Merge pull request #11866 from panyx0718/move_func

Move some v2 codes to a legacy directory.

Author: panyx0718

CMakeLists.txt

@@ -196,7 +196,7 @@ include(inference_lib)      # add paddle fluid inference libraries
 
 
 include_directories("${PADDLE_SOURCE_DIR}")
-include_directories("${PADDLE_SOURCE_DIR}/paddle/cuda/include")
+include_directories("${PADDLE_SOURCE_DIR}/paddle/legacy/cuda/include")
 include_directories("${CMAKE_CURRENT_BINARY_DIR}/proto")
 include_directories("${CMAKE_CURRENT_BINARY_DIR}/go/pserver/client/c")
 
@@ -240,7 +240,7 @@ add_subdirectory(proto)
 if(NOT MOBILE_INFERENCE AND NOT WITH_FLUID_ONLY)
     # "add_subdirectory(go)" should be placed after the following loine,
     # because it depends on paddle/optimizer.
-    add_subdirectory(paddle/optimizer)
+    add_subdirectory(paddle/legacy/optimizer)
 endif()
 
 # "add_subdirectory(paddle)" and "add_subdirectory(python)" should be

CONTRIBUTING.md

@@ -159,4 +159,4 @@ This will enable VLOG messages generated by `buddy_allocator.{h,cc}` and in the
 - verbose level 1: [framework](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/framework)
 - verbose level 3: [operators](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators)
 - verbose level 5: [memory](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/memory), [platform](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/platform)
-- verbose level 7: [math](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/math)
+- verbose level 7: [math](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/legacy/math)

