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

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

CMakeLists.txt

@@ -116,13 +116,14 @@ else()
 endif()
 
 set(WITH_MKLML ${WITH_MKL})
-if (WITH_MKL AND AVX2_FOUND)
-    set(WITH_MKLDNN ON)
-else()
-    message(STATUS "Do not have AVX2 intrinsics and disabled MKL-DNN")
-    set(WITH_MKLDNN OFF)
+if (NOT DEFINED WITH_MKLDNN)
+    if (WITH_MKL AND AVX2_FOUND)
+        set(WITH_MKLDNN ON)
+    else()
+        message(STATUS "Do not have AVX2 intrinsics and disabled MKL-DNN")
+        set(WITH_MKLDNN OFF)
+    endif()
 endif()
-
 ########################################################################################
 
 include(external/mklml)     # download mklml package

cmake/external/boost.cmake

@@ -23,8 +23,12 @@ set(BOOST_PROJECT       "extern_boost")
 # checked that the devtools package of CentOS 6 installs boost 1.41.0.
 # So we use 1.41.0 here.
 set(BOOST_VER           "1.41.0")
-set(BOOST_TAR           "boost_1_41_0")
-set(BOOST_URL           "http://paddlepaddledeps.cdn.bcebos.com/${BOOST_TAR}.tar.gz")
+if((NOT DEFINED BOOST_TAR) OR (NOT DEFINED BOOST_URL))
+    message(STATUS "use pre defined download url")
+    set(BOOST_TAR "boost_1_41_0" CACHE STRING "" FORCE)
+    set(BOOST_URL "http://paddlepaddledeps.cdn.bcebos.com/${BOOST_TAR}.tar.gz" CACHE STRING "" FORCE)
+endif()
+MESSAGE(STATUS "BOOST_TAR: ${BOOST_TAR}, BOOST_URL: ${BOOST_URL}")
 set(BOOST_SOURCES_DIR ${THIRD_PARTY_PATH}/boost)
 set(BOOST_DOWNLOAD_DIR  "${BOOST_SOURCES_DIR}/src/${BOOST_PROJECT}")
 set(BOOST_INCLUDE_DIR "${BOOST_DOWNLOAD_DIR}/${BOOST_TAR}" CACHE PATH "boost include directory." FORCE)

cmake/external/mklml.cmake

@@ -27,8 +27,12 @@ ENDIF()
 INCLUDE(ExternalProject)
 
 SET(MKLML_PROJECT       "extern_mklml")
-SET(MKLML_VER           "mklml_lnx_2018.0.3.20180406")
-SET(MKLML_URL           "http://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.tgz")
+IF((NOT DEFINED MKLML_VER) OR (NOT DEFINED MKLML_URL))
+  MESSAGE(STATUS "use pre defined download url")
+  SET(MKLML_VER "mklml_lnx_2018.0.3.20180406" CACHE STRING "" FORCE)
+  SET(MKLML_URL "http://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.tgz" CACHE STRING "" FORCE)
+ENDIF()
+MESSAGE(STATUS "MKLML_VER: ${MKLML_VER}, MKLML_URL: ${MKLML_URL}")
 SET(MKLML_SOURCE_DIR    "${THIRD_PARTY_PATH}/mklml")
 SET(MKLML_DOWNLOAD_DIR  "${MKLML_SOURCE_DIR}/src/${MKLML_PROJECT}")
 SET(MKLML_DST_DIR       "mklml")

cmake/inference_lib.cmake

@@ -162,4 +162,24 @@ copy(pybind_lib
   DSTS ${dst_dir}/${module}
 )
 
+# CMakeCache Info
+copy(cmake_cache
+  SRCS ${CMAKE_CURRENT_BINARY_DIR}/CMakeCache.txt
+  DSTS ${CMAKE_INSTALL_PREFIX})
+
 add_custom_target(inference_lib_dist DEPENDS ${inference_lib_dist_dep}) 
+
+# paddle fluid version
+execute_process(
+  COMMAND ${GIT_EXECUTABLE} log --pretty=format:%H -1
+  OUTPUT_VARIABLE PADDLE_GIT_COMMIT)
+set(version_file ${CMAKE_INSTALL_PREFIX}/version.txt)
+file(WRITE ${version_file}
+  "GIT COMMIT ID: ${PADDLE_GIT_COMMIT}\n"
+  "WITH_MKL: ${WITH_MKL}\n"
+  "WITH_GPU: ${WITH_GPU}\n")
+if(WITH_GPU)
+  file(APPEND ${version_file}
+    "CUDA version: ${CUDA_VERSION}\n"
+    "CUDNN version: v${CUDNN_MAJOR_VERSION}\n")
+endif()

doc/fluid/howto/optimization/cpu_profiling_cn.md

+# CPU性能调优
+
 此教程会介绍如何使用Python的cProfile包、Python库yep、Google perftools来进行性能分析 (profiling) 与调优（performance tuning）。
 
 Profling 指发现性能瓶颈。系统中的瓶颈可能和程序员开发过程中想象的瓶颈相去甚远。Tuning 指消除瓶颈。性能优化的过程通常是不断重复地 profiling 和 tuning。
@@ -8,7 +10,7 @@ PaddlePaddle 用户一般通过调用 Python API 编写深度学习程序。大
 * Python 与 C++ 混合代码的性能分析
 
 
-# Python代码的性能分析
+## Python代码的性能分析
 
 ### 生成性能分析文件
 

doc/fluid/howto/optimization/cpu_profiling_en.md

+# Tune CPU performance
+
 This tutorial introduces techniques we use to profile and tune the
 CPU performance of PaddlePaddle.  We will use Python packages
 `cProfile` and `yep`, and Google's `perftools`.
@@ -14,7 +16,7 @@ the profiling and tuning of
 1. the Python code and
 1. the mixture of Python and C++ code.
 
-# Profiling the Python Code
+## Profiling the Python Code
 
 ### Generate the Performance Profiling File
 

doc/v2/build_and_install/pip_install_cn.rst

@@ -10,20 +10,38 @@ PaddlePaddle可以使用常用的Python包管理工具
 使用pip安装
 ------------------------------
 
-
-执行下面的命令即可在当前机器上安装PaddlePaddle的运行时环境，并自动下载安装依赖软件，版本为cpu_avx_openblas。
+执行下面的命令即可在当前机器上安装PaddlePaddle的运行时环境，并自动下载安装依赖软件。
 
   .. code-block:: bash
 
      pip install paddlepaddle
 
+当前的默认版本为0.12.0，cpu_avx_openblas，您可以通过指定版本号来安装其它版本，例如:
+
+  .. code-block:: bash
+
+      pip install paddlepaddle==0.11.0
+
 
-如果需要安装支持GPU的版本（cuda7.5_cudnn5_avx_openblas），需要执行：
+如果需要安装支持GPU的版本（cuda8.0_cudnn5_avx_openblas），需要执行：
 
   .. code-block:: bash
 
      pip install paddlepaddle-gpu
 
+当前的默认版本也是0.12.0，PaddlePaddle针对不同需求提供了更多版本的安装包，部分列表如下：
+
+=================================   ========================================
+版本号                               版本说明
+=================================   ========================================
+paddlepaddle-gpu==0.12.0            使用CUDA 8.0和cuDNN 5编译的0.12.0版本
+paddlepaddle-gpu==0.11.0.post87     使用CUDA 8.0和cuDNN 7编译的0.11.0版本
+paddlepaddle-gpu==0.11.0.post8      使用CUDA 8.0和cuDNN 5编译的0.11.0版本
+paddlepaddle-gpu==0.11.0            使用CUDA 7.5和cuDNN 5编译的0.11.0版本
+=================================   ========================================
+
+您可以在 `Release History <https://pypi.org/project/paddlepaddle-gpu/#history>`_ 中找到paddlepaddle-gpu的各个发行版本。
+
 如果需要获取并安装最新的（开发分支）PaddlePaddle，可以从我们的CI系统中下载最新的whl安装包和c-api开发包并安装，
 您可以从下面的表格中找到需要的版本：
 
@@ -37,12 +55,11 @@ PaddlePaddle可以使用常用的Python包管理工具
     :header: "版本说明", "cp27-cp27mu", "cp27-cp27m"
     :widths: 1, 3, 3
 
-    "cpu_avx_mkl", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
-    "cpu_avx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
-    "cpu_noavx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
-    "cuda7.5_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
-    "cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
-    "cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
+    "cpu_avx_mkl", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_"
+    "cpu_avx_openblas", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_"
+    "cpu_noavx_openblas", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_"
+    "cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`_"
+    "cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`_"
 
 .. _pip_dependency:
 
@@ -69,7 +86,7 @@ PaddlePaddle发布的安装包会尽量对齐 `manylinux1 <https://www.python.or
 ------------------------------
 
 - paddlepaddle*.whl is not a supported wheel on this platform.
-  
+
   出现这个问题的主要原因是，没有找到和当前系统匹配的paddlepaddle安装包。请检查Python版本是否为2.7系列。另外最新的pip官方源中的安装包默认是manylinux1标准，需要使用最新的pip (>9.0.0) 才可以安装。可以使用下面的命令更新您的pip：
 
     .. code-block:: bash

doc/v2/build_and_install/pip_install_en.rst

@@ -12,20 +12,38 @@ Install using pip
 ------------------------------
 
 Run the following command to install PaddlePaddle on the current
-machine, it will also download requirements, the version is cpu_avx_openblas.
+machine, it will also download requirements.
 
   .. code-block:: bash
 
      pip install paddlepaddle
 
+the default version is 0.12.0, cpu_avx_openblas, you can specify the versions to satisfy your demands, like:
 
-If you wish to install GPU version (cuda7.5_cudnn5_avx_openblas), just run:
+  .. code-block:: bash
+
+      pip install paddlepaddle==0.11.0
+
+If you need to install a GPU-enabled version (cuda8.0_cudnn5_avx_openblas), you need to run:
 
   .. code-block:: bash
 
      pip install paddlepaddle-gpu
 
-If you wish to install the latest develop branch PaddlePaddle, 
+The default version is also 0.12.0, PaddlePaddle provides several versions of packages for different needs, as shown in the table:
+
+=================================   ========================================
+版本号                               版本说明
+=================================   ========================================
+paddlepaddle-gpu==0.12.0            0.12.0 built with CUDA 8.0 and cuDNN 5
+paddlepaddle-gpu==0.11.0.post87     0.11.0 built with CUDA 8.0 and cuDNN 7
+paddlepaddle-gpu==0.11.0.post8      0.11.0 built with CUDA 8.0 and cuDNN 5
+paddlepaddle-gpu==0.11.0            0.11.0 built with CUDA 7.5 and cuDNN 5
+=================================   ========================================
+
+You can find all versions released of paddlepaddle-gpu in `Release History <https://pypi.org/project/paddlepaddle-gpu/#history>`_ .
+
+If you wish to install the latest develop branch PaddlePaddle,
 you can download the latest whl package from our CI system. Access
 the below links, log in as guest, then click at the "Artifact"
 tab, you'll find the download link of whl packages.
@@ -40,12 +58,11 @@ If the links below shows up the login form, just click "Log in as guest" to star
     :header: "version", "cp27-cp27mu", "cp27-cp27m"
     :widths: 1, 3, 3
 
-    "cpu_avx_mkl", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
-    "cpu_avx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
-    "cpu_noavx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
-    "cuda7.5_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
-    "cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
-    "cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
+    "cpu_avx_mkl", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_"
+    "cpu_avx_openblas", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_"
+    "cpu_noavx_openblas", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_"
+    "cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`_"
+    "cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`_"
 
 .. _pip_dependency:
 
