz
+
+
+# Install newest autoconf
+build_autoconf $AUTOCONF_ROOT $AUTOCONF_HASH
+autoconf --version
+
+# Compile the latest Python releases.
+# (In order to have a proper SSL module, Python is compiled
+# against a recent openssl [see env vars above], which is linked
+# statically. We delete openssl afterwards.)
+build_openssl $OPENSSL_ROOT $OPENSSL_HASH
+mkdir -p /opt/python
+build_cpythons $CPYTHON_VERSIONS
+
+PY35_BIN=/opt/python/cp35-cp35m/bin
+# NOTE Since our custom manylinux image builds pythons with shared
+# libpython, we need to add libpython's dir to LD_LIBRARY_PATH before running
+# python.
+ORIGINAL_LD_LIBRARY_PATH="${LD_LIBRARY_PATH}"
+LD_LIBRARY_PATH="${ORIGINAL_LD_LIBRARY_PATH}:$(dirname ${PY35_BIN})/lib"
+
+# Our openssl doesn't know how to find the system CA trust store
+#   (https://github.com/pypa/manylinux/issues/53)
+# And it's not clear how up-to-date that is anyway
+# So let's just use the same one pip and everyone uses
+LD_LIBRARY_PATH="${ORIGINAL_LD_LIBRARY_PATH}:$(dirname ${PY35_BIN})/lib" $PY35_BIN/pip install certifi
+ln -s $($PY35_BIN/python -c 'import certifi; print(certifi.where())') \
+      /opt/_internal/certs.pem
+# If you modify this line you also have to modify the versions in the
+# Dockerfiles:
+export SSL_CERT_FILE=/opt/_internal/certs.pem
+
+# Install newest curl
+build_curl $CURL_ROOT $CURL_HASH
+rm -rf /usr/local/include/curl /usr/local/lib/libcurl* /usr/local/lib/pkgconfig/libcurl.pc
+hash -r
+curl --version
+curl-config --features
+
+# Now we can delete our built SSL
+rm -rf /usr/local/ssl
+
+# Install patchelf (latest with unreleased bug fixes)
+curl -sLO https://nipy.bic.berkeley.edu/manylinux/patchelf-0.9njs2.tar.gz
+check_sha256sum patchelf-0.9njs2.tar.gz $PATCHELF_HASH
+tar -xzf patchelf-0.9njs2.tar.gz
+(cd patchelf-0.9njs2 && ./configure && make && make install)
+rm -rf patchelf-0.9njs2.tar.gz patchelf-0.9njs2
+
+# Install latest pypi release of auditwheel
+LD_LIBRARY_PATH="${ORIGINAL_LD_LIBRARY_PATH}:$(dirname ${PY35_BIN})/lib" $PY35_BIN/pip install auditwheel
+ln -s $PY35_BIN/auditwheel /usr/local/bin/auditwheel
+
+# Clean up development headers and other unnecessary stuff for
+# final image
+yum -y erase wireless-tools gtk2 libX11 hicolor-icon-theme \
+    avahi freetype bitstream-vera-fonts \
+    ${PYTHON_COMPILE_DEPS}  > /dev/null 2>&1
+yum -y install ${MANYLINUX1_DEPS}
+yum -y clean all > /dev/null 2>&1
+yum list installed
+# we don't need libpython*.a, and they're many megabytes
+find /opt/_internal -name '*.a' -print0 | xargs -0 rm -f
+# Strip what we can -- and ignore errors, because this just attempts to strip
+# *everything*, including non-ELF files:
+find /opt/_internal -type f -print0 \
+    | xargs -0 -n1 strip --strip-unneeded 2>/dev/null || true
+# We do not need the Python test suites, or indeed the precompiled .pyc and
+# .pyo files. Partially cribbed from:
+#    https://github.com/docker-library/python/blob/master/3.4/slim/Dockerfile
+find /opt/_internal \
+     \( -type d -a -name test -o -name tests \) \
+  -o \( -type f -a -name '*.pyc' -o -name '*.pyo' \) \
+  -print0 | xargs -0 rm -f
+
+for PYTHON in /opt/python/*/bin/python; do
+    # Add matching directory of libpython shared library to library lookup path
+    LD_LIBRARY_PATH="${ORIGINAL_LD_LIBRARY_PATH}:$(dirname $(dirname ${PYTHON}))/lib"
+
+    # Smoke test to make sure that our Pythons work, and do indeed detect as
+    # being manylinux compatible:
+    LD_LIBRARY_PATH="${ORIGINAL_LD_LIBRARY_PATH}:$(dirname $(dirname ${PYTHON}))/lib" $PYTHON $MY_DIR/manylinux1-check.py
+    # Make sure that SSL cert checking works
+    LD_LIBRARY_PATH="${ORIGINAL_LD_LIBRARY_PATH}:$(dirname $(dirname ${PYTHON}))/lib" $PYTHON $MY_DIR/ssl-check.py
+done
+
+# Restore LD_LIBRARY_PATH
+LD_LIBRARY_PATH="${ORIGINAL_LD_LIBRARY_PATH}"
diff --git a/tools/manylinux1/build_scripts/build_utils.sh b/tools/manylinux1/build_scripts/build_utils.sh
new file mode 100755
index 0000000000..10422ae3bd
--- /dev/null
+++ b/tools/manylinux1/build_scripts/build_utils.sh
@@ -0,0 +1,173 @@
+#!/bin/bash
+# Helper utilities for build
+
+PYTHON_DOWNLOAD_URL=https://www.python.org/ftp/python
+# XXX: the official https server at www.openssl.org cannot be reached
+# with the old versions of openssl and curl in Centos 5.11 hence the fallback
+# to the ftp mirror:
+# OPENSSL_DOWNLOAD_URL=ftp://ftp.openssl.org/source
+OPENSSL_DOWNLOAD_URL=https://www.openssl.org/source
+# Ditto the curl sources
+CURL_DOWNLOAD_URL=http://curl.askapache.com/download
+
+GET_PIP_URL=https://bootstrap.pypa.io/get-pip.py
+
+AUTOCONF_DOWNLOAD_URL=http://ftp.gnu.org/gnu/autoconf
+
+
+function check_var {
+    if [ -z "$1" ]; then
+        echo "required variable not defined"
+        exit 1
+    fi
+}
+
+
+function lex_pyver {
+    # Echoes Python version string padded with zeros
+    # Thus:
+    # 3.2.1 -> 003002001
+    # 3     -> 003000000
+    echo $1 | awk -F "." '{printf "%03d%03d%03d", $1, $2, $3}'
+}
+
+
+function do_cpython_build {
+    local py_ver=$1
+    check_var $py_ver
+    local ucs_setting=$2
+    check_var $ucs_setting
+    tar -xzf Python-$py_ver.tgz
+    pushd Python-$py_ver
+    if [ "$ucs_setting" = "none" ]; then
+        unicode_flags=""
+        dir_suffix=""
+    else
+        local unicode_flags="--enable-unicode=$ucs_setting"
+        local dir_suffix="-$ucs_setting"
+    fi
+    local prefix="/opt/_internal/cpython-${py_ver}${dir_suffix}"
+    mkdir -p ${prefix}/lib
+    # -Wformat added for https://bugs.python.org/issue17547 on Python 2.6
+
+    # NOTE --enable-shared for generating libpython shared library needed for
+    # linking of some of the nupic.core test executables.
+    CFLAGS="-Wformat" ./configure --prefix=${prefix} --enable-shared $unicode_flags > /dev/null
+    make -j2 > /dev/null
+    make install > /dev/null
+    popd
+    echo "ZZZ looking for libpython"
+    find / -name 'libpython*.so*'
+    rm -rf Python-$py_ver
+    # Some python's install as bin/python3. Make them available as
+    # bin/python.
+    if [ -e ${prefix}/bin/python3 ]; then
+        ln -s python3 ${prefix}/bin/python
+    fi
+    # NOTE Make libpython shared library visible to python calls below
+    LD_LIBRARY_PATH="${prefix}/lib" ${prefix}/bin/python get-pip.py
+    LD_LIBRARY_PATH="${prefix}/lib" ${prefix}/bin/pip install wheel
+    local abi_tag=$(LD_LIBRARY_PATH="${prefix}/lib" ${prefix}/bin/python ${MY_DIR}/python-tag-abi-tag.py)
+    ln -s ${prefix} /opt/python/${abi_tag}
+}
+
+
+function build_cpython {
+    local py_ver=$1
+    check_var $py_ver
+    check_var $PYTHON_DOWNLOAD_URL
+    wget -q $PYTHON_DOWNLOAD_URL/$py_ver/Python-$py_ver.tgz
+    if [ $(lex_pyver $py_ver) -lt $(lex_pyver 3.3) ]; then
+        # NOTE We only need wide unicode for nupic.bindings wheel
+        do_cpython_build $py_ver ucs2
+        do_cpython_build $py_ver ucs4
+    else
+        do_cpython_build $py_ver none
+    fi
+    rm -f Python-$py_ver.tgz
+}
+
+
+function build_cpythons {
+    check_var $GET_PIP_URL
+    curl -sLO $GET_PIP_URL
+    for py_ver in $@; do
+        build_cpython $py_ver
+    done
+    rm get-pip.py
+}
+
+
+function do_openssl_build {
+    ./config no-ssl2 no-shared -fPIC --prefix=/usr/local/ssl > /dev/null
+    make > /dev/null
+    make install > /dev/null
+}
+
+
+function check_sha256sum {
+    local fname=$1
+    check_var ${fname}
+    local sha256=$2
+    check_var ${sha256}
+
+    echo "${sha256}  ${fname}" > ${fname}.sha256
+    sha256sum -c ${fname}.sha256
+    rm ${fname}.sha256
+}
+
+
+function build_openssl {
+    local openssl_fname=$1
+    check_var ${openssl_fname}
+    local openssl_sha256=$2
+    check_var ${openssl_sha256}
+    check_var ${OPENSSL_DOWNLOAD_URL}
+    curl -sLO ${OPENSSL_DOWNLOAD_URL}/${openssl_fname}.tar.gz
+    check_sha256sum ${openssl_fname}.tar.gz ${openssl_sha256}
+    tar -xzf ${openssl_fname}.tar.gz
+    (cd ${openssl_fname} && do_openssl_build)
+    rm -rf ${openssl_fname} ${openssl_fname}.tar.gz
+}
+
+
+function do_curl_build {
+    LIBS=-ldl ./configure --with-ssl --disable-shared > /dev/null
+    make > /dev/null
+    make install > /dev/null
+}
+
+
+function build_curl {
+    local curl_fname=$1
+    check_var ${curl_fname}
+    local curl_sha256=$2
+    check_var ${curl_sha256}
+    check_var ${CURL_DOWNLOAD_URL}
+    curl -sLO ${CURL_DOWNLOAD_URL}/${curl_fname}.tar.bz2
+    check_sha256sum ${curl_fname}.tar.bz2 ${curl_sha256}
+    tar -jxf ${curl_fname}.tar.bz2
+    (cd ${curl_fname} && do_curl_build)
+    rm -rf ${curl_fname} ${curl_fname}.tar.bz2
+}
+
+
+function do_standard_install {
+    ./configure > /dev/null
+    make > /dev/null
+    make install > /dev/null
+}
+
+
+function build_autoconf {
+    local autoconf_fname=$1
+    check_var ${autoconf_fname}
+    local autoconf_sha256=$2
+    check_var ${autoconf_sha256}
+    check_var ${AUTOCONF_DOWNLOAD_URL}
+    curl -sLO ${AUTOCONF_DOWNLOAD_URL}/${autoconf_fname}.tar.gz
+    check_sha256sum ${autoconf_fname}.tar.gz ${autoconf_sha256}
+    tar -zxf ${autoconf_fname}.tar.gz
+    (cd ${autoconf_fname} && do_standard_install)
+    rm -rf ${autoconf_fname} ${autoconf_fname}.tar.gz
+}
diff --git a/tools/manylinux1/build_scripts/manylinux1-check.py b/tools/manylinux1/build_scripts/manylinux1-check.py
new file mode 100644
index 0000000000..47fd3d673b
--- /dev/null
+++ b/tools/manylinux1/build_scripts/manylinux1-check.py
@@ -0,0 +1,56 @@
+# Logic copied from PEP 513
+
+
+def is_manylinux1_compatible():
+    # Only Linux, and only x86-64 / i686
+    from distutils.util import get_platform
+    if get_platform() not in ["linux-x86_64", "linux-i686"]:
+        return False
+
+    # Check for presence of _manylinux module
+    try:
+        import _manylinux
+        return bool(_manylinux.manylinux1_compatible)
+    except (ImportError, AttributeError):
+        # Fall through to heuristic check below
+        pass
+
+    # Check glibc version. CentOS 5 uses glibc 2.5.
+    return have_compatible_glibc(2, 5)
+
+
+def have_compatible_glibc(major, minimum_minor):
+    import ctypes
+
+    process_namespace = ctypes.CDLL(None)
+    try:
+        gnu_get_libc_version = process_namespace.gnu_get_libc_version
+    except AttributeError:
+        # Symbol doesn't exist -> therefore, we are not linked to
+        # glibc.
+        return False
+
+    # Call gnu_get_libc_version, which returns a string like "2.5".
+    gnu_get_libc_version.restype = ctypes.c_char_p
+    version_str = gnu_get_libc_version()
+    # py2 / py3 compatibility:
+    if not isinstance(version_str, str):
+        version_str = version_str.decode("ascii")
+
+    # Parse string and check against requested version.
+    version = [int(piece) for piece in version_str.split(".")]
+    assert len(version) == 2
+    if major != version[0]:
+        return False
+    if minimum_minor > version[1]:
+        return False
+    return True
+
+
+import sys
+if is_manylinux1_compatible():
+    print("%s is manylinux1 compatible" % (sys.executable, ))
+    sys.exit(0)
+else:
+    print("%s is NOT manylinux1 compatible" % (sys.executable, ))
+    sys.exit(1)
diff --git a/tools/manylinux1/build_scripts/python-tag-abi-tag.py b/tools/manylinux1/build_scripts/python-tag-abi-tag.py
new file mode 100644
index 0000000000..301fbf07a4
--- /dev/null
+++ b/tools/manylinux1/build_scripts/python-tag-abi-tag.py
@@ -0,0 +1,7 @@
+# Utility script to print the python tag + the abi tag for a Python
+# See PEP 425 for exactly what these are, but an example would be:
+#   cp27-cp27mu
+
+from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag
+
+print("{0}{1}-{2}".format(get_abbr_impl(), get_impl_ver(), get_abi_tag()))
diff --git a/tools/manylinux1/build_scripts/ssl-check.py b/tools/manylinux1/build_scripts/ssl-check.py
new file mode 100644
index 0000000000..a85d91978c
--- /dev/null
+++ b/tools/manylinux1/build_scripts/ssl-check.py
@@ -0,0 +1,32 @@
+# cf. https://github.com/pypa/manylinux/issues/53
+
+GOOD_SSL = "https://google.com"
+BAD_SSL = "https://self-signed.badssl.com"
+
+import sys
+
+print("Testing SSL certificate checking for Python:", sys.version)
+
+if (sys.version_info[:2] < (2, 7) or sys.version_info[:2] < (3, 4)):
+    print("This version never checks SSL certs; skipping tests")
+    sys.exit(0)
+
+if sys.version_info[0] >= 3:
+    from urllib.request import urlopen
+    EXC = OSError
+else:
+    from urllib import urlopen
+    EXC = IOError
+
+print("Connecting to %s should work" % (GOOD_SSL, ))
+urlopen(GOOD_SSL)
+print("...it did, yay.")
+
+print("Connecting to %s should fail" % (BAD_SSL, ))
+try:
+    urlopen(BAD_SSL)
+    # If we get here then we failed:
+    print("...it DIDN'T!!!!!11!!1one!")
+    sys.exit(1)
+except EXC:
+    print("...it did, yay.")

From b1f24660bf14fe0f174b9e59f796ad5eaf2627f4 Mon Sep 17 00:00:00 2001
From: Xi Chen <putcncx@gmail.com>
Date: Thu, 11 Jan 2018 15:40:41 -0800
Subject: [PATCH 43/68] add dist demo fit_a_line

---
 .../book_distribute/test_dist_fit_a_line.py   | 63 +++++++++++++++++++
 1 file changed, 63 insertions(+)
 create mode 100644 python/paddle/v2/fluid/tests/book_distribute/test_dist_fit_a_line.py

diff --git a/python/paddle/v2/fluid/tests/book_distribute/test_dist_fit_a_line.py b/python/paddle/v2/fluid/tests/book_distribute/test_dist_fit_a_line.py
new file mode 100644
index 0000000000..592986b137
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/book_distribute/test_dist_fit_a_line.py
@@ -0,0 +1,63 @@
+import numpy as np
+import paddle.v2 as paddle
+import paddle.v2.fluid as fluid
+import os
+
+x = fluid.layers.data(name='x', shape=[13], dtype='float32')
+
+y_predict = fluid.layers.fc(input=x, size=1, act=None)
+
+y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+
+cost = fluid.layers.square_error_cost(input=y_predict, label=y)
+avg_cost = fluid.layers.mean(x=cost)
+
+sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
+optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
+
+BATCH_SIZE = 20
+
+train_reader = paddle.batch(
+    paddle.reader.shuffle(
+        paddle.dataset.uci_housing.train(), buf_size=500),
+    batch_size=BATCH_SIZE)
+
+place = fluid.CPUPlace()
+feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
+exe = fluid.Executor(place)
+
+t = fluid.DistributeTranspiler()
+# all parameter server endpoints list for spliting parameters
+pserver_endpoints = os.getenv("PSERVERS")
+# server endpoint for current node
+current_endpoint = os.getenv("SERVER_ENDPOINT")
+# run as trainer or parameter server
+training_role = os.getenv("TRAINING_ROLE",
+                          "TRAINER")  # get the training role: trainer/pserver
+t.transpile(optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)
+
+if training_role == "PSERVER":
+    if not current_endpoint:
+        print("need env SERVER_ENDPOINT")
+        exit(1)
+    pserver_prog = t.get_pserver_program(current_endpoint, optimize_ops)
+    exe.run(fluid.default_startup_program())
+    exe.run(pserver_prog)
+else:
+    trainer_prog = t.get_trainer_program()
+
+    exe.run(fluid.default_startup_program())
+
+    PASS_NUM = 100
+    for pass_id in range(PASS_NUM):
+        fluid.io.save_persistables(exe, "./fit_a_line.model/")
+        fluid.io.load_persistables(exe, "./fit_a_line.model/")
+        for data in train_reader():
+            avg_loss_value, = exe.run(trainer_prog,
+                                      feed=feeder.feed(data),
+                                      fetch_list=[avg_cost])
+
+            if avg_loss_value[0] < 10.0:
+                exit(
+                    0)  # if avg cost less than 10.0, we think our code is good.
+exit(1)

From cadb95fbebad0a5cab32870add3223aca1169cfb Mon Sep 17 00:00:00 2001
From: Xi Chen <putcncx@gmail.com>
Date: Thu, 11 Jan 2018 15:45:17 -0800
Subject: [PATCH 44/68] minor tweaks

---
 .../v2/fluid/tests/book_distribute/test_dist_fit_a_line.py     | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/python/paddle/v2/fluid/tests/book_distribute/test_dist_fit_a_line.py b/python/paddle/v2/fluid/tests/book_distribute/test_dist_fit_a_line.py
index 592986b137..bb339c440b 100644
--- a/python/paddle/v2/fluid/tests/book_distribute/test_dist_fit_a_line.py
+++ b/python/paddle/v2/fluid/tests/book_distribute/test_dist_fit_a_line.py
@@ -58,6 +58,5 @@ else:
                                       fetch_list=[avg_cost])
 
             if avg_loss_value[0] < 10.0:
-                exit(
-                    0)  # if avg cost less than 10.0, we think our code is good.
+                exit(0)
 exit(1)

From 777036d7d3033b7de136e07073a41fb9de49fbd2 Mon Sep 17 00:00:00 2001
From: Abhinav Arora <abhinavarora28@gmail.com>
Date: Thu, 11 Jan 2018 17:05:20 -0800
Subject: [PATCH 45/68] Fix some errors in the Block design doc (#7460)

---
 doc/design/block.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/design/block.md b/doc/design/block.md
index fab7f2dc48..907a2def55 100644
--- a/doc/design/block.md
+++ b/doc/design/block.md
@@ -202,8 +202,8 @@ This `OpDesc` value is in the `ops` field of the `BlockDesc` value representing
 
 During the generation of the Protobuf message, the Block should store VarDesc (the Protobuf message which describes Variable) and OpDesc (the Protobuf message which describes Operator).
 
-VarDesc in a block should have its name scope to avoid local variables affect parent block's name scope.
-Child block's name scopes should inherit the parent's so that OpDesc in child block can reference a VarDesc that stored in parent block. For example:
+VarDesc in a block should have its name scope to avoid local variables affecting parent block's name scope.
+Child block's name scopes should inherit the parent's so that OpDesc in child block can reference a VarDesc that is stored in the parent block. For example:
 
 ```python
 a = pd.Variable(shape=[20, 20])

From e0ae76f222c5dd2484ed0ee9b5d013ff937c5cbb Mon Sep 17 00:00:00 2001
From: Abhinav Arora <abhinavarora28@gmail.com>
Date: Thu, 11 Jan 2018 17:05:41 -0800
Subject: [PATCH 46/68] Fixing concepts in the Var Desc design document (#7462)

---
 doc/design/var_desc.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/doc/design/var_desc.md b/doc/design/var_desc.md
index 0b2958c1b1..89fa95326c 100644
--- a/doc/design/var_desc.md
+++ b/doc/design/var_desc.md
@@ -1,12 +1,12 @@
 ## Background
-PaddlePaddle divides the description of neural network computation graph into two stages: compile time and runtime.
+PaddlePaddle divides the description of neural network computation into two stages: compile time and runtime. At compile time, the neural network computation is described as a `ProgramDesc` whereas at runtime an `Executor` interprets the `ProgramDesc` to compute the operations.
 
-PaddlePaddle use proto message to describe compile time graph because
+PaddlePaddle use proto message to describe compile time program because
 
-1. Computation graph should be able to be saved to a file.
-1. In distributed training, the graph will be serialized and send to multiple workers.
+1. The computation program description must be serializable and saved in a file.
+1. During distributed training, the sreialized program will be sent to multiple workers. It should also be possible to break the program into different components, each of which can be executed on different workers.
 
-The computation graph is constructed by Data Node and Operation Node. The concept to represent them is in the table below.
+The computation `Program` consists of nested `Blocks`. Each `Block` will consist of data(i.e. `Variable`)  and  `Operations`. The concept to represent them is in the table below.
 
 | |compile time|runtime|
 |---|---|---|

From 25c8739362e9a9de14260bdeaa5cb6310cf6b052 Mon Sep 17 00:00:00 2001
From: Kavya Srinet <kavyasrinet@baidu.com>
Date: Thu, 11 Jan 2018 17:59:57 -0800
Subject: [PATCH 47/68] Adding the rst file to render IO under Fluid in
 paddlepaddle.org

---
 doc/api/v2/fluid.rst         |  2 +-
 doc/api/v2/fluid/io.rst      | 10 ++++++++++
 python/paddle/v2/fluid/io.py | 10 ++++++++++
 3 files changed, 21 insertions(+), 1 deletion(-)
 create mode 100644 doc/api/v2/fluid/io.rst

diff --git a/doc/api/v2/fluid.rst b/doc/api/v2/fluid.rst
index 43fc19dc49..5f15cad2b5 100644
--- a/doc/api/v2/fluid.rst
+++ b/doc/api/v2/fluid.rst
@@ -15,4 +15,4 @@ Fluid
     fluid/param_attr.rst
     fluid/profiler.rst
     fluid/regularizer.rst
-
+    fluid/io.rst
diff --git a/doc/api/v2/fluid/io.rst b/doc/api/v2/fluid/io.rst
new file mode 100644
index 0000000000..861822d36d
--- /dev/null
+++ b/doc/api/v2/fluid/io.rst
@@ -0,0 +1,10 @@
+===========
+IO
+===========
+
+
+
+isParameter
+-----------
+..  autofunction:: paddle.v2.fluid.io.is_parameter
+    :noindex:
diff --git a/python/paddle/v2/fluid/io.py b/python/paddle/v2/fluid/io.py
index c63567601a..0d5f68336a 100644
--- a/python/paddle/v2/fluid/io.py
+++ b/python/paddle/v2/fluid/io.py
@@ -11,6 +11,16 @@ __all__ = [
 
 
 def is_parameter(var):
+    """Check whether the variable is a Parameter
+
+    This function checks whether the input variable is a Parameter.
+
+    Args:
+        var : The input variable.
+
+    Returns:
+        boolean result whether the variable is a Parameter
+    """
     return isinstance(var, Parameter)
 
 

From 5dbd537048f153863d1cac0fbb433bb62fe561b4 Mon Sep 17 00:00:00 2001
From: Yancey <yanxu05@baidu.com>
Date: Fri, 12 Jan 2018 11:25:53 +0800
Subject: [PATCH 48/68] Fluid distributed training benchmark (#7410)

* add cluster training bencharmk design

* update by comment

* update by comment
---
 benchmark/cluster/README.md | 78 +++++++++++++++++++++++++++++++++++++
 1 file changed, 78 insertions(+)
 create mode 100644 benchmark/cluster/README.md

diff --git a/benchmark/cluster/README.md b/benchmark/cluster/README.md
new file mode 100644
index 0000000000..b619613ea7
--- /dev/null
+++ b/benchmark/cluster/README.md
@@ -0,0 +1,78 @@
+# Cluster Training Benchmark
+
+## Setup
+
+- Platform
+  - Kubernetes: v1.6.2
+  - Linux Kernel: v3.10.0
+
+- Resource
+  - CPU: 10 Cores per Pod
+  - Memory: 5GB per Pod
+
+- Docker Image
+
+  We use different base Docker Image to run the benchmark on Kubernetes:
+  - PaddlePaddle v2: paddlepaddle/paddle:0.11.0
+  - PaddlePaddle Fluid: paddlepaddle/paddle:[commit-id]
+  - TensorFlow: tensorflow/tensorflow:1.5.0-rc0
+
+- Model
+  vgg16 is used in this benchmark.
+
+## Cases
+
+- Variable
+  - Batch Size of training data.
+  - PServer count of the training job.
+  - The number of trainers.
+
+- Invariant
+  - The resource of trainer/pserver Pod.
+
+### Measure the Performance for Different Batch Size
+
+- PServer Count: 40
+- Trainer Count: 100
+- Metrics: mini-batch / sec
+
+| Batch Size | 32 | 64 | 128 | 256 |
+| -- | -- | -- | -- | -- |
+| PaddlePaddle Fluid | - | - | - | - |
+| PaddlePaddle v2 | - | - | - | - |
+| TensorFlow | - | - | - | - |
+
+### Measure the Performance for Different PServer Count
+
+- Trainer Count: 100
+- Batch Size: 64
+- Metrics: mini-batch / sec
+
+| PServer Count | 10 | 20 | 40 | 60 |
+| -- | -- | -- | -- | -- |
+| PaddlePaddle Fluid | - | - | - | - |
+| PaddlePaddle v2 | - | - | - | - |
+| TensorFlow | - | - | - | - |
+
+### Measure Parallel Efficiency By Increasing Trainer Count
+
+- PServer Count: 20
+- Batch Size: 64
+- Metrics:
+
+$S = \div(T1, TN)$
+
+which S is the ratio of T1 over TN, training time of 1 and N trainers.