doc/v2/design/interface/00.why_plain_c.md

@@ -65,7 +65,7 @@ paddle_error paddle_matrix_get_shape(paddle_matrix matrix,
 而在CPP里面实现这个C的接口，文件 `paddle_matrix.cpp`
 
 ```cpp
-#include "paddle/math/matrix.h"
+#include "paddle/legacy/math/matrix.h"
 extern "C"
 paddle_error paddle_matrix_shape(paddle_matrix matrix,
                                  uint64_t *width,

doc/v2/dev/new_layer_cn.rst

@@ -58,7 +58,7 @@ PaddlePaddle的base layer类可以自动计算上面的导数。
 实现C++类
 ===================
 
-一个网络层的C++类需要实现初始化，前向和后向。全连接层的实现位于:code:`paddle/gserver/layers/FullyConnectedLayer.h`及:code:`paddle/gserver/layers/FullyConnectedLayer.cpp`。这里我们展示一份简化过的代码。
+一个网络层的C++类需要实现初始化，前向和后向。全连接层的实现位于:code:`paddle/legacy/gserver/layers/FullyConnectedLayer.h`及:code:`paddle/legacy/gserver/layers/FullyConnectedLayer.cpp`。这里我们展示一份简化过的代码。
 
 这个类需要继承 :code:`paddle::Layer` 这个基类，并且需要重写基类中的以下几个虚函数：
 
@@ -153,7 +153,7 @@ PaddlePaddle的base layer类可以自动计算上面的导数。
 
 - 每个层在其 :code:`forward` 函数的开头必须调用 :code:`Layer::forward(passType);` 。
 - 之后使用 :code:`reserveOutput(batchSize, size);` 为输出分配内存。由于我们支持训练数据有不同的批次大小，所以这一步是必要的。 :code:`reserveOutput`  会相应地改变输出的尺寸。为了保证效率，如果需要扩大矩阵，我们会重新分配内存；如果需要缩减矩阵，我们会继续使用现有的内存块。
-- 之后使用矩阵运算函数来计算 :math:`\sum_i W_i x + b`。:code:`getInput(i).value` 返回第i个输入矩阵。每个输入都是一个 :math:`batchSize \times dim` 的矩阵，每行表示一个批次中的单个输入。对于我们支持的全部矩阵操作，请参考 :code:`paddle/math/Matrix.h`和:code:`paddle/math/BaseMatrix.h` 。
+- 之后使用矩阵运算函数来计算 :math:`\sum_i W_i x + b`。:code:`getInput(i).value` 返回第i个输入矩阵。每个输入都是一个 :math:`batchSize \times dim` 的矩阵，每行表示一个批次中的单个输入。对于我们支持的全部矩阵操作，请参考 :code:`paddle/legacy/math/Matrix.h`和:code:`paddle/legacy/math/BaseMatrix.h` 。
 - 最终，使用 :code:`forwardActivation();` 进行激活操作。这会自动进行网络配置中声明的激活操作。
 
 
@@ -262,15 +262,15 @@ PaddlePaddle的base layer类可以自动计算上面的导数。
     REGISTER_LAYER(fc, FullyConnectedLayer);
     }
 
-若 :code:`cpp` 被放在 :code:`paddle/gserver/layers` 目录下，其会自动被加入编译列表。
+若 :code:`cpp` 被放在 :code:`paddle/legacy/gserver/layers` 目录下，其会自动被加入编译列表。
 
 
 写梯度检查单元测试
 ===============================
 
 写梯度检查单元测试是一个验证新实现的层是否正确的相对简单的办法。梯度检查单元测试通过有限差分法来验证一个层的梯度。首先对输入做一个小的扰动 :math:`\Delta x` ，然后观察到输出的变化为 :math:`\Delta y` ，那么，梯度就可以通过这个方程计算得到 :math:`\frac{\Delta y}{\Delta x }` 。之后，再用这个梯度去和 :code:`backward` 函数得到的梯度去对比，以保证梯度计算的正确性。需要注意的是梯度检查仅仅验证了梯度的计算，并不保证 :code:`forward` 和 :code:`backward` 函数的实现是正确的。你需要一些更复杂的单元测试来保证你实现的网络层是正确的。
 
-所有网络层的梯度检查单测都位于 :code:`paddle/gserver/tests/test_LayerGrad.cpp` 。我们建议你在写新网络层时把测试代码放入新的文件中。下面列出了全连接层的梯度检查单元测试。它包含以下几步：
+所有网络层的梯度检查单测都位于 :code:`paddle/legacy/gserver/tests/test_LayerGrad.cpp` 。我们建议你在写新网络层时把测试代码放入新的文件中。下面列出了全连接层的梯度检查单元测试。它包含以下几步：
 
 + 生成网络层配置。网络层配置包含以下几项：
    - 偏置参数的大小。（例子中是4096）
@@ -322,7 +322,7 @@ PaddlePaddle的base layer类可以自动计算上面的导数。
       }
     }
 
-如果你要为了测试而增加新的文件，例如 :code:`paddle/gserver/tests/testFCGrad.cpp` ，你需要把该文件加入 :code:`paddle/gserver/tests/CMakeLists.txt` 中。下面给出了一个例子。当你执行命令 :code:`make tests` 时，所有的单测都会被执行一次。注意，有些层可能需要高精度来保证梯度检查单测正确执行。你需要在配置cmake时将 :code:`WITH_DOUBLE` 设置为 `ON` 。
+如果你要为了测试而增加新的文件，例如 :code:`paddle/legacy/gserver/tests/testFCGrad.cpp` ，你需要把该文件加入 :code:`paddle/legacy/gserver/tests/CMakeLists.txt` 中。下面给出了一个例子。当你执行命令 :code:`make tests` 时，所有的单测都会被执行一次。注意，有些层可能需要高精度来保证梯度检查单测正确执行。你需要在配置cmake时将 :code:`WITH_DOUBLE` 设置为 `ON` 。
 
 .. code-block:: bash
 

doc/v2/dev/new_layer_en.rst

@@ -58,7 +58,7 @@ Finally we can use chain rule to calculate :math:`\frac{\partial z}{\partial x}`
 Implement C++ Class
 ===================
 
-The C++ class of the layer implements the initialization, forward, and backward part of the layer. The fully connected layer is at :code:`paddle/gserver/layers/FullyConnectedLayer.h` and :code:`paddle/gserver/layers/FullyConnectedLayer.cpp`. We list simplified version of the code below.