@@ -79,7 +96,7 @@ FAQ
 ------------------------------
 
 - paddlepaddle*.whl is not a supported wheel on this platform.
-  
+
   The main cause of this issue is that your current platform is
   not supported. Please check that you are using Python 2.7 series.
   Besides, pypi only supports manylinux1 standard, you'll need to

paddle/fluid/framework/data_type_transform.cc

@@ -91,6 +91,12 @@ void TransDataType(const OpKernelType& kernel_type_for_var,
     case proto::VarType::BOOL:
       framework::VisitDataType(dst_type, CastDataType<bool>(in, out, ctx));
       break;
+    case proto::VarType::INT16:
+      framework::VisitDataType(dst_type, CastDataType<bool>(in, out, ctx));
+      break;
+    case proto::VarType::UINT8:
+      framework::VisitDataType(dst_type, CastDataType<bool>(in, out, ctx));
+      break;
     default:
       PADDLE_THROW("Not support type %d", src_type);
   }

paddle/fluid/framework/details/multi_devices_graph_builder.cc

@@ -98,7 +98,7 @@ bool MultiDevSSAGraphBuilder::IsDistTrainOp(const OpDesc &op,
     return false;
   };
 
-  if (op.Type() == "split") {
+  if (op.Type() == "split" || op.Type() == "split_byref") {
     return checker(op.OutputArgumentNames(), send_op->InputArgumentNames());
   } else if (op.Type() == "concat") {
     return checker(op.InputArgumentNames(), send_op->OutputArgumentNames());

paddle/fluid/inference/tests/book/test_inference_image_classification.cc

@@ -21,6 +21,7 @@ DEFINE_string(fp16_dirname, "", "Directory of the float16 inference model.");
 DEFINE_int32(batch_size, 1, "Batch size of input data");
 DEFINE_int32(repeat, 1, "Running the inference program repeat times");
 DEFINE_bool(skip_cpu, false, "Skip the cpu test");
+DEFINE_bool(use_mkldnn, false, "Use MKLDNN to run inference");
 
 TEST(inference, image_classification) {
   if (FLAGS_dirname.empty() || FLAGS_batch_size < 1 || FLAGS_repeat < 1) {
@@ -58,8 +59,10 @@ TEST(inference, image_classification) {
     // Run inference on CPU
     LOG(INFO) << "--- CPU Runs: ---";
     LOG(INFO) << "Batch size is " << FLAGS_batch_size;
+    LOG(INFO) << "FLAGS_use_mkldnn: " << FLAGS_use_mkldnn;
     TestInference<paddle::platform::CPUPlace, false, true>(
-        dirname, cpu_feeds, cpu_fetchs1, FLAGS_repeat, is_combined);
+        dirname, cpu_feeds, cpu_fetchs1, FLAGS_repeat, is_combined,
+        FLAGS_use_mkldnn);
     LOG(INFO) << output1.dims();
   }
 

paddle/fluid/inference/tests/test_helper.h

@@ -133,11 +133,24 @@ std::vector<std::vector<int64_t>> GetFeedTargetShapes(
   return feed_target_shapes;
 }
 
+void EnableMKLDNN(
+    const std::unique_ptr<paddle::framework::ProgramDesc>& program) {
+  for (size_t bid = 0; bid < program->Size(); ++bid) {
+    auto* block = program->MutableBlock(bid);
+    for (auto* op : block->AllOps()) {
+      if (op->HasAttr("use_mkldnn")) {
+        op->SetAttr("use_mkldnn", true);
+      }
+    }
+  }
+}
+
 template <typename Place, bool CreateVars = true, bool PrepareContext = false>
 void TestInference(const std::string& dirname,
                    const std::vector<paddle::framework::LoDTensor*>& cpu_feeds,
                    const std::vector<paddle::framework::LoDTensor*>& cpu_fetchs,
-                   const int repeat = 1, const bool is_combined = false) {
+                   const int repeat = 1, const bool is_combined = false,
+                   const bool use_mkldnn = false) {
   // 1. Define place, executor, scope
   auto place = Place();
   auto executor = paddle::framework::Executor(place);
@@ -169,6 +182,9 @@ void TestInference(const std::string& dirname,
         "init_program",
         paddle::platform::DeviceContextPool::Instance().Get(place));
     inference_program = InitProgram(&executor, scope, dirname, is_combined);
+    if (use_mkldnn) {
+      EnableMKLDNN(inference_program);
+    }
   }
   // Disable the profiler and print the timing information
   paddle::platform::DisableProfiler(paddle::platform::EventSortingKey::kDefault,

paddle/fluid/operators/CMakeLists.txt

@@ -204,6 +204,7 @@ if(WITH_DISTRIBUTE)
     set_source_files_properties(send_recv_op_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
     cc_test(test_send_recv SRCS send_recv_op_test.cc DEPS prefetch_op send_op listen_and_serv_op sum_op executor)
     if(WITH_GPU)
+        set_source_files_properties(test_send_nccl_id.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
         cc_test(test_send_nccl_id SRCS test_send_nccl_id.cc DEPS send_op listen_and_serv_op executor)
         op_library(gen_nccl_id_op DEPS nccl_common sendrecvop_grpc)
         set_source_files_properties(gen_nccl_id_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})

paddle/fluid/operators/activation_mkldnn_op.cc

@@ -15,6 +15,7 @@
 #include "mkldnn.hpp"
 #include "paddle/fluid/operators/activation_op.h"
 #include "paddle/fluid/operators/mkldnn_activation_op.h"
+#include "paddle/fluid/platform/mkldnn_helper.h"
 
 namespace paddle {
 namespace operators {
@@ -23,6 +24,18 @@ using paddle::framework::Tensor;
 using paddle::platform::MKLDNNDeviceContext;
 
 namespace {
+std::string gethash(const mkldnn::memory::dims &operand_dims,
+                    const mkldnn::algorithm algorithm) {
+  auto dim2str = [](const mkldnn::memory::dims &operand_dims) {
+    std::string dstr = "";
+    for (size_t i = 0; i < operand_dims.size(); ++i) {
+      dstr += std::to_string(operand_dims[i]) + "-";
+    }
+    return dstr;
+  };
+  return dim2str(operand_dims) + std::to_string(algorithm);
+}
+
 template <typename T, typename ExecContext>
 void eltwise_forward(const ExecContext &ctx, mkldnn::algorithm algorithm,
                      const T alpha = 0, const T beta = 0) {
@@ -37,42 +50,70 @@ void eltwise_forward(const ExecContext &ctx, mkldnn::algorithm algorithm,
   const auto *src_data = src->template data<T>();
 
   auto *dst = ctx.template Output<Tensor>("Out");
-  const T *dst_data = dst->template mutable_data<T>(ctx.GetPlace());
+  T *dst_data = dst->template mutable_data<T>(ctx.GetPlace());
 
   // get memory dim
   PADDLE_ENFORCE(src->dims().size() == 2 || src->dims().size() == 4,
                  "Input dim must be with 2 or 4");
   std::vector<int> src_tz = framework::vectorize2int(src->dims());
 
-  // create memory description
-  auto data_md = src_tz.size() == 2
-                     ? platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
-                                               mkldnn::memory::format::nc)
-                     : platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
-                                               mkldnn::memory::format::nchw);
-
-  // create memory primitives
-  auto src_memory =
-      mkldnn::memory({data_md, mkldnn_engine},
-                     static_cast<void *>(const_cast<float *>(src_data)));
-  auto dst_memory =
-      mkldnn::memory({data_md, mkldnn_engine},
-                     static_cast<void *>(const_cast<float *>(dst_data)));
-
-  auto forward_desc = mkldnn::eltwise_forward::desc(
-      mkldnn::prop_kind::forward_training, algorithm, data_md, alpha, beta);
-
-  // save prim desc into global device context to be referred in backward path
-  const std::string key = ctx.op().Output("Out");
-  const std::string key_eltwise_pd = key + "@eltwise_pd";
-  auto forward_pd = std::make_shared<mkldnn::eltwise_forward::primitive_desc>(
-      forward_desc, mkldnn_engine);
-  dev_ctx.SetBlob(key_eltwise_pd, forward_pd);
-
-  auto eltwise = mkldnn::eltwise_forward(*forward_pd, src_memory, dst_memory);
+  const std::string key = gethash(src_tz, algorithm);
+  const std::string key_src_data =
+      key + ctx.op().Output("Out") + "@eltwise_fwd_src_data";
+  const std::string key_src_mem = key + "@eltwise_fwd_src_mem";
+  const std::string key_dst_mem = key + "@eltwise_fwd_dst_mem";
+  const std::string key_fwd = key + "@eltwise_fwd";
+
+  auto p_fwd = std::static_pointer_cast<mkldnn::eltwise_forward>(
+      dev_ctx.GetBlob(key_fwd));
+
+  // save input data to be referred in backward path
+  auto p_src_data = std::make_shared<const T *>(src_data);
+  dev_ctx.SetBlob(key_src_data, p_src_data);
+
+  if (p_fwd == nullptr) {
+    // create memory description
+    auto data_md = src_tz.size() == 2
+                       ? platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
+                                                 mkldnn::memory::format::nc)
+                       : platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
+                                                 mkldnn::memory::format::nchw);
+
+    // create memory primitives
+    auto p_src_mem = std::make_shared<mkldnn::memory>(mkldnn::memory(
+        {data_md, mkldnn_engine}, platform::to_void_cast(src_data)));
+    dev_ctx.SetBlob(key_src_mem, p_src_mem);
+
+    auto p_dst_mem = std::make_shared<mkldnn::memory>(mkldnn::memory(
+        {data_md, mkldnn_engine}, platform::to_void_cast(dst_data)));
+    dev_ctx.SetBlob(key_dst_mem, p_dst_mem);
+
+    auto fwd_desc = mkldnn::eltwise_forward::desc(
+        mkldnn::prop_kind::forward_training, algorithm, data_md, alpha, beta);
+    auto p_fwd_pd = std::make_shared<mkldnn::eltwise_forward::primitive_desc>(
+        fwd_desc, mkldnn_engine);
+    const std::string key_fwd_pd = key + "eltwise_fwd_pd";
+    dev_ctx.SetBlob(key_fwd_pd, p_fwd_pd);
+    p_fwd = std::make_shared<mkldnn::eltwise_forward>(
+        *p_fwd_pd, *(p_src_mem.get()), *(p_dst_mem.get()));
+    dev_ctx.SetBlob(key_fwd, p_fwd);
+  } else {
+    // primitives already exist
+    auto p_src_mem =
+        std::static_pointer_cast<mkldnn::memory>(dev_ctx.GetBlob(key_src_mem));
+    PADDLE_ENFORCE(p_src_mem != nullptr,
+                   "Fail to find eltwise p_src_mem in device context.");
+    auto p_dst_mem =
+        std::static_pointer_cast<mkldnn::memory>(dev_ctx.GetBlob(key_dst_mem));
+    PADDLE_ENFORCE(p_dst_mem != nullptr,
+                   "Fail to find eltwise p_src_mem in device context.");
+
+    p_src_mem->set_data_handle(platform::to_void_reinterpret_cast(src_data));
+    p_dst_mem->set_data_handle(dst_data);
+  }
 
   // push primitive to stream and wait until it's executed
-  std::vector<mkldnn::primitive> pipeline = {eltwise};
+  std::vector<mkldnn::primitive> pipeline = {*(p_fwd.get())};
   mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
 }
 
@@ -83,8 +124,7 @@ void eltwise_grad(const ExecContext &ctx, mkldnn::algorithm algorithm,
   const auto &mkldnn_engine = dev_ctx.GetEngine();
 
