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

cmake/ccache.cmake

 # Use ccache if found ccache program
 
-find_program(CCACHE_FOUND ccache)
+find_program(CCACHE_PATH ccache)
 
-if(CCACHE_FOUND)
+if(CCACHE_PATH)
     message(STATUS "Ccache is founded, use ccache to speed up compile.")
-    set_property(GLOBAL PROPERTY RULE_LAUNCH_COMPILE ccache)
-    set_property(GLOBAL PROPERTY RULE_LAUNCH_LINK ccache)
-endif(CCACHE_FOUND)
+    set_property(GLOBAL PROPERTY RULE_LAUNCH_COMPILE ${CCACHE_PATH})
+    set_property(GLOBAL PROPERTY RULE_LAUNCH_LINK ${CCACHE_PATH})
+endif(CCACHE_PATH)
\ No newline at end of file

cmake/external/protobuf.cmake

@@ -16,6 +16,14 @@ INCLUDE(ExternalProject)
 
 FIND_PACKAGE(Protobuf 3.1)
 
+IF(PROTOBUF_FOUND)
+    EXEC_PROGRAM(${PROTOBUF_PROTOC_EXECUTABLE} ARGS --version OUTPUT_VARIABLE PROTOBUF_VERSION)
+    STRING(REGEX MATCH "[0-9]+.[0-9]+" PROTOBUF_VERSION "${PROTOBUF_VERSION}")
+    IF (${PROTOBUF_VERSION} VERSION_LESS "3.1.0")
+        SET(PROTOBUF_FOUND OFF)
+    ENDIF()
+ENDIF(PROTOBUF_FOUND)
+
 IF(NOT PROTOBUF_FOUND)
     SET(PROTOBUF_SOURCES_DIR ${THIRD_PARTY_PATH}/protobuf)
     SET(PROTOBUF_INSTALL_DIR ${THIRD_PARTY_PATH}/install/protobuf)

cmake/util.cmake

@@ -71,21 +71,10 @@ function(link_paddle_exe TARGET_NAME)
         generate_rdma_links()
     endif()
 
-    if(WITH_METRIC)
-        if(WITH_GPU)
-            set(METRIC_LIBS paddle_metric_learning paddle_dserver_lib metric metric_cpu)
-        else()
-            set(METRIC_LIBS paddle_metric_learning paddle_dserver_lib metric_cpu)
-        endif()
-    else()
-        set(METRIC_LIBS "")
-    endif()
-
     target_circle_link_libraries(${TARGET_NAME}
         ARCHIVE_START
         paddle_gserver
         paddle_function
-        ${METRIC_LIBS}
         ARCHIVE_END
         paddle_pserver
         paddle_trainer_lib
@@ -95,7 +84,6 @@ function(link_paddle_exe TARGET_NAME)
         paddle_parameter
         paddle_proto
         paddle_cuda
-        ${METRIC_LIBS}
         ${EXTERNAL_LIBS}
         ${CMAKE_THREAD_LIBS_INIT}
         ${CMAKE_DL_LIBS}

doc/faq/index_cn.rst

@@ -286,3 +286,16 @@ PaddlePaddle的参数使用名字 :code:`name` 作为参数的ID，相同名字
 ..      code-block:: bash
 
         paddle train --use_gpu=true --trainer_count=2 --gpu_id=2
+
+
+12. 训练过程中出现 :code:`Floating point exception`, 训练因此退出怎么办?
+------------------------------------------------------------------------
+
+Paddle二进制在运行时捕获了浮点数异常，只要出现浮点数异常(即训练过程中出现NaN或者Inf)，立刻退出。浮点异常通常的原因是浮点数溢出、除零等问题。
+主要原因包括两个方面:
+
+* 训练过程中参数或者训练过程中的梯度尺度过大，导致参数累加，乘除等时候，导致了浮点数溢出。
+* 模型一直不收敛，发散到了一个数值特别大的地方。
+* 训练数据有问题，导致参数收敛到了一些奇异的情况。或者输入数据尺度过大，有些特征的取值达到数百万，这时进行矩阵乘法运算就可能导致浮点数溢出。
+
+主要的解决办法是减小学习律或者对数据进行归一化处理。

doc/getstarted/build_and_install/docker_install_cn.rst

@@ -4,138 +4,137 @@ PaddlePaddle的Docker容器使用方式
 PaddlePaddle目前唯一官方支持的运行的方式是Docker容器。因为Docker能在所有主要操作系统（包括Linux，Mac OS X和Windows）上运行。 请注意，您需要更改 `Dockers设置 <https://github.com/PaddlePaddle/Paddle/issues/627>`_ 才能充分利用Mac OS X和Windows上的硬件资源。
 
 
-通过Docker容器开发PaddlePaddle
+纯CPU和GPU的docker镜像使用说明
 ------------------------------
 
-开发人员可以在Docker中开发PaddlePaddle。这样开发人员可以以一致的方式在不同的平台上工作 - Linux，Mac OS X和Windows。
+对于每一个PaddlePaddle版本，我们都会发布两个Docker镜像：纯CPU的和GPU的。
+我们通过设置 `dockerhub.com <https://hub.docker.com/r/paddledev/paddle/>`_ 自动生成最新的docker镜像：
+`paddledev/paddle:0.10.0rc1-cpu` 和 `paddledev/paddle:0.10.0rc1-gpu`。
 
-1. 将开发环境构建为Docker镜像
-   
-   .. code-block:: bash
+以交互容器方式运行纯CPU的镜像：
 
-      git clone --recursive https://github.com/PaddlePaddle/Paddle
-      cd Paddle
-      docker build -t paddle:dev -f paddle/scripts/docker/Dockerfile .
+.. code-block:: bash
 
+    docker run -it --rm paddledev/paddle:0.10.0rc1-cpu /bin/bash
 
-   请注意，默认情况下，:code:`docker build` 不会将源码导入到镜像中并编译它。如果我们想这样做，需要设置一个参数：
+或者，可以以后台进程方式运行容器：
 
-   .. code-block:: bash
+.. code-block:: bash
 
-      docker build -t paddle:dev -f paddle/scripts/docker/Dockerfile --build-arg BUILD_AND_INSTALL=ON .
+    docker run -d -p 2202:22 -p 8888:8888 paddledev/paddle:0.10.0rc1-cpu
 
+然后用密码 :code:`root` SSH进入容器：
 
-2. 运行开发环境
+.. code-block:: bash
 
-   当我们编译好了 :code:`paddle:dev`， 我们可以在docker容器里做开发，源代码可以通过挂载本地文件来被载入Docker的开发环境里面：
-   
-   .. code-block:: bash
+    ssh -p 2202 root@localhost
 
-      docker run -d -p 2202:22 -v $PWD:/paddle paddle:dev
+SSH方式的一个优点是我们可以从多个终端进入容器。比如，一个终端运行vi，另一个终端运行Python。另一个好处是我们可以把PaddlePaddle容器运行在远程服务器上，并在笔记本上通过SSH与其连接。
 