+The C++ class of the layer implements the initialization, forward, and backward part of the layer. The fully connected layer is at :code:`paddle/legacy/gserver/layers/FullyConnectedLayer.h` and :code:`paddle/legacy/gserver/layers/FullyConnectedLayer.cpp`. We list simplified version of the code below.
 
 It needs to derive the base class :code:`paddle::Layer`, and it needs to override the following functions:
 
@@ -154,7 +154,7 @@ The implementation of the forward part has the following steps.
 
 - Every layer must call :code:`Layer::forward(passType);` at the beginning of its :code:`forward` function.
 - Then it allocates memory for the output using :code:`reserveOutput(batchSize, size);`. This step is necessary because we support the batches to have different batch sizes. :code:`reserveOutput` will change the size of the output accordingly. For the sake of efficiency, we will allocate new memory if we want to expand the matrix, but we will reuse the existing memory block if we want to shrink the matrix.
-- Then it computes :math:`\sum_i W_i x + b` using Matrix operations. :code:`getInput(i).value` retrieve the matrix of the i-th input. Each input is a :math:`batchSize \times dim` matrix, where each row represents an single input in a batch. For a complete lists of supported matrix operations, please refer to :code:`paddle/math/Matrix.h` and :code:`paddle/math/BaseMatrix.h`.
+- Then it computes :math:`\sum_i W_i x + b` using Matrix operations. :code:`getInput(i).value` retrieve the matrix of the i-th input. Each input is a :math:`batchSize \times dim` matrix, where each row represents an single input in a batch. For a complete lists of supported matrix operations, please refer to :code:`paddle/legacy/math/Matrix.h` and :code:`paddle/legacy/math/BaseMatrix.h`.
 - Finally it applies the activation function using :code:`forwardActivation();`. It will automatically applies the corresponding activation function specifies in the network configuration.
 
 
@@ -263,15 +263,15 @@ Finally, you can use :code:`REGISTER_LAYER(fc, FullyConnectedLayer);` to registe
     REGISTER_LAYER(fc, FullyConnectedLayer);
     }
 
-If the :code:`cpp` file is put into :code:`paddle/gserver/layers`, it will be automatically added to the compilation list.
+If the :code:`cpp` file is put into :code:`paddle/legacy/gserver/layers`, it will be automatically added to the compilation list.
 
 
 Write Gradient Check Unit Test
 ===============================
 
 An easy way to verify the correctness of new layer's implementation is to write a gradient check unit test. Gradient check unit test utilizes finite difference method to verify the gradient of a layer. It modifies the input with a small perturbation :math:`\Delta x` and observes the changes of output :math:`\Delta y`, the gradient can be computed as :math:`\frac{\Delta y}{\Delta x }`. This gradient can be compared with the gradient computed by the :code:`backward` function of the layer to ensure the correctness of the gradient computation. Notice that the gradient check only tests the correctness of the gradient computation, it does not necessarily guarantee the correctness of the implementation of the :code:`forward` and :code:`backward` function. You need to write more sophisticated unit tests to make sure your layer is implemented correctly.
 
-All the gradient check unit tests are located in :code:`paddle/gserver/tests/test_LayerGrad.cpp`. You are recommended to put your test into a new test file if you are planning to write a new layer. The gradient test of the gradient check unit test of the fully connected layer is listed below. It has the following steps.