   // get buffers
-  const auto *x = ctx.template Input<Tensor>("X");
-  const auto *src = x->template data<T>();
+  const auto *out = ctx.template Input<Tensor>("Out");
 
   auto *dout = ctx.template Input<Tensor>(framework::GradVarName("Out"));
   const auto *diff_dst = dout->template data<T>();
@@ -94,45 +134,73 @@ void eltwise_grad(const ExecContext &ctx, mkldnn::algorithm algorithm,
   const T *diff_src = dx->template mutable_data<T>(ctx.GetPlace());
 
   // get memory dim
-  std::vector<int> src_tz = framework::vectorize2int(x->dims());
-
-  // create memory description
-  auto data_md = src_tz.size() == 2
-                     ? platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
-                                               mkldnn::memory::format::nc)
-                     : platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
-                                               mkldnn::memory::format::nchw);
-
-  // create memory primitives
-  auto src_memory = mkldnn::memory(
-      {data_md, mkldnn_engine}, static_cast<void *>(const_cast<float *>(src)));
-  auto diff_src_memory =
-      mkldnn::memory({data_md, mkldnn_engine},
-                     static_cast<void *>(const_cast<float *>(diff_src)));
-  auto diff_dst_memory =
-      mkldnn::memory({data_md, mkldnn_engine},
-                     static_cast<void *>(const_cast<float *>(diff_dst)));
-
-  auto backward_desc =
-      mkldnn::eltwise_backward::desc(algorithm, data_md, data_md, alpha, beta);
-
-  // retrieve eltwise primitive desc from device context
-  const std::string key = ctx.op().Input("Out");
-  const std::string key_eltwise_pd = key + "@eltwise_pd";
-  const std::shared_ptr<void> forward_pd = dev_ctx.GetBlob(key_eltwise_pd);
-  PADDLE_ENFORCE(forward_pd != nullptr,
-                 "Fail to find eltwise_pd in device context");
-  auto *p_forward_pd =
-      static_cast<mkldnn::eltwise_forward::primitive_desc *>(forward_pd.get());
-
-  auto eltwise_bwd_prim_desc = mkldnn::eltwise_backward::primitive_desc(
-      backward_desc, mkldnn_engine, *p_forward_pd);
-
-  auto eltwise_bwd = mkldnn::eltwise_backward(eltwise_bwd_prim_desc, src_memory,
-                                              diff_dst_memory, diff_src_memory);
+  std::vector<int> src_tz = framework::vectorize2int(out->dims());
+
+  const std::string key = gethash(src_tz, algorithm);
+  const std::string key_diff_src_mem = key + "@eltwise_diff_src_mem";
+  const std::string key_diff_dst_mem = key + "@eltwise_diff_dst_mem";
+  const std::string key_grad = key + "@eltwise_grad";
+
+  const std::string key_src_data =
+      key + ctx.op().Input("Out") + "@eltwise_fwd_src_data";
+  const auto p_src_data =
+      std::static_pointer_cast<T *>(dev_ctx.GetBlob(key_src_data));
+
+  const std::string key_src_mem = key + "@eltwise_fwd_src_mem";
+  auto p_src_mem =
+      std::static_pointer_cast<mkldnn::memory>(dev_ctx.GetBlob(key_src_mem));
+  p_src_mem->set_data_handle(*p_src_data.get());
+
+  auto p_grad = std::static_pointer_cast<mkldnn::eltwise_forward::primitive>(
+      dev_ctx.GetBlob(key_grad));
+
+  if (p_grad == nullptr) {
+    // create memory description
+    auto data_md = src_tz.size() == 2
+                       ? platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
+                                                 mkldnn::memory::format::nc)
+                       : platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
+                                                 mkldnn::memory::format::nchw);
+
+    // create memory primitives
+    std::shared_ptr<void> p_diff_src_mem =
+        std::make_shared<mkldnn::memory>(mkldnn::memory(
+            {data_md, mkldnn_engine}, platform::to_void_cast(diff_src)));
+    dev_ctx.SetBlob(key_diff_src_mem, p_diff_src_mem);
+    std::shared_ptr<void> p_diff_dst_mem =
+        std::make_shared<mkldnn::memory>(mkldnn::memory(
+            {data_md, mkldnn_engine}, platform::to_void_cast(diff_dst)));
+    dev_ctx.SetBlob(key_diff_dst_mem, p_diff_dst_mem);
+
+    auto bwd_desc = mkldnn::eltwise_backward::desc(algorithm, data_md, data_md,
+                                                   alpha, beta);
+
+    const std::string key_fwd_pd = key + "eltwise_fwd_pd";
+    auto *p_fwd_pd = static_cast<mkldnn::eltwise_forward::primitive_desc *>(
+        dev_ctx.GetBlob(key_fwd_pd).get());
+
+    auto eltwise_bwd_prim_desc = mkldnn::eltwise_backward::primitive_desc(
+        bwd_desc, mkldnn_engine, *p_fwd_pd);
+
+    p_grad = std::make_shared<mkldnn::eltwise_backward>(
+        eltwise_bwd_prim_desc, *static_cast<mkldnn::memory *>(p_src_mem.get()),
+        *(static_cast<mkldnn::memory *>(p_diff_dst_mem.get())),
+        *(static_cast<mkldnn::memory *>(p_diff_src_mem.get())));
+  } else {
+    // primitives already exist
+    auto p_diff_src_mem = std::static_pointer_cast<mkldnn::memory>(
+        dev_ctx.GetBlob(key_diff_src_mem));
+    auto p_diff_dst_mem = std::static_pointer_cast<mkldnn::memory>(
+        dev_ctx.GetBlob(key_diff_dst_mem));
+
+    p_diff_src_mem->set_data_handle(
+        platform::to_void_reinterpret_cast(diff_src));
+    p_diff_dst_mem->set_data_handle(
+        platform::to_void_reinterpret_cast(diff_dst));
+  }
 
   // push primitive to stream and wait until it's executed
-  std::vector<mkldnn::primitive> pipeline = {eltwise_bwd};
+  std::vector<mkldnn::primitive> pipeline = {*(p_grad.get())};
   mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
 }
 }  // anonymous namespace

paddle/fluid/operators/activation_op.cc

@@ -41,7 +41,7 @@ namespace operators {
                                                                              \
    protected:                                                                \
     std::unique_ptr<::paddle::framework::OpDesc> Apply() const override {    \
-      auto *op = new ::paddle::framework::OpDesc();                          \
+      auto* op = new ::paddle::framework::OpDesc();                          \
       op->SetType(#KERNEL_TYPE "_grad");                                     \
       op->SetInput("Out", Output("Out"));                                    \
       op->SetInput(::paddle::framework::GradVarName("Out"),                  \
@@ -54,23 +54,50 @@ namespace operators {
     }                                                                        \
   }
 
+framework::OpKernelType GetKernelType(const framework::ExecutionContext& ctx,
+                                      const framework::OperatorWithKernel& oper,
+                                      const std::string& name) {
+  framework::LibraryType library{framework::LibraryType::kPlain};
+#ifdef PADDLE_WITH_MKLDNN
+  auto it = oper.Attrs().find("use_mkldnn");
+  if (library == framework::LibraryType::kPlain && it != oper.Attrs().end() &&
+      platform::CanMKLDNNBeUsed(ctx)) {
+    library = framework::LibraryType::kMKLDNN;
+  }
+#endif
+  framework::DataLayout layout = framework::DataLayout::kAnyLayout;
+  return framework::OpKernelType(
+      framework::ToDataType(ctx.Input<framework::Tensor>(name)->type()),
+      ctx.GetPlace(), layout, library);
+}
+
 class ActivationOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
-  void InferShape(framework::InferShapeContext *ctx) const override {
+  void InferShape(framework::InferShapeContext* ctx) const override {
     ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
     ctx->ShareLoD("X", /*->*/ "Out");
   }
+
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return GetKernelType(ctx, *this, "X");
+  }
 };
 
 class ActivationOpGrad : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
-  void InferShape(framework::InferShapeContext *ctx) const override {
+  void InferShape(framework::InferShapeContext* ctx) const override {
     ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("Out"));
   }
+
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return GetKernelType(ctx, *this, "Out");
+  }
 };
 
 __attribute__((unused)) constexpr char SigmoidDoc[] = R"DOC(

paddle/fluid/operators/beam_search_op.h

@@ -14,10 +14,6 @@ limitations under the License. */
 
 #pragma once
 
-#ifdef PADDLE_WITH_TESTING
-#include "gtest/gtest.h"
-#endif
-
 #include <string>
 #include <vector>
 #include "paddle/fluid/framework/lod_tensor.h"

paddle/fluid/operators/detection_map_op.cc

@@ -51,7 +51,8 @@ class DetectionMAPOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_EQ(label_dims.size(), 2,
                       "The rank of Input(Label) must be 2, "
                       "the shape is [N, 6].");
-    PADDLE_ENFORCE_EQ(label_dims[1], 6, "The shape is of Input(Label) [N, 6].");
+    PADDLE_ENFORCE(label_dims[1] == 6 || label_dims[1] == 5,
+                   "The shape of Input(Label) is [N, 6] or [N, 5].");
 
     if (ctx->HasInput("PosCount")) {
       PADDLE_ENFORCE(ctx->HasInput("TruePos"),
@@ -88,9 +89,10 @@ class DetectionMAPOpMaker : public framework::OpProtoAndCheckerMaker {
              "offset is N + 1, if LoD[i + 1] - LoD[i] == 0, means there is "
              "no detected data.");
     AddInput("Label",
-             "(LoDTensor) A 2-D LoDTensor with shape[N, 6] represents the"
+             "(LoDTensor) A 2-D LoDTensor represents the"
              "Labeled ground-truth data. Each row has 6 values: "
-             "[label, is_difficult, xmin, ymin, xmax, ymax], N is the total "
+             "[label, xmin, ymin, xmax, ymax, is_difficult] or 5 values: "
+             "[label, xmin, ymin, xmax, ymax], where N is the total "
              "number of ground-truth data in this mini-batch. For each "
              "instance, the offsets in first dimension are called LoD, "
              "the number of offset is N + 1, if LoD[i + 1] - LoD[i] == 0, "

paddle/fluid/operators/detection_map_op.h

@@ -72,7 +72,7 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
     auto* out_false_pos = ctx.Output<framework::LoDTensor>("AccumFalsePos");
 
     float overlap_threshold = ctx.Attr<float>("overlap_threshold");
-    float evaluate_difficult = ctx.Attr<bool>("evaluate_difficult");
+    bool evaluate_difficult = ctx.Attr<bool>("evaluate_difficult");
     auto ap_type = GetAPType(ctx.Attr<std::string>("ap_type"));
     int class_num = ctx.Attr<int>("class_num");
 
