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

Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into add_py36_py37_ubuntu_dockerfile

.github/ISSUE_TEMPLATE/---feature-request-.md

+---
+name: 建议(Feature request)
+about: 您可以提出您的建议。 You could use this template for reporting a suggestion  issue.
+
+---
+
+欢迎您对PaddlePaddle提出建议，非常感谢您对PaddlePaddle的贡献！
+在留下您的建议时，辛苦您同步提供如下信息：
+- 版本、环境信息
+1）PaddlePaddle版本：请提供您的PaddlePaddle版本号，例如1.1
+2）CPU/GPU：您是否使用GPU进行训练，如是，请提供您的CUDA和cuDNN版本号
+3）系统环境：请您描述系统类型、版本，例如Mac OS 10.14
+- 复现信息：如为报错，请给出复现环境、复现步骤
+- 建议描述：请您详细描述，您认为需优化的功能
+
+Thank you for contributing to PaddlePaddle.
+Before submitting the issue, you could search issue in the github in case that there was a similar issue submitted or resolved before.
+Please make sure that this is a feature request. 
+**System information**
+-PaddlePaddle version （eg.1.1）or CommitID
+-CPU: including CPUMKL/OpenBlas/MKLDNN version
+-GPU: including CUDA/CUDNN version
+-OS Platform (eg.Mac OS 10.14)
+**To Reproduce**
+Steps to reproduce the behavior
+**Describe the feature and the current behavior/state.**
+**Any Other info.**

.github/ISSUE_TEMPLATE/---inference-issue-.md

+---
+name: 预测（Inference Issue）
+about: 您可以提问预测中报错、应用等问题。 You could use this template for reporting an inference issue.
+
+---
+
+为使您的问题得到快速解决，在建立Issue前，请您先通过如下方式搜索是否有相似问题:【搜索issue关键字】【使用labels筛选】【官方文档】
+
+如果您没有查询到相似问题，为快速解决您的提问，建立issue时请提供如下细节信息：
+- 标题：简洁、精准描述您的问题，例如“最新预测库的API文档在哪儿 ”
+- 版本、环境信息：
+    1）PaddlePaddle版本：请提供您的PaddlePaddle版本号（如1.1）或CommitID
+    2）CPU：预测若用CPU，请提供CPU型号，MKL/OpenBlas/MKLDNN/等数学库使用情况
+    3）GPU：预测若用GPU，请提供GPU型号、CUDA和CUDNN版本号
+    4）系统环境：请您描述系统类型、版本（如Mac OS 10.14），Python版本
+-预测信息
+    1）C++预测：请您提供预测库安装包的版本信息，及其中的version.txt文件
+    2）CMake包含路径的完整命令
+    3）API信息（如调用请提供）
+    4）预测库来源：官网下载/特殊环境（如BCLOUD编译）
+- 复现信息：如为报错，请给出复现环境、复现步骤
+- 问题描述：请详细描述您的问题，同步贴出报错信息、日志/代码关键片段
+
+Thank you for contributing to PaddlePaddle.
+Before submitting the issue, you could search issue in the github in case that th
+If there is no solution,please make sure that this is an inference issue including the following details :
+**System information**
+-PaddlePaddle version （eg.1.1）or CommitID
+-CPU: including CPUMKL/OpenBlas/MKLDNN version
+-GPU: including CUDA/CUDNN version
+-OS Platform (eg.Mac OS 10.14)
+-Python version
+-Cmake orders
+-C++version.txt
+-API information
+**To Reproduce**
+Steps to reproduce the behavior
+**Describe your current behavior**
+**Code to reproduce the issue**
+**Other info / logs**

.github/ISSUE_TEMPLATE/---installation-issue-.md

+---
+name: 安装（Installation Issue）
+about: 您可以提问安装、编译出现报错等问题。 You could use this template for reporting an installation
+   issue.
+
+---
+
+为使您的问题得到快速解决，在建立Issue前，请您先通过如下方式搜索是否有相似问题:【搜索issue关键字】【使用labels筛选】【官方文档】
+
+建立issue时，为快速解决问题，请您根据使用情况给出如下信息：
+- 标题：请包含关键词“安装错误”/“编译错误”，例如“Mac编译错误”
+- 版本、环境信息：
+    1）PaddlePaddle版本：请提供您的PaddlePaddle版本号（如1.1）或CommitID
+    2）CPU：请提供CPU型号，MKL/OpenBlas/MKLDNN/等数学库的使用情况
+    3）GPU：请提供GPU型号，CUDA和CUDNN版本号
+    4）系统环境：请说明系统类型、版本（如Mac OS 10.14）、Python版本
+- 安装方式信息：
+1）pip安装/docker安装
+2）本地编译：请提供cmake命令，编译命令
+3）docker编译：请提供docker镜像，编译命令            
+  特殊环境请注明：如离线安装等
+- 复现信息：如为报错，请给出复现环境、复现步骤
+- 问题描述：请详细描述您的问题，同步贴出报错信息、日志/代码关键片段
+
+Thank you for contributing to PaddlePaddle.
+Before submitting the issue, you could search issue in Github in case that there was a similar issue submitted or resolved before.
+If there is no solution,please make sure that this is an installation issue including the following details:
+**System information**
+-PaddlePaddle version （eg.1.1）or CommitID
+-CPU: including CPUMKL/OpenBlas/MKLDNN version
+-GPU: including CUDA/CUDNN version
+-OS Platform (eg. Mac OS 10.14)
+-Python version
+- Install method: pip install/install with docker/build from source(without docker)/build within docker
+- Other special cases that you think may be related to this problem, eg. offline install, special internet condition   
+**To Reproduce**
+Steps to reproduce the behavior
+**Describe your current behavior**
+**Code to reproduce the issue**
+**Other info / logs**

.github/ISSUE_TEMPLATE/---model-issue-.md

+---
+name: 模型（Model Issue）
+about: 您可以提问模型、算法、数据集方向的使用报错等问题。You could use this template for reporting a model/
+  algorithm/dataset  issue.
+
+---
+
+为使您的问题得到快速解决，在建立Issue前，请您先通过如下方式搜索是否有相似问题:【搜索issue关键字】【使用labels筛选】【官方文档】
+
+建立issue时，为快速解决问题，请您根据使用情况给出如下信息：
+- 标题：简洁、精准描述您的问题，例如“ssd 模型前置lstm报错  ”
+- 版本、环境信息：
+    1）PaddlePaddle版本：请提供PaddlePaddle版本号，例如1.1或CommitID
+    2）CPU：请提供CPU型号，MKL/OpenBlas/MKLDNN/等数学库的使用情况
+    3）GPU：请提供GPU型号，CUDA和CUDNN版本号
+    4）系统环境：请说明系统类型、版本（例如Mac OS 10.14），Python版本
+- 模型信息
+    1）模型名称 2）使用数据集名称 3）使用算法名称 4）模型链接
+- 复现信息：如为报错，请给出复现环境、复现步骤
+- 问题描述：请详细描述您的问题，同步贴出报错信息、日志/代码关键片段
+
+Thank you for contributing to PaddlePaddle.
+Before submitting the issue, you could search issue in the github.Probably there was a similar issue submitted or resolved before.
+If there is no solution,please make sure that this is a issue of models including the following details:
+**System information**
+-PaddlePaddle version （eg.1.1）or CommitID
+-CPU: including CPUMKL/OpenBlas/MKLDNN version
+-GPU: including CUDA/CUDNN version
+-OS Platform (eg.Mac OS 10.14)
+-Python version
+-Name of Models&Dataset/details of operator
+**To Reproduce**
+Steps to reproduce the behavior
+**Describe your current behavior**
+**Code to reproduce the issue**
+**Other info / logs**

.github/ISSUE_TEMPLATE/---others-.md

+---
+name: 其他（Others）
+about: 如上述分类未包含您的问题，可在此提出。 You could use this template for reporting other issues
+
+---
+
+为使您的问题得到快速解决，在建立Issues前，请您先通过如下方式搜索是否有相似问题:【搜索issue关键字】【使用labels筛选】【官方文档】
+
+如果您没有查询到相似问题，为快速解决您的提问，建立issue时请提供如下细节信息：
+- 标题：简洁、精准概括您的问题
+- 版本、环境信息：
+    1）PaddlePaddle版本：请提供您的PaddlePaddle版本号，例如1.1或CommitID
+    2）CPU/GPU：如果您使用GPU训练，请提供GPU驱动版本、CUDA和cuDNN版本号
+    3）系统环境：请您描述系统类型、版本，例如Mac OS 10.14
+    4）Python版本号
+    5）显存信息
+- 复现信息：如为报错，请给出复现环境、复现步骤
+- 问题描述：请详细描述您的问题，同步贴出报错信息、日志/代码关键片段
+
+Thank you for contributing to PaddlePaddle.
+Before submitting the issue, you could search issue in the github in case that there was a similar issue submitted or resolved before.
+If there is no solution,please provide us with the following details :
+**System information**
+-PaddlePaddle version （eg.1.1）or CommitID
+-CPU: including CPUMKL/OpenBlas/MKLDNN version
+-GPU: including CUDA/cuDNN version
+-OS Platform and Distribution(eg.Mac OS 10.14)
+-Python version 
+**To Reproduce**
+Steps to reproduce the behavior
+**Describe your current behavior**
+**Code to reproduce the issue**
+**Other info / logs**

.github/ISSUE_TEMPLATE/---training-issue-.md

+---
+name: 训练（Training issue）
+about: 您可以提问训练中报错、应用、出core等问题。 You could use this template for reporting an training
+   issue.
+
+---
+
+为使您的问题得到快速解决，在建立Issues前，请您先通过如下方式搜索是否有相似问题:【搜索issue关键字】【使用labels筛选】【官方文档】
+
+如果您没有查询到相似问题，为快速解决您的提问，建立issue时请提供如下细节信息：
+- 标题：简洁、精准概括您的问题，例如“Insufficient Memory xxx" ”
+- 版本、环境信息：
+    1）PaddlePaddle版本：请提供您的PaddlePaddle版本号，例如1.1或CommitID
+    2）CPU：预测若用CPU，请提供CPU型号，MKL/OpenBlas/MKLDNN/等数学库使用情况
+    3）GPU：预测若用GPU，请提供GPU型号、CUDA和CUDNN版本号
+    4）系统环境：请您描述系统类型、版本，例如Mac OS 10.14，Python版本
+- 训练信息
+    1）单机/多机，单卡/多卡
+    2）显存信息
+    3）Operator信息
+- 复现信息：如为报错，请给出复现环境、复现步骤
+- 问题描述：请详细描述您的问题，同步贴出报错信息、日志、可复现的代码片段
+
+Thank you for contributing to PaddlePaddle.
+Before submitting the issue, you could search issue in the github in case that there was a similar issue submitted or resolved before.
+If there is no solution,please make sure that this is a training issue including the following details:
+**System information**
+-PaddlePaddle version （eg.1.1）or CommitID
+-CPU: including CPUMKL/OpenBlas/MKLDNN version
+-GPU: including CUDA/CUDNN version
+-OS Platform (eg.Mac OS 10.14)
+-Other imformation: Distriuted training/informantion of operator/
+Graphics card storage
+**To Reproduce**
+Steps to reproduce the behavior
+**Describe your current behavior**
+**Code to reproduce the issue**
+**Other info / logs**

.gitignore

+python/paddle/fluid/tests/unittests/reader_reset_test.recordio
 paddle/operators/check_t.save
 paddle/operators/check_tensor.ls
 paddle/operators/tensor.save

AUTHORS.md

@@ -25,6 +25,7 @@
 | kexinzhao | Ke-Xin Zhao |
 | kuke | Yi-Bing Liu |
 | lcy-seso | Ying Cao |
+| cjld | Dun Liang |
 | lipeng-unisound | Peng Li |
 | liuyuan | Yuan Liu |
 | livc | Zhao Li |
@@ -42,6 +43,7 @@
 | QiJune | Jun Qi |
 | qingqing01 | Qing-Qing Dang |
 | reyoung | Yang Yu |
+| Sand3r- | Michal Gallus |
 | Superjom | Chun-Wei Yan |
 | tensor-tang | Jian Tang |
 | tianbingsz | Tian-Bing Xu |

CMakeLists.txt

@@ -130,6 +130,21 @@ if (APPLE OR WIN32)
         "Disable MKL for building on mac and windows" FORCE)
 endif()
 
+if (WIN32)
+    set(WITH_AVX OFF CACHE STRING
+            "Disable AVX when compiling for Windows" FORCE)
+    set(WITH_DSO OFF CACHE STRING
+            "Disable DSO when compiling for Windows" FORCE)
+    set(WITH_MKL OFF CACHE STRING
+            "Disable MKL when compiling for Windows" FORCE)
+    set(WITH_DISTRIBUTE OFF CACHE STRING
+            "Disable DISTRIBUTE when compiling for Windows" FORCE)
+    set(WITH_C_API OFF CACHE STRING
+            "Disable C_API when compiling for Windows" FORCE)
+    set(WITH_FLUID_ONLY ON CACHE STRING
+            "Enable FLUID_ONLY when compiling for Windows" FORCE)
+endif()
+
 set(THIRD_PARTY_PATH "${CMAKE_BINARY_DIR}/third_party" CACHE STRING
   "A path setting third party libraries download & build directories.")
 
@@ -189,12 +204,14 @@ include(external/eigen)     # download eigen3
 include(external/pybind11)  # download pybind11
 include(external/cares)
 include(external/cub)
+include(external/rocprim)
 include(external/xxhash)    # download xxhash
-
-if (NOT WIN32)
-# there is no official support of snappystream, warpctc, nccl, cupti in windows
+include(external/dlpack)
 include(external/snappy)    # download snappy
 include(external/snappystream) # download snappystream
+
+if (NOT WIN32)
+# there is no official support of warpctc, nccl, cupti in windows
 include(external/warpctc)   # download, build, install warpctc
 include(cupti)
 endif (NOT WIN32)
@@ -302,6 +319,14 @@ set(PADDLE_PYTHON_BUILD_DIR "${CMAKE_CURRENT_BINARY_DIR}/python/build")
 set(CMAKE_CXX_FLAGS_RELWITHDEBINFO "-O3 -g -DNDEBUG")
 set(CMAKE_C_FLAGS_RELWITHDEBINFO "-O3 -g -DNDEBUG")
 
+if (ON_INFER)
+    message(STATUS "On inference mode, will take place some specific optimization.")
+    add_definitions(-DPADDLE_ON_INFERENCE)
+else()
+    #TODO(luotao), combine this warning with `make inference_lib_dist` command.
+    message(WARNING "On inference mode, will take place some specific optimization. Turn on the ON_INFER flag when building inference_lib only.")
+endif()
+
 add_subdirectory(paddle)
 if(WITH_PYTHON)
     add_subdirectory(python)
@@ -312,10 +337,3 @@ if(WITH_DOC)
     find_python_module(recommonmark REQUIRED)
     add_subdirectory(doc)
 endif()
-
-if (ON_INFER)
-    message(STATUS "On inference mode, will take place some specific optimization.")
-else()
-    #TODO(luotao), combine this warning with `make inference_lib_dist` command.
-    message(WARNING "On inference mode, will take place some specific optimization. Turn on the ON_INFER flag when building inference_lib only.")
-endif()

cmake/external/dlpack.cmake

+include(ExternalProject)
+
+set(DLPACK_SOURCE_DIR ${THIRD_PARTY_PATH}/dlpack)
+set(DLPACK_INCLUDE_DIR ${DLPACK_SOURCE_DIR}/src/extern_dlpack/include)
+
+include_directories(${DLPACK_INCLUDE_DIR})
+
+ExternalProject_Add(
+  extern_dlpack
+  ${EXTERNAL_PROJECT_LOG_ARGS}
+  GIT_REPOSITORY "https://github.com/dmlc/dlpack.git"
+  GIT_TAG        "v0.2"
+  PREFIX         ${DLPACK_SOURCE_DIR}
+  UPDATE_COMMAND ""
+  CONFIGURE_COMMAND ""
+  BUILD_COMMAND     ""
+  INSTALL_COMMAND   ""
+  TEST_COMMAND      ""
+)
+
+if(${CMAKE_VERSION} VERSION_LESS "3.3.0")
+  set(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/dlpack_dummy.c)
+  file(WRITE ${dummyfile} "const char *dummy = \"${dummyfile}\";")
+  add_library(dlpack STATIC ${dummyfile})
+else()
+  add_library(dlpack INTERFACE)
+endif()
+
+add_dependencies(dlpack extern_dlpack)
+
+LIST(APPEND externl_project_dependencies dlpack)

cmake/external/eigen.cmake

@@ -17,7 +17,7 @@ if(WITH_AMD_GPU)
         extern_eigen3
         ${EXTERNAL_PROJECT_LOG_ARGS}
         GIT_REPOSITORY  "https://github.com/sabreshao/hipeigen.git"
-        GIT_TAG         0cba03ff9f8f9f70bbd92ac5857b031aa8fed6f9
+        GIT_TAG         7cb2b6e5a4b4a1efe658abb215cd866c6fb2275e
         PREFIX          ${EIGEN_SOURCE_DIR}
         UPDATE_COMMAND  ""
         CONFIGURE_COMMAND ""

cmake/external/gtest.cmake

@@ -50,7 +50,11 @@ IF(WITH_TESTING)
         CMAKE_ARGS      -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
                         -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
                         -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
+                        -DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
+                        -DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
                         -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
+                        -DCMAKE_C_FLAGS_DEBUG=${CMAKE_C_FLAGS_DEBUG}
+                        -DCMAKE_C_FLAGS_RELEASE=${CMAKE_C_FLAGS_RELEASE}
                         -DCMAKE_INSTALL_PREFIX=${GTEST_INSTALL_DIR}
                         -DCMAKE_POSITION_INDEPENDENT_CODE=ON
                         -DBUILD_GMOCK=ON

cmake/external/mkldnn.cmake

@@ -53,7 +53,7 @@ ExternalProject_Add(
     ${EXTERNAL_PROJECT_LOG_ARGS}
     DEPENDS             ${MKLDNN_DEPENDS}
     GIT_REPOSITORY      "https://github.com/01org/mkl-dnn.git"
-    GIT_TAG             "21fb5f2af1dd14e132af4f1b79160977ee487818"
+    GIT_TAG             "830a10059a018cd2634d94195140cf2d8790a75a"
     PREFIX              ${MKLDNN_SOURCES_DIR}
     UPDATE_COMMAND      ""
     CMAKE_ARGS          -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}

cmake/external/rocprim.cmake

+if (NOT WITH_AMD_GPU)
+    return()
+endif()
+
+# rocprim is "ROCm Parallel Primitives" for short.
+# It is a header-only library providing HIP and HC parallel primitives
+# for developing performant GPU-accelerated code on AMD ROCm platform.
+
+if("x${HCC_HOME}" STREQUAL "x")
+  set(HCC_HOME "/opt/rocm/hcc")
+endif()
+
+INCLUDE(ExternalProject)
+
+SET(ROCPRIM_SOURCE_DIR ${THIRD_PARTY_PATH}/rocprim)
+SET(ROCPRIM_INSTALL_DIR  ${THIRD_PARTY_PATH}/install/rocprim)
+SET(ROCPRIM_INCLUDE_DIR ${ROCPRIM_INSTALL_DIR}/include)
+
+ExternalProject_Add(
+    extern_rocprim
+    GIT_REPOSITORY "https://github.com/ROCmSoftwarePlatform/rocPRIM.git"
+    GIT_TAG        5bd41b96ab8d8343330fb2c3e1b96775bde3b3fc 
+    PREFIX         ${ROCPRIM_SOURCE_DIR}
+    UPDATE_COMMAND  ""
+    CMAKE_ARGS     -DCMAKE_CXX_COMPILER=${HCC_HOME}/bin/hcc
+    CMAKE_ARGS     -DONLY_INSTALL=ON
+    CMAKE_ARGS     -DBUILD_TEST=OFF
+    CMAKE_ARGS     -DCMAKE_INSTALL_PREFIX=${ROCPRIM_INSTALL_DIR}
+
+    INSTALL_DIR    ${ROCPRIM_INSTALL_DIR}
+    ${EXTERNAL_PROJECT_LOG_ARGS}
+)
+
+INCLUDE_DIRECTORIES(${ROCPRIM_INCLUDE_DIR})
+
+if (${CMAKE_VERSION} VERSION_LESS "3.3.0")
+    set(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/rocprim_dummy.c)
+    file(WRITE ${dummyfile} "const char *dummy_rocprim = \"${dummyfile}\";")
+    add_library(rocprim STATIC ${dummyfile})
+else()
+    add_library(rocprim INTERFACE)
+endif()
+
+add_dependencies(rocprim extern_rocprim)

cmake/external/snappy.cmake

@@ -24,7 +24,11 @@ set(SNAPPY_SOURCES_DIR ${THIRD_PARTY_PATH}/snappy)
 set(SNAPPY_INSTALL_DIR ${THIRD_PARTY_PATH}/install/snappy)
 set(SNAPPY_INCLUDE_DIR "${SNAPPY_INSTALL_DIR}/include" CACHE PATH "snappy include directory." FORCE)
 
-set(SNAPPY_LIBRARIES "${SNAPPY_INSTALL_DIR}/lib/libsnappy.a")
+if (WIN32)
+    set(SNAPPY_LIBRARIES "${SNAPPY_INSTALL_DIR}/lib/snappy.lib")
+else(WIN32)
+    set(SNAPPY_LIBRARIES "${SNAPPY_INSTALL_DIR}/lib/libsnappy.a")
+endif (WIN32)
 
 ExternalProject_Add(
     extern_snappy
@@ -34,8 +38,12 @@ ExternalProject_Add(
     UPDATE_COMMAND  ""
     CMAKE_ARGS      -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
                     -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
-                    -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
                     -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
+                    -DCMAKE_C_FLAGS_DEBUG=${CMAKE_C_FLAGS_DEBUG}
+                    -DCMAKE_C_FLAGS_RELEASE=${CMAKE_C_FLAGS_RELEASE}
+                    -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
+                    -DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
+                    -DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
                     -DCMAKE_INSTALL_PREFIX=${SNAPPY_INSTALL_DIR}
                     -DCMAKE_INSTALL_LIBDIR=${SNAPPY_INSTALL_DIR}/lib
                     -DCMAKE_POSITION_INDEPENDENT_CODE=ON

cmake/external/snappystream.cmake

@@ -18,15 +18,19 @@ ENDIF()
 
 include (ExternalProject)
 
-# NOTE: snappy is needed when linking with recordio
-
 set(SNAPPYSTREAM_SOURCES_DIR ${THIRD_PARTY_PATH}/snappy_stream)
 set(SNAPPYSTREAM_INSTALL_DIR ${THIRD_PARTY_PATH}/install/snappy_stream)
 set(SNAPPYSTREAM_INCLUDE_DIR "${SNAPPYSTREAM_INSTALL_DIR}/include" CACHE PATH "snappy stream include directory." FORCE)
 
-set(SNAPPYSTREAM_LIBRARIES "${SNAPPYSTREAM_INSTALL_DIR}/lib/libsnappystream.a")
+if(WIN32)
+    # Fix me, VS2015 come without VLA support
+    set(SNAPPYSTREAM_LIBRARIES "${SNAPPYSTREAM_INSTALL_DIR}/lib/snappystream.lib")
+    MESSAGE(WARNING, "In windows, snappystream has no compile support for windows,
+    please build it manually and put it at " ${SNAPPYSTREAM_INSTALL_DIR})
+else(WIN32)
+    set(SNAPPYSTREAM_LIBRARIES "${SNAPPYSTREAM_INSTALL_DIR}/lib/libsnappystream.a")
 
-ExternalProject_Add(
+    ExternalProject_Add(
             extern_snappystream
             GIT_REPOSITORY "https://github.com/hoxnox/snappystream.git"
             GIT_TAG "0.2.8"
@@ -34,8 +38,12 @@ ExternalProject_Add(
             UPDATE_COMMAND  ""
             CMAKE_ARGS      -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
                             -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
-                        -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
                             -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
+                            -DCMAKE_C_FLAGS_DEBUG=${CMAKE_C_FLAGS_DEBUG}
+                            -DCMAKE_C_FLAGS_RELEASE=${CMAKE_C_FLAGS_RELEASE}
+                            -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
+                            -DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
+                            -DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
                             -DCMAKE_INSTALL_PREFIX=${SNAPPY_INSTALL_DIR}
                             -DCMAKE_INSTALL_LIBDIR=${SNAPPY_INSTALL_DIR}/lib
                             -DCMAKE_POSITION_INDEPENDENT_CODE=ON
@@ -47,7 +55,8 @@ ExternalProject_Add(
                             -DCMAKE_INSTALL_LIBDIR:PATH=${SNAPPYSTREAM_INSTALL_DIR}/lib
                             -DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
             DEPENDS snappy
-)
+    )
+endif(WIN32)
 
 add_library(snappystream STATIC IMPORTED GLOBAL)
 set_property(TARGET snappystream PROPERTY IMPORTED_LOCATION ${SNAPPYSTREAM_LIBRARIES})

cmake/flags.cmake

@@ -129,6 +129,9 @@ set(COMMON_FLAGS
     -Wno-error=parentheses-equality # Warnings in pybind11
     -Wno-error=ignored-attributes  # Warnings in Eigen, gcc 6.3
     -Wno-error=terminate  # Warning in PADDLE_ENFORCE
+    -Wno-error=int-in-bool-context # Warning in Eigen gcc 7.2
+    -Wimplicit-fallthrough=0 # Warning in tinyformat.h
+    -Wno-error=maybe-uninitialized # Warning in boost gcc 7.2
 )
 
 set(GPU_COMMON_FLAGS

cmake/generic.cmake

@@ -351,6 +351,9 @@ function(cc_test TARGET_NAME)
     cmake_parse_arguments(cc_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
     add_executable(${TARGET_NAME} ${cc_test_SRCS})
     target_link_libraries(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main lod_tensor memory gtest gflags glog)
+    if(WIN32)
+      target_link_libraries(${TARGET_NAME} shlwapi)
+    endif(WIN32)
     add_dependencies(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main lod_tensor memory gtest gflags glog)
     add_test(NAME ${TARGET_NAME}
              COMMAND ${TARGET_NAME} ${cc_test_ARGS}
@@ -451,11 +454,15 @@ function(hip_library TARGET_NAME)
       else()
         add_library(${TARGET_NAME} STATIC ${_cmake_options} ${_generated_files} ${_sources})
         set_target_properties(${TARGET_NAME} PROPERTIES LINKER_LANGUAGE CXX)
-        target_link_libraries(${TARGET_NAME} /opt/rocm/hip/lib/libhip_hcc.so /opt/rocm/hip/lib/libhip_device.a)
+        target_link_libraries(${TARGET_NAME} /opt/rocm/hip/lib/libhip_hcc.so /opt/rocm/hip/lib/libhip_device.a /opt/rocm/rccl/lib/librccl.so /opt/rocm/hiprand/lib/libhiprand.so)
         find_fluid_modules(${TARGET_NAME})
       endif()
-      if (hip_library_DEPS)
-	add_dependencies(${TARGET_NAME} ${hip_library_DEPS})
+      if("${hip_library_DEPS}" MATCHES "ARCHIVE_START")
+        # Support linking flags: --whole-archive (Linux) / -force_load (MacOS).
+        # WARNING: Please don't use ARCHIVE_START&ARCHIVE_END if TARGET_NAME will be linked by other libraries.
+        target_circle_link_libraries(${TARGET_NAME} ${hip_library_DEPS})
+        list(REMOVE_ITEM hip_library_DEPS ARCHIVE_START ARCHIVE_END)
+      else()
         target_link_libraries(${TARGET_NAME} ${hip_library_DEPS})
       endif()
       # cpplint code style

cmake/hip.cmake

@@ -3,6 +3,8 @@ if(NOT WITH_AMD_GPU)
 endif()
 
 include_directories("/opt/rocm/include")
+include_directories("/opt/rocm/hip/include")
+include_directories("/opt/rocm/miopen/include")
 include_directories("/opt/rocm/hipblas/include")
 include_directories("/opt/rocm/hiprand/include")
 include_directories("/opt/rocm/rocrand/include")
@@ -11,20 +13,40 @@ include_directories("/opt/rocm/thrust")
 
 list(APPEND EXTERNAL_LIBS "-L/opt/rocm/lib/ -lhip_hcc")
 
-set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -fPIC -DPADDLE_WITH_HIP -std=c++14" )
+set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -fPIC -DPADDLE_WITH_HIP -std=c++11" )
 
 if(WITH_DSO)
   set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DPADDLE_USE_DSO")
 endif(WITH_DSO)
 
-if(WITH_DOUBLE)
-  set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DPADDLE_TYPE_DOUBLE")
-endif(WITH_DOUBLE)
-
 if(WITH_TESTING)
   set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DPADDLE_WITH_TESTING")
 endif(WITH_TESTING)
 
+if(WITH_DISTRIBUTE)
+  set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DPADDLE_WITH_DISTRIBUTE")
+endif(WITH_DISTRIBUTE)
+
+if(WITH_GRPC)
+  set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DPADDLE_WITH_GRPC")
+endif(WITH_GRPC)
+
+if(NOT WITH_GOLANG)
+  set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DPADDLE_WITHOUT_GOLANG")
+endif(NOT WITH_GOLANG)
+
+if(WITH_MKLDNN)
+  set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DPADDLE_WITH_MKLDNN")
+endif(WITH_MKLDNN)
+
+set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DANY_IMPL_ANY_CAST_MOVEABLE")
+
+if(NOT WITH_RDMA)
+  set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DPADDLE_DISABLE_RDMA")
+endif(NOT WITH_RDMA)
+
+
+
 if(CMAKE_BUILD_TYPE  STREQUAL "Debug")
     list(APPEND HIP_HCC_FLAGS  ${CMAKE_CXX_FLAGS_DEBUG})
 elseif(CMAKE_BUILD_TYPE  STREQUAL "RelWithDebInfo")

cmake/inference_lib.cmake

@@ -166,8 +166,8 @@ copy(framework_lib DEPS ${framework_lib_deps}
 
 set(module "memory")
 copy(memory_lib
-        SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/detail/*.h
-        DSTS ${dst_dir}/${module} ${dst_dir}/${module}/detail
+        SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/detail/*.h ${src_dir}/${module}/allocation/*.h
+        DSTS ${dst_dir}/${module} ${dst_dir}/${module}/detail ${dst_dir}/${module}/allocation
         )
 
 set(inference_deps paddle_fluid_shared paddle_fluid)

cmake/operators.cmake

@@ -84,9 +84,7 @@ function(op_library TARGET)
     endif()
     if (WIN32)
     # remove windows unsupported op, because windows has no nccl, no warpctc such ops.
-    foreach(windows_unsupport_op "nccl_op" "gen_nccl_id_op" "warpctc_op" "hierarchical_sigmoid_op"
-     "crf_decoding_op" "select_op" "lstmp_op" "gru_op" "fusion_gru_op" "lstm_op" "fusion_lstm_op" "cumsum_op"
-      "fusion_seqconv_eltadd_relu_op" "channel_send_op" "channel_create_op" "channel_close_op" "channel_recv_op")
+    foreach(windows_unsupport_op "nccl_op" "gen_nccl_id_op" "warpctc_op")
         if ("${TARGET}" STREQUAL "${windows_unsupport_op}")
           return()
         endif()

cmake/simd.cmake

@@ -57,43 +57,46 @@ int main()
     return 0;
 }" SSE3_FOUND)
 
-# Check AVX
-set(CMAKE_REQUIRED_FLAGS ${AVX_FLAG})
-set(AVX_FOUND_EXITCODE 1 CACHE STRING "Result from TRY_RUN" FORCE)
-CHECK_CXX_SOURCE_RUNS("
-#include <immintrin.h>
-int main()
-{
+# disable AVX by default on windows
+if(NOT WIN32)
+    # Check AVX
+    set(CMAKE_REQUIRED_FLAGS ${AVX_FLAG})
+    set(AVX_FOUND_EXITCODE 1 CACHE STRING "Result from TRY_RUN" FORCE)
+    CHECK_CXX_SOURCE_RUNS("
+    #include <immintrin.h>
+    int main()
+    {
         __m256 a = _mm256_set_ps (-1.0f, 2.0f, -3.0f, 4.0f, -1.0f, 2.0f, -3.0f, 4.0f);
         __m256 b = _mm256_set_ps (1.0f, 2.0f, 3.0f, 4.0f, 1.0f, 2.0f, 3.0f, 4.0f);
         __m256 result = _mm256_add_ps (a, b);
         return 0;
-}" AVX_FOUND)
+    }" AVX_FOUND)
 
-# Check AVX 2
-set(CMAKE_REQUIRED_FLAGS ${AVX2_FLAG})
-set(AVX2_FOUND_EXITCODE 1 CACHE STRING "Result from TRY_RUN" FORCE)
-CHECK_CXX_SOURCE_RUNS("
-#include <immintrin.h>
-int main()
-{
+    # Check AVX 2
+    set(CMAKE_REQUIRED_FLAGS ${AVX2_FLAG})
+    set(AVX2_FOUND_EXITCODE 1 CACHE STRING "Result from TRY_RUN" FORCE)
+    CHECK_CXX_SOURCE_RUNS("
+    #include <immintrin.h>
+    int main()
+    {
         __m256i a = _mm256_set_epi32 (-1, 2, -3, 4, -1, 2, -3, 4);
         __m256i result = _mm256_abs_epi32 (a);
         return 0;
-}" AVX2_FOUND)
+    }" AVX2_FOUND)
 
-# Check AVX512F
-set(CMAKE_REQUIRED_FLAGS ${AVX512F_FLAG})
-set(AVX512F_FOUND_EXITCODE 1 CACHE STRING "Result from TRY_RUN" FORCE)
-CHECK_CXX_SOURCE_RUNS("
-#include <immintrin.h>
-int main()
-{
+    # Check AVX512F
+    set(CMAKE_REQUIRED_FLAGS ${AVX512F_FLAG})
+    set(AVX512F_FOUND_EXITCODE 1 CACHE STRING "Result from TRY_RUN" FORCE)
+    CHECK_CXX_SOURCE_RUNS("
+    #include <immintrin.h>
+    int main()
+    {
         __m512i a = _mm512_set_epi32 (-1, 2, -3, 4, -1, 2, -3, 4,
                                       13, -5, 6, -7, 9, 2, -6, 3);
         __m512i result = _mm512_abs_epi32 (a);
         return 0;
-}" AVX512F_FOUND)
+    }" AVX512F_FOUND)
+endif(NOT WIN32)
 
 set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_RETAINED})
 mark_as_advanced(MMX_FOUND SSE2_FOUND SSE3_FOUND AVX_FOUND AVX2_FOUND AVX512F_FOUND)

paddle/fluid/API.spec

@@ -103,6 +103,7 @@ paddle.fluid.layers.beam_search ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 's
 paddle.fluid.layers.row_conv ArgSpec(args=['input', 'future_context_size', 'param_attr', 'act'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.layers.multiplex ArgSpec(args=['inputs', 'index'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.layer_norm ArgSpec(args=['input', 'scale', 'shift', 'begin_norm_axis', 'epsilon', 'param_attr', 'bias_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(True, True, 1, 1e-05, None, None, None, None))
+paddle.fluid.layers.group_norm ArgSpec(args=['input', 'groups', 'epsilon', 'param_attr', 'bias_attr', 'act', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(1e-05, None, None, None, 'NCHW', None))
 paddle.fluid.layers.softmax_with_cross_entropy ArgSpec(args=['logits', 'label', 'soft_label', 'ignore_index', 'numeric_stable_mode', 'return_softmax'], varargs=None, keywords=None, defaults=(False, -100, False, False))
 paddle.fluid.layers.smooth_l1 ArgSpec(args=['x', 'y', 'inside_weight', 'outside_weight', 'sigma'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.layers.one_hot ArgSpec(args=['input', 'depth'], varargs=None, keywords=None, defaults=None)
@@ -275,7 +276,7 @@ paddle.fluid.layers.hard_shrink ArgSpec(args=['x', 'threshold'], varargs=None, k
 paddle.fluid.layers.cumsum ArgSpec(args=['x', 'axis', 'exclusive', 'reverse'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.layers.thresholded_relu ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.prior_box ArgSpec(args=['input', 'image', 'min_sizes', 'max_sizes', 'aspect_ratios', 'variance', 'flip', 'clip', 'steps', 'offset', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, [1.0], [0.1, 0.1, 0.2, 0.2], False, False, [0.0, 0.0], 0.5, None, False))
-paddle.fluid.layers.density_prior_box ArgSpec(args=['input', 'image', 'densities', 'fixed_sizes', 'fixed_ratios', 'variance', 'clip', 'steps', 'offset', 'name'], varargs=None, keywords=None, defaults=(None, None, None, [0.1, 0.1, 0.2, 0.2], False, [0.0, 0.0], 0.5, None))
+paddle.fluid.layers.density_prior_box ArgSpec(args=['input', 'image', 'densities', 'fixed_sizes', 'fixed_ratios', 'variance', 'clip', 'steps', 'offset', 'flatten_to_2d', 'name'], varargs=None, keywords=None, defaults=(None, None, None, [0.1, 0.1, 0.2, 0.2], False, [0.0, 0.0], 0.5, False, None))
 paddle.fluid.layers.multi_box_head ArgSpec(args=['inputs', 'image', 'base_size', 'num_classes', 'aspect_ratios', 'min_ratio', 'max_ratio', 'min_sizes', 'max_sizes', 'steps', 'step_w', 'step_h', 'offset', 'variance', 'flip', 'clip', 'kernel_size', 'pad', 'stride', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None, 0.5, [0.1, 0.1, 0.2, 0.2], True, False, 1, 0, 1, None, False))
 paddle.fluid.layers.bipartite_match ArgSpec(args=['dist_matrix', 'match_type', 'dist_threshold', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.layers.target_assign ArgSpec(args=['input', 'matched_indices', 'negative_indices', 'mismatch_value', 'name'], varargs=None, keywords=None, defaults=(None, None, None))

paddle/fluid/CMakeLists.txt

@@ -3,13 +3,9 @@ add_subdirectory(platform)
 add_subdirectory(framework)
 add_subdirectory(operators)
 add_subdirectory(string)
-
-add_subdirectory(pybind)
-if (NOT WIN32)
 add_subdirectory(recordio)
-endif(NOT WIN32)
+add_subdirectory(pybind)
 
 # NOTE: please add subdirectory inference at last.
 add_subdirectory(inference)
-
 add_subdirectory(train)

paddle/fluid/framework/CMakeLists.txt

@@ -31,9 +31,7 @@ function(windows_symbolic TARGET)
 endfunction()
 
 add_subdirectory(ir)
-if (NOT WIN32)
 add_subdirectory(details)
-endif (NOT WIN32)
 # ddim lib
 proto_library(framework_proto SRCS framework.proto)
 
@@ -68,11 +66,7 @@ if(WITH_GPU)
 else()
   cc_test(mixed_vector_test SRCS mixed_vector_test.cc DEPS place memory device_context tensor)
 endif()
-if (NOT WIN32)
-  cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto recordio version)
-else()
-  cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto version)
-endif (NOT WIN32)
+cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto recordio version)
 
 cc_test(lod_tensor_test SRCS lod_tensor_test.cc DEPS lod_tensor memory)
 nv_test(lod_tensor_gpu_test SRCS lod_tensor_test.cu DEPS lod_tensor)
@@ -122,13 +116,8 @@ cc_test(op_proto_maker_test SRCS op_proto_maker_test.cc DEPS op_proto_maker)
 cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto)
 cc_library(shape_inference SRCS shape_inference.cc DEPS ddim attribute device_context)
 
-if (NOT WIN32)
 cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope glog
     shape_inference data_transform lod_tensor profiler)
-else()
-cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope glog
-    shape_inference data_transform lod_tensor)
-endif(NOT WIN32)
 
 cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry device_context)
 
@@ -183,12 +172,10 @@ else()
   cc_test(test_naive_executor SRCS naive_executor_test.cc DEPS naive_executor elementwise_add_op)
 endif()
 
-if (NOT WIN32)
 cc_library(parallel_executor SRCS parallel_executor.cc DEPS
         threaded_ssa_graph_executor scope_buffered_ssa_graph_executor
         graph build_strategy
         fast_threaded_ssa_graph_executor)
-endif() # NOT WIN32
 
 cc_library(prune SRCS prune.cc DEPS framework_proto)
 cc_test(prune_test SRCS prune_test.cc DEPS op_info prune recurrent_op device_context)
@@ -205,3 +192,6 @@ cc_test(tuple_test SRCS tuple_test.cc )
 if (NOT WIN32)
 cc_test(rw_lock_test SRCS rw_lock_test.cc)
 endif (NOT WIN32)
+
+cc_library(dlpack_tensor SRCS dlpack_tensor.cc DEPS tensor dlpack)
+cc_test(dlpack_tensor_test SRCS dlpack_tensor_test.cc DEPS dlpack_tensor glog)

paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h

@@ -13,9 +13,9 @@
 // limitations under the License.
 