-   以上代码会启动一个带有PaddlePaddle开发环境的docker容器，源代码会被挂载到 :code:`/paddle` 。
 
-   请注意， :code:`paddle:dev` 的默认入口是 :code:`sshd` 。以上的 :code:`docker run` 命令其实会启动一个在2202端口监听的SSHD服务器。这样，我们就能SSH进入我们的开发容器了：
-   
-   .. code-block:: bash
+以上方法在GPU镜像里也能用－只是请不要忘记按装CUDA驱动，以及告诉Docker：
 
-      ssh root@localhost -p 2202
+.. code-block:: bash
 
-3. 在Docker开发环境中编译与安装PaddlPaddle代码
+    export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')"
+    export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}')
+    docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:0.10.0rc1-gpu
 
-   当在容器里面的时候，可以用脚本 :code:`paddle/scripts/docker/build.sh` 来编译、安装与测试PaddlePaddle：
-   
-   .. code-block:: bash
-		      
-      /paddle/paddle/scripts/docker/build.sh
 
-   以上指令会在 :code:`/paddle/build` 中编译PaddlePaddle。通过以下指令可以运行单元测试：
-   
-   .. code-block:: bash
+运行PaddlePaddle书籍
+---------------------
 
-      cd /paddle/build
-      ctest
+Jupyter Notebook是一个开源的web程序，大家可以通过它制作和分享带有代码、公式、图表、文字的交互式文档。用户可以通过网页浏览文档。
 
-4. 在Docker容器中运行PaddlePaddle书籍
+PaddlePaddle书籍是为用户和开发者制作的一个交互式的Jupyter Nodebook。
+如果您想要更深入了解deep learning，PaddlePaddle书籍一定是您最好的选择。
 
-   Jupyter Notebook是一个开源的web程序，大家可以通过它制作和分享带有代码、公式、图表、文字的交互式文档。用户可以通过网页浏览文档。
+当您进入容器内之后，只用运行以下命令：
 
-   PaddlePaddle书籍是为用户和开发者制作的一个交互式的Jupyter Nodebook。
-   如果您想要更深入了解deep learning，PaddlePaddle书籍一定是您最好的选择。
-   
-   当您进入容器内之后，只用运行以下命令：
+.. code-block:: bash
+        
+    jupyter notebook
 
-   .. code-block:: bash
-		   
-      jupyter notebook
+然后在浏览器中输入以下网址：
+    
+.. code-block:: text
 
-   然后在浏览器中输入以下网址：
-      
-   .. code-block:: text
+    http://localhost:8888/
 
-      http://localhost:8888/
+就这么简单，享受您的旅程！
 
-   就这么简单，享受您的旅程！
 
-纯CPU和GPU的docker镜像
-----------------------
+非AVX镜像
+---------
 
-对于每一个PaddlePaddle版本，我们都会发布两个Docker镜像：纯CPU的和GPU的。我们通过设置 `dockerhub.com <https://hub.docker.com/r/paddledev/paddle/>`_ 自动运行以下两个命令：
+纯CPU镜像以及GPU镜像都会用到AVX指令集，但是2008年之前生产的旧电脑不支持AVX。以下指令能检查Linux电脑是否支持AVX：
 
 .. code-block:: bash
 
-   docker build -t paddle:cpu -f paddle/scripts/docker/Dockerfile --build-arg BUILD_AND_INSTALL=ON .
-   docker build -t paddle:gpu -f paddle/scripts/docker/Dockerfile.gpu --build-arg BUILD_AND_INSTALL=ON .
+   if cat /proc/cpuinfo | grep -i avx; then echo Yes; else echo No; fi
 
-以交互容器方式运行纯CPU的镜像：
+如果输出是No，我们就需要手动编译一个非AVX版本的镜像：
 
 .. code-block:: bash
 
-    docker run -it --rm paddledev/paddle:0.10.0rc1-cpu /bin/bash
+   cd ~
+   git clone https://github.com/PaddlePaddle/Paddle.git
+   cd Paddle
+   docker build --build-arg WITH_AVX=OFF -t paddle:cpu-noavx -f paddle/scripts/docker/Dockerfile .
+   docker build --build-arg WITH_AVX=OFF -t paddle:gpu-noavx -f paddle/scripts/docker/Dockerfile.gpu .
 
-或者，可以以后台进程方式运行容器：
 
-.. code-block:: bash
+通过Docker容器开发PaddlePaddle
+------------------------------
 
-    docker run -d -p 2202:22 paddledev/paddle:0.10.0rc1-cpu
+开发人员可以在Docker中开发PaddlePaddle。这样开发人员可以以一致的方式在不同的平台上工作 - Linux，Mac OS X和Windows。
 
-然后用密码 :code:`root` SSH进入容器：
+1. 将开发环境构建为Docker镜像
+   
+   .. code-block:: bash
 
-.. code-block:: bash
+      git clone --recursive https://github.com/PaddlePaddle/Paddle
+      cd Paddle
+      docker build -t paddle:dev -f paddle/scripts/docker/Dockerfile .
 
-    ssh -p 2202 root@localhost
 
-SSH方式的一个优点是我们可以从多个终端进入容器。比如，一个终端运行vi，另一个终端运行Python。另一个好处是我们可以把PaddlePaddle容器运行在远程服务器上，并在笔记本上通过SSH与其连接。
+   请注意，默认情况下，:code:`docker build` 不会将源码导入到镜像中并编译它。如果我们想这样做，需要设置一个参数：
 
+   .. code-block:: bash
 
-以上方法在GPU镜像里也能用－只是请不要忘记按装CUDA驱动，以及告诉Docker：
+      docker build -t paddle:dev -f paddle/scripts/docker/Dockerfile --build-arg BUILD_AND_INSTALL=ON .
 
-.. code-block:: bash
 
-    export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')"
-    export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}')
-    docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:0.10.0rc1-gpu
+2. 运行开发环境
 
+   当我们编译好了 :code:`paddle:dev`， 我们可以在docker容器里做开发，源代码可以通过挂载本地文件来被载入Docker的开发环境里面：
+   
+   .. code-block:: bash
 
-非AVX镜像
----------
+      docker run -d -p 2202:22 -v $PWD:/paddle paddle:dev
 
-纯CPU镜像以及GPU镜像都会用到AVX指令集，但是2008年之前生产的旧电脑不支持AVX。以下指令能检查Linux电脑是否支持AVX：
+   以上代码会启动一个带有PaddlePaddle开发环境的docker容器，源代码会被挂载到 :code:`/paddle` 。
 
+   请注意， :code:`paddle:dev` 的默认入口是 :code:`sshd` 。以上的 :code:`docker run` 命令其实会启动一个在2202端口监听的SSHD服务器。这样，我们就能SSH进入我们的开发容器了：
+   
+   .. code-block:: bash
 
-.. code-block:: bash
+      ssh root@localhost -p 2202
 
-   if cat /proc/cpuinfo | grep -i avx; then echo Yes; else echo No; fi
+3. 在Docker开发环境中编译与安装PaddlPaddle代码
 
-如果输出是No，我们就需要手动编译一个非AVX版本的镜像：
+   当在容器里面的时候，可以用脚本 :code:`paddle/scripts/docker/build.sh` 来编译、安装与测试PaddlePaddle：
+   
+   .. code-block:: bash
+		      
+      /paddle/paddle/scripts/docker/build.sh
 
-.. code-block:: bash
+   以上指令会在 :code:`/paddle/build` 中编译PaddlePaddle。通过以下指令可以运行单元测试：
+   
+   .. code-block:: bash
 
-   cd ~
-   git clone https://github.com/PaddlePaddle/Paddle.git
-   cd Paddle
-   docker build --build-arg WITH_AVX=OFF -t paddle:cpu-noavx -f paddle/scripts/docker/Dockerfile .
-   docker build --build-arg WITH_AVX=OFF -t paddle:gpu-noavx -f paddle/scripts/docker/Dockerfile.gpu .
+      cd /paddle/build
+      ctest
 
 
 文档

doc/getstarted/build_and_install/docker_install_en.rst

@@ -9,6 +9,100 @@ Please be aware that you will need to change `Dockers settings
 of your hardware resource on Mac OS X and Windows.
 