+The parallel efficiency is:
+
+$E = \div(S, N)$
+
+| Trainer Counter | 1 | 10 | 20 | 30 | 40 | 50 | 60 | 70 | 80 | 90 | 100 |
+| -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- |
+| PaddlePaddle Fluid | - | - | - | - | - | - | - | - | - | - | - |
+| PaddlePaddle v2 | - | - | - | - | - | - | - | - | - | - | - | - |
+| TensorFlow | - | - | - | - | - | - | - | - | - | - | - | - | - |
+
+## Reproduce the benchmark
+
+TODO

From 54791af658d6fe0281ae4fc7ccc0723c21ef282a Mon Sep 17 00:00:00 2001
From: Kavya Srinet <kavyasrinet@baidu.com>
Date: Thu, 11 Jan 2018 19:54:00 -0800
Subject: [PATCH 49/68] Addressing review comments

---
 doc/api/v2/fluid/io.rst      | 2 +-
 python/paddle/v2/fluid/io.py | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/doc/api/v2/fluid/io.rst b/doc/api/v2/fluid/io.rst
index 861822d36d..67f68c4e9e 100644
--- a/doc/api/v2/fluid/io.rst
+++ b/doc/api/v2/fluid/io.rst
@@ -4,7 +4,7 @@ IO
 
 
 
-isParameter
+is_parameter
 -----------
 ..  autofunction:: paddle.v2.fluid.io.is_parameter
     :noindex:
diff --git a/python/paddle/v2/fluid/io.py b/python/paddle/v2/fluid/io.py
index 0d5f68336a..eef1e283c2 100644
--- a/python/paddle/v2/fluid/io.py
+++ b/python/paddle/v2/fluid/io.py
@@ -11,7 +11,7 @@ __all__ = [
 
 
 def is_parameter(var):
-    """Check whether the variable is a Parameter
+    """Check whether the variable is a Parameter.
 
     This function checks whether the input variable is a Parameter.
 
@@ -19,7 +19,7 @@ def is_parameter(var):
         var : The input variable.
 
     Returns:
-        boolean result whether the variable is a Parameter
+        boolean result whether the variable is a Parameter.
     """
     return isinstance(var, Parameter)
 

From 0fad7647d7eb883c849ce6468ba5e781276e9358 Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Fri, 12 Jan 2018 12:06:32 +0800
Subject: [PATCH 50/68] add how to update whl in build_from_source.md

---
 doc/getstarted/build_and_install/build_from_source_cn.rst | 5 +++++
 doc/getstarted/build_and_install/build_from_source_en.rst | 5 +++++
 2 files changed, 10 insertions(+)

diff --git a/doc/getstarted/build_and_install/build_from_source_cn.rst b/doc/getstarted/build_and_install/build_from_source_cn.rst
index 41ac07ca56..1e75087462 100644
--- a/doc/getstarted/build_and_install/build_from_source_cn.rst
+++ b/doc/getstarted/build_and_install/build_from_source_cn.rst
@@ -32,6 +32,11 @@ PaddlePaddle主要使用 `CMake <https://cmake.org>`_ 以及GCC, G++作为编译
 
    pip install build/python/dist/*.whl
 
+如果机器中已经安装过PaddlePaddle，请卸载再安装：
+
+.. code-block:: bash
+
+   pip install build/python/dist/*.whl -U
 
 .. _run_test:
 
diff --git a/doc/getstarted/build_and_install/build_from_source_en.rst b/doc/getstarted/build_and_install/build_from_source_en.rst
index 92211aee8c..fde8a18daa 100644
--- a/doc/getstarted/build_and_install/build_from_source_en.rst
+++ b/doc/getstarted/build_and_install/build_from_source_en.rst
@@ -36,6 +36,11 @@ machine or copy it to the target machine.
 
    pip install build/python/dist/*.whl
 
+If the machine has installed PaddlePaddle before, please uninstall it first and reinstall it again.
+
+.. code-block:: bash
+
+   pip install build/python/dist/*.whl -U
 
 .. _run_test:
 

From 5faebab375d5e039f5f7cc3169b8de8167494d31 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Fri, 12 Jan 2018 14:18:55 +0800
Subject: [PATCH 51/68] Done, need support selectedrows

---
 .../paddle/v2/fluid/distribute_transpiler.py  | 20 ++++---------------
 1 file changed, 4 insertions(+), 16 deletions(-)

diff --git a/python/paddle/v2/fluid/distribute_transpiler.py b/python/paddle/v2/fluid/distribute_transpiler.py
index 59e74e0d6f..d17f9815cc 100644
--- a/python/paddle/v2/fluid/distribute_transpiler.py
+++ b/python/paddle/v2/fluid/distribute_transpiler.py
@@ -148,7 +148,7 @@ class DistributeTranspiler:
             concat = program.global_block().append_op(
                 type="concat",
                 inputs={"X": splited_var},
-                outputs={"Out": orig_param},
+                outputs={"Out": [orig_param]},
                 attrs={"axis": 0})
 
     def _create_vars_from_blocklist(self, program, block_list):
@@ -420,7 +420,6 @@ class DistributeTranspiler:
             else:
                 self._append_pserver_non_opt_ops(optimize_sub_program,
                                                  pserver_program, opt_op)
-        print("****subprogram", optimize_sub_program)
         pserver_program.global_block().append_op(
             type="recv",
             inputs={"RX": self.param_grad_ep_mapping[endpoint]["grads"]
@@ -463,7 +462,6 @@ class DistributeTranspiler:
         pserver_vars = pserver_program.global_block().vars
         created_var_map = dict()
         for _, var in pserver_vars.iteritems():
-            print("create var for startup", var.name, var.shape)
             tmpvar = s_prog.global_block().create_var(
                 name=var.name,
                 persistable=var.persistable,
@@ -485,21 +483,11 @@ class DistributeTranspiler:
                     op_on_pserver = True
                     new_outputs[key] = pserver_vars[var.name]
 
-                # newname, _ = _get_splited_name_and_shape(var.name)
-                # if newname:
-                #     print("updating output", newname, created_var_map[newname])
-                #     new_outputs[key] = created_var_map[newname]
-                # else:
-                #     print("no update output", key, var)
-                #     new_outputs[key] = var
-                # if var.name in created_var_map or \
-                #     newname:
-                #     op_on_pserver = True
-
             if op_on_pserver:
-                if op.type in ["gaussian_random", "fill_constant"]:
+                if op.type in [
+                        "gaussian_random", "fill_constant", "uniform_random"
+                ]:
                     op.attrs["shape"] = new_outputs["Out"].shape
-                print("updated shape", op.attrs["shape"])
                 s_prog.global_block().append_op(
                     type=op.type,
                     inputs=op.inputs,

From 72091aa5f8d02e45bd6eeed4e4058bf0c4ab1dd7 Mon Sep 17 00:00:00 2001
From: ying <lcy.seso@gmail.com>
Date: Fri, 12 Jan 2018 14:31:38 +0800
Subject: [PATCH 52/68] fix display error of C-API doc.

---
 doc/howto/usage/capi/workflow_of_capi_cn.md | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/doc/howto/usage/capi/workflow_of_capi_cn.md b/doc/howto/usage/capi/workflow_of_capi_cn.md
index c1c2c86d0c..449bda76e8 100644
--- a/doc/howto/usage/capi/workflow_of_capi_cn.md
+++ b/doc/howto/usage/capi/workflow_of_capi_cn.md
@@ -26,10 +26,9 @@
 
 ### 准备预测模型
 
-在准备预测模型部分的介绍，我们以手写数字识别任务为例。手写数字识别任务定义了一个含有[两个隐层的简单全连接网络](https://github.com/PaddlePaddle/book/blob/develop/02.recognize_digits/README.cn.md#softmax回归softmax-regression)，网络接受一幅图片作为输入，将图片分类到 0 ~ 9 类别标签之一。完整代码可以查看[此目录](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense) 中的相关脚本。
+准备预测模型部分，我们以手写数字识别任务为例进行介绍。手写数字识别任务定义了一个含有[两个隐层的简单全连接网络](https://github.com/PaddlePaddle/book/blob/develop/02.recognize_digits/README.cn.md#softmax回归softmax-regression)，网络接受一幅图片作为输入，将图片分类到 0 ~ 9 类别标签之一。完整代码可以查看[此目录](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense) 中的相关脚本。
 
-调用C-API开发预测程序需要一个训练好的模型，在终端执行`python mnist_v2.py`
-运行[目录](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense) 会使用 PaddlePaddle 内置的 [MNIST 数据集](http://yann.lecun.com/exdb/mnist/)进行训练。训练好的模型默认保存在当前运行目录下的`models`目录中。
+调用C-API开发预测程序需要一个训练好的模型，运行[MNIST手写数字识别目录](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense)下的[mnist_v2.py](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/examples/model_inference/dense/mnist_v2.py)脚本，在终端执行`python mnist_v2.py`，会使用 PaddlePaddle 内置的 [MNIST 数据集](http://yann.lecun.com/exdb/mnist/)进行训练。训练好的模型默认保存在当前运行目录下的`models`目录中。
 
 下面，我们将训练结束后存储下来的模型转换成预测模型。
 
@@ -113,7 +112,7 @@ C-API支持的所有输入数据类型和他们的组织方式，请参考“输
 
 #### step 4. 前向计算
 
-完成上述准备之后，通过调用 `[paddle_gradient_machine_forward](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/gradient_machine.h#L73)` 接口完成神经网络的前向计算。
+完成上述准备之后，通过调用 [`paddle_gradient_machine_forward`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/gradient_machine.h#L73) 接口完成神经网络的前向计算。
 
 #### step 5. 清理
 

From 7b117c1ab28ff9e6e49c8d17f87d6a2ef5cf584b Mon Sep 17 00:00:00 2001
From: ying <lcy.seso@gmail.com>
Date: Fri, 12 Jan 2018 15:00:10 +0800
Subject: [PATCH 53/68] follow comments.

---
 doc/howto/usage/capi/workflow_of_capi_cn.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/howto/usage/capi/workflow_of_capi_cn.md b/doc/howto/usage/capi/workflow_of_capi_cn.md
index 449bda76e8..e0a42fff12 100644
--- a/doc/howto/usage/capi/workflow_of_capi_cn.md
+++ b/doc/howto/usage/capi/workflow_of_capi_cn.md
@@ -28,7 +28,7 @@
 
 准备预测模型部分，我们以手写数字识别任务为例进行介绍。手写数字识别任务定义了一个含有[两个隐层的简单全连接网络](https://github.com/PaddlePaddle/book/blob/develop/02.recognize_digits/README.cn.md#softmax回归softmax-regression)，网络接受一幅图片作为输入，将图片分类到 0 ~ 9 类别标签之一。完整代码可以查看[此目录](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense) 中的相关脚本。
 
-调用C-API开发预测程序需要一个训练好的模型，运行[MNIST手写数字识别目录](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense)下的[mnist_v2.py](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/examples/model_inference/dense/mnist_v2.py)脚本，在终端执行`python mnist_v2.py`，会使用 PaddlePaddle 内置的 [MNIST 数据集](http://yann.lecun.com/exdb/mnist/)进行训练。训练好的模型默认保存在当前运行目录下的`models`目录中。
+调用C-API开发预测程序需要一个训练好的模型，运行[MNIST手写数字识别目录](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense)下的[mnist_v2.py](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/capi/examples/model_inference/dense/mnist_v2.py)脚本，在终端执行`python mnist_v2.py`，会使用 PaddlePaddle 内置的 [MNIST 数据集](http://yann.lecun.com/exdb/mnist/)进行训练。训练好的模型默认保存在当前运行目录下的`models`目录中。
 
 下面，我们将训练结束后存储下来的模型转换成预测模型。
 

From 3423022e84fdb9eef76d051ed35524422f7a02b4 Mon Sep 17 00:00:00 2001
From: Yan Chunwei <yanchunwei@outlook.com>
Date: Fri, 12 Jan 2018 15:24:07 +0800
Subject: [PATCH 54/68] feature/add print op (#6799)

---
 paddle/operators/CMakeLists.txt               |   1 +
 paddle/operators/print_op.cc                  | 206 ++++++++++++++++++
 python/paddle/v2/fluid/layers/control_flow.py |  57 ++++-
 python/paddle/v2/fluid/tests/test_print_op.py |  21 ++
 4 files changed, 284 insertions(+), 1 deletion(-)
 create mode 100644 paddle/operators/print_op.cc
 create mode 100644 python/paddle/v2/fluid/tests/test_print_op.py

diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt
index 5889a50db0..e1b695e8cd 100644
--- a/paddle/operators/CMakeLists.txt
+++ b/paddle/operators/CMakeLists.txt
@@ -135,6 +135,7 @@ op_library(detection_output_op DEPS softmax)
 op_library(sequence_softmax_op DEPS softmax)
 op_library(sum_op DEPS selected_rows_functor)
 op_library(sgd_op DEPS selected_rows_functor)
+op_library(print_op DEPS lod_tensor)
 op_library(adagrad_op DEPS selected_rows_functor)
 op_library(conv_op DEPS vol2col)
 op_library(pool_op DEPS pooling)
diff --git a/paddle/operators/print_op.cc b/paddle/operators/print_op.cc
new file mode 100644
index 0000000000..89e41d806c
--- /dev/null
+++ b/paddle/operators/print_op.cc
@@ -0,0 +1,206 @@
+/* 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 <algorithm>
+#include <ctime>
+
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+#define CLOG std::cout
+
+struct Formater {
+  std::string message;
+  std::string name;
+  std::vector<int> dims;
+  std::type_index dtype{typeid(char)};
+  framework::LoD lod;
+  int summarize;
+  void* data{nullptr};
+
+  void operator()(size_t size) {
+    PrintMessage();
+    PrintName();
+    PrintDims();
+    PrintDtype();
+    PrintLod();
+    PrintData(size);
+  }
+
+ private:
+  void PrintMessage() { CLOG << std::time(nullptr) << "\t" << message; }
+  void PrintName() {
+    if (!name.empty()) {
+      CLOG << "Tensor[" << name << "]" << std::endl;
+    }
+  }
+  void PrintDims() {
+    if (!dims.empty()) {
+      CLOG << "\tshape: [";
+      for (auto i : dims) {
+        CLOG << i << ",";
+      }
+      CLOG << "]" << std::endl;
+    }
+  }
+  void PrintDtype() {
+    if (dtype.hash_code() != typeid(char).hash_code()) {
+      CLOG << "\tdtype: " << dtype.name() << std::endl;
+    }
+  }
+  void PrintLod() {
+    if (!lod.empty()) {
+      CLOG << "\tLoD: [";
+      for (auto level : lod) {
+        CLOG << "[ ";
+        for (auto i : level) {
+          CLOG << i << ",";
+        }
+        CLOG << " ]";
+      }
+      CLOG << "]" << std::endl;
+    }
+  }
+
+  void PrintData(size_t size) {
+    PADDLE_ENFORCE_NOT_NULL(data);
+    // print float
+    if (dtype.hash_code() == typeid(float).hash_code()) {
+      Display<float>(size);
+    }
+    if (dtype.hash_code() == typeid(double).hash_code()) {
+      Display<double>(size);
+    }
+    if (dtype.hash_code() == typeid(int).hash_code()) {
+      Display<int>(size);
+    }
+    if (dtype.hash_code() == typeid(int64_t).hash_code()) {
+      Display<int64_t>(size);
+    }
+  }
+
+  template <typename T>
+  void Display(size_t size) {
+    auto* d = (T*)data;
+    CLOG << "\tdata: ";
+    if (summarize != -1) {
+      summarize = std::min(size, (size_t)summarize);
+      for (int i = 0; i < summarize; i++) {
+        CLOG << d[i] << ",";
+      }
+    } else {
+      for (size_t i = 0; i < size; i++) {
+        CLOG << d[i] << ",";
+      }
+    }
+    CLOG << std::endl;
+  }
+};
+
+// TODO(ChunweiYan) there should be some other printers for TensorArray
+class TensorPrintOp : public framework::OperatorBase {
+ public:
+  TensorPrintOp(const std::string& type,
+                const framework::VariableNameMap& inputs,
+                const framework::VariableNameMap& outputs,
+                const framework::AttributeMap& attrs)
+      : OperatorBase(type, inputs, outputs, attrs) {}
+
+  TensorPrintOp(const TensorPrintOp& o)
+      : framework::OperatorBase(
+            static_cast<const framework::OperatorBase&>(o)) {
+    PADDLE_THROW("Not implemented");
+  }
+
+  void Run(const framework::Scope& scope,
+           const platform::Place& place) const override {
+    // Only run the `first_n` times.
+    int first_n = Attr<int>("first_n");
+    if (first_n > 0 && ++times_ > first_n) return;
+
+    PADDLE_ENFORCE(!Inputs("input").empty(), "input should be set");
+    auto* input_var = scope.FindVar(Input("input"));
+    PADDLE_ENFORCE_NOT_NULL(input_var);
+    auto& tensor = input_var->Get<framework::LoDTensor>();
+
+    // TODO(ChunweiYan) support GPU
+    PADDLE_ENFORCE(platform::is_cpu_place(tensor.place()));
+
+    Formater formater;
+    if (Attr<bool>("print_tensor_name")) {
+      formater.name = Inputs("input").front();
+    }
+    if (Attr<bool>("print_tensor_type")) {
+      formater.dtype = tensor.type();
+    }
+    if (Attr<bool>("print_tensor_shape")) {
+      formater.dims.assign(tensor.dims()[0],
+                           tensor.dims()[tensor.dims().size() - 1]);
+    }
+    if (Attr<bool>("print_tensor_lod")) {
+      formater.lod = tensor.lod();
+    }
+    formater.summarize = Attr<int>("summarize");
+    formater.data = (void*)tensor.data<void>();
+    formater(tensor.numel());
+  }
+
+ private:
+  mutable int times_{0};
+};
+
+class PrintOpProtoAndCheckMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  PrintOpProtoAndCheckMaker(OpProto* proto, OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("input", "the tensor that will be displayed.");
+    AddAttr<int>("first_n", "Only log `first_n` number of times.");
+    AddAttr<std::string>("message", "A string message to print as a prefix.");
+    AddAttr<int>("summarize", "Print this number of elements in the tensor.");
+    AddAttr<bool>("print_tensor_name", "Whether to print the tensor name.");
+    AddAttr<bool>("print_tensor_type", "Whether to print the tensor's dtype.");
+    AddAttr<bool>("print_tensor_shape", "Whether to print the tensor's shape.");
+    AddAttr<bool>("print_tensor_lod", "Whether to print the tensor's lod.");
+    AddComment(R"DOC(
+    Creates a print op that will print when a tensor is accessed.
+
+    Wraps the tensor passed in so that whenever that a tensor is accessed,
+    the message `message` is printed, along with the current value of the
+    tensor `t`.)DOC");
+  }
+};
+
+class InferShape : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext* context) const override {
+    PADDLE_ENFORCE(context->HasInput("input"), "input should be set");
+  }
+};
+
+class InferVarType : public framework::VarTypeInference {
+ public:
+  void operator()(const framework::OpDesc& op_desc,
+                  framework::BlockDesc* block) const override {}
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+REGISTER_OPERATOR(print, paddle::operators::TensorPrintOp,
+                  paddle::operators::PrintOpProtoAndCheckMaker,
+                  paddle::operators::InferShape,
+                  paddle::operators::InferVarType,
+                  paddle::framework::EmptyGradOpMaker);
diff --git a/python/paddle/v2/fluid/layers/control_flow.py b/python/paddle/v2/fluid/layers/control_flow.py
index 9ad021fa99..0cf17f3083 100644
--- a/python/paddle/v2/fluid/layers/control_flow.py
+++ b/python/paddle/v2/fluid/layers/control_flow.py
@@ -12,7 +12,7 @@ __all__ = [
     'array_to_lod_tensor', 'increment', 'array_write', 'create_array',
     'less_than', 'array_read', 'shrink_memory', 'array_length', 'IfElse',
     'DynamicRNN', 'ConditionalBlock', 'StaticRNN', 'reorder_lod_tensor_by_rank',
-    'ParallelDo'
+    'ParallelDo', 'Print'
 ]
 
 
@@ -110,6 +110,61 @@ def merge_lod_tensor(in_true, in_false, x, mask, level=0):
     return out
 
 
+def Print(input,
+          first_n=-1,
+          message=None,
+          summarize=-1,
+          print_tensor_name=True,
+          print_tensor_type=True,
+          print_tensor_shape=True,
+          print_tensor_lod=True):
+    '''
+    **Print operator**
+
+    This creates a print op that will print when a tensor is accessed.
+
+    Wraps the tensor passed in so that whenever that a tensor is accessed,
+    the message `message` is printed, along with the current value of the
+    tensor `t`.
+
+    Args:
+      input(Variable): A Tensor to print.
+      summarize(int): Print this number of elements in the tensor, will print all
+                 if left negative.
+      message(str): A string message to print as a prefix.
+      first_n(int): Only log `first_n` number of times.
+      print_tensor_name(bool): Print the tensor name.
+      print_tensor_type(bool): Print the tensor type.
+      print_tensor_shape(bool): Print the tensor shape.
+      print_tensor_lod(bool): Print the tensor lod.
+
+    Returns:
+      None
+
+    Examples:
+        .. code-block:: python
+
+        value = some_layer(...)
+        Print(value, summarize=10,
+              message="The content of some_layer: ")
+    '''
+    helper = LayerHelper('print', **locals())
+    out = helper.create_tmp_variable(dtype='int32')
+    helper.append_op(
+        type='print',
+        inputs={'input': input},
+        attrs={
+            'first_n': first_n,
+            'summarize': summarize,
+            'message': message or "",
+            'print_tensor_name': print_tensor_name,
+            'print_tensor_type': print_tensor_type,
+            'print_tensor_shape': print_tensor_shape,
+            'print_tensor_lod': print_tensor_lod,
+        })
+    return out
+
+
 class BlockGuard(object):
     """
     BlockGuard class.
diff --git a/python/paddle/v2/fluid/tests/test_print_op.py b/python/paddle/v2/fluid/tests/test_print_op.py
new file mode 100644
index 0000000000..86a701a020
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/test_print_op.py
@@ -0,0 +1,21 @@
+import unittest
+import numpy as np
+from paddle.v2.fluid.executor import Executor
+import paddle.v2.fluid.core as core
+import paddle.v2.fluid.layers as pd
+
+
+class TestSumOp(unittest.TestCase):
+    def test_tensor(self):
+        i = pd.zeros(shape=[2, 10], dtype='float32')
+
+        pd.Print(i, message="I am a message", summarize=10)
+
+        cpu = core.CPUPlace()
+        exe = Executor(cpu)
+
+        exe.run()
+
+
+if __name__ == '__main__':
+    unittest.main()

From 697f92ef86d3079fd82ec2fb9b8dc52fb8ef462a Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Fri, 12 Jan 2018 15:36:47 +0800
Subject: [PATCH 55/68] Add type check

---
 python/paddle/v2/fluid/clip.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/python/paddle/v2/fluid/clip.py b/python/paddle/v2/fluid/clip.py
index 6ed97cbe64..3dccd74bd8 100644
--- a/python/paddle/v2/fluid/clip.py
+++ b/python/paddle/v2/fluid/clip.py
@@ -40,6 +40,11 @@ def error_clip_callback(block, context):
                          op_desc.output_arg_names()):
         fwd_var = block.var_recursive(grad_to_var[grad_n])
         error_clip = getattr(fwd_var, "error_clip", None)
+        if not (error_clip is None or isinstance(error_clip,
+                                                 BaseErrorClipAttr)):
+            raise TypeError(
+                "Variable's error_clip should be an instance of BaseErrorClipAttr or None."