 #pragma once
+#include <ThreadPool.h>
 #include <string>
 #include <vector>
-#include "ThreadPool.h"
 #include "paddle/fluid/framework/blocking_queue.h"
 #include "paddle/fluid/framework/details/exception_holder.h"
 #include "paddle/fluid/framework/details/execution_strategy.h"

paddle/fluid/framework/dlpack_tensor.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 "paddle/fluid/framework/dlpack_tensor.h"
+
+namespace paddle {
+namespace framework {
+
+namespace internal {
+template <typename T>
+static ::DLDataType GetDLDataTypeCode() {
+  ::DLDataType dtype;
+  if (std::is_same<T, platform::float16>::value ||
+      std::is_floating_point<T>::value) {
+    dtype.code = kDLFloat;
+  } else if (std::is_unsigned<T>::value) {
+    dtype.code = kDLUInt;
+  } else if (std::is_integral<T>::value) {
+    dtype.code = kDLInt;
+  } else {
+    PADDLE_THROW("Unsupported data type %s", typeid(T).name());
+  }
+  dtype.bits = 8 * sizeof(T);
+  dtype.lanes = 1;
+  return dtype;
+}
+
+static DLDataType GetDLDataTypeFromTypeIndex(const std::type_index &type) {
+#define REG_DL_DATA_TYPE(type) \
+  { std::type_index(typeid(type)), GetDLDataTypeCode<type>() }
+  static const std::unordered_map<std::type_index, ::DLDataType>
+      type_to_dtype_map({
+          REG_DL_DATA_TYPE(platform::float16),  // NOLINT
+          REG_DL_DATA_TYPE(float),              // NOLINT
+          REG_DL_DATA_TYPE(double),             // NOLINT
+          REG_DL_DATA_TYPE(int),                // NOLINT
+          REG_DL_DATA_TYPE(int64_t),            // NOLINT
+          REG_DL_DATA_TYPE(bool),               // NOLINT
+          REG_DL_DATA_TYPE(size_t),             // NOLINT
+          REG_DL_DATA_TYPE(int16_t),            // NOLINT
+          REG_DL_DATA_TYPE(uint8_t),            // NOLINT
+          REG_DL_DATA_TYPE(int8_t)              // NOLINT
+      });
+  static auto type_to_dtype_map_end_it = type_to_dtype_map.end();
+  auto it = type_to_dtype_map.find(type);
+  PADDLE_ENFORCE(it != type_to_dtype_map_end_it, "Unsupported data type %s",
+                 type.name());
+  return it->second;
+#undef REG_DL_DATA_TYPE
+}
+
+struct DLContextVisitor : public boost::static_visitor<::DLContext> {
+  inline ::DLContext operator()(const platform::CPUPlace &place) const {
+    DLContext ctx;
+    ctx.device_type = kDLCPU;
+    ctx.device_id = 0;
+    return ctx;
+  }
+
+  inline ::DLContext operator()(const platform::CUDAPlace &place) const {
+#ifdef PADDLE_WITH_CUDA
+    DLContext ctx;
+    ctx.device_type = kDLGPU;
+    ctx.device_id = place.device;
+    return ctx;
+#else
+    PADDLE_THROW("platform::CUDAPlace is not supported in CPU only version");
+#endif
+  }
+
+  inline ::DLContext operator()(const platform::CUDAPinnedPlace &place) const {
+#ifdef PADDLE_WITH_CUDA
+    DLContext ctx;
+    ctx.device_type = kDLCPUPinned;
+    ctx.device_id = 0;
+    return ctx;
+#else
+    PADDLE_THROW(
+        "platform::CUDAPinnedPlace is not supported in CPU only version");
+#endif
+  }
+};
+}  // namespace internal
+
+DLPackTensor::DLPackTensor(const Tensor &tensor, LaneType lanes) {
+  // init data, data buffer
+  t_.data = const_cast<void *>(tensor.data<void>());
+
+  // init ctx, DLContext type with device_type and device_id
+  auto place = tensor.place();
+  t_.ctx = boost::apply_visitor(internal::DLContextVisitor(), place);
+
+  // init dtype
+  t_.dtype = internal::GetDLDataTypeFromTypeIndex(tensor.type());
+  t_.dtype.lanes = lanes;
+
+  // init ndim, tensor rank
+  auto &dims = tensor.dims();
+  using DimType = decltype(t_.ndim);  // int
+  t_.ndim = static_cast<DimType>(dims.size());
+
+  // init shape, tensor dims
+  t_.shape = shape_;
+  for (DimType i = 0; i < t_.ndim; ++i) {
+    t_.shape[i] = dims[i];
+  }
+
+  // init strides, nullptr means the tensor is compact
+  t_.strides = nullptr;
+
+  // init byte_offset
+  t_.byte_offset = 0;
+}
+
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/dlpack_tensor.h

+// 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.
+
+#pragma once
+
+#include <dlpack/dlpack.h>
+#include "paddle/fluid/framework/tensor.h"
+
+namespace paddle {
+namespace framework {
+
+class DLPackTensor {
+ public:
+  using LaneType = decltype(::DLTensor::dtype.lanes);  // uint16_t
+  using ShapeType =
+      std::remove_reference<decltype(::DLTensor::shape[0])>::type;  // int64_t
+
+  // lanes is only used in CPU to enable vectorization
+  explicit DLPackTensor(const Tensor& tensor, LaneType lanes = 1);
+
+  inline operator const ::DLTensor&() const { return t_; }
+
+  inline operator ::DLTensor&() { return t_; }
+
+ private:
+  ::DLTensor t_;
+
+  // The shape in DLTensor is defined as int64_t*
+  // Add this member to make TVMTensor init without heap allocation
+  ShapeType shape_[9];
+};
+
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/dlpack_tensor_test.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 "paddle/fluid/framework/dlpack_tensor.h"
+#include <glog/logging.h>
+#include <gtest/gtest.h>
+#include <vector>
+
+namespace paddle {
+namespace framework {
+
+namespace {  // NOLINT
+template <typename T>
+constexpr uint8_t GetDLDataTypeCode() {
+  return std::is_same<platform::float16, T>::value ||
+                 std::is_floating_point<T>::value
+             ? static_cast<uint8_t>(kDLFloat)
+             : (std::is_unsigned<T>::value
+                    ? static_cast<uint8_t>(kDLUInt)
+                    : (std::is_integral<T>::value ? static_cast<uint8_t>(kDLInt)
+                                                  : static_cast<uint8_t>(-1)));
+}
+}  // NOLINT
+
+template <typename T>
+void TestMain(const platform::Place &place, uint16_t lanes) {
+  DDim dims{4, 5, 6, 7};
+  Tensor tensor;
+  tensor.Resize(dims);
+  void *p = tensor.mutable_data<T>(place);
+
+  DLPackTensor dlpack_tensor(tensor, lanes);
+  ::DLTensor &dl_tensor = dlpack_tensor;
+
+  CHECK_EQ(p, dl_tensor.data);
+  if (platform::is_cpu_place(place)) {
+    CHECK_EQ(kDLCPU, dl_tensor.ctx.device_type);
+    CHECK_EQ(0, dl_tensor.ctx.device_id);
+  } else if (platform::is_gpu_place(place)) {
+    CHECK_EQ(kDLGPU, dl_tensor.ctx.device_type);
+    CHECK_EQ(boost::get<platform::CUDAPlace>(place).device,
+             dl_tensor.ctx.device_id);
+  } else if (platform::is_cuda_pinned_place(place)) {
+    CHECK_EQ(kDLCPUPinned, dl_tensor.ctx.device_type);
+    CHECK_EQ(0, dl_tensor.ctx.device_id);
+  } else {
+    CHECK_EQ(false, true);
+  }
+
+  CHECK_EQ(dims.size(), dl_tensor.ndim);
+  for (auto i = 0; i < dims.size(); ++i) {
+    CHECK_EQ(dims[i], dl_tensor.shape[i]);
+  }
+
+  CHECK_EQ(dl_tensor.strides == nullptr, true);
+  CHECK_EQ(static_cast<uint64_t>(0), dl_tensor.byte_offset);
+
+  CHECK_EQ(lanes, dl_tensor.dtype.lanes);
+  CHECK_EQ(sizeof(T) * 8, dl_tensor.dtype.bits);
+
+  CHECK_EQ(GetDLDataTypeCode<T>(), dl_tensor.dtype.code);
+}
+
+template <typename T>
+void TestMainLoop() {
+#ifdef PADDLE_WITH_CUDA
+  std::vector<platform::Place> places{platform::CPUPlace(),
+                                      platform::CUDAPlace(0),
+                                      platform::CUDAPinnedPlace()};
+  if (platform::GetCUDADeviceCount() > 1) {
+    places.emplace_back(platform::CUDAPlace(1));
+  }
+#else
+  std::vector<platform::Place> places{platform::CPUPlace()};
+#endif
+  std::vector<uint16_t> lanes{1, 2};
+  for (auto &p : places) {
+    for (auto &l : lanes) {
+      TestMain<T>(p, l);
+    }
+  }
+}
+
+#define PADDLE_DLPACK_TEST(type) \
+  TEST(dlpack, test_##type) { TestMainLoop<type>(); }
+
+using float16 = platform::float16;
+PADDLE_DLPACK_TEST(float16);
+PADDLE_DLPACK_TEST(float);
+PADDLE_DLPACK_TEST(double);
+PADDLE_DLPACK_TEST(int);
+PADDLE_DLPACK_TEST(int64_t);
+PADDLE_DLPACK_TEST(bool);
+PADDLE_DLPACK_TEST(size_t);
+PADDLE_DLPACK_TEST(int16_t);
+PADDLE_DLPACK_TEST(uint8_t);
+PADDLE_DLPACK_TEST(int8_t);
+
+#undef PADDLE_DLPACK_TEST
+
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/eigen.h

@@ -13,11 +13,6 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
-// logging.h and windows.h conflict
-#define GLOG_NO_ABBREVIATED_SEVERITIES
-// solve static linking error in windows
-// https://github.com/google/glog/issues/301
-#define GOOGLE_GLOG_DLL_DECL
 
 #include "paddle/fluid/framework/tensor.h"
 #include "unsupported/Eigen/CXX11/Tensor"

paddle/fluid/framework/ir/graph_helper.cc

@@ -15,8 +15,15 @@ limitations under the License. */
 #include "paddle/fluid/framework/ir/graph_helper.h"
 #include <algorithm>
 #include <deque>
+#include <fstream>
+#include <iosfwd>
+#include <ostream>
 #include <unordered_set>
 
+DEFINE_string(print_sub_graph_dir, "",
+              "FLAGS_print_sub_graph_dir is used "
+              "to print the nodes of sub_graphs.");
+
 namespace paddle {
 namespace framework {
 namespace ir {
@@ -164,12 +171,15 @@ size_t GraphNum(const Graph &graph) {
     graph_nodes.emplace_back(g_nodes);
   }
 
-  if (VLOG_IS_ON(100)) {
-    VLOG(100) << "graph_num: " << graph_nodes.size();
-    for (auto &g_n : graph_nodes) {
-      VLOG(100) << "graph_nodes: " << g_n.size();
-      if (g_n.size() < 10) {
+  if (FLAGS_print_sub_graph_dir.size()) {
+    if (graph_nodes.size() > 1) {
       std::stringstream out;
+      for (auto &g_n : graph_nodes) {
+        out << "graph_nodes: " << g_n.size() << "\n";
+      }
+      out << "\n\n";
+      for (auto &g_n : graph_nodes) {
+        out << "graph_nodes: " << g_n.size();
         for (auto &node : g_n) {
           out << "\nNode: " << node->Name() << " in [";
           for (auto &n : node->inputs) {
@@ -181,8 +191,12 @@ size_t GraphNum(const Graph &graph) {
           }
           out << "]";
         }
-        VLOG(100) << out.str();
+        out << "\n\n\n";
       }
+      std::unique_ptr<std::ostream> fout(
+          new std::ofstream(FLAGS_print_sub_graph_dir));
+      PADDLE_ENFORCE(fout->good());
+      *fout << out.str();
     }
   }
 

paddle/fluid/framework/op_desc.cc

@@ -252,6 +252,12 @@ void OpDesc::SetAttr(const std::string &name, const Attribute &v) {
         this->attrs_[name] = std::vector<int>();
         break;
       }
+      case proto::AttrType::LONGS: {
+        VLOG(110) << "SetAttr: " << Type() << ", " << name
+                  << " from LONGS to LONGS";
+        this->attrs_[name] = std::vector<int64_t>();
+        break;
+      }
       case proto::AttrType::FLOATS: {
         VLOG(110) << "SetAttr: " << Type() << ", " << name
                   << " from INTS to FLOATS";

paddle/fluid/framework/op_registry.h

@@ -23,11 +23,6 @@ limitations under the License. */
 #include <unordered_map>
 #include <unordered_set>
 
-#if defined(_WIN32)
-#define GLOG_NO_ABBREVIATED_SEVERITIES  // msvc conflict logging with windows.h
-#define GOOGLE_GLOG_DLL_DECL
-#endif
-
 #include "glog/logging.h"  // For VLOG()
 #include "paddle/fluid/framework/attribute.h"
 #include "paddle/fluid/framework/details/op_registry.h"

paddle/fluid/framework/operator.cc

@@ -11,8 +11,6 @@ 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. */
-#define GLOG_NO_ABBREVIATED_SEVERITIES
-#define GOOGLE_GLOG_DLL_DECL
 
 #include <gflags/gflags.h>
 #include <glog/logging.h>

paddle/fluid/framework/operator.h

@@ -20,8 +20,6 @@ limitations under the License. */
 #include <tuple>
 #include <unordered_map>
 #include <vector>
-#define GLOG_NO_ABBREVIATED_SEVERITIES
-#define GOOGLE_GLOG_DLL_DECL
 
 #include "glog/logging.h"  // For VLOG
 #include "paddle/fluid/framework/attribute.h"
@@ -100,6 +98,7 @@ class OperatorBase {
 
   const std::string& Type() const { return type_; }
 
+  bool HasAttr(const std::string& name) const { return attrs_.count(name); }
   template <typename T>
   inline const T& Attr(const std::string& name) const {
     PADDLE_ENFORCE(attrs_.count(name) != 0, "%s should be in AttributeMap",

paddle/fluid/framework/parallel_executor.cc

@@ -171,8 +171,17 @@ ParallelExecutor::ParallelExecutor(
   }
   // If the loss_var_name is given, the number of graph should be only one.
   if (loss_var_name.size()) {
-    PADDLE_ENFORCE_EQ(ir::GraphNum(*graph), 1,
-                      "The number of graph should be only one");
+    size_t graph_num = ir::GraphNum(*graph);
+    if (graph_num > 1) {
+      LOG(WARNING)
+          << "The number of graph should be only one, "
+             "but the current graph has "
+          << ir::GraphNum(*graph)
+          << " sub_graphs. If you want to see the nodes of the "
+             "sub_graphs, you should use 'FLAGS_print_sub_graph_dir' "
+             "to specify the output dir. NOTES: if you not do training, "
+             "please don't pass loss_var_name.";
+    }
   }
 
   if (exec_strategy.type_ == ExecutionStrategy::kDefault) {

paddle/fluid/inference/analysis/CMakeLists.txt

@@ -7,16 +7,17 @@ set(analysis_deps # analysis_deps can be extended accross the project
 add_subdirectory(ir_passes)
 add_subdirectory(passes)
 
-cc_library(ir_pass_manager SRCS ir_pass_manager.cc DEPS graph pass ${INFER_IR_PASSES})
+cc_library(analysis_helper SRCS helper.cc DEPS framework_proto proto_desc graph paddle_fluid_api)
+
+cc_library(ir_pass_manager SRCS ir_pass_manager.cc DEPS graph pass ${INFER_IR_PASSES} analysis_helper)
 
 cc_library(argument SRCS argument.cc DEPS scope proto_desc)
 cc_library(analysis_pass SRCS analysis_pass.cc DEPS proto_desc)
 
 cc_library(analysis SRCS
   analyzer.cc
-  helper.cc
   analysis_pass
-  DEPS ${analysis_deps}
+  DEPS ${analysis_deps} analysis_helper
   )
 
 cc_test(test_dot SRCS dot_tester.cc DEPS analysis)
@@ -34,4 +35,4 @@ function(inference_analysis_test TARGET)
   endif()
 endfunction(inference_analysis_test)
 
-inference_analysis_test(test_analyzer SRCS analyzer_tester.cc EXTRA_DEPS paddle_inference_api)
+inference_analysis_test(test_analyzer SRCS analyzer_tester.cc EXTRA_DEPS reset_tensor_array paddle_inference_api)

paddle/fluid/inference/analysis/analyzer_tester.cc

@@ -30,6 +30,7 @@ TEST(Analyzer, analysis_without_tensorrt) {
   Argument argument;
   argument.SetModelDir(FLAGS_inference_model_dir);
   argument.SetIrAnalysisPasses({"infer_clean_graph_pass"});
+  argument.SetUseGPU(false);
 
   Analyzer analyser;
   analyser.Run(&argument);
@@ -41,6 +42,7 @@ TEST(Analyzer, analysis_with_tensorrt) {
   argument.SetTensorRtWorkspaceSize(1 << 20);
   argument.SetModelDir(FLAGS_inference_model_dir);
   argument.SetIrAnalysisPasses({"infer_clean_graph_pass"});
+  argument.SetUseGPU(false);
 
   Analyzer analyser;
   analyser.Run(&argument);

paddle/fluid/inference/analysis/argument.h

@@ -116,6 +116,7 @@ struct Argument {
                       std::vector<std::string>);
 
   DECL_ARGUMENT_FIELD(use_gpu, UseGPU, bool);
+  DECL_ARGUMENT_FIELD(gpu_device_id, GPUDeviceId, int);
   DECL_ARGUMENT_FIELD(use_tensorrt, UseTensorRT, bool);
   DECL_ARGUMENT_FIELD(tensorrt_node_teller, TensorRtNodeTeller,
                       std::function<bool(const framework::ir::Node*)>);

paddle/fluid/inference/analysis/ir_passes/CMakeLists.txt

@@ -4,4 +4,6 @@ set(analysis_deps ${analysis_deps}
         subgraph_detector tensorrt_subgraph_pass
         CACHE INTERNAL "")
 
+set(pass_file ${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/paddle_inference_pass.h)
+file(APPEND ${pass_file} "USE_PASS(tensorrt_subgraph_pass);\n")
 set(INFER_IR_PASSES ${INFER_IR_PASSES} tensorrt_subgraph_pass CACHE INTERNAL "")

paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.cc

@@ -114,7 +114,7 @@ void TensorRtSubgraphPass::CreateTensorRTOp(framework::ir::Node *node,
   // it is either an OP's input or an OP's output.
 
   auto &subgraph_nodes = *Agent(node).subgraph();
-  for (size_t index = 0; index < block_desc.OpSize(); index++) {
+  for (size_t index = 0; index < block_desc.OpSize(); ++index) {
     framework::proto::OpDesc *op = block_desc.Op(index)->Proto();
     auto correspond_node = subgraph_nodes[index];
     PADDLE_ENFORCE_EQ(correspond_node->Name(), op->type());

paddle/fluid/inference/analysis/passes/ir_analysis_compose_pass.cc

@@ -45,7 +45,8 @@ void IrAnalysisComposePass::InitTensorRTAttrs(Argument *argument) {
       std::unordered_set<std::string> teller_set(
           {"mul", "conv2d", "pool2d", "relu", "softmax", "sigmoid",
            "depthwise_conv2d", "batch_norm", "concat", "tanh", "pad",
-           "elementwise_add", "dropout", "split", "prelu", "conv2d_transpose"});
+           "elementwise_add", "elementwise_mul", "dropout", "split", "prelu",
+           "conv2d_transpose", "leaky_relu"});
       if (!node->IsOp()) return false;
 
       if (teller_set.count(node->Op()->Type())) {

paddle/fluid/inference/analysis/passes/ir_graph_build_pass.cc

@@ -30,15 +30,28 @@ void IrGraphBuildPass::RunImpl(Argument *argument) {
   if (!argument->scope_valid()) {
     argument->SetScope(new framework::Scope);
   }
+  PADDLE_ENFORCE(argument->use_gpu_valid());
+
+  // The load program should run on the same device with the inference program,
+  // so that the parameters will on the same device, or they will keep copying
+  // between difference devices.
+  platform::Place place;
+  if (argument->use_gpu()) {
+    PADDLE_ENFORCE(argument->gpu_device_id_valid());
+    place = platform::CUDAPlace(argument->gpu_device_id());
+  } else {
+    place = platform::CPUPlace();
+  }
 
   if (argument->model_dir_valid()) {
-    auto program = LoadModel(argument->model_dir(), argument->scope_ptr());
+    auto program =
+        LoadModel(argument->model_dir(), argument->scope_ptr(), place);
     argument->SetMainProgram(program.release());
   } else if (argument->model_program_path_valid() &&
              argument->model_params_path_valid()) {
     auto program =
         LoadModel(argument->model_program_path(), argument->model_params_path(),
-                  argument->scope_ptr());
+                  argument->scope_ptr(), place);
     argument->SetMainProgram(program.release());
   } else {
     PADDLE_THROW(
@@ -52,16 +65,15 @@ void IrGraphBuildPass::RunImpl(Argument *argument) {
 }
 
 std::unique_ptr<framework::ProgramDesc> IrGraphBuildPass::LoadModel(
-    const std::string &path, framework::Scope *scope) {
-  platform::CPUPlace place;
+    const std::string &path, framework::Scope *scope,
+    const platform::Place &place) {
   framework::Executor exe(place);
   return Load(&exe, scope, path);
 }
 
 std::unique_ptr<framework::ProgramDesc> IrGraphBuildPass::LoadModel(
     const std::string &program_path, const std::string &params_path,
-    framework::Scope *scope) {
-  platform::CPUPlace place;
+    framework::Scope *scope, const platform::Place &place) {
   framework::Executor exe(place);
   return Load(&exe, scope, program_path, params_path);
 }

paddle/fluid/inference/analysis/passes/ir_graph_build_pass.h

@@ -17,6 +17,7 @@
 #include <string>
 #include "paddle/fluid/framework/scope.h"
 #include "paddle/fluid/inference/analysis/analysis_pass.h"
+#include "paddle/fluid/platform/place.h"
 
 namespace paddle {
 namespace inference {
@@ -32,11 +33,12 @@ class IrGraphBuildPass : public AnalysisPass {
   std::string repr() const override;
 
  private:
-  std::unique_ptr<framework::ProgramDesc> LoadModel(const std::string &path,
-                                                    framework::Scope *scope);
+  std::unique_ptr<framework::ProgramDesc> LoadModel(
+      const std::string &path, framework::Scope *scope,
+      const platform::Place &place);
   std::unique_ptr<framework::ProgramDesc> LoadModel(
       const std::string &program_path, const std::string &params_path,
-      framework::Scope *scope);
+      framework::Scope *scope, const platform::Place &place);
 
   std::string model_binary_str_;
 };

paddle/fluid/inference/api/CMakeLists.txt

@@ -27,11 +27,10 @@ endif()
 cc_library(reset_tensor_array SRCS details/reset_tensor_array.cc DEPS lod_tensor scope)
 cc_library(analysis_config SRCS analysis_config.cc DEPS lod_tensor paddle_pass_builder)
 cc_library(paddle_pass_builder SRCS paddle_pass_builder.cc)
-cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS lod_tensor scope paddle_pass_builder reset_tensor_array analysis_config analysis_config paddle_pass_builder)
-cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis naive_executor zero_copy_tensor reset_tensor_array analysis_config paddle_pass_builder)
-cc_library(zero_copy_tensor SRCS details/zero_copy_tensor.cc DEPS paddle_inference_api)
-cc_library(zero_copy_tensor_dummy SRCS details/zero_copy_tensor_dummy.cc DEPS paddle_inference_api)
-
+cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis naive_executor zero_copy_tensor reset_tensor_array analysis_config paddle_pass_builder ir_pass_manager)
+cc_library(zero_copy_tensor SRCS details/zero_copy_tensor.cc DEPS scope lod_tensor enforce)
+cc_library(zero_copy_tensor_dummy SRCS details/zero_copy_tensor_dummy.cc)
+cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS lod_tensor scope paddle_pass_builder reset_tensor_array analysis_config analysis_config paddle_pass_builder DEPS zero_copy_tensor)
 
 cc_test(test_paddle_inference_api
         SRCS api_tester.cc

paddle/fluid/inference/api/analysis_predictor.cc

@@ -285,6 +285,7 @@ void AnalysisPredictor::OptimizeInferenceProgram() {
   status_program_optimized_ = true;
 
   argument_.SetUseGPU(config_.use_gpu);
+  argument_.SetGPUDeviceId(config_.device);
   // Analyze inference_program
   if (!config_.model_dir.empty()) {
     argument_.SetModelDir(config_.model_dir);
@@ -491,8 +492,7 @@ bool AnalysisPredictor::LoadParameters() {
   }
 
   // Use NaiveExecutor to Load parameters.
-  platform::CPUPlace place;
-  framework::NaiveExecutor e(place);
+  framework::NaiveExecutor e(place_);
   e.Prepare(scope_.get(), *load_program, 0, false);
   e.Run();
   VLOG(3) << "get " << scope_->LocalVarNames().size() << " vars after load";
@@ -551,4 +551,5 @@ USE_TRT_CONVERTER(pad);
 USE_TRT_CONVERTER(split);
 USE_TRT_CONVERTER(prelu);
 USE_TRT_CONVERTER(conv2d_transpose);
+USE_TRT_CONVERTER(leaky_relu);
 #endif

paddle/fluid/inference/api/api_impl.h

@@ -14,12 +14,6 @@ limitations under the License. */
 
 #pragma once
 
-// logging.h and windows.h conflict
-#define GLOG_NO_ABBREVIATED_SEVERITIES
-// solve static linking error in windows
-// https://github.com/google/glog/issues/301
-#define GOOGLE_GLOG_DLL_DECL
-
 #include <glog/logging.h>
 #include <map>
 #include <memory>

paddle/fluid/inference/api/paddle_pass_builder.h

@@ -116,8 +116,12 @@ class CpuPassStrategy : public PassStrategy {
 class GpuPassStrategy : public PassStrategy {
  public:
   GpuPassStrategy() : PassStrategy({}) {
+    // TODO(NHZlX) Problem with Data synchronization between GPU and CPU
+    // When running in GPU mode, the parameters are all on GPU. But the
+    // opearations of "conv_bn_fuse_pass" are on CPU.
     passes_.assign({
-        "infer_clean_graph_pass", "conv_bn_fuse_pass",
+        "infer_clean_graph_pass",
+        // "infer_clean_graph_pass", "conv_bn_fuse_pass",
     });
   }
 

paddle/fluid/inference/tensorrt/convert/CMakeLists.txt

 # Add TRT tests
 nv_library(tensorrt_converter
            SRCS mul_op.cc conv2d_op.cc fc_op.cc pool2d_op.cc elementwise_op.cc
-batch_norm_op.cc activation_op.cc softmax_op.cc concat_op.cc dropout_op.cc
-pad_op.cc split_op.cc prelu_op.cc
+                batch_norm_op.cc activation_op.cc softmax_op.cc concat_op.cc dropout_op.cc
+                pad_op.cc split_op.cc prelu_op.cc leaky_relu_op.cc
            DEPS tensorrt_engine tensorrt_plugin operator scope framework_proto op_registry)
 
 nv_test(test_op_converter SRCS test_op_converter.cc DEPS
@@ -18,9 +18,10 @@ nv_test(test_trt_activation_op SRCS test_activation_op.cc activation_op.cc
 nv_test(test_trt_conv_op SRCS test_conv2d_op.cc conv2d_op.cc
         DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine conv_op conv_transpose_op SERIAL)
 nv_test(test_trt_pool2d_op SRCS test_pool2d_op.cc pool2d_op.cc
-        DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine pool_op SERIAL)
+        DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine pool_op tensorrt_plugin SERIAL)
 nv_test(test_trt_elementwise_op SRCS test_elementwise_op.cc elementwise_op.cc
-        DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine elementwise_add_op SERIAL)
+        DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine tensorrt_plugin
+             elementwise_add_op elementwise_mul_op SERIAL)
 nv_test(test_trt_softmax_op SRCS test_softmax_op.cc softmax_op.cc
         DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine softmax_op SERIAL)
 nv_test(test_trt_batch_norm_op SRCS test_batch_norm_op.cc batch_norm_op.cc
@@ -37,3 +38,5 @@ nv_test(test_trt_split_op SRCS test_split_op.cc split_op.cc
 nv_test(test_trt_prelu_op SRCS test_prelu_op.cc prelu_op.cc
         DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine tensorrt_plugin
         prelu_op SERIAL)
+nv_test(test_trt_leaky_relu_op SRCS test_leaky_relu_op.cc leaky_relu_op.cc
+        DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine activation_op SERIAL)

paddle/fluid/inference/tensorrt/convert/elementwise_op.cc

@@ -4,7 +4,7 @@ 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
+    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,
@@ -13,11 +13,25 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
+#include "paddle/fluid/inference/tensorrt/plugin/elementwise_op_plugin.h"
 
 namespace paddle {
 namespace inference {
 namespace tensorrt {
 
+static bool CheckDims(const nvinfer1::Dims& dims_x,
+                      const nvinfer1::Dims& dims_y) {
+  if (dims_x.nbDims != dims_y.nbDims) {
+    return false;
+  }
+  for (int i = 0; i < dims_x.nbDims; i++) {
+    if (dims_x.d[i] != dims_y.d[i]) {
+      return false;
+    }
+  }
+  return true;
+}
+
 class ElementwiseWeightOpConverter : public OpConverter {
  public:
   ElementwiseWeightOpConverter() {}
@@ -26,7 +40,7 @@ class ElementwiseWeightOpConverter : public OpConverter {
     // Here the two nullptr looks strange, that's because the
     // framework::OpDesc's constructor is strange.
     framework::OpDesc op_desc(op, nullptr);
-    VLOG(3) << "convert a fluid elementwise op to tensorrt IScaleLayer";
+    VLOG(3) << "Convert a fluid elementwise op to TensorRT IScaleLayer";
 
     PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1);
     PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1);  // Y is a weight
@@ -106,10 +120,12 @@ class ElementwiseTensorOpConverter : public OpConverter {
   ElementwiseTensorOpConverter() {}
   void operator()(const framework::proto::OpDesc& op,
                   const framework::Scope& scope, bool test_mode) override {
+    auto op_pair = ops.find(op_type_);
+    PADDLE_ENFORCE(op_pair != ops.end(), "Wrong elementwise op type!");
+
     // Here the two nullptr looks strange, that's because the
     // framework::OpDesc's constructor is strange.
     framework::OpDesc op_desc(op, nullptr);
-    VLOG(3) << "convert a fluid elementwise op to tensorrt IScaleLayer";
 
     PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1);
     PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1);  // Y is a weight
@@ -120,29 +136,35 @@ class ElementwiseTensorOpConverter : public OpConverter {
     nvinfer1::Dims dims_x = X->getDimensions();
     nvinfer1::Dims dims_y = Y->getDimensions();
 
+    int axis = boost::get<int>(op_desc.GetAttr("axis"));
+    auto output_name = op_desc.Output("Out")[0];
+    if (CheckDims(dims_x, dims_y)) {
       // The two input tensor should have the same dims
-    PADDLE_ENFORCE(dims_x.nbDims >= 3);
-    if (dims_x.nbDims == dims_y.nbDims) {
-      for (int i = 0; i < dims_x.nbDims; i++) {
-        if (dims_x.d[i] != dims_y.d[i])
-          PADDLE_THROW("TensorRT unsupported tensor shape for Elementwise op!");
-      }
-    } else {
-      PADDLE_THROW("TensorRT unsupported tensor shape for Elementwise op!");
-    }
+      VLOG(3) << "Convert a fluid elementwise op to TensorRT IElementWiseLayer";
 
-    auto op_pair = ops.find(op_type_);
-    if (op_pair == ops.end()) {
-      PADDLE_THROW("Wrong elementwise op type!");
-    }
       nvinfer1::IElementWiseLayer* layer = TRT_ENGINE_ADD_LAYER(
           engine_, ElementWise, *const_cast<nvinfer1::ITensor*>(X),
           *const_cast<nvinfer1::ITensor*>(Y), op_pair->second);
 
-    auto output_name = op_desc.Output("Out")[0];
       layer->setName(("elementwise (Output: " + output_name + ")").c_str());
       layer->getOutput(0)->setName(output_name.c_str());
       engine_->SetITensor(output_name, layer->getOutput(0));
+    } else {
+      VLOG(3) << "Convert a fluid elementwise op to TensorRT "
+                 "ElementWisePluginLayer";
+
+      plugin::ElementWisePlugin* plugin =
+          new plugin::ElementWisePlugin(op_pair->second, dims_x, dims_y, axis);
+      plugin->AddInput(X);
+      plugin->AddInput(Y);
+      nvinfer1::IPluginLayer* layer = engine_->AddPlugin(
+          const_cast<nvinfer1::ITensor* const*>(plugin->GetInputs().data()), 2,
+          reinterpret_cast<plugin::PluginTensorRT*>(plugin));
+
+      layer->setName(("elementwise (Output: " + output_name + ")").c_str());
+      layer->getOutput(0)->setName(output_name.c_str());
+      engine_->SetITensor(output_name, layer->getOutput(0));
+    }
     if (test_mode) {  // the test framework can not determine which is the
                       // output, so place the declaration inside.
       engine_->DeclareOutput(output_name);

paddle/fluid/inference/tensorrt/convert/leaky_relu_op.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 "paddle/fluid/inference/tensorrt/convert/op_converter.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+// LeakyRelu converter from fluid to tensorRT
+class LeakyReluOpConverter : public OpConverter {
+ public:
+  void operator()(const framework::proto::OpDesc& op,
+                  const framework::Scope& scope, bool test_mode) override {
+    VLOG(4) << "convert fluid leaky_relu op to tensorrt layer";
+
+    framework::OpDesc op_desc(op, nullptr);
+    // Declare inputs
+    int input_num = op_desc.Input("X").size();
+    PADDLE_ENFORCE(input_num == 1);
+    auto* input = engine_->GetITensor(op_desc.Input("X")[0]);
+    // Get output
+    size_t output_num = op_desc.Output("Out").size();
+    PADDLE_ENFORCE(output_num == 1);
+    // Get attrs
+    float alpha = boost::get<float>(op_desc.GetAttr("alpha"));
+
+    platform::CPUPlace place;
+    std::unique_ptr<framework::LoDTensor> alpha_tensor(
+        new framework::LoDTensor());
+    alpha_tensor->Resize(framework::make_ddim({2}));
+    float* alpha_data = alpha_tensor->mutable_data<float>(place);
+    alpha_data[0] = alpha;
+    alpha_data[1] = 1.f - alpha;
+    // the leaky relu formula y = (x > 0) ? x : alpha * x is equal to
+    // y = alpha * x + (x > 0) ? (1 - alpha) * x : 0
+    TensorRTEngine::Weight scale{nvinfer1::DataType::kFLOAT, &alpha_data[0], 1};
+    TensorRTEngine::Weight shift{nvinfer1::DataType::kFLOAT, nullptr, 0};
+    TensorRTEngine::Weight power{nvinfer1::DataType::kFLOAT, nullptr, 0};
+    // y_scale = alpha * x
+    auto* scale_layer = TRT_ENGINE_ADD_LAYER(
+        engine_, Scale, *input, nvinfer1::ScaleMode::kUNIFORM, shift.get(),
+        scale.get(), power.get());
+    PADDLE_ENFORCE(nullptr != scale_layer);
+    // y_relu = (x > 0) : x : 0
+    auto* relu_layer = TRT_ENGINE_ADD_LAYER(engine_, Activation, *input,
+                                            nvinfer1::ActivationType::kRELU);
+    PADDLE_ENFORCE(nullptr != relu_layer);
+    //
+    TensorRTEngine::Weight sub_scale{nvinfer1::DataType::kFLOAT, &alpha_data[1],
+                                     1};
+    auto* scale_relu_layer =
+        TRT_ENGINE_ADD_LAYER(engine_, Scale, *(relu_layer->getOutput(0)),
+                             nvinfer1::ScaleMode::kUNIFORM, shift.get(),
+                             sub_scale.get(), power.get());
+    PADDLE_ENFORCE(nullptr != scale_relu_layer);
+    auto* output_layer =
+        TRT_ENGINE_ADD_LAYER(engine_, ElementWise, *(scale_layer->getOutput(0)),
+                             *(scale_relu_layer->getOutput(0)),
+                             nvinfer1::ElementWiseOperation::kSUM);
+    PADDLE_ENFORCE(nullptr != output_layer);
+    // keep alpha tensor to avoid release it's memory
+    std::string alpha_name = op_desc.Output("Out")[0] + "_alpha";
+    PADDLE_ENFORCE(engine_->weight_map.find(alpha_name) ==
+                   engine_->weight_map.end());
+    engine_->weight_map[alpha_name] = std::move(alpha_tensor);
+
+    std::string layer_name = "leaky_relu (Output: ";
+    auto output_name = op_desc.Output("Out")[0];
+    output_layer->getOutput(0)->setName(output_name.c_str());
+    engine_->SetITensor(output_name, output_layer->getOutput(0));
+    layer_name += output_name;
+    if (test_mode) {
+      engine_->DeclareOutput(output_name);
+    }
+    output_layer->setName((layer_name + ")").c_str());
+  }
+};
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle
+
+REGISTER_TRT_OP_CONVERTER(leaky_relu, LeakyReluOpConverter);

paddle/fluid/inference/tensorrt/convert/op_converter.h

@@ -61,7 +61,7 @@ class OpConverter {
       // TODO(xingzhaolong): all mul, sub, div
       // static std::unordered_set<std::string> add_weight_op_set {"add", "mul",
       // "sub", "div"};
-      static std::unordered_set<std::string> add_weight_op_set{"add"};
+      static std::unordered_set<std::string> add_weight_op_set{"add", "mul"};
       PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1UL);
       int op_type_len = op_desc.Type().size();
       std::string op_type = op_desc.Type().substr(op_type_len - 3, op_type_len);

paddle/fluid/inference/tensorrt/convert/pool2d_op.cc

@@ -13,25 +13,57 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
+#include "paddle/fluid/inference/tensorrt/plugin/avg_pool_op_plugin.h"
 
 namespace paddle {
 namespace inference {
 namespace tensorrt {
 
+void DealCeilMode(const nvinfer1::Dims &input_shape, std::vector<int> ksize,
+                  std::vector<int> strides, std::vector<int> paddings,
+                  nvinfer1::DimsHW *pre_pad, nvinfer1::DimsHW *post_pad,
+                  int input_dims) {
+  int input_height = input_shape.d[input_dims - 2];
+  int input_width = input_shape.d[input_dims - 1];
+  int floor_h_output_size =
+      (input_height - ksize[0] + 2 * paddings[0]) / strides[0] + 1;
+  int ceil_h_output_size =
+      (input_height - ksize[0] + 2 * paddings[0] + strides[0] - 1) /
+          strides[0] +
+      1;
+
+  int floor_w_output_size =
+      (input_width - ksize[1] + 2 * paddings[1]) / strides[1] + 1;
+  int ceil_w_output_size =
+      (input_width - ksize[1] + 2 * paddings[1] + strides[1] - 1) / strides[1] +
+      1;
+  if (floor_h_output_size != ceil_h_output_size) {
+    post_pad->h() = strides[0] - 1;
+  }
+
+  if (floor_w_output_size != ceil_w_output_size) {
+    post_pad->w() = strides[1] - 1;
+  }
+}
+
 /*
  * Pool2dOp, IPoolingLayer in TRT. This Layer doesn't has weights.
  */
 class Pool2dOpConverter : public OpConverter {
  public:
-  void operator()(const framework::proto::OpDesc& op,
-                  const framework::Scope& scope, bool test_mode) override {
-    VLOG(3)
+  void operator()(const framework::proto::OpDesc &op,
+                  const framework::Scope &scope, bool test_mode) override {
+    VLOG(40)
         << "convert a fluid pool2d op to tensorrt pool2d layer without bias";
     framework::OpDesc op_desc(op, nullptr);
     // Declare inputs
     PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1);
     PADDLE_ENFORCE_EQ(op_desc.Output("Out").size(), 1);
-    auto* input1 = engine_->GetITensor(op_desc.Input("X")[0]);
+    auto *input1 = engine_->GetITensor(op_desc.Input("X")[0]);
+    nvinfer1::Dims input_shape = input1->getDimensions();
+    int input_dims = input_shape.nbDims;
+
+    PADDLE_ENFORCE_EQ(input_dims, 3UL);
 
     bool global_pooling = boost::get<bool>(op_desc.GetAttr("global_pooling"));
     std::string pool_type =
@@ -44,23 +76,6 @@ class Pool2dOpConverter : public OpConverter {
         boost::get<std::vector<int>>(op_desc.GetAttr("paddings"));
     bool ceil_mode = boost::get<bool>(op_desc.GetAttr("ceil_mode"));
 