 
+Usage of CPU-only and GPU Images
+----------------------------------
+
+For each version of PaddlePaddle, we release 2 Docker images, a
+CPU-only one and a CUDA GPU one.  We do so by configuring
+`dockerhub.com <https://hub.docker.com/r/paddledev/paddle/>`_
+automatically generate the latest docker images `paddledev/paddle:0.10.0rc1-cpu`
+and `paddledev/paddle:0.10.0rc1-gpu`.
+
+To run the CPU-only image as an interactive container:
+
+.. code-block:: bash
+
+    docker run -it --rm paddledev/paddle:0.10.0rc1-cpu /bin/bash
+
+or, we can run it as a daemon container
+
+.. code-block:: bash
+
+    docker run -d -p 2202:22 -p 8888:8888 paddledev/paddle:0.10.0rc1-cpu
+
+and SSH to this container using password :code:`root`:
+
+.. code-block:: bash
+
+    ssh -p 2202 root@localhost
+
+An advantage of using SSH is that we can connect to PaddlePaddle from
+more than one terminals.  For example, one terminal running vi and
+another one running Python interpreter.  Another advantage is that we
+can run the PaddlePaddle container on a remote server and SSH to it
+from a laptop.
+
+Above methods work with the GPU image too -- just please don't forget
+to install CUDA driver and let Docker knows about it:
+
+.. code-block:: bash
+
+    export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')"
+    export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}')
+    docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:0.10.0rc1-gpu
+
+
+PaddlePaddle Book
+------------------
+
+The Jupyter Notebook is an open-source web application that allows
+you to create and share documents that contain live code, equations,
+visualizations and explanatory text in a single browser.
+
+PaddlePaddle Book is an interactive Jupyter Notebook for users and developers. 
+We already exposed port 8888 for this book. If you want to
+dig deeper into deep learning, PaddlePaddle Book definitely is your best choice.
+
+Once you are inside the container, simply issue the command:
+
+.. code-block:: bash
+        
+    jupyter notebook
+
+Then, you would back and paste the address into the local browser:
+    
+.. code-block:: text
+
+    http://localhost:8888/
+
+That's all. Enjoy your journey!
+
+
+Non-AVX Images
+--------------
+
+Please be aware that the CPU-only and the GPU images both use the AVX
+instruction set, but old computers produced before 2008 do not support
+AVX.  The following command checks if your Linux computer supports
+AVX:
+
+.. code-block:: bash
+
+   if cat /proc/cpuinfo | grep -i avx; then echo Yes; else echo No; fi
+
+
+If it doesn't, we will need to build non-AVX images manually from
+source code:
+
+.. code-block:: bash
+
+   cd ~
+   git clone https://github.com/PaddlePaddle/Paddle.git
+   cd Paddle
+   docker build --build-arg WITH_AVX=OFF -t paddle:cpu-noavx -f paddle/scripts/docker/Dockerfile .
+   docker build --build-arg WITH_AVX=OFF -t paddle:gpu-noavx -f paddle/scripts/docker/Dockerfile.gpu .
+
+
 Development Using Docker
 ------------------------
 
@@ -82,103 +176,6 @@ Windows -- in a consistent way.
       cd /paddle/build
       ctest
 
-4. Run PaddlePaddle Book under Docker Container
-
-   The Jupyter Notebook is an open-source web application that allows
-   you to create and share documents that contain live code, equations,
-   visualizations and explanatory text in a single browser.
-
-   PaddlePaddle Book is an interactive Jupyter Notebook for users and developers. 
-   We already exposed port 8888 for this book. If you want to
-   dig deeper into deep learning, PaddlePaddle Book definitely is your best choice.
-
-   Once you are inside the container, simply issue the command:
-
-   .. code-block:: bash
-		   
-      jupyter notebook
-
-   Then, you would back and paste the address into the local browser:
-      
-   .. code-block:: text
-
-      http://localhost:8888/
-
-   That's all. Enjoy your journey!
-
-CPU-only and GPU Images
------------------------
-
-For each version of PaddlePaddle, we release 2 Docker images, a
-CPU-only one and a CUDA GPU one.  We do so by configuring
-`dockerhub.com <https://hub.docker.com/r/paddledev/paddle/>`_
-automatically runs the following commands:
-
-.. code-block:: bash
-
-   docker build -t paddle:cpu -f paddle/scripts/docker/Dockerfile --build-arg BUILD_AND_INSTALL=ON .
-   docker build -t paddle:gpu -f paddle/scripts/docker/Dockerfile.gpu --build-arg BUILD_AND_INSTALL=ON .
-
-
-To run the CPU-only image as an interactive container:
-
-.. code-block:: bash
-
-    docker run -it --rm paddledev/paddle:0.10.0rc1-cpu /bin/bash
-
-or, we can run it as a daemon container
-
-.. code-block:: bash
-
-    docker run -d -p 2202:22 paddledev/paddle:0.10.0rc1-cpu
-
-and SSH to this container using password :code:`root`:
-
-.. code-block:: bash
-
-    ssh -p 2202 root@localhost
-
-An advantage of using SSH is that we can connect to PaddlePaddle from
-more than one terminals.  For example, one terminal running vi and
-another one running Python interpreter.  Another advantage is that we
-can run the PaddlePaddle container on a remote server and SSH to it
-from a laptop.
-
-
-Above methods work with the GPU image too -- just please don't forget
-to install CUDA driver and let Docker knows about it:
-
-.. code-block:: bash
-
-    export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')"
-    export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}')
-    docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:0.10.0rc1-gpu
-
-
-Non-AVX Images
---------------
-
-Please be aware that the CPU-only and the GPU images both use the AVX
-instruction set, but old computers produced before 2008 do not support
-AVX.  The following command checks if your Linux computer supports
-AVX:
-
-.. code-block:: bash
-
-   if cat /proc/cpuinfo | grep -i avx; then echo Yes; else echo No; fi
-
-
-If it doesn't, we will need to build non-AVX images manually from
-source code:
-
-.. code-block:: bash
-
-   cd ~
-   git clone https://github.com/PaddlePaddle/Paddle.git
-   cd Paddle
-   docker build --build-arg WITH_AVX=OFF -t paddle:cpu-noavx -f paddle/scripts/docker/Dockerfile .
-   docker build --build-arg WITH_AVX=OFF -t paddle:gpu-noavx -f paddle/scripts/docker/Dockerfile.gpu .
-
 