+All the gradient check unit tests are located in :code:`paddle/legacy/gserver/tests/test_LayerGrad.cpp`. You are recommended to put your test into a new test file if you are planning to write a new layer. The gradient test of the gradient check unit test of the fully connected layer is listed below. It has the following steps.
 
 + Create layer configuration. A layer configuration can include the following attributes:
    - size of the bias parameter. (4096 in our example)
@@ -323,7 +323,7 @@ All the gradient check unit tests are located in :code:`paddle/gserver/tests/tes
       }
     }
 
-If you are creating a new file for the test, such as :code:`paddle/gserver/tests/testFCGrad.cpp`, you need to add the file to :code:`paddle/gserver/tests/CMakeLists.txt`. An example is given below. All the unit tests will run when you execute the command :code:`make tests`. Notice that some layers might need high accuracy for the gradient check unit tests to work well. You need to configure :code:`WITH_DOUBLE` to `ON` when configuring cmake.
+If you are creating a new file for the test, such as :code:`paddle/legacy/gserver/tests/testFCGrad.cpp`, you need to add the file to :code:`paddle/legacy/gserver/tests/CMakeLists.txt`. An example is given below. All the unit tests will run when you execute the command :code:`make tests`. Notice that some layers might need high accuracy for the gradient check unit tests to work well. You need to configure :code:`WITH_DOUBLE` to `ON` when configuring cmake.
 
 .. code-block:: bash
 

doc/v2/faq/parameter/index_cn.rst

@@ -196,6 +196,6 @@ PaddlePaddle保存的模型参数文件内容由16字节头信息和网络参数
         obj="process",
         args={"src_dict_path": src_dict_path})
 
-完整源码可参考 `sequence_recurrent <https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/gserver/tests/sequence_recurrent.py>`_ 示例。
+完整源码可参考 `sequence_recurrent <https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/legacy/gserver/tests/sequence_recurrent.py>`_ 示例。
 
 

doc/v2/howto/optimization/gpu_profiling_cn.rst

@@ -50,12 +50,12 @@ GPU则还需要高并行性，才能发挥其全部能力。这正是它们速
 **nvprof** 是Nvidia性能分析工具， **nvvp** 则是带GUI的Nvidia可视化性能分析工具。
 在这个教程中，我们主要会介绍nvprof和nvvp。
 
-:code:`test_GpuProfiler` from :code:`paddle/math/tests` directory will be used to evaluate
+:code:`test_GpuProfiler` from :code:`paddle/legacy/math/tests` directory will be used to evaluate
 above profilers.
 
-:code:`paddle/math/test` 目录中的 :code:`test_GpuProfiler` 就是用于展示上述分析工具的用法。
+:code:`paddle/legacy/math/test` 目录中的 :code:`test_GpuProfiler` 就是用于展示上述分析工具的用法。
 
-.. literalinclude:: ../../../../paddle/math/tests/test_GpuProfiler.cpp
+.. literalinclude:: ../../../../paddle/legacy/math/tests/test_GpuProfiler.cpp
    :language: c++
    :lines: 137-151
    :linenos:
@@ -83,7 +83,7 @@ program crashes when CPU version of PaddlePaddle invokes them.
 
 1. 加入 :code:`REGISTER_TIMER_INFO` 和 :code:`printAllStatus` 函数（如高亮部分）。
 
-    .. literalinclude:: ../../../../paddle/math/tests/test_GpuProfiler.cpp
+    .. literalinclude:: ../../../../paddle/legacy/math/tests/test_GpuProfiler.cpp
         :language: c++
         :lines: 137-151
         :emphasize-lines: 8-12,14
@@ -101,8 +101,8 @@ program crashes when CPU version of PaddlePaddle invokes them.
     .. code-block:: bash
         :emphasize-lines: 1,12-15
 
-        > ./paddle/math/tests/test_GpuProfiler
-        I1117 11:13:42.313065 2522362816 Util.cpp:155] commandline: ./paddle/math/tests/test_GpuProfiler
+        > ./paddle/legacy/math/tests/test_GpuProfiler
+        I1117 11:13:42.313065 2522362816 Util.cpp:155] commandline: ./paddle/legacy/math/tests/test_GpuProfiler
         I1117 11:13:42.845065 2522362816 Util.cpp:130] Calling runInitFunctions
         I1117 11:13:42.845208 2522362816 Util.cpp:143] Call runInitFunctions done.
         [==========] Running 1 test from 1 test case.
@@ -130,7 +130,7 @@ nvprof 工具
 