-    nvinfer1::Dims input_shape = input1->getDimensions();
-    int nbDims = input_shape.nbDims;
-    nvinfer1::DimsHW nv_ksize(ksize[0], ksize[1]);
-    nvinfer1::DimsHW nv_strides(strides[0], strides[1]);
-    nvinfer1::DimsHW nv_paddings(paddings[0], paddings[1]);
-
-    if (global_pooling == true) {
-      nv_ksize.d[0] = input_shape.d[nbDims - 2];
-      nv_ksize.d[1] = input_shape.d[nbDims - 1];
-      nv_strides.h() = 1;
-      nv_strides.w() = 1;
-      nv_paddings.h() = 0;
-      nv_paddings.w() = 0;
-    }
-
-    PADDLE_ENFORCE_EQ(input1->getDimensions().nbDims, 3UL);
-
     nvinfer1::PoolingType nv_pool_type = nvinfer1::PoolingType::kMAX;
     if (pool_type == "max") {
       nv_pool_type = nvinfer1::PoolingType::kMAX;
@@ -70,42 +85,63 @@ class Pool2dOpConverter : public OpConverter {
       PADDLE_THROW("TensorRT unsupported pooling type!");
     }
 
-    if (ceil_mode) {
-      nvinfer1::DimsHW pre_pad(0, 0);
-      nvinfer1::DimsHW post_pad(0, 0);
-      int input_height = input_shape.d[nbDims - 2];
-      int input_width = input_shape.d[nbDims - 1];
-      int floor_h_output_size =
-          (input_height - ksize[0] + 2 * paddings[0]) / strides[0] + 1;
-      int ceil_h_output_size =
-          (input_height - ksize[0] + 2 * paddings[0] + strides[0] - 1) /
-              strides[0] +
-          1;
+    nvinfer1::DimsHW nv_ksize(ksize[0], ksize[1]);
+    nvinfer1::DimsHW nv_strides(strides[0], strides[1]);
+    nvinfer1::DimsHW nv_paddings(paddings[0], paddings[1]);
 
-      int floor_w_output_size =
-          (input_width - ksize[1] + 2 * paddings[1]) / strides[1] + 1;
-      int ceil_w_output_size =
-          (input_width - ksize[1] + 2 * paddings[1] + strides[1] - 1) /
-              strides[1] +
-          1;
-      if (floor_h_output_size != ceil_h_output_size) {
-        post_pad.h() = strides[0] - 1;
-      }
+    nvinfer1::ILayer *layer = nullptr;
 
-      if (floor_w_output_size != ceil_w_output_size) {
-        post_pad.w() = strides[1] - 1;
+    if (global_pooling == true) {
+      nv_ksize.d[0] = input_shape.d[input_dims - 2];
+      nv_ksize.d[1] = input_shape.d[input_dims - 1];
+      auto *layer = TRT_ENGINE_ADD_LAYER(
+          engine_, Pooling, *const_cast<nvinfer1::ITensor *>(input1),
+          nv_pool_type, nv_ksize);
+      PADDLE_ENFORCE_NOT_NULL(layer, "pool layer could not be created.");
+      auto output_name = op_desc.Output("Out")[0];
+      layer->setName(("pool2d (Output: " + output_name + ")").c_str());
+      layer->getOutput(0)->setName(output_name.c_str());
+      engine_->SetITensor(output_name, layer->getOutput(0));
+      if (test_mode) {
+        engine_->DeclareOutput(output_name);
+      }
+      return;
     }
-      auto* layer = TRT_ENGINE_ADD_LAYER(
-          engine_, Padding, *const_cast<nvinfer1::ITensor*>(input1), pre_pad,
+
+    if (pool_type == "max") {
+      nvinfer1::DimsHW pre_pad(paddings[0], paddings[1]);
+      nvinfer1::DimsHW post_pad(paddings[0], paddings[1]);
+      if (ceil_mode) {
+        // If ceil mode is true, we will pad the appropriate size to the input.
+        DealCeilMode(input_shape, ksize, strides, paddings, &pre_pad, &post_pad,
+                     input_dims);
+        auto *pad_layer = TRT_ENGINE_ADD_LAYER(
+            engine_, Padding, *const_cast<nvinfer1::ITensor *>(input1), pre_pad,
             post_pad);
-      input1 = layer->getOutput(0);
+        PADDLE_ENFORCE_NOT_NULL(
+            pad_layer, "pad layer in poolOp converter could not be created.");
+        input1 = pad_layer->getOutput(0);
       }
-    auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Pooling,
-                                       *const_cast<nvinfer1::ITensor*>(input1),
+      auto *pool_layer = TRT_ENGINE_ADD_LAYER(
+          engine_, Pooling, *const_cast<nvinfer1::ITensor *>(input1),
           nv_pool_type, nv_ksize);
-    PADDLE_ENFORCE_NOT_NULL(layer, "pool layer could not be created.");
-    layer->setStride(nv_strides);
-    layer->setPadding(nv_paddings);
+      PADDLE_ENFORCE_NOT_NULL(pool_layer, "pool layer could not be created.");
+      pool_layer->setStride(nv_strides);
+      pool_layer->setPadding(nv_paddings);
+      layer = pool_layer;
+    } else {
+      // Average pooling needs to exclude the padding pixels from the average
+      // mean.
+      // It is not supported well by TRT, we use a plugin here.
+      std::vector<int> input_shape_v;
+      for (int i = 0; i < input_dims; i++) {
+        input_shape_v.push_back(input_shape.d[i]);
+      }
+      plugin::AvgPoolPlugin *plugin = new plugin::AvgPoolPlugin(
+          ceil_mode, ksize, strides, paddings, input_shape_v);
+      auto *avg_pool_layer = engine_->AddPlugin(&input1, 1, plugin);
+      layer = avg_pool_layer;
+    }
 
     auto output_name = op_desc.Output("Out")[0];
     layer->setName(("pool2d (Output: " + output_name + ")").c_str());

paddle/fluid/inference/tensorrt/convert/prelu_op.cc

@@ -54,7 +54,7 @@ class PReluOpConverter : public OpConverter {
     TensorRTEngine::Weight alpha_rt(nvinfer1::DataType::kFLOAT,
                                     static_cast<void*>(alpha_data),
                                     alpha_tensor_device->numel());
-    PReluPlugin* plugin = new PReluPlugin(alpha_rt, mode);
+    plugin::PReluPlugin* plugin = new plugin::PReluPlugin(alpha_rt, mode);
     nvinfer1::IPluginLayer* layer =
         engine_->AddPlugin(&input, input_num, plugin);
     // keep alpha tensor to avoid release it's memory

paddle/fluid/inference/tensorrt/convert/split_op.cc

@@ -19,9 +19,6 @@ namespace paddle {
 namespace inference {
 namespace tensorrt {
 
-/*
- * SplitOp.
- */
 class SplitOpConverter : public OpConverter {
  public:
   void operator()(const framework::proto::OpDesc& op,
@@ -40,17 +37,12 @@ class SplitOpConverter : public OpConverter {
     int axis = boost::get<int>(op_desc.GetAttr("axis"));
     std::vector<int> output_lengths =
         boost::get<std::vector<int>>(op_desc.GetAttr("sections"));
+    // split on batch is not supported in TensorRT
     PADDLE_ENFORCE(axis != 0);
-    if (axis < 0) {
-      axis += input_dims.nbDims;
-    } else {
-      axis -= 1;
-    }
+    axis += (axis < 0) ? input_dims.nbDims : -1;
 
     PADDLE_ENFORCE(output_lengths.size() == output_num);
-
-    //
-    SplitPlugin* plugin = new SplitPlugin(axis, output_lengths);
+    plugin::SplitPlugin* plugin = new plugin::SplitPlugin(axis, output_lengths);
     nvinfer1::IPluginLayer* layer =
         engine_->AddPlugin(&input, input_num, plugin);
 

paddle/fluid/inference/tensorrt/convert/test_elementwise_op.cc

@@ -20,13 +20,12 @@ namespace paddle {
 namespace inference {
 namespace tensorrt {
 
-TEST(elementwise_op, add_weight_test) {
+TEST(elementwise_op, add_weight) {
   std::unordered_set<std::string> parameters({"elementwise_add-Y"});
   framework::Scope scope;
   TRTConvertValidation validator(10, parameters, scope, 1 << 15);
   validator.DeclInputVar("elementwise_add-X", nvinfer1::DimsCHW(10, 3, 3));
   validator.DeclParamVar("elementwise_add-Y", nvinfer1::Dims3(10, 1, 1));
-  // validator.DeclParamVar("mul-Y", nvinfer1::Dims2(8, 2));
   validator.DeclOutputVar("elementwise_add-Out", nvinfer1::DimsCHW(10, 3, 3));
 
   // Prepare Op description
@@ -44,30 +43,65 @@ TEST(elementwise_op, add_weight_test) {
   validator.Execute(8);
 }
 
-TEST(elementwise_op, add_tensor_test) {
+TEST(elementwise_op, native) {
+  for (std::string type : {"add", "mul"}) {
+    int batch_size = 8;
     std::unordered_set<std::string> parameters;
     framework::Scope scope;
-  TRTConvertValidation validator(8, parameters, scope, 1 << 15);
-  validator.DeclInputVar("elementwise_add-X", nvinfer1::DimsCHW(10, 3, 3));
-  validator.DeclInputVar("elementwise_add-Y", nvinfer1::Dims3(10, 3, 3));
-  // validator.DeclParamVar("mul-Y", nvinfer1::Dims2(8, 2));
-  validator.DeclOutputVar("elementwise_add-Out", nvinfer1::DimsCHW(10, 3, 3));
+    TRTConvertValidation validator(batch_size, parameters, scope, 1 << 15);
+    validator.DeclInputVar("elementwise_" + type + "-X",
+                           nvinfer1::DimsCHW(10, 3, 3));
+    validator.DeclInputVar("elementwise_" + type + "-Y",
+                           nvinfer1::Dims3(10, 3, 3));
+    validator.DeclOutputVar("elementwise_" + type + "-Out",
+                            nvinfer1::DimsCHW(10, 3, 3));
 
     // Prepare Op description
     framework::OpDesc desc;
-  desc.SetType("elementwise_add");
-  desc.SetInput("X", {"elementwise_add-X"});
-  desc.SetInput("Y", {"elementwise_add-Y"});
-  desc.SetOutput("Out", {"elementwise_add-Out"});
+    desc.SetType("elementwise_" + type);
+    desc.SetInput("X", {"elementwise_" + type + "-X"});
+    desc.SetInput("Y", {"elementwise_" + type + "-Y"});
+    desc.SetOutput("Out", {"elementwise_" + type + "-Out"});
 
-  // the defalut axis of elementwise op is -1
+    int axis = -1;
+    desc.SetAttr("axis", axis);
 
     validator.SetOp(*desc.Proto());
+    validator.Execute(batch_size);
+  }
+}
 
-  validator.Execute(8);
+TEST(elementwise_op, plugin) {
+  for (std::string type : {"add", "mul"}) {
+    int batch_size = 8;
+    std::unordered_set<std::string> parameters;
+    framework::Scope scope;
+    TRTConvertValidation validator(batch_size, parameters, scope, 1 << 15);
+    validator.DeclInputVar("elementwise_" + type + "-X",
+                           nvinfer1::DimsCHW(10, 3, 3));
+    validator.DeclInputVar("elementwise_" + type + "-Y",
+                           nvinfer1::Dims3(10, 1, 1));
+    validator.DeclOutputVar("elementwise_" + type + "-Out",
+                            nvinfer1::DimsCHW(10, 3, 3));
+
+    // Prepare Op description
+    framework::OpDesc desc;
+    desc.SetType("elementwise_" + type);
+    desc.SetInput("X", {"elementwise_" + type + "-X"});
+    desc.SetInput("Y", {"elementwise_" + type + "-Y"});
+    desc.SetOutput("Out", {"elementwise_" + type + "-Out"});
+
+    int axis = -1;
+    desc.SetAttr("axis", axis);
+
+    validator.SetOp(*desc.Proto());
+    validator.Execute(batch_size);
+  }
 }
 
 }  // namespace tensorrt
 }  // namespace inference
 }  // namespace paddle
+
 USE_OP(elementwise_add);
+USE_OP(elementwise_mul);

paddle/fluid/inference/tensorrt/convert/test_leaky_relu_op.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 <gtest/gtest.h>
+#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
+#include "paddle/fluid/inference/tensorrt/convert/ut_helper.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+TEST(leaky_relu_op, test_leaky_relu) {
+  std::unordered_set<std::string> parameters;
+  framework::Scope scope;
+  TRTConvertValidation validator(10, parameters, scope, 1000);
+  validator.DeclInputVar("leaky_relu_input", nvinfer1::DimsCHW(3, 2, 2));
+  validator.DeclOutputVar("leaky_relu_out", nvinfer1::DimsCHW(3, 2, 2));
+
+  // Prepare Op description
+  framework::OpDesc desc;
+  desc.SetType("leaky_relu");
+  desc.SetInput("X", {"leaky_relu_input"});
+  desc.SetOutput("Out", {"leaky_relu_out"});
+
+  desc.SetAttr("alpha", 0.1f);
+
+  validator.SetOp(*desc.Proto());
+
+  validator.Execute(1);
+}
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle
+
+// USE_OP(leaky_relu);
+USE_OP(leaky_relu);

paddle/fluid/inference/tensorrt/convert/test_mul_op.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
+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. */
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
 
 #include <gtest/gtest.h>
 #include "paddle/fluid/framework/op_registry.h"

paddle/fluid/inference/tensorrt/convert/test_pool2d_op.cc

@@ -20,20 +20,21 @@ namespace paddle {
 namespace inference {
 namespace tensorrt {
 
-void test_pool2d(bool global_pooling, bool ceil_mode) {
+void test_pool2d(bool global_pooling, bool ceil_mode,
+                 std::string pool_type = "max") {
   framework::Scope scope;
   std::unordered_set<std::string> parameters;
   TRTConvertValidation validator(5, parameters, scope, 1 << 15);
 
   // The ITensor's Dims should not contain the batch size.
   // So, the ITensor's Dims of input and output should be C * H * W.
-  validator.DeclInputVar("pool2d-X", nvinfer1::Dims3(3, 13, 14));
+  validator.DeclInputVar("pool2d-X", nvinfer1::Dims3(3, 6, 7));
   if (global_pooling)
     validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 1, 1));
   else if (ceil_mode)
-    validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 6, 7));
+    validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 3, 4));
   else
-    validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 6, 6));
+    validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 3, 3));
 
   // Prepare Op description
   framework::OpDesc desc;
@@ -41,10 +42,10 @@ void test_pool2d(bool global_pooling, bool ceil_mode) {
   desc.SetInput("X", {"pool2d-X"});
   desc.SetOutput("Out", {"pool2d-Out"});
 
-  std::vector<int> ksize({3, 3});
+  std::vector<int> ksize({2, 2});
   std::vector<int> strides({2, 2});
   std::vector<int> paddings({0, 0});
-  std::string pooling_t = "max";
+  std::string pooling_t = pool_type;
 
   desc.SetAttr("pooling_type", pooling_t);
   desc.SetAttr("ksize", ksize);
@@ -63,7 +64,8 @@ void test_pool2d(bool global_pooling, bool ceil_mode) {
 TEST(Pool2dOpConverter, normal) { test_pool2d(false, false); }
 TEST(Pool2dOpConverter, test_global_pooling) { test_pool2d(true, false); }
 
-TEST(Pool2dOpConverter, test_ceil_mode) { test_pool2d(false, true); }
+TEST(Pool2dOpConverter, max_ceil_test) { test_pool2d(false, true); }
+TEST(Pool2dOpConverter, avg_ceil_test) { test_pool2d(false, true, "avg"); }
 
 }  // namespace tensorrt
 }  // namespace inference

paddle/fluid/inference/tensorrt/convert/test_split_op.cc

@@ -20,30 +20,92 @@ namespace paddle {
 namespace inference {
 namespace tensorrt {
 
-TEST(split_op, test) {
+template <int BatchSize, int Axis>
+void TensorRTSplitTest(const std::vector<int> &in_shape,
+                       const std::vector<int> &sections) {
   std::unordered_set<std::string> parameters({""});
   framework::Scope scope;
-  TRTConvertValidation validator(10, parameters, scope, 1000);
-  validator.DeclInputVar("split_input", nvinfer1::DimsCHW(3, 2, 2));
-  validator.DeclOutputVar("split_out1", nvinfer1::DimsCHW(2, 2, 2));
-  validator.DeclOutputVar("split_out2", nvinfer1::DimsCHW(1, 2, 2));
+  TRTConvertValidation validator(BatchSize + 1, parameters, scope, 10000);
+
+  auto make_dim = [](const std::vector<int> &shape) {
+    nvinfer1::DimsCHW dim;
+    dim.c() = shape[0];
+    dim.h() = shape[1];
+    dim.w() = shape[2];
+    return dim;
+  };
+  validator.DeclInputVar("split_input", make_dim(in_shape));
+  std::vector<std::string> output_vars;
+  for (size_t i = 0; i < sections.size(); ++i) {
+    auto out_shape = in_shape;
+    out_shape[Axis - 1] = sections[i];
+    std::string output_name = "split_out" + std::to_string(i);
+    validator.DeclOutputVar(output_name, make_dim(out_shape));
+    output_vars.push_back(output_name);
+  }
 
   // Prepare Op description
   framework::OpDesc desc;
   desc.SetType("split");
   desc.SetInput("X", {"split_input"});
-  desc.SetOutput("Out", {"split_out1", "split_out2"});
+  desc.SetOutput("Out", output_vars);
 
-  int num = 0;
-  int axis = 1;
-  std::vector<int> output_lengths = {2, 1};
-  desc.SetAttr("axis", axis);
-  desc.SetAttr("num", num);
-  desc.SetAttr("sections", output_lengths);
+  desc.SetAttr("axis", Axis);
+  desc.SetAttr("num", 0);
+  desc.SetAttr("sections", sections);
 
   validator.SetOp(*desc.Proto());
 
-  validator.Execute(1);
+  validator.Execute(BatchSize);
+}
+
+// batch = 0, axis = 1, same shape
+TEST(split_op, test_same_shape_axis1_batch1) {
+  TensorRTSplitTest<1, 1>({4, 2, 2}, {2, 2});
+}
+// batch = 0, axis = 1, different shape
+TEST(split_op, test_different_shape_axis1_batch1) {
+  TensorRTSplitTest<1, 1>({3, 2, 2}, {2, 1});
+}
+// batch = 10, axis = 1, same shape
+TEST(split_op, test_same_shape_axis1_batch10) {
+  TensorRTSplitTest<10, 1>({4, 2, 2}, {2, 2});
+}
+// batch = 10, axis = 1, different shape
+TEST(split_op, test_different_shape_axis1_batch10) {
+  TensorRTSplitTest<10, 1>({3, 2, 2}, {2, 1});
+}
+// batch = 0, axis = 2, same shape
+TEST(split_op, test_same_shape_axis2_batch1) {
+  TensorRTSplitTest<1, 2>({3, 4, 2}, {2, 2});
+}
+// batch = 0, axis = 2, different shape
+TEST(split_op, test_different_shape_axis2_batch1) {
+  TensorRTSplitTest<1, 2>({3, 3, 2}, {2, 1});
+}
+// batch = 10, axis = 2, same shape
+TEST(split_op, test_same_shape_axis2_batch10) {
+  TensorRTSplitTest<10, 2>({3, 4, 2}, {2, 2});
+}
+// batch = 10, axis = 2, different shape
+TEST(split_op, test_different_shape_axis2_batch10) {
+  TensorRTSplitTest<10, 2>({3, 3, 2}, {2, 1});
+}
+// batch = 0, axis = 3, same shape
+TEST(split_op, test_same_shape_axis3_batch1) {
+  TensorRTSplitTest<1, 3>({3, 2, 4}, {2, 2});
+}
+// batch = 0, axis = 3, different shape
+TEST(split_op, test_different_shape_axis3_batch1) {
+  TensorRTSplitTest<1, 3>({3, 2, 3}, {2, 1});
+}
+// batch = 10, axis = 3, same shape
+TEST(split_op, test_same_shape_axis3_batch10) {
+  TensorRTSplitTest<10, 3>({3, 2, 4}, {2, 2});
+}
+// batch = 10, axis = 3, different shape
+TEST(split_op, test_different_shape_axis3_batch10) {
+  TensorRTSplitTest<10, 3>({3, 2, 3}, {2, 1});
 }
 
 }  // namespace tensorrt

paddle/fluid/inference/tensorrt/convert/ut_helper.h

@@ -4,7 +4,7 @@ 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
+    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,

paddle/fluid/inference/tensorrt/engine.cc

@@ -257,9 +257,10 @@ void TensorRTEngine::freshDeviceId() {
 }
 
 nvinfer1::IPluginLayer *TensorRTEngine::AddPlugin(
-    nvinfer1::ITensor *const *inputs, int nbInputs, PluginTensorRT *plugin) {
+    nvinfer1::ITensor *const *inputs, int num_inputs,
+    plugin::PluginTensorRT *plugin) {
   owned_plugin_.emplace_back(plugin);
-  return infer_network_.get()->addPluginExt(inputs, nbInputs, *plugin);
+  return infer_network_.get()->addPluginExt(inputs, num_inputs, *plugin);
 }
 
 }  // namespace tensorrt

paddle/fluid/inference/tensorrt/engine.h

@@ -128,7 +128,7 @@ class TensorRTEngine : public EngineBase {
   int GetRuntimeBatch();
   int GetDevice() { return device_; }
   nvinfer1::IPluginLayer* AddPlugin(nvinfer1::ITensor* const* inputs,
-                                    int nbInputs, PluginTensorRT*);
+                                    int num_inputs, plugin::PluginTensorRT*);
 
   // A pointer to CPU memory is needed of the TRT weight.
   // Before TRT runs, fluid loads weight into GPU storage.
@@ -171,7 +171,7 @@ class TensorRTEngine : public EngineBase {
 
   // The specific GPU id that the TensorRTEngine bounded to.
   int device_;
-  std::vector<std::unique_ptr<PluginTensorRT>> owned_plugin_;
+  std::vector<std::unique_ptr<plugin::PluginTensorRT>> owned_plugin_;
 
   // TensorRT related internal members
   template <typename T>

paddle/fluid/inference/tensorrt/plugin/CMakeLists.txt

-nv_library(tensorrt_plugin SRCS trt_plugin.cc split_op_plugin.cu prelu_op_plugin.cu DEPS enforce device_context)
+nv_library(tensorrt_plugin
+           SRCS trt_plugin.cc split_op_plugin.cu elementwise_op_plugin.cu prelu_op_plugin.cu
+           avg_pool_op_plugin.cu
+           DEPS enforce tensorrt_engine)

paddle/fluid/inference/tensorrt/plugin/avg_pool_op_plugin.cu

+// 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 "paddle/fluid/inference/tensorrt/plugin/avg_pool_op_plugin.h"
+#include "paddle/fluid/operators/math/pooling.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+namespace plugin {
+
+nvinfer1::Dims AvgPoolPlugin::getOutputDimensions(
+    int index, const nvinfer1::Dims* inputDims, int nbInputs) {
+  assert(nbInputs == 1);
+  assert(index == 0);
+  assert(inputDims[0].nbDims == 3);
+  nvinfer1::Dims const& input_dims = inputDims[0];
+
+  nvinfer1::Dims output_dims = input_dims;
+
+  output_dims.d[1] = output_shape_[1];
+  output_dims.d[2] = output_shape_[2];
+  return output_dims;
+}
+
+int AvgPoolPlugin::enqueue(int batchSize, const void* const* inputs,
+                           void** outputs, void* workspace,
+                           cudaStream_t stream) {
+  auto const& input_dims = this->getInputDims(0);
+  int input_size = 0;
+  float const* idata = reinterpret_cast<float const*>(inputs[0]);
+  float** odatas = reinterpret_cast<float**>(outputs);
+
+  paddle::operators::math::AvgPool<float> pool_process;
+  paddle::operators::math::Pool2dDirectCUDAFunctor<
+      paddle::operators::math::AvgPool<float>, float>
+      pool2d_forward;
+
+  std::vector<int> input_shape = input_shape_;
+  std::vector<int> output_shape = output_shape_;
+  input_shape.insert(input_shape.begin(), batchSize);
+  output_shape.insert(output_shape.begin(), batchSize);
+
+  pool2d_forward(idata, input_shape, output_shape, ksize_, strides_, paddings_,
+                 pool_process, true, odatas[0], stream);
+
+  return cudaGetLastError() != cudaSuccess;
+}
+
+}  // namespace plugin
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/tensorrt/plugin/avg_pool_op_plugin.h

+// 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.
+
+#pragma once
+#include <cassert>
+#include <vector>
+#include "paddle/fluid/inference/tensorrt/plugin/trt_plugin.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+namespace plugin {
+
+class AvgPoolPlugin : public PluginTensorRT {
+ private:
+  bool ceil_mode_;
+  std::vector<int> ksize_;
+  std::vector<int> strides_;
+  std::vector<int> paddings_;
+  std::vector<int> input_shape_;
+  std::vector<int> output_shape_;
+
+ protected:
+  size_t getSerializationSize() override {
+    return SerializedSize(ceil_mode_) + SerializedSize(ksize_) +
+           SerializedSize(strides_) + SerializedSize(paddings_) +
+           SerializedSize(input_shape_) + getBaseSerializationSize();
+  }
+
+  // TRT will call this func when we need to serialize the configuration of
+  // tensorrt.
+  // It should not be called by users.
+  void serialize(void *buffer) override {
+    serializeBase(buffer);
+    SerializeValue(&buffer, ceil_mode_);
+    SerializeValue(&buffer, ksize_);
+    SerializeValue(&buffer, strides_);
+    SerializeValue(&buffer, paddings_);
+    SerializeValue(&buffer, input_shape_);
+  }
+
+ public:
+  AvgPoolPlugin(bool ceil_mode, std::vector<int> ksize,
+                std::vector<int> strides, std::vector<int> paddings,
+                std::vector<int> input_shape)
+      : ceil_mode_(ceil_mode),
+        ksize_(ksize),
+        strides_(strides),
+        paddings_(paddings),
+        input_shape_(input_shape) {
+    int output_h, output_w;
+    output_shape_ = input_shape_;
+    if (!ceil_mode_) {
+      output_h =
+          (input_shape[1] - ksize_[0] + 2 * paddings_[0]) / strides_[0] + 1;
+      output_w =
+          (input_shape[2] - ksize_[1] + 2 * paddings_[1]) / strides_[1] + 1;
+    } else {
+      output_h =
+          (input_shape[1] - ksize_[0] + 2 * paddings_[0] + strides_[0] - 1) /
+              strides_[0] +
+          1;
+      output_w =
+          (input_shape[2] - ksize_[1] + 2 * paddings_[1] + strides_[1] - 1) /
+              strides_[1] +
+          1;
+    }
+    output_shape_[1] = output_h;
+    output_shape_[2] = output_w;
+  }
+
+  // It was used for tensorrt deserialization.
+  // It should not be called by users.
+  AvgPoolPlugin(void const *serialData, size_t serialLength) {
+    deserializeBase(serialData, serialLength);
+    DeserializeValue(&serialData, &serialLength, &ceil_mode_);
+    DeserializeValue(&serialData, &serialLength, &ksize_);
+    DeserializeValue(&serialData, &serialLength, &strides_);
+    DeserializeValue(&serialData, &serialLength, &paddings_);
+    DeserializeValue(&serialData, &serialLength, &input_shape_);
+  }
+
+  AvgPoolPlugin *clone() const override {
+    return new AvgPoolPlugin(ceil_mode_, ksize_, strides_, paddings_,
+                             input_shape_);
+  }
+
+  const char *getPluginType() const override { return "avg_pool"; }
+  int getNbOutputs() const override { return 1; }
+  nvinfer1::Dims getOutputDimensions(int index, const nvinfer1::Dims *inputs,
+                                     int nbInputDims) override;
+  int initialize() override { return 0; }
+  int enqueue(int batchSize, const void *const *inputs, void **outputs,
+              void *workspace, cudaStream_t stream) override;
+};
+
+}  // namespace plugin
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/tensorrt/plugin/elementwise_op_plugin.cu

+/* 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 <glog/logging.h>
+#include "paddle/fluid/inference/tensorrt/plugin/elementwise_op_plugin.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+namespace plugin {
+
+namespace details {
+
+template <typename T>
+struct Add {
+  __device__ T operator()(const T& a, const T& b) const { return a + b; }
+};
+
+template <typename T>
+struct Mul {
+  __device__ T operator()(const T& a, const T& b) const { return a * b; }
+};
+
+template <typename T, typename Operator>
+__global__ void ColumnWiseKernel(Operator op, const T* x, const T* y, T* out,
+                                 int batch_size, int num_rows, int num_cols) {
+  for (int batch_id = 0; batch_id < batch_size; ++batch_id) {
+    int row = blockIdx.x;
+    for (; row < num_rows; row += gridDim.x) {
+      T value_y = y[batch_id * num_rows + row];
+      int col = threadIdx.x;
+      int offset = (batch_id * num_rows + row) * num_cols;
+      for (; col < num_cols; col += blockDim.x) {
+        T value_x = x[offset + col];
+        out[offset + col] = op(value_x, value_y);
+      }
+    }
+  }
+}
+
+template <typename T, typename Operator>
+static void ElementWise(Operator op, const T* x, const T* y, T* out,
+                        int batch_size, int prev, int midd, int post,
+                        cudaStream_t stream) {
+  const int kThreadsPerBlock = 1024;
+  const int kMaximumBlocks = 65535;
+  if (prev == 1) {
+    int num_threads = (post > kThreadsPerBlock) ? kThreadsPerBlock
+                                                : (((post + 31) >> 5) << 5);
+    int num_blocks = (midd < kMaximumBlocks) ? midd : kMaximumBlocks;
+    ColumnWiseKernel<<<num_blocks, num_threads, 0, stream>>>(
+        op, x, y, out, batch_size, midd, post);
+  } else if (post == 1) {
+    PADDLE_THROW("Not implemented.");
+  } else {
+    PADDLE_THROW("Not implemented.");
+  }
+}
+
+}  // namespace details
+
+nvinfer1::Dims ElementWisePlugin::getOutputDimensions(
+    int index, const nvinfer1::Dims* input_dims, int num_inputs) {
+  PADDLE_ENFORCE_EQ(index, 0);
+  PADDLE_ENFORCE_EQ(num_inputs, 2);
+  PADDLE_ENFORCE_NOT_NULL(input_dims);
+  return input_dims[0];
+}
+
+int ElementWisePlugin::initialize() {
+  PADDLE_ENFORCE_GT(dims_y_.nbDims, 0);
+
+  axis_ = (axis_ == -1) ? dims_x_.nbDims - dims_y_.nbDims : axis_;
+  int trimed_nb_dims = dims_y_.nbDims;
+  for (; trimed_nb_dims > 0; --trimed_nb_dims) {
+    if (dims_y_.d[trimed_nb_dims - 1] != 1) {
+      break;
+    }
+  }
+  dims_y_.nbDims = trimed_nb_dims;
+
+  PADDLE_ENFORCE_GE(dims_x_.nbDims, dims_y_.nbDims + axis_);
+  PADDLE_ENFORCE_LT(axis_, dims_x_.nbDims);
+
+  prev_size_ = 1;
+  midd_size_ = 1;
+  post_size_ = 1;
+  for (int i = 0; i < axis_; ++i) {
+    prev_size_ *= dims_x_.d[i];
+  }
+
+  for (int i = 0; i < dims_y_.nbDims; ++i) {
+    PADDLE_ENFORCE_EQ(dims_x_.d[i + axis_], dims_y_.d[i],
+                      "Broadcast dimension mismatch.");
+    midd_size_ *= dims_y_.d[i];
+  }
+
+  for (int i = axis_ + dims_y_.nbDims; i < dims_x_.nbDims; ++i) {
+    post_size_ *= dims_x_.d[i];
+  }
+  return 0;
+}
+
+int ElementWisePlugin::enqueue(int batch_size, const void* const* inputs,
+                               void** outputs, void* workspace,
+                               cudaStream_t stream) {
+  const float* x = reinterpret_cast<const float*>(inputs[0]);
+  const float* y = reinterpret_cast<const float*>(inputs[1]);
+  float* out = reinterpret_cast<float*>(outputs[0]);
+
+  if (type_ == nvinfer1::ElementWiseOperation::kSUM) {
+    details::ElementWise(details::Add<float>(), x, y, out, batch_size,
+                         prev_size_, midd_size_, post_size_, stream);
+  } else if (type_ == nvinfer1::ElementWiseOperation::kPROD) {
+    details::ElementWise(details::Mul<float>(), x, y, out, batch_size,
+                         prev_size_, midd_size_, post_size_, stream);
+  } else {
+    PADDLE_THROW("Not implemented.");
+  }
+
+  return cudaGetLastError() != cudaSuccess;
+}
+
+}  // namespace plugin
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/tensorrt/plugin/elementwise_op_plugin.h

+/* 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. */
+
+#pragma once
+
+#include <vector>
+#include "paddle/fluid/inference/tensorrt/plugin/trt_plugin.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+namespace plugin {
+
+class ElementWisePlugin : public PluginTensorRT {
+ public:
+  ElementWisePlugin(nvinfer1::ElementWiseOperation type,
+                    nvinfer1::Dims const &dims_x, nvinfer1::Dims const &dims_y,
+                    int axis)
+      : type_(type),
+        dims_x_(dims_x),
+        dims_y_(dims_y),
+        axis_(axis),
+        prev_size_(1),
+        midd_size_(1),
+        post_size_(1) {}
+
+  ElementWisePlugin(void const *serial_data, size_t serial_length) {
+    deserializeBase(serial_data, serial_length);
+    DeserializeValue(&serial_data, &serial_length, &axis_);
+    DeserializeValue(&serial_data, &serial_length, &dims_x_);
+    DeserializeValue(&serial_data, &serial_length, &dims_y_);
+  }
+
+  ElementWisePlugin *clone() const override {
+    // return new ElementWisePlugin(dims_x_, dims_y_, axis_);
+    return nullptr;
+  }
+
+  const char *getPluginType() const override { return "elementwise"; }
+
+  nvinfer1::Dims getOutputDimensions(int index,
+                                     const nvinfer1::Dims *input_dims,
+                                     int num_inputs) override;
+
+  int initialize() override;
+
+  // execute the layer
+  int enqueue(int batch_size, const void *const *inputs, void **outputs,
+              void *workspace, cudaStream_t stream);
+
+ protected:
+  size_t getSerializationSize() override {
+    return SerializedSize(axis_) + SerializedSize(dims_x_) +
+           SerializedSize(dims_y_) + getBaseSerializationSize();
+  }
+
+  void serialize(void *buffer) override {
+    serializeBase(buffer);
+    SerializeValue(&buffer, axis_);
+    SerializeValue(&buffer, dims_x_);
+    SerializeValue(&buffer, dims_y_);
+  }
+
+  nvinfer1::ElementWiseOperation type_;
+  nvinfer1::Dims dims_x_;
+  nvinfer1::Dims dims_y_;
+  int axis_;
+  int prev_size_;
+  int midd_size_;
+  int post_size_;
+};
+
+}  // namespace plugin
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/tensorrt/plugin/prelu_op_plugin.cu

@@ -20,6 +20,7 @@
 namespace paddle {
 namespace inference {
 namespace tensorrt {
+namespace plugin {
 
 static const int CUDA_NUM_THREADS = 1024;
 static const int CUDA_MAX_NUM_BLOCKS = 65535;
@@ -126,6 +127,7 @@ int PReluPlugin::enqueue(int batchSize, const void *const *inputs,
   return cudaGetLastError() != cudaSuccess;
 }
 
+}  // namespace plugin
 }  // namespace tensorrt
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/tensorrt/plugin/prelu_op_plugin.h

@@ -21,6 +21,7 @@
 namespace paddle {
 namespace inference {
 namespace tensorrt {
+namespace plugin {
 
 class PReluPlugin : public PluginTensorRT {
   TensorRTEngine::Weight alpha_;
@@ -63,6 +64,7 @@ class PReluPlugin : public PluginTensorRT {
               void *workspace, cudaStream_t stream) override;
 };
 
+}  // namespace plugin
 }  // namespace tensorrt
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/tensorrt/plugin/serialize.h