@@ -175,14 +175,20 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
     for (int n = 0; n < batch_size; ++n) {
       std::map<int, std::vector<Box>> boxes;
       for (size_t i = label_index[n]; i < label_index[n + 1]; ++i) {
-        Box box(labels(i, 2), labels(i, 3), labels(i, 4), labels(i, 5));
         int label = labels(i, 0);
-        auto is_difficult = labels(i, 1);
-        if (std::abs(is_difficult - 0.0) < 1e-6)
-          box.is_difficult = false;
-        else
-          box.is_difficult = true;
-        boxes[label].push_back(box);
+        if (input_label.dims()[1] == 6) {
+          Box box(labels(i, 2), labels(i, 3), labels(i, 4), labels(i, 5));
+          auto is_difficult = labels(i, 1);
+          if (std::abs(is_difficult - 0.0) < 1e-6)
+            box.is_difficult = false;
+          else
+            box.is_difficult = true;
+          boxes[label].push_back(box);
+        } else {
+          PADDLE_ENFORCE_EQ(input_label.dims()[1], 5);
+          Box box(labels(i, 1), labels(i, 2), labels(i, 3), labels(i, 4));
+          boxes[label].push_back(box);
+        }
       }
       gt_boxes->push_back(boxes);
     }

paddle/fluid/operators/is_empty_op.cc

@@ -12,45 +12,41 @@ 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/fluid/operators/is_empty_op.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/operator.h"
 
 namespace paddle {
 namespace operators {
 
-constexpr char kInput[] = "X";
-constexpr char kOutput[] = "Out";
-
-class IsEmptyOp : public framework::OperatorBase {
+class IsEmptyOp : public framework::OperatorWithKernel {
  public:
-  IsEmptyOp(const std::string &type, const framework::VariableNameMap &inputs,
-            const framework::VariableNameMap &outputs,
-            const framework::AttributeMap &attrs)
-      : OperatorBase(type, inputs, outputs, attrs) {}
+  using framework::OperatorWithKernel::OperatorWithKernel;
 
- private:
-  void RunImpl(const framework::Scope &scope,
-               const platform::Place &place) const override {
-    // get input
-    auto *var = scope.FindVar(Input(kInput));
-    PADDLE_ENFORCE_NOT_NULL(var);
-    auto &tensor = var->Get<framework::LoDTensor>();
-    // get output
-    auto *out = scope.FindVar(Output(kOutput));
-    PADDLE_ENFORCE_NOT_NULL(out);
-    auto *out_tensor = out->GetMutable<framework::LoDTensor>();
+ protected:
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of IsEmptyOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of IsEmptyOp should not be null.");
+    ctx->SetOutputDim("Out", {1});
+  }
 
-    out_tensor->Resize({1});
-    out_tensor->mutable_data<bool>(platform::CPUPlace())[0] =
-        framework::product(tensor.dims()) == 0;
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    framework::OpKernelType kt = framework::OpKernelType(
+        framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
+        platform::CPUPlace());
+    return kt;
   }
 };
 
-class IsEmptyOpProtoMaker : public framework::OpProtoAndCheckerMaker {
+class IsEmptyOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
-    AddInput(kInput, "(Tensor) Tensor which is to be checked.");
-    AddOutput(kOutput, "(Tensor) a boolean Tensor that indicate empty or not.");
+    AddInput("X", "(LoDTensor) Tensor which is to be checked.");
+    AddOutput("Out",
+              "(LoDTensor) a boolean Tensor that indicate empty or not.");
     AddComment(R"DOC(
 IsEmpty Operator which checks whether a tensor is empty.
 
@@ -62,5 +58,12 @@ It will just return product(tensor.ddims()) > 0;
 }  // namespace operators
 }  // namespace paddle
 
-REGISTER_OP_WITHOUT_GRADIENT(is_empty, paddle::operators::IsEmptyOp,
-                             paddle::operators::IsEmptyOpProtoMaker);
+namespace ops = paddle::operators;
+
+REGISTER_OPERATOR(is_empty, ops::IsEmptyOp, ops::IsEmptyOpMaker,
+                  paddle::framework::EmptyGradOpMaker);
+REGISTER_OP_CPU_KERNEL(
+    is_empty, ops::IsEmptyOpKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::IsEmptyOpKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::IsEmptyOpKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::IsEmptyOpKernel<paddle::platform::CPUDeviceContext, int64_t>);

paddle/fluid/operators/is_empty_op.h

+/* 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. */
+
+#pragma once
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/operator.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext, typename T>
+class IsEmptyOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    // get input
+    auto* input_tensor = context.Input<framework::LoDTensor>("X");
+    // get output
+    auto* output_tensor = context.Output<framework::LoDTensor>("Out");
+
+    output_tensor->mutable_data<bool>(platform::CPUPlace())[0] =
+        framework::product(input_tensor->dims()) == 0;
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/mkldnn_activation_op.h

@@ -13,6 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include <string>
+
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/detail/safe_ref.h"
@@ -60,52 +62,5 @@ class MKLDNNActivationGradKernel
   }
 };
 
-namespace {  // NOLINT
-framework::OpKernelType GetKernelType(
-    const framework::ExecutionContext& ctx,
-    const framework::OperatorWithKernel& oper) {
-  framework::LibraryType library{framework::LibraryType::kPlain};
-#ifdef PADDLE_WITH_MKLDNN
-  if (library == framework::LibraryType::kPlain &&
-      platform::CanMKLDNNBeUsed(ctx)) {
-    library = framework::LibraryType::kMKLDNN;
-  }
-#endif
-  framework::DataLayout layout = framework::DataLayout::kAnyLayout;
-  return framework::OpKernelType(
-      framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
-      ctx.GetPlace(), layout, library);
-}
-}  // anonymous namespace
-
-class ActivationWithMKLDNNOp : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
-  void InferShape(framework::InferShapeContext* ctx) const override {
-    ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
-    ctx->ShareLoD("X", /*->*/ "Out");
-  }
-
-  framework::OpKernelType GetExpectedKernelType(
-      const framework::ExecutionContext& ctx) const override {
-    return GetKernelType(ctx, *this);
-  }
-};
-
-class ActivationWithMKLDNNOpGrad : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
-  void InferShape(framework::InferShapeContext* ctx) const override {
-    ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("Out"));
-  }
-
-  framework::OpKernelType GetExpectedKernelType(
-      const framework::ExecutionContext& ctx) const override {
-    return GetKernelType(ctx, *this);
-  }
-};
-
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/pool_mkldnn_op.cc

@@ -18,6 +18,26 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
+using mkldnn::memory;  // Note: paddle has also "memory" namespace
+using mkldnn::pooling_forward;
+using mkldnn::pooling_backward;
+
+// Generate keys for storing/retriving primitives for this operator
+// TODO(jczaja): Make hashing function more optimial
+static std::string gethash(memory::dims& input_dims, std::string& pooling_type,
+                           std::vector<int>& ksize, std::vector<int>& strides,
+                           std::vector<int>& paddings, std::string suffix) {
+  auto dims2str = [](memory::dims& operand_dims) {
+    std::string dstr = "";
+    for (size_t i = 0; i < operand_dims.size(); ++i) {
+      dstr += std::to_string(operand_dims[i]) + "-";
+    }
+    return dstr;
+  };
+  return dims2str(input_dims) + dims2str(ksize) + dims2str(strides) +
+         dims2str(paddings) + pooling_type + suffix;
+}
+
 template <typename T>
 class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
  public:
@@ -34,10 +54,6 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
 
     // Get an unique name from "argument" name of "Out" variable
     // This name will be used as key when saving info into device context
-    const std::string key = ctx.op().Output("Out");
-    const std::string key_pool_pd = key + "@pool_pd";
-    const std::string key_pool_workspace_memory =
-        key + "@pool_workspace_memory";
 
     std::string pooling_type = ctx.Attr<std::string>("pooling_type");
     std::vector<int> ksize = ctx.Attr<std::vector<int>>("ksize");
@@ -63,37 +79,71 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     std::vector<int> src_tz = paddle::framework::vectorize2int(input->dims());
     std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());
 
-    // TODO(pzelazko-intel): support more formats
-    auto src_md = platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
-                                          mkldnn::memory::format::nchw);
-    auto dst_md = platform::MKLDNNMemDesc(dst_tz, mkldnn::memory::f32,
-                                          mkldnn::memory::format::nchw);
-
-    std::shared_ptr<mkldnn::pooling_forward::primitive_desc> pool_pd =
-        CreatePrimitiveDesc(src_md, dst_md, strides, paddings, ksize,
-                            pooling_type, mkldnn_engine);
-
-    // save pool_pd into global device context to be referred in backward path
-    dev_ctx.SetBlob(key_pool_pd, pool_pd);
-
-    std::shared_ptr<mkldnn::memory> workspace_memory =
-        CreateWorkspaceMemory(pool_pd, pooling_type, mkldnn_engine);
-
-    // save pool_workspace_memory to be referred in backward path
-    dev_ctx.SetBlob(key_pool_workspace_memory, workspace_memory);
-
-    auto src_memory =
-        mkldnn::memory({src_md, mkldnn_engine},
-                       static_cast<void*>(const_cast<T*>(input_data)));
-    auto dst_memory =
-        mkldnn::memory({dst_md, mkldnn_engine},
-                       static_cast<void*>(const_cast<T*>(output_data)));
+    const std::string key = gethash(src_tz, pooling_type, ksize, strides,
+                                    paddings, ctx.op().Output("Out"));
+    const std::string key_pool_p = key + "@pool_p";
+    const std::string key_pool_pd = key + "@pool_pd";
+    const std::string key_pool_src_mem_p = key + "@pool_src_mem_p";
+    const std::string key_pool_dst_mem_p = key + "@pool_dst_mem_p";
+    const std::string key_pool_workspace_memory =
+        key + "@pool_workspace_memory";
 
-    auto pool_prim = mkldnn::pooling_forward(*pool_pd, src_memory, dst_memory,
-                                             *workspace_memory);
+    auto pool_p =
+        std::static_pointer_cast<pooling_forward>(dev_ctx.GetBlob(key_pool_p));
+    if (pool_p == nullptr) {
+      // TODO(pzelazko-intel): support more formats
+
+      auto src_md =
+          platform::MKLDNNMemDesc(src_tz, platform::MKLDNNGetDataType<T>(),
+                                  mkldnn::memory::format::nchw);
+      auto dst_md =
+          platform::MKLDNNMemDesc(dst_tz, platform::MKLDNNGetDataType<T>(),
+                                  mkldnn::memory::format::nchw);
+
+      std::shared_ptr<pooling_forward::primitive_desc> pool_pd =
+          CreatePrimitiveDesc(src_md, dst_md, strides, paddings, ksize,
+                              pooling_type, mkldnn_engine);
+
+      // save pool_pd into global device context to be referred in backward path
+      dev_ctx.SetBlob(key_pool_pd, pool_pd);
+
+      std::shared_ptr<mkldnn::memory> workspace_memory =
+          CreateWorkspaceMemory(pool_pd, pooling_type, mkldnn_engine);
+
+      // save pool_workspace_memory to be referred in backward path
+      dev_ctx.SetBlob(key_pool_workspace_memory, workspace_memory);
+
+      auto pool_src_memory_p = std::make_shared<memory>(
+          memory::primitive_desc{src_md, mkldnn_engine},
+          static_cast<void*>(const_cast<T*>(input_data)));
+      dev_ctx.SetBlob(key_pool_src_mem_p, pool_src_memory_p);
+
+      auto pool_dst_memory_p = std::make_shared<memory>(
+          memory::primitive_desc{dst_md, mkldnn_engine},
+          static_cast<void*>(output_data));
+      dev_ctx.SetBlob(key_pool_dst_mem_p, pool_dst_memory_p);
+
+      pool_p = std::make_shared<pooling_forward>(
+          *pool_pd, *(pool_src_memory_p.get()), *(pool_dst_memory_p.get()),
+          *workspace_memory);
+      dev_ctx.SetBlob(key_pool_p, pool_p);
+    } else {
+      // Primitives already exist
+      auto pool_src_memory_p =
+          std::static_pointer_cast<memory>(dev_ctx.GetBlob(key_pool_src_mem_p));
+      PADDLE_ENFORCE(pool_src_memory_p != nullptr,
+                     "Fail to find pooling src mem_p in device context");
+      auto pool_dst_memory_p =
+          std::static_pointer_cast<memory>(dev_ctx.GetBlob(key_pool_dst_mem_p));
+      PADDLE_ENFORCE(pool_dst_memory_p != nullptr,
+                     "Fail to find pooling dst mem_p in device context");
+      pool_src_memory_p->set_data_handle(
+          reinterpret_cast<void*>(const_cast<T*>(input_data)));
+      pool_dst_memory_p->set_data_handle(output_data);
+    }
 