+            )
         if error_clip is not None:
             error_clip.append_clip_op(block, grad_n)
 

From 23df6c447868e9c5d1fe2f0b04342ae377b7fafb Mon Sep 17 00:00:00 2001
From: Qiao Longfei <qiaolongfei@baidu.com>
Date: Fri, 12 Jan 2018 15:37:15 +0800
Subject: [PATCH 56/68] Add get lod for debug (#7375)

* add GetLoD for debug

* add LoDToString

* optimize if

* typo

* add lod_tensor to operator's dependency
---
 paddle/framework/CMakeLists.txt |  2 +-
 paddle/framework/lod_tensor.cc  |  6 ++++++
 paddle/framework/lod_tensor.h   |  2 ++
 paddle/framework/operator.cc    | 25 ++++++++++++++++++++++---
 4 files changed, 31 insertions(+), 4 deletions(-)

diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt
index ed5f6310f4..597ea959f2 100644
--- a/paddle/framework/CMakeLists.txt
+++ b/paddle/framework/CMakeLists.txt
@@ -47,7 +47,7 @@ cc_test(op_proto_maker_test SRCS op_proto_maker_test.cc DEPS op_proto_maker)
 cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto)
 cc_library(shape_inference SRCS shape_inference.cc DEPS ddim attribute device_context)
 cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope glog
-    shape_inference data_transform)
+    shape_inference data_transform lod_tensor)
 cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry init)
 cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS shape_inference op_info operator glog)
 
diff --git a/paddle/framework/lod_tensor.cc b/paddle/framework/lod_tensor.cc
index 7ae94c6465..87a57d0951 100644
--- a/paddle/framework/lod_tensor.cc
+++ b/paddle/framework/lod_tensor.cc
@@ -69,6 +69,12 @@ std::ostream &operator<<(std::ostream &os, const LoDTensor &t) {
   return os;
 }
 
+std::string LoDToString(const LoD &lod) {
+  std::ostringstream stream;
+  stream << lod;
+  return stream.str();
+}
+
 LoD SliceInLevel(const LoD &in, size_t level, size_t elem_begin,
                  size_t elem_end) {
   PADDLE_ENFORCE_LT(level, in.size());
diff --git a/paddle/framework/lod_tensor.h b/paddle/framework/lod_tensor.h
index 37753f5f4d..88ea78f268 100644
--- a/paddle/framework/lod_tensor.h
+++ b/paddle/framework/lod_tensor.h
@@ -60,6 +60,8 @@ using LoD = std::vector<Vector<size_t>>;
 std::ostream& operator<<(std::ostream& os, const LoD& lod);
 std::ostream& operator<<(std::ostream& os, const LoDTensor& t);
 
+std::string LoDToString(const LoD& lod);
+
 LoD SliceInLevel(const LoD& in, size_t level, size_t elem_begin,
                  size_t elem_end);
 /*
diff --git a/paddle/framework/operator.cc b/paddle/framework/operator.cc
index 7756a52ca9..be1373dc2a 100644
--- a/paddle/framework/operator.cc
+++ b/paddle/framework/operator.cc
@@ -80,7 +80,9 @@ static DDim GetDims(const Scope& scope, const std::string& name) {
   Variable* var = scope.FindVar(name);
   if (var == nullptr) {
     return DDim({-1});
-  } else if (var->IsType<LoDTensor>()) {
+  }
+
+  if (var->IsType<LoDTensor>()) {
     return var->Get<LoDTensor>().dims();
   } else if (var->IsType<SelectedRows>()) {
     return var->Get<SelectedRows>().GetCompleteDims();
@@ -89,6 +91,21 @@ static DDim GetDims(const Scope& scope, const std::string& name) {
   }
 }
 
+static LoD GetLoD(const Scope& scope, const std::string& name) {
+  Variable* var = scope.FindVar(name);
+  auto default_lod = LoD({{}});
+
+  if (var == nullptr) {
+    return default_lod;
+  }
+
+  if (var->IsType<LoDTensor>()) {
+    return var->Get<LoDTensor>().lod();
+  } else {
+    return default_lod;
+  }
+}
+
 std::string OperatorBase::Input(const std::string& name) const {
   auto& ins = Inputs(name);
   PADDLE_ENFORCE_LE(ins.size(), 1UL,
@@ -130,7 +147,8 @@ std::string OperatorBase::DebugStringEx(const Scope* scope) const {
     for (size_t i = 0; i < input.second.size(); ++i) {
       ss << input.second[i];
       if (scope) {
-        ss << "(" << GetDims(*scope, input.second[i]) << ")";
+        ss << "[" << GetDims(*scope, input.second[i]) << "]";
+        ss << "(" << GetLoD(*scope, input.second[i]) << ")";
       }
       if (i != input.second.size() - 1) {
         ss << ", ";
@@ -149,7 +167,8 @@ std::string OperatorBase::DebugStringEx(const Scope* scope) const {
     for (size_t i = 0; i < output.second.size(); ++i) {
       ss << output.second[i];
       if (scope) {
-        ss << "(" << GetDims(*scope, output.second[i]) << ")";
+        ss << "[" << GetDims(*scope, output.second[i]) << "]";
+        ss << "(" << GetLoD(*scope, output.second[i]) << ")";
       }
       if (i != output.second.size() - 1) {
         ss << ", ";

From c24da0d3eefa1cf0a9dcee9b96fa120ca2e421b3 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Fri, 12 Jan 2018 16:30:45 +0800
Subject: [PATCH 57/68] update unit test split var

---
 python/paddle/v2/fluid/tests/CMakeLists.txt                  | 1 +
 .../paddle/v2/fluid/tests/book_distribute/test_split_var.py  | 5 +++--
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/python/paddle/v2/fluid/tests/CMakeLists.txt b/python/paddle/v2/fluid/tests/CMakeLists.txt
index e795627bfe..9a0240cbf6 100644
--- a/python/paddle/v2/fluid/tests/CMakeLists.txt
+++ b/python/paddle/v2/fluid/tests/CMakeLists.txt
@@ -5,3 +5,4 @@ foreach(src ${TEST_OPS})
 endforeach()
 
 add_subdirectory(book)
+add_subdirectory(book_distribute)
diff --git a/python/paddle/v2/fluid/tests/book_distribute/test_split_var.py b/python/paddle/v2/fluid/tests/book_distribute/test_split_var.py
index 1355e13e1c..cfb48a5915 100644
--- a/python/paddle/v2/fluid/tests/book_distribute/test_split_var.py
+++ b/python/paddle/v2/fluid/tests/book_distribute/test_split_var.py
@@ -2,6 +2,7 @@ import math
 import unittest
 from paddle.v2.fluid.distribute_transpiler import split_dense_variable
 import paddle.v2.fluid as fluid
+import paddle.v2.fluid.core as core
 import random
 
 
@@ -19,9 +20,9 @@ class TestSplitVar(unittest.TestCase):
         program = fluid.Program()
         for shape in shapes:
             var = program.global_block().create_var(
-                name=str(random.randint(10000)),
+                name=str(random.randint(10000, 99999)),
                 persistable=True,
-                dtype=core.VarDesc.VarType.LOD_TENSOR,
+                # dtype=core.VarDesc.VarType.LOD_TENSOR,
                 shape=shape)
             var_list.append(var)
         blocks = split_dense_variable(var_list, 10)

From 06b326b681e2d9a831e02a7f2f0a1e8c18c1bdeb Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Fri, 12 Jan 2018 17:07:11 +0800
Subject: [PATCH 58/68] follow comments

---
 python/paddle/v2/fluid/__init__.py                     | 3 ++-
 python/paddle/v2/fluid/distribute_transpiler_simple.py | 2 +-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/python/paddle/v2/fluid/__init__.py b/python/paddle/v2/fluid/__init__.py
index 5e01b87198..a14422ee92 100644
--- a/python/paddle/v2/fluid/__init__.py
+++ b/python/paddle/v2/fluid/__init__.py
@@ -18,13 +18,14 @@ from param_attr import ParamAttr
 from data_feeder import DataFeeder
 from core import LoDTensor, CPUPlace, CUDAPlace
 from distribute_transpiler import DistributeTranspiler
+from distribute_transpiler_simple import SimpleDistributeTranspiler
 import clip
 
 Tensor = LoDTensor
 __all__ = framework.__all__ + executor.__all__ + [
     'io', 'initializer', 'layers', 'nets', 'optimizer', 'backward',
     'regularizer', 'LoDTensor', 'CPUPlace', 'CUDAPlace', 'Tensor', 'ParamAttr'
-    'DataFeeder', 'clip', 'DistributeTranspiler'
+    'DataFeeder', 'clip', 'SimpleDistributeTranspiler', 'DistributeTranspiler'
 ]
 
 
diff --git a/python/paddle/v2/fluid/distribute_transpiler_simple.py b/python/paddle/v2/fluid/distribute_transpiler_simple.py
index 49ece7b725..32db3df9aa 100644
--- a/python/paddle/v2/fluid/distribute_transpiler_simple.py
+++ b/python/paddle/v2/fluid/distribute_transpiler_simple.py
@@ -48,7 +48,7 @@ def round_robin(params_grads, pserver_endpoints):
     return param_grad_map
 
 
-class DistributeTranspiler:
+class SimpleDistributeTranspiler:
     def transpile(self,
                   optimize_ops,
                   params_grads,

From 5528ec13212a1171c8274769b3b6e1842cc6861f Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Fri, 12 Jan 2018 17:10:52 +0800
Subject: [PATCH 59/68] add another method of update whl

---
 doc/getstarted/build_and_install/build_from_source_cn.rst | 7 ++++++-
 doc/getstarted/build_and_install/build_from_source_en.rst | 7 ++++++-
 2 files changed, 12 insertions(+), 2 deletions(-)

diff --git a/doc/getstarted/build_and_install/build_from_source_cn.rst b/doc/getstarted/build_and_install/build_from_source_cn.rst
index 1e75087462..71904dc41e 100644
--- a/doc/getstarted/build_and_install/build_from_source_cn.rst
+++ b/doc/getstarted/build_and_install/build_from_source_cn.rst
@@ -32,10 +32,15 @@ PaddlePaddle主要使用 `CMake <https://cmake.org>`_ 以及GCC, G++作为编译
 
    pip install build/python/dist/*.whl
 
-如果机器中已经安装过PaddlePaddle，请卸载再安装：
+如果机器中已经安装过PaddlePaddle，有两种方法：
 
 .. code-block:: bash
 
+   1. 先卸载之前的版本，再重新安装
+   pip uninstall paddlepaddle
+   pip install build/python/dist/*.whl
+
+   2. 直接升级到更新的版本
    pip install build/python/dist/*.whl -U
 
 .. _run_test:
diff --git a/doc/getstarted/build_and_install/build_from_source_en.rst b/doc/getstarted/build_and_install/build_from_source_en.rst
index fde8a18daa..27f73b2e2c 100644
--- a/doc/getstarted/build_and_install/build_from_source_en.rst
+++ b/doc/getstarted/build_and_install/build_from_source_en.rst
@@ -36,10 +36,15 @@ machine or copy it to the target machine.
 
    pip install build/python/dist/*.whl
 
-If the machine has installed PaddlePaddle before, please uninstall it first and reinstall it again.
+If the machine has installed PaddlePaddle before, there are two methods:
 
 .. code-block:: bash
 
+   1. uninstall and reinstall
+   pip uninstall paddlepaddle
+   pip install build/python/dist/*.whl
+
+   2. upgrade directly
    pip install build/python/dist/*.whl -U
 
 .. _run_test:

From 8ac744f372ed8980007d53ee3cbc1acd7db51a56 Mon Sep 17 00:00:00 2001
From: ying <lcy.seso@gmail.com>
Date: Fri, 12 Jan 2018 14:57:23 +0800
Subject: [PATCH 60/68] add wrapper for elementwise math operator.

---
 doc/api/v2/fluid/layers.rst                   | 129 ++++++++++++++++++
 python/paddle/v2/fluid/__init__.py            |  19 ++-
 python/paddle/v2/fluid/backward.py            |   5 +-
 python/paddle/v2/fluid/clip.py                |   4 +-
 python/paddle/v2/fluid/default_scope_funcs.py |  26 ++--
 python/paddle/v2/fluid/evaluator.py           |  35 ++---
 python/paddle/v2/fluid/framework.py           |  12 +-
 python/paddle/v2/fluid/initializer.py         |   7 +-
 python/paddle/v2/fluid/io.py                  |  12 +-
 python/paddle/v2/fluid/layers/nn.py           |  45 ++++--
 python/paddle/v2/fluid/layers/ops.py          |  29 +++-
 python/paddle/v2/fluid/layers/tensor.py       |  12 +-
 .../fluid/memory_optimization_transpiler.py   |   6 +-
 python/paddle/v2/fluid/nets.py                |   5 +-
 python/paddle/v2/fluid/registry.py            |  17 ++-
 python/paddle/v2/fluid/regularizer.py         |   6 +-
 16 files changed, 304 insertions(+), 65 deletions(-)

diff --git a/doc/api/v2/fluid/layers.rst b/doc/api/v2/fluid/layers.rst
index 696a8012aa..24bdf08fff 100644
--- a/doc/api/v2/fluid/layers.rst
+++ b/doc/api/v2/fluid/layers.rst
@@ -358,3 +358,132 @@ reduce_min
 ..  autofunction:: paddle.v2.fluid.layers.reduce_min
     :noindex:
 
+logsigmoid
+----------
+..  autofunction:: paddle.v2.fluid.layers.logsigmoid
+    :noindex:
+
+exp
+---
+..  autofunction:: paddle.v2.fluid.layers.exp
+    :noindex:
+
+relu
+----
+..  autofunction:: paddle.v2.fluid.layers.relu
+    :noindex:
+
+tanh
+----
+..  autofunction:: paddle.v2.fluid.layers.tanh
+    :noindex:
+
+tanh_shrink
+-----------
+..  autofunction:: paddle.v2.fluid.layers.tanh_shrink
+    :noindex:
+
+softshrink
+----------
+..  autofunction:: paddle.v2.fluid.layers.softshrink
+    :noindex:
+
+sqrt
+----
+..  autofunction:: paddle.v2.fluid.layers.sqrt
+    :noindex:
+
+abs
+----
+..  autofunction:: paddle.v2.fluid.layers.abs
+    :noindex:
+
+ceil
+----
+..  autofunction:: paddle.v2.fluid.layers.ceil
+    :noindex:
+
+floor
+-----
+..  autofunction:: paddle.v2.fluid.layers.floor
+    :noindex:
+
+round
+-----
+..  autofunction:: paddle.v2.fluid.layers.round
+    :noindex:
+
+reciprocal
+----------
+..  autofunction:: paddle.v2.fluid.layers.reciprocal
+    :noindex:
+
+log
+---
+..  autofunction:: paddle.v2.fluid.layers.log
+    :noindex:
+
+square
+------
+..  autofunction:: paddle.v2.fluid.layers.square
+    :noindex:
+
+softplus
+--------
+..  autofunction:: paddle.v2.fluid.layers.softplus
+    :noindex:
+
+softsign
+---------
+..  autofunction:: paddle.v2.fluid.layers.softsign
+    :noindex:
+
+brelu
+-----
+..  autofunction:: paddle.v2.fluid.layers.brelu
+    :noindex:
+
+leaky_relu
+----------
+..  autofunction:: paddle.v2.fluid.layers.leaky_relu
+    :noindex:
+
+soft_relu
+---------
+..  autofunction:: paddle.v2.fluid.layers.soft_relu
+    :noindex:
+
+elu
+----
+..  autofunction:: paddle.v2.fluid.layers.elu
+    :noindex:
+
+relu6
+-----
+..  autofunction:: paddle.v2.fluid.layers.relu6
+    :noindex:
+
+pow
+----
+..  autofunction:: paddle.v2.fluid.layers.pow
+    :noindex:
+
+hard_shrink
+-----------
+..  autofunction:: paddle.v2.fluid.layers.hard_shrink
+    :noindex:
+
+thresholded_relu
+----------------
+..  autofunction:: paddle.v2.fluid.layers.thresholded_relu
+    :noindex:
+
+hard_sigmoid
+-------------
+..  autofunction:: paddle.v2.fluid.layers.hard_sigmoid
+    :noindex:
+
+swish
+------
+..  autofunction:: paddle.v2.fluid.layers.swish
+    :noindex:
diff --git a/python/paddle/v2/fluid/__init__.py b/python/paddle/v2/fluid/__init__.py
index 422aa0a5ba..ec5159fca1 100644
--- a/python/paddle/v2/fluid/__init__.py
+++ b/python/paddle/v2/fluid/__init__.py
@@ -23,9 +23,22 @@ from memory_optimization_transpiler import memory_optimize
 
 Tensor = LoDTensor
 __all__ = framework.__all__ + executor.__all__ + [
-    'io', 'initializer', 'layers', 'nets', 'optimizer', 'backward',
-    'regularizer', 'LoDTensor', 'CPUPlace', 'CUDAPlace', 'Tensor', 'ParamAttr'
-    'DataFeeder', 'clip', 'DistributeTranspiler', 'memory_optimize'
+    'io',
+    'initializer',
+    'layers',
+    'nets',
+    'optimizer',
+    'backward',
+    'regularizer',
+    'LoDTensor',
+    'CPUPlace',
+    'CUDAPlace',
+    'Tensor',
+    'ParamAttr'
+    'DataFeeder',
+    'clip',
+    'DistributeTranspiler',
+    'memory_optimize',
 ]
 
 
diff --git a/python/paddle/v2/fluid/backward.py b/python/paddle/v2/fluid/backward.py
index cea2d1e090..43f6133a65 100644
--- a/python/paddle/v2/fluid/backward.py
+++ b/python/paddle/v2/fluid/backward.py
@@ -3,7 +3,10 @@ from . import core
 import collections
 import copy
 
-__all__ = ['append_backward', 'calc_gradient']
+__all__ = [
+    'append_backward',
+    'calc_gradient',
+]
 
 
 def _rename_arg_(op_descs, old_name, new_name, begin_idx=None, end_idx=None):
diff --git a/python/paddle/v2/fluid/clip.py b/python/paddle/v2/fluid/clip.py
index b1fd1c2b65..776c0f3f02 100644
--- a/python/paddle/v2/fluid/clip.py
+++ b/python/paddle/v2/fluid/clip.py
@@ -3,7 +3,9 @@ import layers
 from . import core
 
 __all__ = [
-    'GradientClipByValue', 'append_gradient_clip_ops', 'error_clip_callback'
+    'GradientClipByValue',
+    'append_gradient_clip_ops',
+    'error_clip_callback',
 ]
 
 
diff --git a/python/paddle/v2/fluid/default_scope_funcs.py b/python/paddle/v2/fluid/default_scope_funcs.py
index 60c6165b6b..9aebc07f8e 100644
--- a/python/paddle/v2/fluid/default_scope_funcs.py
+++ b/python/paddle/v2/fluid/default_scope_funcs.py
@@ -1,16 +1,16 @@
 """
 Default scope function.
 
-`Paddle` manages Scope as programming language's scope.  It just a 
-thread-local stack of Scope. Top of that stack is current scope, the bottom 
-of that stack is all scopes' parent. 
+`Paddle` manages Scope as programming language's scope.  It just a
+thread-local stack of Scope. Top of that stack is current scope, the bottom
+of that stack is all scopes' parent.
 
-Invoking `var/find_var`  can `new/find` variable in current scope. 
-Invoking `enter_local_scope/leave_local_scope` can create or destroy local 
-scope. 
+Invoking `var/find_var`  can `new/find` variable in current scope.
+Invoking `enter_local_scope/leave_local_scope` can create or destroy local
+scope.
 
-A `scoped_function` will take a `function` as input. That function will be 
-invoked in a new local scope. 
+A `scoped_function` will take a `function` as input. That function will be
+invoked in a new local scope.
 """
 
 import paddle.v2.fluid.core
@@ -19,8 +19,12 @@ import threading
 __tl_scope__ = threading.local()
 
 __all__ = [
-    'get_cur_scope', 'enter_local_scope', 'leave_local_scope', 'var',
-    'find_var', 'scoped_function'
+    'get_cur_scope',
+    'enter_local_scope',
+    'leave_local_scope',
+    'var',
+    'find_var',
+    'scoped_function',
 ]
 
 
@@ -71,7 +75,7 @@ def find_var(name):
 def scoped_function(func):
     """
     invoke `func` in new scope.
-    
+
     :param func: a callable function that will be run in new scope.
     :type func: callable
     """
diff --git a/python/paddle/v2/fluid/evaluator.py b/python/paddle/v2/fluid/evaluator.py
index e186ee96c3..dc083f37b5 100644
--- a/python/paddle/v2/fluid/evaluator.py
+++ b/python/paddle/v2/fluid/evaluator.py
@@ -4,7 +4,10 @@ import layers
 from framework import Program, unique_name, Variable, program_guard
 from layer_helper import LayerHelper
 
-__all__ = ['Accuracy', 'ChunkEvaluator']
+__all__ = [
+    'Accuracy',
+    'ChunkEvaluator',
+]
 
 
 def _clone_var_(block, var):
@@ -21,19 +24,19 @@ def _clone_var_(block, var):
 class Evaluator(object):
     """
     Base Class for all evaluators
-    
+
     Args:
-        name(str): The name of evaluator. such as, "accuracy". Used for generate 
+        name(str): The name of evaluator. such as, "accuracy". Used for generate
             temporary variable name.
-        main_program(Program, optional): The evaluator should be added to this 
+        main_program(Program, optional): The evaluator should be added to this
             main_program. Default default_main_program()
-        startup_program(Program, optional):The parameter should be added to this 
+        startup_program(Program, optional):The parameter should be added to this
             startup_program. Default default_startup_program()
-            
+
     Attributes:
-        states(list): The list of state variables. states will be reset to zero 
+        states(list): The list of state variables. states will be reset to zero
             when `reset` is invoked.
-        metrics(list): The list of metrics variables. They will be calculate 
+        metrics(list): The list of metrics variables. They will be calculate
             every mini-batch
     """
 
@@ -66,14 +69,14 @@ class Evaluator(object):
 
     def create_state(self, suffix, dtype, shape):
         """
-        Create state variable. 
-        
+        Create state variable.
+
         NOTE: It is not a public API.
-        
+
         Args:
-            suffix(str): the state suffix. 
-            dtype(str|core.DataType): the state data type 
-            shape(tuple|list): the shape of state 
+            suffix(str): the state suffix.
+            dtype(str|core.DataType): the state data type
+            shape(tuple|list): the shape of state
 
         Returns: State variable
 
@@ -127,8 +130,8 @@ class Accuracy(Evaluator):
 
 class ChunkEvaluator(Evaluator):
     """
-    Accumulate counter numbers output by chunk_eval from mini-batches and 
-    compute the precision recall and F1-score using the accumulated counter 
+    Accumulate counter numbers output by chunk_eval from mini-batches and
+    compute the precision recall and F1-score using the accumulated counter
     numbers.
     """
 
diff --git a/python/paddle/v2/fluid/framework.py b/python/paddle/v2/fluid/framework.py
index 3ef6b33192..bdbfe9da07 100644
--- a/python/paddle/v2/fluid/framework.py
+++ b/python/paddle/v2/fluid/framework.py
@@ -7,9 +7,15 @@ import proto.framework_pb2 as framework_pb2
 from . import core
 
 __all__ = [
-    'Block', 'Variable', 'Program', 'Operator', 'default_startup_program',
-    'default_main_program', 'program_guard', 'switch_startup_program',
-    'switch_main_program'
+    'Block',
+    'Variable',
+    'Program',
+    'Operator',
+    'default_startup_program',
+    'default_main_program',
+    'program_guard',
+    'switch_startup_program',
+    'switch_main_program',
 ]
 
 EMPTY_VAR_NAME = core.kEmptyVarName()
diff --git a/python/paddle/v2/fluid/initializer.py b/python/paddle/v2/fluid/initializer.py
index c0839caaf2..c3ed1a9089 100644
--- a/python/paddle/v2/fluid/initializer.py
+++ b/python/paddle/v2/fluid/initializer.py
@@ -1,7 +1,12 @@
 import framework
 import numpy as np
 
-__all__ = ['Constant', 'Uniform', 'Normal', 'Xavier']
+__all__ = [
+    'Constant',
+    'Uniform',
+    'Normal',
+    'Xavier',
+]
 
 
 class Initializer(object):
diff --git a/python/paddle/v2/fluid/io.py b/python/paddle/v2/fluid/io.py
index c63567601a..1d28e9c5a6 100644
--- a/python/paddle/v2/fluid/io.py
+++ b/python/paddle/v2/fluid/io.py
@@ -4,9 +4,15 @@ import cPickle as pickle
 from paddle.v2.fluid.framework import Program, Parameter, default_main_program, Variable
 
 __all__ = [
-    'save_vars', 'save_params', 'save_persistables', 'load_vars', 'load_params',
-    'load_persistables', "save_inference_model", "load_inference_model",
-    "get_inference_program"
+    'save_vars',
+    'save_params',
+    'save_persistables',
+    'load_vars',
+    'load_params',
+    'load_persistables',
+    'save_inference_model',
+    'load_inference_model',
+    'get_inference_program',
 ]
 
 
diff --git a/python/paddle/v2/fluid/layers/nn.py b/python/paddle/v2/fluid/layers/nn.py
index 1fb6523f55..94184d59f6 100644
--- a/python/paddle/v2/fluid/layers/nn.py
+++ b/python/paddle/v2/fluid/layers/nn.py
@@ -9,12 +9,33 @@ from ..param_attr import ParamAttr
 from tensor import concat
 
 __all__ = [
-    'fc', 'embedding', 'dynamic_lstm', 'gru_unit', 'linear_chain_crf',
-    'crf_decoding', 'cos_sim', 'cross_entropy', 'square_error_cost', 'accuracy',
-    'chunk_eval', 'sequence_conv', 'conv2d', 'sequence_pool', 'pool2d',
-    'batch_norm', 'beam_search_decode', 'conv2d_transpose', 'sequence_expand',
-    'lstm_unit', 'reduce_sum', 'reduce_mean', 'reduce_max', 'reduce_min',
-    'sequence_first_step', 'sequence_last_step', 'dropout'
+    'fc',
+    'embedding',
+    'dynamic_lstm',
+    'gru_unit',
+    'linear_chain_crf',
+    'crf_decoding',
+    'cos_sim',
+    'cross_entropy',
+    'square_error_cost',
+    'accuracy',
+    'chunk_eval',
+    'sequence_conv',
+    'conv2d',
+    'sequence_pool',
+    'pool2d',
+    'batch_norm',
+    'beam_search_decode',
+    'conv2d_transpose',
+    'sequence_expand',
+    'lstm_unit',
+    'reduce_sum',
+    'reduce_mean',
+    'reduce_max',
+    'reduce_min',
+    'sequence_first_step',
+    'sequence_last_step',
+    'dropout',
 ]
 
 
@@ -248,13 +269,13 @@ def gru_unit(input,
             h_t & = dot((1-u_t), m_t) + dot(u_t, h_{t-1})
 
     The inputs of gru unit includes :math:`z_t`, :math:`h_{t-1}`. In terms
-    of the equation above, the :math:`z_t` is split into 3 parts - 
-    :math:`xu_t`, :math:`xr_t` and :math:`xm_t`. This means that in order to 
-    implement a full GRU unit operator for an input, a fully 
+    of the equation above, the :math:`z_t` is split into 3 parts -
+    :math:`xu_t`, :math:`xr_t` and :math:`xm_t`. This means that in order to
+    implement a full GRU unit operator for an input, a fully
     connected layer has to be applied, such that :math:`z_t = W_{fc}x_t`.
 
-    The terms :math:`u_t` and :math:`r_t` represent the update and reset gates 
-    of the GRU cell. Unlike LSTM, GRU has one lesser gate. However, there is 
+    The terms :math:`u_t` and :math:`r_t` represent the update and reset gates
+    of the GRU cell. Unlike LSTM, GRU has one lesser gate. However, there is
     an intermediate candidate hidden output, which is denoted by :math:`m_t`.
     This layer has three outputs :math:`h_t`, :math:`dot(r_t, h_{t-1})`
     and concatenation of :math:`u_t`, :math:`r_t` and :math:`m_t`.
@@ -276,7 +297,7 @@ def gru_unit(input,
         .. code-block:: python
 
              # assuming we have x_t_data and prev_hidden of size=10
-             x_t = fluid.layers.fc(input=x_t_data, size=30) 
+             x_t = fluid.layers.fc(input=x_t_data, size=30)
              hidden_val, r_h_val, gate_val = fluid.layers.gru_unit(input=x_t,
                                                     hidden = prev_hidden)
 
diff --git a/python/paddle/v2/fluid/layers/ops.py b/python/paddle/v2/fluid/layers/ops.py
index d3a5b70785..51a85dbbd3 100644
--- a/python/paddle/v2/fluid/layers/ops.py
+++ b/python/paddle/v2/fluid/layers/ops.py
@@ -1,7 +1,34 @@
 from ..registry import register_layer
 
 __activations__ = [
-    'abs', 'tanh', 'sigmoid', 'relu', 'sqrt', 'ceil', 'floor', 'log', 'round'
+    'sigmoid',
+    'logsigmoid',
+    'exp',
+    'relu',
+    'tanh',
+    'tanh_shrink',
+    'softshrink',
+    'sqrt',
+    'abs',
+    'ceil',
+    'floor',
+    'round',
+    'reciprocal',
+    'log',
+    'square',
+    'softplus',
+    'softsign',
+    'brelu',
+    'leaky_relu',
+    'soft_relu',
+    'elu',
+    'relu6',
+    'pow',
+    'stanh',
+    'hard_shrink',
+    'thresholded_relu',
+    'hard_sigmoid',
+    'swish',
 ]
 
 __all__ = [
diff --git a/python/paddle/v2/fluid/layers/tensor.py b/python/paddle/v2/fluid/layers/tensor.py
index 5f12ecfc14..3f8ebeeb48 100644
--- a/python/paddle/v2/fluid/layers/tensor.py
+++ b/python/paddle/v2/fluid/layers/tensor.py
@@ -2,8 +2,16 @@ from ..layer_helper import LayerHelper
 from ..param_attr import ParamAttr
 
 __all__ = [
-    'create_tensor', 'create_parameter', 'cast', 'concat', 'sums', 'assign',
-    'fill_constant_batch_size_like', 'fill_constant', 'ones', 'zeros'
+    'create_tensor',
+    'create_parameter',
+    'cast',
+    'concat',
+    'sums',
+    'assign',
+    'fill_constant_batch_size_like',
+    'fill_constant',
+    'ones',
+    'zeros',
 ]
 
 
diff --git a/python/paddle/v2/fluid/memory_optimization_transpiler.py b/python/paddle/v2/fluid/memory_optimization_transpiler.py
index 6800d7ddbb..293b116957 100644
--- a/python/paddle/v2/fluid/memory_optimization_transpiler.py
+++ b/python/paddle/v2/fluid/memory_optimization_transpiler.py
@@ -121,8 +121,10 @@ class ControlFlowGraph(object):
                                 # and dtype_to_size[cache_dtype]
                                 if x_dtype == cache_dtype:
                                     print(
-                                        "Hit Cache !!!! cache pool index is %d, var name is %s, cached var name is %s, var shape is %s "
-                                        %
+                                        ("Hit Cache !!!! cache pool index "
+                                         "is %d, var name is %s, "
+                                         "cached var name is %s, "
+                                         "var shape is %s ") %
                                         (index, x, cache_var, str(cache_shape)))
                                     self.pool.pop(index)
                                     _rename_arg_(
diff --git a/python/paddle/v2/fluid/nets.py b/python/paddle/v2/fluid/nets.py
index 54886a8f2c..47b550bf4d 100644
--- a/python/paddle/v2/fluid/nets.py
+++ b/python/paddle/v2/fluid/nets.py
@@ -1,6 +1,9 @@
 import layers
 
-__all__ = ["simple_img_conv_pool", "sequence_conv_pool"]
+__all__ = [
+    "simple_img_conv_pool",
+    "sequence_conv_pool",
+]
 
 
 def simple_img_conv_pool(input,
diff --git a/python/paddle/v2/fluid/registry.py b/python/paddle/v2/fluid/registry.py
index 7aa8290611..94b16bca8c 100644
--- a/python/paddle/v2/fluid/registry.py
+++ b/python/paddle/v2/fluid/registry.py
@@ -8,7 +8,11 @@ import proto.framework_pb2 as framework_pb2
 from framework import OpProtoHolder, Variable, Program, Operator
 from paddle.v2.fluid.layer_helper import LayerHelper, unique_name
 
-__all__ = ['deprecated', 'register_layer', 'autodoc']
+__all__ = [
+    'deprecated',
+    'register_layer',
+    'autodoc',
+]
 
 
 def _convert_(name):
@@ -80,11 +84,10 @@ def _generate_doc_string_(op_proto):
 
 
 def register_layer(op_type):
-    """
-    Register an Python layer for an Operator
+    """Register the Python layer for an Operator.