@@ -14,10 +14,15 @@
 
 #pragma once
 
-#include <cassert>
 #include <cstring>
 #include <type_traits>
 #include <vector>
+#include "paddle/fluid/platform/enforce.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+namespace plugin {
 
 template <typename T>
 inline void SerializeValue(void** buffer, T const& value);
@@ -26,7 +31,7 @@ template <typename T>
 inline void DeserializeValue(void const** buffer, size_t* buffer_size,
                              T* value);
 
-namespace {
+namespace details {
 
 template <typename T, class Enable = void>
 struct Serializer {};
@@ -36,10 +41,12 @@ struct Serializer<T, typename std::enable_if<std::is_arithmetic<T>::value ||
                                              std::is_enum<T>::value ||
                                              std::is_pod<T>::value>::type> {
   static size_t SerializedSize(T const& value) { return sizeof(T); }
+
   static void Serialize(void** buffer, T const& value) {
     std::memcpy(*buffer, &value, sizeof(T));
     reinterpret_cast<char*&>(*buffer) += sizeof(T);
   }
+
   static void Deserialize(void const** buffer, size_t* buffer_size, T* value) {
     assert(*buffer_size >= sizeof(T));
     std::memcpy(value, *buffer, sizeof(T));
@@ -51,10 +58,12 @@ struct Serializer<T, typename std::enable_if<std::is_arithmetic<T>::value ||
 template <>
 struct Serializer<const char*> {
   static size_t SerializedSize(const char* value) { return strlen(value) + 1; }
+
   static void Serialize(void** buffer, const char* value) {
-    std::strcpy(static_cast<char*>(*buffer), value);
+    std::strcpy(static_cast<char*>(*buffer), value);  // NOLINT
     reinterpret_cast<char*&>(*buffer) += strlen(value) + 1;
   }
+
   static void Deserialize(void const** buffer, size_t* buffer_size,
                           const char** value) {
     *value = static_cast<char const*>(*buffer);
@@ -73,39 +82,46 @@ struct Serializer<std::vector<T>,
   static size_t SerializedSize(std::vector<T> const& value) {
     return sizeof(value.size()) + value.size() * sizeof(T);
   }
+
   static void Serialize(void** buffer, std::vector<T> const& value) {
     SerializeValue(buffer, value.size());
     size_t nbyte = value.size() * sizeof(T);
     std::memcpy(*buffer, value.data(), nbyte);
     reinterpret_cast<char*&>(*buffer) += nbyte;
   }
+
   static void Deserialize(void const** buffer, size_t* buffer_size,
                           std::vector<T>* value) {
     size_t size;
     DeserializeValue(buffer, buffer_size, &size);
     value->resize(size);
     size_t nbyte = value->size() * sizeof(T);
-    assert(*buffer_size >= nbyte);
+    PADDLE_ENFORCE_GE(*buffer_size, nbyte);
     std::memcpy(value->data(), *buffer, nbyte);
     reinterpret_cast<char const*&>(*buffer) += nbyte;
     *buffer_size -= nbyte;
   }
 };
 
-}  // namespace
+}  // namespace details
 
 template <typename T>
 inline size_t SerializedSize(T const& value) {
-  return Serializer<T>::SerializedSize(value);
+  return details::Serializer<T>::SerializedSize(value);
 }
 
 template <typename T>
 inline void SerializeValue(void** buffer, T const& value) {
-  return Serializer<T>::Serialize(buffer, value);
+  return details::Serializer<T>::Serialize(buffer, value);
 }
 
 template <typename T>
 inline void DeserializeValue(void const** buffer, size_t* buffer_size,
                              T* value) {
-  return Serializer<T>::Deserialize(buffer, buffer_size, value);
+  return details::Serializer<T>::Deserialize(buffer, buffer_size, value);
 }
+
+}  // namespace plugin
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/tensorrt/plugin/split_op_plugin.cu

@@ -12,70 +12,167 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include <stdio.h>
-#include <cassert>
+#include <cuda_fp16.h>
+#include <algorithm>
 #include "paddle/fluid/inference/tensorrt/plugin/split_op_plugin.h"
 
 namespace paddle {
 namespace inference {
 namespace tensorrt {
+namespace plugin {
 
-nvinfer1::Dims SplitPlugin::getOutputDimensions(int index,
-                                                const nvinfer1::Dims* inputDims,
-                                                int nbInputs) {
-  assert(nbInputs == 1);
-  assert(index < this->getNbOutputs());
-  nvinfer1::Dims const& input_dims = inputDims[0];
-  nvinfer1::Dims output_dims = input_dims;
+// copied from operators::math::SplitFunctor
+template <typename T>
+__global__ void SplitKernel(const T* input_data, const int in_row,
+                            const int in_col, const int* out_cols,
+                            int out_cols_size, T** outputs_data) {
+  int tid_x = blockIdx.x * blockDim.x + threadIdx.x;
+  int curr_segment = 0;
+  int curr_offset = out_cols[0];
+  for (; tid_x < in_col; tid_x += blockDim.x * gridDim.x) {
+    int curr_col_offset = out_cols[curr_segment + 1];
+    while (curr_col_offset <= tid_x) {
+      curr_offset = curr_col_offset;
+      ++curr_segment;
+      curr_col_offset = out_cols[curr_segment + 1];
+    }
+
+    int local_col = tid_x - curr_offset;
+    int segment_width = curr_col_offset - curr_offset;
+    T* output_ptr = outputs_data[curr_segment];
+    if (output_ptr != nullptr) {
+      int tid_y = blockIdx.y * blockDim.y + threadIdx.y;
+      for (; tid_y < in_row; tid_y += blockDim.y * gridDim.y)
+        output_ptr[tid_y * segment_width + local_col] =
+            input_data[tid_y * in_col + tid_x];
+    }
+  }
+}
+
+template <typename T>
+__global__ void SplitKernel(const T* input_data, const int in_row,
+                            const int in_col, const int fixed_out_col,
+                            T** outputs_data) {
+  int tid_x = blockIdx.x * blockDim.x + threadIdx.x;
+  for (; tid_x < in_col; tid_x += blockDim.x * gridDim.x) {
+    int split = tid_x / fixed_out_col;
+    int in_offset = tid_x - split * fixed_out_col;
+    T* output_ptr = outputs_data[split];
+    if (output_ptr != nullptr) {
+      int tid_y = blockIdx.y * blockDim.y + threadIdx.y;
+      for (; tid_y < in_row; tid_y += blockDim.y * gridDim.y)
+        output_ptr[tid_y * fixed_out_col + in_offset] =
+            input_data[tid_y * in_col + tid_x];
+    }
+  }
+}
+
+nvinfer1::Dims SplitPlugin::getOutputDimensions(
+    int index, const nvinfer1::Dims* input_dims, int num_inputs) {
+  PADDLE_ENFORCE_EQ(num_inputs, 1);
+  PADDLE_ENFORCE_LT(index, this->getNbOutputs());
+
+  nvinfer1::Dims output_dims = input_dims[0];
   output_dims.d[axis_] = output_length_.at(index);
   return output_dims;
 }
 
 int SplitPlugin::initialize() {
+  PADDLE_ENFORCE_LE(axis_, nvinfer1::Dims::MAX_DIMS);
+  // notice input dims is [C, H, W]
+  nvinfer1::Dims dims = this->getInputDims(0);
+  outer_rows_ = 1;
+  inner_cols_ = 1;
+  for (int i = 0; i < axis_; ++i) {
+    outer_rows_ *= dims.d[i];
+  }
+  for (int i = axis_ + 1; i < dims.nbDims; ++i) {
+    inner_cols_ *= dims.d[i];
+  }
+  same_shape_ = true;
   std::vector<int> segment_offsets(1, 0);
   for (int i = 0; i < this->getNbOutputs(); ++i) {
-    segment_offsets.push_back(segment_offsets.back() + output_length_[i]);
-  }
-  segment_offsets_ = segment_offsets;
-  nvinfer1::Dims dims = this->getInputDims(0);
-  nx_ = 1;
-  for (int i = dims.nbDims - 1; i > axis_; --i) {
-    nx_ *= dims.d[i];
+    if (output_length_[i] != output_length_[0]) {
+      same_shape_ = false;
     }
-  ny_ = dims.d[axis_];
-  nz_ = 1;
-  for (int i = axis_ - 1; i >= 0; --i) {
-    nz_ *= dims.d[i];
+    segment_offsets.push_back(segment_offsets.back() +
+                              output_length_[i] * inner_cols_);
   }
+  inner_cols_ *= dims.d[axis_];
+  d_segment_offsets_ = segment_offsets;
+  segment_offsets_ = std::move(segment_offsets);
+  d_output_ptrs_.resize(this->getNbOutputs(), nullptr);
   return 0;
 }
 
+template <typename T>
+inline void Split(cudaStream_t stream, const bool same_shape,
+                  const int outer_rows, const int inner_cols,
+                  const std::vector<int>& segment_offsets,
+                  const int* d_segment_offsets, const T* input, T** outputs) {
+  const int kThreadsPerBlock = 1024;
+  const int kMaxBlocks = 65535;
+  int block_cols = kThreadsPerBlock;
+  if (inner_cols < kThreadsPerBlock) {  // block_cols is aligned by 32.
+    block_cols = ((inner_cols + 31) >> 5) << 5;
+  }
+  int block_rows = kThreadsPerBlock / block_cols;
+  dim3 block_size = dim3(block_cols, block_rows, 1);
+
+  int grid_cols =
+      std::min((inner_cols + block_cols - 1) / block_cols, kMaxBlocks);
+  int grid_rows =
+      std::min(kMaxBlocks / grid_cols, std::max(outer_rows / block_rows, 1));
+  dim3 grid_size = dim3(grid_cols, grid_rows, 1);
+
+  if (same_shape) {
+    SplitKernel<<<grid_size, block_size, 0, stream>>>(
+        input, outer_rows, inner_cols, segment_offsets[1], outputs);
+  } else {
+    SplitKernel<<<grid_size, block_size, 0, stream>>>(
+        input, outer_rows, inner_cols, d_segment_offsets,
+        static_cast<int>(segment_offsets.size()), outputs);
+  }
+}
+
 int SplitPlugin::enqueue(int batchSize, const void* const* inputs,
                          void** outputs, void* workspace, cudaStream_t stream) {
-  auto const& input_dims = this->getInputDims(0);
-  int input_size = 0;
-  float const* idata = reinterpret_cast<float const*>(inputs[0]);
-  float** odatas = reinterpret_cast<float**>(outputs);
-
-  // kernel impl here.
-  int inputBatchOffset = nx_ * ny_ * nz_;
-  for (size_t i = 0; i < this->getNbOutputs(); i++) {
-    for (size_t j = 0; j < batchSize; j++) {
+  float const* input_ptr = reinterpret_cast<float const*>(inputs[0]);
+  if (((batchSize == 1 && axis_ == 0) || axis_ == -1) &&
+      this->getNbOutputs() < 10) {
+    float** output_ptrs = reinterpret_cast<float**>(outputs);
+    int data_type_size = (this->getDataType() == nvinfer1::DataType::kFLOAT)
+                             ? sizeof(float)
+                             : sizeof(__half);
+    for (int i = 0; i < this->getNbOutputs(); ++i) {
+      PADDLE_ENFORCE(
           cudaMemcpyAsync(
-          odatas[i] +
-              j * (segment_offsets_[i + 1] - segment_offsets_[i]) * nx_ *
-                  sizeof(float),
-          inputs[0] +
-              (inputBatchOffset * j + segment_offsets_[i] * nx_) *
-                  sizeof(float),
-          (segment_offsets_[i + 1] - segment_offsets_[i]) * nx_ * sizeof(float),
-          cudaMemcpyDeviceToDevice, stream);
+              output_ptrs[i], input_ptr + segment_offsets_[i],
+              (segment_offsets_[i + 1] - segment_offsets_[i]) * data_type_size,
+              cudaMemcpyDeviceToDevice, stream) == cudaSuccess);
+    }
+  } else {
+    outer_rows_ *= batchSize;
+    const int* d_segment_offsets_ptr =
+        thrust::raw_pointer_cast(&d_segment_offsets_[0]);
+    float** output_ptrs = thrust::raw_pointer_cast(&d_output_ptrs_[0]);
+    PADDLE_ENFORCE(cudaMemcpyAsync(output_ptrs, outputs,
+                                   this->getNbOutputs() * sizeof(float*),
+                                   cudaMemcpyHostToDevice,
+                                   stream) == cudaSuccess);
+    if (this->getDataType() == nvinfer1::DataType::kFLOAT) {
+      Split(stream, same_shape_, outer_rows_, inner_cols_, segment_offsets_,
+            d_segment_offsets_ptr, input_ptr, output_ptrs);
+    } else {
+      Split(stream, same_shape_, outer_rows_, inner_cols_, segment_offsets_,
+            d_segment_offsets_ptr, (__half*)input_ptr,  // NOLINT
+            (__half**)output_ptrs);                     // NOLINT
     }
   }
-
   return cudaGetLastError() != cudaSuccess;
 }
 
-}  // tensorrt
-}  // inference
-}  // paddle
+}  // namespace plugin
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/tensorrt/plugin/split_op_plugin.h

@@ -14,61 +14,63 @@
 
 #pragma once
 
+#include <thrust/device_vector.h>
+#include <vector>
 #include "paddle/fluid/inference/tensorrt/plugin/trt_plugin.h"
 
 namespace paddle {
 namespace inference {
 namespace tensorrt {
+namespace plugin {
 
 class SplitPlugin : public PluginTensorRT {
-  int axis_;
-  std::vector<int> output_length_;
-  int nx_, ny_, nz_;
-  std::vector<int> segment_offsets_;
+ public:
+  SplitPlugin(int axis, std::vector<int> const &output_lengths)
+      : axis_(axis), same_shape_(true), output_length_(output_lengths) {}
+
+  SplitPlugin(void const *serial_data, size_t serial_length) {
+    deserializeBase(serial_data, serial_length);
+    DeserializeValue(&serial_data, &serial_length, &axis_);
+    DeserializeValue(&serial_data, &serial_length, &output_length_);
+  }
+
+  SplitPlugin *clone() const override {
+    return new SplitPlugin(axis_, output_length_);
+  }
+
+  const char *getPluginType() const override { return "split"; }
+  int getNbOutputs() const override { return output_length_.size(); }
+  nvinfer1::Dims getOutputDimensions(int index,
+                                     const nvinfer1::Dims *input_dims,
+                                     int num_inputs) override;
+
+  int initialize() override;
+  int enqueue(int batchSize, const void *const *inputs, void **outputs,
+              void *workspace, cudaStream_t stream) override;
 
  protected:
-  virtual size_t getSerializationSize() override {
+  size_t getSerializationSize() override {
     return SerializedSize(axis_) + SerializedSize(output_length_) +
            getBaseSerializationSize();
   }
 
-  // TRT will call this func when we need to serialize the configuration of
-  // tensorrt.
-  // It should not be called by users.
-  virtual void serialize(void *buffer) override {
+  void serialize(void *buffer) override {
     serializeBase(buffer);
     SerializeValue(&buffer, axis_);
     SerializeValue(&buffer, output_length_);
   }
 
- public:
-  SplitPlugin(int axis, std::vector<int> const &output_lengths)
-      : axis_(axis), output_length_(output_lengths) {
-    assert(axis <= nvinfer1::Dims::MAX_DIMS);
-  }
-
-  // It was used for tensorrt deserialization.
-  // It should not be called by users.
-  SplitPlugin(void const *serialData, size_t serialLength) {
-    deserializeBase(serialData, serialLength);
-    DeserializeValue(&serialData, &serialLength, &axis_);
-    DeserializeValue(&serialData, &serialLength, &output_length_);
-  }
-
-  SplitPlugin *clone() const override {
-    return new SplitPlugin(axis_, output_length_);
-  }
-
-  virtual const char *getPluginType() const override { return "split"; }
-  virtual int getNbOutputs() const override { return output_length_.size(); }
-  virtual nvinfer1::Dims getOutputDimensions(int index,
-                                             const nvinfer1::Dims *inputs,
-                                             int nbInputDims) override;
-  virtual int initialize() override;
-  virtual int enqueue(int batchSize, const void *const *inputs, void **outputs,
-                      void *workspace, cudaStream_t stream) override;
+  int axis_;
+  int outer_rows_;
+  int inner_cols_;
+  bool same_shape_;
+  std::vector<int> output_length_;
+  std::vector<int> segment_offsets_;
+  thrust::device_vector<int> d_segment_offsets_;
+  thrust::device_vector<float *> d_output_ptrs_;
 };
 
-}  // tensorrt
-}  // inference
-}  // paddle
+}  // namespace plugin
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/tensorrt/plugin/trt_plugin.cc

@@ -17,6 +17,7 @@
 namespace paddle {
 namespace inference {
 namespace tensorrt {
+namespace plugin {
 
 void PluginTensorRT::serializeBase(void*& buffer) {
   SerializeValue(&buffer, input_dims_);
@@ -25,12 +26,12 @@ void PluginTensorRT::serializeBase(void*& buffer) {
   SerializeValue(&buffer, data_format_);
 }
 
-void PluginTensorRT::deserializeBase(void const*& serialData,
-                                     size_t& serialLength) {
-  DeserializeValue(&serialData, &serialLength, &input_dims_);
-  DeserializeValue(&serialData, &serialLength, &max_batch_size_);
-  DeserializeValue(&serialData, &serialLength, &data_type_);
-  DeserializeValue(&serialData, &serialLength, &data_format_);
+void PluginTensorRT::deserializeBase(void const*& serial_data,
+                                     size_t& serial_length) {
+  DeserializeValue(&serial_data, &serial_length, &input_dims_);
+  DeserializeValue(&serial_data, &serial_length, &max_batch_size_);
+  DeserializeValue(&serial_data, &serial_length, &data_type_);
+  DeserializeValue(&serial_data, &serial_length, &data_format_);
 }
 
 size_t PluginTensorRT::getBaseSerializationSize() {
@@ -44,18 +45,17 @@ bool PluginTensorRT::supportsFormat(nvinfer1::DataType type,
           (format == nvinfer1::PluginFormat::kNCHW));
 }
 
-void PluginTensorRT::configureWithFormat(const nvinfer1::Dims* inputDims,
-                                         int nbInputs,
-                                         const nvinfer1::Dims* outputDims,
-                                         int nbOutputs, nvinfer1::DataType type,
-                                         nvinfer1::PluginFormat format,
-                                         int maxBatchSize) {
+void PluginTensorRT::configureWithFormat(
+    const nvinfer1::Dims* input_dims, int num_inputs,
+    const nvinfer1::Dims* output_dims, int num_outputs, nvinfer1::DataType type,
+    nvinfer1::PluginFormat format, int max_batch_size) {
   data_type_ = type;
   data_format_ = format;
-  input_dims_.assign(inputDims, inputDims + nbInputs);
-  max_batch_size_ = maxBatchSize;
+  input_dims_.assign(input_dims, input_dims + num_inputs);
+  max_batch_size_ = max_batch_size;
 }
 
+}  // namespace plugin
 }  // namespace tensorrt
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/tensorrt/plugin/trt_plugin.h

@@ -14,23 +14,30 @@
 
 #pragma once
 
-#include <cassert>
+#include <NvInfer.h>
 #include <cstring>
-#include <iostream>
 #include <unordered_map>
 #include <vector>
-#include "NvInfer.h"
 
 #include "paddle/fluid/inference/tensorrt/plugin/serialize.h"
+#include "paddle/fluid/platform/enforce.h"
+#include "paddle/fluid/platform/profiler.h"
+
+DECLARE_bool(profile);
 
 namespace paddle {
 namespace inference {
 namespace tensorrt {
+namespace plugin {
 
 class PluginTensorRT : public nvinfer1::IPluginExt {
  public:
   PluginTensorRT() {}
+  // It was used for TensorRT deserialization.
+  // It should not be called by users.
   PluginTensorRT(const void* serialized_data, size_t length) {}
+  virtual ~PluginTensorRT() {}
+
   nvinfer1::Dims const& getInputDims(int index) const {
     return input_dims_.at(index);
   }
@@ -38,43 +45,66 @@ class PluginTensorRT : public nvinfer1::IPluginExt {
   nvinfer1::DataType getDataType() const { return data_type_; }
   nvinfer1::PluginFormat getDataFormat() const { return data_format_; }
   virtual const char* getPluginVersion() const { return "1"; }
+
+  void AddInput(nvinfer1::ITensor* input) { inputs_.push_back(input); }
+  std::vector<nvinfer1::ITensor*>& GetInputs() { return inputs_; }
+
+  virtual nvinfer1::IPluginExt* clone() const = 0;
+  virtual const char* getPluginType() const = 0;
+
+  // Following functions are inherit from nvinfer1::IPluginExt
+  // Get the number of outputs from the layer
+  int getNbOutputs() const { return 1; }
+  // Get the dimension of an output tensor
+  virtual nvinfer1::Dims getOutputDimensions(int index,
+                                             const nvinfer1::Dims* input_dims,
+                                             int num_inputs) = 0;
+  // Find the workspace size required by the layer
   size_t getWorkspaceSize(int) const override { return 0; }
+
+  // Initialize the layer for execution.
+  // This is called when the engine is created.
+  int initialize() override { return 0; }
+  // Shutdown the layer. This is called when the engine is destroyed
   void terminate() override {}
-  virtual ~PluginTensorRT() {}
+  // Execute the layer
+  virtual int enqueue(int batch_size, const void* const* inputs, void** outputs,
+                      void* workspace, cudaStream_t stream) = 0;
+
+  // Find the size of the serialization buffer required
+  virtual size_t getSerializationSize() = 0;
+  // Serialize the layer config to buffer.
+  // TensorRT will call this func to serialize the configuration of TensorRT
+  // engine. It should not be called by users.
+  virtual void serialize(void* buffer) = 0;
+
   // Check format support. The default is FLOAT32 and NCHW.
   bool supportsFormat(nvinfer1::DataType type,
                       nvinfer1::PluginFormat format) const override;
-  void configureWithFormat(const nvinfer1::Dims* inputDims, int nbInputs,
-                           const nvinfer1::Dims* outputDims, int nbOutputs,
+  // Configure the layer
+  void configureWithFormat(const nvinfer1::Dims* input_dims, int num_inputs,
+                           const nvinfer1::Dims* output_dims, int num_outputs,
                            nvinfer1::DataType type,
                            nvinfer1::PluginFormat format,
-                           int maxBatchSize) override;
-
-  // *NOTE* The following functions need to be overrided in the subclass.
-  virtual nvinfer1::IPluginExt* clone() const = 0;
-  virtual const char* getPluginType() const = 0;
-  // Initialize the layer for execution. This is called when the engine is
-  // created.
-  int initialize() override { return 0; }
-  // Serialize the layer config to buffer.
-  virtual void serialize(void* buffer) = 0;
-  virtual size_t getSerializationSize() = 0;
-  virtual int enqueue(int batchSize, const void* const* inputs, void** outputs,
-                      void* workspace, cudaStream_t stream) = 0;
+                           int max_batch_size) override;
 
  protected:
   // Deserialize input_dims, max_batch_size, data_type, data_format
-  void deserializeBase(void const*& serialData, size_t& serialLength);
+  void deserializeBase(void const*& serial_data,  // NOLINT
+                       size_t& serial_length);    // NOLINT
   size_t getBaseSerializationSize();
   // Serialize input_dims, max_batch_size, data_type, data_format
-  void serializeBase(void*& buffer);
+  void serializeBase(void*& buffer);  // NOLINT
 
   std::vector<nvinfer1::Dims> input_dims_;
   size_t max_batch_size_;
   nvinfer1::DataType data_type_;
   nvinfer1::PluginFormat data_format_;
+
+  std::vector<nvinfer1::ITensor*> inputs_;
 };
 
+}  // namespace plugin
 }  // namespace tensorrt
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/tests/api/CMakeLists.txt

 set(INFERENCE_EXTRA_DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis_predictor)
 
+if(WITH_GPU AND TENSORRT_FOUND)
+    set(INFERENCE_EXTRA_DEPS ${INFERENCE_EXTRA_DEPS} analysis ${analysis_deps} ir_pass_manager analysis_predictor)
+endif()
+
 function(download_model install_dir model_name)
     if (NOT EXISTS ${install_dir})
         inference_download_and_uncompress(${install_dir} ${INFERENCE_URL} ${model_name})
@@ -27,14 +31,14 @@ function(inference_analysis_api_test_with_fake_data target install_dir filename
 endfunction()
 
 # RNN1
-if(NOT APPLE)
+if(NOT APPLE AND WITH_MKLML)
     set(RNN1_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/rnn1")
     download_model_and_data(${RNN1_INSTALL_DIR} "rnn1%2Fmodel.tar.gz" "rnn1%2Fdata.txt.tar.gz")
     inference_analysis_api_test(test_analyzer_rnn1 ${RNN1_INSTALL_DIR} analyzer_rnn1_tester.cc)
 else()
-    # TODO: fix this test on MACOS, the reason is that
-    # fusion_seqexpand_concat_fc_op is not supported on MACOS
-    message(WARNING "These tests has been disabled in OSX before being fixed: \n test_analyzer_rnn1")
+    # TODO: fix this test on MACOS and OPENBLAS, the reason is that
+    # fusion_seqexpand_concat_fc_op is not supported on MACOS and OPENBLAS
+    message(WARNING "These tests has been disabled in OSX or WITH_MKL=OFF before being fixed: \n test_analyzer_rnn1")
 endif()
 
 # RNN2
@@ -109,6 +113,6 @@ if(WITH_GPU AND TENSORRT_FOUND)
        inference_download_and_uncompress(${TRT_MODEL_INSTALL_DIR} ${INFERENCE_URL}/tensorrt_test "trt_test_models.tar.gz")
    endif()
    inference_analysis_test(test_trt_models SRCS trt_models_tester.cc
-      EXTRA_DEPS ${INFERENCE_EXTRA_DEPS} analysis ${analysis_deps} ir_pass_manager analysis_predictor
+      EXTRA_DEPS ${INFERENCE_EXTRA_DEPS}
         ARGS --infer_model=${TRT_MODEL_INSTALL_DIR}/trt_test_models SERIAL)
 endif()

paddle/fluid/inference/tests/api/tester_helper.h

@@ -51,7 +51,7 @@ void PrintConfig(const PaddlePredictor::Config *config, bool use_analysis) {
     LOG(INFO) << *reinterpret_cast<const contrib::AnalysisConfig *>(config);
     return;
   }
-  LOG(INFO) << *config;
+  LOG(INFO) << *reinterpret_cast<const NativeConfig *>(config);
 }
 
 void CompareResult(const std::vector<PaddleTensor> &outputs,
@@ -222,7 +222,23 @@ void TestMultiThreadPrediction(
       // The inputs of each thread are all the same.
       std::vector<PaddleTensor> outputs_tid;
       auto &predictor = predictors[tid];
-      LOG(INFO) << "running thread " << tid;
+
+      // warmup run
+      LOG(INFO) << "Running thread " << tid << ", warm up run...";
+      {
+        Timer warmup_timer;
+        warmup_timer.tic();
+        predictor->Run(inputs[0], outputs, batch_size);
+        PrintTime(batch_size, 1, num_threads, tid, warmup_timer.toc(), 1);
+#if !defined(_WIN32)
+        if (FLAGS_profile) {
+          paddle::platform::ResetProfiler();
+        }
+#endif
+      }
+
+      LOG(INFO) << "Thread " << tid << " run " << num_times << " times...";
+      {
         Timer timer;
         timer.tic();
         for (int i = 0; i < num_times; i++) {
@@ -235,6 +251,7 @@ void TestMultiThreadPrediction(
         total_time += time;
         PrintTime(batch_size, num_times, num_threads, tid, time / num_times,
                   inputs.size());
+      }
     });
   }
   for (int i = 0; i < num_threads; ++i) {

paddle/fluid/inference/tests/api/trt_models_tester.cc

@@ -145,5 +145,3 @@ TEST(TensorRT_mobilenet, analysis) {
 
 }  // namespace inference
 }  // namespace paddle
-
-USE_PASS(tensorrt_subgraph_pass);

paddle/fluid/memory/allocation/best_fit_allocator_test.cc

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #include "paddle/fluid/memory/allocation/best_fit_allocator.h"
+#include <random>
 #include <thread>  // NOLINT
 #include <vector>
 #include "gtest/gtest.h"

paddle/fluid/memory/allocation/best_fit_allocator_test.cu

@@ -12,6 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+#include <random>
 #include <thread>  // NOLINT
 #include <vector>
 #include "gtest/gtest.h"

paddle/fluid/memory/allocation/cpu_allocator.h

@@ -15,6 +15,12 @@
 #pragma once
 #include "paddle/fluid/memory/allocation/allocator.h"
 
+#ifdef _WIN32
+#define posix_memalign_free _aligned_free
+#define posix_memalign(p, a, s) \
+  (((*(p)) = _aligned_malloc((s), (a))), *(p) ? 0 : errno)
+#endif
+
 namespace paddle {
 namespace memory {
 namespace allocation {

paddle/fluid/operators/CMakeLists.txt

@@ -22,9 +22,7 @@ if(WITH_DISTRIBUTE)
     add_subdirectory(distributed_ops)
 endif()
 
-if (NOT WIN32)
-    add_subdirectory(reader)
-endif()
+add_subdirectory(reader)
 
 if (NOT WIN32)
     add_subdirectory(nccl)
@@ -34,29 +32,39 @@ if (WITH_GPU AND TENSORRT_FOUND)
     add_subdirectory(tensorrt)
 endif()
 
-register_operators(EXCLUDES warpctc_op conv_fusion_op)
-
-# warpctc_cudnn need cudnn 7 above
+SET(OP_HEADER_DEPS xxhash)
 if (WITH_GPU)
+    SET(OP_HEADER_DEPS ${OP_HEADER_DEPS} cub)
+endif()
+
+register_operators(EXCLUDES warpctc_op conv_fusion_op DEPS ${OP_HEADER_DEPS})
+
+# warpctc_op needs cudnn 7 above
+if (WITH_GPU AND NOT WIN32)
     if (${CUDNN_MAJOR_VERSION} VERSION_LESS 7)
         op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale SRCS warpctc_op.cc warpctc_op.cu.cc)
     else()
         op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale)
     endif()
+    # conv_fusion_op needs cudnn 7 above
+    if (NOT ${CUDNN_MAJOR_VERSION} VERSION_LESS 7)
         op_library(conv_fusion_op)
         file(APPEND ${pybind_file} "USE_CUDA_ONLY_OP(conv2d_fusion);\n")
+    endif()
 else()
     op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale)
 endif()
 
-set(COMMON_OP_DEPS "")
+set(COMMON_OP_DEPS ${OP_HEADER_DEPS})
 
-set(COMMON_OP_DEPS ${COMMON_OP_DEPS} xxhash selected_rows_functor selected_rows lod_tensor maxouting unpooling pooling lod_rank_table context_project sequence_pooling executor dynload_warpctc sequence_padding sequence_scale cos_sim_functor memory jit_kernel concat_and_split cross_entropy softmax vol2col im2col sampler)
+set(COMMON_OP_DEPS ${COMMON_OP_DEPS} selected_rows_functor selected_rows lod_tensor maxouting unpooling pooling lod_rank_table context_project sequence_pooling executor)
 if (NOT WIN32)
-  set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence2batch lstm_compute matrix_bit_code gru_compute activation_functions)
+    set(COMMON_OP_DEPS ${COMMON_OP_DEPS} dynload_warpctc)
 endif()
+set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence_padding sequence_scale cos_sim_functor memory jit_kernel concat_and_split cross_entropy softmax vol2col im2col sampler)
+set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence2batch lstm_compute matrix_bit_code gru_compute activation_functions)
 if (WITH_GPU)
-  set(COMMON_OP_DEPS ${COMMON_OP_DEPS} depthwise_conv cub)
+  set(COMMON_OP_DEPS ${COMMON_OP_DEPS} depthwise_conv)
 endif()
 
 # FIXME(typhoonzero): operator deps may not needed.

paddle/fluid/operators/conv_fusion_op.cu.cc

@@ -22,6 +22,7 @@ DECLARE_bool(cudnn_exhaustive_search);
 namespace paddle {
 namespace operators {
 
+#if CUDNN_VERSION >= 7001
 using Tensor = framework::Tensor;
 using ScopedTensorDescriptor = platform::ScopedTensorDescriptor;
 using ScopedFilterDescriptor = platform::ScopedFilterDescriptor;
@@ -178,10 +179,13 @@ class CUDNNConvFusionOpKernel : public framework::OpKernel<T> {
     workspace_handle.RunFunc(cudnn_func, workspace_size_in_bytes);
   }
 };
+#endif
 
 }  // namespace operators
 }  // namespace paddle
 
+#if CUDNN_VERSION >= 7001
 namespace ops = paddle::operators;
 REGISTER_OP_CUDA_KERNEL(conv2d_fusion, ops::CUDNNConvFusionOpKernel<float>,
                         ops::CUDNNConvFusionOpKernel<double>);
+#endif

paddle/fluid/operators/detection/CMakeLists.txt

@@ -22,7 +22,7 @@ iou_similarity_op.cu)
 detection_library(mine_hard_examples_op SRCS mine_hard_examples_op.cc)
 detection_library(multiclass_nms_op SRCS multiclass_nms_op.cc poly_util.cc gpc.cc)
 detection_library(prior_box_op SRCS prior_box_op.cc prior_box_op.cu)
-detection_library(density_prior_box_op SRCS density_prior_box_op.cc)
+detection_library(density_prior_box_op SRCS density_prior_box_op.cc density_prior_box_op.cu)
 detection_library(anchor_generator_op SRCS anchor_generator_op.cc
 anchor_generator_op.cu)
 detection_library(target_assign_op SRCS target_assign_op.cc

paddle/fluid/operators/detection/density_prior_box_op.cc

@@ -39,17 +39,15 @@ class DensityPriorBoxOp : public framework::OperatorWithKernel {
     auto fixed_sizes = ctx->Attrs().Get<std::vector<float>>("fixed_sizes");
     auto fixed_ratios = ctx->Attrs().Get<std::vector<float>>("fixed_ratios");
     auto densities = ctx->Attrs().Get<std::vector<int>>("densities");
+    bool flatten = ctx->Attrs().Get<bool>("flatten_to_2d");
 
     PADDLE_ENFORCE_EQ(fixed_sizes.size(), densities.size(),
                       "The number of fixed_sizes and densities must be equal.");
     size_t num_priors = 0;
-    if ((fixed_sizes.size() > 0) && (densities.size() > 0)) {
     for (size_t i = 0; i < densities.size(); ++i) {
-        if (fixed_ratios.size() > 0) {
       num_priors += (fixed_ratios.size()) * (pow(densities[i], 2));
     }
-      }
-    }
+    if (!flatten) {
       std::vector<int64_t> dim_vec(4);
       dim_vec[0] = input_dims[2];
       dim_vec[1] = input_dims[3];
@@ -57,6 +55,11 @@ class DensityPriorBoxOp : public framework::OperatorWithKernel {
       dim_vec[3] = 4;
       ctx->SetOutputDim("Boxes", framework::make_ddim(dim_vec));
       ctx->SetOutputDim("Variances", framework::make_ddim(dim_vec));
+    } else {
+      int64_t dim0 = input_dims[2] * input_dims[3] * num_priors;
+      ctx->SetOutputDim("Boxes", {dim0, 4});
+      ctx->SetOutputDim("Variances", {dim0, 4});
+    }
   }
 
  protected:
@@ -64,7 +67,7 @@ class DensityPriorBoxOp : public framework::OperatorWithKernel {
       const framework::ExecutionContext& ctx) const override {
     return framework::OpKernelType(
         framework::ToDataType(ctx.Input<framework::Tensor>("Input")->type()),
-        platform::CPUPlace());
+        ctx.GetPlace());
   }
 };
 
@@ -101,7 +104,10 @@ class DensityPriorBoxOpMaker : public framework::OpProtoAndCheckerMaker {
         });
     AddAttr<bool>("clip", "(bool) Whether to clip out-of-boundary boxes.")
         .SetDefault(true);
-
+    AddAttr<bool>("flatten_to_2d",
+                  "(bool) Whether to flatten to 2D and "
+                  "the second dim is 4.")
+        .SetDefault(false);
     AddAttr<float>(
         "step_w",
         "Density prior boxes step across width, 0.0 for auto calculation.")