 Documentation
 -------------

doc/howto/usage/cmd_parameter/arguments_cn.md

@@ -228,16 +228,6 @@
 <td class="left"></td><td class="left"></td><td class="left">√</td><td class="left">√</td>
 </tr>
 
-<tr>
-<td class="left" rowspan = "2">度量学习(metric learning)</td><td class="left">external</td>
-<td class="left">√</td><td class="left">√</td><td class="left">√</td><td class="left">√</td>
-</tr>
-
-<tr>
-<td class="left">data_server_port</td>
-<td class="left"></td><td class="left">√</td><td class="left"></td><td class="left">√</td>
-</tr>
-
 <tr>
 <td class="left" rowspan = "16">参数服务器(PServer)</td><td class="left">start_pserver</td>
 <td class="left"></td><td class="left">√</td><td class="left"></td><td class="left">√</td>

doc/howto/usage/cmd_parameter/arguments_en.md

@@ -228,16 +228,6 @@ It looks like there are a lot of arguments. However, most of them are for develo
 <td class="left"></td><td class="left"></td><td class="left">√</td><td class="left">√</td>
 </tr>
 
-<tr>
-<td class="left" rowspan = "2">metric learning</td><td class="left">external</td>
-<td class="left">√</td><td class="left">√</td><td class="left">√</td><td class="left">√</td>
-</tr>
-
-<tr>
-<td class="left">data_server_port</td>
-<td class="left"></td><td class="left">√</td><td class="left"></td><td class="left">√</td>
-</tr>
-
 <tr>
 <td class="left" rowspan = "16">PServer</td><td class="left">start_pserver</td>
 <td class="left"></td><td class="left">√</td><td class="left"></td><td class="left">√</td>

doc/howto/usage/cmd_parameter/detail_introduction_cn.md

@@ -180,15 +180,6 @@
   - 用户可以自定义beam search的方法，编译成动态库，供PaddlePaddle加载。 该参数用于指定动态库路径.
   - 类型: string (默认: "", null).
 
-## 度量学习(Metric Learning)
-* `--external`
-   - 指示是否使用外部机器进行度量学习.
-   - 类型: bool (默认: 0).
-
-* `--data_server_port`
-  - 数据服务器(data server)的监听端口，主要用在度量学习中.
-  - 类型: int32 (默认: 21134).
-
 ## 数据支持(DataProvider)
 
 * `--memory_threshold_on_load_data`

doc/howto/usage/cmd_parameter/detail_introduction_en.md

@@ -184,15 +184,6 @@
   - Specify shared dynamic library. It can be defined out of paddle by user.
   - type: string (default: "", null).
 
-## Metric Learning
-* `--external`
-   - Whether to use external machine for metric learning.
-   - type: bool (default: 0).
-
-* `--data_server_port`
-  - Listening port for dserver (data server), dserver is mainly used in metric learning.
-  - type: int32 (default: 21134).
-
 ## DataProvider
 
 * `--memory_threshold_on_load_data`

paddle/gserver/gradientmachines/MultiGradientMachine.cpp

@@ -24,9 +24,6 @@ limitations under the License. */
 DEFINE_bool(allow_only_one_model_on_one_gpu,
             true,
             "If true, do not allow multiple models on one GPU device");
-#ifdef PADDLE_METRIC_LEARNING
-DECLARE_bool(external);
-#endif
 
 namespace paddle {
 
@@ -45,11 +42,7 @@ MultiGradientMachine::MultiGradientMachine(const ModelConfig& config,
       trainerBarrier_(FLAGS_trainer_count),
       allBarrier_(FLAGS_trainer_count + 1),
       inArgsCopied_(false) {
-#ifdef PADDLE_METRIC_LEARNING
-  isPassGrad_ = FLAGS_external;
-#else
   isPassGrad_ = false;
-#endif
   numThreads_ = FLAGS_trainer_count;
   if (useGpu) {
     //! TODO(yuyang18): When useGpu=false && paddle is not compiled with gpu,

paddle/gserver/layers/CRFDecodingLayer.cpp

@@ -24,7 +24,7 @@ bool CRFDecodingLayer::init(const LayerMap& layerMap,
     return false;
   }
   crf_.reset(new LinearChainCRF(
-      numClasses_, parameter_->getBuf(PARAMETER_VALUE)->getData(), nullptr));
+      numClasses_, parameter_->getBuf(PARAMETER_VALUE)->getData()));
   return true;
 }
 

paddle/gserver/layers/CRFLayer.cpp

@@ -42,6 +42,7 @@ bool CRFLayer::init(const LayerMap& layerMap,
   CHECK_EQ(parameters_[0]->getSize(), numClasses_ * (numClasses_ + 2));
 
   parameter_ = parameters_[0];
+  weight_.reset(new Weight(numClasses_ + 2, numClasses_, parameter_));
 
   // We don't need sequenceStartPositions because each sample of output_ is
   // for the cost of one sequence.
@@ -69,11 +70,7 @@ void CRFLayer::forward(PassType passType) {
 
   for (size_t i = 0; i < numSequences; ++i) {
     if (i >= crfs_.size()) {
-      crfs_.emplace_back(numClasses_,
-                         parameter_->getBuf(PARAMETER_VALUE)->getData(),
-                         parameter_->getBuf(PARAMETER_GRADIENT)
-                             ? parameter_->getBuf(PARAMETER_GRADIENT)->getData()
-                             : nullptr);
+      crfs_.emplace_back(numClasses_, weight_->getW()->getData());
     }
     output_.value->getData()[i] =
         crfs_[i].forward(output.value->getData() + numClasses_ * starts[i],
@@ -93,22 +90,25 @@ void CRFLayer::backward(const UpdateCallback& callback) {
   const int* starts = label.sequenceStartPositions->getData(false);
   int numSequences = label.sequenceStartPositions->getSize() - 1;
 
+  bool needWGrad = weight_->getWGrad() ? true : false;
   for (int i = 0; i < numSequences; ++i) {
     crfs_[i].backward(output.value->getData() + numClasses_ * starts[i],
-                      output.grad->getData() + numClasses_ * starts[i],
                       label.ids->getData() + starts[i],
-                      starts[i + 1] - starts[i]);
-    if (weightLayer_) {
-      real weight = getInputValue(*weightLayer_)->getElement(i, 0);
-      MatrixPtr grad = output.grad->subRowMatrix(starts[i], starts[i + 1]);
-      grad->mulScalar(weight);
+                      starts[i + 1] - starts[i],
+                      needWGrad);
+    real instanceWeight = weightLayer_
+                              ? getInputValue(*weightLayer_)->getElement(i, 0)
+                              : real(1.0f);
+    instanceWeight *= coeff_;
+
+    MatrixPtr grad = output.grad->subRowMatrix(starts[i], starts[i + 1]);
+    grad->add(*crfs_[i].getXGrad(), real(1.0f), instanceWeight);
+    if (needWGrad) {
+      weight_->getWGrad()->add(
+          *crfs_[i].getWGrad(), real(1.0f), instanceWeight);
     }
   }
 