     // push primitive to stream and wait until it's executed
-    std::vector<mkldnn::primitive> pipeline{pool_prim};
+    std::vector<mkldnn::primitive> pipeline{*(pool_p.get())};
     mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
   }
 
@@ -120,9 +170,10 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     mkldnn::memory::primitive_desc workspace_md =
         pooling_type == "max"
             ? pool_pd->workspace_primitive_desc()
-            : mkldnn::memory::primitive_desc(
-                  {{}, mkldnn::memory::f32, mkldnn::memory::format::nchw},
-                  engine);
+            : mkldnn::memory::primitive_desc({{},
+                                              platform::MKLDNNGetDataType<T>(),
+                                              mkldnn::memory::format::nchw},
+                                             engine);
 
     auto p_workspace_memory = new mkldnn::memory(workspace_md);
     return std::unique_ptr<mkldnn::memory>(p_workspace_memory);
@@ -140,13 +191,6 @@ class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
     const Tensor* out_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
     Tensor* in_x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
 
-    // Get an unique name from "argument" name of "Out" variable
-    // This name will be used as key when referring info from device context
-    const std::string key = ctx.op().Input("Out");
-    const std::string key_pool_pd = key + "@pool_pd";
-    const std::string key_pool_workspace_memory =
-        key + "@pool_workspace_memory";
-
     std::string pooling_type = ctx.Attr<std::string>("pooling_type");
     std::vector<int> ksize = ctx.Attr<std::vector<int>>("ksize");
     std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
@@ -171,43 +215,76 @@ class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
     std::vector<int> diff_dst_tz =
         paddle::framework::vectorize2int(out_grad->dims());
 
-    auto diff_src_md = platform::MKLDNNMemDesc(diff_src_tz, mkldnn::memory::f32,
-                                               mkldnn::memory::format::nchw);
-    auto diff_dst_md = platform::MKLDNNMemDesc(diff_dst_tz, mkldnn::memory::f32,
-                                               mkldnn::memory::format::nchw);
-
-    // Retrieve pool_pd/pool_workspace_memory from device context
-    auto pool_pd =
-        std::static_pointer_cast<mkldnn::pooling_forward::primitive_desc>(
-            dev_ctx.GetBlob(key_pool_pd));
-    PADDLE_ENFORCE(pool_pd != nullptr,
-                   "Fail to find pool_pd in device context");
-
-    auto workspace_memory = std::static_pointer_cast<mkldnn::memory>(
-        dev_ctx.GetBlob(key_pool_workspace_memory));
-    PADDLE_ENFORCE(workspace_memory != nullptr,
-                   "Fail to find workspace_memory in device context");
-
-    auto pool_bwd_desc = mkldnn::pooling_backward::desc(
-        pooling_type == "max" ? mkldnn::algorithm::pooling_max
-                              : mkldnn::algorithm::pooling_avg,
-        diff_src_md, diff_dst_md, strides, ksize, paddings, paddings,
-        mkldnn::padding_kind::zero);
-    auto pool_bwd_pd = mkldnn::pooling_backward::primitive_desc(
-        pool_bwd_desc, mkldnn_engine, *pool_pd);
-
-    auto diff_src_memory =
-        mkldnn::memory({diff_src_md, mkldnn_engine},
-                       static_cast<void*>(const_cast<T*>(in_x_grad_data)));
-    auto diff_dst_memory =
-        mkldnn::memory({diff_dst_md, mkldnn_engine},
-                       static_cast<void*>(const_cast<T*>(out_grad_data)));
+    // Get an unique name from "argument" name of "Out" variable
+    // This name will be used as key when referring info from device context
+    const std::string key = gethash(diff_src_tz, pooling_type, ksize, strides,
+                                    paddings, ctx.op().Input("Out"));
+    const std::string key_pool_bwd_p = key + "@pool_bwd_p";
+    const std::string key_pool_diff_src_mem_p = key + "@pool_diff_src_mem_p";
+    const std::string key_pool_diff_dst_mem_p = key + "@pool_diff_dst_mem_p";
+    const std::string key_pool_pd = key + "@pool_pd";
+    const std::string key_pool_workspace_memory =
+        key + "@pool_workspace_memory";
 
-    auto bwd_prim = mkldnn::pooling_backward(
-        pool_bwd_pd, diff_dst_memory, *workspace_memory, diff_src_memory);
+    auto pool_bwd_p = std::static_pointer_cast<pooling_backward>(
+        dev_ctx.GetBlob(key_pool_bwd_p));
+    if (pool_bwd_p == nullptr) {
+      auto diff_src_md =
+          platform::MKLDNNMemDesc(diff_src_tz, platform::MKLDNNGetDataType<T>(),
+                                  mkldnn::memory::format::nchw);
+      auto diff_dst_md =
+          platform::MKLDNNMemDesc(diff_dst_tz, platform::MKLDNNGetDataType<T>(),
+                                  mkldnn::memory::format::nchw);
+      // Retrieve pool_pd/pool_workspace_memory from device context
+      auto pool_pd =
+          std::static_pointer_cast<mkldnn::pooling_forward::primitive_desc>(
+              dev_ctx.GetBlob(key_pool_pd));
+      PADDLE_ENFORCE(pool_pd != nullptr,
+                     "Fail to find pool_pd in device context");
+
+      auto workspace_memory = std::static_pointer_cast<mkldnn::memory>(
+          dev_ctx.GetBlob(key_pool_workspace_memory));
+      PADDLE_ENFORCE(workspace_memory != nullptr,
+                     "Fail to find workspace_memory in device context");
+
+      auto pool_diff_src_memory_p = std::make_shared<memory>(memory(
+          {diff_src_md, mkldnn_engine}, static_cast<void*>(in_x_grad_data)));
+      dev_ctx.SetBlob(key_pool_diff_src_mem_p, pool_diff_src_memory_p);
+
+      auto pool_diff_dst_memory_p = std::make_shared<memory>(
+          memory({diff_dst_md, mkldnn_engine},
+                 static_cast<void*>(const_cast<T*>(out_grad_data))));
+      dev_ctx.SetBlob(key_pool_diff_dst_mem_p, pool_diff_dst_memory_p);
+
+      auto pool_bwd_desc = mkldnn::pooling_backward::desc(
+          pooling_type == "max" ? mkldnn::algorithm::pooling_max
+                                : mkldnn::algorithm::pooling_avg,
+          diff_src_md, diff_dst_md, strides, ksize, paddings, paddings,
+          mkldnn::padding_kind::zero);
+      auto pool_bwd_pd = mkldnn::pooling_backward::primitive_desc(
+          pool_bwd_desc, mkldnn_engine, *pool_pd);
+
+      pool_bwd_p = std::make_shared<pooling_backward>(
+          pool_bwd_pd, *(pool_diff_dst_memory_p.get()), *workspace_memory,
+          *(pool_diff_src_memory_p));
+      dev_ctx.SetBlob(key_pool_bwd_p, pool_bwd_p);
+    } else {
+      // Primitives already exist
+      auto pool_diff_src_memory_p = std::static_pointer_cast<memory>(
+          dev_ctx.GetBlob(key_pool_diff_src_mem_p));
+      PADDLE_ENFORCE(pool_diff_src_memory_p != nullptr,
+                     "Fail to find pooling src mem_p in device context");
+      auto pool_diff_dst_memory_p = std::static_pointer_cast<memory>(
+          dev_ctx.GetBlob(key_pool_diff_dst_mem_p));
+      PADDLE_ENFORCE(pool_diff_dst_memory_p != nullptr,
+                     "Fail to find pooling dst mem_p in device context");
+      pool_diff_src_memory_p->set_data_handle(
+          reinterpret_cast<void*>(in_x_grad_data));
+      pool_diff_dst_memory_p->set_data_handle(const_cast<T*>(out_grad_data));
+    }
 
     // push primitive to stream and wait until it's executed
-    std::vector<mkldnn::primitive> pipeline{bwd_prim};
+    std::vector<mkldnn::primitive> pipeline{*(pool_bwd_p.get())};
     mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
   }  // Compute()
 };

paddle/fluid/operators/warpctc_op.h

@@ -186,8 +186,7 @@ class WarpCTCKernel : public framework::OpKernel<T> {
 
     // warpctc accesses labels in CPU memory
     Tensor warpctc_label;
-    TensorCopy(*label, platform::CPUPlace(), ctx.device_context(),
-               &warpctc_label);
+    TensorCopySync(*label, platform::CPUPlace(), &warpctc_label);
     const int* warpctc_label_data = warpctc_label.data<int>();
     // warpctc stores loss in CPU memory
     Tensor warpctc_loss;

paddle/fluid/platform/CMakeLists.txt

@@ -49,7 +49,7 @@ nv_test(device_context_test SRCS device_context_test.cu DEPS device_context gpu_
 nv_test(cudnn_helper_test SRCS cudnn_helper_test.cc DEPS dynload_cuda)
 nv_test(transform_test SRCS transform_test.cu DEPS memory place device_context)
 
-cc_library(device_tracer SRCS device_tracer.cc DEPS boost profiler_proto ${GPU_CTX_DEPS})
+cc_library(device_tracer SRCS device_tracer.cc DEPS boost profiler_proto framework_proto ${GPU_CTX_DEPS})
 cc_library(profiler SRCS profiler.cc DEPS device_context device_tracer)
 cc_test(profiler_test SRCS profiler_test.cc DEPS profiler)
 

paddle/fluid/platform/mkldnn_helper.h

@@ -38,6 +38,11 @@ void* to_void_cast(const Type* t) {
   return static_cast<void*>(const_cast<Type*>(t));
 }
 
+template <typename Type>
+void* to_void_reinterpret_cast(const Type* t) {
+  return reinterpret_cast<void*>(const_cast<Type*>(t));
+}
+
 template <class Type>
 using tf_desc = typename Type::desc;
 
@@ -71,5 +76,15 @@ inline bool CanMKLDNNBeUsed(const framework::ExecutionContext& ctx) {
   return use_mkldnn && platform::is_cpu_place(ctx.GetPlace());
 }
 