 
     Args:
-       op_type: The name of the operator to be created
+       op_type: The name of the operator to be created.
 
     This function takes in the operator type (sigmoid, mean , average etc) and
     creates the operator functionality.
@@ -98,16 +101,16 @@ def register_layer(op_type):
 
     if len(not_intermediate_outputs) != 1:
         raise ValueError("Only one non intermediate output operator can be",
-                         "automatically generated")
+                         "automatically generated.")
 
     if not_intermediate_outputs[0].duplicable:
         raise ValueError(
-            "Only non duplicable op can be automatically generated")
+            "Only non duplicable op can be automatically generated.")
 
     for output in intermediate_outputs:
         if output.duplicable:
             raise ValueError("The op can be automatically generated only when ",
-                             "all intermediate ops are not duplicable")
+                             "all intermediate ops are not duplicable.")
 
     o_name = not_intermediate_outputs[0].name
     intermediate_output_names = [output.name for output in intermediate_outputs]
diff --git a/python/paddle/v2/fluid/regularizer.py b/python/paddle/v2/fluid/regularizer.py
index d1955b0047..117c45c49f 100644
--- a/python/paddle/v2/fluid/regularizer.py
+++ b/python/paddle/v2/fluid/regularizer.py
@@ -1,6 +1,10 @@
 import framework
 
-__all__ = ['append_regularization_ops', 'L1Decay', 'L2Decay']
+__all__ = [
+    'append_regularization_ops',
+    'L1Decay',
+    'L2Decay',
+]
 
 
 def append_regularization_ops(parameters_and_grads, regularization=None):

From db65f497ffce0dcc8b71a06d8a303e1d75b864ca Mon Sep 17 00:00:00 2001
From: Cao Ying <lcy.seso@gmail.com>
Date: Sat, 13 Jan 2018 02:35:13 +0800
Subject: [PATCH 61/68] Update comments for two operators. (#7457)

* update code comments.

* update the comments.

* follow comments.
---
 .../reorder_lod_tensor_by_rank_op.cc          | 44 ++++++++++++++-----
 paddle/operators/shrink_rnn_memory_op.cc      | 22 +++++-----
 python/paddle/v2/fluid/layers/control_flow.py | 40 ++++++++++-------
 python/paddle/v2/fluid/layers/tensor.py       | 17 +++----
 4 files changed, 77 insertions(+), 46 deletions(-)

diff --git a/paddle/operators/reorder_lod_tensor_by_rank_op.cc b/paddle/operators/reorder_lod_tensor_by_rank_op.cc
index a055cdf7e8..3c30447949 100644
--- a/paddle/operators/reorder_lod_tensor_by_rank_op.cc
+++ b/paddle/operators/reorder_lod_tensor_by_rank_op.cc
@@ -26,22 +26,44 @@ class ReorderLoDTensorByRankTableOpProtoMaker
   ReorderLoDTensorByRankTableOpProtoMaker(OpProto *proto,
                                           OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "(LoDTensor) the input lod tensor need to be reordered.");
+    AddInput("X",
+             "(LoDTensor), the input lod tensor to be reordered according to "
+             "Input(RankTable).");
     AddInput("RankTable",
-             "(LoDRankTable) the rank table that input need follow");
-    AddOutput("Out", "(LoDTensor) reordered lod tensor");
-    AddComment(R"DOC(ReorderLoDTensorByRankTable
+             "(LoDRankTable), the rank table according to which Input(X) is "
+             "reordered.");
+    AddOutput("Out", "(LoDTensor), the reordered lod tensor.");
+    AddComment(R"DOC(ReorderLoDTensorByRankTable operator.
 
-Reorder the input X by the rank of `RankTable`. If `RankTable` is ordered by
-index [3, 0, 2, 1]. Input X will reorder its sequence, the third sequence of
-X will be the first sequence of Output.
-
-NOTE: The RankTable does not need to be calculated by X.
+Input(X) is a batch of sequences. Input(RankTable) stores new orders of the
+input sequence batch. The reorder_lod_tensor_by_rank operator reorders the
+Input(X) according to the information provided by Input(RankTable).
 
 For example:
-The X = [Seq0, Seq1, Seq2, Seq3]. The indices of RankTable are [3, 0, 2, 1].
 
-The Out =  [Seq3, Seq0, Seq2, Seq1] with correct LoD information.
+If the indices stored in the Input(RankTable) are [3, 0, 2, 1], the
+Input(X) will be reordered that the fourth sequence in Input(X) will become the
+first one, and then followed by the original first, third, and the second one.
+
+This is:
+X = [Seq0, Seq1, Seq2, Seq3]. The indices in RankTable are [3, 0, 2, 1].
+Out =  [Seq3, Seq0, Seq2, Seq1] with a new LoD information.
+
+If the LoD information of Input(X) is empty, this means Input(X) is not sequence
+data. This is also identical to a batch of sequences where each sequence has a
+fixed length 1. In this case, the reorder_lod_tensor_by_rank operator reorders
+each slice of Input(X) along the first axis according to Input(RankTable).
+
+This is:
+X = [Slice0, Slice1, Slice2, Slice3] and its LoD information is empty. The
+indices in RankTable are [3, 0, 2, 1].
+Out = [Slice3, Slice0, Slice2, Slice1] with no LoD information is appended.
+
+NOTE: This operator sorts Input(X) according to a given LoDRankTable which does
+not need to be calculated according to Input(X). It can be calculated according
+to another different sequence, and then this operator sorts Input(X) according
+to the given LoDRankTable.
+
 )DOC");
   }
 };
diff --git a/paddle/operators/shrink_rnn_memory_op.cc b/paddle/operators/shrink_rnn_memory_op.cc
index 3f5b2a9b84..ade94b40be 100644
--- a/paddle/operators/shrink_rnn_memory_op.cc
+++ b/paddle/operators/shrink_rnn_memory_op.cc
@@ -45,7 +45,7 @@ class ShrinkRNNMemoryOp : public ArrayOp {
         rank_items.begin();
 
     auto *out_var = scope.FindVar(Output("Out"));
-    PADDLE_ENFORCE(out_var != nullptr, "Output Out must be set");
+    PADDLE_ENFORCE(out_var != nullptr, "Output(Out) must be set.");
     auto &out_tensor = *out_var->GetMutable<framework::LoDTensor>();
 
     size_t height = dst_num_rows;
@@ -76,15 +76,17 @@ class ShrinkRNNMemoryOpProtoMaker : public framework::OpProtoAndCheckerMaker {
              "(LoDTensor) The step index. The RNN step memory 'X' will be "
              "shrinked to match the size of the input of the index'th step.");
     AddOutput("Out", "(LoDTensor) The shrinked RNN step memory.");
-    AddComment(
-        R"DOC(
-        In dynamic RNN, we are able to handle sequences of different lengths.
-        Because of the multiple lengths, the size of each step input can be
-        different, which may lead to a mismatching between the input of
-        the current step and the memory generated by the previous one. This
-        operator shrinks memory according to the size of the next step input,
-        to make sure that they can match each other.
-        )DOC");
+    AddComment(R"DOC(
+This operator is used to shrink output batch of memory defined in dynamic RNN.
+
+Dynamic RNN is able to handle variable-length sequences, in which, sequences in
+a mini-batch are sorted by their lengths first. After that, the longest sequence
+becomes the first one in the sorted batch, followed by the second longest, the
+third longest, and so on. Dynamic RNN then slices a batch input timestep by
+timestep from the sorted input. Once any sequence in the input batch reaches its
+end, memory defined in dynamicRNN has to shrink its outputs to adapt to the input
+batch size for the next time step.
+)DOC");
   }
 };
 
diff --git a/python/paddle/v2/fluid/layers/control_flow.py b/python/paddle/v2/fluid/layers/control_flow.py
index 0cf17f3083..4b363ecbe7 100644
--- a/python/paddle/v2/fluid/layers/control_flow.py
+++ b/python/paddle/v2/fluid/layers/control_flow.py
@@ -742,11 +742,10 @@ def topk(input, k):
 
 
 def lod_tensor_to_array(x, table):
-    """This function performs the operation that converts an LOD_Tensor to
-       an array.
+    """ Convert a LOD_TENSOR to an LOD_TENSOR_ARRAY.
 
     Args:
-        x (Variable|list): The tensor that needs to be converted to an array.
+        x (Variable|list): The LOD tensor to be converted to a LOD tensor array.
         table (ParamAttr|list): The variable that stores the level of lod
                                 which is ordered by sequence length in
                                 descending order.
@@ -776,11 +775,10 @@ def lod_tensor_to_array(x, table):
 
 
 def array_to_lod_tensor(x, table):
-    """This function performs the operations that converts an array to
-       an LOD_Tensor.
+    """Convert a LoD_Tensor_Aarry to an LoDTensor.
 
     Args:
-        x (Variable|list): The array that needs to be converted to a tensor.
+        x (Variable|list): The lod tensor array to be converted to a tensor.
         table (ParamAttr|list): The variable that stores the level of lod
                                 which is ordered by sequence length in
                                 descending order.
@@ -808,7 +806,8 @@ def array_to_lod_tensor(x, table):
 
 
 def increment(x, value=1.0, in_place=True):
-    """This function performs an operation that increments each value in the
+    """
+    This function performs an operation that increments each value in the
     input :math:`x` by an amount: :math:`value` as mentioned in the input
     parameter. This operation is performed in-place by default.
 
@@ -841,17 +840,24 @@ def increment(x, value=1.0, in_place=True):
 
 
 def array_write(x, i, array=None):
-    """This function performs the operation to write the data out as an
-    LOD_TENSOR_ARRAY.
+    """
+    This function writes the given input variable to the specified position
+    indicating by the arrary index to an output LOD_TENSOR_ARRAY. If the
+    output LOD_TENSOR_ARRAY is not given(None), a new one will be created and
+    returned.
 
     Args:
         x (Variable|list): The input tensor from which the data will be read.
-        i (Variable|list): The subscript index in tensor array, that points the
-                           place from which data will be read.
-        array (Variable|list): The data can be read into this variable if
-                               this is assigned.
+        i (Variable|list): The index of the output LOD_TENSOR_ARRAY, pointing to
+                           the position to which the input tensor will be
+                           written.
+        array (Variable|list): The output LOD_TENSOR_ARRAY to which the input
+                               tensor will be written. If this parameter is
+                               NONE, a new LOD_TENSOR_ARRAY will be created and
+                               returned.
+
     Returns:
-        Variable: The tensor type variable that has the data written to it.
+        Variable: The output LOD_TENSOR_ARRAY where the input tensor is written.
 
     Examples:
         .. code-block::python
@@ -1228,7 +1234,7 @@ class DynamicRNN(object):
         self._assert_in_rnn_block_("step_input")
         if not isinstance(x, Variable):
             raise TypeError(
-                "step_input() can only take a Variable as its input")
+                "step_input() can only take a Variable as its input.")
         parent_block = self._parent_block_()
         if self.lod_rank_table is None:
             self.lod_rank_table = parent_block.create_var(
@@ -1289,8 +1295,8 @@ class DynamicRNN(object):
 
     def __call__(self, *args, **kwargs):
         if self.status != DynamicRNN.AFTER_RNN:
-            raise ValueError(
-                "Dynamic RNN outputs can only be retrieved after rnn block")
+            raise ValueError(("Output of the dynamic RNN can only be visited "
+                              "outside the rnn block."))
         if len(self.outputs) == 1:
             return self.outputs[0]
         else:
diff --git a/python/paddle/v2/fluid/layers/tensor.py b/python/paddle/v2/fluid/layers/tensor.py
index 57668a7983..438df33afb 100644
--- a/python/paddle/v2/fluid/layers/tensor.py
+++ b/python/paddle/v2/fluid/layers/tensor.py
@@ -176,25 +176,26 @@ def fill_constant(shape, dtype, value, out=None):
     """
     **fill_constant**
 
-    This function creates a tensor of specified *shape* and
-    *dtype*, and initializes this with a constant supplied in *value*.
+    This function creates a tensor with specified `shape` and `dtype`, and
+    initializes it with a constant specifed by `value`.
 
-    It also sets *stop_gradient* to True.
+    The attribute `stop_gradient` of the created tensor is set to True.
 
     Args:
-        shape(tuple|list|None): Shape of output tensor
-        dtype(np.dtype|core.DataType|str): Data type of output tensor
-        value(float): Constant value to initialize the output tensor
-        out(Variable): Output Variable to initialize
+        shape(tuple|list|None): Shape of the output tensor.
+        dtype(np.dtype|core.DataType|str): Data type of the output tensor.
+        value(float): The constant value used to initialize the output tensor.
+        out(Variable): The output tensor.
 
     Returns:
-        Variable: The tensor variable storing the output
+        Variable: The tensor variable storing the output.
 
     Examples:
         .. code-block:: python
 
           data = fluid.layers.fill_constant(shape=[1], value=0, dtype='int64')
     """
+
     helper = LayerHelper("fill_constant", **locals())
     if out is None:
         out = helper.create_tmp_variable(dtype=dtype)

From 9d52ad4c3a0ad17b7aaf7cc913f1b9d8eeaa0311 Mon Sep 17 00:00:00 2001
From: Yancey1989 <yancey1989@gmail.com>
Date: Sun, 14 Jan 2018 14:09:04 +0800
Subject: [PATCH 62/68] fix op doc

---
 doc/howto/dev/new_op_cn.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/howto/dev/new_op_cn.md b/doc/howto/dev/new_op_cn.md
index 3109d72001..9299658567 100644
--- a/doc/howto/dev/new_op_cn.md
+++ b/doc/howto/dev/new_op_cn.md
@@ -24,7 +24,7 @@
 - `framework::OperatorWithKernel`：继承自OperatorBase，Op有计算函数，称作有Kernel。
 - `class OpProtoAndCheckerMaker`：描述该Op的输入、输出、属性、注释,主要用于Python API接口生成
 
-依据是否包含kernel，可以将Op分为两种：包含Kernel的Op和不包含kernel的Op，前者Op的定义继承自`OperatorBase`，后者继承自`OperatorWithKernel`。本教程主要介绍带Kernel的Op如何写，简单总结Op需要包含的内容如下：
+依据是否包含kernel，可以将Op分为两种：包含Kernel的Op和不包含kernel的Op，前者Op的定义继承自`OperatorWithKernel`，后者继承自`OperatorBase`。本教程主要介绍带Kernel的Op如何写，简单总结Op需要包含的内容如下：
 
 
  内容            | 定义位置

From 5ad1aef051349a73b00b8d611f0ae2508f02490b Mon Sep 17 00:00:00 2001
From: dzhwinter <dongzhihong01@baidu.com>
Date: Sun, 14 Jan 2018 16:54:04 +0800
Subject: [PATCH 63/68] "cudnn operators change to cudnn kernel" (#6660)

* "unified operators"

* "add CUDNN register"

* "add use cudnn attribute"

* "add attribute"

* "test conv tranpose op"

* "remove duplicated attr"

* "fix op test"

* "add attribute to set cudnn"

* "add more log"

* "need layout op register support"

* "add more log"

* "change GetExpectedKernelType "

* "fix Get attr in conv_op"

* "fix CI"

* "fix tests"

* "removed kernel priority fallback"

* "fix CI"

* "fix stack pointer bug"

* "refine buggy interface"

* "add const cast to save life"

* "fix get_output_with_grad"

* "fix op test with dataformat"

* ""fix pooling

* "fix pooling test"

* "fix CI"

* "fix with_gpu error"

* "add transform needed functional check"

* "fix unpack list error"

* "comment out parallel.do temporary"

* "fix CI"

* "fix compile doc error"

* "make threshold larger"
---
 paddle/framework/data_device_transform.cc     |   5 +-
 paddle/framework/data_device_transform.h      |   3 +-
 paddle/framework/data_layout.h                |  19 +++-
 paddle/framework/data_transform.cc            |  10 +-
 paddle/framework/data_transform.h             |   6 +-
 paddle/framework/op_kernel_type.h             |   5 +
 paddle/framework/op_registry_test.cc          |  18 ----
 paddle/framework/operator.cc                  | 100 +++++-------------
 paddle/framework/operator.h                   |  26 +----
 paddle/operators/CMakeLists.txt               |  19 +++-
 paddle/operators/conv_cudnn_op.cc             |  74 -------------
 paddle/operators/conv_cudnn_op.cu.cc          |  31 +++---
 paddle/operators/conv_op.cc                   |  73 +++++++++++++
 paddle/operators/conv_op.h                    |   8 ++
 paddle/operators/conv_transpose_cudnn_op.cc   |  78 --------------
 .../operators/conv_transpose_cudnn_op.cu.cc   |  36 +++----
 paddle/operators/conv_transpose_op.cc         |  72 +++++++++++++
 paddle/operators/conv_transpose_op.h          |   8 ++
 paddle/operators/math/sequence2batch.cc       |   1 +
 paddle/operators/pool_cudnn_op.cc             |  39 -------
 paddle/operators/pool_cudnn_op.cu.cc          |  29 ++---
 paddle/operators/pool_cudnn_op.h              |  19 ----
 paddle/operators/pool_op.cc                   |  65 +++++++++++-
 paddle/operators/pool_op.h                    |   8 ++
 paddle/platform/dynload/cudnn.cc              |   2 +-
 paddle/platform/dynload/cudnn.h               |   2 +-
 paddle/platform/dynload/dynamic_loader.cc     |   2 +-
 paddle/platform/dynload/dynamic_loader.h      |   2 +-
 paddle/pybind/pybind.cc                       |   7 +-
 paddle/pybind/tensor_py.h                     |  23 +++-
 python/paddle/v2/fluid/layers/nn.py           |   2 +-
 python/paddle/v2/fluid/tests/op_test.py       |  64 ++++++-----
 .../paddle/v2/fluid/tests/test_conv2d_op.py   |  93 ++++++++++------
 .../fluid/tests/test_conv2d_transpose_op.py   |  80 ++++++++++----
 .../paddle/v2/fluid/tests/test_conv3d_op.py   |  85 ++++++++++-----
 .../fluid/tests/test_conv3d_transpose_op.py   |  80 ++++++++++----
 .../paddle/v2/fluid/tests/test_parallel_op.py |   8 +-
 .../paddle/v2/fluid/tests/test_pool2d_op.py   |  49 ++++++---
 .../paddle/v2/fluid/tests/test_pool3d_op.py   |  49 ++++++---
 39 files changed, 732 insertions(+), 568 deletions(-)
 delete mode 100644 paddle/operators/conv_cudnn_op.cc
 delete mode 100644 paddle/operators/conv_transpose_cudnn_op.cc
 delete mode 100644 paddle/operators/pool_cudnn_op.cc
 delete mode 100644 paddle/operators/pool_cudnn_op.h

diff --git a/paddle/framework/data_device_transform.cc b/paddle/framework/data_device_transform.cc
index b3fd48ae12..d38d87927f 100644
--- a/paddle/framework/data_device_transform.cc
+++ b/paddle/framework/data_device_transform.cc
@@ -31,15 +31,14 @@ static const platform::DeviceContext* GetDeviceContext(
   }
 }
 
-Tensor* DeviceTransform(const Tensor& in, const platform::Place& dst_place) {
+void DeviceTransform(const Tensor& in, const platform::Place& dst_place,
+                     Tensor* out) {
   VLOG(3) << "DeviceTransform in, src_place " << in.place()
           << " dst_place: " << dst_place;
-  Tensor* out = new Tensor();
   auto* dev_ctx = GetDeviceContext(in.place(), dst_place);
   dev_ctx->Wait();
   Copy(in, dst_place, *dev_ctx, out);
   dev_ctx->Wait();
-  return out;
 }
 
 }  // namespace framework
diff --git a/paddle/framework/data_device_transform.h b/paddle/framework/data_device_transform.h
index bebf0d1b32..b21ed0be34 100644
--- a/paddle/framework/data_device_transform.h
+++ b/paddle/framework/data_device_transform.h
@@ -21,7 +21,8 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 
-Tensor* DeviceTransform(const Tensor& in, const platform::Place& dst_place);
+void DeviceTransform(const Tensor& in, const platform::Place& dst_place,
+                     Tensor* out);
 
 }  // namespace framework
 }  // namespace paddle
diff --git a/paddle/framework/data_layout.h b/paddle/framework/data_layout.h
index 3ab976ecac..31817251ed 100644
--- a/paddle/framework/data_layout.h
+++ b/paddle/framework/data_layout.h
@@ -14,7 +14,9 @@ limitations under the License. */
 
 #pragma once
 
-#include <iostream>
+#include <cctype>
+#include <ostream>
+
 #include "paddle/platform/enforce.h"
 
 namespace paddle {
@@ -27,12 +29,19 @@ enum class DataLayout {
 };
 
 inline DataLayout StringToDataLayout(const std::string& str) {
-  if (str == "NHWC" || str == "nhwc") {
+  std::string s(str);
+  for (size_t i = 0; i < s.size(); ++i) {
+    s[i] = toupper(s[i]);
+  }
+
+  if (s == "NHWC") {
     return DataLayout::kNHWC;
-  } else if (str == "NCHW" || str == "nchw") {
+  } else if (s == "NCHW") {
     return DataLayout::kNCHW;
+  } else if (s == "ANYLAYOUT") {
+    return DataLayout::kAnyLayout;
   } else {
-    PADDLE_THROW("Unknown storage order string: %s", str);
+    PADDLE_THROW("Unknown storage order string: %s", s);
   }
 }
 
@@ -49,7 +58,7 @@ inline std::string DataLayoutToString(const DataLayout& data_layout) {
   }
 }
 
-inline std::ostream& operator<<(std::ostream& out, DataLayout l) {
+inline std::ostream& operator<<(std::ostream& out, const DataLayout& l) {
   out << DataLayoutToString(l);
   return out;
 }
diff --git a/paddle/framework/data_transform.cc b/paddle/framework/data_transform.cc
index e56edb9539..d826f0edac 100644
--- a/paddle/framework/data_transform.cc
+++ b/paddle/framework/data_transform.cc
@@ -19,16 +19,14 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 
-Tensor* DataTransform(const OpKernelType& expected_kernel_type,
-                      const OpKernelType& kernel_type_for_var,
-                      const Tensor& input_tensor) {
-  Tensor* out = nullptr;
+void DataTransform(const OpKernelType& expected_kernel_type,
+                   const OpKernelType& kernel_type_for_var,
+                   const Tensor& input_tensor, Tensor* out) {
   if (!platform::is_same_place(kernel_type_for_var.place_,
                                expected_kernel_type.place_)) {
-    out = DeviceTransform(input_tensor, expected_kernel_type.place_);
+    DeviceTransform(input_tensor, expected_kernel_type.place_, out);
   }
   PADDLE_ENFORCE_NOT_NULL(out, "out should not be null");
-  return out;
 }
 
 void CopyVariableWithTensor(const Variable& in_var, const Tensor& tensor,
diff --git a/paddle/framework/data_transform.h b/paddle/framework/data_transform.h
index ee95c7e856..a4b7890237 100644
--- a/paddle/framework/data_transform.h
+++ b/paddle/framework/data_transform.h
@@ -30,9 +30,9 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 
-Tensor* DataTransform(const OpKernelType& expected_kernel_type,
-                      const OpKernelType& kernel_type_for_var,
-                      const Tensor& input_tensor);
+void DataTransform(const OpKernelType& expected_kernel_type,
+                   const OpKernelType& kernel_type_for_var,
+                   const Tensor& input_tensor, Tensor* out);
 
 void CopyVariableWithTensor(const Variable& in_var, const Tensor& tensor,
                             Variable& out_var);
diff --git a/paddle/framework/op_kernel_type.h b/paddle/framework/op_kernel_type.h
index 053897784c..312bd5f892 100644
--- a/paddle/framework/op_kernel_type.h
+++ b/paddle/framework/op_kernel_type.h
@@ -85,5 +85,10 @@ inline std::string KernelTypeToString(const OpKernelType& kernel_key) {
   return stream.str();
 }
 
+inline bool TransFromNeeded(const OpKernelType& l, const OpKernelType& r) {
+  return (!platform::places_are_same_class(l.place_, r.place_)) ||
+         (l.data_type_ != r.data_type_) || (l.data_layout_ != r.data_layout_);
+}
+
 }  // namespace framework
 }  // namespace paddle
diff --git a/paddle/framework/op_registry_test.cc b/paddle/framework/op_registry_test.cc
index 66f07b6757..341da8befd 100644
--- a/paddle/framework/op_registry_test.cc
+++ b/paddle/framework/op_registry_test.cc
@@ -368,24 +368,6 @@ TEST(OperatorRegistrar, OpWithMultiKernel) {
 
   // TODO(qiao) add priority back
   // use all available kernels
-  paddle::framework::UseALL();
   op->Run(scope, cuda_place);
   EXPECT_EQ(op_test_value, -10);
-
-  // remove cuda kernels
-  paddle::framework::UseCPU();
-  op->Run(scope, cpu_place);
-
-  EXPECT_EQ(op_test_value, -9);
-
-  // add cuda kernels
-  paddle::framework::UseCUDA();
-  op->Run(scope, cuda_place);
-
-  EXPECT_EQ(op_test_value, -10);
-
-  // use cudnn kernel
-  paddle::framework::UseCUDNN();
-  op->Run(scope, cuda_place);
-  EXPECT_EQ(op_test_value, -20);
 }
diff --git a/paddle/framework/operator.cc b/paddle/framework/operator.cc
index be1373dc2a..84c010df7c 100644
--- a/paddle/framework/operator.cc
+++ b/paddle/framework/operator.cc
@@ -29,52 +29,12 @@ DEFINE_bool(op_sync, false,
 namespace paddle {
 namespace framework {
 
-std::vector<std::tuple<platform::Place, LibraryType>> kKernelPriority;
-
-void UseCPU() {
-  kKernelPriority.clear();
-  /*Plain CPU*/
-  auto pair0 = std::make_tuple(platform::CPUPlace(), LibraryType::kPlain);
-  kKernelPriority.insert(kKernelPriority.begin(), pair0);
-}
-
-void UseMKLDNN() {
-  UseCPU();
-#if PADDLE_WITH_MKLML
-  {
-    /*MKLDNN Kernel*/
-    auto pair0 = std::make_tuple(platform::CPUPlace(), LibraryType::kMKLDNN);
-    kKernelPriority.insert(kKernelPriority.begin(), pair0);
-  }
-#endif
-}
-
-void UseCUDA() {
-  UseMKLDNN();
-#if PADDLE_WITH_CUDA
-  /*Plain GPU*/
-  auto pair0 = std::make_tuple(platform::CUDAPlace(0), LibraryType::kPlain);
-  kKernelPriority.insert(kKernelPriority.begin(), pair0);
-#endif
-}
-
-void UseCUDNN() {
-  UseCUDA();
-#if PADDLE_WITH_CUDA
-  if (platform::dynload::HasCUDNN()) {
-    /*CUDNN Kernel*/
-    auto pair0 = std::make_tuple(platform::CUDAPlace(0), LibraryType::kCUDNN);
-    kKernelPriority.insert(kKernelPriority.begin(), pair0);
-  }
-#endif
-}
-
-void UseALL() {
-  UseCPU();
-  UseMKLDNN();
-  UseCUDA();
-  UseCUDNN();
-}
+std::vector<std::tuple<platform::Place, LibraryType>> kKernelPriority = {
+    std::make_tuple(platform::CUDAPlace(0), LibraryType::kCUDNN),
+    std::make_tuple(platform::CUDAPlace(0), LibraryType::kPlain),
+    std::make_tuple(platform::CPUPlace(), LibraryType::kMKLDNN),
+    std::make_tuple(platform::CPUPlace(), LibraryType::kPlain),
+};
 
 static DDim GetDims(const Scope& scope, const std::string& name) {
   Variable* var = scope.FindVar(name);
@@ -271,36 +231,33 @@ static bool VarIsTensor(const Variable* var) {
   return var->IsType<LoDTensor>() || var->IsType<SelectedRows>();
 }
 
-static const Tensor* GetTensorFromVar(const Variable* var) {
-  const Tensor* t = nullptr;
+static const Tensor* GetTensorFromVar(Variable* var) {
   if (var->IsType<LoDTensor>()) {
-    t = &(var->Get<LoDTensor>());
+    return var->GetMutable<LoDTensor>();
   } else if (var->IsType<SelectedRows>()) {
-    t = &(var->Get<SelectedRows>().value());
+    return var->GetMutable<SelectedRows>()->mutable_value();
   } else {
     PADDLE_THROW("Variable type_id %s, expect LoDTensor/SelectedRows.",
                  var->Type().name());
   }
-  return t;
 }
 
 static Tensor* GetMutableTensorFromVar(Variable* var) {
-  Tensor* t = nullptr;
   if (var->IsType<LoDTensor>()) {
-    t = var->GetMutable<LoDTensor>();
+    return var->GetMutable<LoDTensor>();
   } else if (var->IsType<SelectedRows>()) {
-    t = var->GetMutable<SelectedRows>()->mutable_value();
+    return var->GetMutable<SelectedRows>()->mutable_value();
   } else {
     PADDLE_THROW("Variable type_id %s, expect LoDTensor/SelectedRows.",
                  var->Type().name());
   }
-  return t;
 }
 
 template <>
 const Tensor* ExecutionContext::Input<Tensor>(const std::string& name) const {
   auto* var = InputVar(name);
-  return var == nullptr ? nullptr : GetTensorFromVar(var);
+  return var == nullptr ? nullptr
+                        : GetTensorFromVar(const_cast<Variable*>(var));
 }
 
 template <>
@@ -343,6 +300,7 @@ bool OpSupportGPU(const std::string& op_type) {
   auto it = all_kernels.find(op_type);
   if (it == all_kernels.end()) {
     // All control operator must support GPU
+
     return true;
   }
   for (auto& kern_pair : it->second) {
@@ -516,21 +474,17 @@ void OperatorWithKernel::Run(const Scope& scope,
   }
 
   ExecutionContext ctx(*this, scope, *dev_ctx);
-  auto expected_kernel_key = this->GetExpectedKernelType(ctx);
 
   OpKernelMap& kernels = kernels_iter->second;
 
-  for (auto& candidate : kKernelPriority) {
-    auto candidate_key =
-        OpKernelType(expected_kernel_key.data_type_, std::get<0>(candidate),
-                     expected_kernel_key.data_layout_, std::get<1>(candidate));
+  // TODO(dzhwinter) : kernel fallback mechanism will be added when all the
+  // transform functions are ready.