-  if (coeff_ != real(1.0f)) {
-    output.grad->mulScalar(coeff_);
-  }
-
   parameter_->incUpdate(callback);
 }
 

paddle/gserver/layers/CRFLayer.h

@@ -38,8 +38,9 @@ protected:
   size_t numClasses_;
   ParameterPtr parameter_;
   std::vector<LinearChainCRF> crfs_;
-  LayerPtr weightLayer_;  // weight for each sequence
-  real coeff_;            // weight for the layer
+  LayerPtr weightLayer_;            // weight for each sequence
+  std::unique_ptr<Weight> weight_;  // parameters
+  real coeff_;                      // weight for the layer
 };
 
 }  // namespace paddle

paddle/gserver/layers/Layer.cpp

@@ -381,8 +381,7 @@ void Layer::backwardActivation() {
 void Layer::forwardDropOut() {
   auto& outV = getOutputValue();
 
-  if (passType_ == PASS_TRAIN || passType_ == PASS_METRIC_TRAIN ||
-      passType_ == PASS_METRIC_TRAIN_WITH_NOERROR) {
+  if (passType_ == PASS_TRAIN) {
     // new dropOutMask_ if dropOutMask_ is null ptr
     Matrix::resizeOrCreate(dropOutMask_,
                            outV->getHeight(),

paddle/gserver/layers/LinearChainCRF.cpp

@@ -17,18 +17,12 @@ limitations under the License. */
 
 namespace paddle {
 
-LinearChainCRF::LinearChainCRF(int numClasses, real* para, real* grad)
+LinearChainCRF::LinearChainCRF(int numClasses, real* para)
     : numClasses_(numClasses) {
   a_ = Matrix::create(para, 1, numClasses_);
   b_ = Matrix::create(para + numClasses_, 1, numClasses_);
   w_ = Matrix::create(para + 2 * numClasses_, numClasses_, numClasses_);
 
-  if (grad) {
-    da_ = Matrix::create(grad, 1, numClasses_);
-    db_ = Matrix::create(grad + numClasses_, 1, numClasses_);
-    dw_ = Matrix::create(grad + 2 * numClasses_, numClasses_, numClasses_);
-  }
-
   ones_ = Matrix::create(1, numClasses_);
   ones_->one();
 
@@ -107,19 +101,24 @@ real LinearChainCRF::forward(real* x, int* s, int length) {
   return -ll;
 }
 
-void LinearChainCRF::backward(real* x, real* dx, int* s, int length) {
+void LinearChainCRF::backward(real* x, int* s, int length, bool needWGrad) {
   MatrixPtr matX = Matrix::create(x, length, numClasses_);
-  MatrixPtr matDX = Matrix::create(dx, length, numClasses_);
-  MatrixPtr matGrad = Matrix::create(length, numClasses_);
+  Matrix::resizeOrCreate(matGrad_, length, numClasses_);
   Matrix::resizeOrCreate(beta_, length, numClasses_);
   real* b = b_->getData();
-  real* dw = dw_ ? dw_->getData() : nullptr;
+  if (needWGrad) {
+    Matrix::resizeOrCreate(matWGrad_, numClasses_ + 2, numClasses_);
+    matWGrad_->zeroMem();
+    da_ = matWGrad_->subRowMatrix(0, 1);
+    db_ = matWGrad_->subRowMatrix(1, 2);
+    dw_ = matWGrad_->subRowMatrix(2, numClasses_ + 2);
+  }
 
   real* alpha = alpha_->getData();
   real* beta = beta_->getData();
   real* expW = expW_->getData();
   real* expX = expX_->getData();
-  real* grad = matGrad->getData();
+  real* grad = matGrad_->getData();
 
   for (int i = 0; i < numClasses_; ++i) {
     beta[(length - 1) * numClasses_ + i] = exp(b[i]);
@@ -140,39 +139,38 @@ void LinearChainCRF::backward(real* x, real* dx, int* s, int length) {
     normalizeL1(beta + k * numClasses_, numClasses_);
   }
 
-  matGrad->dotMul(*alpha_, *beta_);
-  matGrad->rowNormalizeL1(*matGrad);
+  matGrad_->dotMul(*alpha_, *beta_);
+  matGrad_->rowNormalizeL1(*matGrad_);
   for (int k = 0; k < length; ++k) {
     grad[k * numClasses_ + s[k]] -= (real)1;
   }
-  matDX->add(*matGrad);
-  if (da_) {
-    da_->add(*matGrad->subMatrix(/* startRow= */ 0, /* numRows= */ 1));
-  }
-  if (db_) {
-    db_->add(*matGrad->subMatrix(/* startRow= */ length - 1, 1));
-  }
 
-  beta_->dotMul(*beta_, *expX_);
-  beta_->rowNormalizeL1(*beta_);
+  if (needWGrad) {
+    da_->add(*matGrad_->subMatrix(/* startRow= */ 0, /* numRows= */ 1));
+    db_->add(*matGrad_->subMatrix(/* startRow= */ length - 1, 1));
 
-  for (int k = 1; dw && k < length; ++k) {
-    real sum = 0;
-    for (int i = 0; i < numClasses_; ++i) {
-      for (int j = 0; j < numClasses_; ++j) {
-        sum += expW[i * numClasses_ + j] * alpha[(k - 1) * numClasses_ + i] *
-               beta[k * numClasses_ + j];
+    beta_->dotMul(*beta_, *expX_);
+    beta_->rowNormalizeL1(*beta_);
+
+    real* dw = dw_->getData();
+    for (int k = 1; k < length; ++k) {
+      real sum = 0;
+      for (int i = 0; i < numClasses_; ++i) {
+        for (int j = 0; j < numClasses_; ++j) {
+          sum += expW[i * numClasses_ + j] * alpha[(k - 1) * numClasses_ + i] *
+                 beta[k * numClasses_ + j];
+        }
       }
-    }
-    sum = 1 / sum;
-    for (int i = 0; i < numClasses_; ++i) {
-      for (int j = 0; j < numClasses_; ++j) {
-        dw[i * numClasses_ + j] += sum * expW[i * numClasses_ + j] *
-                                   alpha[(k - 1) * numClasses_ + i] *
-                                   beta[k * numClasses_ + j];
+      sum = 1 / sum;
+      for (int i = 0; i < numClasses_; ++i) {
+        for (int j = 0; j < numClasses_; ++j) {
+          dw[i * numClasses_ + j] += sum * expW[i * numClasses_ + j] *
+                                     alpha[(k - 1) * numClasses_ + i] *
+                                     beta[k * numClasses_ + j];
+        }
       }
+      dw[s[k - 1] * numClasses_ + s[k]] -= (real)1;
     }
-    dw[s[k - 1] * numClasses_ + s[k]] -= (real)1;
   }
 }
 

paddle/gserver/layers/LinearChainCRF.h

@@ -21,7 +21,7 @@ namespace paddle {
 class LinearChainCRF {
 public:
   /**
-   * The size of para and grad must be \f$(numClasses + 2) * numClasses\f$.
+   * The size of para must be \f$(numClasses + 2) * numClasses\f$.
    * The first numClasses values of para are for starting weights (\f$a\f$).
    * The next numClasses values of para are for ending weights (\f$b\f$),
    * The remaning values are for transition weights (\f$w\f$).
@@ -34,7 +34,7 @@ public:
    * all possible
    * sequences is \f$1\f$, and \f$x\f$ is the input feature to the CRF.
    */
-  LinearChainCRF(int numClasses, real* para, real* grad);
+  LinearChainCRF(int numClasses, real* para);
 