+template <typename Type>
+mkldnn::memory::data_type MKLDNNGetDataType() {
+  return mkldnn::memory::data_undef;
+}
+
+template <>
+inline mkldnn::memory::data_type MKLDNNGetDataType<float>() {
+  return mkldnn::memory::f32;
+}
+
 }  // namespace platform
 }  // namespace paddle

paddle/fluid/pybind/protobuf.cc

@@ -238,6 +238,7 @@ void BindVarDsec(pybind11::module *m) {
 
   pybind11::enum_<pd::proto::VarType::Type>(var_desc, "VarType", "")
       .value("BOOL", pd::proto::VarType::BOOL)
+      .value("UINT8", pd::proto::VarType::UINT8)
       .value("INT16", pd::proto::VarType::INT16)
       .value("INT32", pd::proto::VarType::INT32)
       .value("INT64", pd::proto::VarType::INT64)

paddle/fluid/train/demo/CMakeLists.txt

+cmake_minimum_required(VERSION 3.0)
+
+project(cpp_train_demo CXX C)
+
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
+
+if(NOT DEFINED PADDLE_LIB)
+  message(FATAL_ERROR "please set PADDLE_LIB with -DPADDLE_LIB=/paddle/lib/dir")
+endif()
+
+option(WITH_MKLDNN     "Compile PaddlePaddle with MKLDNN"                                   OFF)
+option(WITH_MKL        "Compile PaddlePaddle with MKL support, default use openblas."       OFF)
+
+include_directories("${PADDLE_LIB}")
+include_directories("${PADDLE_LIB}/third_party/install/protobuf/include")
+include_directories("${PADDLE_LIB}/third_party/install/glog/include")
+include_directories("${PADDLE_LIB}/third_party/install/gflags/include")
+include_directories("${PADDLE_LIB}/third_party/install/snappy/include")
+include_directories("${PADDLE_LIB}/third_party/install/snappystream/include")
+include_directories("${PADDLE_LIB}/third_party/install/zlib/include")
+
+include_directories("${PADDLE_LIB}/third_party/boost")
+include_directories("${PADDLE_LIB}/third_party/eigen3")
+
+link_directories("${PADDLE_LIB}/third_party/install/snappy/lib")
+link_directories("${PADDLE_LIB}/third_party/install/snappystream/lib")
+link_directories("${PADDLE_LIB}/third_party/install/protobuf/lib")
+link_directories("${PADDLE_LIB}/third_party/install/glog/lib")
+link_directories("${PADDLE_LIB}/third_party/install/gflags/lib")
+link_directories("${PADDLE_LIB}/third_party/install/zlib/lib")
+
+add_executable(demo_trainer demo_trainer.cc)
+
+if(WITH_MKLDNN)
+  include_directories("${PADDLE_LIB}/third_party/install/mkldnn/include")
+  set(MKLDNN_LIB ${PADDLE_LIB}/third_party/install/mkldnn/lib/libmkldnn.so.0)
+endif()
+
+if(WITH_MKL)
+  include_directories("${PADDLE_LIB}/third_party/install/mklml/include")
+  set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel.so)
+else()
+  if(APPLE)
+    set(MATH_LIB cblas)
+  else(APPLE)
+    set(MATH_LIB ${PADDLE_LIB}/third_party/install/openblas/lib/libopenblas.a)
+  endif(APPLE)
+endif()
+
+if(APPLE)
+  set(MACOS_LD_FLAGS "-undefined dynamic_lookup -Wl,-all_load -framework CoreFoundation -framework Security")
+else(APPLE)
+  set(ARCHIVE_START "-Wl,--whole-archive")
+  set(ARCHIVE_END "-Wl,--no-whole-archive")
+  set(EXTERNAL_LIB "-lrt -ldl -lpthread")
+endif(APPLE)
+
+target_link_libraries(demo_trainer
+        ${MACOS_LD_FLAGS}
+        ${ARCHIVE_START}
+        ${PADDLE_LIB}/paddle/fluid/inference/libpaddle_fluid.a
+        ${ARCHIVE_END}
+        ${MATH_LIB}
+        ${MKLDNN_LIB}
+        glog gflags protobuf snappystream snappy z
+        ${EXTERNAL_LIB})

paddle/fluid/train/demo/README.md

+
+### step 1. build paddle lib
+
+```
+
+# WITH_MKL=ON|OFF
+# WITH_MKLDNN=ON|OFF
+
+PADDLE_LIB=/paddle/lib/dir
+cmake .. -DCMAKE_INSTALL_PREFIX=$PADDLE_LIB \
+         -DCMAKE_BUILD_TYPE=Release \
+         -DWITH_FLUID_ONLY=ON \
+         -DWITH_GPU=OFF \
+         -DWITH_STYLE_CHECK=OFF \
+         -DWITH_MKL=OFF \
+         -DWITH_MKLDNN=OFF
+make -j8
+make -j8 inference_lib_dist
+```
+
+### step 2. generate program desc
+```
+# please install paddle before run this scripe
+pip install --upgrade paddlepaddle-*.whl
+python demo_network.py
+```
+
+This will generate two program desc files:
+  - startup_program: used to init all parameters
+  - main_program: main logic of the network
+
+### step 3. build demo_trainer and run it.
+
+
+```
+# Make a build dir at the same dir of this README.md document.
+# The demo dir can be put anywhere.
+mkdir build
+cd build
+
+# WITH_MKL=ON|OFF
+# WITH_MKLDNN=ON|OFF
+PADDLE_LIB=/paddle/lib/dir
+
+# PADDLE_LIB is the same with CMAKE_INSTALL_PREFIX when building the lib
+cmake .. -DPADDLE_LIB=$PADDLE_LIB \
+         -DWITH_MKLDNN=OFF \
+         -DWITH_MKL=OFF
+make
+
+# copy startup_program and main_program to this dir
+cp ../startup_program .
+cp ../main_program .
+
+# run demo cpp trainer
+./demo_trainer
+
+```
+
+The output will be:
+```
+step: 0 loss: 1069.02
+step: 1 loss: 1069.02
+step: 2 loss: 1069.02
+....
+```

paddle/fluid/train/demo/demo_network.py

+#   Copyright (c) 2018 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 paddle.fluid as fluid
+import paddle.fluid.framework as framework
+
+
+def train_network(with_optimize):
+    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(cost)
+
+    if with_optimize:
+        sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.00001)
+        sgd_optimizer.minimize(avg_cost)
+    else:
+        fluid.backward.append_backward(avg_cost)
+
+
+def save_program_desc(network_func):
+    startup_program = framework.Program()
+    train_program = framework.Program()
+
+    with framework.program_guard(train_program, startup_program):
+        network_func(with_optimize=False)
+
+    with open("startup_program", "w") as f:
+        f.write(startup_program.desc.serialize_to_string())
+    with open("main_program", "w") as f:
+        f.write(train_program.desc.serialize_to_string())
+
+
+save_program_desc(train_network)

paddle/fluid/train/demo/demo_trainer.cc

+//   Copyright (c) 2018 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.
+
+#include <fstream>
+
+#include "paddle/fluid/framework/executor.h"
+#include "paddle/fluid/framework/init.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/program_desc.h"
+#include "paddle/fluid/framework/tensor_util.h"
+#include "paddle/fluid/platform/device_context.h"
+#include "paddle/fluid/platform/place.h"
+
+namespace paddle {
+namespace train {
+
+void ReadBinaryFile(const std::string& filename, std::string* contents) {
+  std::ifstream fin(filename, std::ios::in | std::ios::binary);
+  PADDLE_ENFORCE(static_cast<bool>(fin), "Cannot open file %s", filename);
+  fin.seekg(0, std::ios::end);
+  contents->clear();
+  contents->resize(fin.tellg());
+  fin.seekg(0, std::ios::beg);
+  fin.read(&(contents->at(0)), contents->size());
+  fin.close();
+}
+
+std::unique_ptr<paddle::framework::ProgramDesc> Load(
+    paddle::framework::Executor* executor, const std::string& model_filename) {
+  VLOG(3) << "loading model from " << model_filename;
+  std::string program_desc_str;
+  ReadBinaryFile(model_filename, &program_desc_str);
+
+  std::unique_ptr<paddle::framework::ProgramDesc> main_program(
+      new paddle::framework::ProgramDesc(program_desc_str));
+  return main_program;
+}
+
+}  // namespace train
+}  // namespace paddle
+
+int main() {
+  paddle::framework::InitDevices(false);
+
+  const auto cpu_place = paddle::platform::CPUPlace();
+
+  paddle::framework::Executor executor(cpu_place);
+  paddle::framework::Scope scope;
+  auto startup_program = paddle::train::Load(&executor, "startup_program");
+  auto train_program = paddle::train::Load(&executor, "main_program");
+
+  std::string loss_name = "";
+  for (auto op_desc : train_program->Block(0).AllOps()) {
+    if (op_desc->Type() == "mean") {
+      loss_name = op_desc->Output("Out")[0];
+      break;
+    }
+  }
+
+  PADDLE_ENFORCE_NE(loss_name, "", "loss not found");
+
+  // init all parameters
+  executor.Run(*startup_program.get(), &scope, 0);
+
+  // prepare data
+  auto x_var = scope.Var("x");
+  auto x_tensor = x_var->GetMutable<paddle::framework::LoDTensor>();
+  x_tensor->Resize({2, 13});
+
+  auto x_data = x_tensor->mutable_data<float>(cpu_place);
+  for (int i = 0; i < 2 * 13; ++i) {
+    x_data[i] = static_cast<float>(i);
+  }
+
+  auto y_var = scope.Var("y");
+  auto y_tensor = y_var->GetMutable<paddle::framework::LoDTensor>();
+  y_tensor->Resize({2, 1});
+  auto y_data = y_tensor->mutable_data<float>(cpu_place);
+  for (int i = 0; i < 2 * 1; ++i) {
+    y_data[i] = static_cast<float>(i);
+  }
+
+  auto loss_var = scope.Var(loss_name);
+
+  for (int i = 0; i < 10; ++i) {
+    executor.Run(*train_program.get(), &scope, 0, false, true);
+    std::cout << "step: " << i << " loss: "
+              << loss_var->Get<paddle::framework::LoDTensor>().data<float>()[0]
+              << std::endl;
+  }
+  return 0;
+}

paddle/scripts/paddle_docker_build.sh

@@ -14,20 +14,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-function container_running() {
-    name=$1
-    docker ps -a --format "{{.Names}}" | grep "${name}" > /dev/null
-    return $?
-}
-
 function start_build_docker() {
     docker pull $IMG
 
-    if container_running "${CONTAINER_ID}"; then
-        docker stop "${CONTAINER_ID}" 1>/dev/null
-        docker rm -f "${CONTAINER_ID}" 1>/dev/null
-    fi
-
     apt_mirror='s#http://archive.ubuntu.com/ubuntu#mirror://mirrors.ubuntu.com/mirrors.txt#g'
     DOCKER_ENV=$(cat <<EOL
         -e FLAGS_fraction_of_gpu_memory_to_use=0.15 \
@@ -61,7 +50,6 @@ EOL
     fi
     set -ex
     ${DOCKER_CMD} run -it \
-        --name $CONTAINER_ID \
         ${DOCKER_ENV} \
         -e SCRIPT_NAME=$0 \
         -v $PADDLE_ROOT:/paddle \
@@ -75,10 +63,8 @@ EOL
 function main() {
     DOCKER_REPO="paddlepaddle/paddle"
     VERSION="latest-dev"
-    CONTAINER_ID="${USER}_paddle_dev"
     PADDLE_ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}")/../../" && pwd )"
     if [ "$1" == "build_android" ]; then
-        CONTAINER_ID="${USER}_paddle_dev_android"
         VERSION="latest-dev-android"
     fi
     IMG=${DOCKER_REPO}:${VERSION}

python/paddle/fluid/evaluator.py

@@ -273,10 +273,11 @@ class DetectionMAP(Evaluator):
             [M, 6]. The layout is [label, confidence, xmin, ymin, xmax, ymax].
         gt_label (Variable): The ground truth label index, which is a LoDTensor
             with shape [N, 1].
-        gt_difficult (Variable): Whether this ground truth is a difficult
-            bounding box (bbox), which is a LoDTensor [N, 1].
         gt_box (Variable): The ground truth bounding box (bbox), which is a
             LoDTensor with shape [N, 6]. The layout is [xmin, ymin, xmax, ymax].
+        gt_difficult (Variable|None): Whether this ground truth is a difficult
+            bounding bbox, which can be a LoDTensor [N, 1] or not set. If None,
+            it means all the ground truth labels are not difficult bbox.
         class_num (int): The class number.
         background_label (int): The index of background label, the background
             label will be ignored. If set to -1, then all categories will be
@@ -284,7 +285,8 @@ class DetectionMAP(Evaluator):
         overlap_threshold (float): The threshold for deciding true/false
             positive, 0.5 by defalut.
         evaluate_difficult (bool): Whether to consider difficult ground truth
-            for evaluation, True by defalut.
+            for evaluation, True by defalut. This argument does not work when
+            gt_difficult is None.
         ap_version (string): The average precision calculation ways, it must be
             'integral' or '11point'. Please check
             https://sanchom.wordpress.com/tag/average-precision/ for details.
@@ -295,7 +297,7 @@ class DetectionMAP(Evaluator):
 
         exe = fluid.executor(place)
         map_evaluator = fluid.Evaluator.DetectionMAP(input,
-            gt_label, gt_difficult, gt_box)
+            gt_label, gt_box, gt_difficult)
         cur_map, accum_map = map_evaluator.get_map_var()
         fetch = [cost, cur_map, accum_map]
         for epoch in PASS_NUM:
@@ -313,8 +315,8 @@ class DetectionMAP(Evaluator):
                  input,
                  gt_label,
                  gt_box,
-                 gt_difficult,
-                 class_num,
+                 gt_difficult=None,
+                 class_num=None,
                  background_label=0,
                  overlap_threshold=0.5,
                  evaluate_difficult=True,
@@ -322,8 +324,11 @@ class DetectionMAP(Evaluator):
         super(DetectionMAP, self).__init__("map_eval")
 
         gt_label = layers.cast(x=gt_label, dtype=gt_box.dtype)
-        gt_difficult = layers.cast(x=gt_difficult, dtype=gt_box.dtype)
-        label = layers.concat([gt_label, gt_difficult, gt_box], axis=1)
+        if gt_difficult:
+            gt_difficult = layers.cast(x=gt_difficult, dtype=gt_box.dtype)
+            label = layers.concat([gt_label, gt_difficult, gt_box], axis=1)
+        else:
+            label = layers.concat([gt_label, gt_box], axis=1)
 