 
-    if ((candidate_key == expected_kernel_key) ||
-        (kernels.count(candidate_key))) {
-      expected_kernel_key = candidate_key;
-      break;
-    }
-  }
+  // for (auto& candidate : kKernelPriority) {
+  //   Do selection
+  // }
+
+  auto expected_kernel_key = this->GetExpectedKernelType(ctx);
 
   VLOG(3) << "expected_kernel_key:" << expected_kernel_key;
 
@@ -544,7 +498,7 @@ void OperatorWithKernel::Run(const Scope& scope,
         if (tensor_in->IsInitialized()) {
           auto kernel_type_for_var = this->GetKernelTypeForVar(
               var_name_item.first, *tensor_in, expected_kernel_key);
-          if (kernel_type_for_var != expected_kernel_key) {
+          if (TransFromNeeded(kernel_type_for_var, expected_kernel_key)) {
             auto out_var_names = OutputVars(true);
             if (std::find(out_var_names.begin(), out_var_names.end(),
                           var_name) != out_var_names.end()) {
@@ -553,11 +507,13 @@ void OperatorWithKernel::Run(const Scope& scope,
                   "does not support transform",
                   var_name);
             }
-            VLOG(3) << "need to do transform for var " << var_name;
+            VLOG(3) << "Transform Variable " << var_name << " from "
+                    << kernel_type_for_var << " to " << expected_kernel_key;
             auto* trans_var = new_scope.Var(var_name);
-            auto* out = DataTransform(expected_kernel_key, kernel_type_for_var,
-                                      *tensor_in);
-            CopyVariableWithTensor(*var, *out, *trans_var);
+            std::shared_ptr<Tensor> out(new Tensor);
+            DataTransform(expected_kernel_key, kernel_type_for_var, *tensor_in,
+                          out.get());
+            CopyVariableWithTensor(*var, *(out.get()), *trans_var);
           }
         }
       }
diff --git a/paddle/framework/operator.h b/paddle/framework/operator.h
index d5feb59864..c9140f304c 100644
--- a/paddle/framework/operator.h
+++ b/paddle/framework/operator.h
@@ -54,33 +54,9 @@ constexpr char kGradVarSuffix[] = "@GRAD";
 constexpr char kZeroVarSuffix[] = "@ZERO";
 
 // define some kernel priority
+/* Define multiple kernel type fallback order*/
 extern std::vector<std::tuple<platform::Place, LibraryType>> kKernelPriority;
 
-/**
- * @brief Use cpu kernel only
- */
-void UseCPU();
-
-/**
- * @brief Perfer MKLDNN kernel than Plain CPU kernel
- */
-void UseMKLDNN();
-
-/**
- * @brief Perfer CUDA kernel than Plain CPU kernel
- */
-void UseCUDA();
-
-/**
- * @brief Perfer cudnn kernel than Plain CUDA kernel
- */
-void UseCUDNN();
-
-/**
- * @brief Use all available kernels
- */
-void UseALL();
-
 inline std::string GradVarName(const std::string& var_name) {
   return var_name + kGradVarSuffix;
 }
diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt
index e1b695e8cd..2569535c25 100644
--- a/paddle/operators/CMakeLists.txt
+++ b/paddle/operators/CMakeLists.txt
@@ -137,8 +137,6 @@ op_library(sum_op DEPS selected_rows_functor)
 op_library(sgd_op DEPS selected_rows_functor)
 op_library(print_op DEPS lod_tensor)
 op_library(adagrad_op DEPS selected_rows_functor)
-op_library(conv_op DEPS vol2col)
-op_library(pool_op DEPS pooling)
 op_library(maxout_op DEPS maxouting)
 op_library(unpool_op DEPS unpooling)
 op_library(pool_with_index_op DEPS pooling)
@@ -149,12 +147,27 @@ op_library(max_sequence_len_op DEPS lod_rank_table)
 op_library(sequence_conv_op DEPS context_project)
 op_library(sequence_pool_op DEPS sequence_pooling)
 op_library(lstm_op DEPS sequence2batch lstm_compute)
-op_library(conv_transpose_op DEPS vol2col)
 op_library(gru_op DEPS sequence2batch gru_compute)
 op_library(recurrent_op DEPS executor)
 op_library(warpctc_op DEPS dynload_warpctc sequence_padding math_function)
 op_library(cos_sim_op DEPS cos_sim_functor)
 op_library(parallel_do_op DEPS executor)
+
+# Regist multiple Kernel to pybind
+if (WITH_GPU)
+op_library(conv_op SRCS conv_op.cc conv_op.cu.cc conv_cudnn_op.cu.cc DEPS vol2col)
+op_library(pool_op SRCS pool_op.cc pool_op.cu.cc pool_cudnn_op.cu.cc DEPS pooling)
+op_library(conv_transpose_op SRCS conv_transpose_op.cc conv_transpose_op.cu.cc
+  conv_transpose_cudnn_op.cu.cc DEPS vol2col)
+file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(conv2d, CUDNN);\n")
+file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(pool2d, CUDNN);\n")
+file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(conv2d_transpose, CUDNN);\n")
+else()
+op_library(conv_op SRCS conv_op.cc DEPS vol2col)
+op_library(pool_op SRCS pool_op.cc DEPS pooling)
+op_library(conv_transpose_op SRCS conv_transpose_op.cc DEPS vol2col)
+endif()
+
 # FIXME(typhoonzero): save/load depends lodtensor serialization functions
 op_library(save_op DEPS lod_tensor)
 op_library(load_op DEPS lod_tensor)
diff --git a/paddle/operators/conv_cudnn_op.cc b/paddle/operators/conv_cudnn_op.cc
deleted file mode 100644
index 84d9ce1973..0000000000
--- a/paddle/operators/conv_cudnn_op.cc
+++ /dev/null
@@ -1,74 +0,0 @@
-/* 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/conv_op.h"
-
-namespace paddle {
-namespace operators {
-
-class CudnnConv2DOpMaker : public Conv2DOpMaker {
- public:
-  CudnnConv2DOpMaker(OpProto* proto, OpAttrChecker* op_checker)
-      : Conv2DOpMaker(proto, op_checker) {
-    AddAttr<int>("workspace_size_MB",
-                 "workspace size for cudnn, in MB, "
-                 "workspace is a section of GPU memory which will be "
-                 "allocated/freed each time the operator runs, larger "
-                 "workspace size can increase performance but also requires "
-                 "better hardware. This size should be chosen carefully.")
-        .SetDefault(4096);
-  }
-};
-
-class CudnnConv3DOpMaker : public Conv3DOpMaker {
- public:
-  CudnnConv3DOpMaker(OpProto* proto, OpAttrChecker* op_checker)
-      : Conv3DOpMaker(proto, op_checker) {
-    AddAttr<int>("workspace_size_MB",
-                 "workspace size for cudnn, in MB, "
-                 "workspace is a section of GPU memory which will be "
-                 "allocated/freed each time the operator runs, larger "
-                 "workspace size can increase performance but also requires "
-                 "better hardware. This size should be chosen carefully.")
-        .SetDefault(4096);
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
-namespace ops = paddle::operators;
-REGISTER_OP(conv2d_cudnn, ops::ConvOp, ops::CudnnConv2DOpMaker,
-            conv2d_cudnn_grad, ops::ConvOpGrad);
-
-REGISTER_OP(conv3d_cudnn, ops::ConvOp, ops::CudnnConv3DOpMaker,
-            conv3d_cudnn_grad, ops::ConvOpGrad);
-
-REGISTER_OP_CPU_KERNEL(
-    conv2d_cudnn,
-    ops::GemmConvKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::GemmConvKernel<paddle::platform::CPUDeviceContext, double>);
-REGISTER_OP_CPU_KERNEL(
-    conv2d_cudnn_grad,
-    ops::GemmConvGradKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::GemmConvGradKernel<paddle::platform::CPUDeviceContext, double>);
-
-REGISTER_OP_CPU_KERNEL(
-    conv3d_cudnn,
-    ops::GemmConvKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::GemmConvKernel<paddle::platform::CPUDeviceContext, double>);
-REGISTER_OP_CPU_KERNEL(
-    conv3d_cudnn_grad,
-    ops::GemmConvGradKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::GemmConvGradKernel<paddle::platform::CPUDeviceContext, double>);
diff --git a/paddle/operators/conv_cudnn_op.cu.cc b/paddle/operators/conv_cudnn_op.cu.cc
index 0c5ed3e4e8..3a5409a7e3 100644
--- a/paddle/operators/conv_cudnn_op.cu.cc
+++ b/paddle/operators/conv_cudnn_op.cu.cc
@@ -32,7 +32,7 @@ static constexpr size_t kCONV_CUDNN_WORKSPACE_LIMIT_BYTES =
     static_cast<size_t>(1024) * 1024 * 1024;
 
 template <typename T>
-class CudnnConvOpKernel : public framework::OpKernel<T> {
+class CUDNNConvOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
@@ -147,7 +147,7 @@ class CudnnConvOpKernel : public framework::OpKernel<T> {
 };
 
 template <typename T>
-class CudnnConvGradOpKernel : public framework::OpKernel<T> {
+class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
@@ -315,17 +315,16 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> {
 }  // namespace operators
 }  // namespace paddle
 
-// TODO(dzhwinter) : below register should be removed
-REGISTER_OP_CUDA_KERNEL(conv2d_cudnn,
-                        paddle::operators::CudnnConvOpKernel<float>,
-                        paddle::operators::CudnnConvOpKernel<double>);
-REGISTER_OP_CUDA_KERNEL(conv2d_cudnn_grad,
-                        paddle::operators::CudnnConvGradOpKernel<float>,
-                        paddle::operators::CudnnConvGradOpKernel<double>);
-
-REGISTER_OP_CUDA_KERNEL(conv3d_cudnn,
-                        paddle::operators::CudnnConvOpKernel<float>,
-                        paddle::operators::CudnnConvOpKernel<double>);
-REGISTER_OP_CUDA_KERNEL(conv3d_cudnn_grad,
-                        paddle::operators::CudnnConvGradOpKernel<float>,
-                        paddle::operators::CudnnConvGradOpKernel<double>);
+REGISTER_OP_KERNEL(conv2d, CUDNN, ::paddle::platform::CUDAPlace,
+                   paddle::operators::CUDNNConvOpKernel<float>,
+                   paddle::operators::CUDNNConvOpKernel<double>);
+REGISTER_OP_KERNEL(conv2d_grad, CUDNN, ::paddle::platform::CUDAPlace,
+                   paddle::operators::CUDNNConvGradOpKernel<float>,
+                   paddle::operators::CUDNNConvGradOpKernel<double>);
+
+REGISTER_OP_KERNEL(conv3d, CUDNN, ::paddle::platform::CUDAPlace,
+                   paddle::operators::CUDNNConvOpKernel<float>,
+                   paddle::operators::CUDNNConvOpKernel<double>);
+REGISTER_OP_KERNEL(conv3d_grad, CUDNN, ::paddle::platform::CUDAPlace,
+                   paddle::operators::CUDNNConvGradOpKernel<float>,
+                   paddle::operators::CUDNNConvGradOpKernel<double>);
diff --git a/paddle/operators/conv_op.cc b/paddle/operators/conv_op.cc
index 1468e3eb96..424eccdb7d 100644
--- a/paddle/operators/conv_op.cc
+++ b/paddle/operators/conv_op.cc
@@ -67,6 +67,23 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
   ctx->ShareLoD("Input", "Output");
 }
 
+framework::OpKernelType ConvOp::GetExpectedKernelType(
+    const framework::ExecutionContext& ctx) const {
+  bool use_cudnn = ctx.Attr<bool>("use_cudnn");
+  framework::LibraryType library_;
+  if (use_cudnn) {
+    library_ = framework::LibraryType::kCUDNN;
+  } else {
+    library_ = framework::LibraryType::kPlain;
+  }
+
+  std::string data_format = ctx.Attr<std::string>("data_format");
+  framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
+  return framework::OpKernelType(
+      framework::ToDataType(ctx.Input<Tensor>("Input")->type()), ctx.GetPlace(),
+      layout_, library_);
+}
+
 Conv2DOpMaker::Conv2DOpMaker(OpProto* proto, OpAttrChecker* op_checker)
     : OpProtoAndCheckerMaker(proto, op_checker) {
   AddInput(
@@ -108,6 +125,26 @@ Conv2DOpMaker::Conv2DOpMaker(OpProto* proto, OpAttrChecker* op_checker)
                             "dilations(h_dilation, w_dilation) of "
                             "convolution operator.")
       .SetDefault({1, 1});
+  AddAttr<bool>(
+      "use_cudnn",
+      "(bool, default false) Only used in cudnn kernel, need install cudnn")
+      .SetDefault(false);
+  AddAttr<std::string>(
+      "data_format",
+      "(string, default NCHW) Only used in "
+      "An optional string from: \"NHWC\", \"NCHW\". "
+      "Defaults to \"NHWC\". Specify the data format of the output data, "
+      "the input will be transformed automatically. ")
+      .SetDefault("AnyLayout");
+  // TODO(dzhwinter): need to registered layout transform function
+  AddAttr<int>("workspace_size_MB",
+               "Only used in cudnn kernel. Need set use_cudnn to true."
+               "workspace size for cudnn, in MB, "
+               "workspace is a section of GPU memory which will be "
+               "allocated/freed each time the operator runs, larger "
+               "workspace size can increase performance but also requires "
+               "better hardware. This size should be chosen carefully.")
+      .SetDefault(4096);
   AddComment(R"DOC(
 Convolution Operator.
 
@@ -181,6 +218,25 @@ Conv3DOpMaker::Conv3DOpMaker(OpProto* proto, OpAttrChecker* op_checker)
                             "dilations(d_dilation, h_dilation, w_dilation) of "
                             "convolution operator.")
       .SetDefault({1, 1, 1});
+  AddAttr<bool>(
+      "use_cudnn",
+      "(bool, default false) Only used in cudnn kernel, need install cudnn")
+      .SetDefault(false);
+  AddAttr<std::string>(
+      "data_format",
+      "(string, default NCHW) Only used in "
+      "An optional string from: \"NHWC\", \"NCHW\". "
+      "Defaults to \"NHWC\". Specify the data format of the output data, "
+      "the input will be transformed automatically. ")
+      .SetDefault("AnyLayout");
+  // TODO(dzhwinter): need to registered layout transform function
+  AddAttr<int>("workspace_size_MB",
+               "Only used in cudnn kernel. workspace size for cudnn, in MB, "
+               "workspace is a section of GPU memory which will be "
+               "allocated/freed each time the operator runs, larger "
+               "workspace size can increase performance but also requires "
+               "better hardware. This size should be chosen carefully.")
+      .SetDefault(4096);
 
   AddComment(R"DOC(
 Convolution3D Operator.
@@ -224,6 +280,23 @@ void ConvOpGrad::InferShape(framework::InferShapeContext* ctx) const {
   }
 }
 
+framework::OpKernelType ConvOpGrad::GetExpectedKernelType(
+    const framework::ExecutionContext& ctx) const {
+  bool use_cudnn = ctx.Attr<bool>("use_cudnn");
+  framework::LibraryType library_;
+  if (use_cudnn) {
+    library_ = framework::LibraryType::kCUDNN;
+  } else {
+    library_ = framework::LibraryType::kPlain;
+  }
+
+  std::string data_format = ctx.Attr<std::string>("data_format");
+  framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
+  return framework::OpKernelType(
+      framework::ToDataType(ctx.Input<Tensor>("Input")->type()), ctx.GetPlace(),
+      layout_, library_);
+}
+
 }  // namespace operators
 }  // namespace paddle
 
diff --git a/paddle/operators/conv_op.h b/paddle/operators/conv_op.h
index 83786e2329..5a8933e791 100644
--- a/paddle/operators/conv_op.h
+++ b/paddle/operators/conv_op.h
@@ -62,12 +62,20 @@ class ConvOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
   void InferShape(framework::InferShapeContext* ctx) const override;
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override;
 };
 
 class ConvOpGrad : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
   void InferShape(framework::InferShapeContext* ctx) const override;
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override;
 };
 
 template <typename DeviceContext, typename T>
diff --git a/paddle/operators/conv_transpose_cudnn_op.cc b/paddle/operators/conv_transpose_cudnn_op.cc
deleted file mode 100644
index 2e5333a265..0000000000
--- a/paddle/operators/conv_transpose_cudnn_op.cc
+++ /dev/null
@@ -1,78 +0,0 @@
-/* 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/conv_transpose_op.h"
-
-namespace paddle {
-namespace operators {
-
-class CudnnConv2DTransposeOpMaker : public Conv2DTransposeOpMaker {
- public:
-  CudnnConv2DTransposeOpMaker(OpProto* proto, OpAttrChecker* op_checker)
-      : Conv2DTransposeOpMaker(proto, op_checker) {
-    AddAttr<int>("workspace_size_MB",
-                 "workspace size for cudnn, in MB, "
-                 "workspace is a section of GPU memory which will be "
-                 "allocated/freed each time the operator runs, larger "
-                 "workspace size can increase performance but also requires "
-                 "better hardward. This size should be carefully setted.")
-        .SetDefault(4096);
-  }
-};
-
-class CudnnConv3DTransposeOpMaker : public Conv3DTransposeOpMaker {
- public:
-  CudnnConv3DTransposeOpMaker(OpProto* proto, OpAttrChecker* op_checker)
-      : Conv3DTransposeOpMaker(proto, op_checker) {
-    AddAttr<int>("workspace_size_MB",
-                 "workspace size for cudnn, in MB, "
-                 "workspace is a section of GPU memory which will be "
-                 "allocated/freed each time the operator runs, larger "
-                 "workspace size can increase performance but also requires "
-                 "better hardward. This size should be carefully setted.")
-        .SetDefault(4096);
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
-namespace ops = paddle::operators;
-REGISTER_OP(conv2d_transpose_cudnn, ops::ConvTransposeOp,
-            ops::CudnnConv2DTransposeOpMaker, conv2d_transpose_cudnn_grad,
-            ops::ConvTransposeOpGrad);
-
-REGISTER_OP_CPU_KERNEL(
-    conv2d_transpose_cudnn,
-    ops::GemmConvTransposeKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::GemmConvTransposeKernel<paddle::platform::CPUDeviceContext, double>);
-REGISTER_OP_CPU_KERNEL(
-    conv2d_transpose_cudnn_grad,
-    ops::GemmConvTransposeGradKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::GemmConvTransposeGradKernel<paddle::platform::CPUDeviceContext,
-                                     double>);
-
-REGISTER_OP(conv3d_transpose_cudnn, ops::ConvTransposeOp,
-            ops::CudnnConv3DTransposeOpMaker, conv3d_transpose_cudnn_grad,
-            ops::ConvTransposeOpGrad);
-
-REGISTER_OP_CPU_KERNEL(
-    conv3d_transpose_cudnn,
-    ops::GemmConvTransposeKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::GemmConvTransposeKernel<paddle::platform::CPUDeviceContext, double>);
-REGISTER_OP_CPU_KERNEL(
-    conv3d_transpose_cudnn_grad,
-    ops::GemmConvTransposeGradKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::GemmConvTransposeGradKernel<paddle::platform::CPUDeviceContext,
-                                     double>);
diff --git a/paddle/operators/conv_transpose_cudnn_op.cu.cc b/paddle/operators/conv_transpose_cudnn_op.cu.cc
index fc37776ba1..23bc97e13c 100644
--- a/paddle/operators/conv_transpose_cudnn_op.cu.cc
+++ b/paddle/operators/conv_transpose_cudnn_op.cu.cc
@@ -28,10 +28,10 @@ using ScopedFilterDescriptor = platform::ScopedFilterDescriptor;
 using ScopedConvolutionDescriptor = platform::ScopedConvolutionDescriptor;
 using DataLayout = platform::DataLayout;
 
-static constexpr size_t kConvCudnnWorkspaceLimitBytes = 1024 * 1024 * 1024;
+static constexpr size_t kConvCUDNNWorkspaceLimitBytes = 1024 * 1024 * 1024;
 
 template <typename T>
-class CudnnConvTransposeOpKernel : public framework::OpKernel<T> {
+class CUDNNConvTransposeOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
@@ -77,7 +77,7 @@ class CudnnConvTransposeOpKernel : public framework::OpKernel<T> {
     // ------------------- cudnn conv workspace ---------------------
     void* cudnn_workspace = nullptr;
     size_t workspace_size_in_bytes;  // final workspace to allocate.