   /**
    * Calculate the negative log likelihood of s given x.
@@ -45,29 +45,45 @@ public:
 
   /**
    * Calculate the gradient with respect to x, a, b, and w.
-   * The gradient of x will be stored in dx.
    * backward() can only be called after a corresponding call to forward() with
    * the same x, s and length.
-   * @note The gradient is added to dx and grad (provided at constructor).
+   * The gradient with respect to a, b, and w will not be calculated if
+   * needWGrad is false.
+   * @note Please call getWGrad() and getXGrad() to get the gradient with
+   * respect to (a, b, w) and x respectively.
    */
-  void backward(real* x, real* dx, int* s, int length);
+  void backward(real* x, int* s, int length, bool needWGrad);
 
   /**
    * Find the most probable sequence given x. The result will be stored in s.
    */
   void decode(real* x, int* s, int length);
 
+  /*
+   * Return the gradient with respect to (a, b, w). It can only be called after
+   * a corresponding call to backward().
+   */
+  MatrixPtr getWGrad() { return matWGrad_; }
+
+  /*
+   * Return the gradient with respect to x. It can only be called after a
+   * corresponding call to backward().
+   */
+  MatrixPtr getXGrad() { return matGrad_; }
+
 protected:
   int numClasses_;
   MatrixPtr a_;
   MatrixPtr b_;
   MatrixPtr w_;
+  MatrixPtr matWGrad_;
   MatrixPtr da_;
   MatrixPtr db_;
   MatrixPtr dw_;
   MatrixPtr ones_;
 
   MatrixPtr expX_;
+  MatrixPtr matGrad_;
   MatrixPtr alpha_;
   MatrixPtr beta_;
   MatrixPtr maxX_;

paddle/gserver/tests/CMakeLists.txt

@@ -18,6 +18,14 @@ add_unittest_without_exec(test_LayerGrad
 add_test(NAME test_LayerGrad
     COMMAND test_LayerGrad)
 
+################ test_CRFLayerGrad ####################
+add_unittest_without_exec(test_CRFLayerGrad
+    test_CRFLayerGrad.cpp
+    LayerGradUtil.cpp)
+add_test(NAME test_CRFLayerGrad
+    COMMAND test_CRFLayerGrad)
+
+
 add_unittest_without_exec(test_ActivationGrad
     test_ActivationGrad.cpp
     LayerGradUtil.cpp)

paddle/gserver/tests/test_CRFLayerGrad.cpp

+/* Copyright (c) 2016 Baidu, Inc. 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 <gtest/gtest.h>
+#include "ModelConfig.pb.h"
+#include "paddle/gserver/layers/DataLayer.h"
+#include "paddle/gserver/layers/LinearChainCRF.h"
+#include "paddle/trainer/Trainer.h"
+
+#include "LayerGradUtil.h"
+#include "paddle/testing/TestUtil.h"
+
+using namespace paddle;  // NOLINT
+
+DECLARE_int32(gpu_id);
+DECLARE_bool(thread_local_rand_use_global_seed);
+
+static inline bool getNextSequence(std::vector<int>& seq, int numClasses) {
+  for (auto& v : seq) {
+    if (++v < numClasses) {
+      return true;
+    }
+    v = 0;
+  }
+  return false;
+}
+
+// log(exp(x) + exp(y))
+static inline real logSum(real x, real y) {
+  real maxValue = std::max(x, y);
+  if (std::isinf(maxValue)) {
+    return -std::numeric_limits<real>::infinity();
+  } else {
+    return maxValue + log(exp(x - maxValue) + exp(y - maxValue));
+  }
+}
+
+static inline std::vector<int> genRandLabels(int numClasses, int length) {
+  std::vector<int> labels(length);
+  for (int i = 0; i < length; ++i) {
+    labels[i] = rand() % numClasses;  // NOLINT
+  }
+  return labels;
+}
+
+TEST(CRFLayer, cost) {
+  const int numClasses = 4;
+  CpuVector para(numClasses * (numClasses + 2));
+  real* a = para.getData();
+  real* b = para.getData() + numClasses;
+  real* w = para.getData() + 2 * numClasses;
+  LinearChainCRF crf(4, para.getData());
+  for (int length : {1, 2, 3, 10}) {
+    for (int tries = 0; tries < 10; ++tries) {
+      CpuMatrix x(length, numClasses);
+      x.randomizeUniform();
+      para.randnorm(0, 2);
+
+      std::vector<int> goldenLabels = genRandLabels(numClasses, length);
+
+      real cost = crf.forward(x.getData(), goldenLabels.data(), length);
+
+      real logZ = -std::numeric_limits<real>::infinity();
+      real logNominator = -std::numeric_limits<real>::infinity();
+      std::vector<int> testResult(length, 0);
+      do {
+        real score = a[testResult.front()];
+        score += x.getElement(0, testResult.front());
+        for (int k = 1; k < length; ++k) {
+          score += x.getElement(k, testResult[k]) +
+                   w[numClasses * testResult[k - 1] + testResult[k]];
+        }
+        score += b[testResult.back()];
+        logZ = logSum(logZ, score);
+
+        if (goldenLabels == testResult) {
+          logNominator = score;
+        }
+      } while (getNextSequence(testResult, numClasses));
+
+      real trueCost = -logNominator + logZ;
+
+      real diff = fabs(trueCost - cost);
+      diff /= fabs(cost) < fabs(trueCost) ? fabs(cost) : fabs(trueCost);
+      VLOG(1) << "cost=" << cost << " trueCost=" << trueCost << " diff=" << diff
+              << std::endl;
+      if (typeid(real) == typeid(double)) {  // NOLINT
+        EXPECT_LE(diff, 1e-10);
+      } else {
+        EXPECT_LE(diff, 5e-3);
+      }
+    }
+  }
+}
+
+inline real epsilon() { return typeid(real) == typeid(double) ? 1e-10 : 0.06; }
+
+TestConfig initTestConfig(size_t numClasses, bool withWeight) {
+  TestConfig config;
+  config.layerConfig.set_type("crf");
+  config.layerConfig.set_size(numClasses);
+  config.biasSize = 0;
+
+  config.inputDefs.push_back({INPUT_SEQUENCE_DATA,
+                              "layer_0",
+                              numClasses,
+                              numClasses * (numClasses + 2)});
+  config.layerConfig.add_inputs();
+  config.inputDefs.push_back(
+      {INPUT_SEQUENCE_LABEL, "layer_label", numClasses, 0});
+  config.layerConfig.add_inputs();
+
+  if (withWeight) {
+    config.inputDefs.push_back({INPUT_DENSE_DIM_DATA, "layer_weight", 1, 0});
+    config.layerConfig.add_inputs();
+  }
+
+  return config;
+}
+
+TEST(Layer, CRFLayer) {
+  size_t numClasses = 10;
+  for (int tries = 0; tries < 5; ++tries) {
+    TestConfig config = initTestConfig(numClasses, /* withWeight= */ false);
+    for (int length : {1, 3, 100}) {
+      // Not support GPU now
+      testLayerGrad(config,
+                    "crf",
+                    length,
+                    /* trans= */ false,
+                    /* useGpu= */ false,
+                    /* useWeight= */ false,
+                    epsilon());
+    }
+  }
+}
+
+TEST(Layer, CRFLayerUseWeight) {
+  size_t numClasses = 10;
+  for (int tries = 0; tries < 5; ++tries) {
+    TestConfig config = initTestConfig(numClasses, /* withWeight= */ true);
+    for (int length : {1, 3, 100}) {
+      // Not support GPU now
+      testLayerGrad(config,
+                    "crf",
+                    length,
+                    /* trans= */ false,
+                    /* useGpu= */ false,
+                    /* useWeight= */ false,
+                    epsilon());
+    }
+  }
+}
+
+int main(int argc, char** argv) {
+  initMain(argc, argv);
+  hl_start();
+  hl_init(FLAGS_gpu_id);
+  FLAGS_thread_local_rand_use_global_seed = true;
+  srand(1);
+  testing::InitGoogleTest(&argc, argv);
+  return RUN_ALL_TESTS();
+}