         # calculate mean average precision (mAP) of current mini-batch
         map = layers.detection_map(

python/paddle/fluid/framework.py

@@ -72,6 +72,8 @@ def convert_np_dtype_to_dtype_(np_dtype):
         return core.VarDesc.VarType.INT64
     elif dtype == np.bool:
         return core.VarDesc.VarType.BOOL
+    elif dtype == np.uint8:
+        return core.VarDesc.VarType.UINT8
     else:
         raise ValueError("Not supported numpy dtype " + str(dtype))
 

python/paddle/fluid/layers/control_flow.py

@@ -49,6 +49,7 @@ __all__ = [
     'reorder_lod_tensor_by_rank',
     'ParallelDo',
     'Print',
+    'is_empty',
 ]
 
 
@@ -1097,7 +1098,7 @@ class ConditionalBlock(object):
         input_set = set([ipt.name for ipt in self.inputs])
 
         param_list = [
-            parent_block.var(each_name) for each_name in params
+            parent_block.var_recursive(each_name) for each_name in params
             if each_name not in input_set
         ]
 
@@ -1562,3 +1563,40 @@ def reorder_lod_tensor_by_rank(x, rank_table):
                 'RankTable': [rank_table]},
         outputs={'Out': [out]})
     return out
+
+
+def is_empty(x, cond=None, **ignored):
+    """
+    **Is Empty**
+
+    This layer returns the truth value of whether the variable is empty.
+
+    Args:
+        x(Variable): Operand of *is_empty*
+        cond(Variable|None): Optional output variable to store the result
+                             of *is_empty*
+
+    Returns:
+        Variable: The tensor variable storing the output of *is_empty*.
+
+    Raises:
+        TypeError: If input cond is not a variable, or cond's dtype is
+                   not bool
+
+    Examples:
+        .. code-block:: python
+
+          less = fluid.layers.is_empty(x=input)
+    """
+    helper = LayerHelper("is_empty", **locals())
+    if cond is None:
+        cond = helper.create_tmp_variable(dtype='bool')
+        cond.stop_gradient = True
+    elif not isinstance(cond, Variable):
+        raise TypeError("cond takes a variable")
+    elif cond.dtype != 'bool':
+        raise TypeError("The data type of cond must be bool")
+
+    helper.append_op(
+        type='is_empty', inputs={'X': [x]}, outputs={'Out': [cond]})
+    return cond

python/paddle/fluid/layers/detection.py

@@ -23,6 +23,7 @@ import nn
 import math
 
 __all__ = [
+    'prior_box',
     'multi_box_head',
     'bipartite_match',
     'target_assign',
@@ -564,6 +565,115 @@ def ssd_loss(location,
     return loss
 
 
+def prior_box(input,
+              image,
+              min_sizes,
+              max_sizes=None,
+              aspect_ratios=[1.],
+              variance=[0.1, 0.1, 0.2, 0.2],
+              flip=False,
+              clip=False,
+              steps=[0.0, 0.0],
+              offset=0.5,
+              name=None):
+    """
+    **Prior box operator**
+
+    Generate prior boxes for SSD(Single Shot MultiBox Detector) algorithm.
+    Each position of the input produce N prior boxes, N is determined by
+    the count of min_sizes, max_sizes and aspect_ratios, The size of the
+    box is in range(min_size, max_size) interval, which is generated in
+    sequence according to the aspect_ratios.
+
+    Args:
+       input(Variable): The Input Variables, the format is NCHW.
+       image(Variable): The input image data of PriorBoxOp,
+            the layout is NCHW.
+       min_sizes(list|tuple|float value): min sizes of generated prior boxes.
+       max_sizes(list|tuple|None): max sizes of generated prior boxes.
+            Default: None.
+       aspect_ratios(list|tuple|float value): the aspect ratios of generated
+            prior boxes. Default: [1.].
+       variance(list|tuple): the variances to be encoded in prior boxes.
+            Default:[0.1, 0.1, 0.2, 0.2].
+       flip(bool): Whether to flip aspect ratios. Default:False.
+       clip(bool): Whether to clip out-of-boundary boxes. Default: False.
+       step(list|turple): Prior boxes step across width and height, If
+            step[0] == 0.0/step[1] == 0.0, the prior boxes step across
+            height/weight of the input will be automatically calculated.
+            Default: [0., 0.]
+       offset(float): Prior boxes center offset. Default: 0.5
+       name(str): Name of the prior box op. Default: None.
+
+    Returns:
+        boxes(Variable): the output prior boxes of PriorBox.
+             The layout is [H, W, num_priors, 4].
+             H is the height of input, W is the width of input,
+             num_priors is the total
+             box count of each position of input.
+        Variances(Variable): the expanded variances of PriorBox.
+             The layout is [H, W, num_priors, 4].
+             H is the height of input, W is the width of input
+             num_priors is the total
+             box count of each position of input
+
+
+    Examples:
+        .. code-block:: python
+            box, var = prior_box(
+            input=conv1,
+            image=images,
+            min_sizes=[100.],
+            flip=True,
+            clip=True)
+    """
+    helper = LayerHelper("prior_box", **locals())
+    dtype = helper.input_dtype()
+
+    def _is_list_or_tuple_(data):
+        return (isinstance(data, list) or isinstance(data, tuple))
+
+    if not _is_list_or_tuple_(min_sizes):
+        min_sizes = [min_sizes]
+    if not _is_list_or_tuple_(aspect_ratios):
+        aspect_ratios = [aspect_ratios]
+    if not (_is_list_or_tuple_(steps) and len(steps) == 2):
+        raise ValueError('steps should be a list or tuple ',
+                         'with length 2, (step_width, step_height).')
+
+    min_sizes = list(map(float, min_sizes))
+    aspect_ratios = list(map(float, aspect_ratios))
+    steps = list(map(float, steps))
+
+    attrs = {
+        'min_sizes': min_sizes,
+        'aspect_ratios': aspect_ratios,
+        'variances': variance,
+        'flip': flip,
+        'clip': clip,
+        'step_w': steps[0],
+        'step_h': steps[1],
+        'offset': offset
+    }
+    if max_sizes is not None and len(max_sizes) > 0 and max_sizes[0] > 0:
+        if not _is_list_or_tuple_(max_sizes):
+            max_sizes = [max_sizes]
+        attrs['max_sizes'] = max_sizes
+
+    box = helper.create_tmp_variable(dtype)
+    var = helper.create_tmp_variable(dtype)
+    helper.append_op(
+        type="prior_box",
+        inputs={"Input": input,
+                "Image": image},
+        outputs={"Boxes": box,
+                 "Variances": var},
+        attrs=attrs, )
+    box.stop_gradient = True
+    var.stop_gradient = True
+    return box, var
+
+
 def multi_box_head(inputs,
                    image,
                    base_size,
@@ -660,47 +770,6 @@ def multi_box_head(inputs,
             clip=True)
     """
 
-    def _prior_box_(input,
-                    image,
-                    min_sizes,
-                    max_sizes,
-                    aspect_ratios,
-                    variance,
-                    flip=False,
-                    clip=False,
-                    step_w=0.0,
-                    step_h=0.0,
-                    offset=0.5,
-                    name=None):
-        helper = LayerHelper("prior_box", **locals())
-        dtype = helper.input_dtype()
-
-        attrs = {
-            'min_sizes': min_sizes,
-            'aspect_ratios': aspect_ratios,
-            'variances': variance,
-            'flip': flip,
-            'clip': clip,
-            'step_w': step_w,
-            'step_h': step_h,
-            'offset': offset
-        }
-        if len(max_sizes) > 0 and max_sizes[0] > 0:
-            attrs['max_sizes'] = max_sizes
-
-        box = helper.create_tmp_variable(dtype)
-        var = helper.create_tmp_variable(dtype)
-        helper.append_op(
-            type="prior_box",
-            inputs={"Input": input,
-                    "Image": image},
-            outputs={"Boxes": box,
-                     "Variances": var},
-            attrs=attrs, )
-        box.stop_gradient = True
-        var.stop_gradient = True
-        return box, var
-
     def _reshape_with_axis_(input, axis=1):
         if not (axis > 0 and axis < len(input.shape)):
             raise ValueError("The axis should be smaller than "
@@ -777,11 +846,10 @@ def multi_box_head(inputs,
             aspect_ratio = aspect_ratios[i]
             if not _is_list_or_tuple_(aspect_ratio):
                 aspect_ratio = [aspect_ratio]
+        step = [step_w[i] if step_w else 0.0, step_h[i] if step_w else 0.0]
 
-        box, var = _prior_box_(input, image, min_size, max_size, aspect_ratio,
-                               variance, flip, clip, step_w[i]
-                               if step_w else 0.0, step_h[i]
-                               if step_w else 0.0, offset)
+        box, var = prior_box(input, image, min_size, max_size, aspect_ratio,
+                             variance, flip, clip, step, offset)
 
         box_results.append(box)
         var_results.append(var)

python/paddle/fluid/layers/nn.py

@@ -699,8 +699,8 @@ def dynamic_gru(input,
 def gru_unit(input,
              hidden,
              size,
-             weight=None,
-             bias=None,
+             param_attr=None,
+             bias_attr=None,
              activation='tanh',
              gate_activation='sigmoid'):
     """
@@ -731,8 +731,8 @@ def gru_unit(input,
         input (Variable): The fc transformed input value of current step.
         hidden (Variable): The hidden value of lstm unit from previous step.
         size (integer): The input dimension value.
-        weight (ParamAttr): The weight parameters for gru unit. Default: None
-        bias (ParamAttr): The bias parameters for gru unit. Default: None
+        param_attr (ParamAttr): The weight parameters for gru unit. Default: None
+        bias_attr (ParamAttr): The bias parameters for gru unit. Default: None
         activation (string): The activation type for cell (actNode).
                              Default: 'tanh'
         gate_activation (string): The activation type for gates (actGate).
@@ -764,34 +764,31 @@ def gru_unit(input,
     size = size / 3
 