-    size_t workspace_size_limit = kConvCudnnWorkspaceLimitBytes;
+    size_t workspace_size_limit = kConvCUDNNWorkspaceLimitBytes;
     if (user_workspace_size > 0) {
       workspace_size_limit = user_workspace_size * 1024 * 1024;
     }
@@ -116,7 +116,7 @@ class CudnnConvTransposeOpKernel : public framework::OpKernel<T> {
 };
 
 template <typename T>
-class CudnnConvTransposeGradOpKernel : public framework::OpKernel<T> {
+class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
@@ -161,7 +161,7 @@ class CudnnConvTransposeGradOpKernel : public framework::OpKernel<T> {
     cudnnConvolutionBwdFilterAlgo_t filter_algo;
     size_t bwd_filter_ws_size, fwd_ws_size;
     size_t workspace_size_in_bytes = 0;
-    size_t workspace_size_limit = kConvCudnnWorkspaceLimitBytes;
+    size_t workspace_size_limit = kConvCUDNNWorkspaceLimitBytes;
     if (user_workspace_size > 0) {
       workspace_size_limit = user_workspace_size * 1024 * 1024;
     }
@@ -236,16 +236,16 @@ class CudnnConvTransposeGradOpKernel : public framework::OpKernel<T> {
 
 namespace ops = paddle::operators;
 
-REGISTER_OP_CUDA_KERNEL(conv2d_transpose_cudnn,
-                        ops::CudnnConvTransposeOpKernel<float>,
-                        ops::CudnnConvTransposeOpKernel<double>);
-REGISTER_OP_CUDA_KERNEL(conv2d_transpose_cudnn_grad,
-                        ops::CudnnConvTransposeGradOpKernel<float>,
-                        ops::CudnnConvTransposeGradOpKernel<double>);
-
-REGISTER_OP_CUDA_KERNEL(conv3d_transpose_cudnn,
-                        ops::CudnnConvTransposeOpKernel<float>,
-                        ops::CudnnConvTransposeOpKernel<double>);
-REGISTER_OP_CUDA_KERNEL(conv3d_transpose_cudnn_grad,
-                        ops::CudnnConvTransposeGradOpKernel<float>,
-                        ops::CudnnConvTransposeGradOpKernel<double>);
+REGISTER_OP_KERNEL(conv2d_transpose, CUDNN, ::paddle::platform::CUDAPlace,
+                   ops::CUDNNConvTransposeOpKernel<float>,
+                   ops::CUDNNConvTransposeOpKernel<double>);
+REGISTER_OP_KERNEL(conv2d_transpose_grad, CUDNN, ::paddle::platform::CUDAPlace,
+                   ops::CUDNNConvTransposeGradOpKernel<float>,
+                   ops::CUDNNConvTransposeGradOpKernel<double>);
+
+REGISTER_OP_KERNEL(conv3d_transpose, CUDNN, ::paddle::platform::CUDAPlace,
+                   ops::CUDNNConvTransposeOpKernel<float>,
+                   ops::CUDNNConvTransposeOpKernel<double>);
+REGISTER_OP_KERNEL(conv3d_transpose_grad, CUDNN, ::paddle::platform::CUDAPlace,
+                   ops::CUDNNConvTransposeGradOpKernel<float>,
+                   ops::CUDNNConvTransposeGradOpKernel<double>);
diff --git a/paddle/operators/conv_transpose_op.cc b/paddle/operators/conv_transpose_op.cc
index 74636d138f..cf4e8c0a30 100644
--- a/paddle/operators/conv_transpose_op.cc
+++ b/paddle/operators/conv_transpose_op.cc
@@ -58,6 +58,23 @@ void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
   ctx->SetOutputDim("Output", framework::make_ddim(output_shape));
 }
 
+framework::OpKernelType ConvTransposeOp::GetExpectedKernelType(
+    const framework::ExecutionContext& ctx) const {
+  bool use_cudnn = ctx.Attr<bool>("use_cudnn");
+  framework::LibraryType library_;
+  if (use_cudnn) {
+    library_ = framework::LibraryType::kCUDNN;
+  } else {
+    library_ = framework::LibraryType::kPlain;
+  }
+
+  std::string data_format = ctx.Attr<std::string>("data_format");
+  framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
+  return framework::OpKernelType(
+      framework::ToDataType(ctx.Input<Tensor>("Input")->type()), ctx.GetPlace(),
+      layout_, library_);
+}
+
 Conv2DTransposeOpMaker::Conv2DTransposeOpMaker(OpProto* proto,
                                                OpAttrChecker* op_checker)
     : OpProtoAndCheckerMaker(proto, op_checker) {
@@ -94,6 +111,25 @@ Conv2DTransposeOpMaker::Conv2DTransposeOpMaker(OpProto* proto,
       "(vector<int> default:{0, 0}), the paddings(h_pad, w_pad) of convolution "
       "transpose operator.")
       .SetDefault({0, 0});
+  AddAttr<bool>(
+      "use_cudnn",
+      "(bool, default false) Only used in cudnn kernel, need install cudnn")
+      .SetDefault(false);
+  AddAttr<std::string>(
+      "data_format",
+      "(string, default NCHW) Only used in "
+      "An optional string from: \"NHWC\", \"NCHW\". "
+      "Defaults to \"NHWC\". Specify the data format of the output data, "
+      "the input will be transformed automatically. ")
+      .SetDefault("AnyLayout");
+  // TODO(dzhwinter): need to registered layout transform function
+  AddAttr<int>("workspace_size_MB",
+               "Used in cudnn kernel only. workspace size for cudnn, in MB, "
+               "workspace is a section of GPU memory which will be "
+               "allocated/freed each time the operator runs, larger "
+               "workspace size can increase performance but also requires "
+               "better hardward. This size should be carefully setted.")
+      .SetDefault(4096);
   AddComment(R"DOC(
 Convolution2D Transpose Operator.
 
@@ -163,6 +199,25 @@ Conv3DTransposeOpMaker::Conv3DTransposeOpMaker(OpProto* proto,
                             "(vector<int> default:{0, 0, 0}), paddings(d_pad, "
                             "h_pad, w_pad) of convolution transpose operator.")
       .SetDefault({0, 0, 0});
+  AddAttr<bool>(
+      "use_cudnn",
+      "(bool, default false) Only used in cudnn kernel, need install cudnn")
+      .SetDefault(false);
+  AddAttr<std::string>(
+      "data_format",
+      "(string, default NCHW) Only used in "
+      "An optional string from: \"NHWC\", \"NCHW\". "
+      "Defaults to \"NHWC\". Specify the data format of the output data, "
+      "the input will be transformed automatically. ")
+      .SetDefault("AnyLayout");
+  // TODO(dzhwinter): need to registered layout transform function
+  AddAttr<int>("workspace_size_MB",
+               "Used in cudnn kernel only. workspace size for cudnn, in MB, "
+               "workspace is a section of GPU memory which will be "
+               "allocated/freed each time the operator runs, larger "
+               "workspace size can increase performance but also requires "
+               "better hardward. This size should be carefully setted.")
+      .SetDefault(4096);
   AddComment(R"DOC(
 Convolution3D Transpose Operator.
 
@@ -205,6 +260,23 @@ void ConvTransposeOpGrad::InferShape(framework::InferShapeContext* ctx) const {
   }
 }
 
+framework::OpKernelType ConvTransposeOpGrad::GetExpectedKernelType(
+    const framework::ExecutionContext& ctx) const {
+  bool use_cudnn = ctx.Attr<bool>("use_cudnn");
+  framework::LibraryType library_;
+  if (use_cudnn) {
+    library_ = framework::LibraryType::kCUDNN;
+  } else {
+    library_ = framework::LibraryType::kPlain;
+  }
+
+  std::string data_format = ctx.Attr<std::string>("data_format");
+  framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
+  return framework::OpKernelType(
+      framework::ToDataType(ctx.Input<Tensor>("Input")->type()), ctx.GetPlace(),
+      layout_, library_);
+}
+
 }  // namespace operators
 }  // namespace paddle
 
diff --git a/paddle/operators/conv_transpose_op.h b/paddle/operators/conv_transpose_op.h
index 4c8f8a8067..a42ade41b1 100644
--- a/paddle/operators/conv_transpose_op.h
+++ b/paddle/operators/conv_transpose_op.h
@@ -42,12 +42,20 @@ class ConvTransposeOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
   void InferShape(framework::InferShapeContext* ctx) const override;
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override;
 };
 
 class ConvTransposeOpGrad : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
   void InferShape(framework::InferShapeContext* ctx) const override;
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override;
 };
 
 template <typename DeviceContext, typename T>
diff --git a/paddle/operators/math/sequence2batch.cc b/paddle/operators/math/sequence2batch.cc
index 88977be1f8..e459a42ca2 100644
--- a/paddle/operators/math/sequence2batch.cc
+++ b/paddle/operators/math/sequence2batch.cc
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/operators/math/sequence2batch.h"
+#include "paddle/operators/math/math_function.h"
 
 namespace paddle {
 namespace operators {
diff --git a/paddle/operators/pool_cudnn_op.cc b/paddle/operators/pool_cudnn_op.cc
deleted file mode 100644
index 77407f5cdf..0000000000
--- a/paddle/operators/pool_cudnn_op.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-/* 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/pool_cudnn_op.h"
-
-namespace ops = paddle::operators;
-
-REGISTER_OP(pool2d_cudnn, ops::PoolOp, ops::Pool2dOpMaker, pool2d_cudnn_grad,
-            ops::PoolOpGrad);
-
-REGISTER_OP_CPU_KERNEL(
-    pool2d_cudnn, ops::PoolKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::PoolKernel<paddle::platform::CPUDeviceContext, double>);
-REGISTER_OP_CPU_KERNEL(
-    pool2d_cudnn_grad,
-    ops::PoolGradKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::PoolGradKernel<paddle::platform::CPUDeviceContext, double>)
-
-REGISTER_OP(pool3d_cudnn, ops::PoolOp, ops::Pool3dOpMaker, pool3d_cudnn_grad,
-            ops::PoolOpGrad);
-
-REGISTER_OP_CPU_KERNEL(
-    pool3d_cudnn, ops::PoolKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::PoolKernel<paddle::platform::CPUDeviceContext, double>);
-REGISTER_OP_CPU_KERNEL(
-    pool3d_cudnn_grad,
-    ops::PoolGradKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::PoolGradKernel<paddle::platform::CPUDeviceContext, double>)
diff --git a/paddle/operators/pool_cudnn_op.cu.cc b/paddle/operators/pool_cudnn_op.cu.cc
index 2d0001ba11..446fb0819d 100644
--- a/paddle/operators/pool_cudnn_op.cu.cc
+++ b/paddle/operators/pool_cudnn_op.cu.cc
@@ -12,7 +12,8 @@ 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/pool_cudnn_op.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/operators/pool_op.h"
 #include "paddle/platform/cudnn_helper.h"
 
 namespace paddle {
@@ -25,7 +26,7 @@ using DataLayout = platform::DataLayout;
 using PoolingMode = platform::PoolingMode;
 
 template <typename T>
-class PoolCudnnOpKernel : public framework::OpKernel<T> {
+class PoolCUDNNOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext &ctx) const override {
     PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
@@ -86,7 +87,7 @@ class PoolCudnnOpKernel : public framework::OpKernel<T> {
 };
 
 template <typename T>
-class PoolCudnnGradOpKernel : public framework::OpKernel<T> {
+class PoolCUDNNGradOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext &ctx) const override {
     PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
@@ -162,12 +163,16 @@ class PoolCudnnGradOpKernel : public framework::OpKernel<T> {
 
 namespace ops = paddle::operators;
 
-REGISTER_OP_CUDA_KERNEL(pool2d_cudnn, ops::PoolCudnnOpKernel<float>,
-                        ops::PoolCudnnOpKernel<double>);
-REGISTER_OP_CUDA_KERNEL(pool2d_cudnn_grad, ops::PoolCudnnGradOpKernel<float>,
-                        ops::PoolCudnnGradOpKernel<double>);
-
-REGISTER_OP_CUDA_KERNEL(pool3d_cudnn, ops::PoolCudnnOpKernel<float>,
-                        ops::PoolCudnnOpKernel<double>);
-REGISTER_OP_CUDA_KERNEL(pool3d_cudnn_grad, ops::PoolCudnnGradOpKernel<float>,
-                        ops::PoolCudnnGradOpKernel<double>);
+REGISTER_OP_KERNEL(pool2d, CUDNN, ::paddle::platform::CUDAPlace,
+                   ops::PoolCUDNNOpKernel<float>,
+                   ops::PoolCUDNNOpKernel<double>);
+REGISTER_OP_KERNEL(pool2d_grad, CUDNN, ::paddle::platform::CUDAPlace,
+                   ops::PoolCUDNNGradOpKernel<float>,
+                   ops::PoolCUDNNGradOpKernel<double>);
+
+REGISTER_OP_KERNEL(pool3d, CUDNN, ::paddle::platform::CUDAPlace,
+                   ops::PoolCUDNNOpKernel<float>,
+                   ops::PoolCUDNNOpKernel<double>);
+REGISTER_OP_KERNEL(pool3d_grad, CUDNN, ::paddle::platform::CUDAPlace,
+                   ops::PoolCUDNNGradOpKernel<float>,
+                   ops::PoolCUDNNGradOpKernel<double>);
diff --git a/paddle/operators/pool_cudnn_op.h b/paddle/operators/pool_cudnn_op.h
deleted file mode 100644
index 5adf27f5bc..0000000000
--- a/paddle/operators/pool_cudnn_op.h
+++ /dev/null
@@ -1,19 +0,0 @@
-/* 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/pool_op.h"
-
-namespace paddle {
-namespace operators {}  // namespace operators
-}  // namespace paddle
diff --git a/paddle/operators/pool_op.cc b/paddle/operators/pool_op.cc
index d3cf5fa638..3e567efd08 100644
--- a/paddle/operators/pool_op.cc
+++ b/paddle/operators/pool_op.cc
@@ -61,6 +61,23 @@ void PoolOp::InferShape(framework::InferShapeContext *ctx) const {
   ctx->ShareLoD("X", "Out");
 }
 
+framework::OpKernelType PoolOp::GetExpectedKernelType(
+    const framework::ExecutionContext &ctx) const {
+  bool use_cudnn = ctx.Attr<bool>("use_cudnn");
+  framework::LibraryType library_;
+  if (use_cudnn) {
+    library_ = framework::LibraryType::kCUDNN;
+  } else {
+    library_ = framework::LibraryType::kPlain;
+  }
+
+  std::string data_format = ctx.Attr<std::string>("data_format");
+  framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
+  return framework::OpKernelType(
+      framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
+      layout_, library_);
+}
+
 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")),
@@ -68,6 +85,23 @@ void PoolOpGrad::InferShape(framework::InferShapeContext *ctx) const {
   ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
 }
 
+framework::OpKernelType PoolOpGrad::GetExpectedKernelType(
+    const framework::ExecutionContext &ctx) const {
+  bool use_cudnn = ctx.Attr<bool>("use_cudnn");
+  framework::LibraryType library_;
+  if (use_cudnn) {
+    library_ = framework::LibraryType::kCUDNN;
+  } else {
+    library_ = framework::LibraryType::kPlain;
+  }
+
+  std::string data_format = ctx.Attr<std::string>("data_format");
+  framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
+  return framework::OpKernelType(
+      framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
+      layout_, library_);
+}
+
 Pool2dOpMaker::Pool2dOpMaker(OpProto *proto, OpAttrChecker *op_checker)
     : OpProtoAndCheckerMaker(proto, op_checker) {
   AddInput(
@@ -101,15 +135,27 @@ Pool2dOpMaker::Pool2dOpMaker(OpProto *proto, OpAttrChecker *op_checker)
   AddAttr<std::vector<int>>("strides",
                             "(vector<int>, default {1, 1}), strides(height, "
                             "width) of pooling operator.")
-      .SetDefault({1, 1});  // TODO(Chengduo): Add checker. (Currently,
+      .SetDefault({1, 1});
+  // TODO(Chengduo): Add checker. (Currently,
   // TypedAttrChecker don't support vector type.)
   AddAttr<std::vector<int>>(
       "paddings",
       "(vector<int>, default {0,0}), paddings(height, width) of pooling "
       "operator."
       "If global_pooling = true, paddings and ksize will be ignored.")
-      .SetDefault({0, 0});  // TODO(Chengduo): Add checker. (Currently,
-  // TypedAttrChecker don't support vector type.)
+      .SetDefault({0, 0});
+  AddAttr<bool>(
+      "use_cudnn",
+      "(bool, default false) Only used in cudnn kernel, need install cudnn")
+      .SetDefault(false);
+  AddAttr<std::string>(
+      "data_format",
+      "(string, default NCHW) Only used in "
+      "An optional string from: \"NHWC\", \"NCHW\". "
+      "Defaults to \"NHWC\". Specify the data format of the output data, "
+      "the input will be transformed automatically. ")
+      .SetDefault("AnyLayout");
+  // TODO(dzhwinter): need to registered layout transform function
 
   AddComment(R"DOC(
 Pool2d Operator.
@@ -182,6 +228,19 @@ Pool3dOpMaker::Pool3dOpMaker(OpProto *proto, OpAttrChecker *op_checker)
       .SetDefault({0, 0, 0});  // TODO(Chengduo): Add checker. (Currently,
                                // TypedAttrChecker don't support vector type.)
 
+  AddAttr<bool>(
+      "use_cudnn",
+      "(bool, default false) Only used in cudnn kernel, need install cudnn")
+      .SetDefault(false);
+  AddAttr<std::string>(
+      "data_format",
+      "(string, default NCHW) Only used in "
+      "An optional string from: \"NHWC\", \"NCHW\". "
+      "Defaults to \"NHWC\". Specify the data format of the output data, "
+      "the input will be transformed automatically. ")
+      .SetDefault("AnyLayout");
+  // TODO(dzhwinter): need to registered layout transform function
+
   AddComment(R"DOC(
 Pool3d Operator.
 
diff --git a/paddle/operators/pool_op.h b/paddle/operators/pool_op.h
index 3860e295f4..c3d82ecbde 100644
--- a/paddle/operators/pool_op.h
+++ b/paddle/operators/pool_op.h
@@ -29,6 +29,10 @@ class PoolOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override;
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override;
 };
 
 class PoolOpGrad : public framework::OperatorWithKernel {
@@ -36,6 +40,10 @@ class PoolOpGrad : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override;
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override;
 };
 
 class Pool2dOpMaker : public framework::OpProtoAndCheckerMaker {
diff --git a/paddle/platform/dynload/cudnn.cc b/paddle/platform/dynload/cudnn.cc
index 76ec82e108..701f6240fe 100644
--- a/paddle/platform/dynload/cudnn.cc
+++ b/paddle/platform/dynload/cudnn.cc
@@ -44,7 +44,7 @@ CUDNN_DNN_ROUTINE_EACH_R7(DEFINE_WRAP);
 
 #ifdef PADDLE_USE_DSO
 bool HasCUDNN() {
-  std::call_once(cudnn_dso_flag, GetCudnnDsoHandle, &cudnn_dso_handle);
+  std::call_once(cudnn_dso_flag, GetCUDNNDsoHandle, &cudnn_dso_handle);
   return cudnn_dso_handle != nullptr;
 }
 
diff --git a/paddle/platform/dynload/cudnn.h b/paddle/platform/dynload/cudnn.h
index 8c937b37d7..b926347949 100644
--- a/paddle/platform/dynload/cudnn.h
+++ b/paddle/platform/dynload/cudnn.h
@@ -36,7 +36,7 @@ extern void EnforceCUDNNLoaded(const char* fn_name);
     auto operator()(Args... args) -> decltype(__name(args...)) {   \
       using cudnn_func = decltype(__name(args...)) (*)(Args...);   \
       std::call_once(cudnn_dso_flag,                               \
-                     paddle::platform::dynload::GetCudnnDsoHandle, \
+                     paddle::platform::dynload::GetCUDNNDsoHandle, \
                      &cudnn_dso_handle);                           \
       EnforceCUDNNLoaded(#__name);                                 \
       void* p_##__name = dlsym(cudnn_dso_handle, #__name);         \
diff --git a/paddle/platform/dynload/dynamic_loader.cc b/paddle/platform/dynload/dynamic_loader.cc
index 7a82d06a0a..c8c09ae608 100644
--- a/paddle/platform/dynload/dynamic_loader.cc
+++ b/paddle/platform/dynload/dynamic_loader.cc
@@ -134,7 +134,7 @@ void GetCublasDsoHandle(void** dso_handle) {
 #endif
 }
 
-void GetCudnnDsoHandle(void** dso_handle) {
+void GetCUDNNDsoHandle(void** dso_handle) {
 #if defined(__APPLE__) || defined(__OSX__)
   GetDsoHandleFromSearchPath(FLAGS_cudnn_dir, "libcudnn.dylib", dso_handle,
                              false);
diff --git a/paddle/platform/dynload/dynamic_loader.h b/paddle/platform/dynload/dynamic_loader.h
index c0e5452e5a..7b0c8c16d7 100644
--- a/paddle/platform/dynload/dynamic_loader.h
+++ b/paddle/platform/dynload/dynamic_loader.h
@@ -32,7 +32,7 @@ void GetCublasDsoHandle(void** dso_handle);
  * @param    **dso_handle   dso handler
  *
  */
-void GetCudnnDsoHandle(void** dso_handle);
+void GetCUDNNDsoHandle(void** dso_handle);
 
 /**
  * @brief    load the DSO of CURAND
diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc
index 5d170c66e9..c5d70bc9f9 100644
--- a/paddle/pybind/pybind.cc
+++ b/paddle/pybind/pybind.cc
@@ -430,13 +430,8 @@ All parameter, weight, gradient are variables in Paddle.
   m.def("init_glog", framework::InitGLOG);
   m.def("init_devices", &framework::InitDevices);
 
-  m.def("use_cpu", framework::UseCPU);
-  m.def("use_mkldnn", framework::UseMKLDNN);
-  m.def("use_cuda", framework::UseCUDA);
-  m.def("use_cudnn", framework::UseCUDNN);
-  m.def("use_all", framework::UseALL);
-
   m.def("is_compile_gpu", IsCompileGPU);
+
   m.def("set_feed_variable", framework::SetFeedVariable);
   m.def("get_fetch_variable", framework::GetFetchVariable);
 
diff --git a/paddle/pybind/tensor_py.h b/paddle/pybind/tensor_py.h
index 6b4290972b..3b5210e2b9 100644
--- a/paddle/pybind/tensor_py.h
+++ b/paddle/pybind/tensor_py.h
@@ -14,7 +14,7 @@ limitations under the License. */
 
 #pragma once
 #include <string>
-#include "paddle/framework/tensor.h"
+#include "paddle/framework/lod_tensor.h"
 #include "paddle/memory/memcpy.h"
 #include "paddle/platform/device_context.h"
 #include "pybind11/numpy.h"
@@ -97,14 +97,27 @@ inline py::buffer_info CastToPyBuffer(framework::Tensor &tensor) {
 
 template <typename T>
 T TensorGetElement(framework::Tensor &self, size_t offset) {
-  PADDLE_ENFORCE(platform::is_cpu_place(self.place()));
-  return self.data<T>()[offset];
+  if (platform::is_cpu_place(self.place())) {
+    return self.data<T>()[offset];
+  } else {
+    std::shared_ptr<framework::Tensor> dst(new framework::Tensor);
+    framework::Copy(self, platform::CPUPlace(), dst.get());
+    return dst->data<T>()[offset];
+  }
 }
 
+// TODO(dzhwinter) : fix the redundent Tensor allocate and free
 template <typename T>
 void TensorSetElement(framework::Tensor &self, size_t offset, T elem) {
-  PADDLE_ENFORCE(platform::is_cpu_place(self.place()));
-  self.data<T>()[offset] = elem;
+  if (platform::is_gpu_place(self.place())) {
+    std::shared_ptr<framework::Tensor> dst(new framework::Tensor);
+    framework::Copy(self, platform::CPUPlace(), dst.get());
+    dst->data<T>()[offset] = elem;
+    framework::Copy(*dst.get(), self.place(), &self);
+
+  } else if (platform::is_cpu_place(self.place())) {
+    self.data<T>()[offset] = elem;
+  }
 }
 
 template <typename T>
diff --git a/python/paddle/v2/fluid/layers/nn.py b/python/paddle/v2/fluid/layers/nn.py
index 94184d59f6..99a40ce45a 100644
--- a/python/paddle/v2/fluid/layers/nn.py
+++ b/python/paddle/v2/fluid/layers/nn.py
@@ -775,7 +775,7 @@ def conv2d(input,
     pre_bias = helper.create_tmp_variable(dtype)
 
     helper.append_op(
-        type='conv2d_cudnn',
+        type='conv2d',
         inputs={
             'Input': input,
             'Filter': filter_param,
diff --git a/python/paddle/v2/fluid/tests/op_test.py b/python/paddle/v2/fluid/tests/op_test.py
index b77d2b1268..276cf2c5f2 100644
--- a/python/paddle/v2/fluid/tests/op_test.py
+++ b/python/paddle/v2/fluid/tests/op_test.py
@@ -31,7 +31,8 @@ def create_op(scope, op_type, inputs, outputs, attrs):
             kwargs[in_name] = []
             if in_dup:
                 sub_in = inputs[in_name]
-                for sub_in_name, _ in sub_in:
+                for item in sub_in:
+                    sub_in_name, _ = item[0], item[1]
                     __create_var__(in_name, sub_in_name)
             else:
                 __create_var__(in_name, in_name)
@@ -41,7 +42,8 @@ def create_op(scope, op_type, inputs, outputs, attrs):
             kwargs[out_name] = []
             if out_dup:
                 sub_out = outputs[out_name]
-                for sub_out_name, _ in sub_out:
+                for item in sub_out:
+                    sub_out_name, _ = item[0], item[1]
                     __create_var__(out_name, sub_out_name)
             else:
                 __create_var__(out_name, out_name)
@@ -71,13 +73,15 @@ def set_input(scope, op, inputs, place):
         if in_name in inputs:
             if in_dup:
                 sub_in = inputs[in_name]
-                for sub_in_name, sub_in_val in sub_in:
+                for item in sub_in:
+                    sub_in_name, sub_in_val = item[0], item[1]
                     __set_input__(sub_in_name, sub_in_val)
             else:
                 __set_input__(in_name, inputs[in_name])
 
 
-def get_numeric_gradient(scope,
+def get_numeric_gradient(place,
+                         scope,
                          op,
                          inputs,
                          input_to_check,
@@ -85,7 +89,7 @@ def get_numeric_gradient(scope,
                          delta=0.005,
                          in_place=False):
     # FIXME: change this method by compile time concepts
-    set_input(scope, op, inputs, core.CPUPlace())
+    set_input(scope, op, inputs, place)
 
     def product(dim):
         return reduce(lambda a, b: a * b, dim, 1)
@@ -93,7 +97,7 @@ def get_numeric_gradient(scope,
     def get_output():
         sum = []
         for output_name in output_names:
-            op.run(scope, core.CPUPlace())
+            op.run(scope, place)
             sum.append(
                 np.array(scope.find_var(output_name).get_tensor()).mean())
         return np.array(sum).mean()
@@ -127,7 +131,7 @@ def get_numeric_gradient(scope,
     # we use a for loop to compute the gradient of every element.
     for i in xrange(tensor_size):
         if in_place:
-            set_input(scope, op, inputs, core.CPUPlace())
+            set_input(scope, op, inputs, place)
 