paddle/fluid/operators/detection/density_prior_box_op.cu

+/* 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 "paddle/fluid/operators/detection/density_prior_box_op.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+static __device__ inline T Clip(T in) {
+  return min(max(in, 0.), 1.);
+}
+
+template <typename T>
+static __global__ void GenDensityPriorBox(
+    const int height, const int width, const int im_height, const int im_width,
+    const T offset, const T step_width, const T step_height,
+    const int num_priors, const T* ratios_shift, bool is_clip, const T var_xmin,
+    const T var_ymin, const T var_xmax, const T var_ymax, T* out, T* var) {
+  int gidx = blockIdx.x * blockDim.x + threadIdx.x;
+  int gidy = blockIdx.y * blockDim.y + threadIdx.y;
+  int step_x = blockDim.x * gridDim.x;
+  int step_y = blockDim.y * gridDim.y;
+
+  const T* width_ratio = ratios_shift;
+  const T* height_ratio = ratios_shift + num_priors;
+  const T* width_shift = ratios_shift + 2 * num_priors;
+  const T* height_shift = ratios_shift + 3 * num_priors;
+
+  for (int j = gidy; j < height; j += step_y) {
+    for (int i = gidx; i < width * num_priors; i += step_x) {
+      int h = j;
+      int w = i / num_priors;
+      int k = i % num_priors;
+
+      T center_x = (w + offset) * step_width;
+      T center_y = (h + offset) * step_height;
+
+      T center_x_temp = center_x + width_shift[k];
+      T center_y_temp = center_y + height_shift[k];
+
+      T box_width_ratio = width_ratio[k] / 2.;
+      T box_height_ratio = height_ratio[k] / 2.;
+
+      T xmin = max((center_x_temp - box_width_ratio) / im_width, 0.);
+      T ymin = max((center_y_temp - box_height_ratio) / im_height, 0.);
+      T xmax = min((center_x_temp + box_width_ratio) / im_width, 1.);
+      T ymax = min((center_y_temp + box_height_ratio) / im_height, 1.);
+
+      int out_offset = (j * width * num_priors + i) * 4;
+      out[out_offset] = is_clip ? Clip<T>(xmin) : xmin;
+      out[out_offset + 1] = is_clip ? Clip<T>(ymin) : ymin;
+      out[out_offset + 2] = is_clip ? Clip<T>(xmax) : xmax;
+      out[out_offset + 3] = is_clip ? Clip<T>(ymax) : ymax;
+
+      var[out_offset] = var_xmin;
+      var[out_offset + 1] = var_ymin;
+      var[out_offset + 2] = var_xmax;
+      var[out_offset + 3] = var_ymax;
+    }
+  }
+}
+
+template <typename T>
+class DensityPriorBoxOpCUDAKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* input = ctx.Input<paddle::framework::Tensor>("Input");
+    auto* image = ctx.Input<paddle::framework::Tensor>("Image");
+    auto* boxes = ctx.Output<paddle::framework::Tensor>("Boxes");
+    auto* vars = ctx.Output<paddle::framework::Tensor>("Variances");
+
+    auto variances = ctx.Attr<std::vector<float>>("variances");
+    auto is_clip = ctx.Attr<bool>("clip");
+
+    auto fixed_sizes = ctx.Attr<std::vector<float>>("fixed_sizes");
+    auto fixed_ratios = ctx.Attr<std::vector<float>>("fixed_ratios");
+    auto densities = ctx.Attr<std::vector<int>>("densities");
+
+    T step_w = static_cast<T>(ctx.Attr<float>("step_w"));
+    T step_h = static_cast<T>(ctx.Attr<float>("step_h"));
+    T offset = static_cast<T>(ctx.Attr<float>("offset"));
+
+    auto img_width = image->dims()[3];
+    auto img_height = image->dims()[2];
+
+    auto feature_width = input->dims()[3];
+    auto feature_height = input->dims()[2];
+
+    T step_width, step_height;
+    if (step_w == 0 || step_h == 0) {
+      step_width = static_cast<T>(img_width) / feature_width;
+      step_height = static_cast<T>(img_height) / feature_height;
+    } else {
+      step_width = step_w;
+      step_height = step_h;
+    }
+
+    int num_priors = 0;
+    for (size_t i = 0; i < densities.size(); ++i) {
+      num_priors += (fixed_ratios.size()) * (pow(densities[i], 2));
+    }
+    int step_average = static_cast<int>((step_width + step_height) * 0.5);
+
+    framework::Tensor h_temp;
+    T* tdata = h_temp.mutable_data<T>({num_priors * 4}, platform::CPUPlace());
+    int idx = 0;
+    for (size_t s = 0; s < fixed_sizes.size(); ++s) {
+      auto fixed_size = fixed_sizes[s];
+      int density = densities[s];
+      for (size_t r = 0; r < fixed_ratios.size(); ++r) {
+        float ar = fixed_ratios[r];
+        int shift = step_average / density;
+        float box_width_ratio = fixed_size * sqrt(ar);
+        float box_height_ratio = fixed_size / sqrt(ar);
+        for (int di = 0; di < density; ++di) {
+          for (int dj = 0; dj < density; ++dj) {
+            float center_x_temp = shift / 2. + dj * shift - step_average / 2.;
+            float center_y_temp = shift / 2. + di * shift - step_average / 2.;
+            tdata[idx] = box_width_ratio;
+            tdata[num_priors + idx] = box_height_ratio;
+            tdata[2 * num_priors + idx] = center_x_temp;
+            tdata[3 * num_priors + idx] = center_y_temp;
+            idx++;
+          }
+        }
+      }
+    }
+
+    boxes->mutable_data<T>(ctx.GetPlace());
+    vars->mutable_data<T>(ctx.GetPlace());
+
+    framework::Tensor d_temp;
+    framework::TensorCopySync(h_temp, ctx.GetPlace(), &d_temp);
+
+    // At least use 32 threads, at most 512 threads.
+    // blockx is multiple of 32.
+    int blockx = std::min(((feature_width * num_priors + 31) >> 5) << 5, 512L);
+    int gridx = (feature_width * num_priors + blockx - 1) / blockx;
+    dim3 threads(blockx, 1);
+    dim3 grids(gridx, feature_height);
+
+    auto stream =
+        ctx.template device_context<platform::CUDADeviceContext>().stream();
+    GenDensityPriorBox<T><<<grids, threads, 0, stream>>>(
+        feature_height, feature_width, img_height, img_width, offset,
+        step_width, step_height, num_priors, d_temp.data<T>(), is_clip,
+        variances[0], variances[1], variances[2], variances[3],
+        boxes->data<T>(), vars->data<T>());
+  }
+};  // namespace operators
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(density_prior_box,
+                        ops::DensityPriorBoxOpCUDAKernel<float>,
+                        ops::DensityPriorBoxOpCUDAKernel<double>);

paddle/fluid/operators/detection/density_prior_box_op.h

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+/* 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
@@ -52,18 +52,16 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
       step_height = step_h;
     }
     int num_priors = 0;
-    if (fixed_sizes.size() > 0 && densities.size() > 0) {
     for (size_t i = 0; i < densities.size(); ++i) {
-        if (fixed_ratios.size() > 0) {
       num_priors += (fixed_ratios.size()) * (pow(densities[i], 2));
     }
-      }
-    }
 
     boxes->mutable_data<T>(ctx.GetPlace());
     vars->mutable_data<T>(ctx.GetPlace());
-    auto e_boxes = framework::EigenTensor<T, 4>::From(*boxes).setConstant(0.0);
 
+    auto box_dim = vars->dims();
+    boxes->Resize({feature_height, feature_width, num_priors, 4});
+    auto e_boxes = framework::EigenTensor<T, 4>::From(*boxes).setConstant(0.0);
     int step_average = static_cast<int>((step_width + step_height) * 0.5);
 
     for (int h = 0; h < feature_height; ++h) {
@@ -76,7 +74,6 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
           auto fixed_size = fixed_sizes[s];
           int density = densities[s];
           // Generate density prior boxes with fixed ratios.
-          if (fixed_ratios.size() > 0) {
           for (size_t r = 0; r < fixed_ratios.size(); ++r) {
             float ar = fixed_ratios[r];
             int shift = step_average / density;
@@ -111,7 +108,6 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
         }
       }
     }
-    }
     if (clip) {
       platform::Transform<platform::CPUDeviceContext> trans;
       ClipFunctor<T> clip_func;
@@ -139,6 +135,7 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
     e_vars = var_et.broadcast(Eigen::DSizes<int, 2>(box_num, 1));
 
     vars->Resize(var_dim);
+    boxes->Resize(box_dim);
   }
 };  // namespace operators
 

paddle/fluid/operators/elementwise/elementwise_mul_mkldnn_op.cc

+/* 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. */
+
+#include <mkldnn/include/mkldnn.hpp>
+#include "paddle/fluid/operators/elementwise/elementwise_op.h"
+#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
+
+#include "paddle/fluid/platform/mkldnn_helper.h"
+
+#include "paddle/fluid/operators/math/jit_kernel.h"
+#include "xbyak.h"
+#include "xbyak_util.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::DataLayout;
+using mkldnn::memory;
+
+static mkldnn::memory::format StringToMKLDNNFormat(std::string& format) {
+  std::transform(format.begin(), format.end(), format.begin(), ::tolower);
+
+  if (!format.compare("nchw")) {
+    return memory::format::nchw;
+  } else if (!format.compare("nchw16c")) {
+    return memory::format::nChw16c;
+  } else if (!format.compare("nchw8c")) {
+    return memory::format::nChw8c;
+  } else if (!format.compare("nhwc")) {
+    return memory::format::nhwc;
+  } else {
+    return memory::format::any;
+  }
+}
+
+static void UpdateDataFormat(const framework::ExecutionContext& ctx,
+                             framework::Tensor* tensor, const char* attribute) {
+  if (ctx.op().HasAttr(attribute)) {
+    auto format_as_string = ctx.Attr<std::string>(attribute);
+    auto format = StringToMKLDNNFormat(format_as_string);
+    if (format != memory::format::any) {
+      tensor->set_format(format);
+    }
+  }
+}
+
+template <typename T>
+static void ReorderInput(framework::Tensor* tensor,
+                         const platform::Place& place,
+                         const mkldnn::engine& engine, bool isFourDim) {
+  using platform::to_void_cast;
+  auto dims = paddle::framework::vectorize2int(tensor->dims());
+  framework::Tensor out_tensor;
+  out_tensor.Resize(tensor->dims());
+  out_tensor.set_format(isFourDim ? memory::format::nchw : memory::format::nc);
+  out_tensor.set_layout(tensor->layout());
+  mkldnn::memory input_memory = {
+      {{dims, platform::MKLDNNGetDataType<T>(), tensor->format()}, engine},
+      to_void_cast<T>(tensor->data<T>())};
+  mkldnn::memory output_memory = {
+      {{dims, platform::MKLDNNGetDataType<T>(), out_tensor.format()}, engine},
+      to_void_cast<T>(out_tensor.mutable_data<T>(place))};
+  platform::Reorder(input_memory, output_memory);
+  tensor->ShareDataWith(out_tensor);
+}
+
+template <typename T>
+class ElementwiseMulMKLDNNKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    using Tensor = framework::Tensor;
+
+    int axis = ctx.Attr<int>("axis");
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+    auto* z = ctx.Output<Tensor>("Out");
+    const T* x_data = x->data<T>();
+    const T* y_data = y->data<T>();
+    T* z_data = z->mutable_data<T>(ctx.GetPlace());
+
+    auto x_dims = x->dims();
+    auto y_dims_untrimmed = y->dims();
+    auto x_int_dims = paddle::framework::vectorize2int(x_dims);
+
+    UpdateDataFormat(ctx, (Tensor*)x, "x_data_format");
+    UpdateDataFormat(ctx, (Tensor*)y, "y_data_format");
+
+    Xbyak::util::Cpu cpu;
+    const bool is_avx512_enabled = cpu.has(Xbyak::util::Cpu::tAVX512F);
+    const bool are_dims_divisable = !(x_int_dims[1] % 16);
+    const bool is_x_format_correct = x->format() == memory::format::nChw16c;
+    const bool is_y_format_correct = y->format() == memory::format::nc;
+    if (is_x_format_correct && is_y_format_correct && are_dims_divisable &&
+        is_avx512_enabled) {
+      int pre, n, post;
+      get_mid_dims(x_dims, y_dims_untrimmed, axis, &pre, &n, &post);
+
+      if (post == 1) {
+        PADDLE_THROW("Not implemented when post is 1");
+      } else {
+        // Just check whether it works for RE-Resnext.
+        PADDLE_ENFORCE_EQ(x_dims.size(), 4, "X should have 4 dimensions");
+
+        int n = x_dims[0];
+        int c = x_dims[1];
+        int h = x_dims[2];
+        int w = x_dims[3];
+
+        PADDLE_ENFORCE(y_dims_untrimmed[0] == n && y_dims_untrimmed[1] == c,
+                       "Y should be in nc format");
+
+        constexpr int simd_width = 16;
+        int C = c / simd_width;
+
+        const auto& multiply =
+            math::jitkernel::KernelPool::Instance()
+                .template Get<math::jitkernel::EltwiseMulnChw16cNCKernel<T>>(n);
+
+#pragma omp parallel for collapse(2)
+        for (int ni = 0; ni < n; ni++) {
+          for (int ci = 0; ci < C; ci++) {
+            auto ptr_x =
+                x_data + ni * C * h * w * simd_width + ci * h * w * simd_width;
+
+            auto ptr_y = y_data + ni * C * simd_width + ci * simd_width;
+            auto ptr_z =
+                z_data + ni * C * h * w * simd_width + ci * h * w * simd_width;
+
+            multiply->Compute(ptr_x, ptr_y, ptr_z, h, w);
+          }
+        }
+      }
+
+      z->set_layout(DataLayout::kMKLDNN);
+      z->set_format(x->format());
+    } else {
+      // Fallback to naive version:
+      const bool are_inputs_in_same_format = x->format() == y->format();
+      const bool is_x_nchw = x->format() == memory::format::nchw;
+      const bool is_x_nc = x->format() == memory::format::nc;
+      const bool is_y_nchw = y->format() == memory::format::nchw;
+      const bool is_y_nc = y->format() == memory::format::nc;
+      if (!are_inputs_in_same_format) {
+        using platform::MKLDNNDeviceContext;
+        auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
+        const auto& mkldnn_engine = dev_ctx.GetEngine();
+        if (!(is_x_nchw || is_x_nc))
+          ReorderInput<T>((Tensor*)x, ctx.GetPlace(), mkldnn_engine,
+                          x->dims().size() == 4);
+        if (!(is_y_nchw || is_y_nc))
+          ReorderInput<T>((Tensor*)y, ctx.GetPlace(), mkldnn_engine,
+                          y->dims().size() == 4);
+      }
+
+      auto mul_func = [](T a, T b) -> T { return a * b; };
+
+      TransformFunctor<decltype(mul_func), T,
+                       paddle::platform::CPUDeviceContext, T>
+          functor(
+              x, y, z,
+              ctx.template device_context<paddle::platform::CPUDeviceContext>(),
+              mul_func);
+
+      axis = (axis == -1 ? x_dims.size() - y_dims_untrimmed.size() : axis);
+      PADDLE_ENFORCE(axis >= 0 && axis < x_dims.size(),
+                     "Axis should be in range [0, x_dims)");
+
+      auto y_dims = trim_trailing_singular_dims(y_dims_untrimmed);
+      axis = (y_dims.size() == 0) ? x_dims.size() : axis;
+
+      int pre, n, post;
+      get_mid_dims(x_dims, y_dims, axis, &pre, &n, &post);
+
+      if (post == 1) {
+        functor.RunRowWise(n, pre);
+      } else {
+        functor.RunMidWise(n, pre, post);
+      }
+      z->set_layout(DataLayout::kMKLDNN);
+      z->set_format(x->format());
+    }
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_KERNEL(elementwise_mul, MKLDNN, ::paddle::platform::CPUPlace,
+                   ops::ElementwiseMulMKLDNNKernel<float>)

paddle/fluid/operators/elementwise/elementwise_op.h

@@ -97,6 +97,20 @@ class ElementwiseOpMaker : public framework::OpProtoAndCheckerMaker {
         .EqualGreaterThan(-1);
     AddAttr<bool>("use_mkldnn", "(bool, default false). Used by MKLDNN.")
         .SetDefault(false);
+    AddAttr<std::string>(
+        "x_data_format",
+        "(string, default NCHW) Only used in mkldnn"
+        "An optional string from: \"NHWC\", \"NCHW\", \"NCHW16C\", \"NCHW8C\". "
+        "Defaults to \"\". Specify the data format of the output data, "
+        "the input will be transformed automatically. ")
+        .SetDefault("");
+    AddAttr<std::string>(
+        "y_data_format",
+        "(string, default \"\") Only used in mkldnn"
+        "An optional string from: \"NHWC\", \"NCHW\", \"NCHW16C\", \"NCHW8C\". "
+        "Defaults to \"\". Specify the data format of the output data, "
+        "the input will be transformed automatically. ")
+        .SetDefault("");
     AddComment(string::Sprintf(R"DOC(
 Elementwise %s Operator
 

paddle/fluid/operators/group_norm_op.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 "paddle/fluid/operators/group_norm_op.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+using DataLayout = framework::DataLayout;
+
+class GroupNormOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of GroupNormOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Y"),
+                   "Output(Y) of GroupNormOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Mean"),
+                   "Output(Mean) of GroupNormOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Variance"),
+                   "Output(Variance) of GroupNormOp should not be null.");
+
+    auto x_dim = ctx->GetInputDim("X");
+    auto channel_num = x_dim[1];
+    auto batch_size = x_dim[0];
+    auto groups = ctx->Attrs().Get<int>("groups");
+    PADDLE_ENFORCE_LE(
+        groups, channel_num,
+        "'groups' must be less equal than the number of channels.");
+    PADDLE_ENFORCE_GE(groups, 1, "'groups' must be greater equal than 1.");
+
+    if (ctx->HasInput("Scale")) {
+      PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale").size(), 1UL);
+      PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale")[0], channel_num);
+    }
+    if (ctx->HasInput("Bias")) {
+      PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias").size(), 1UL);
+      PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias")[0], channel_num);
+    }
+
+    ctx->SetOutputDim("Y", ctx->GetInputDim("X"));
+    ctx->SetOutputDim("Mean", {batch_size, groups});
+    ctx->SetOutputDim("Variance", {batch_size, groups});
+    ctx->ShareLoD("X", "Y");
+  }
+};
+
+class GroupNormOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X", "The input tensor.");
+    AddInput("Scale",
+             "Scale is a 1-dimensional tensor of size C"
+             "that is applied to the output.")
+        .AsDispensable();
+    AddInput("Bias",
+             "Bias is a 1-dimensional tensor of size C "
+             "that is applied to the output")
+        .AsDispensable();
+    AddOutput("Y", "Result after normalization.");
+    AddOutput("Mean", "Mean of each group.").AsIntermediate();
+    AddOutput("Variance", "Variance of each group.").AsIntermediate();
+
+    AddAttr<float>("epsilon",
+                   "Constant for numerical stability [default 1e-5].")
+        .SetDefault(1e-5)
+        .AddCustomChecker([](const float &epsilon) {
+          PADDLE_ENFORCE(epsilon >= 0.0f && epsilon <= 1.0f,
+                         "'epsilon' should be between 0.0 and 1.0.");
+        });
+    AddAttr<int>("groups", "The number of groups that divided from channels.")
+        .AddCustomChecker([](const int &groups) {
+          PADDLE_ENFORCE_GT(groups, 0, "'groups' should be greater than zero.");
+        });
+
+    AddComment(R"DOC(
+Group Normalization
+
+Refer to `Group Normalization <https://arxiv.org/abs/1803.08494>`_
+)DOC");
+  }
+};
+
+class GroupNormGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    // check input
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of GroupNormOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Mean"),
+                   "Input(Mean) of GroupNormOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Variance"),
+                   "Input(Variance) of GroupNormOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")),
+                   "Input(Y@GRAD) of GroupNormOp should not be null.");
+
+    // check output
+    if (ctx->HasOutput(framework::GradVarName("X"))) {
+      ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
+    }
+    if (ctx->HasOutput(framework::GradVarName("Scale"))) {
+      ctx->SetOutputDim(framework::GradVarName("Scale"),
+                        ctx->GetInputDim("Scale"));
+    }
+    if (ctx->HasOutput(framework::GradVarName("Bias"))) {
+      ctx->SetOutputDim(framework::GradVarName("Bias"),
+                        ctx->GetInputDim("Bias"));
+    }
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    const auto *var = ctx.InputVar(framework::GradVarName("Y"));
+    if (var == nullptr) {
+      PADDLE_THROW("can't find Y@GRAD");
+    }
+    const Tensor *t = nullptr;
+    if (var->IsType<Tensor>()) {
+      t = &var->Get<Tensor>();
+    } else if (var->IsType<LoDTensor>()) {
+      t = &var->Get<LoDTensor>();
+    }
+    if (t == nullptr) {
+      PADDLE_THROW("can't find Y@GRAD");
+    }
+    return framework::OpKernelType(framework::ToDataType(t->type()),
+                                   ctx.GetPlace());
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(group_norm, ops::GroupNormOp, ops::GroupNormOpMaker,
+                  paddle::framework::DefaultGradOpDescMaker<true>);
+REGISTER_OPERATOR(group_norm_grad, ops::GroupNormGradOp);
+REGISTER_OP_CPU_KERNEL(
+    group_norm, ops::GroupNormKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::GroupNormKernel<paddle::platform::CPUDeviceContext, double>);
+REGISTER_OP_CPU_KERNEL(
+    group_norm_grad,
+    ops::GroupNormGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::GroupNormGradKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/group_norm_op.cu

+/* 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 <cub/cub.cuh>
+#include "paddle/fluid/operators/group_norm_op.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+__global__ void GroupNormForwardGetMeanAndVar(const T* x, int N, int C,
+                                              int imsize, int groups,
+                                              int group_size, T* mean, T* var) {
+  int gid = blockIdx.y;
+  int cid = blockIdx.x;
+  int bid = blockIdx.z;
+  int number = min(group_size, static_cast<int>(C - gid * group_size));
+  int ccid = gid * group_size + cid;
+  if (ccid >= C) return;
+  T x_mean = 0, x_var = 0;
+  for (int imid = threadIdx.x; imid < imsize; imid += blockDim.x) {
+    T val = x[(bid * C + ccid) * imsize + imid];
+    x_mean += val;
+    x_var += val * val;
+  }
+  x_mean /= number * imsize;
+  x_var /= number * imsize;
+  __shared__ T s_mem[2];
+  if (threadIdx.x == 0) {
+    s_mem[0] = s_mem[1] = 0;
+  }
+  __syncthreads();
+  paddle::platform::CudaAtomicAdd(&s_mem[0], x_mean);
+  paddle::platform::CudaAtomicAdd(&s_mem[1], x_var);
+  __syncthreads();
+  if (threadIdx.x == 0) {
+    paddle::platform::CudaAtomicAdd(&mean[bid * groups + gid], s_mem[0]);
+    paddle::platform::CudaAtomicAdd(&var[bid * groups + gid], s_mem[1]);
+  }
+}
+
+template <typename T>
+__global__ void GroupNormForward(const T* x, const T* mean, const T* var,
+                                 const T* scale, const T* bias, int N, int C,
+                                 int imsize, int groups, int group_size,
+                                 T epsilon, T* y, T* real_var) {
+  int gid = blockIdx.y;
+  int cid = blockIdx.x;
+  int bid = blockIdx.z;
+  int ccid = gid * group_size + cid;
+  if (ccid >= C) return;
+  T x_mean = mean[bid * groups + gid];
+  T x_var = var[bid * groups + gid];
+  x_var = x_var - x_mean * x_mean;
+  T var_inv = 1.0 / sqrt(x_var + epsilon);
+  if (cid == 0 && threadIdx.x == 0) real_var[bid * groups + gid] = x_var;
+  for (int imid = threadIdx.x; imid < imsize; imid += blockDim.x) {
+    T val = x[(bid * C + ccid) * imsize + imid];
+    val = (val - x_mean) * var_inv;
+    if (scale) val *= scale[gid * group_size + cid];
+    if (bias) val += bias[gid * group_size + cid];
+    y[(bid * C + ccid) * imsize + imid] = val;
+  }
+}
+
+template <typename T>
+class GroupNormKernel<platform::CUDADeviceContext, T>
+    : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    const float epsilon = ctx.Attr<float>("epsilon");
+    auto* scale = ctx.Input<Tensor>("Scale");
+    auto* bias = ctx.Input<Tensor>("Bias");
+    auto* x = ctx.Input<Tensor>("X");
+
+    auto* y = ctx.Output<Tensor>("Y");
+    auto* mean = ctx.Output<Tensor>("Mean");
+    auto* var = ctx.Output<Tensor>("Variance");
+    const auto groups = ctx.Attr<int>("groups");
+
+    const auto x_dims = x->dims();
+    const int group_size = (x_dims[1] - 1) / groups + 1;
+
+    y->mutable_data<T>(ctx.GetPlace());
+    mean->mutable_data<T>(ctx.GetPlace());
+    var->mutable_data<T>(ctx.GetPlace());
+    math::SetConstant<platform::CUDADeviceContext, T> set_zero;
+    auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
+    Tensor temp_var;
+    temp_var.mutable_data<T>(var->dims(), ctx.GetPlace());
+
+    set_zero(dev_ctx, mean, static_cast<T>(0));
+    set_zero(dev_ctx, &temp_var, static_cast<T>(0));
+
+    auto* x_data = x->data<T>();
+    auto* y_data = y->data<T>();
+    auto* mean_data = mean->data<T>();
+    auto* var_data = var->data<T>();
+    auto* temp_var_data = temp_var.data<T>();
+
+    const T* scale_data = nullptr;
+    if (scale) scale_data = scale->data<T>();
+    const T* bias_data = nullptr;
+    if (bias) bias_data = bias->data<T>();
+
+    int imsize = x_dims[2] * x_dims[3];
+    int block_size = std::min(512, imsize);
+    dim3 grid(group_size, groups, x_dims[0]);
+    dim3 threads(block_size, 1, 1);
+    GroupNormForwardGetMeanAndVar<T><<<grid, threads, 0, dev_ctx.stream()>>>(
+        x_data, x_dims[0], x_dims[1], imsize, groups, group_size, mean_data,
+        temp_var_data);
+    GroupNormForward<T><<<grid, threads, 0, dev_ctx.stream()>>>(
+        x_data, mean_data, temp_var_data, scale_data, bias_data, x_dims[0],
+        x_dims[1], imsize, groups, group_size, epsilon, y_data, var_data);
+  }
+};
+
+template <typename T>
+__global__ void GroupNormBackwardGetMeanAndVar(
+    const T* x, const T* mean, const T* var, const T* scale, const T* d_y,
+    int N, int C, int imsize, int groups, int group_size, T epsilon, T* d_x,
+    T* d_mean, T* d_var, T* d_scale, T* d_bias) {
+  int gid = blockIdx.y;
+  int cid = blockIdx.x;
+  int bid = blockIdx.z;
+  int number = min(group_size, static_cast<int>(C - gid * group_size));
+  int ccid = gid * group_size + cid;
+  if (ccid >= C) return;
+  T x_mean = mean[bid * groups + gid];
+  T x_var = var[bid * groups + gid];
+  T var_inv = 1.0 / sqrt(x_var + epsilon);
+  T d_var_inv = 0, d_x_mean = 0;
+  T d_mean_data = 0, d_var_data = 0, d_scale_data = 0, d_bias_data = 0;
+
+  for (int imid = threadIdx.x; imid < imsize; imid += blockDim.x) {
+    T tmp = x[(bid * C + ccid) * imsize + imid];
+    T val = (tmp - x_mean) * var_inv;
+    T dval = d_y[(bid * C + ccid) * imsize + imid];
+    if (d_bias) d_bias_data += dval;
+    if (d_scale) d_scale_data += val * dval;
+    if (scale) dval = dval * scale[ccid];
+    d_var_data += (tmp - x_mean) * dval;
+    T d_tmp = dval * var_inv;
+    if (d_x) d_x[(bid * C + ccid) * imsize + imid] = d_tmp;
+    d_mean_data -= d_tmp;
+  }
+
+  __shared__ T s_mem[4];
+  if (threadIdx.x == 0) {
+    s_mem[0] = s_mem[1] = 0;
+    if (d_scale) s_mem[2] = 0;
+    if (d_bias) s_mem[3] = 0;
+  }
+  __syncthreads();
+  paddle::platform::CudaAtomicAdd(&s_mem[0], d_mean_data);
+  paddle::platform::CudaAtomicAdd(&s_mem[1], d_var_data);
+  if (d_scale) paddle::platform::CudaAtomicAdd(&s_mem[2], d_scale_data);
+  if (d_bias) paddle::platform::CudaAtomicAdd(&s_mem[3], d_bias_data);
+  __syncthreads();
+  if (threadIdx.x == 0) {
+    paddle::platform::CudaAtomicAdd(&d_mean[bid * groups + gid], s_mem[0]);
+    paddle::platform::CudaAtomicAdd(&d_var[bid * groups + gid], s_mem[1]);
+    if (d_scale) paddle::platform::CudaAtomicAdd(&d_scale[ccid], s_mem[2]);
+    if (d_bias) paddle::platform::CudaAtomicAdd(&d_bias[ccid], s_mem[3]);
+  }
+}
+
+template <typename T>
+__global__ void GroupNormBackward(const T* x, const T* mean, const T* var,
+                                  const T* d_mean, const T* d_var, int N, int C,
+                                  int imsize, int groups, int group_size,
+                                  T epsilon, T* d_x) {
+  int gid = blockIdx.y;
+  int cid = blockIdx.x;
+  int bid = blockIdx.z;
+  int number = min(group_size, static_cast<int>(C - gid * group_size));
+  int ccid = gid * group_size + cid;
+  if (ccid >= C) return;
+  T x_mean = mean[bid * groups + gid];
+  T x_var = var[bid * groups + gid];
+  T d_x_mean = d_mean[bid * groups + gid];
+  T d_var_inv = d_var[bid * groups + gid];
+
+  T d_x_var =
+      -1.0 / (2 * (x_var + epsilon) * sqrt(x_var + epsilon)) * d_var_inv;
+  d_x_mean -= 2 * d_x_var * x_mean;
+  d_x_var /= number * imsize;
+  d_x_mean /= number * imsize;
+  for (int imid = threadIdx.x; imid < imsize; imid += blockDim.x) {
+    T tmp = x[(bid * C + ccid) * imsize + imid];
+    if (d_x)
+      d_x[(bid * C + ccid) * imsize + imid] += d_x_mean + tmp * 2 * d_x_var;
+  }
+}
+
+template <typename T>
+class GroupNormGradKernel<platform::CUDADeviceContext, T>
+    : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    const float epsilon = ctx.Attr<float>("epsilon");
+    auto* x = ctx.Input<Tensor>("X");
+    auto* mean = ctx.Input<Tensor>("Mean");
+    auto* var = ctx.Input<Tensor>("Variance");
+    auto* scale = ctx.Input<Tensor>("Scale");
+    auto* d_y = ctx.Input<Tensor>(framework::GradVarName("Y"));
+    const auto groups = ctx.Attr<int>("groups");
+
+    // init output
+    auto* d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto* d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
+    auto* d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
+
+    const auto& x_dims = x->dims();
+    const int group_size = (x_dims[1] - 1) / groups + 1;
+
+    T* d_x_data = nullptr;
+    if (d_x) {
+      d_x->mutable_data<T>(ctx.GetPlace());
+      d_x_data = d_x->data<T>();
+    }
+    math::SetConstant<platform::CUDADeviceContext, T> set_zero;
+    auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
+
+    Tensor temp_var;
+    temp_var.mutable_data<T>(var->dims(), ctx.GetPlace());
+    set_zero(dev_ctx, &temp_var, static_cast<T>(0));
+    T* temp_var_data = temp_var.data<T>();
+
+    Tensor temp_mean;
+    temp_mean.mutable_data<T>(var->dims(), ctx.GetPlace());
+    set_zero(dev_ctx, &temp_mean, static_cast<T>(0));
+    T* temp_mean_data = temp_mean.data<T>();
+
+    auto* x_data = x->data<T>();
+    auto* y_data = d_y->data<T>();
+    auto* mean_data = mean->data<T>();
+    auto* var_data = var->data<T>();
+    T* d_scale_data = nullptr;
+    if (d_scale) {
+      d_scale->mutable_data<T>(ctx.GetPlace());
+      set_zero(dev_ctx, d_scale, static_cast<T>(0));
+      d_scale_data = d_scale->data<T>();
+    }
+    T* d_bias_data = nullptr;
+    if (d_bias) {
+      d_bias->mutable_data<T>(ctx.GetPlace());
+      set_zero(dev_ctx, d_bias, static_cast<T>(0));
+      d_bias_data = d_bias->data<T>();
+    }
+
+    const T* scale_data = nullptr;
+    if (scale) scale_data = scale->data<T>();
+
+    int imsize = x_dims[2] * x_dims[3];
+    int block_size = std::min(512, imsize);
+    dim3 grid(group_size, groups, x_dims[0]);
+    dim3 threads(block_size, 1, 1);
+    GroupNormBackwardGetMeanAndVar<T><<<grid, threads, 0, dev_ctx.stream()>>>(
+        x_data, mean_data, var_data, scale_data, y_data, x_dims[0], x_dims[1],
+        imsize, groups, group_size, epsilon, d_x_data, temp_mean_data,
+        temp_var_data, d_scale_data, d_bias_data);
+    GroupNormBackward<T><<<grid, threads, 0, dev_ctx.stream()>>>(
+        x_data, mean_data, var_data, temp_mean_data, temp_var_data, x_dims[0],
+        x_dims[1], imsize, groups, group_size, epsilon, d_x_data);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(
+    group_norm,
+    ops::GroupNormKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::GroupNormKernel<paddle::platform::CUDADeviceContext, double>);
+REGISTER_OP_CUDA_KERNEL(
+    group_norm_grad,
+    ops::GroupNormGradKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::GroupNormGradKernel<paddle::platform::CUDADeviceContext, double>);

paddle/fluid/operators/group_norm_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 <algorithm>
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
+#include "paddle/fluid/operators/math/blas.h"
+#include "paddle/fluid/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+using DataLayout = framework::DataLayout;
+
+template <typename DeviceContext, typename T>
+class GroupNormKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    const float epsilon = ctx.Attr<float>("epsilon");
+    auto* scale = ctx.Input<Tensor>("Scale");
+    auto* bias = ctx.Input<Tensor>("Bias");
+    auto* x = ctx.Input<Tensor>("X");
+
+    auto* y = ctx.Output<Tensor>("Y");
+    auto* mean = ctx.Output<Tensor>("Mean");
+    auto* var = ctx.Output<Tensor>("Variance");
+    const auto groups = ctx.Attr<int>("groups");
+
+    const auto x_dims = x->dims();
+    const int group_size = (x_dims[1] - 1) / groups + 1;
+
+    y->mutable_data<T>(ctx.GetPlace());
+    mean->mutable_data<T>(ctx.GetPlace());
+    var->mutable_data<T>(ctx.GetPlace());
+
+    auto* x_data = x->data<T>();
+    auto* y_data = y->data<T>();
+    auto* mean_data = mean->data<T>();
+    auto* var_data = var->data<T>();
+
+    const T* scale_data = nullptr;
+    if (scale) scale_data = scale->data<T>();
+    const T* bias_data = nullptr;
+    if (bias) bias_data = bias->data<T>();
+
+    int imsize = x_dims[2] * x_dims[3];
+    auto* iter_x_data = x_data;
+    auto* iter_y_data = y_data;
+    for (int bid = 0; bid < x_dims[0]; bid++)
+      for (int gid = 0; gid < groups; gid++) {
+        T x_mean = 0, x_var = 0;
+        int number = std::min(group_size,
+                              static_cast<int>(x_dims[1] - gid * group_size));
+        auto* tmp = iter_x_data;
+        for (int cid = 0; cid < number; cid++) {
+          for (int imid = 0; imid < imsize; imid++, iter_x_data++) {
+            x_mean += iter_x_data[0];
+            x_var += iter_x_data[0] * iter_x_data[0];
+          }
+        }
+        x_mean /= number * imsize;
+        x_var /= number * imsize;
+        x_var = x_var - x_mean * x_mean;
+        T var_inv = 1.0 / sqrt(x_var + epsilon);
+        mean_data[bid * groups + gid] = x_mean;
+        var_data[bid * groups + gid] = x_var;
+        for (int cid = 0; cid < number; cid++) {
+          for (int imid = 0; imid < imsize; imid++, tmp++, iter_y_data++) {
+            T val = (tmp[0] - x_mean) * var_inv;
+            if (scale_data) val *= scale_data[gid * group_size + cid];
+            if (bias_data) val += bias_data[gid * group_size + cid];
+            iter_y_data[0] = val;
+          }
+        }
+      }
+  }
+};
+
+template <typename DeviceContext, typename T>
+class GroupNormGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    const float epsilon = ctx.Attr<float>("epsilon");
+    auto* x = ctx.Input<Tensor>("X");
+    auto* mean = ctx.Input<Tensor>("Mean");
+    auto* var = ctx.Input<Tensor>("Variance");
+    auto* scale = ctx.Input<Tensor>("Scale");
+    auto* d_y = ctx.Input<Tensor>(framework::GradVarName("Y"));
+    const auto groups = ctx.Attr<int>("groups");
+
+    // init output
+    auto* d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto* d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
+    auto* d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
+
+    const auto& x_dims = x->dims();
+    const int group_size = (x_dims[1] - 1) / groups + 1;
+
+    // TODO(liangdun): need to check d_x is null
+    math::SetConstant<DeviceContext, T> set_zero;
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+    T* d_x_data = nullptr;
+    if (d_x) {
+      d_x->mutable_data<T>(ctx.GetPlace());
+      set_zero(dev_ctx, d_x, static_cast<T>(0));
+      d_x_data = d_x->data<T>();
+    }
+
+    auto* x_data = x->data<T>();
+    auto* y_data = d_y->data<T>();
+    auto* mean_data = mean->data<T>();
+    auto* var_data = var->data<T>();
+    T* d_scale_data = nullptr;
+    if (d_scale) {
+      d_scale->mutable_data<T>(ctx.GetPlace());
+      set_zero(dev_ctx, d_scale, static_cast<T>(0));
+      d_scale_data = d_scale->data<T>();
+    }
+    T* d_bias_data = nullptr;
+    if (d_bias) {
+      d_bias->mutable_data<T>(ctx.GetPlace());
+      set_zero(dev_ctx, d_bias, static_cast<T>(0));
+      d_bias_data = d_bias->data<T>();
+    }
+
+    const T* scale_data = nullptr;
+    if (scale) scale_data = scale->data<T>();
+
+    int imsize = x_dims[2] * x_dims[3];
+    auto* iter_x_data = x_data;
+    auto* iter_d_x_data = d_x_data;
+    auto* iter_y_data = y_data;
+    for (int bid = 0; bid < x_dims[0]; bid++)
+      for (int gid = 0; gid < groups; gid++) {
+        T x_mean = mean_data[bid * groups + gid];
+        T x_var = var_data[bid * groups + gid];
+        T var_inv = 1.0 / sqrt(x_var + epsilon);
+        int number = std::min(group_size,
+                              static_cast<int>(x_dims[1] - gid * group_size));
+        auto* tmp = iter_x_data;
+        auto* tmp2 = iter_d_x_data;
+        T d_var_inv = 0, d_x_mean = 0;
+        for (int cid = 0; cid < number; cid++) {
+          for (int imid = 0; imid < imsize;
+               imid++, tmp++, iter_y_data++, iter_d_x_data++) {
+            T val = (tmp[0] - x_mean) * var_inv;
+            T dval = iter_y_data[0];
+            if (d_bias_data) d_bias_data[gid * group_size + cid] += dval;
+            if (d_scale_data)
+              d_scale_data[gid * group_size + cid] += val * dval;
+            if (scale_data) dval = scale_data[gid * group_size + cid] * dval;
+
+            d_var_inv += (tmp[0] - x_mean) * dval;
+            T d_tmp = dval * var_inv;
+            if (d_x_data) iter_d_x_data[0] += d_tmp;
+            d_x_mean -= d_tmp;
+          }
+        }
+
+        T d_x_var =
+            -1.0 / (2 * (x_var + epsilon) * sqrt(x_var + epsilon)) * d_var_inv;
+        d_x_mean -= 2 * d_x_var * x_mean;
+        d_x_var /= number * imsize;
+        d_x_mean /= number * imsize;
+
+        iter_d_x_data = tmp2;
+
+        if (d_x_data) {
+          for (int cid = 0; cid < number; cid++) {
+            for (int imid = 0; imid < imsize;
+                 imid++, iter_x_data++, iter_d_x_data++) {
+              iter_d_x_data[0] += d_x_mean;
+              iter_d_x_data[0] += iter_x_data[0] * 2 * d_x_var;
+            }
+          }
+        }
+      }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/hierarchical_sigmoid_op.h

@@ -111,7 +111,7 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
     auto pre_out_mat = EigenMatrix<T>::From(*pre_out);
     auto pre_out_grad_mat = EigenMatrix<T>::From(pre_out_grad);
     auto out_grad_mat = EigenMatrix<T>::From(*out_grad);
-    Eigen::array<int, 2> bcast({{1, static_cast<int>(pre_out_grad.dims()[1])}});
+    Eigen::array<int, 2> bcast{1, static_cast<int>(pre_out_grad.dims()[1])};
 
     // softrelu derivative
     pre_out_grad_mat.device(place) =

paddle/fluid/operators/math/CMakeLists.txt

-if (NOT WIN32)
-    add_subdirectory(detail)
-endif(NOT WIN32)
+add_subdirectory(detail)
 
 function(math_library TARGET)
     # math_library is a function to create math library.
@@ -43,10 +41,8 @@ math_library(depthwise_conv)
 math_library(im2col)
 math_library(sampler)
 
-if (NOT WIN32) # windows do not support avx functions yet.
-    math_library(gru_compute DEPS activation_functions math_function)
-    math_library(lstm_compute DEPS activation_functions)
-endif (NOT WIN32)
+math_library(gru_compute DEPS activation_functions math_function)
+math_library(lstm_compute DEPS activation_functions)
 
 cc_library(blas SRCS blas.cc DEPS cblas framework_proto device_context)
 math_library(math_function DEPS blas)
@@ -58,9 +54,9 @@ math_library(sequence_padding)
 math_library(sequence_pooling DEPS math_function)
 math_library(sequence_scale)
 math_library(softmax DEPS math_function)
-if (NOT WIN32)
-    math_library(matrix_bit_code)
-endif (NOT WIN32)
+
+math_library(matrix_bit_code)
+
 math_library(unpooling)
 math_library(vol2col)
 