     # create weight
-    if weight is None:
-        weight = helper.create_parameter(
-            attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
+    weight = helper.create_parameter(
+        attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
 
+    gate = helper.create_tmp_variable(dtype)
+    reset_hidden_pre = helper.create_tmp_variable(dtype)
+    updated_hidden = helper.create_tmp_variable(dtype)
+    inputs = {'Input': input, 'HiddenPrev': hidden, 'Weight': weight}
     # create bias
-
-    if bias is None:
+    if helper.bias_attr:
         bias_size = [1, 3 * size]
         bias = helper.create_parameter(
             attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
-
-    gate = helper.create_tmp_variable(dtype)
-    reset_hidden_pre = helper.create_tmp_variable(dtype)
-    updated_hidden = helper.create_tmp_variable(dtype)
+        inputs['Bias'] = bias
 
     helper.append_op(
         type='gru_unit',
-        inputs={'Input': input,
-                'HiddenPrev': hidden,
-                'Weight': weight},
+        inputs=inputs,
         outputs={
             'Gate': gate,
             'ResetHiddenPrev': reset_hidden_pre,
             'Hidden': updated_hidden,
         },
         attrs={
-            'activation': 0,
-            'gate_activation': 1,
+            'activation': 2,  # tanh
+            'gate_activation': 1,  # sigmoid
         })
 
     return updated_hidden, reset_hidden_pre, gate

python/paddle/fluid/tests/book/high-level-api/CMakeLists.txt

@@ -9,3 +9,4 @@ endforeach()
 add_subdirectory(fit_a_line)
 add_subdirectory(recognize_digits)
 add_subdirectory(image_classification)
+add_subdirectory(understand_sentiment)

python/paddle/fluid/tests/book/high-level-api/understand_sentiment/CMakeLists.txt

+file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
+string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
+
+# default test
+foreach(src ${TEST_OPS})
+    py_test(${src} SRCS ${src}.py)
+endforeach()

python/paddle/fluid/tests/book/high-level-api/understand_sentiment/notest_understand_sentiment_stacked_lstm.py → python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_stacked_lstm.py

@@ -17,11 +17,13 @@ from __future__ import print_function
 import paddle
 import paddle.fluid as fluid
 from functools import partial
+import numpy as np
 
 CLASS_DIM = 2
 EMB_DIM = 128
 HID_DIM = 512
 STACKED_NUM = 3
+BATCH_SIZE = 128
 
 
 def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
@@ -50,7 +52,7 @@ def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
     return prediction
 
 
-def inference_network(word_dict):
+def inference_program(word_dict):
     data = fluid.layers.data(
         name="words", shape=[1], dtype="int64", lod_level=1)
 
@@ -60,57 +62,71 @@ def inference_network(word_dict):
     return net
 
 
-def train_network(word_dict):
-    prediction = inference_network(word_dict)
+def train_program(word_dict):
+    prediction = inference_program(word_dict)
     label = fluid.layers.data(name="label", shape=[1], dtype="int64")
     cost = fluid.layers.cross_entropy(input=prediction, label=label)
     avg_cost = fluid.layers.mean(cost)
     accuracy = fluid.layers.accuracy(input=prediction, label=label)
-    return avg_cost, accuracy
+    return [avg_cost, accuracy]
 
 
-def train(use_cuda, save_path):
-    BATCH_SIZE = 128
-    EPOCH_NUM = 5
+def train(use_cuda, train_program, save_dirname):
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+    optimizer = fluid.optimizer.Adagrad(learning_rate=0.002)
 
     word_dict = paddle.dataset.imdb.word_dict()
+    trainer = fluid.Trainer(
+        train_func=partial(train_program, word_dict),
+        place=place,
+        optimizer=optimizer)
 
-    train_data = paddle.batch(
+    def event_handler(event):
+        if isinstance(event, fluid.EndEpochEvent):
+            test_reader = paddle.batch(
+                paddle.dataset.imdb.test(word_dict), batch_size=BATCH_SIZE)
+            avg_cost, acc = trainer.test(
+                reader=test_reader, feed_order=['words', 'label'])
+
+            print("avg_cost: %s" % avg_cost)
+            print("acc     : %s" % acc)
+
+            if acc > 0.2:  # Smaller value to increase CI speed
+                trainer.save_params(save_dirname)
+                trainer.stop()
+
+            else:
+                print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
+                    event.epoch + 1, avg_cost, acc))
+                if math.isnan(avg_cost):
+                    sys.exit("got NaN loss, training failed.")
+        elif isinstance(event, fluid.EndStepEvent):
+            print("Step {0}, Epoch {1} Metrics {2}".format(
+                event.step, event.epoch, map(np.array, event.metrics)))
+            if event.step == 1:  # Run 2 iterations to speed CI
+                trainer.save_params(save_dirname)
+                trainer.stop()
+
+    train_reader = paddle.batch(
         paddle.reader.shuffle(
-            paddle.dataset.imdb.train(word_dict), buf_size=1000),
+            paddle.dataset.imdb.train(word_dict), buf_size=25000),
         batch_size=BATCH_SIZE)
 
-    test_data = paddle.batch(
-        paddle.dataset.imdb.test(word_dict), batch_size=BATCH_SIZE)
-
-    def event_handler(event):
-        if isinstance(event, fluid.EndIteration):
-            if (event.batch_id % 10) == 0:
-                avg_cost, accuracy = trainer.test(reader=test_data)
-
-                print('BatchID {1:04}, Loss {2:2.2}, Acc {3:2.2}'.format(
-                    event.batch_id + 1, avg_cost, accuracy))
+    trainer.train(
+        num_epochs=1,
+        event_handler=event_handler,
+        reader=train_reader,
+        feed_order=['words', 'label'])
 
-                if accuracy > 0.01:  # Low threshold for speeding up CI
-                    trainer.params.save(save_path)
-                    return
 
-    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-    trainer = fluid.Trainer(
-        partial(train_network, word_dict),
-        optimizer=fluid.optimizer.Adagrad(learning_rate=0.002),
-        place=place,
-        event_handler=event_handler)
-
-    trainer.train(train_data, EPOCH_NUM, event_handler=event_handler)
-
-
-def infer(use_cuda, save_path):
-    params = fluid.Params(save_path)
+def infer(use_cuda, inference_program, save_dirname=None):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
     word_dict = paddle.dataset.imdb.word_dict()
+
     inferencer = fluid.Inferencer(
-        partial(inference_network, word_dict), params, place=place)
+        infer_func=partial(inference_program, word_dict),
+        param_path=save_dirname,
+        place=place)
 
     def create_random_lodtensor(lod, place, low, high):
         data = np.random.random_integers(low, high,
@@ -131,8 +147,8 @@ def main(use_cuda):
     if use_cuda and not fluid.core.is_compiled_with_cuda():
         return
     save_path = "understand_sentiment_stacked_lstm.inference.model"
-    train(use_cuda, save_path)
-    infer(use_cuda, save_path)
+    train(use_cuda, train_program, save_path)
+    infer(use_cuda, inference_program, save_path)
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/test_detection.py

@@ -109,6 +109,24 @@ class TestDetection(unittest.TestCase):
         print(str(program))
 
 
+class TestPriorBox(unittest.TestCase):
+    def test_prior_box(self):
+        data_shape = [3, 224, 224]
+        images = fluid.layers.data(
+            name='pixel', shape=data_shape, dtype='float32')
+        conv1 = fluid.layers.conv2d(images, 3, 3, 2)
+        box, var = layers.prior_box(
+            input=conv1,
+            image=images,
+            min_sizes=[100.0],
+            aspect_ratios=[1.],
+            flip=True,
+            clip=True)
+        assert len(box.shape) == 4
+        assert box.shape == var.shape
+        assert box.shape[3] == 4
+
+
 class TestMultiBoxHead(unittest.TestCase):
     def test_multi_box_head(self):
         data_shape = [3, 224, 224]

python/paddle/fluid/tests/unittests/test_detection_map_op.py

@@ -160,7 +160,9 @@ class TestDetectionMAPOp(OpTest):
         label_count, true_pos, false_pos = get_input_pos(
             self.class_pos_count, self.true_pos, self.true_pos_lod,
             self.false_pos, self.false_pos_lod)
-        for (label, difficult, xmin, ymin, xmax, ymax) in self.label:
+        for v in self.label:
+            label = v[0]
+            difficult = False if len(v) == 5 else v[1]
             if self.evaluate_difficult:
                 label_count[label] += 1
             elif not difficult:
@@ -245,6 +247,15 @@ class TestDetectionMAPOpSkipDiff(TestDetectionMAPOp):
                        [2, 0.8, 0, 1], [2, 0.1, 1, 0], [3, 0.2, 0, 1]]
 
 
+class TestDetectionMAPOpWithoutDiff(TestDetectionMAPOp):
+    def init_test_case(self):
+        super(TestDetectionMAPOpWithoutDiff, self).init_test_case()
+
+        # label xmin ymin xmax ymax
+        self.label = [[1, 0.1, 0.1, 0.3, 0.3], [1, 0.6, 0.6, 0.8, 0.8],
+                      [2, 0.3, 0.3, 0.6, 0.5], [1, 0.7, 0.1, 0.9, 0.3]]
+
+
 class TestDetectionMAPOp11Point(TestDetectionMAPOp):
     def init_test_case(self):
         super(TestDetectionMAPOp11Point, self).init_test_case()

python/paddle/fluid/tests/unittests/test_is_empty_op.py

@@ -14,42 +14,24 @@
 
 import unittest
 import numpy as np
-from paddle.fluid.op import Operator
-import paddle.fluid.core as core
+from op_test import OpTest
 
 
-def create_tensor(scope, name, np_data):
-    tensor = scope.var(name).get_tensor()
-    tensor.set_dims(np_data.shape)
-    tensor.set(np_data, core.CPUPlace())
-    return tensor
-
-
-class TestIsEmptyOp(unittest.TestCase):
+class TestEmpty(OpTest):
     def setUp(self):
-        self.scope = core.Scope()
-        # create input variables
-        np_data0 = np.array([0, 1, 2])
-        create_tensor(self.scope, "X0", np_data0)
-
-        np_data1 = np.array([1])
-        t = create_tensor(self.scope, "X1", np_data1)
-        t.set_dims([0])
+        self.op_type = "is_empty"
+        self.inputs = {'X': np.array([1, 2, 3])}
+        self.outputs = {'Out': np.array([False])}
 
-        # create output variables
-        self.scope.var("out")
+    def test_check_output(self):
+        self.check_output()
 
-    def test_no_empty(self):
-        self.one_case("X0", False)
 
-    def test_empty(self):
-        self.one_case("X1", True)
-
-    def one_case(self, input, target):
-        op = Operator(type="is_empty", X=input, Out="out")
-        op.run(self.scope, core.CPUPlace())
-        out = self.scope.var("out").get_tensor()
-        self.assertEqual(np.array(out)[0], target)
+class TestNotEmpty(TestEmpty):
+    def setUp(self):
+        self.op_type = "is_empty"
+        self.inputs = {'X': np.array([])}
+        self.outputs = {'Out': np.array([True])}
 
 
 if __name__ == "__main__":

python/paddle/fluid/transpiler/memory_optimization_transpiler.py

@@ -24,7 +24,8 @@ dtype_to_size = {
     core.VarDesc.VarType.INT16: 2,
     core.VarDesc.VarType.INT32: 4,
     core.VarDesc.VarType.INT64: 8,
-    core.VarDesc.VarType.BOOL: 1
+    core.VarDesc.VarType.BOOL: 1,
+    core.VarDesc.VarType.UINT8: 1,
 }
 
 SUB_BLOCK_OPS = [