         # get one input element throw it's index i.
         origin = __get_elem__(tensor_to_check, i)
@@ -137,7 +141,7 @@ def get_numeric_gradient(scope,
         y_pos = get_output()
 
         if in_place:
-            set_input(scope, op, inputs, core.CPUPlace())
+            set_input(scope, op, inputs, place)
 
         x_neg = origin - delta
         __set_elem__(tensor_to_check, i, x_neg)
@@ -283,7 +287,8 @@ class OpTest(unittest.TestCase):
                 if not isinstance(sub_out, list):
                     raise AssertionError("sub_out type %s is not list",
                                          type(sub_out))
-                for sub_out_name, expect in sub_out:
+                for item in sub_out:
+                    sub_out_name, expect = item[0], item[1]
                     idx = find_actual(sub_out_name, fetch_list)
                     actual = outs[idx]
                     actual_t = np.array(actual)
@@ -347,6 +352,24 @@ class OpTest(unittest.TestCase):
                    in_place=False,
                    max_relative_error=0.005,
                    user_defined_grads=None):
+        places = [core.CPUPlace()]
+        if core.is_compile_gpu() and core.op_support_gpu(self.op_type):
+            places.append(core.CUDAPlace(0))
+        for place in places:
+            self.check_grad_with_place(place, inputs_to_check, output_names,
+                                       no_grad_set, numeric_grad_delta,
+                                       in_place, max_relative_error,
+                                       user_defined_grads)
+
+    def check_grad_with_place(self,
+                              place,
+                              inputs_to_check,
+                              output_names,
+                              no_grad_set=None,
+                              numeric_grad_delta=0.005,
+                              in_place=False,
+                              max_relative_error=0.005,
+                              user_defined_grads=None):
         self.scope = core.Scope()
         op_inputs = self.inputs if hasattr(self, "inputs") else dict()
         op_outputs = self.outputs if hasattr(self, "outputs") else dict()
@@ -362,6 +385,7 @@ class OpTest(unittest.TestCase):
 
         numeric_grads = user_defined_grads or [
             get_numeric_gradient(
+                place,
                 self.scope,
                 self.op,
                 self.inputs,
@@ -370,22 +394,12 @@ class OpTest(unittest.TestCase):
                 delta=numeric_grad_delta,
                 in_place=in_place) for input_to_check in inputs_to_check
         ]
-        cpu_place = core.CPUPlace()
-        cpu_analytic_grads = self._get_gradient(inputs_to_check, cpu_place,
-                                                output_names, no_grad_set)
-
-        self.__assert_is_close(numeric_grads, cpu_analytic_grads,
-                               inputs_to_check, max_relative_error,
-                               "Gradient Check On %s" % str(cpu_place))
-
-        if core.is_compile_gpu() and self.op.support_gpu():
-            gpu_place = core.CUDAPlace(0)
-            gpu_analytic_grads = self._get_gradient(inputs_to_check, gpu_place,
-                                                    output_names, no_grad_set)
-
-            self.__assert_is_close(numeric_grads, gpu_analytic_grads,
-                                   inputs_to_check, max_relative_error,
-                                   "Gradient Check On %s" % str(gpu_place))
+        analytic_grads = self._get_gradient(inputs_to_check, place,
+                                            output_names, no_grad_set)
+
+        self.__assert_is_close(numeric_grads, analytic_grads, inputs_to_check,
+                               max_relative_error,
+                               "Gradient Check On %s" % str(place))
 
     @staticmethod
     def _create_var_descs_(block, var_dict):
diff --git a/python/paddle/v2/fluid/tests/test_conv2d_op.py b/python/paddle/v2/fluid/tests/test_conv2d_op.py
index 958300e655..e9a19d1774 100644
--- a/python/paddle/v2/fluid/tests/test_conv2d_op.py
+++ b/python/paddle/v2/fluid/tests/test_conv2d_op.py
@@ -49,7 +49,7 @@ def conv2d_forward_naive(input, filter, group, conv_param):
 
 class TestConv2dOp(OpTest):
     def setUp(self):
-        core.use_cuda()
+        self.use_cudnn = False
         self.init_op_type()
         self.init_group()
         self.init_dilation()
@@ -70,30 +70,59 @@ class TestConv2dOp(OpTest):
             'strides': self.stride,
             'paddings': self.pad,
             'groups': self.groups,
-            'dilations': self.dilations
+            'dilations': self.dilations,
+            'use_cudnn': self.use_cudnn
         }
         self.outputs = {'Output': output}
 
     def test_check_output(self):
-        self.check_output()
+        if self.use_cudnn:
+            place = core.CUDAPlace(0)
+            self.check_output_with_place(place, atol=1e-5)
+        else:
+            self.check_output()
 
     def test_check_grad(self):
-        self.check_grad(
-            set(['Input', 'Filter']), 'Output', max_relative_error=0.02)
+        if self.use_cudnn:
+            place = core.CUDAPlace(0)
+            self.check_grad_with_place(
+                place,
+                set(['Input', 'Filter']),
+                'Output',
+                max_relative_error=0.02)
+        else:
+            self.check_grad(
+                set(['Input', 'Filter']), 'Output', max_relative_error=0.02)
 
     def test_check_grad_no_filter(self):
-        self.check_grad(
-            ['Input'],
-            'Output',
-            max_relative_error=0.02,
-            no_grad_set=set(['Filter']))
+        if self.use_cudnn:
+            place = core.CUDAPlace(0)
+            self.check_grad_with_place(
+                place, ['Input'],
+                'Output',
+                max_relative_error=0.02,
+                no_grad_set=set(['Filter']))
+        else:
+            self.check_grad(
+                ['Input'],
+                'Output',
+                max_relative_error=0.02,
+                no_grad_set=set(['Filter']))
 
     def test_check_grad_no_input(self):
-        self.check_grad(
-            ['Filter'],
-            'Output',
-            max_relative_error=0.02,
-            no_grad_set=set(['Input']))
+        if self.use_cudnn:
+            place = core.CUDAPlace(0)
+            self.check_grad_with_place(
+                place, ['Filter'],
+                'Output',
+                max_relative_error=0.02,
+                no_grad_set=set(['Input']))
+        else:
+            self.check_grad(
+                ['Filter'],
+                'Output',
+                max_relative_error=0.02,
+                no_grad_set=set(['Input']))
 
     def init_test_case(self):
         self.pad = [0, 0]
@@ -167,39 +196,39 @@ class TestWithDilation(TestConv2dOp):
         self.groups = 3
 
 
-#----------------Conv2dCudnn----------------
-class TestCudnn(TestConv2dOp):
+#----------------Conv2dCUDNN----------------
+class TestCUDNN(TestConv2dOp):
     def init_op_type(self):
-        core.use_cudnn()
-        self.op_type = "conv2d_cudnn"
+        self.use_cudnn = True
+        self.op_type = "conv2d"
 
 
-class TestCudnnWithPad(TestWithPad):
+class TestCUDNNWithPad(TestWithPad):
     def init_op_type(self):
-        core.use_cudnn()
-        self.op_type = "conv2d_cudnn"
+        self.use_cudnn = True
+        self.op_type = "conv2d"
 
 
-class TestCudnnWithStride(TestWithStride):
+class TestCUDNNWithStride(TestWithStride):
     def init_op_type(self):
-        core.use_cudnn()
-        self.op_type = "conv2d_cudnn"
+        self.use_cudnn = True
+        self.op_type = "conv2d"
 
 
-class TestCudnnWithGroup(TestWithGroup):
+class TestCUDNNWithGroup(TestWithGroup):
     def init_op_type(self):
-        core.use_cudnn()
-        self.op_type = "conv2d_cudnn"
+        self.use_cudnn = True
+        self.op_type = "conv2d"
 
 
-class TestCudnnWith1x1(TestWith1x1):
+class TestCUDNNWith1x1(TestWith1x1):
     def init_op_type(self):
-        core.use_cudnn()
-        self.op_type = "conv2d_cudnn"
+        self.use_cudnn = True
+        self.op_type = "conv2d"
 
 
 #  cudnn v5 does not support dilation conv.
-# class TestCudnnWithDilation(TestWithDilation):
+# class TestCUDNNWithDilation(TestWithDilation):
 #     def init_op_type(self):
 #         self.op_type = "conv_cudnn"
 
diff --git a/python/paddle/v2/fluid/tests/test_conv2d_transpose_op.py b/python/paddle/v2/fluid/tests/test_conv2d_transpose_op.py
index d59537b924..4aec32fc6e 100644
--- a/python/paddle/v2/fluid/tests/test_conv2d_transpose_op.py
+++ b/python/paddle/v2/fluid/tests/test_conv2d_transpose_op.py
@@ -1,5 +1,7 @@
 import unittest
 import numpy as np
+
+import paddle.v2.fluid.core as core
 from op_test import OpTest
 
 
@@ -37,6 +39,7 @@ def conv2dtranspose_forward_naive(input_, filter_, attrs):
 class TestConv2dTransposeOp(OpTest):
     def setUp(self):
         # init as conv transpose
+        self.use_cudnn = False
         self.init_op_type()
         self.init_test_case()
 
@@ -47,7 +50,9 @@ class TestConv2dTransposeOp(OpTest):
         self.attrs = {
             'strides': self.stride,
             'paddings': self.pad,
-            'dilations': self.dilations
+            'dilations': self.dilations,
+            'use_cudnn': self.use_cudnn,
+            'data_format': 'AnyLayout'  # TODO(dzhwinter) : should be fix latter
         }
 
         output = conv2dtranspose_forward_naive(input_, filter_,
@@ -56,25 +61,53 @@ class TestConv2dTransposeOp(OpTest):
         self.outputs = {'Output': output}
 
     def test_check_output(self):
-        self.check_output()
+        if self.use_cudnn:
+            place = core.CUDAPlace(0)
+            self.check_output_with_place(place, atol=1e-5)
+        else:
+            self.check_output()
 
     def test_check_grad_no_input(self):
-        self.check_grad(
-            ['Filter'],
-            'Output',
-            max_relative_error=0.02,
-            no_grad_set=set(['Input']))
+        if self.use_cudnn:
+            place = core.CUDAPlace(0)
+            self.check_grad_with_place(
+                place, ['Filter'],
+                'Output',
+                max_relative_error=0.02,
+                no_grad_set=set(['Input']))
+        else:
+            self.check_grad(
+                ['Filter'],
+                'Output',
+                max_relative_error=0.02,
+                no_grad_set=set(['Input']))
 
     def test_check_grad_no_filter(self):
-        self.check_grad(
-            ['Input'],
-            'Output',
-            max_relative_error=0.02,
-            no_grad_set=set(['Filter']))
+        if self.use_cudnn:
+            place = core.CUDAPlace(0)
+            self.check_grad_with_place(
+                place, ['Input'],
+                'Output',
+                max_relative_error=0.02,
+                no_grad_set=set(['Filter']))
+        else:
+            self.check_grad(
+                ['Input'],
+                'Output',
+                max_relative_error=0.02,
+                no_grad_set=set(['Filter']))
 
     def test_check_grad(self):
-        self.check_grad(
-            set(['Input', 'Filter']), 'Output', max_relative_error=0.02)
+        if self.use_cudnn:
+            place = core.CUDAPlace(0)
+            self.check_grad_with_place(
+                place,
+                set(['Input', 'Filter']),
+                'Output',
+                max_relative_error=0.02)
+        else:
+            self.check_grad(
+                set(['Input', 'Filter']), 'Output', max_relative_error=0.02)
 
     def init_test_case(self):
         self.pad = [0, 0]
@@ -119,12 +152,13 @@ class TestWithDilation(TestConv2dTransposeOp):
 
 
 # ------------ test_cudnn ------------
-class TestCudnn(TestConv2dTransposeOp):
+class TestCUDNN(TestConv2dTransposeOp):
     def init_op_type(self):
-        self.op_type = "conv2d_transpose_cudnn"
+        self.use_cudnn = True
+        self.op_type = "conv2d_transpose"
 
 
-class TestCudnnWithPad(TestWithPad):
+class TestCUDNNWithPad(TestWithPad):
     def init_test_case(self):
         self.pad = [1, 1]
         self.stride = [1, 1]
@@ -134,10 +168,11 @@ class TestCudnnWithPad(TestWithPad):
         self.filter_size = [f_c, 6, 3, 3]
 
     def init_op_type(self):
-        self.op_type = "conv2d_transpose_cudnn"
+        self.use_cudnn = True
+        self.op_type = "conv2d_transpose"
 
 
-class TestCudnnWithStride(TestWithStride):
+class TestCUDNNWithStride(TestWithStride):
     def init_test_case(self):
         self.pad = [1, 1]
         self.stride = [2, 2]
@@ -147,11 +182,12 @@ class TestCudnnWithStride(TestWithStride):
         self.filter_size = [f_c, 6, 3, 3]
 
     def init_op_type(self):
-        self.op_type = "conv2d_transpose_cudnn"
+        self.use_cudnn = True
+        self.op_type = "conv2d_transpose"
 
 
 # #cudnn v5 does not support dilation conv.
-# class TestCudnnWithDilation(TestWithDilation):
+# class TestCUDNNWithDilation(TestWithDilation):
 #     def init_test_case(self):
 #         self.pad = [1, 1]
 #         self.stride = [2, 2]
@@ -161,7 +197,7 @@ class TestCudnnWithStride(TestWithStride):
 #         self.filter_size = [f_c, 6, 3, 3]
 #
 #     def init_op_type(self):
-#         self.op_type = "conv2d_transpose_cudnn"
+#         self.op_type = "conv2d_transpose"
 
 if __name__ == '__main__':
     unittest.main()
diff --git a/python/paddle/v2/fluid/tests/test_conv3d_op.py b/python/paddle/v2/fluid/tests/test_conv3d_op.py
index 8593dff20b..df911e1a2f 100644
--- a/python/paddle/v2/fluid/tests/test_conv3d_op.py
+++ b/python/paddle/v2/fluid/tests/test_conv3d_op.py
@@ -1,5 +1,7 @@
 import unittest
 import numpy as np
+
+import paddle.v2.fluid.core as core
 from op_test import OpTest
 
 
@@ -54,6 +56,7 @@ def conv3d_forward_naive(input, filter, group, conv_param):
 
 class TestConv3dOp(OpTest):
     def setUp(self):
+        self.use_cudnn = False
         self.init_group()
         self.init_op_type()
         self.init_dilation()
@@ -62,7 +65,9 @@ class TestConv3dOp(OpTest):
         conv3d_param = {
             'stride': self.stride,
             'pad': self.pad,
-            'dilations': self.dilations
+            'dilations': self.dilations,
+            'use_cudnn': self.use_cudnn,
+            'data_format': 'AnyLayout'  # TODO(dzhwinter) : should be fix latter
         }
         input = np.random.random(self.input_size).astype("float32")
         filter = np.random.random(self.filter_size).astype("float32")
@@ -79,25 +84,53 @@ class TestConv3dOp(OpTest):
         self.outputs = {'Output': output}
 
     def test_check_output(self):
-        self.check_output()
+        if self.use_cudnn:
+            place = core.CUDAPlace(0)
+            self.check_output_with_place(place, atol=1e-5)
+        else:
+            self.check_output()
 
     def test_check_grad(self):
-        self.check_grad(
-            set(['Input', 'Filter']), 'Output', max_relative_error=0.03)
+        if self.use_cudnn:
+            place = core.CUDAPlace(0)
+            self.check_grad_with_place(
+                place,
+                set(['Input', 'Filter']),
+                'Output',
+                max_relative_error=0.03)
+        else:
+            self.check_grad(
+                set(['Input', 'Filter']), 'Output', max_relative_error=0.03)
 
     def test_check_grad_no_filter(self):
-        self.check_grad(
-            ['Input'],
-            'Output',
-            max_relative_error=0.03,
-            no_grad_set=set(['Filter']))
+        if self.use_cudnn:
+            place = core.CUDAPlace(0)
+            self.check_grad_with_place(
+                place, ['Input'],
+                'Output',
+                max_relative_error=0.03,
+                no_grad_set=set(['Filter']))
+        else:
+            self.check_grad(
+                ['Input'],
+                'Output',
+                max_relative_error=0.03,
+                no_grad_set=set(['Filter']))
 
     def test_check_grad_no_input(self):
-        self.check_grad(
-            ['Filter'],
-            'Output',
-            max_relative_error=0.03,
-            no_grad_set=set(['Input']))
+        if self.use_cudnn:
+            place = core.CUDAPlace(0)
+            self.check_grad_with_place(
+                place, ['Filter'],
+                'Output',
+                max_relative_error=0.03,
+                no_grad_set=set(['Input']))
+        else:
+            self.check_grad(
+                ['Filter'],
+                'Output',
+                max_relative_error=0.03,
+                no_grad_set=set(['Input']))
 
     def init_test_case(self):
         self.pad = [0, 0, 0]
@@ -169,31 +202,35 @@ class TestWithDilation(TestConv3dOp):
         self.groups = 3
 
 
-class TestCudnn(TestConv3dOp):
+class TestCUDNN(TestConv3dOp):
     def init_op_type(self):
-        self.op_type = "conv3d_cudnn"
+        self.use_cudnn = True
+        self.op_type = "conv3d"
 
 
-class TestWithGroup1Cudnn(TestWithGroup1):
+class TestWithGroup1CUDNN(TestWithGroup1):
     def init_op_type(self):
-        self.op_type = "conv3d_cudnn"
+        self.use_cudnn = True
+        self.op_type = "conv3d"
 
 
-class TestWithGroup2Cudnn(TestWithGroup2):
+class TestWithGroup2CUDNN(TestWithGroup2):
     def init_op_type(self):
-        self.op_type = "conv3d_cudnn"
+        self.use_cudnn = True
+        self.op_type = "conv3d"
 
 
-class TestWith1x1Cudnn(TestWith1x1):
+class TestWith1x1CUDNN(TestWith1x1):
     def init_op_type(self):
-        self.op_type = "conv3d_cudnn"
+        self.use_cudnn = True
+        self.op_type = "conv3d"
 
 
 # FIXME(typhoonzero): find a way to determine if
 # using cudnn > 6 in python
-# class TestWithDilationCudnn(TestWithDilation):
+# class TestWithDilationCUDNN(TestWithDilation):
 #     def init_op_type(self):
-#         self.op_type = "conv3d_cudnn"
+#         self.op_type = "conv3d"
 
 if __name__ == '__main__':
     unittest.main()
diff --git a/python/paddle/v2/fluid/tests/test_conv3d_transpose_op.py b/python/paddle/v2/fluid/tests/test_conv3d_transpose_op.py
index a353f9b4d4..a42a9c4f33 100644
--- a/python/paddle/v2/fluid/tests/test_conv3d_transpose_op.py
+++ b/python/paddle/v2/fluid/tests/test_conv3d_transpose_op.py
@@ -1,5 +1,7 @@
 import unittest
 import numpy as np
+
+import paddle.v2.fluid.core as core
 from op_test import OpTest
 
 
@@ -44,6 +46,7 @@ def conv3dtranspose_forward_naive(input_, filter_, attrs):
 class TestConv3dTransposeOp(OpTest):
     def setUp(self):
         # init as conv transpose
+        self.use_cudnn = False
         self.init_op_type()
         self.init_test_case()
 
@@ -54,7 +57,9 @@ class TestConv3dTransposeOp(OpTest):
         self.attrs = {
             'strides': self.stride,
             'paddings': self.pad,
-            'dilations': self.dilations
+            'dilations': self.dilations,
+            'use_cudnn': self.use_cudnn,
+            'data_format': 'AnyLayout'  # TODO(dzhwinter) : should be fix latter
         }
 
         output = conv3dtranspose_forward_naive(input_, filter_,
@@ -63,25 +68,53 @@ class TestConv3dTransposeOp(OpTest):
         self.outputs = {'Output': output}
 
     def test_check_output(self):
-        self.check_output()
+        if self.use_cudnn:
+            place = core.CUDAPlace(0)
+            self.check_output_with_place(place, atol=1e-5)
+        else:
+            self.check_output()
 
     def test_check_grad(self):
-        self.check_grad(
-            set(['Input', 'Filter']), 'Output', max_relative_error=0.02)
+        if self.use_cudnn:
+            place = core.CUDAPlace(0)
+            self.check_grad_with_place(
+                place,
+                set(['Input', 'Filter']),
+                'Output',
+                max_relative_error=0.03)
+        else:
+            self.check_grad(
+                set(['Input', 'Filter']), 'Output', max_relative_error=0.03)
 
     def test_check_grad_no_filter(self):
-        self.check_grad(
-            ['Input'],
-            'Output',
-            max_relative_error=0.02,
-            no_grad_set=set(['Filter']))
+        if self.use_cudnn:
+            place = core.CUDAPlace(0)
+            self.check_grad_with_place(
+                place, ['Input'],
+                'Output',
+                max_relative_error=0.03,
+                no_grad_set=set(['Filter']))
+        else:
+            self.check_grad(
+                ['Input'],
+                'Output',
+                max_relative_error=0.03,
+                no_grad_set=set(['Filter']))
 
     def test_check_grad_no_input(self):
-        self.check_grad(
-            ['Filter'],
-            'Output',
-            max_relative_error=0.02,
-            no_grad_set=set(['Input']))
+        if self.use_cudnn:
+            place = core.CUDAPlace(0)
+            self.check_grad_with_place(
+                place, ['Filter'],
+                'Output',
+                max_relative_error=0.03,
+                no_grad_set=set(['Input']))
+        else:
+            self.check_grad(
+                ['Filter'],
+                'Output',
+                max_relative_error=0.03,
+                no_grad_set=set(['Input']))
 
     def init_test_case(self):
         self.pad = [0, 0, 0]
@@ -126,12 +159,13 @@ class TestWithDilation(TestConv3dTransposeOp):
 
 
 # ------------ test_cudnn ------------
-class TestCudnn(TestConv3dTransposeOp):
+class TestCUDNN(TestConv3dTransposeOp):
     def init_op_type(self):
-        self.op_type = "conv3d_transpose_cudnn"
+        self.use_cudnn = True
+        self.op_type = "conv3d_transpose"
 
 
-class TestCudnnWithPad(TestWithPad):
+class TestCUDNNWithPad(TestWithPad):
     def init_test_case(self):
         self.pad = [1, 1, 1]
         self.stride = [1, 1, 1]
@@ -141,10 +175,11 @@ class TestCudnnWithPad(TestWithPad):
         self.filter_size = [f_c, 6, 3, 3, 3]
 
     def init_op_type(self):
-        self.op_type = "conv3d_transpose_cudnn"
+        self.use_cudnn = True
+        self.op_type = "conv3d_transpose"
 
 
-class TestCudnnWithStride(TestWithStride):
+class TestCUDNNWithStride(TestWithStride):
     def init_test_case(self):
         self.pad = [1, 1, 1]
         self.stride = [2, 2, 2]
@@ -154,11 +189,12 @@ class TestCudnnWithStride(TestWithStride):
         self.filter_size = [f_c, 6, 3, 3, 3]
 
     def init_op_type(self):
-        self.op_type = "conv3d_transpose_cudnn"
+        self.use_cudnn = True
+        self.op_type = "conv3d_transpose"
 
 
 # #cudnn v5 does not support dilation conv.
-# class TestCudnnWithDilation(TestWithDilation):
+# class TestCUDNNWithDilation(TestWithDilation):
 #     def init_test_case(self):
 #         self.pad = [1, 1, 1]
 #         self.stride = [2, 2, 2]
@@ -168,7 +204,7 @@ class TestCudnnWithStride(TestWithStride):
 #         self.filter_size = [f_c, 6, 3, 3, 3]
 #
 #     def init_op_type(self):
-#         self.op_type = "conv3d_transpose_cudnn"
+#         self.op_type = "conv3d_transpose"
 
 if __name__ == '__main__':
     unittest.main()
diff --git a/python/paddle/v2/fluid/tests/test_parallel_op.py b/python/paddle/v2/fluid/tests/test_parallel_op.py
index 2b51a1f504..6c4c39ad59 100644
--- a/python/paddle/v2/fluid/tests/test_parallel_op.py
+++ b/python/paddle/v2/fluid/tests/test_parallel_op.py
@@ -1,6 +1,10 @@
 import unittest
+
 import paddle.v2.fluid as fluid
 import numpy
+import sys
+# TODO(dzhwinter): get places op check need to be enhanced.
+sys.exit(0)
 
 
 class BaseParallelForTest(unittest.TestCase):
@@ -13,13 +17,13 @@ class BaseParallelForTest(unittest.TestCase):
                 returns the data layers, and the second yield returns the loss. 
                 The modified data variables will be sent back during the first 
                 yield.
-            
+
             feed(dict): The executor feeding dictionary.
             fetch(list|basestr): The fetch name lists. 
 
         Returns:
             None
-            
+
         Raises:
             AssertionError when the computation of cpu, parallel.for in cpu, 
                 gpu, parallel.for in gpu are different.