@@ -76,13 +72,12 @@ if(WITH_GPU)
 endif()
 cc_test(concat_test SRCS concat_test.cc DEPS concat_and_split)
 cc_test(cpu_vec_test SRCS cpu_vec_test.cc DEPS blas cpu_info)
-if (NOT WIN32)
-    set(JIT_KERNEL_SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_rnn.cc jit_kernel_crf_decode.cc jit_kernel_layer_norm.cc)
-    set(JIT_KERNEL_DEPS cpu_info cblas gflags enforce)
-    if(WITH_XBYAK)
+
+set(JIT_KERNEL_SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_rnn.cc jit_kernel_crf_decode.cc jit_kernel_layer_norm.cc)
+set(JIT_KERNEL_DEPS cpu_info cblas gflags enforce)
+if(WITH_XBYAK)
     list(APPEND JIT_KERNEL_SRCS jit_gen.cc jit_code.cc)
     list(APPEND JIT_KERNEL_DEPS xbyak)
-    endif()
-    cc_library(jit_kernel SRCS ${JIT_KERNEL_SRCS} DEPS ${JIT_KERNEL_DEPS})
-    cc_test(jit_kernel_test SRCS jit_kernel_test.cc DEPS jit_kernel)
-endif (NOT WIN32)
+endif()
+cc_library(jit_kernel SRCS ${JIT_KERNEL_SRCS} DEPS ${JIT_KERNEL_DEPS})
+cc_test(jit_kernel_test SRCS jit_kernel_test.cc DEPS jit_kernel)

paddle/fluid/operators/math/blas_impl.cu.h

@@ -16,6 +16,9 @@
 
 #include "paddle/fluid/operators/math/math_function.h"
 #include "paddle/fluid/platform/dynload/cublas.h"
+#include "paddle/fluid/platform/gpu_info.h"
+
+DECLARE_bool(enable_cublas_tensor_op_math);
 
 namespace paddle {
 namespace operators {
@@ -42,11 +45,44 @@ struct CUBlas<float> {
   }
 
   template <typename... ARGS>
-  static void GEMM_BATCH(ARGS... args) {
+  static void GEMM_STRIDED_BATCH(ARGS... args) {
 #if CUDA_VERSION >= 8000
     PADDLE_ENFORCE(platform::dynload::cublasSgemmStridedBatched(args...));
 #else
     PADDLE_THROW("SgemmStridedBatched is not supported on cuda <= 7.5");
+#endif
+  }
+
+  // NOTES: GEMM_EX can use Tensor Core to accelerate matrix multiply.
+  // https://docs.nvidia.com/cuda/cublas/index.html#cublassetmathmode
+  template <typename... ARGS>
+  static void GEMM_EX(platform::CUDADeviceContext *dev_ctx,
+                      cublasOperation_t transa, cublasOperation_t transb, int m,
+                      int n, int k, const float *alpha, const void *A,
+                      cudaDataType_t Atype, int lda, const void *B,
+                      cudaDataType_t Btype, int ldb, const float *beta, void *C,
+                      cudaDataType_t Ctype, int ldc) {
+    // Because the gcc 4.8 doesn't expand template parameter pack that
+    // appears in a lambda-expression, I can not use template parameter pack
+    // here.
+    auto cublas_call = [&]() {
+#if CUDA_VERSION >= 8000
+      VLOG(5) << "use_tensor_op_math: "
+              << (platform::TensorCoreAvailable() ? "True" : "False");
+      PADDLE_ENFORCE(platform::dynload::cublasSgemmEx(
+          dev_ctx->cublas_handle(), transa, transb, m, n, k, alpha, A, Atype,
+          lda, B, Btype, ldb, beta, C, Ctype, ldc));
+#else
+      PADDLE_THROW("cublasSgemmEx is supported on cuda >= 8.0");
+#endif
+    };
+
+#if CUDA_VERSION >= 9000
+    // NOTES: To use Tensor Core, we should change the cublas config,
+    // but the cublas may be hold by multi-thread.
+    dev_ctx->CublasCall(cublas_call, CUBLAS_TENSOR_OP_MATH);
+#else
+    cublas_call();
 #endif
   }
 };
@@ -69,13 +105,18 @@ struct CUBlas<double> {
   }
 
   template <typename... ARGS>
-  static void GEMM_BATCH(ARGS... args) {
+  static void GEMM_STRIDED_BATCH(ARGS... args) {
 #if CUDA_VERSION >= 8000
     PADDLE_ENFORCE(platform::dynload::cublasDgemmStridedBatched(args...));
 #else
     PADDLE_THROW("DgemmStridedBatched is not supported on cuda <= 7.5");
 #endif
   }
+
+  template <typename... ARGS>
+  static void GEMM_EX(ARGS... args) {
+    PADDLE_THROW("Currently there are not cublasDgemmEx.");
+  }
 };
 
 template <>
@@ -96,10 +137,12 @@ struct CUBlas<platform::float16> {
                                        reinterpret_cast<__half *>(C), ldc));
   }
 
-  static void GEMM_BATCH(cublasHandle_t handle, cublasOperation_t transa,
+  static void GEMM_STRIDED_BATCH(cublasHandle_t handle,
+                                 cublasOperation_t transa,
                                  cublasOperation_t transb, int m, int n, int k,
-                         const float16 *alpha, const float16 *A, int lda,
-                         long long int strideA, const float16 *B,  // NOLINT
+                                 const float16 *alpha, const float16 *A,
+                                 int lda, long long int strideA,  // NOLINT
+                                 const float16 *B,                // NOLINT
                                  int ldb, long long int strideB,  // NOLINT
                                  const float16 *beta, float16 *C, int ldc,
                                  long long int strideC,  // NOLINT
@@ -114,6 +157,45 @@ struct CUBlas<platform::float16> {
         ldc, strideC, batchCount));
 #else
     PADDLE_THROW("HgemmStridedBatched is not supported on cuda <= 7.5");
+#endif
+  }
+
+  // NOTES: GEMM_EX can use Tensor Core to accelerate matrix multiply.
+  // https://docs.nvidia.com/cuda/cublas/index.html#cublassetmathmode
+  template <typename... ARGS>
+  static void GEMM_EX(platform::CUDADeviceContext *dev_ctx,
+                      cublasOperation_t transa, cublasOperation_t transb, int m,
+                      int n, int k, const void *alpha, const void *A,
+                      cudaDataType_t Atype, int lda, const void *B,
+                      cudaDataType_t Btype, int ldb, const void *beta, void *C,
+                      cudaDataType_t Ctype, int ldc,
+                      cudaDataType_t computeType) {
+    auto cublas_call = [&]() {
+#if CUDA_VERSION >= 8000
+      cublasGemmAlgo_t algo = CUBLAS_GEMM_DFALT;
+#if CUDA_VERSION >= 9000
+      bool use_tensor_op_math = platform::TensorCoreAvailable();
+      if (use_tensor_op_math) {
+        algo = CUBLAS_GEMM_DFALT_TENSOR_OP;
+      }
+      VLOG(5) << "use_tensor_op_math: "
+              << (use_tensor_op_math ? "True" : "False");
+#endif  // CUDA_VERSION >= 9000
+
+      PADDLE_ENFORCE(platform::dynload::cublasGemmEx(
+          dev_ctx->cublas_handle(), transa, transb, m, n, k, alpha, A, Atype,
+          lda, B, Btype, ldb, beta, C, Ctype, ldc, computeType, algo));
+#else
+      PADDLE_THROW("cublasGemmEx is supported on cuda >= 8.0");
+#endif
+    };
+
+#if CUDA_VERSION >= 9000
+    // NOTES: To use Tensor Core, we should change the cublas config,
+    // but the cublas may be hold by multi-thread.
+    dev_ctx->CublasCall(cublas_call, CUBLAS_TENSOR_OP_MATH);
+#else
+    cublas_call();
 #endif
   }
 };
@@ -133,8 +215,21 @@ void Blas<platform::CUDADeviceContext>::GEMM(CBLAS_TRANSPOSE transA,
   cublasOperation_t cuTransB =
       (transB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
 
-  CUBlas<T>::GEMM(context_.cublas_handle(), cuTransB, cuTransA, N, M, K, &alpha,
-                  B, ldb, A, lda, &beta, C, N);
+#if CUDA_VERSION >= 8000
+  if (FLAGS_enable_cublas_tensor_op_math && std::is_same<T, float>::value) {
+    auto &cuda_ctx = const_cast<platform::CUDADeviceContext &>(context_);
+    CUBlas<T>::GEMM_EX(&cuda_ctx, cuTransB, cuTransA, N, M, K, &alpha, B,
+                       CUDA_R_32F, ldb, A, CUDA_R_32F, lda, &beta, C,
+                       CUDA_R_32F, N);
+  } else {
+#endif  // CUDA_VERSION >= 8000
+
+    CUBlas<T>::GEMM(context_.cublas_handle(), cuTransB, cuTransA, N, M, K,
+                    &alpha, B, ldb, A, lda, &beta, C, N);
+
+#if CUDA_VERSION >= 8000
+  }
+#endif  // CUDA_VERSION >= 8000
 }
 
 template <>
@@ -157,30 +252,18 @@ inline void Blas<platform::CUDADeviceContext>::GEMM(
   PADDLE_ENFORCE_GE(context_.GetComputeCapability(), 53,
                     "cublas fp16 gemm requires GPU compute capability >= 53");
 
-#if CUDA_VERSION >= 8000
   float h_alpha = static_cast<float>(alpha);
   float h_beta = static_cast<float>(beta);
 
-  cublasGemmAlgo_t algo = CUBLAS_GEMM_DFALT;
-#if CUDA_VERSION >= 9000
-  if (context_.GetComputeCapability() >= 70) {
-    PADDLE_ENFORCE(platform::dynload::cublasSetMathMode(
-        context_.cublas_handle(), CUBLAS_TENSOR_OP_MATH));
-    algo = CUBLAS_GEMM_DFALT_TENSOR_OP;
-  } else {
-    PADDLE_ENFORCE(platform::dynload::cublasSetMathMode(
-        context_.cublas_handle(), CUBLAS_DEFAULT_MATH));
-  }
-#endif  // CUDA_VERSION >= 9000
-
+#if CUDA_VERSION >= 8000
   // cublasHgemm does true FP16 computation which is slow for non-Volta
   // GPUs. So use cublasGemmEx instead which does pesudo FP16 computation:
   // input/output in fp16, computation in fp32, which can also be accelerated
   // using tensor cores in volta GPUs.
-  PADDLE_ENFORCE(platform::dynload::cublasGemmEx(
-      context_.cublas_handle(), cuTransB, cuTransA, N, M, K, &h_alpha, B,
-      CUDA_R_16F, ldb, A, CUDA_R_16F, lda, &h_beta, C, CUDA_R_16F, N,
-      CUDA_R_32F, algo));
+  auto &cuda_ctx = const_cast<platform::CUDADeviceContext &>(context_);
+  CUBlas<platform::float16>::GEMM_EX(
+      &cuda_ctx, cuTransB, cuTransA, N, M, K, &h_alpha, B, CUDA_R_16F, ldb, A,
+      CUDA_R_16F, lda, &h_beta, C, CUDA_R_16F, N, CUDA_R_32F);
 #else
   // CUDA 7.5 does not support cublasGemmEx, hence we fall back to use hgemm
   CUBlas<platform::float16>::GEMM(context_.cublas_handle(), cuTransB, cuTransA,
@@ -199,8 +282,38 @@ void Blas<platform::CUDADeviceContext>::GEMM(bool transA, bool transB, int M,
   // the cblas convention.
   cublasOperation_t cuTransA = transA ? CUBLAS_OP_T : CUBLAS_OP_N;
   cublasOperation_t cuTransB = transB ? CUBLAS_OP_T : CUBLAS_OP_N;
-  CUBlas<T>::GEMM(context_.cublas_handle(), cuTransB, cuTransA, N, M, K, &alpha,
-                  B, ldb, A, lda, &beta, C, ldc);
+
+#if CUDA_VERSION >= 8000
+  if (FLAGS_enable_cublas_tensor_op_math && std::is_same<T, float>::value) {
+    auto &cuda_ctx = const_cast<platform::CUDADeviceContext &>(context_);
+    CUBlas<T>::GEMM_EX(&cuda_ctx, cuTransB, cuTransA, N, M, K, &alpha, B,
+                       CUDA_R_32F, ldb, A, CUDA_R_32F, lda, &beta, C,
+                       CUDA_R_32F, ldc);
+  } else {
+#endif  // CUDA_VERSION >= 8000
+
+    CUBlas<T>::GEMM(context_.cublas_handle(), cuTransB, cuTransA, N, M, K,
+                    &alpha, B, ldb, A, lda, &beta, C, ldc);
+
+#if CUDA_VERSION >= 8000
+  }
+#endif  // CUDA_VERSION >= 8000
+}
+
+template <>
+template <>
+inline void Blas<platform::CUDADeviceContext>::GEMM(
+    bool transA, bool transB, int M, int N, int K, platform::float16 alpha,
+    const platform::float16 *A, int lda, const platform::float16 *B, int ldb,
+    platform::float16 beta, platform::float16 *C, int ldc) const {
+  // Note that cublas follows fortran order, so the order is different from
+  // the cblas convention.
+  cublasOperation_t cuTransA = transA ? CUBLAS_OP_T : CUBLAS_OP_N;
+  cublasOperation_t cuTransB = transB ? CUBLAS_OP_T : CUBLAS_OP_N;
+
+  CUBlas<platform::float16>::GEMM(context_.cublas_handle(), cuTransB, cuTransA,
+                                  N, M, K, &alpha, B, ldb, A, lda, &beta, C,
+                                  ldc);
 }
 
 template <>
@@ -238,9 +351,34 @@ void Blas<platform::CUDADeviceContext>::BatchedGEMM(
       (transB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
   const int64_t strideC = M * N;
 
-  CUBlas<T>::GEMM_BATCH(context_.cublas_handle(), cuTransB, cuTransA, N, M, K,
-                        &alpha, B, ldb, strideB, A, lda, strideA, &beta, C, ldc,
-                        strideC, batchCount);
+#if CUDA_VERSION >= 9010
+  if (FLAGS_enable_cublas_tensor_op_math && std::is_same<T, float>::value) {
+    auto cublas_call = [&]() {
+      cublasGemmAlgo_t algo = CUBLAS_GEMM_DFALT;
+      bool use_tensor_op_math = platform::TensorCoreAvailable();
+      if (use_tensor_op_math) {
+        algo = CUBLAS_GEMM_DFALT_TENSOR_OP;
+      }
+      VLOG(5) << "use_tensor_op_math: "
+              << (use_tensor_op_math ? "True" : "False");
+
+      PADDLE_ENFORCE(platform::dynload::cublasGemmStridedBatchedEx(
+          context_.cublas_handle(), cuTransB, cuTransA, N, M, K, &alpha, B,
+          CUDA_R_32F, ldb, strideB, A, CUDA_R_32F, lda, strideA, &beta, C,
+          CUDA_R_32F, ldc, strideC, batchCount, CUDA_R_32F, algo));
+    };
+    auto &dev_ctx = const_cast<platform::CUDADeviceContext &>(context_);
+    dev_ctx.CublasCall(cublas_call, CUBLAS_TENSOR_OP_MATH);
+  } else {
+#endif  // CUDA_VERSION >= 9010
+
+    CUBlas<T>::GEMM_STRIDED_BATCH(context_.cublas_handle(), cuTransB, cuTransA,
+                                  N, M, K, &alpha, B, ldb, strideB, A, lda,
+                                  strideA, &beta, C, ldc, strideC, batchCount);
+
+#if CUDA_VERSION >= 9010
+  }
+#endif  // CUDA_VERSION >= 9010
 }
 
 }  // namespace math

paddle/fluid/operators/math/detail/activation_functions.h

@@ -15,6 +15,7 @@ limitations under the License. */
 #pragma once
 #include <math.h>
 #include <string>
+
 #include "paddle/fluid/platform/enforce.h"
 #include "paddle/fluid/platform/hostdevice.h"
 

paddle/fluid/operators/math/jit_code.cc

@@ -13,8 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/math/jit_code.h"
-#include "paddle/fluid/operators/math/jit_kernel.h"
-#include "paddle/fluid/platform/cpu_info.h"
+#include "paddle/fluid/operators/math/jit_kernel.h"  // TODO(TJ): remove me
 
 namespace paddle {
 namespace operators {
@@ -60,99 +59,60 @@ void VXXJitCode::generate() {
     offset += sizeof(float) * YMM_FLOAT_BLOCK;
   }
   int rest = num_ % YMM_FLOAT_BLOCK;
+  while (rest > 0) {
+    int block = XMM_FLOAT_BLOCK;
     if (rest >= 4) {
+      block = 4;
       if (scalar_index_ != 1) {
         vmovups(xmm_src1, ptr[param1 + offset]);
       }
       if (scalar_index_ != 2) {
         vmovups(xmm_src2, ptr[param2 + offset]);
       }
-    if (type_ == operand_type::mul) {
-      vmulps(xmm_dst, xmm_src1, xmm_src2);
-    } else if (type_ == operand_type::add) {
-      vaddps(xmm_dst, xmm_src1, xmm_src2);
-    }
-    if (with_relu_) {
-      vmaxps(xmm_dst, xmm_zero, xmm_dst);
+    } else if (rest >= 2) {
+      block = 2;
+      if (scalar_index_ != 1) {
+        vmovq(xmm_src1, ptr[param1 + offset]);
       }
-    vmovups(ptr[param3 + offset], xmm_dst);
-    offset += sizeof(float) * 4;
-    rest -= 4;
+      if (scalar_index_ != 2) {
+        vmovq(xmm_src2, ptr[param2 + offset]);
       }
-  if (rest >= 2) {
+    } else {
+      block = 1;
       if (scalar_index_ != 1) {
-      vmovups(xmm_src1, ptr[param1 + offset]);
+        vmovss(xmm_src1, ptr[param1 + offset]);
       }
       if (scalar_index_ != 2) {
-      vmovups(xmm_src2, ptr[param2 + offset]);
+        vmovss(xmm_src2, ptr[param2 + offset]);
       }
-    if (type_ == operand_type::mul) {
+    }
+    switch (type_) {
+      case operand_type::mul:
         vmulps(xmm_dst, xmm_src1, xmm_src2);
-    } else if (type_ == operand_type::add) {
+        break;
+      case operand_type::add:
         vaddps(xmm_dst, xmm_src1, xmm_src2);
+        break;
+      default:
+        break;
     }
     if (with_relu_) {
       vmaxps(xmm_dst, xmm_zero, xmm_dst);
     }
+    if (rest >= 4) {
+      vmovups(ptr[param3 + offset], xmm_dst);
+    } else if (rest >= 2) {
       vmovq(ptr[param3 + offset], xmm_dst);
-    offset += sizeof(float) * 2;
-    rest -= 2;
-  }
-  if (rest > 0) {
-    if (scalar_index_ != 1) {
-      vmovups(xmm_src1, ptr[param1 + offset]);
-    }
-    if (scalar_index_ != 2) {
-      vmovups(xmm_src2, ptr[param2 + offset]);
-    }
-    if (type_ == operand_type::mul) {
-      vmulss(xmm_dst, xmm_src1, xmm_src2);
-    } else if (type_ == operand_type::add) {
-      vaddss(xmm_dst, xmm_src1, xmm_src2);
-    }
-    if (with_relu_) {
-      vmaxps(xmm_dst, xmm_zero, xmm_dst);
-    }
+    } else {
       vmovss(ptr[param3 + offset], xmm_dst);
     }
+    offset += sizeof(float) * block;
+    rest -= block;
+  }
   ret();
 }
 
-#define ALIGN32 __attribute__((aligned(32)))
-#define EXP_HIG 88.3762626647949f
-#define EXP_LOW -88.3762626647949f
-#define CEPHES_LOG2EF 1.44269504088896341
-#define CEPHES_EXP_C1 0.693359375
-#define CEPHES_EXP_C2 -2.12194440e-4
-#define CEPHES_EXP_P0 1.9875691500E-4
-#define CEPHES_EXP_P1 1.3981999507E-3
-#define CEPHES_EXP_P2 8.3334519073E-3
-#define CEPHES_EXP_P3 4.1665795894E-2
-#define CEPHES_EXP_P4 1.6666665459E-1
-#define CEPHES_EXP_P5 5.0000001201E-1
-
-#define REPEAT_8TIMES(val) val, val, val, val, val, val, val, val
-
-#define OFFSET_EXP_ONE 0 * YMM_FLOAT_BLOCK * sizeof(float)
-#define OFFSET_EXP_TWO 1 * YMM_FLOAT_BLOCK * sizeof(float)
-#define OFFSET_EXP_0P5 2 * YMM_FLOAT_BLOCK * sizeof(float)
-#define OFFSET_EXP_HIG 3 * YMM_FLOAT_BLOCK * sizeof(float)
-#define OFFSET_EXP_LOW 4 * YMM_FLOAT_BLOCK * sizeof(float)
-#define OFFSET_EXP_LOG2EF 5 * YMM_FLOAT_BLOCK * sizeof(float)
-#define OFFSET_EXP_C1 6 * YMM_FLOAT_BLOCK * sizeof(float)
-#define OFFSET_EXP_C2 7 * YMM_FLOAT_BLOCK * sizeof(float)
-#define OFFSET_EXP_P0 8 * YMM_FLOAT_BLOCK * sizeof(float)
-#define OFFSET_EXP_P1 9 * YMM_FLOAT_BLOCK * sizeof(float)
-#define OFFSET_EXP_P2 10 * YMM_FLOAT_BLOCK * sizeof(float)
-#define OFFSET_EXP_P3 11 * YMM_FLOAT_BLOCK * sizeof(float)
-#define OFFSET_EXP_P4 12 * YMM_FLOAT_BLOCK * sizeof(float)
-#define OFFSET_EXP_P5 13 * YMM_FLOAT_BLOCK * sizeof(float)
-#define OFFSET_EXP_MAX_INPUT 14 * YMM_FLOAT_BLOCK * sizeof(float)
-#define OFFSET_SIGMOID_MAX 15 * YMM_FLOAT_BLOCK * sizeof(float)
-#define OFFSET_SIGMOID_MIN 16 * YMM_FLOAT_BLOCK * sizeof(float)
-
-static const float exp_float_consts[] ALIGN32 = {
-    REPEAT_8TIMES(1.f),
+const float exp_float_consts[] ALIGN32 = {REPEAT_8TIMES(1.f),
                                           REPEAT_8TIMES(2.f),
                                           REPEAT_8TIMES(0.5f),
                                           REPEAT_8TIMES(EXP_HIG),
@@ -170,147 +130,12 @@ static const float exp_float_consts[] ALIGN32 = {
                                           REPEAT_8TIMES(SIGMOID_THRESHOLD_MAX),
                                           REPEAT_8TIMES(SIGMOID_THRESHOLD_MIN)};
 
-static const int exp_int_0x7f[] ALIGN32 = {REPEAT_8TIMES(0x7f)};
-static int g_tmp_mem[16] ALIGN32 = {0};
+const int exp_int_0x7f[] ALIGN32 = {REPEAT_8TIMES(0x7f)};
+int g_tmp_mem[16] ALIGN32 = {0};
 
 bool VActJitCode::init(int d, operand_type type) {
-  bool ok = MayIUse(avx);
-  if (type == operand_type::relu) {
-    return ok;
-  } else if (type == operand_type::exp) {
-    // exp is slower than mkl when d >= 256
-    return ok && d % 8 == 0 && d < 256;
-  } else {
-    // TODO(TJ): support more
-    return ok && d % 8 == 0;
-  }
-}
-
-void VActJitCode::relu_ymm(ymm_t& ymm_dst, ymm_t& ymm_src, ymm_t& ymm_zero) {
-  vmaxps(ymm_dst, ymm_zero, ymm_src);
-}
-
-void VActJitCode::exp_ymm(ymm_t& ymm_dst, ymm_t& ymm_src, int fx_idx,
-                          int fy_idx, int mask_idx, int tmp_idx) {
-  assert(ymm_src.getIdx() != ymm_dst.getIdx());  // TODO(TJ): use enfore
-  // check all idx can not equal
-  ymm_t ymm_fx = ymm_t(fx_idx);
-  ymm_t ymm_fy = ymm_t(fy_idx);
-  ymm_t ymm_mask = ymm_t(mask_idx);
-  ymm_t ymm_tmp = ymm_t(tmp_idx);
-  reg64_t reg_ptr_global = rax;
-  push(reg_ptr_global);
-  mov(reg_ptr_global, reinterpret_cast<size_t>(exp_float_consts));
-  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_EXP_HIG]);
-  vminps(ymm_src, ymm_src, ymm_tmp);
-  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_EXP_LOW]);
-  vmaxps(ymm_src, ymm_src, ymm_tmp);
-  // express exp(x) as exp(g + n*log(2))
-  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_EXP_LOG2EF]);
-  vmulps(ymm_fx, ymm_src, ymm_tmp);
-  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_EXP_0P5]);
-  vaddps(ymm_fx, ymm_fx, ymm_tmp);
-  vroundps(ymm_fy, ymm_fx, 0x01);
-  // if greater, substract 1
-  vcmpgtps(ymm_mask, ymm_fy, ymm_fx);
-  vmovaps(ymm_tmp, ptr[reg_ptr_global]);
-  vandps(ymm_mask, ymm_mask, ymm_tmp);
-  vsubps(ymm_fx, ymm_fy, ymm_mask);
-  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_EXP_C1]);
-  vmulps(ymm_fy, ymm_fx, ymm_tmp);
-  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_EXP_C2]);
-  ymm_t ymm_z = ymm_t(ymm_mask.getIdx());
-  vmulps(ymm_z, ymm_fx, ymm_tmp);
-  vsubps(ymm_src, ymm_src, ymm_fy);
-  vsubps(ymm_src, ymm_src, ymm_z);
-  vmulps(ymm_z, ymm_src, ymm_src);
-  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_EXP_P0]);
-  vmulps(ymm_dst, ymm_src, ymm_tmp);
-  for (size_t i = OFFSET_EXP_P1; i < OFFSET_EXP_P5;
-       i += (YMM_FLOAT_BLOCK * sizeof(float))) {
-    vmovaps(ymm_tmp, ptr[reg_ptr_global + i]);  // P1~P4
-    vaddps(ymm_dst, ymm_dst, ymm_tmp);
-    vmulps(ymm_dst, ymm_dst, ymm_src);
-  }
-  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_EXP_P5]);
-  vaddps(ymm_dst, ymm_dst, ymm_tmp);
-  vmulps(ymm_dst, ymm_dst, ymm_z);
-  vaddps(ymm_dst, ymm_dst, ymm_src);
-  vmovaps(ymm_tmp, ptr[reg_ptr_global]);
-  vaddps(ymm_dst, ymm_dst, ymm_tmp);
-  // build 2^n
-  ymm_t ymm_int = ymm_fx;
-  vcvttps2dq(ymm_int, ymm_fx);
-  mov(reg_ptr_global, reinterpret_cast<size_t>(exp_int_0x7f));
-  vmovdqa(ymm_tmp, ptr[reg_ptr_global]);
-  if (MayIUse(avx2)) {
-    vpaddd(ymm_int, ymm_int, ymm_tmp);
-    vpslld(ymm_int, ymm_int, 23);
-  } else if (MayIUse(avx)) {
-    xmm_t xtmp1 = xmm_t(ymm_int.getIdx());
-    xmm_t xtmp2 = xmm_t(ymm_tmp.getIdx());
-    reg64_t reg_ptr_tmp = reg_ptr_global;
-    mov(reg_ptr_tmp, reinterpret_cast<size_t>(g_tmp_mem));
-    vmovdqa(ptr[reg_ptr_tmp], ymm_int);
-    vmovdqa(ptr[reg_ptr_tmp + YMM_FLOAT_BLOCK * sizeof(float)], ymm_tmp);
-    vpaddd(xtmp1, xtmp1, xtmp2);
-    vpslld(xtmp1, xtmp1, 23);
-    vmovdqa(ptr[reg_ptr_tmp], xtmp1);
-    // next 128bits
-    vmovdqa(xtmp1, ptr[reg_ptr_tmp + 4 /*xmm float block*/ * sizeof(float)]);
-    vmovdqa(xtmp2,
-            ptr[reg_ptr_tmp +
-                (YMM_FLOAT_BLOCK + 4 /*xmm float block*/) * sizeof(float)]);
-    vpaddd(xtmp1, xtmp1, xtmp2);
-    vpslld(xtmp1, xtmp1, 23);
-    vmovdqa(ptr[reg_ptr_tmp + 4 /*xmm float block*/ * sizeof(float)], xtmp1);
-    // load out
-    vmovdqa(ymm_int, ptr[reg_ptr_tmp]);
-  }
-  vmulps(ymm_dst, ymm_dst, ymm_int);
-  pop(reg_ptr_global);
-}
-
-void VActJitCode::sigmoid_ymm(ymm_t& ymm_dst, ymm_t& ymm_src, int fx_idx,
-                              int fy_idx, int mask_idx, int tmp_idx) {
-  // y = 1 / (1 + e^-x)
-  ymm_t ymm_tmp = ymm_t(tmp_idx);
-  reg64_t reg_ptr_global = rax;
-  push(reg_ptr_global);
-  mov(reg_ptr_global, reinterpret_cast<size_t>(exp_float_consts));
-  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_SIGMOID_MAX]);
-  vminps(ymm_src, ymm_src, ymm_tmp);
-  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_SIGMOID_MIN]);
-  vmaxps(ymm_src, ymm_src, ymm_tmp);
-  vxorps(ymm_tmp, ymm_tmp, ymm_tmp);
-  vsubps(ymm_src, ymm_tmp, ymm_src);
-  exp_ymm(ymm_dst, ymm_src, fx_idx, fy_idx, mask_idx, tmp_idx);
-  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_EXP_ONE]);
-  vaddps(ymm_dst, ymm_dst, ymm_tmp);
-  vdivps(ymm_dst, ymm_tmp, ymm_dst);
-  pop(reg_ptr_global);
-}
-
-void VActJitCode::tanh_ymm(ymm_t& ymm_dst, ymm_t& ymm_src, int fx_idx,
-                           int fy_idx, int mask_idx, int tmp_idx) {
-  // y = 2 / (1 + e^(-2x)) - 1
-  ymm_t ymm_tmp = ymm_t(tmp_idx);
-  ymm_t ymm_zero = ymm_t(mask_idx);
-  reg64_t reg_ptr_global = rax;
-  push(reg_ptr_global);
-  mov(reg_ptr_global, reinterpret_cast<size_t>(exp_float_consts));
-  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_EXP_TWO]);
-  vxorps(ymm_zero, ymm_zero, ymm_zero);
-  vsubps(ymm_tmp, ymm_zero, ymm_tmp);
-  vmulps(ymm_src, ymm_src, ymm_tmp);
-  exp_ymm(ymm_dst, ymm_src, fx_idx, fy_idx, mask_idx, tmp_idx);
-  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_EXP_ONE]);
-  vaddps(ymm_dst, ymm_dst, ymm_tmp);
-  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_EXP_TWO]);
-  vdivps(ymm_dst, ymm_tmp, ymm_dst);
-  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_EXP_ONE]);
-  vsubps(ymm_dst, ymm_dst, ymm_tmp);
-  pop(reg_ptr_global);
+  // TODO(TJ): implement avx512, avx_exp is slower than mkl when d >= 256
+  return MayIUse(avx);
 }
 
 void VActJitCode::generate() {
@@ -324,16 +149,16 @@ void VActJitCode::generate() {
     vmovups(ymm_src, ptr[param1 + offset]);
     switch (type_) {
       case operand_type::relu:
-        relu_ymm(ymm_dst, ymm_src, ymm_zero);
+        relu_jmm<ymm_t>(ymm_dst, ymm_src, ymm_zero);
         break;
       case operand_type::exp:
-        exp_ymm(ymm_dst, ymm_src, 2, 3, 4, 5);
+        exp_jmm<ymm_t>(ymm_dst, ymm_src, 2, 3, 4, 5);
         break;
       case operand_type::sigmoid:
-        sigmoid_ymm(ymm_dst, ymm_src, 2, 3, 4, 5);
+        sigmoid_jmm<ymm_t>(ymm_dst, ymm_src, 2, 3, 4, 5);
         break;
       case operand_type::tanh:
-        tanh_ymm(ymm_dst, ymm_src, 2, 3, 4, 5);
+        tanh_jmm<ymm_t>(ymm_dst, ymm_src, 2, 3, 4, 5);
         break;
       case operand_type::identity:
         break;
@@ -343,31 +168,45 @@ void VActJitCode::generate() {
     vmovups(ptr[param2 + offset], ymm_dst);
     offset += sizeof(float) * YMM_FLOAT_BLOCK;
   }
-  if (type_ != operand_type::relu) {
-    // TODO(TJ): remove me
-    ret();
-    return;
-  }
   int rest = num_ % YMM_FLOAT_BLOCK;
+  while (rest > 0) {
+    int block = XMM_FLOAT_BLOCK;
     if (rest >= 4) {
+      block = 4;
       vmovups(xmm_src, ptr[param1 + offset]);
-    vmaxps(xmm_dst, xmm_zero, xmm_src);
-    vmovups(ptr[param2 + offset], xmm_dst);
-    offset += sizeof(float) * 4;
-    rest -= 4;
+    } else if (rest >= 2) {
+      block = 2;
+      vmovq(xmm_src, ptr[param1 + offset]);
+    } else {
+      block = 1;
+      vmovss(xmm_src, ptr[param1 + offset]);
     }
-  if (rest >= 2) {
-    vmovups(xmm_src, ptr[param1 + offset]);
-    vmaxps(xmm_dst, xmm_zero, xmm_src);
-    vmovq(ptr[param2 + offset], xmm_dst);
-    offset += sizeof(float) * 2;
-    rest -= 2;
+    switch (type_) {
+      case operand_type::relu:
+        relu_jmm<xmm_t>(xmm_dst, xmm_src, xmm_zero);
+        break;
+      case operand_type::exp:
+        exp_jmm<xmm_t>(xmm_dst, xmm_src, 2, 3, 4, 5);
+        break;
+      case operand_type::sigmoid:
+        sigmoid_jmm<xmm_t>(xmm_dst, xmm_src, 2, 3, 4, 5);
+        break;
+      case operand_type::tanh:
+        tanh_jmm<xmm_t>(xmm_dst, xmm_src, 2, 3, 4, 5);
+        break;
+      default:
+        break;
     }
-  if (rest > 0) {
-    vmovups(xmm_src, ptr[param1 + offset]);
-    vmaxps(xmm_dst, xmm_zero, xmm_src);
+    if (rest >= 4) {
+      vmovups(ptr[param2 + offset], xmm_dst);
+    } else if (rest >= 2) {
+      vmovq(ptr[param2 + offset], xmm_dst);
+    } else {
       vmovss(ptr[param2 + offset], xmm_dst);
     }
+    offset += sizeof(float) * block;
+    rest -= block;
+  }
   ret();
 }
 

paddle/fluid/operators/math/jit_code.h

@@ -16,6 +16,8 @@ limitations under the License. */
 
 #include <string>
 #include "paddle/fluid/operators/math/jit_gen.h"
+#include "paddle/fluid/platform/cpu_info.h"
+
 namespace paddle {
 namespace operators {
 namespace math {
@@ -40,6 +42,51 @@ typedef enum {
   identity
 } operand_type;
 
+extern const float exp_float_consts[];
+extern const int exp_int_0x7f[];
+extern int g_tmp_mem[];
+
+// TODO(TJ): move these to some proper place
+#define SIGMOID_THRESHOLD_MIN -40.0
+#define SIGMOID_THRESHOLD_MAX 13.0
+#define EXP_MAX_INPUT 40.0
+#define XMM_FLOAT_BLOCK 4
+#define YMM_FLOAT_BLOCK 8
+#define ZMM_FLOAT_BLOCK 16
+
+#define ALIGN32 __attribute__((aligned(32)))
+#define EXP_HIG 88.3762626647949f
+#define EXP_LOW -88.3762626647949f
+#define CEPHES_LOG2EF 1.44269504088896341
+#define CEPHES_EXP_C1 0.693359375
+#define CEPHES_EXP_C2 -2.12194440e-4
+#define CEPHES_EXP_P0 1.9875691500E-4
+#define CEPHES_EXP_P1 1.3981999507E-3
+#define CEPHES_EXP_P2 8.3334519073E-3
+#define CEPHES_EXP_P3 4.1665795894E-2
+#define CEPHES_EXP_P4 1.6666665459E-1
+#define CEPHES_EXP_P5 5.0000001201E-1
+
+#define REPEAT_8TIMES(val) val, val, val, val, val, val, val, val
+
+#define OFFSET_EXP_ONE 0 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_TWO 1 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_0P5 2 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_HIG 3 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_LOW 4 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_LOG2EF 5 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_C1 6 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_C2 7 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_P0 8 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_P1 9 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_P2 10 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_P3 11 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_P4 12 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_P5 13 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_MAX_INPUT 14 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_SIGMOID_MAX 15 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_SIGMOID_MIN 16 * YMM_FLOAT_BLOCK * sizeof(float)
+
 // function: vec = Operand(vec(or scalar), vec(or scalar)) (maybe with relu)
 class VXXJitCode : public JitCode {
  public:
@@ -127,21 +174,140 @@ class VActJitCode : public JitCode {
   void generate() override;
 
  protected:
-  // compute relu with ymm
-  void relu_ymm(const Xbyak::Ymm& dst, const Xbyak::Ymm& src,
-                const Xbyak::Ymm& zero);
+  // compute relu with ymm, xmm
+  template <typename JMM>
+  void relu_jmm(JMM& dst, JMM& src, JMM& zero) {  // NOLINT
+    vmaxps(dst, src, zero);
+  }
 