paddle/gserver/tests/test_LayerGrad.cpp

@@ -276,27 +276,6 @@ TEST(Layer, AddtoLayer) {
   }
 }
 
-TEST(Layer, CRFLayer) {
-  TestConfig config;
-  config.layerConfig.set_type("crf");
-  config.layerConfig.set_size(10);
-  config.biasSize = 0;
-
-  config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 120});
-  config.inputDefs.push_back({INPUT_SEQUENCE_LABEL, "layer_1", 10, 0});
-  config.layerConfig.add_inputs();
-  config.layerConfig.add_inputs();
-
-  // Not support GPU now
-  testLayerGrad(config,
-                "crf",
-                100,
-                /* trans */ false,
-                /* useGpu */ false,
-                false /*useWeight*/,
-                0.03 /*epsilon*/);
-}
-
 TEST(Layer, CTCLayer) {
   TestConfig config;
   config.layerConfig.set_type("ctc");

paddle/gserver/tests/test_LinearChainCRF.cpp

@@ -36,7 +36,7 @@ TEST(LinearChainCRF, decoding) {
   real* a = para.getData();
   real* b = para.getData() + numClasses;
   real* w = para.getData() + 2 * numClasses;
-  LinearChainCRF crf(4, para.getData(), nullptr);
+  LinearChainCRF crf(4, para.getData());
   for (int length : {1, 2, 3, 10}) {
     for (int tries = 0; tries < 10; ++tries) {
       CpuMatrix x(length, numClasses);

paddle/pserver/BaseClient.h

@@ -30,9 +30,6 @@ namespace paddle {
  * the first solution arms with sendThreads_/recvThreads_ and sendJobQueue_/
  * recvJobQueue_. the second solution use some shared thread pool to manage
  * connections.
- * In addition to pserver, metric learning also uses network to exchange
- * features within multi-machines, so this class just abstracts some basic
- * threads and queue buffer creation for them
  */
 class BaseClient {
 protected:

paddle/pserver/ParameterServer2.cpp

@@ -367,11 +367,8 @@ void ParameterServer2::addGradient(const SendParameterRequest& request,
                                    std::vector<Buffer>* outputBuffers) {
   VLOG(1) << "pserver: addGradient";
 
-/// forwardbackward delta from all trainers
-/// indicate the fluctuation caused by forwardbackward.
-#ifndef PADDLE_METRIC_LEARNING
-  // @TODO(yanfei):
-  // add support tuning forwardbackward balance for metric learning
+  // forwardbackward delta from all trainers
+  // indicate the fluctuation caused by forwardbackward.
   if (!numPassFinishClients_) {
     REGISTER_BARRIER_DELTA_SERVER_SET(
         *statSet_,
@@ -381,7 +378,6 @@ void ParameterServer2::addGradient(const SendParameterRequest& request,
         request.forwardbackward_time(),
         isSparseServer_ ? "_sparseUpdater" : "_denseUpdater");
   }
-#endif
 
   {
     /// approximately pure network overhead

paddle/trainer/Trainer.h

@@ -30,10 +30,6 @@ limitations under the License. */
 #include "TrainerConfigHelper.h"
 #include "TrainerInternal.h"
 
-#ifdef PADDLE_METRIC_LEARNING
-#include "paddle/internals/metric_learning/MetricTrainer.h"
-#endif
-
 DECLARE_int32(num_passes);
 
 namespace paddle {
@@ -201,12 +197,8 @@ protected:
   // parameter util
   std::unique_ptr<ParameterUtil> paramUtil_;
 
-#ifdef PADDLE_METRIC_LEARNING
-  MetricTrainer trainerInternal_;
-#else
   // trainer Internal
   TrainerInternal trainerInternal_;
-#endif
 };
 
 }  // namespace paddle

paddle/utils/Flags.cpp

@@ -30,7 +30,6 @@ DEFINE_bool(parallel_nn,
 DEFINE_int32(trainer_count, 1, "Defined how many trainers to train");
 DEFINE_int32(gpu_id, 0, "Which gpu core to use");
 DEFINE_int32(port, 20134, "Listening port for pserver");
-DEFINE_int32(data_server_port, 21134, "Listening port for dserver");
 DEFINE_int32(ports_num,
              1,
              "Number of ports for sending dense parameter,"

paddle/utils/Flags.h

@@ -19,7 +19,6 @@ limitations under the License. */
 DECLARE_bool(parallel_nn);
 DECLARE_int32(async_count);
 DECLARE_int32(port);
-DECLARE_int32(data_server_port);
 DECLARE_bool(use_gpu);
 DECLARE_int32(gpu_id);
 DECLARE_int32(trainer_count);

paddle/utils/GlobalConstants.h

@@ -23,11 +23,6 @@ enum PassType {
   PASS_TEST,    // Test pass
   PASS_GC,      // Gradient Check pass
   PASS_METRIC,  // pass for generate template output with no drop rate.
-  // pass for metric learning training with metric learning error, only used
-  // when we are doing KNN evaluation.
-  PASS_METRIC_TRAIN,
-  PASS_METRIC_TRAIN_WITH_NOERROR,  // Pass for metric learning training
-                                   // with no evaluation.
 };
 