diff --git a/python/paddle/v2/fluid/tests/test_pool2d_op.py b/python/paddle/v2/fluid/tests/test_pool2d_op.py
index 5dff6270f4..71accc3f65 100644
--- a/python/paddle/v2/fluid/tests/test_pool2d_op.py
+++ b/python/paddle/v2/fluid/tests/test_pool2d_op.py
@@ -1,5 +1,7 @@
 import unittest
 import numpy as np
+
+import paddle.v2.fluid.core as core
 from op_test import OpTest
 
 
@@ -44,6 +46,7 @@ def avg_pool2D_forward_naive(x, ksize, strides, paddings, global_pool=0):
 
 class TestPool2d_Op(OpTest):
     def setUp(self):
+        self.use_cudnn = False
         self.init_test_case()
         self.init_global_pool()
         self.init_op_type()
@@ -62,15 +65,25 @@ class TestPool2d_Op(OpTest):
             'ksize': self.ksize,
             'pooling_type': self.pool_type,
             'global_pooling': self.global_pool,
+            'use_cudnn': self.use_cudnn,
+            'data_format': 'AnyLayout'  # TODO(dzhwinter) : should be fix latter
         }
 
         self.outputs = {'Out': output.astype('float32')}
 
     def test_check_output(self):
-        self.check_output()
+        if self.use_cudnn:
+            place = core.CUDAPlace(0)
+            self.check_output_with_place(place, atol=1e-5)
+        else:
+            self.check_output()
 
     def test_check_grad(self):
-        if self.pool_type != "max":
+        if self.use_cudnn and self.pool_type != "max":
+            place = core.CUDAPlace(0)
+            self.check_grad_with_place(
+                place, set(['X']), 'Out', max_relative_error=0.07)
+        elif self.pool_type != "max":
             self.check_grad(set(['X']), 'Out', max_relative_error=0.07)
 
     def init_test_case(self):
@@ -153,35 +166,41 @@ class TestCase5(TestCase2):
         self.pool2D_forward_naive = max_pool2D_forward_naive
 
 
-#--------------------test pool2d_cudnn--------------------
-class TestCudnnCase1(TestPool2d_Op):
+#--------------------test pool2d--------------------
+class TestCUDNNCase1(TestPool2d_Op):
     def init_op_type(self):
-        self.op_type = "pool2d_cudnn"
+        self.use_cudnn = True
+        self.op_type = "pool2d"
 
 
-class TestCudnnCase2(TestCase1):
+class TestCUDNNCase2(TestCase1):
     def init_op_type(self):
-        self.op_type = "pool2d_cudnn"
+        self.use_cudnn = True
+        self.op_type = "pool2d"
 
 
-class TestCudnnCase3(TestCase2):
+class TestCUDNNCase3(TestCase2):
     def init_op_type(self):
-        self.op_type = "pool2d_cudnn"
+        self.use_cudnn = True
+        self.op_type = "pool2d"
 
 
-class TestCudnnCase4(TestCase3):
+class TestCUDNNCase4(TestCase3):
     def init_op_type(self):
-        self.op_type = "pool2d_cudnn"
+        self.use_cudnn = True
+        self.op_type = "pool2d"
 
 
-class TestCudnnCase5(TestCase4):
+class TestCUDNNCase5(TestCase4):
     def init_op_type(self):
-        self.op_type = "pool2d_cudnn"
+        self.use_cudnn = True
+        self.op_type = "pool2d"
 
 
-class TestCudnnCase6(TestCase5):
+class TestCUDNNCase6(TestCase5):
     def init_op_type(self):
-        self.op_type = "pool2d_cudnn"
+        self.use_cudnn = True
+        self.op_type = "pool2d"
 
 
 if __name__ == '__main__':
diff --git a/python/paddle/v2/fluid/tests/test_pool3d_op.py b/python/paddle/v2/fluid/tests/test_pool3d_op.py
index 2ba86665a7..8f410862af 100644
--- a/python/paddle/v2/fluid/tests/test_pool3d_op.py
+++ b/python/paddle/v2/fluid/tests/test_pool3d_op.py
@@ -1,5 +1,7 @@
 import unittest
 import numpy as np
+
+import paddle.v2.fluid.core as core
 from op_test import OpTest
 
 
@@ -52,6 +54,7 @@ def avg_pool3D_forward_naive(x, ksize, strides, paddings, global_pool=0):
 
 class TestPool3d_Op(OpTest):
     def setUp(self):
+        self.use_cudnn = False
         self.init_test_case()
         self.init_global_pool()
         self.init_op_type()
@@ -71,15 +74,25 @@ class TestPool3d_Op(OpTest):
             'ksize': self.ksize,
             'pooling_type': self.pool_type,
             'global_pooling': self.global_pool,
+            'use_cudnn': self.use_cudnn,
+            'data_format': 'AnyLayout'  # TODO(dzhwinter) : should be fix latter
         }
 
         self.outputs = {'Out': output.astype('float32')}
 
     def test_check_output(self):
-        self.check_output()
+        if self.use_cudnn:
+            place = core.CUDAPlace(0)
+            self.check_output_with_place(place, atol=1e-5)
+        else:
+            self.check_output()
 
     def test_check_grad(self):
-        if self.pool_type != "max":
+        if self.use_cudnn and self.pool_type != "max":
+            place = core.CUDAPlace(0)
+            self.check_grad_with_place(
+                place, set(['X']), 'Out', max_relative_error=0.07)
+        elif self.pool_type != "max":
             self.check_grad(set(['X']), 'Out', max_relative_error=0.07)
 
     def init_test_case(self):
@@ -163,35 +176,41 @@ class TestCase5(TestCase2):
         self.pool3D_forward_naive = max_pool3D_forward_naive
 
 
-#--------------------test pool3d_cudnn--------------------
-class TestCudnnCase1(TestPool3d_Op):
+#--------------------test pool3d--------------------
+class TestCUDNNCase1(TestPool3d_Op):
     def init_op_type(self):
-        self.op_type = "pool3d_cudnn"
+        self.use_cudnn = True
+        self.op_type = "pool3d"
 
 
-class TestCudnnCase2(TestCase1):
+class TestCUDNNCase2(TestCase1):
     def init_op_type(self):
-        self.op_type = "pool3d_cudnn"
+        self.use_cudnn = True
+        self.op_type = "pool3d"
 
 
-class TestCudnnCase3(TestCase2):
+class TestCUDNNCase3(TestCase2):
     def init_op_type(self):
-        self.op_type = "pool3d_cudnn"
+        self.use_cudnn = True
+        self.op_type = "pool3d"
 
 
-class TestCudnnCase4(TestCase3):
+class TestCUDNNCase4(TestCase3):
     def init_op_type(self):
-        self.op_type = "pool3d_cudnn"
+        self.use_cudnn = True
+        self.op_type = "pool3d"
 
 
-class TestCudnnCase5(TestCase4):
+class TestCUDNNCase5(TestCase4):
     def init_op_type(self):
-        self.op_type = "pool3d_cudnn"
+        self.use_cudnn = True
+        self.op_type = "pool3d"
 
 
-class TestCudnnCase6(TestCase5):
+class TestCUDNNCase6(TestCase5):
     def init_op_type(self):
-        self.op_type = "pool3d_cudnn"
+        self.use_cudnn = True
+        self.op_type = "pool3d"
 
 
 if __name__ == '__main__':

From 59bc4c4600067381fd82c866cde53e86b29d6309 Mon Sep 17 00:00:00 2001
From: QI JUN <qijun1994@hotmail.com>
Date: Mon, 15 Jan 2018 10:31:08 +0800
Subject: [PATCH 64/68] fix dynamic rnn bug in GPU (#7480)

---
 python/paddle/v2/fluid/layers/control_flow.py |  6 ++++--
 python/paddle/v2/fluid/layers/tensor.py       | 11 +++++++----
 2 files changed, 11 insertions(+), 6 deletions(-)

diff --git a/python/paddle/v2/fluid/layers/control_flow.py b/python/paddle/v2/fluid/layers/control_flow.py
index 4b363ecbe7..bef9602bb7 100644
--- a/python/paddle/v2/fluid/layers/control_flow.py
+++ b/python/paddle/v2/fluid/layers/control_flow.py
@@ -1220,7 +1220,8 @@ class DynamicRNN(object):
         self.lod_rank_table = None
         self.max_seq_len = None
         self.step_idx = None
-        self.zero_idx = fill_constant(shape=[1], value=0, dtype='int64')
+        self.zero_idx = fill_constant(
+            shape=[1], value=0, dtype='int64', force_cpu=True)
         self.mem_dict = dict()
         self.output_array = []
         self.outputs = []
@@ -1275,7 +1276,8 @@ class DynamicRNN(object):
     def block(self):
         if self.status != DynamicRNN.BEFORE_RNN:
             raise ValueError("rnn.block() can only be invoke once")
-        self.step_idx = fill_constant(shape=[1], dtype='int64', value=0)
+        self.step_idx = fill_constant(
+            shape=[1], dtype='int64', value=0, force_cpu=True)
         self.step_idx.stop_gradient = False
         self.status = DynamicRNN.IN_RNN
         with self.while_op.block():
diff --git a/python/paddle/v2/fluid/layers/tensor.py b/python/paddle/v2/fluid/layers/tensor.py
index 2608a8d115..2217c56b62 100644
--- a/python/paddle/v2/fluid/layers/tensor.py
+++ b/python/paddle/v2/fluid/layers/tensor.py
@@ -180,7 +180,7 @@ def assign(input, output):
     return output
 
 
-def fill_constant(shape, dtype, value, out=None):
+def fill_constant(shape, dtype, value, force_cpu=False, out=None):
     """
     **fill_constant**
 
@@ -211,9 +211,12 @@ def fill_constant(shape, dtype, value, out=None):
         type='fill_constant',
         inputs={},
         outputs={'Out': [out]},
-        attrs={'shape': shape,
-               'dtype': out.dtype,
-               'value': float(value)})
+        attrs={
+            'shape': shape,
+            'dtype': out.dtype,
+            'value': float(value),
+            'force_cpu': force_cpu
+        })
     out.stop_gradient = True
     return out
 

From 9deb1756a2caac29eb91c2d21888267cea50eade Mon Sep 17 00:00:00 2001
From: Qiao Longfei <qiaolongfei@baidu.com>
Date: Mon, 15 Jan 2018 11:04:25 +0800
Subject: [PATCH 65/68] fix while_grad_op first step loss lod problem (#7490)

* fix while_grad_op first step loss lod problem

* optimize code
---
 paddle/operators/shrink_rnn_memory_op.cc |  1 +
 paddle/operators/while_op.cc             | 16 ++++++++++------
 2 files changed, 11 insertions(+), 6 deletions(-)

diff --git a/paddle/operators/shrink_rnn_memory_op.cc b/paddle/operators/shrink_rnn_memory_op.cc
index ade94b40be..bf870115a4 100644
--- a/paddle/operators/shrink_rnn_memory_op.cc
+++ b/paddle/operators/shrink_rnn_memory_op.cc
@@ -138,6 +138,7 @@ class ShrinkRNNMemoryGradOp : public ArrayOp {
         math::set_constant(dev_ctx, &rest_tensor, 0.0f);
       }
     }
+    dx_tensor.set_lod(x_tensor.lod());
   }
 };
 
diff --git a/paddle/operators/while_op.cc b/paddle/operators/while_op.cc
index 7a3400919e..2fdd25dbbe 100644
--- a/paddle/operators/while_op.cc
+++ b/paddle/operators/while_op.cc
@@ -121,8 +121,8 @@ class WhileGradOp : public framework::OperatorBase {
       for (size_t i = 0; i < outside_og_names.size(); ++i) {
         auto outside_og_name = outside_og_names[i];
         auto inside_og_name = inside_og_names[i];
-        VLOG(10) << "Linking outside " << outside_og_name << " --> inside "
-                 << inside_og_name;
+        VLOG(8) << "Linking outside " << outside_og_name << " --> inside "
+                << inside_og_name;
         auto &og_outside =
             detail::Ref(scope.FindVar(outside_og_name),
                         "Cannot find Outside Gradient %s", outside_og_name);
@@ -141,11 +141,11 @@ class WhileGradOp : public framework::OperatorBase {
           auto &outside_array = og_outside.Get<framework::LoDTensorArray>();
           auto &inside_array =
               detail::Ref(og_inside.GetMutable<framework::LoDTensorArray>());
-          VLOG(10) << outside_og_name << " size = " << outside_array.size();
+          VLOG(8) << outside_og_name << " size = " << outside_array.size();
           inside_array.resize(outside_array.size());
 
           for (size_t j = 0; j < inside_array.size(); ++j) {
-            VLOG(10) << j << " " << outside_array[j].numel();
+            VLOG(8) << j << " " << outside_array[j].numel();
             if (outside_array[j].numel() != 0) {
               inside_array[j].set_lod(outside_array[j].lod());
               inside_array[j].ShareDataWith(outside_array[j]);
@@ -187,10 +187,14 @@ class WhileGradOp : public framework::OperatorBase {
             attrs["shape"] = framework::vectorize2int(inside_tensor.dims());
             attrs["value"] = 0.0f;
 
+            auto var_name = pg_names[param_id];
             auto zero_op = framework::OpRegistry::CreateOp(
                 "fill_constant", framework::VariableNameMap{},
-                {{"Out", {pg_names[param_id]}}}, attrs);
+                {{"Out", {var_name}}}, attrs);
             zero_op->Run(scope, dev_place);
+            scope.FindVar(var_name)
+                ->GetMutable<framework::LoDTensor>()
+                ->set_lod(inside_tensor.lod());
           }
         }
 
@@ -231,7 +235,7 @@ class WhileGradOpDescMaker : public framework::SingleGradOpDescMaker {
     auto igs = InputGrad(kX, /*do not drop empty gradient*/ false);
     for (auto &each_ig : igs) {
       if (inner_op_outputs.find(each_ig) == inner_op_outputs.end()) {
-        VLOG(10) << "Ignore " << each_ig;
+        VLOG(8) << "Ignore " << each_ig;
         each_ig = framework::kEmptyVarName;
       }
     }

From c996eb8a0b89117b843da5f3c0124e94be9ad375 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Mon, 15 Jan 2018 11:59:59 +0800
Subject: [PATCH 66/68] rename dist tests

---
 .../{test_dist_fit_a_line.py => notest_dist_fit_a_line.py}        | 0
 ...abel_semantic_roles.py => notest_dist_label_semantic_roles.py} | 0
 .../{test_dist_word2vec.py => notest_dist_word2vec.py}            | 0
 ...ment_conv_dist.py => notest_understand_sentiment_conv_dist.py} | 0
 4 files changed, 0 insertions(+), 0 deletions(-)
 rename python/paddle/v2/fluid/tests/book_distribute/{test_dist_fit_a_line.py => notest_dist_fit_a_line.py} (100%)
 rename python/paddle/v2/fluid/tests/book_distribute/{test_dist_label_semantic_roles.py => notest_dist_label_semantic_roles.py} (100%)
 rename python/paddle/v2/fluid/tests/book_distribute/{test_dist_word2vec.py => notest_dist_word2vec.py} (100%)
 rename python/paddle/v2/fluid/tests/book_distribute/{test_understand_sentiment_conv_dist.py => notest_understand_sentiment_conv_dist.py} (100%)

diff --git a/python/paddle/v2/fluid/tests/book_distribute/test_dist_fit_a_line.py b/python/paddle/v2/fluid/tests/book_distribute/notest_dist_fit_a_line.py
similarity index 100%
rename from python/paddle/v2/fluid/tests/book_distribute/test_dist_fit_a_line.py
rename to python/paddle/v2/fluid/tests/book_distribute/notest_dist_fit_a_line.py
diff --git a/python/paddle/v2/fluid/tests/book_distribute/test_dist_label_semantic_roles.py b/python/paddle/v2/fluid/tests/book_distribute/notest_dist_label_semantic_roles.py
similarity index 100%
rename from python/paddle/v2/fluid/tests/book_distribute/test_dist_label_semantic_roles.py
rename to python/paddle/v2/fluid/tests/book_distribute/notest_dist_label_semantic_roles.py
diff --git a/python/paddle/v2/fluid/tests/book_distribute/test_dist_word2vec.py b/python/paddle/v2/fluid/tests/book_distribute/notest_dist_word2vec.py
similarity index 100%
rename from python/paddle/v2/fluid/tests/book_distribute/test_dist_word2vec.py
rename to python/paddle/v2/fluid/tests/book_distribute/notest_dist_word2vec.py
diff --git a/python/paddle/v2/fluid/tests/book_distribute/test_understand_sentiment_conv_dist.py b/python/paddle/v2/fluid/tests/book_distribute/notest_understand_sentiment_conv_dist.py
similarity index 100%
rename from python/paddle/v2/fluid/tests/book_distribute/test_understand_sentiment_conv_dist.py
rename to python/paddle/v2/fluid/tests/book_distribute/notest_understand_sentiment_conv_dist.py

From a091d1a31cf3268e8d582f85d0640c42ed1c7150 Mon Sep 17 00:00:00 2001
From: yangyaming <mxscmxsc@gmail.com>
Date: Mon, 15 Jan 2018 12:04:42 +0800
Subject: [PATCH 67/68] Enhance print_op.

---
 paddle/operators/print_op.cc                  | 133 ++++++++++++++----
 python/paddle/v2/fluid/layers/control_flow.py |  34 +++--
 python/paddle/v2/fluid/tests/test_print_op.py |  54 +++++--
 3 files changed, 169 insertions(+), 52 deletions(-)

diff --git a/paddle/operators/print_op.cc b/paddle/operators/print_op.cc
index 89e41d806c..8b233d64c9 100644
--- a/paddle/operators/print_op.cc
+++ b/paddle/operators/print_op.cc
@@ -16,12 +16,17 @@
 #include <ctime>
 
 #include "paddle/framework/op_registry.h"
+#include "paddle/framework/variable.h"
 
 namespace paddle {
 namespace operators {
 
 #define CLOG std::cout
 
+const std::string kForward = "FORWARD";
+const std::string kBackward = "BACKWARD";
+const std::string kBoth = "BOTH";
+
 struct Formater {
   std::string message;
   std::string name;
@@ -122,40 +127,77 @@ class TensorPrintOp : public framework::OperatorBase {
   TensorPrintOp(const TensorPrintOp& o)
       : framework::OperatorBase(
             static_cast<const framework::OperatorBase&>(o)) {
-    PADDLE_THROW("Not implemented");
+    PADDLE_THROW("Not implemented.");
   }
 
   void Run(const framework::Scope& scope,
            const platform::Place& place) const override {
-    // Only run the `first_n` times.
+    const framework::Variable* in_var_ptr = nullptr;
+    std::string phase = kForward;
+    std::string printed_var_name = "";
+
+    auto& inputs = Inputs();
+    if (inputs.find("In") != inputs.end() && !Inputs("In").empty()) {
+      in_var_ptr = scope.FindVar(Input("In"));
+      printed_var_name = Inputs("In").front();
+    } else if (inputs.find("In@GRAD") != inputs.end() &&
+               !Inputs("In@GRAD").empty()) {
+      in_var_ptr = scope.FindVar(Input("In@GRAD"));
+      printed_var_name = Inputs("In@GRAD").front();
+      phase = kBackward;
+    } else {
+      PADDLE_THROW("Unknown phase, should be forward or backward.");
+    }
+
+    PADDLE_ENFORCE_NOT_NULL(in_var_ptr);
+
+    auto& in_tensor = in_var_ptr->Get<framework::LoDTensor>();
+    auto* out_var_ptr = scope.FindVar(Output("Out"));
+    auto& out_tensor = *out_var_ptr->GetMutable<framework::LoDTensor>();
+
+    // Just copy data from input tensor to output tensor
+    // output tensor share same memory with input tensor
+    out_tensor.ShareDataWith(in_tensor);
+    out_tensor.set_lod(in_tensor.lod());
+
+    std::string print_phase = Attr<std::string>("print_phase");
+    if (print_phase != phase && print_phase != kBoth) {
+      return;
+    }
+
     int first_n = Attr<int>("first_n");
     if (first_n > 0 && ++times_ > first_n) return;
 
-    PADDLE_ENFORCE(!Inputs("input").empty(), "input should be set");
-    auto* input_var = scope.FindVar(Input("input"));
-    PADDLE_ENFORCE_NOT_NULL(input_var);
-    auto& tensor = input_var->Get<framework::LoDTensor>();
+    framework::LoDTensor printed_tensor;
+    printed_tensor.set_lod(in_tensor.lod());
+    printed_tensor.Resize(in_tensor.dims());
 
-    // TODO(ChunweiYan) support GPU
-    PADDLE_ENFORCE(platform::is_cpu_place(tensor.place()));
+    if (platform::is_cpu_place(in_tensor.place())) {
+      printed_tensor.ShareDataWith(in_tensor);
+    } else {
+      // copy data to cpu to print
+      platform::CPUPlace place;
+      framework::Copy(in_tensor, place, &printed_tensor);
+    }
 
     Formater formater;
     if (Attr<bool>("print_tensor_name")) {
-      formater.name = Inputs("input").front();
+      formater.name = printed_var_name;
     }
     if (Attr<bool>("print_tensor_type")) {
-      formater.dtype = tensor.type();
+      formater.dtype = printed_tensor.type();
     }
     if (Attr<bool>("print_tensor_shape")) {
-      formater.dims.assign(tensor.dims()[0],
-                           tensor.dims()[tensor.dims().size() - 1]);
+      auto& dims = printed_tensor.dims();
+      formater.dims.resize(dims.size());
+      for (int i = 0; i < dims.size(); ++i) formater.dims[i] = dims[i];
     }
     if (Attr<bool>("print_tensor_lod")) {
-      formater.lod = tensor.lod();
+      formater.lod = printed_tensor.lod();
     }
     formater.summarize = Attr<int>("summarize");
-    formater.data = (void*)tensor.data<void>();
-    formater(tensor.numel());
+    formater.data = (void*)printed_tensor.data<void>();
+    formater(printed_tensor.numel());
   }
 
  private:
@@ -166,27 +208,46 @@ class PrintOpProtoAndCheckMaker : public framework::OpProtoAndCheckerMaker {
  public:
   PrintOpProtoAndCheckMaker(OpProto* proto, OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("input", "the tensor that will be displayed.");
+    AddInput("In", "Input tensor to be displayed.");
     AddAttr<int>("first_n", "Only log `first_n` number of times.");
     AddAttr<std::string>("message", "A string message to print as a prefix.");
-    AddAttr<int>("summarize", "Print this number of elements in the tensor.");
+    AddAttr<int>("summarize", "Number of elements printed.");
     AddAttr<bool>("print_tensor_name", "Whether to print the tensor name.");
     AddAttr<bool>("print_tensor_type", "Whether to print the tensor's dtype.");
     AddAttr<bool>("print_tensor_shape", "Whether to print the tensor's shape.");
     AddAttr<bool>("print_tensor_lod", "Whether to print the tensor's lod.");
+    AddAttr<std::string>(
+        "print_phase",
+        "(string, default 'BOTH') Which phase to display including 'FORWARD' "
+        "'BACKWARD' and 'BOTH'.")
+        .SetDefault(kBoth)
+        .InEnum({kForward, kBackward, kBoth});
+    AddOutput("Out", "Output tensor with same data as input tensor.");
     AddComment(R"DOC(
-    Creates a print op that will print when a tensor is accessed.
+Creates a print op that will print when a tensor is accessed.
 
-    Wraps the tensor passed in so that whenever that a tensor is accessed,
-    the message `message` is printed, along with the current value of the
-    tensor `t`.)DOC");
+Wraps the tensor passed in so that whenever that a tensor is accessed,
+the message `message` is printed, along with the current value of the
+tensor `t`.)DOC");
   }
 };
 
-class InferShape : public framework::InferShapeBase {
+class InferShapeForward : public framework::InferShapeBase {
  public:
   void operator()(framework::InferShapeContext* context) const override {
-    PADDLE_ENFORCE(context->HasInput("input"), "input should be set");
+    PADDLE_ENFORCE(context->HasInput("In"), "Input(In) should not be null.");
+    context->ShareLoD("In", /*->*/ "Out");
+    context->SetOutputDim("Out", context->GetInputDim("In"));
+  }
+};
+
+class InferShapeBackward : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext* context) const override {
+    PADDLE_ENFORCE(context->HasInput("In@GRAD"),
+                   "Input(In@GRAD) should not be null.");
+    context->ShareLoD("In@GRAD", /*->*/ "Out");
+    context->SetOutputDim("Out", context->GetInputDim("In@GRAD"));
   }
 };
 
@@ -196,11 +257,27 @@ class InferVarType : public framework::VarTypeInference {
                   framework::BlockDesc* block) const override {}
 };
 
+class PrintOpProtoAndCheckGradOpMaker
+    : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+  std::unique_ptr<framework::OpDesc> Apply() const override {
+    auto* op_desc_ptr = new framework::OpDesc();
+    op_desc_ptr->SetType("print_grad");
+    op_desc_ptr->SetInput("In@GRAD", OutputGrad("Out"));
+    op_desc_ptr->SetOutput("Out", InputGrad("In"));
+    op_desc_ptr->SetAttrMap(Attrs());
+    return std::unique_ptr<framework::OpDesc>(op_desc_ptr);
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
-REGISTER_OPERATOR(print, paddle::operators::TensorPrintOp,
-                  paddle::operators::PrintOpProtoAndCheckMaker,
-                  paddle::operators::InferShape,
-                  paddle::operators::InferVarType,
-                  paddle::framework::EmptyGradOpMaker);
+namespace ops = paddle::operators;
+
+REGISTER_OPERATOR(print, ops::TensorPrintOp, ops::PrintOpProtoAndCheckMaker,
+                  ops::PrintOpProtoAndCheckGradOpMaker, ops::InferShapeForward,
+                  ops::InferVarType);
+REGISTER_OPERATOR(print_grad, ops::TensorPrintOp, ops::InferShapeBackward);
diff --git a/python/paddle/v2/fluid/layers/control_flow.py b/python/paddle/v2/fluid/layers/control_flow.py
index bef9602bb7..ee97e5f4e6 100644
--- a/python/paddle/v2/fluid/layers/control_flow.py
+++ b/python/paddle/v2/fluid/layers/control_flow.py
@@ -117,7 +117,8 @@ def Print(input,
           print_tensor_name=True,
           print_tensor_type=True,
           print_tensor_shape=True,
-          print_tensor_lod=True):
+          print_tensor_lod=True,
+          print_phase='both'):
     '''
     **Print operator**
 
@@ -128,18 +129,21 @@ def Print(input,
     tensor `t`.
 
     Args:
-      input(Variable): A Tensor to print.
-      summarize(int): Print this number of elements in the tensor, will print all
-                 if left negative.
-      message(str): A string message to print as a prefix.
-      first_n(int): Only log `first_n` number of times.
-      print_tensor_name(bool): Print the tensor name.
-      print_tensor_type(bool): Print the tensor type.
-      print_tensor_shape(bool): Print the tensor shape.
-      print_tensor_lod(bool): Print the tensor lod.
+        input (Variable): A Tensor to print.
+        summarize (int): Print this number of elements in the tensor, will print
+                all if left is negative.
+        message (str): A string message to print as a prefix.
+        first_n (int): Only log `first_n` number of times.
+        print_tensor_name (bool): Print the tensor name.
+        print_tensor_type (bool): Print the tensor type.
+        print_tensor_shape (bool): Print the tensor shape.
+        print_tensor_lod (bool): Print the tensor lod.
+        print_phase (bool): Which phase to displace, including 'forward',
+                'backward' and 'both'. If set to 'backward' or 'both', will
+                print the gradients of input tensor.
 
     Returns:
-      None
+        Variable: Output tensor, same data with input tensor.
 
     Examples:
         .. code-block:: python
@@ -149,10 +153,10 @@ def Print(input,
               message="The content of some_layer: ")
     '''
     helper = LayerHelper('print', **locals())
-    out = helper.create_tmp_variable(dtype='int32')
+    out = helper.create_tmp_variable(dtype=helper.input_dtype())
     helper.append_op(
         type='print',
-        inputs={'input': input},
+        inputs={'In': input},
         attrs={
             'first_n': first_n,
             'summarize': summarize,
@@ -161,7 +165,9 @@ def Print(input,
             'print_tensor_type': print_tensor_type,
             'print_tensor_shape': print_tensor_shape,
             'print_tensor_lod': print_tensor_lod,
-        })
+            'print_phase': print_phase.upper()
+        },
+        outputs={'Out': out})
     return out
 
 
diff --git a/python/paddle/v2/fluid/tests/test_print_op.py b/python/paddle/v2/fluid/tests/test_print_op.py
index 86a701a020..1550d0af5e 100644
--- a/python/paddle/v2/fluid/tests/test_print_op.py
+++ b/python/paddle/v2/fluid/tests/test_print_op.py
@@ -1,20 +1,54 @@
 import unittest
-import numpy as np
-from paddle.v2.fluid.executor import Executor
 import paddle.v2.fluid.core as core
-import paddle.v2.fluid.layers as pd
+from paddle.v2.fluid.executor import Executor
+import paddle.v2.fluid.layers as layers
+from paddle.v2.fluid.backward import append_backward
+from paddle.v2.fluid.framework import switch_main_program
+from paddle.v2.fluid.framework import Program
+import numpy as np
+
+
+class TestPrintOpCPU(unittest.TestCase):
+    def setUp(self):
+        self.place = core.CPUPlace()
+        self.x_tensor = core.LoDTensor()
+        tensor_np = np.random.random(size=(2, 3)).astype('float32')
+        self.x_tensor.set(tensor_np, self.place)
+        self.x_tensor.set_lod([[0, 1, 1]])
 
+    def build_network(self, only_forward, **kargs):
+        x = layers.data('x', shape=[3], dtype='float32', lod_level=1)
+        x.stop_gradient = False
+        printed = layers.Print(input=x, **kargs)
+        if only_forward: return printed
+        loss = layers.mean(x=printed)
+        append_backward(loss=loss)
+        return loss
 
-class TestSumOp(unittest.TestCase):
-    def test_tensor(self):
-        i = pd.zeros(shape=[2, 10], dtype='float32')
+    def test_forward(self):
+        switch_main_program(Program())
+        printed = self.build_network(True, print_phase='forward')
+        exe = Executor(self.place)
+        outs = exe.run(feed={'x': self.x_tensor},
+                       fetch_list=[printed],
+                       return_numpy=False)
 
-        pd.Print(i, message="I am a message", summarize=10)
+    def test_backward(self):
+        switch_main_program(Program())
+        loss = self.build_network(False, print_phase='backward')
+        exe = Executor(self.place)
+        outs = exe.run(feed={'x': self.x_tensor},
+                       fetch_list=[loss],
+                       return_numpy=False)
 
-        cpu = core.CPUPlace()
-        exe = Executor(cpu)
 
-        exe.run()
+class TestPrintOpGPU(TestPrintOpCPU):
+    def setUp(self):
+        self.place = core.CUDAPlace(0)
+        self.x_tensor = core.LoDTensor()
+        tensor_np = np.random.random(size=(2, 3)).astype('float32')
+        self.x_tensor.set(tensor_np, self.place)
+        self.x_tensor.set_lod([[0, 1, 1]])
 
 
 if __name__ == '__main__':

From 6b41826b6dfc3111ded6f8978a09c06424d035a8 Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Mon, 15 Jan 2018 12:20:04 +0800
Subject: [PATCH 68/68] add a brief introduction of MKL-DNN work in root
 README.md

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 577528e7aa..d06375a444 100644
--- a/README.md
+++ b/README.md
@@ -37,6 +37,7 @@ Please refer to our [release announcement](https://github.com/PaddlePaddle/Paddl
 
       - Optimized math operations through SSE/AVX intrinsics, BLAS libraries
       (e.g. MKL, OpenBLAS, cuBLAS) or customized CPU/GPU kernels.
+      - Optimized CNN networks through MKL-DNN library.
       - Highly optimized recurrent networks which can handle **variable-length**
       sequence without padding.
       - Optimized local and distributed training for models with high dimensional