 1. 将 :code:`REGISTER_GPU_PROFILER` 函数加到代码中（参考强调部分）。
 
-    .. literalinclude:: ../../../../paddle/math/tests/test_GpuProfiler.cpp
+    .. literalinclude:: ../../../../paddle/legacy/math/tests/test_GpuProfiler.cpp
         :language: c++
         :lines: 137-151
         :emphasize-lines: 6-7
@@ -147,13 +147,13 @@ nvprof 工具
 
     .. code-block:: bash
 
-        nvprof  ./paddle/math/tests/test_GpuProfiler
+        nvprof  ./paddle/legacy/math/tests/test_GpuProfiler
 
 然后，您就能获得如下的分析结果：
 
 .. code-block:: bash
 
-    ==78544== Profiling application: ./paddle/math/tests/test_GpuProfiler
+    ==78544== Profiling application: ./paddle/legacy/math/tests/test_GpuProfiler
     ==78544== Profiling result:
     Time(%)     Time     Calls       Avg       Min       Max  Name
     27.60%  9.6305ms         5  1.9261ms  3.4560us  6.4035ms  [CUDA memcpy HtoD]

doc/v2/howto/optimization/gpu_profiling_en.rst

@@ -51,10 +51,10 @@ For general GPU profiling, a bunch of tools are provided from both NVIDIA and th
 **nvprof** is Nvidia profiler and **nvvp** is (GUI based) Nvidia visual profiler.
 In this tutorial, we will focus on nvprof and nvvp.
 
-:code:`test_GpuProfiler` from :code:`paddle/math/tests` directory will be used to evaluate
+:code:`test_GpuProfiler` from :code:`paddle/legacy/math/tests` directory will be used to evaluate
 above profilers.
 
-.. literalinclude:: ../../../../paddle/math/tests/test_GpuProfiler.cpp
+.. literalinclude:: ../../../../paddle/legacy/math/tests/test_GpuProfiler.cpp
    :language: c++
    :lines: 137-151
    :linenos:
@@ -80,7 +80,7 @@ As a simple example, consider the following:
 
 1. Add :code:`REGISTER_TIMER_INFO` and :code:`printAllStatus` functions (see the emphasize-lines).
 
-    .. literalinclude:: ../../../../paddle/math/tests/test_GpuProfiler.cpp
+    .. literalinclude:: ../../../../paddle/legacy/math/tests/test_GpuProfiler.cpp
         :language: c++
         :lines: 137-151
         :emphasize-lines: 8-12,14
@@ -98,8 +98,8 @@ As a simple example, consider the following:
     .. code-block:: bash
         :emphasize-lines: 1,12-15
 
-        > ./paddle/math/tests/test_GpuProfiler
-        I1117 11:13:42.313065 2522362816 Util.cpp:155] commandline: ./paddle/math/tests/test_GpuProfiler
+        > ./paddle/legacy/math/tests/test_GpuProfiler
+        I1117 11:13:42.313065 2522362816 Util.cpp:155] commandline: ./paddle/legacy/math/tests/test_GpuProfiler
         I1117 11:13:42.845065 2522362816 Util.cpp:130] Calling runInitFunctions
         I1117 11:13:42.845208 2522362816 Util.cpp:143] Call runInitFunctions done.
         [==========] Running 1 test from 1 test case.
@@ -127,7 +127,7 @@ To use this command line profiler **nvprof**, you can simply issue the following
 
 1. Add :code:`REGISTER_GPU_PROFILER` function (see the emphasize-lines).
 
-    .. literalinclude:: ../../../../paddle/math/tests/test_GpuProfiler.cpp
+    .. literalinclude:: ../../../../paddle/legacy/math/tests/test_GpuProfiler.cpp
         :language: c++
         :lines: 137-151
         :emphasize-lines: 6-7
@@ -144,13 +144,13 @@ To use this command line profiler **nvprof**, you can simply issue the following
 
     .. code-block:: bash
 
-        nvprof  ./paddle/math/tests/test_GpuProfiler
+        nvprof  ./paddle/legacy/math/tests/test_GpuProfiler
 
 Then, you can get the following profiling result:
 
 .. code-block:: bash
 
-    ==78544== Profiling application: ./paddle/math/tests/test_GpuProfiler
+    ==78544== Profiling application: ./paddle/legacy/math/tests/test_GpuProfiler
     ==78544== Profiling result:
     Time(%)     Time     Calls       Avg       Min       Max  Name
     27.60%  9.6305ms         5  1.9261ms  3.4560us  6.4035ms  [CUDA memcpy HtoD]