 enum ParameterType {

python/paddle/trainer/config_parser.py

@@ -2301,14 +2301,9 @@ def Generator(
 
 @config_layer('expand')
 class ExpandLayer(LayerBase):
-    def __init__(self,
-                 name,
-                 inputs,
-                 trans_type='non-seq',
-                 device=None,
-                 bias=False):
+    def __init__(self, name, inputs, trans_type='non-seq', bias=False, **xargs):
         super(ExpandLayer, self).__init__(
-            name, 'expand', 0, inputs=inputs, device=device)
+            name, 'expand', 0, inputs=inputs, **xargs)
         config_assert(
             len(self.inputs) == 2, 'ExpandLayer takes 2 and only 2 inputs')
         self.config.trans_type = trans_type
@@ -2339,11 +2334,10 @@ class MaxLayer(LayerBase):
                  inputs,
                  trans_type='non-seq',
                  active_type='linear',
-                 device=None,
                  bias=False,
-                 output_max_index=None):
-        super(MaxLayer, self).__init__(
-            name, 'max', 0, inputs=inputs, device=device)
+                 output_max_index=None,
+                 **xargs):
+        super(MaxLayer, self).__init__(name, 'max', 0, inputs=inputs, **xargs)
         config_assert(len(self.inputs) == 1, 'MaxLayer must have 1 input')
         self.config.trans_type = trans_type
         self.config.active_type = active_type
@@ -2390,15 +2384,15 @@ class SequenceLastInstanceLayer(LayerBase):
                  inputs,
                  active_type='linear',
                  trans_type='non-seq',
-                 device=None,
-                 bias=False):
+                 bias=False,
+                 **xargs):
         super(SequenceLastInstanceLayer, self).__init__(
             name,
             'seqlastins',
             0,
             inputs=inputs,
-            device=device,
-            active_type=active_type)
+            active_type=active_type,
+            **xargs)
         config_assert(
             len(inputs) == 1, 'SequenceLastInstanceLayer must have 1 input')
         self.config.trans_type = trans_type
@@ -2410,39 +2404,29 @@ class SequenceLastInstanceLayer(LayerBase):
 
 @config_layer('seqfirstins')
 class SequenceFirstInstanceLayer(SequenceLastInstanceLayer):
-    def __init__(
-            self,
-            name,
-            inputs,
-            active_type='linear',
-            trans_type='non-seq',
-            device=None,
-            bias=False, ):
+    def __init__(self,
+                 name,
+                 inputs,
+                 active_type='linear',
+                 trans_type='non-seq',
+                 bias=False,
+                 **xargs):
         super(SequenceFirstInstanceLayer, self).__init__(
-            name,
-            inputs=inputs,
-            active_type=active_type,
-            device=device,
-            bias=bias)
+            name, inputs=inputs, active_type=active_type, bias=bias, **xargs)
         self.config.trans_type = trans_type
         self.config.select_first = True
 
 
 @config_layer('seqconcat')
 class SequenceConcatLayer(LayerBase):
-    def __init__(self,
-                 name,
-                 inputs,
-                 active_type='linear',
-                 device=None,
-                 bias=False):
+    def __init__(self, name, inputs, active_type='linear', bias=False, **xargs):
         super(SequenceConcatLayer, self).__init__(
             name,
             'seqconcat',
             0,
             inputs=inputs,
-            device=device,
-            active_type=active_type)
+            active_type=active_type,
+            **xargs)
         config_assert(
             len(inputs) == 2, 'SequenceConcatLayer must have 2 inputs')
         for input_index in xrange(len(self.inputs)):
@@ -2458,15 +2442,15 @@ class SequenceReshapeLayer(LayerBase):
                  size,
                  inputs,
                  active_type='linear',
-                 device=None,
-                 bias=False):
+                 bias=False,
+                 **xargs):
         super(SequenceReshapeLayer, self).__init__(
             name,
             'seqreshape',
             size,
             inputs=inputs,
-            device=device,
-            active_type=active_type)
+            active_type=active_type,
+            **xargs)
         config_assert(
             len(inputs) == 1, 'SequenceReshapeLayer must have 1 inputs')
         self.set_layer_size(size)
@@ -2475,19 +2459,9 @@ class SequenceReshapeLayer(LayerBase):
 
 @config_layer('subseq')
 class SubSequenceLayer(LayerBase):
-    def __init__(self,
-                 name,
-                 inputs,
-                 active_type='linear',
-                 device=None,
-                 bias=False):
+    def __init__(self, name, inputs, active_type='linear', bias=False, **xargs):
         super(SubSequenceLayer, self).__init__(
-            name,
-            'subseq',
-            0,
-            inputs=inputs,
-            device=device,
-            active_type=active_type)
+            name, 'subseq', 0, inputs=inputs, active_type=active_type, **xargs)
         config_assert(len(inputs) == 3, 'SubSequenceLayer must have 3 inputs')
         input_layer0 = self.get_input_layer(0)
         size = input_layer0.size
@@ -2644,15 +2618,10 @@ class AverageLayer(LayerBase):
                  average_strategy='average',
                  trans_type='non-seq',
                  active_type='linear',
-                 device=None,
-                 bias=False):
+                 bias=False,
+                 **xargs):
         super(AverageLayer, self).__init__(
-            name,
-            'average',
-            0,
-            inputs=inputs,
-            device=device,
-            active_type=active_type)
+            name, 'average', 0, inputs=inputs, active_type=active_type, **xargs)
         self.config.average_strategy = average_strategy
         self.config.trans_type = trans_type
         config_assert(len(inputs) == 1, 'AverageLayer must have 1 input')
@@ -2676,9 +2645,9 @@ class CosSimLayer(LayerBase):
 
 @config_layer('tensor')
 class TensorLayer(LayerBase):
-    def __init__(self, name, size, inputs, device=None, bias=True, **xargs):
+    def __init__(self, name, size, inputs, bias=True, **xargs):
         super(TensorLayer, self).__init__(
-            name, 'tensor', size, inputs=inputs, device=device, **xargs)
+            name, 'tensor', size, inputs=inputs, **xargs)
         config_assert(len(self.inputs) == 2, 'TensorLayer must have 2 inputs')
         config_assert(size > 0, 'size must be positive')
         config_assert(inputs[1].parameter_name == None,
@@ -3029,7 +2998,7 @@ class CRFLayer(LayerBase):
         super(CRFLayer, self).__init__(name, 'crf', size, inputs, device=device)
         config_assert(2 <= len(self.inputs) <= 3,
                       'CRFLayer must have 2 or 3 inputs')
-        self.create_input_parameter(0, size * (size + 2), [size, size + 2])
+        self.create_input_parameter(0, size * (size + 2), [size + 2, size])
         self.config.coeff = coeff
 
 
@@ -3051,7 +3020,7 @@ class CRFDecodingLayer(LayerBase):
         config_assert(
             len(self.inputs) <= 2,
             'CRFDecodingLayer cannot have more than 2 inputs')
-        self.create_input_parameter(0, size * (size + 2), [size, size + 2])
+        self.create_input_parameter(0, size * (size + 2), [size + 2, size])
 
 
 @config_layer('ctc')

python/paddle/trainer_config_helpers/tests/configs/protostr/test_cost_layers.protostr

@@ -239,9 +239,9 @@ parameters {
   name: "___crf_layer_0__.w0"
   size: 24
   initial_mean: 0.0
-  initial_std: 0.5
-  dims: 4
+  initial_std: 0.408248290464
   dims: 6
+  dims: 4
   initial_strategy: 0
   initial_smart: true
 }