-  // compute exp with ymm
-  void exp_ymm(const Xbyak::Ymm& dst, const Xbyak::Ymm& src, int fx_idx = 2,
-               int fy_idx = 3, int mask_idx = 4, int tmp_idx = 5);
+  // compute exp with ymm, xmm
+  template <typename JMM>
+  void exp_jmm(JMM& dst, JMM& src, int fx_idx = 2, int fy_idx = 3,  // NOLINT
+               int mask_idx = 4, int tmp_idx = 5) {
+    using namespace platform::jit;         // NOLINT
+    assert(src.getIdx() != dst.getIdx());  // TODO(TJ): use enfore
+    // check all idx can not equal
+    JMM jmm_fx = JMM(fx_idx);
+    JMM jmm_fy = JMM(fy_idx);
+    JMM jmm_mask = JMM(mask_idx);
+    JMM jmm_tmp = JMM(tmp_idx);
+    reg64_t reg_ptr_global = rax;
+    push(reg_ptr_global);
+    mov(reg_ptr_global, reinterpret_cast<size_t>(exp_float_consts));
+    vmovaps(jmm_tmp, ptr[reg_ptr_global + OFFSET_EXP_HIG]);
+    vminps(src, src, jmm_tmp);
+    vmovaps(jmm_tmp, ptr[reg_ptr_global + OFFSET_EXP_LOW]);
+    vmaxps(src, src, jmm_tmp);
+    // express exp(x) as exp(g + n*log(2))
+    vmovaps(jmm_tmp, ptr[reg_ptr_global + OFFSET_EXP_LOG2EF]);
+    vmulps(jmm_fx, src, jmm_tmp);
+    vmovaps(jmm_tmp, ptr[reg_ptr_global + OFFSET_EXP_0P5]);
+    vaddps(jmm_fx, jmm_fx, jmm_tmp);
+    vroundps(jmm_fy, jmm_fx, 0x01);
+    // if greater, substract 1
+    vcmpgtps(jmm_mask, jmm_fy, jmm_fx);
+    vmovaps(jmm_tmp, ptr[reg_ptr_global]);
+    vandps(jmm_mask, jmm_mask, jmm_tmp);
+    vsubps(jmm_fx, jmm_fy, jmm_mask);
+    vmovaps(jmm_tmp, ptr[reg_ptr_global + OFFSET_EXP_C1]);
+    vmulps(jmm_fy, jmm_fx, jmm_tmp);
+    vmovaps(jmm_tmp, ptr[reg_ptr_global + OFFSET_EXP_C2]);
+    JMM ymm_z = JMM(jmm_mask.getIdx());
+    vmulps(ymm_z, jmm_fx, jmm_tmp);
+    vsubps(src, src, jmm_fy);
+    vsubps(src, src, ymm_z);
+    vmulps(ymm_z, src, src);
+    vmovaps(jmm_tmp, ptr[reg_ptr_global + OFFSET_EXP_P0]);
+    vmulps(dst, src, jmm_tmp);
+    for (size_t i = OFFSET_EXP_P1; i < OFFSET_EXP_P5;
+         i += (YMM_FLOAT_BLOCK * sizeof(float))) {
+      vmovaps(jmm_tmp, ptr[reg_ptr_global + i]);  // P1~P4
+      vaddps(dst, dst, jmm_tmp);
+      vmulps(dst, dst, src);
+    }
+    vmovaps(jmm_tmp, ptr[reg_ptr_global + OFFSET_EXP_P5]);
+    vaddps(dst, dst, jmm_tmp);
+    vmulps(dst, dst, ymm_z);
+    vaddps(dst, dst, src);
+    vmovaps(jmm_tmp, ptr[reg_ptr_global]);
+    vaddps(dst, dst, jmm_tmp);
+    // build 2^n
+    JMM ymm_int = jmm_fx;
+    vcvttps2dq(ymm_int, jmm_fx);
+    mov(reg_ptr_global, reinterpret_cast<size_t>(exp_int_0x7f));
+    vmovdqa(jmm_tmp, ptr[reg_ptr_global]);
+    if (MayIUse(avx2) || std::is_same<JMM, xmm_t>::value) {
+      vpaddd(ymm_int, ymm_int, jmm_tmp);
+      vpslld(ymm_int, ymm_int, 23);
+    } else if (MayIUse(avx)) {
+      xmm_t xtmp1 = xmm_t(ymm_int.getIdx());
+      xmm_t xtmp2 = xmm_t(jmm_tmp.getIdx());
+      reg64_t reg_ptr_tmp = reg_ptr_global;
+      mov(reg_ptr_tmp, reinterpret_cast<size_t>(g_tmp_mem));
+      vmovdqa(ptr[reg_ptr_tmp], ymm_int);
+      vmovdqa(ptr[reg_ptr_tmp + YMM_FLOAT_BLOCK * sizeof(float)], jmm_tmp);
+      vpaddd(xtmp1, xtmp1, xtmp2);
+      vpslld(xtmp1, xtmp1, 23);
+      vmovdqa(ptr[reg_ptr_tmp], xtmp1);
+      // next 128bits
+      vmovdqa(xtmp1, ptr[reg_ptr_tmp + XMM_FLOAT_BLOCK * sizeof(float)]);
+      vmovdqa(xtmp2, ptr[reg_ptr_tmp +
+                         (YMM_FLOAT_BLOCK + XMM_FLOAT_BLOCK) * sizeof(float)]);
+      vpaddd(xtmp1, xtmp1, xtmp2);
+      vpslld(xtmp1, xtmp1, 23);
+      vmovdqa(ptr[reg_ptr_tmp + XMM_FLOAT_BLOCK * sizeof(float)], xtmp1);
+      // load out
+      vmovdqa(ymm_int, ptr[reg_ptr_tmp]);
+    }
+    vmulps(dst, dst, ymm_int);
+    pop(reg_ptr_global);
+  }
 
-  // compute sigmoid with ymm
-  void sigmoid_ymm(const Xbyak::Ymm& dst, const Xbyak::Ymm& src, int fx_idx = 2,
-                   int fy_idx = 3, int mask_idx = 4, int tmp_idx = 5);
+  // compute sigmoid with ymm, xmm
+  template <typename JMM>
+  void sigmoid_jmm(JMM& dst, JMM& src, int fx_idx = 2,  // NOLINT
+                   int fy_idx = 3, int mask_idx = 4, int tmp_idx = 5) {
+    // y = 1 / (1 + e^-x)
+    JMM jmm_tmp = JMM(tmp_idx);
+    reg64_t reg_ptr_global = rax;
+    push(reg_ptr_global);
+    mov(reg_ptr_global, reinterpret_cast<size_t>(exp_float_consts));
+    vmovaps(jmm_tmp, ptr[reg_ptr_global + OFFSET_SIGMOID_MAX]);
+    vminps(src, src, jmm_tmp);
+    vmovaps(jmm_tmp, ptr[reg_ptr_global + OFFSET_SIGMOID_MIN]);
+    vmaxps(src, src, jmm_tmp);
+    vxorps(jmm_tmp, jmm_tmp, jmm_tmp);
+    vsubps(src, jmm_tmp, src);
+    exp_jmm<JMM>(dst, src, fx_idx, fy_idx, mask_idx, tmp_idx);
+    vmovaps(jmm_tmp, ptr[reg_ptr_global + OFFSET_EXP_ONE]);
+    vaddps(dst, dst, jmm_tmp);
+    vdivps(dst, jmm_tmp, dst);
+    pop(reg_ptr_global);
+  }
 
-  // compute tanh with ymm
-  void tanh_ymm(const Xbyak::Ymm& dst, const Xbyak::Ymm& src, int fx_idx = 2,
-                int fy_idx = 3, int mask_idx = 4, int tmp_idx = 5);
+  // compute tanh with ymm, xmm
+  template <typename JMM>
+  void tanh_jmm(JMM& dst, JMM& src, int fx_idx = 2, int fy_idx = 3,  // NOLINT
+                int mask_idx = 4, int tmp_idx = 5) {
+    // y = 2 / (1 + e^(-2x)) - 1
+    JMM jmm_tmp = JMM(tmp_idx);
+    JMM jmm_zero = JMM(mask_idx);
+    reg64_t reg_ptr_global = rax;
+    push(reg_ptr_global);
+    mov(reg_ptr_global, reinterpret_cast<size_t>(exp_float_consts));
+    vmovaps(jmm_tmp, ptr[reg_ptr_global + OFFSET_EXP_TWO]);
+    vxorps(jmm_zero, jmm_zero, jmm_zero);
+    vsubps(jmm_tmp, jmm_zero, jmm_tmp);
+    vmulps(src, src, jmm_tmp);
+    exp_jmm<JMM>(dst, src, fx_idx, fy_idx, mask_idx, tmp_idx);
+    vmovaps(jmm_tmp, ptr[reg_ptr_global + OFFSET_EXP_ONE]);
+    vaddps(dst, dst, jmm_tmp);
+    vmovaps(jmm_tmp, ptr[reg_ptr_global + OFFSET_EXP_TWO]);
+    vdivps(dst, jmm_tmp, dst);
+    vmovaps(jmm_tmp, ptr[reg_ptr_global + OFFSET_EXP_ONE]);
+    vsubps(dst, dst, jmm_tmp);
+    pop(reg_ptr_global);
+  }
 
  protected:
   int num_;
@@ -156,6 +322,42 @@ class VActJitCode : public JitCode {
   ymm_t ymm_dst = ymm_t(1);
 };
 
+#ifdef PADDLE_WITH_MKLDNN
+struct EltwiseMulnChw16cNC : public Xbyak::CodeGenerator {
+  explicit EltwiseMulnChw16cNC(size_t code_size = 256 * 1024)
+      : Xbyak::CodeGenerator(code_size) {
+    // RDI is ptr x_input
+    // RSI is ptr y_input
+    // RDX is ptr output
+    // RCX is height
+    // r8 is width
+
+    push(rbx);
+
+    xor_(rax, rax);
+    xor_(r10, r10);
+    vmovups(zmm3, ptr[rsi]);
+
+    L("h_loop");
+    xor_(rbx, rbx);
+    L("w_loop");
+    vmovups(zmm2, ptr[rdi + rax]);
+    vmulps(zmm1, zmm2, zmm3);
+    vmovups(ptr[rdx + rax], zmm1);
+    add(rax, 64);
+    inc(rbx);
+    cmp(r8, rbx);
+    jnz("w_loop");
+    inc(r10);
+    cmp(r10, rcx);
+    jnz("h_loop");
+
+    pop(rbx);
+    ret();
+  }
+};
+#endif
+
 }  // namespace gen
 }  // namespace jitkernel
 }  // namespace math

paddle/fluid/operators/math/jit_kernel.h

@@ -26,6 +26,7 @@ namespace operators {
 namespace math {
 namespace jitkernel {
 
+// TODO(TJ): move these to some proper place
 #define SIGMOID_THRESHOLD_MIN -40.0
 #define SIGMOID_THRESHOLD_MAX 13.0
 #define EXP_MAX_INPUT 40.0
@@ -94,6 +95,15 @@ class VAddBiasKernel : public Kernel {
   void (*Compute)(const T *, const T *, T *, int);
 };
 
+#ifdef PADDLE_WITH_MKLDNN
+template <typename T>
+class EltwiseMulnChw16cNCKernel : public Kernel {
+ public:
+  // nChw16c = nChw16c .* NC
+  void (*Compute)(const float *, const float *, float *, int, int);
+};
+#endif
+
 template <typename T>
 class VActKernel : public Kernel {
  public:

paddle/fluid/operators/math/jit_kernel_blas.cc

@@ -226,6 +226,44 @@ bool VAddKernelImpl<double>::useMKL(int d) {
 }
 #endif
 
+#ifdef PADDLE_WITH_MKLDNN
+/* EltwiseMul for nChw16c & NC inputs JitKernel */
+template <typename T>
+class EltwiseMulnChw16cNCKernelImpl
+    : public math::jitkernel::EltwiseMulnChw16cNCKernel<T> {
+ public:
+  JITKERNEL_DECLARE_STATIC_FUNC;
+  explicit EltwiseMulnChw16cNCKernelImpl(int d)
+      : EltwiseMulnChw16cNCKernel<T>() {
+    using mul_func_t = void (*)(const float*, const float*, float*, int, int);
+#ifdef PADDLE_WITH_XBYAK
+    if (useJIT(d)) {
+      // roughly estimate the size of code
+      size_t sz = 96 + d / YMM_FLOAT_BLOCK * 4 * 8;
+      sz = sz > 4096 ? sz : 4096;
+      jitcode_.reset(new gen::EltwiseMulnChw16cNC(sz));
+      this->Compute = (mul_func_t)jitcode_->getCode();
+      return;
+    }
+#endif
+    PADDLE_THROW(
+        "This kernel shouldn't be used in Non-Xbyak, Non-MKL-DNN "
+        "environemnt");
+  }
+
+#ifdef PADDLE_WITH_XBYAK
+
+ private:
+  std::unique_ptr<gen::EltwiseMulnChw16cNC> jitcode_{nullptr};
+};
+
+template <>
+bool EltwiseMulnChw16cNCKernelImpl<float>::useJIT(int d) {
+  return true;
+}
+#endif
+#endif
+
 /* VAddRelu JitKernel */
 template <typename T>
 class VAddReluKernelImpl : public VAddReluKernel<T> {
@@ -394,6 +432,9 @@ REGISTER_JITKERNEL(vscal, VScalKernel);
 REGISTER_JITKERNEL(vaddbias, VAddBiasKernel);
 REGISTER_JITKERNEL(vrelu, VReluKernel);
 REGISTER_JITKERNEL(videntity, VIdentityKernel);
+#ifdef PADDLE_WITH_MKLDNN
+REGISTER_JITKERNEL(eltwise_mul_nchw16c, EltwiseMulnChw16cNCKernel);
+#endif
 
 }  // namespace jitkernel
 }  // namespace math

paddle/fluid/operators/math/jit_kernel_test.cc

@@ -33,6 +33,9 @@ limitations under the License. */
 
 constexpr int repeat = 20000;
 
+// TODO(TJ): benchmark and test should be seperated,
+// benchmark should verify more sizes
+
 inline double GetCurrentUS() {
   struct timeval time;
   gettimeofday(&time, NULL);
@@ -66,7 +69,7 @@ void vrelu_intri8(const int n, const float* x, float* y) {
 
 TEST(JitKernel, vrelu) {
   namespace jit = paddle::operators::math::jitkernel;
-  for (int d : {7, 8, 15, 16, 30, 256, 512}) {
+  for (int d : {3, 7, 8, 15, 16, 30, 256, 512}) {
     std::vector<float> x(d);
     std::vector<float> zref(d), ztgt(d);
     RandomVec<float>(d, x.data(), -10.f, 1.f);
@@ -156,7 +159,7 @@ void vexp_mkl(const int n, const float* x, float* y) {
 
 TEST(JitKernel, vexp) {
   namespace jit = paddle::operators::math::jitkernel;
-  for (int d : {7, 8, 15, 16, 30, 128, 256}) {
+  for (int d : {1, 3, 4, 6, 7, 8, 12, 15, 16, 20, 30, 128, 256}) {
     std::vector<float> x(d);
     std::vector<float> zref(d), ztgt(d);
     RandomVec<float>(d, x.data(), -2.f, 2.f);
@@ -231,7 +234,7 @@ void vsigmoid_better(
 
 TEST(JitKernel, vsigmoid) {
   namespace jit = paddle::operators::math::jitkernel;
-  for (int d : {7, 8, 15, 16, 30, 32, 64, 100, 128, 256}) {
+  for (int d : {1, 3, 4, 6, 7, 8, 15, 16, 30, 32, 64, 100, 128, 256}) {
     std::vector<float> x(d);
     std::vector<float> zref(d), ztgt(d);
     RandomVec<float>(d, x.data(), -2.f, 2.f);
@@ -295,7 +298,7 @@ void vtanh_better(
 
 TEST(JitKernel, vtanh) {
   namespace jit = paddle::operators::math::jitkernel;
-  for (int d : {7, 8, 15, 16, 30, 32, 64, 100, 128, 256}) {
+  for (int d : {1, 2, 3, 4, 5, 6, 7, 8, 15, 16, 30, 32, 64, 100, 128, 256}) {
     std::vector<float> x(d);
     std::vector<float> zref(d), ztgt(d);
     RandomVec<float>(d, x.data(), -2.f, 2.f);
@@ -386,7 +389,7 @@ void lstm_ctht_better(
 
 TEST(JitKernel, lstm) {
   namespace jit = paddle::operators::math::jitkernel;
-  for (int d : {7, 8, 15, 16, 30, 32, 64, 100}) {
+  for (int d : {1, 2, 3, 4, 5, 6, 7, 8, 15, 16, 30, 32, 64, 100}) {
     int d4 = d * 4;
     int d3 = d * 3;
     std::vector<float> x(d4), xref(d4);
@@ -759,7 +762,7 @@ TEST(JitKernel, vaddrelu) {
     float* zref_data = zref.data();
     auto trefs = GetCurrentUS();
     for (int i = 0; i < repeat; ++i) {
-      vadd_ref(d, x_data, y_data, zref_data);
+      vaddrelu_ref(d, x_data, y_data, zref_data);
     }
     auto trefe = GetCurrentUS();
     auto tmkls = GetCurrentUS();

paddle/fluid/operators/math/matrix_bit_code.h

@@ -67,7 +67,7 @@ inline constexpr size_t FindLastSet(size_t x) {
              : (std::is_same<size_t, unsigned long>::value  // NOLINT
                     ? (x ? 8 * sizeof(x) - __builtin_clzl(x) : 0)
                     : (x ? 8 * sizeof(x) - __builtin_clzll(x) : 0));
-
+}
 #else
 // windows don't have built-in clz, ctz function
 template <typename T>
@@ -92,7 +92,6 @@ inline int clz(const T& value) {
 
 inline size_t FindLastSet(size_t x) { return sizeof(size_t) * 8 - clz(x); }
 #endif  // !_WIN32
-}
 
 struct SimpleCode {
   SimpleCode(size_t code, size_t num_classes) : c_(code + num_classes) {}

paddle/fluid/operators/math/pooling.cu

@@ -153,6 +153,37 @@ __global__ void KernelMaxPool2DGrad(
   }
 }
 
+template <typename PoolProcess, typename T>
+void Pool2dDirectCUDAFunctor<PoolProcess, T>::operator()(
+    const T* input, const std::vector<int>& input_shape,
+    const std::vector<int>& output_shape, const std::vector<int>& ksize,
+    const std::vector<int>& strides, const std::vector<int>& paddings,
+    PoolProcess pool_compute, bool exclusive, T* output, cudaStream_t stream) {
+  const int batch_size = input_shape[0];
+  const int input_channels = input_shape[1];
+  const int input_height = input_shape[2];
+  const int input_width = input_shape[3];
+  const int output_channels = output_shape[1];
+  const int output_height = output_shape[2];
+  const int output_width = output_shape[3];
+  const int ksize_height = ksize[0];
+  const int ksize_width = ksize[1];
+  const int stride_height = strides[0];
+  const int stride_width = strides[1];
+  const int padding_height = paddings[0];
+  const int padding_width = paddings[1];
+
+  int nthreads = batch_size * output_channels * output_height * output_width;
+  int blocks = (nthreads + 1024 - 1) / 1024;
+  dim3 threads(1024, 1);
+  dim3 grid(blocks, 1);
+
+  KernelPool2D<PoolProcess, T><<<grid, threads, 0, stream>>>(
+      nthreads, input, input_channels, input_height, input_width, output_height,
+      output_width, ksize_height, ksize_width, stride_height, stride_width,
+      padding_height, padding_width, pool_compute, exclusive, output);
+}
+
 /*
  * All tensors are in NCHW format.
  * Ksize, strides, paddings are two elements. These two elements represent
@@ -291,6 +322,11 @@ class MaxPool2dGradFunctor<platform::CUDADeviceContext, T> {
   }
 };
 
+template class Pool2dDirectCUDAFunctor<paddle::operators::math::MaxPool<float>,
+                                       float>;
+template class Pool2dDirectCUDAFunctor<paddle::operators::math::AvgPool<float>,
+                                       float>;
+
 template class MaxPool2dGradFunctor<platform::CUDADeviceContext, float>;
 template class MaxPool2dGradFunctor<platform::CUDADeviceContext, double>;
 

paddle/fluid/operators/math/pooling.h

@@ -82,6 +82,19 @@ class AvgPoolGrad {
  * This is different from average pooling. So we rewrite the max_pool_grad:
  * MaxPool2dGradFunctor, MaxPool3dGradFunctor.
  */
+#ifdef PADDLE_WITH_CUDA
+template <typename PoolProcess, typename T>
+class Pool2dDirectCUDAFunctor {
+ public:
+  void operator()(const T* input, const std::vector<int>& input_shape,
+                  const std::vector<int>& output_shape,
+                  const std::vector<int>& ksize,
+                  const std::vector<int>& strides,
+                  const std::vector<int>& paddings, PoolProcess pool_compute,
+                  bool exclusive, T* output, cudaStream_t stream);
+};
+#endif
+
 template <typename DeviceContext, typename PoolProcess, typename T>
 class Pool2dFunctor {
  public:

paddle/fluid/operators/math/softmax.h

@@ -19,7 +19,8 @@ namespace paddle {
 namespace operators {
 namespace math {
 
-template <typename DeviceContext, typename T, bool is_test>
+template <typename DeviceContext, typename T, bool is_test,
+          typename Enable = void>
 class SoftmaxFunctor {
  public:
   void operator()(const DeviceContext& context, const framework::Tensor* X,

paddle/fluid/operators/math/softmax_impl.h

@@ -16,6 +16,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/tensor.h"
 
+#include "paddle/fluid/operators/math/blas.h"
 namespace paddle {
 namespace operators {
 namespace math {
@@ -32,8 +33,8 @@ struct ValueClip {
   }
 };
 
-template <typename DeviceContext, typename T, bool is_test>
-void SoftmaxFunctor<DeviceContext, T, is_test>::operator()(
+template <typename DeviceContext, typename T, bool is_test, typename Enable>
+void SoftmaxFunctor<DeviceContext, T, is_test, Enable>::operator()(
     const DeviceContext& context, const framework::Tensor* X,
     framework::Tensor* Y) {
   auto logits = EigenMatrix<T>::From(*X);
@@ -65,36 +66,46 @@ void SoftmaxFunctor<DeviceContext, T, is_test>::operator()(
                                                  .broadcast(one_by_class));
 }
 
-template <typename DeviceContext, typename T>
-class SoftmaxFunctor<DeviceContext, T, true> {
+template <class DeviceContext>
+using enable_if_CPU = typename std::enable_if<
+    std::is_same<DeviceContext, platform::CPUDeviceContext>::value>::type;
+
+template <typename DeviceContext>
+class SoftmaxFunctor<DeviceContext, float, true, enable_if_CPU<DeviceContext>> {
   void operator()(const DeviceContext& context, const framework::Tensor* X,
                   framework::Tensor* Y) {
-    auto logits = EigenMatrix<T>::From(*X);
-    auto softmax = EigenMatrix<T>::From(*Y);
-
+    auto in_dims = X->dims();
+    auto out_dims = Y->dims();
+    const float* in_data = X->data<float>();
+    float* out_data = Y->data<float>();
     const int kBatchDim = 0;
     const int kClassDim = 1;
+    // 2D data. Batch x C
+    const int batch_size = in_dims[kBatchDim];
+    const int num_classes = in_dims[kClassDim];
+    std::vector<float> entities(batch_size);
+    auto blas = math::GetBlas<DeviceContext, float>(context);
+    for (int n = 0; n < batch_size; ++n) {
+      entities[n] = in_data[n * num_classes];
+      for (int c = 1; c < num_classes; ++c) {
+        entities[n] = in_data[n * num_classes + c] > entities[n]
+                          ? in_data[n * num_classes + c]
+                          : entities[n];
+      }
+      for (int c = 0; c < num_classes; ++c) {
+        out_data[n * num_classes + c] =
+            in_data[n * num_classes + c] - entities[n];
+      }
+    }
 
-    const int batch_size = logits.dimension(kBatchDim);
-    const int num_classes = logits.dimension(kClassDim);
-
-    Eigen::DSizes<int, 1> along_class(kClassDim);
-    Eigen::DSizes<int, 2> batch_by_one(batch_size, 1);
-    Eigen::DSizes<int, 2> one_by_class(1, num_classes);
-
-    auto shifted_logits = (logits -
-                           logits.maximum(along_class)
-                               .eval()
-                               .reshape(batch_by_one)
-                               .broadcast(one_by_class));
-
-    softmax.device(*context.eigen_device()) = shifted_logits.exp();
-    softmax.device(*context.eigen_device()) = (softmax *
-                                               softmax.sum(along_class)
-                                                   .inverse()
-                                                   .eval()
-                                                   .reshape(batch_by_one)
-                                                   .broadcast(one_by_class));
+    blas.VEXP(num_classes * batch_size, out_data, out_data);
+    for (int n = 0; n < batch_size; ++n) {
+      entities[n] = out_data[n * num_classes];
+      for (int c = 1; c < num_classes; ++c) {
+        entities[n] += out_data[n * num_classes + c];
+      }
+      blas.SCAL(num_classes, 1.0f / entities[n], &out_data[n * num_classes]);
+    }
   }
 };
 

paddle/fluid/operators/roi_align_op.cc

@@ -35,10 +35,10 @@ class ROIAlignOp : public framework::OperatorWithKernel {
                    "The format of input tensor is NCHW.");
     PADDLE_ENFORCE(rois_dims.size() == 2,
                    "ROIs should be a 2-D LoDTensor of shape (num_rois, 4)"
-                   "given as [[x1, y1, x2, y2], …].");
+                   "given as [[x1, y1, x2, y2], ...].");
     PADDLE_ENFORCE(rois_dims[1] == 4,
                    "ROIs should be a 2-D LoDTensor of shape (num_rois, 4)"
-                   "given as [[x1, y1, x2, y2], …].");
+                   "given as [[x1, y1, x2, y2], ...].");
     int pooled_height = ctx->Attrs().Get<int>("pooled_height");
     int pooled_width = ctx->Attrs().Get<int>("pooled_width");
     float spatial_scale = ctx->Attrs().Get<float>("spatial_scale");
@@ -103,7 +103,7 @@ class ROIAlignOpMaker : public framework::OpProtoAndCheckerMaker {
              "(LoDTensor), "
              "ROIs (Regions of Interest) to pool over. "
              "should be a 2-D LoDTensor of shape (num_rois, 4)"
-             "given as [[x1, y1, x2, y2], …]. "
+             "given as [[x1, y1, x2, y2], ...]. "
              "(x1, y1) is the top left coordinates, and "
              "(x2, y2) is the bottom right coordinates.");
     AddOutput("Out",

paddle/fluid/operators/roi_pool_op.cc

@@ -40,10 +40,10 @@ class ROIPoolOp : public framework::OperatorWithKernel {
                    "The format of input tensor is NCHW.");
     PADDLE_ENFORCE(rois_dims.size() == 2,
                    "ROIs should be a 2-D LoDTensor of shape (num_rois, 4)"
-                   "given as [[x1, y1, x2, y2], …].");
+                   "given as [[x1, y1, x2, y2], ...].");
     PADDLE_ENFORCE(rois_dims[1] == kROISize,
                    "ROIs should be a 2-D LoDTensor of shape (num_rois, 4)"
-                   "given as [[x1, y1, x2, y2], …].");
+                   "given as [[x1, y1, x2, y2], ...].");
 
     int pooled_height = ctx->Attrs().Get<int>("pooled_height");
     int pooled_width = ctx->Attrs().Get<int>("pooled_width");
@@ -110,7 +110,7 @@ class ROIPoolOpMaker : public framework::OpProtoAndCheckerMaker {
              "(LoDTensor), "
              "ROIs (Regions of Interest) to pool over. "
              "should be a 2-D LoDTensor of shape (num_rois, 4)"
-             "given as [[x1, y1, x2, y2], …]. "
+             "given as [[x1, y1, x2, y2], ...]. "
              "Where batch_id is the id of the data, "
              "(x1, y1) is the top left coordinates, and "
              "(x2, y2) is the bottom right coordinates.");

paddle/fluid/operators/softmax_op.h

@@ -35,8 +35,10 @@ class SoftmaxKernel : public framework::OpKernel<T> {
     Tensor X_2d = framework::ReshapeToMatrix(*X, rank - 1);
     Tensor Out_2d = framework::ReshapeToMatrix(*Out, rank - 1);
 
-#ifdef ON_INFER
-    math::SoftmaxFunctor<DeviceContext, T, true>()(
+#ifdef PADDLE_ON_INFERENCE
+    math::SoftmaxFunctor<
+        DeviceContext, T,
+        std::is_same<DeviceContext, platform::CPUDeviceContext>::value>()(
         context.template device_context<DeviceContext>(), &X_2d, &Out_2d);
 #else
     math::SoftmaxFunctor<DeviceContext, T, false>()(

paddle/fluid/operators/stack_op.h

@@ -147,20 +147,32 @@ class StackKernel : public framework::OpKernel<T> {
     auto &dim = x[0]->dims();
     for (auto i = 0; i < axis; ++i) pre *= dim[i];
     for (auto i = axis; i < dim.size(); ++i) post *= dim[i];
-    int total_num = pre * n * post;
 
-    auto &dev_ctx = ctx.template device_context<DeviceContext>();
 #ifdef __NVCC__
+    int total_num = pre * n * post;
+    auto &dev_ctx = ctx.template device_context<DeviceContext>();
+
     thrust::device_vector<const T *> device_x_vec(x_datas);
     auto x_data_arr = device_x_vec.data().get();
-#else
-    auto x_data_arr = x_datas.data();
-#endif
+
     StackFunctorForRange(dev_ctx, x_data_arr, y_data, total_num, n, post);
-#ifdef __NVCC__
+
     // Wait() must be called because device_x_vec may be destructed before
     // kernel ends
     dev_ctx.Wait();
+#else
+    auto x_data_arr = x_datas.data();
+
+    size_t x_offset = 0;
+    size_t y_offset = 0;
+    for (int i = 0; i < pre; i++) {
+      for (int j = 0; j < n; j++) {
+        std::memcpy(y_data + y_offset, x_data_arr[j] + x_offset,
+                    post * sizeof(T));
+        y_offset += post;
+      }
+      x_offset += post;
+    }
 #endif
   }
 };

paddle/fluid/platform/CMakeLists.txt

-if (NOT WIN32)
 proto_library(profiler_proto SRCS profiler.proto DEPS framework_proto)
 py_proto_compile(profiler_py_proto SRCS profiler.proto)
 
@@ -6,11 +5,19 @@ add_custom_target(profiler_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch _
 
 add_dependencies(profiler_py_proto profiler_py_proto_init)
 
+if (NOT WIN32)
 add_custom_command(TARGET profiler_py_proto POST_BUILD
         COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_BINARY_DIR}/python/paddle/fluid/proto/profiler
         COMMAND cp *.py ${PADDLE_BINARY_DIR}/python/paddle/fluid/proto/profiler
         COMMENT "Copy generated python proto into directory paddle/fluid/proto/profiler."
         WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
+else(NOT WIN32)
+string(REPLACE "/" "\\" proto_dstpath "${PADDLE_BINARY_DIR}/python/paddle/fluid/proto/profiler/")
+add_custom_command(TARGET profiler_py_proto POST_BUILD
+        COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_BINARY_DIR}/python/paddle/fluid/proto/profiler
+        COMMAND copy /Y *.py ${proto_dstpath}
+        COMMENT "Copy generated python proto into directory paddle/fluid/proto/profiler."
+        WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
 endif(NOT WIN32)
 
 if(WITH_GPU)
@@ -60,12 +67,9 @@ cc_test(init_test SRCS init_test.cc DEPS device_context)
 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)
 
-
-if (NOT WIN32)
 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)
-endif(NOT WIN32)
 
 nv_test(float16_gpu_test SRCS float16_test.cu DEPS lod_tensor)
 cc_test(float16_test SRCS float16_test.cc DEPS lod_tensor)

paddle/fluid/platform/cpu_helper.cc

@@ -29,6 +29,13 @@ namespace platform {
 
 void SetNumThreads(int num_threads) {
 #ifdef PADDLE_USE_OPENBLAS
+// windows has no support for openblas multi-thread
+// please refer to: https://github.com/PaddlePaddle/Paddle/issues/7234
+#ifdef _WIN32
+  if (num_threads > 1) {
+    num_threads = 1;
+  }
+#endif
   int real_num_threads = num_threads > 1 ? num_threads : 1;
   openblas_set_num_threads(real_num_threads);
 #elif defined(PADDLE_WITH_MKLML)

paddle/fluid/platform/device_context.h

@@ -143,6 +143,39 @@ class CudnnWorkspaceHandle {
   std::unique_ptr<std::lock_guard<std::mutex>> guard_;
 };
 
+#if CUDA_VERSION >= 9000
+class ScopedCublasMathMode {
+ public:
+  ScopedCublasMathMode(cublasHandle_t handle, cublasMath_t new_math_mode)
+      : handle_(handle) {
+    need_reset = false;
+    PADDLE_ENFORCE(
+        platform::dynload::cublasGetMathMode(handle_, &old_math_mode_),
+        "Failed to get old cublas math mode");
+    if (old_math_mode_ != new_math_mode) {
+      PADDLE_ENFORCE(
+          platform::dynload::cublasSetMathMode(handle_, new_math_mode),
+          "Failed to set old cublas math mode");
+      need_reset = true;
+    }
+  }
+
+  ~ScopedCublasMathMode() {
+    if (need_reset) {
+      PADDLE_ENFORCE(
+          platform::dynload::cublasSetMathMode(handle_, old_math_mode_),
+          "Failed to set old cublas math mode");
+    }
+  }
+
+ private:
+  cublasHandle_t handle_;
+  cublasMath_t old_math_mode_;
+  bool need_reset;
+};
+
+#endif
+
 class CUDADeviceContext : public DeviceContext {
  public:
   explicit CUDADeviceContext(CUDAPlace place);
@@ -199,6 +232,18 @@ class CUDADeviceContext : public DeviceContext {
     callback_manager_->Wait();
   }
 
+#if CUDA_VERSION >= 9000
+  /*! \brief CublasCall may need to change cublas's config,
+   *  but the cublas may be hold by multi-thread, so we should
+   *  add lock here. */
+  template <typename Callback>
+  void CublasCall(Callback callback, cublasMath_t new_math) {
+    std::lock_guard<std::mutex> guard(cublas_mtx_);
+    ScopedCublasMathMode scoped_cublas_math(cublas_handle_, new_math);
+    callback();
+  }
+#endif
+
  private:
   CUDAPlace place_;
 
@@ -220,6 +265,8 @@ class CUDADeviceContext : public DeviceContext {
   // If we use mtx_ for StreamCallbackManager, deadlock may occur sometimes
   mutable std::mutex callback_mtx_;
   std::unique_ptr<StreamCallbackManager> callback_manager_;
+
+  mutable std::mutex cublas_mtx_;
 };
 
 template <>

paddle/fluid/platform/device_tracer.h

@@ -13,17 +13,11 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 #pragma once
 
-#if !defined(_WIN32)
-#include <sys/time.h>
-#else
-#include <windows.h>
-#endif  // !_WIN32
-
-#include <time.h>
 #include <chrono>  // NOLINT
 #include <string>
 
 #include "paddle/fluid/platform/dynload/cupti.h"
+#include "paddle/fluid/platform/port.h"
 #include "paddle/fluid/platform/profiler.pb.h"
 
 namespace paddle {
@@ -32,15 +26,11 @@ namespace platform {
 ///////////////////////
 // WARN: Under Development. Don't depend on it yet.
 //////////////////////
-#if !defined(_WIN32)
 inline uint64_t PosixInNsec() {
   struct timeval tv;
   gettimeofday(&tv, nullptr);
   return 1000 * (static_cast<uint64_t>(tv.tv_sec) * 1000000 + tv.tv_usec);
 }
-#else
-inline uint64_t PosixInNsec() { return static_cast<uint64_t>(0); }
-#endif  // !_WIN32
 
 // DeviceTracer performs the following tasks:
 // 1. Register cuda callbacks for various events: kernel, memcpy, etc.

paddle/fluid/platform/dynload/cublas.h

@@ -61,9 +61,6 @@ extern void *cublas_dso_handle;
   extern DynLoad__##__name __name
 #endif
 
-#define DECLARE_DYNAMIC_LOAD_CUBLAS_V2_WRAP(__name) \
-  DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(__name)
-
 #define CUBLAS_BLAS_ROUTINE_EACH(__macro) \
   __macro(cublasSaxpy_v2);                \
   __macro(cublasDaxpy_v2);                \
@@ -93,22 +90,23 @@ CUBLAS_BLAS_ROUTINE_EACH(DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP)
 
 // APIs available after CUDA 8.0
 #if CUDA_VERSION >= 8000
-#define CUBLAS_BLAS_ROUTINE_EACH_R2(__macro) \
-  __macro(cublasGemmEx);                     \
-  __macro(cublasSgemmStridedBatched);        \
-  __macro(cublasDgemmStridedBatched);        \
-  __macro(cublasCgemmStridedBatched);        \
-  __macro(cublasZgemmStridedBatched);        \
-  __macro(cublasHgemmStridedBatched);
-
-CUBLAS_BLAS_ROUTINE_EACH_R2(DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP)
+DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasGemmEx);
+DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasSgemmStridedBatched);
+DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasDgemmStridedBatched);
+DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasCgemmStridedBatched);
+DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasZgemmStridedBatched);
+DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasHgemmStridedBatched);
 #endif
 
 // APIs available after CUDA 9.0
 #if CUDA_VERSION >= 9000
-#define CUBLAS_BLAS_ROUTINE_EACH_R3(__macro) __macro(cublasSetMathMode);
+DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasSetMathMode);
+DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasGetMathMode);
+#endif
 
-CUBLAS_BLAS_ROUTINE_EACH_R3(DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP)
+#if CUDA_VERSION >= 9010
+DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasGemmBatchedEx);
+DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(cublasGemmStridedBatchedEx);
 #endif
 
 #undef DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP

paddle/fluid/platform/dynload/cudnn.h

@@ -13,8 +13,6 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
-#define GLOG_NO_ABBREVIATED_SEVERITIES
-#define GOOGLE_GLOG_DLL_DECL
 #include <glog/logging.h>
 
 #include <cudnn.h>

paddle/fluid/platform/enforce.h

@@ -18,12 +18,6 @@ limitations under the License. */
 #include <cxxabi.h>  // for __cxa_demangle
 #endif               // __GNUC__
 
-#if defined(_WIN32)
-#define NOMINMAX  // msvc max/min macro conflict with std::min/max
-#define GLOG_NO_ABBREVIATED_SEVERITIES  // msvc conflict logging with windows.h
-#define GOOGLE_GLOG_DLL_DECL
-#endif
-
 #ifdef PADDLE_WITH_CUDA
 #include <cublas_v2.h>
 #include <cudnn.h>
@@ -127,14 +121,14 @@ struct EOFException : public std::exception {
 #define UNLIKELY(condition) __builtin_expect(static_cast<bool>(condition), 0)
 #else
 // there is no equivalent intrinsics in msvc.
-#define UNLIKELY(condition) (condition == 0)
+#define UNLIKELY(condition) (condition)
 #endif
 
 #if !defined(_WIN32)
 #define LIKELY(condition) __builtin_expect(static_cast<bool>(condition), 1)
 #else
 // there is no equivalent intrinsics in msvc.
-#define LIKELY(condition) (condition != 0)
+#define LIKELY(condition) (condition)
 #endif
 
 template <typename... Args>
@@ -248,7 +242,6 @@ inline void throw_on_error(T e) {
   throw_on_error(e, "");
 }
 
-#if !defined(_WIN32)
 #define PADDLE_THROW(...)                                              \
   do {                                                                 \
     throw ::paddle::platform::EnforceNotMet(                           \
@@ -272,17 +265,6 @@ inline void throw_on_error(T e) {
 #define PADDLE_ENFORCE(...) ::paddle::platform::throw_on_error(__VA_ARGS__);
 #endif  // REPLACE_ENFORCE_GLOG
 
-#else  // !_WIN32
-// disable enforce, caused by the varardic macro exception error
-#define PADDLE_THROW(x)                                      \
-  do {                                                       \
-    throw std::make_exception_ptr(                           \
-        std::runtime_error("Windows disable the enforce.")); \
-  } while (false)
-
-#define PADDLE_ENFORCE(x, ...) x
-#endif  // !_WIN32
-
 #define PADDLE_THROW_EOF()                                                     \
   do {                                                                         \
     throw ::paddle::platform::EOFException("There is no next data.", __FILE__, \
@@ -302,20 +284,6 @@ inline void throw_on_error(T e) {
  *    extra messages is also supported, for example:
  *    PADDLE_ENFORCE(a, b, "some simple enforce failed between %d numbers", 2)
  */
-#if !defined(_WIN32)
-#define PADDLE_ENFORCE_EQ(__VAL0, __VAL1, ...) \
-  __PADDLE_BINARY_COMPARE(__VAL0, __VAL1, ==, !=, __VA_ARGS__)
-#define PADDLE_ENFORCE_NE(__VAL0, __VAL1, ...) \
-  __PADDLE_BINARY_COMPARE(__VAL0, __VAL1, !=, ==, __VA_ARGS__)
-#define PADDLE_ENFORCE_GT(__VAL0, __VAL1, ...) \
-  __PADDLE_BINARY_COMPARE(__VAL0, __VAL1, >, <=, __VA_ARGS__)
-#define PADDLE_ENFORCE_GE(__VAL0, __VAL1, ...) \
-  __PADDLE_BINARY_COMPARE(__VAL0, __VAL1, >=, <, __VA_ARGS__)
-#define PADDLE_ENFORCE_LT(__VAL0, __VAL1, ...) \
-  __PADDLE_BINARY_COMPARE(__VAL0, __VAL1, <, >=, __VA_ARGS__)
-#define PADDLE_ENFORCE_LE(__VAL0, __VAL1, ...) \
-  __PADDLE_BINARY_COMPARE(__VAL0, __VAL1, <=, >, __VA_ARGS__)
-
 #define PADDLE_ENFORCE_NOT_NULL(__VAL, ...)                  \
   do {                                                       \
     if (UNLIKELY(nullptr == (__VAL))) {                      \
@@ -335,27 +303,19 @@ inline void throw_on_error(T e) {
                    paddle::string::Sprintf("" __VA_ARGS__));            \
     }                                                                   \
   } while (0)
-#else
-#define PADDLE_ENFORCE_EQ(__VAL0, __VAL1, ...) ((__VAL0) == (__VAL1))
-#define PADDLE_ENFORCE_NE(__VAL0, __VAL1, ...) ((__VAL0) != (__VAL1))
-#define PADDLE_ENFORCE_GT(__VAL0, __VAL1, ...) ((__VAL0) > (__VAL1))
-#define PADDLE_ENFORCE_GE(__VAL0, __VAL1, ...) ((__VAL0) >= (__VAL1))
-#define PADDLE_ENFORCE_LT(__VAL0, __VAL1, ...) ((__VAL0) < (__VAL1))
-#define PADDLE_ENFORCE_LE(__VAL0, __VAL1, ...) ((__VAL0) <= (__VAL1))
 
-#define __PADDLE_BINARY_COMPARE(__VAL0, __VAL1, __CMP, __INV_CMP, ...) \
-  do {                                                                 \
-    if (!((__VAL0)__CMP(__VAL1))) {                                    \
-      PADDLE_THROW("Windows disable the enforce. Enforce failed.");    \
-    }                                                                  \
-  } while (0)
-#define PADDLE_ENFORCE_NOT_NULL(__VAL1, ...)                       \
-  do {                                                             \
-    if (nullptr == (__VAL1)) {                                     \
-      PADDLE_THROW("Windows disable the enforce. Enforce failed"); \
-    }                                                              \
-  } while (0)
-#endif  // !_WIN32
+#define PADDLE_ENFORCE_EQ(__VAL0, __VAL1, ...) \
+  __PADDLE_BINARY_COMPARE(__VAL0, __VAL1, ==, !=, __VA_ARGS__)
+#define PADDLE_ENFORCE_NE(__VAL0, __VAL1, ...) \
+  __PADDLE_BINARY_COMPARE(__VAL0, __VAL1, !=, ==, __VA_ARGS__)
+#define PADDLE_ENFORCE_GT(__VAL0, __VAL1, ...) \
+  __PADDLE_BINARY_COMPARE(__VAL0, __VAL1, >, <=, __VA_ARGS__)
+#define PADDLE_ENFORCE_GE(__VAL0, __VAL1, ...) \
+  __PADDLE_BINARY_COMPARE(__VAL0, __VAL1, >=, <, __VA_ARGS__)
+#define PADDLE_ENFORCE_LT(__VAL0, __VAL1, ...) \
+  __PADDLE_BINARY_COMPARE(__VAL0, __VAL1, <, >=, __VA_ARGS__)
+#define PADDLE_ENFORCE_LE(__VAL0, __VAL1, ...) \
+  __PADDLE_BINARY_COMPARE(__VAL0, __VAL1, <=, >, __VA_ARGS__)
 
 }  // namespace platform
 }  // namespace paddle

paddle/fluid/platform/gpu_info.cc

@@ -26,6 +26,16 @@ DEFINE_double(fraction_of_gpu_memory_to_use, 0.92,
               "additional trunks of the same size will be requested from gpu "
               "until the gpu has no memory left for another trunk.");
 
+DEFINE_bool(
+    enable_cublas_tensor_op_math, false,
+    "The enable_cublas_tensor_op_math indicate whether to use Tensor Core, "
+    "but it may loss precision. Currently, There are two CUDA libraries that"
+    " use Tensor Cores, cuBLAS and cuDNN. cuBLAS uses Tensor Cores to speed up"
+    " GEMM computations(the matrices must be either half precision or single "
+    "precision); cuDNN uses Tensor Cores to speed up both convolutions(the "
+    "input and output must be half precision) and recurrent neural networks "
+    "(RNNs).");
+
 namespace paddle {
 namespace platform {
 
@@ -64,6 +74,16 @@ int GetCUDADriverVersion(int id) {
   return driver_version;
 }
 
+bool TensorCoreAvailable() {
+#if CUDA_VERSION >= 9000
+  int device = GetCurrentDeviceId();
+  int driver_version = GetCUDAComputeCapability(device);
+  return driver_version >= 70;
+#else
+  return false;
+#endif
+}
+
 int GetCUDAMultiProcessors(int id) {
   PADDLE_ENFORCE_LT(id, GetCUDADeviceCount(), "id must less than GPU count");
   int count;

paddle/fluid/platform/gpu_info.h

@@ -35,6 +35,9 @@ int GetCUDARuntimeVersion(int id);
 //! Get the driver version of the ith GPU
 int GetCUDADriverVersion(int id);
 
+//! Wheter the current device support TensorCore
+bool TensorCoreAvailable();
+
 //! Get the MultiProcessors of the ith GPU.
 int GetCUDAMultiProcessors(int i);
 

paddle/fluid/platform/init.cc

@@ -38,6 +38,7 @@ std::once_flag p2p_init_flag;
 
 void InitGflags(std::vector<std::string> argv) {
   std::call_once(gflags_init_flag, [&]() {
+    FLAGS_logtostderr = true;
     argv.insert(argv.begin(), "dummy");
     int argc = argv.size();
     char **arr = new char *[argv.size()];
@@ -116,13 +117,6 @@ void InitDevices(bool init_p2p, const std::vector<int> devices) {
   places.emplace_back(platform::CPUPlace());
   platform::DeviceContextPool::Init(places);
 
-// windows has no support for openblas multi-thread
-#ifdef _WIN32
-  if (FLAGS_paddle_num_threads > 1) {
-    FLAGS_paddle_num_threads = 1;
-  }
-#endif
-
 #ifndef PADDLE_WITH_MKLDNN
   platform::SetNumThreads(FLAGS_paddle_num_threads);
 #endif

paddle/fluid/platform/init.h

@@ -16,9 +16,6 @@ limitations under the License. */
 #include <string>
 #include <vector>
 
-#define GLOG_NO_ABBREVIATED_SEVERITIES
-#define GOOGLE_GLOG_DLL_DECL
-
 #include "gflags/gflags.h"
 #include "glog/logging.h"
 

paddle/fluid/platform/port.h

@@ -17,6 +17,7 @@
 #include <cstdio>
 #include <stdexcept>
 
+#include <time.h>
 #include <memory>
 #include <string>
 
@@ -27,8 +28,13 @@
 #include <dlfcn.h>     //  dladdr
 #include <execinfo.h>  // backtrace
 #include <sys/stat.h>
+#include <sys/time.h>
 #include <algorithm>  // std::accumulate
 #else
+#define NOMINMAX  // msvc max/min macro conflict with std::min/max
+// solve static linking error in windows
+// https://github.com/google/glog/issues/301
+#define GOOGLE_GLOG_DLL_DECL
 #include <io.h>  // _popen, _pclose
 #include <stdio.h>
 #include <windows.h>
@@ -57,6 +63,25 @@ static void *dlopen(const char *filename, int flag) {
   return reinterpret_cast<void *>(hModule);
 }
 
+static int gettimeofday(struct timeval *tp, void *tzp) {
+  time_t clock;
+  struct tm tm;
+  SYSTEMTIME wtm;
+
+  GetLocalTime(&wtm);
+  tm.tm_year = wtm.wYear - 1900;
+  tm.tm_mon = wtm.wMonth - 1;
+  tm.tm_mday = wtm.wDay;
+  tm.tm_hour = wtm.wHour;
+  tm.tm_min = wtm.wMinute;
+  tm.tm_sec = wtm.wSecond;
+  tm.tm_isdst = -1;
+  clock = mktime(&tm);
+  tp->tv_sec = clock;
+  tp->tv_usec = wtm.wMilliseconds * 1000;
+
+  return (0);
+}
 #endif  // !_WIN32
 
 static void ExecShellCommand(const std::string &cmd, std::string *message) {
@@ -132,11 +157,13 @@ static void MkDir(const char *path) {
     }
   }
 #else
-  CreateDirectory(path, NULL);
+  BOOL return_value = CreateDirectory(path, NULL);
+  if (!return_value) {
     auto errorno = GetLastError();
     if (errorno != ERROR_ALREADY_EXISTS) {
       throw std::runtime_error(path_error);
     }
+  }
 #endif  // !_WIN32
 }
 

paddle/fluid/platform/profiler.cc

@@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/platform/profiler.h"
+#include "paddle/fluid/platform/port.h"
 
-#include <sys/time.h>
 #include <algorithm>
 #include <iomanip>
 #include <limits>

paddle/fluid/platform/profiler.h

@@ -69,7 +69,6 @@ void PushEvent(const std::string& name, const DeviceContext* dev_ctx);
 
 void PopEvent(const std::string& name, const DeviceContext* dev_ctx);
 
-#if !defined(_WIN32)
 struct RecordEvent {
   // dev_ctx can be set to nullptr if device is cpu.
   RecordEvent(const std::string& name, const DeviceContext* dev_ctx);
@@ -106,15 +105,6 @@ struct RecordBlock {
   std::string name_;
   uint64_t start_ns_;
 };
-#else
-// windows do not support profiler temporarily.
-struct RecordEvent {
-  RecordEvent(const std::string& name, const DeviceContext* dev_ctx) {}
-};
-struct RecordBlock {
-  explicit RecordBlock(int block_id) {}
-};
-#endif
 
 // Return the event list of all threads. Assumed the returned value calls
 // event_lists, event_lists[i][j] represents the j-th Event of i-th thread.

paddle/fluid/platform/stream_callback_manager.h

@@ -45,16 +45,15 @@ class StreamCallbackManager {
   inline void AddCallback(Callback &&callback) const {
     auto *stream_callback_context =
         new StreamCallbackContext(this, std::forward<Callback>(callback));
-    PADDLE_ENFORCE(
 #if CUDA_VERSION >= 10000
-        cudaLaunchHostFunc(stream_, StreamCallbackManager::StreamCallbackFunc,
-                           stream_callback_context)
-#else
-        cudaStreamAddCallback(stream_,
+    PADDLE_ENFORCE(cudaLaunchHostFunc(stream_,
                                       StreamCallbackManager::StreamCallbackFunc,
-                              stream_callback_context, 0)
+                                      stream_callback_context));  // NOLINT
+#else
+    PADDLE_ENFORCE(cudaStreamAddCallback(
+        stream_, StreamCallbackManager::StreamCallbackFunc,
+        stream_callback_context, 0));  // NOLINT
 #endif
-            );  // NOLINT
   }
 
   void Wait() const { thread_pool_.reset(new ThreadPool(1)); }

paddle/fluid/pybind/CMakeLists.txt

 
-set(PYBIND_DEPS pybind python proto_desc memory executor prune  feed_fetch_method pass_builder)
-set(PYBIND_SRCS pybind.cc exception.cc protobuf.cc const_value.cc)
-if(NOT WIN32)
-  list(APPEND PYBIND_DEPS parallel_executor profiler)
-  list(APPEND PYBIND_SRCS recordio.cc)
-endif(NOT WIN32)
+set(PYBIND_DEPS pybind python proto_desc memory executor prune feed_fetch_method pass_builder parallel_executor profiler)
+set(PYBIND_SRCS pybind.cc exception.cc protobuf.cc const_value.cc recordio.cc)
 if(WITH_PYTHON)
   if(WITH_AMD_GPU)
     hip_library(paddle_pybind SHARED
       SRCS ${PYBIND_SRCS}
-      DEPS ${PYBIND_DEPS}
-      ${GLOB_OP_LIB} ${GLOB_OPERATOR_DEPS})
+      DEPS ARCHIVE_START ${PYBIND_DEPS}
+      ${GLOB_OP_LIB} ${GLOB_OPERATOR_DEPS} ARCHIVE_END)
   else()
     cc_library(paddle_pybind SHARED
       SRCS ${PYBIND_SRCS}

paddle/fluid/pybind/pybind.cc

@@ -21,13 +21,6 @@ limitations under the License. */
 #include <utility>
 #include <vector>
 
-#if defined(_WIN32)
-#define NOMINMAX
-#define GLOG_NO_ABBREVIATED_SEVERITIES  // msvc conflict logging with windows.h
-#define GOOGLE_GLOG_DLL_DECL
-#include <Windows.h>
-#endif
-
 #include "paddle/fluid/framework/executor.h"
 #include "paddle/fluid/framework/feed_fetch_method.h"
 #include "paddle/fluid/framework/framework.pb.h"
@@ -36,9 +29,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/lod_tensor_array.h"
 #include "paddle/fluid/framework/op_registry.h"
-#ifndef _WIN32
 #include "paddle/fluid/framework/parallel_executor.h"
-#endif
 #include "paddle/fluid/framework/prune.h"
 #include "paddle/fluid/framework/reader.h"
 #include "paddle/fluid/framework/selected_rows.h"
@@ -46,6 +37,7 @@ limitations under the License. */
 #include "paddle/fluid/memory/allocation/allocator_strategy.h"
 #include "paddle/fluid/operators/activation_op.h"
 #include "paddle/fluid/operators/reader/lod_tensor_blocking_queue.h"
+#include "paddle/fluid/platform/cpu_info.h"
 #include "paddle/fluid/platform/enforce.h"
 #include "paddle/fluid/platform/init.h"
 #include "paddle/fluid/platform/place.h"
@@ -95,6 +87,9 @@ bool IsCompiledWithDIST() {
 }
 
 PYBIND11_PLUGIN(core) {
+  // Not used, just make sure cpu_info.cc is linked.
+  paddle::platform::CpuTotalPhysicalMemory();
+
   paddle::memory::allocation::UseAllocatorStrategyGFlag();
   py::module m("core", "C++ core of PaddlePaddle");
 
@@ -359,19 +354,16 @@ All parameter, weight, gradient are variables in Paddle.
              return self.GetMutable<platform::Communicator>();
            },
            py::return_value_policy::reference)
+#endif
       .def("get_reader",
            [](Variable &self) -> framework::ReaderHolder * {
              PADDLE_ENFORCE(self.IsType<framework::ReaderHolder>());
              return self.GetMutable<framework::ReaderHolder>();
            },
-           py::return_value_policy::reference)
-#endif
-      ;
+           py::return_value_policy::reference);
 
-#if !defined(_WIN32)
   py::class_<framework::ReaderHolder>(m, "Reader", "")
       .def("reset", &framework::ReaderHolder::ResetAll);
-#endif
 
   using LoDTensorBlockingQueue =
       ::paddle::operators::reader::LoDTensorBlockingQueue;
@@ -640,7 +632,6 @@ All parameter, weight, gradient are variables in Paddle.
 #endif
 #endif
 
-#ifndef _WIN32
   py::enum_<platform::ProfilerState>(m, "ProfilerState", py::arithmetic())
       .value("kDisabled", platform::ProfilerState::kDisabled)
       .value("kCPU", platform::ProfilerState::kCPU)
@@ -661,7 +652,6 @@ All parameter, weight, gradient are variables in Paddle.
   m.def("disable_profiler", platform::DisableProfiler);
   m.def("is_profiler_enabled", platform::IsProfileEnabled);
   m.def("reset_profiler", platform::ResetProfiler);
-#endif
 
   py::class_<ir::Pass, std::shared_ptr<ir::Pass>> pass(m, "Pass");
   pass.def(py::init())
@@ -690,7 +680,6 @@ All parameter, weight, gradient are variables in Paddle.
       .def("remove_pass",
            [](ir::PassBuilder &self, size_t idx) { self.RemovePass(idx); });
 
-#ifndef _WIN32
   // -- python binds for parallel executor.
   py::class_<ParallelExecutor> pe(m, "ParallelExecutor");
   py::class_<ExecutionStrategy> exec_strategy(pe, "ExecutionStrategy", R"DOC(
@@ -918,7 +907,6 @@ All parameter, weight, gradient are variables in Paddle.
       });
 
   BindRecordIOWriter(&m);
-#endif
   return m.ptr();
 }
 }  // namespace pybind

paddle/scripts/paddle_build.sh

@@ -94,6 +94,30 @@ function cmake_gen() {
             else
                 exit 1
             fi
+        elif [ "$1" == "cp36-cp36m" ]; then
+            if [ -d "/Library/Frameworks/Python.framework/Versions/3.6" ]; then
+                export LD_LIBRARY_PATH=/Library/Frameworks/Python.framework/Versions/3.6/lib/
+                export DYLD_LIBRARY_PATH=/Library/Frameworks/Python.framework/Versions/3.6/lib/
+                export PATH=/Library/Frameworks/Python.framework/Versions/3.6/bin/:${PATH}
+                PYTHON_FLAGS="-DPYTHON_EXECUTABLE:FILEPATH=/Library/Frameworks/Python.framework/Versions/3.6/bin/python3
+            -DPYTHON_INCLUDE_DIR:PATH=/Library/Frameworks/Python.framework/Versions/3.6/include/python3.6m/
+            -DPYTHON_LIBRARY:FILEPATH=/Library/Frameworks/Python.framework/Versions/3.6/lib/libpython3.6m.dylib"
+                WITH_FLUID_ONLY=${WITH_FLUID_ONLY:-ON}
+            else
+                exit 1
+            fi
+        elif [ "$1" == "cp37-cp37m" ]; then
+            if [ -d "/Library/Frameworks/Python.framework/Versions/3.7" ]; then
+                export LD_LIBRARY_PATH=/Library/Frameworks/Python.framework/Versions/3.7/lib/
+                export DYLD_LIBRARY_PATH=/Library/Frameworks/Python.framework/Versions/3.7/lib/
+                export PATH=/Library/Frameworks/Python.framework/Versions/3.7/bin/:${PATH}
+                PYTHON_FLAGS="-DPYTHON_EXECUTABLE:FILEPATH=/Library/Frameworks/Python.framework/Versions/3.7/bin/python3
+            -DPYTHON_INCLUDE_DIR:PATH=/Library/Frameworks/Python.framework/Versions/3.7/include/python3.7m/
+            -DPYTHON_LIBRARY:FILEPATH=/Library/Frameworks/Python.framework/Versions/3.7/lib/libpython3.7m.dylib"
+                WITH_FLUID_ONLY=${WITH_FLUID_ONLY:-ON}
+            else
+                exit 1
+            fi
         fi
     else
         if [ "$1" != "" ]; then
@@ -116,6 +140,18 @@ function cmake_gen() {
                 export PYTHON_FLAGS="-DPYTHON_EXECUTABLE:FILEPATH=/opt/_internal/cpython-3.5.1/bin/python3
             -DPYTHON_INCLUDE_DIR:PATH=/opt/_internal/cpython-3.5.1/include/python3.5m
             -DPYTHON_LIBRARIES:FILEPATH=/opt/_internal/cpython-3.5.1/lib/libpython3.so"
+            elif [ "$1" == "cp36-cp36m" ]; then
+                export LD_LIBRARY_PATH=/opt/_internal/cpython-3.6.0/lib/:${LD_LIBRARY_PATH}
+                export PATH=/opt/_internal/cpython-3.6.0/bin/:${PATH}
+                export PYTHON_FLAGS="-DPYTHON_EXECUTABLE:FILEPATH=/opt/_internal/cpython-3.6.0/bin/python3
+            -DPYTHON_INCLUDE_DIR:PATH=/opt/_internal/cpython-3.6.0/include/python3.6m
+            -DPYTHON_LIBRARIES:FILEPATH=/opt/_internal/cpython-3.6.0/lib/libpython3.so"
+            elif [ "$1" == "cp37-cp37m" ]; then
+                export LD_LIBRARY_PATH=/opt/_internal/cpython-3.7.0/lib/:${LD_LIBRARY_PATH}
+                export PATH=/opt/_internal/cpython-3.7.0/bin/:${PATH}
+                export PYTHON_FLAGS="-DPYTHON_EXECUTABLE:FILEPATH=/opt/_internal/cpython-3.7.0/bin/python3
+            -DPYTHON_INCLUDE_DIR:PATH=/opt/_internal/cpython-3.7.0/include/python3.7m
+            -DPYTHON_LIBRARIES:FILEPATH=/opt/_internal/cpython-3.7.0/lib/libpython3.so"
            fi
         fi
     fi
@@ -419,7 +455,7 @@ function assert_api_not_changed() {
     source .env/bin/activate
     pip install ${PADDLE_ROOT}/build/python/dist/*whl
     python ${PADDLE_ROOT}/tools/print_signatures.py paddle.fluid > new.spec
-    if [ "$1" == "cp35-cp35m" ]; then
+    if [ "$1" == "cp35-cp35m" ] || [ "$1" == "cp36-cp36m" ] || [ "$1" == "cp37-cp37m" ]; then
         # Use sed to make python2 and python3 sepc keeps the same
         sed -i 's/arg0: str/arg0: unicode/g' new.spec
         sed -i "s/\(.*Transpiler.*\).__init__ ArgSpec(args=\['self'].*/\1.__init__ /g" new.spec

paddle/testing/paddle_gtest_main.cc

@@ -28,7 +28,7 @@ int main(int argc, char** argv) {
   for (int i = 0; i < argc; ++i) {
     new_argv.push_back(argv[i]);
   }
-#ifdef PADDLE_WITH_CUDA
+#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
   new_argv.push_back(
       strdup("--tryfromenv=fraction_of_gpu_memory_to_use,allocator_strategy"));
 #else

python/paddle/fluid/__init__.py

@@ -115,8 +115,8 @@ def __bootstrap__():
         'use_pinned_memory', 'check_nan_inf', 'benchmark', 'eager_delete_scope',
         'use_mkldnn', 'use_ngraph', 'initial_cpu_memory_in_mb',
         'init_allocated_mem', 'free_idle_memory', 'paddle_num_threads',
-        "dist_threadpool_size", 'cpu_deterministic', 'eager_delete_tensor_gb',
-        'allocator_strategy', 'reader_queue_speed_test_mode'
+        "dist_threadpool_size", 'eager_delete_tensor_gb', 'allocator_strategy',
+        'reader_queue_speed_test_mode', 'print_sub_graph_dir'
     ]
     if os.name != 'nt':
         read_env_flags.append('warpctc_dir')
@@ -133,7 +133,8 @@ def __bootstrap__():
     if core.is_compiled_with_cuda():
         read_env_flags += [
             'fraction_of_gpu_memory_to_use', 'cudnn_deterministic',
-            'conv_workspace_size_limit', 'cudnn_exhaustive_search'
+            'enable_cublas_tensor_op_math', 'conv_workspace_size_limit',
+            'cudnn_exhaustive_search'
         ]
     core.init_gflags([sys.argv[0]] +
                      ["--tryfromenv=" + ",".join(read_env_flags)])

python/paddle/fluid/contrib/inferencer.py

@@ -15,15 +15,13 @@
 from __future__ import print_function
 
 import contextlib
-import os
 
 from .. import core
 
 from .. import executor
 from .. import framework
 from .. import io
-if os.name != 'nt':
-    from .. import parallel_executor
+from .. import parallel_executor
 from .. import unique_name
 from .trainer import check_and_get_place
 

python/paddle/fluid/contrib/trainer.py

@@ -28,8 +28,7 @@ from .. import framework
 from .. import io
 # optimizer is same as the parameter of Trainer.__init__. Rename it to opt_module
 from .. import optimizer as opt_module
-if os.name != 'nt':
-    from .. import parallel_executor
+from .. import parallel_executor
 from ..transpiler import distribute_transpiler
 
 __all__ = [

python/paddle/fluid/contrib/utils/__init__.py

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+from . import hdfs_utils
+from .hdfs_utils import *
+
+__all__ = hdfs_utils.__all__

python/paddle/fluid/contrib/utils/hdfs_utils.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.
+"""HDFS Utils"""
+
+import os
+import subprocess
+import multiprocessing
+from datetime import datetime
+
+import re
+import copy
+import errno
+
+import logging
+
+__all__ = ["HDFSClient", "multi_download"]
+
+logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s')
+_logger = logging.getLogger("hdfs_utils")
+_logger.setLevel(logging.INFO)
+
+
+class HDFSClient(object):
+    def __init__(self, hadoop_home, configs):
+        self.pre_commands = []
+        hadoop_bin = '%s/bin/hadoop' % hadoop_home
+        self.pre_commands.append(hadoop_bin)
+        dfs = 'fs'
+        self.pre_commands.append(dfs)
+
+        for k, v in configs.iteritems():
+            config_command = '-D%s=%s' % (k, v)
+            self.pre_commands.append(config_command)
+
+    def __run_hdfs_cmd(self, commands, retry_times=5):
+        whole_commands = copy.deepcopy(self.pre_commands)
+        whole_commands.extend(commands)
+
+        print('Running system command: {0}'.format(' '.join(whole_commands)))
+
+        ret_code = 0
+        ret_out = None
+        ret_err = None
+        for x in range(retry_times + 1):
+            proc = subprocess.Popen(
+                whole_commands, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+            (output, errors) = proc.communicate()
+            ret_code, ret_out, ret_err = proc.returncode, output, errors
+            if ret_code:
+                _logger.warn(
+                    'Times: %d, Error running command: %s. Return code: %d, Error: %s'
+                    % (x, ' '.join(whole_commands), proc.returncode, errors))
+            else:
+                break
+        return ret_code, ret_out, ret_err
+
+    def upload(self, hdfs_path, local_path, overwrite=False, retry_times=5):
+        """
+            upload the local file to hdfs
+            args:
+                local_file_path: the local file path
+                remote_file_path: default value(${OUTPUT_PATH}/${SYS_USER_ID}/${SYS_JOB_ID}/tmp)
+            return:
+                True or False
+        """
+        assert hdfs_path is not None
+        assert local_path is not None and os.path.exists(local_path)
+
+        if os.path.isdir(local_path):
+            _logger.warn(
+                "The Local path: {} is dir and I will support it later, return".
+                format(local_path))
+            return
+
+        base = os.path.basename(local_path)
+        if not self.is_exist(hdfs_path):
+            self.makedirs(hdfs_path)
+        else:
+            if self.is_exist(os.path.join(hdfs_path, base)):
+                if overwrite:
+                    _logger.error(
+                        "The HDFS path: {} is exist and overwrite is True, delete it".
+                        format(hdfs_path))
+                    self.delete(hdfs_path)
+                else:
+                    _logger.error(
+                        "The HDFS path: {} is exist and overwrite is False, return".
+                        format(hdfs_path))
+                    return False
+
+        put_commands = ["-put", local_path, hdfs_path]
+        returncode, output, errors = self.__run_hdfs_cmd(put_commands,
+                                                         retry_times)
+        if returncode:
+            _logger.error("Put local path: {} to HDFS path: {} failed".format(
+                local_path, hdfs_path))
+            return False
+        else:
+            _logger.info("Put local path: {} to HDFS path: {} successfully".
+                         format(local_path, hdfs_path))
+            return True
+
+    def download(self, hdfs_path, local_path, overwrite=False, unzip=False):
+        """
+            download from hdfs
+            args:
+                local_file_path: the local file path
+                remote_file_path: remote dir on hdfs
+            return:
+                True or False
+        """
+        _logger.info('Downloading %r to %r.', hdfs_path, local_path)
+        _logger.info('Download of %s to %r complete.', hdfs_path, local_path)
+
+        if not self.is_exist(hdfs_path):
+            print("HDFS path: {} do not exist".format(hdfs_path))
+            return False
+        if self.is_dir(hdfs_path):
+            _logger.error(
+                "The HDFS path: {} is dir and I will support it later, return".
+                format(hdfs_path))
+
+        if os.path.exists(local_path):
+            base = os.path.basename(hdfs_path)
+            local_file = os.path.join(local_path, base)
+            if os.path.exists(local_file):
+                if overwrite:
+                    os.remove(local_file)
+                else:
+                    _logger.error(
+                        "The Local path: {} is exist and overwrite is False, return".
+                        format(local_file))
+                    return False
+
+        self.make_local_dirs(local_path)
+
+        download_commands = ["-get", hdfs_path, local_path]
+        returncode, output, errors = self.__run_hdfs_cmd(download_commands)
+        if returncode:
+            _logger.error("Get local path: {} from HDFS path: {} failed".format(
+                local_path, hdfs_path))
+            return False
+        else:
+            _logger.info("Get local path: {} from HDFS path: {} successfully".
+                         format(local_path, hdfs_path))
+            return True
+
+    def is_exist(self, hdfs_path=None):
+        """
+            whether the remote hdfs path exists?
+            args:
+                remote_file_path: default value(${OUTPUT_PATH}/${SYS_USER_ID}/${SYS_JOB_ID}/tmp)
+                fs_name: The default values are the same as in the job configuration
+                fs_ugi: The default values are the same as in the job configuration
+            return:
+                True or False
+        """
+        exist_cmd = ['-test', '-e', hdfs_path]
+        returncode, output, errors = self.__run_hdfs_cmd(
+            exist_cmd, retry_times=1)
+
+        if returncode:
+            _logger.error("HDFS is_exist HDFS path: {} failed".format(
+                hdfs_path))
+            return False
+        else:
+            _logger.info("HDFS is_exist HDFS path: {} successfully".format(
+                hdfs_path))
+            return True
+
+    def is_dir(self, hdfs_path=None):
+        """
+            whether the remote hdfs path exists?
+            args:
+                remote_file_path: default value(${OUTPUT_PATH}/${SYS_USER_ID}/${SYS_JOB_ID}/tmp)
+                fs_name: The default values are the same as in the job configuration
+                fs_ugi: The default values are the same as in the job configuration
+            return:
+                True or False
+        """
+
+        if not self.is_exist(hdfs_path):
+            return False
+
+        dir_cmd = ['-test', '-d', hdfs_path]
+        returncode, output, errors = self.__run_hdfs_cmd(dir_cmd, retry_times=1)
+
+        if returncode:
+            _logger.error("HDFS path: {} failed is not a directory".format(
+                hdfs_path))
+            return False
+        else:
+            _logger.info("HDFS path: {} successfully is a directory".format(
+                hdfs_path))
+            return True
+
+    def delete(self, hdfs_path):
+        """Remove a file or directory from HDFS.
+
+        :param hdfs_path: HDFS path.
+        :param recursive: Recursively delete files and directories. By default,
+          this method will raise an :class:`HdfsError` if trying to delete a
+          non-empty directory.
+
+        This function returns `True` if the deletion was successful and `False` if
+        no file or directory previously existed at `hdfs_path`.
+
+        """
+        _logger.info('Deleting %r.', hdfs_path)
+
+        if not self.is_exist(hdfs_path):
+            _logger.warn("HDFS path: {} do not exist".format(hdfs_path))
+            return True
+
+        if self.is_dir(hdfs_path):
+            del_cmd = ['-rmr', hdfs_path]
+        else:
+            del_cmd = ['-rm', hdfs_path]
+
+        returncode, output, errors = self.__run_hdfs_cmd(del_cmd, retry_times=0)
+
+        if returncode:
+            _logger.error("HDFS path: {} delete files failure".format(
+                hdfs_path))
+            return False
+        else:
+            _logger.info("HDFS path: {} delete files successfully".format(
+                hdfs_path))
+            return True
+
+    def rename(self, hdfs_src_path, hdfs_dst_path, overwrite=False):
+        """Move a file or folder.
+
+        :param hdfs_src_path: Source path.
+        :param hdfs_dst_path: Destination path. If the path already exists and is
+          a directory, the source will be moved into it. If the path exists and is
+          a file, or if a parent destination directory is missing, this method will
+          raise an :class:`HdfsError`.
+
+        """
+        assert hdfs_src_path is not None
+        assert hdfs_dst_path is not None
+
+        if not self.is_exist(hdfs_src_path):
+            _logger.info("HDFS path do not exist: {}".format(hdfs_src_path))
+        if self.is_exist(hdfs_dst_path) and not overwrite:
+            _logger.error("HDFS path is exist: {} and overwrite=False".format(
+                hdfs_dst_path))
+
+        rename_command = ['-mv', hdfs_src_path, hdfs_dst_path]
+        returncode, output, errors = self.__run_hdfs_cmd(
+            rename_command, retry_times=1)
+
+        if returncode:
+            _logger.error("HDFS rename path: {} to {} failed".format(
+                hdfs_src_path, hdfs_dst_path))
+            return False
+        else:
+            _logger.info("HDFS rename path: {} to {} successfully".format(
+                hdfs_src_path, hdfs_dst_path))
+            return True
+
+    @staticmethod
+    def make_local_dirs(local_path):
+        try:
+            os.makedirs(local_path)
+        except OSError as e:
+            if e.errno != errno.EEXIST:
+                raise
+
+    def makedirs(self, hdfs_path):
+        """Create a remote directory, recursively if necessary.
+
+        :param hdfs_path: Remote path. Intermediate directories will be created
+          appropriately.
+        """
+        _logger.info('Creating directories to %r.', hdfs_path)
+        assert hdfs_path is not None
+
+        if self.is_exist(hdfs_path):
+            return
+
+        mkdirs_commands = ['-mkdir', hdfs_path]
+        returncode, output, errors = self.__run_hdfs_cmd(
+            mkdirs_commands, retry_times=1)
+
+        if returncode:
+            _logger.error("HDFS mkdir path: {} failed".format(hdfs_path))
+            return False
+        else:
+            _logger.error("HDFS mkdir path: {} successfully".format(hdfs_path))
+            return True
+
+    def ls(self, hdfs_path):
+        assert hdfs_path is not None
+
+        if not self.is_exist(hdfs_path):
+            return []
+
+        ls_commands = ['-ls', hdfs_path]
+        returncode, output, errors = self.__run_hdfs_cmd(
+            ls_commands, retry_times=1)
+
+        if returncode:
+            _logger.error("HDFS list path: {} failed".format(hdfs_path))
+            return []
+        else:
+            _logger.info("HDFS list path: {} successfully".format(hdfs_path))
+
+            ret_lines = []
+            regex = re.compile('\s+')
+            out_lines = output.strip().split("\n")
+            for line in out_lines:
+                re_line = regex.split(line)
+                if len(re_line) == 8:
+                    ret_lines.append(re_line[7])
+            return ret_lines
+
+    def lsr(self, hdfs_path, only_file=True, sort=True):
+        def sort_by_time(v1, v2):
+            v1_time = datetime.strptime(v1[1], '%Y-%m-%d %H:%M')
+            v2_time = datetime.strptime(v2[1], '%Y-%m-%d %H:%M')
+            return v1_time > v2_time
+
+        assert hdfs_path is not None
+
+        if not self.is_exist(hdfs_path):
+            return []
+
+        ls_commands = ['-lsr', hdfs_path]
+        returncode, output, errors = self.__run_hdfs_cmd(
+            ls_commands, retry_times=1)
+
+        if returncode:
+            _logger.error("HDFS list all files: {} failed".format(hdfs_path))
+            return []
+        else:
+            _logger.info("HDFS list all files: {} successfully".format(
+                hdfs_path))
+            lines = []
+            regex = re.compile('\s+')
+            out_lines = output.strip().split("\n")
+            for line in out_lines:
+                re_line = regex.split(line)
+                if len(re_line) == 8:
+                    if only_file and re_line[0][0] == "d":
+                        continue
+                    else:
+                        lines.append(
+                            (re_line[7], re_line[5] + " " + re_line[6]))
+            if sort:
+                sorted(lines, cmp=sort_by_time)
+            ret_lines = [ret[0] for ret in lines]
+            return ret_lines
+
+
+def multi_upload(client,
+                 hdfs_path,
+                 local_path,
+                 multi_processes=5,
+                 overwrite=False):
+    """
+    :param overwrite: will overwrite hdfs file or not
+    :param multi_processes: the upload data process at the same time, default=5
+    :param client: instance of HDFSClient
+    :param hdfs_path: path on hdfs
+    :param local_path: path on local
+    :return:
+    """
+
+    def __subprocess_upload(datas):
+        for data in datas:
+            re_path = os.path.relpath(os.path.dirname(data), local_path)
+            hdfs_re_path = os.path.join(hdfs_path, re_path)
+            client.upload(hdfs_re_path, data, overwrite, retry_times=5)
+
+    def get_local_files(path):
+        rlist = []
+
+        if not os.path.isdir(path):
+            return rlist
+
+        for dirname, folder, files in os.walk(path):
+            for i in files:
+                t = os.path.join(dirname, i)
+                rlist.append(t)
+        return rlist
+
+    assert isinstance(client, HDFSClient)
+
+    all_files = get_local_files(local_path)
+    if not all_files:
+        _logger.info("there are nothing need to upload, exit")
+        return
+    _logger.info("Start {} multi process to upload datas".format(
+        multi_processes))
+    procs = []
+    for i in range(multi_processes):
+        process_datas = all_files[i::multi_processes]
+        p = multiprocessing.Process(
+            target=__subprocess_upload, args=(process_datas, ))
+        procs.append(p)
+        p.start()
+
+    # complete the processes
+    for proc in procs:
+        proc.join()
+
+    _logger.info("Finish {} multi process to upload datas".format(
+        multi_processes))
+
+
+def multi_download(client,
+                   hdfs_path,
+                   local_path,
+                   trainer_id,
+                   trainers,
+                   multi_processes=5):
+    """
+    multi_download
+    :param client: instance of HDFSClient
+    :param hdfs_path: path on hdfs
+    :param local_path: path on local
+    :param trainer_id: current trainer id
+    :param trainers: all trainers number
+    :param multi_processes: the download data process at the same time, default=5
+    :return: None
+    """
+
+    def __subprocess_download(datas):
+        for data in datas:
+            re_path = os.path.relpath(os.path.dirname(data), hdfs_path)
+            local_re_path = os.path.join(local_path, re_path)
+            client.download(data, local_re_path)
+
+    assert isinstance(client, HDFSClient)
+
+    client.make_local_dirs(local_path)
+    _logger.info("Make local dir {} successfully".format(local_path))
+
+    all_need_download = client.lsr(hdfs_path, sort=True)
+    need_download = all_need_download[trainer_id::trainers]
+    _logger.info("Get {} files From all {} files need to be download from {}".
+                 format(len(need_download), len(all_need_download), hdfs_path))
+
+    _logger.info("Start {} multi process to download datas".format(
+        multi_processes))
+    procs = []
+    for i in range(multi_processes):
+        process_datas = need_download[i::multi_processes]
+        p = multiprocessing.Process(
+            target=__subprocess_download, args=(process_datas, ))
+        procs.append(p)
+        p.start()
+
+    # complete the processes
+    for proc in procs:
+        proc.join()
+
+    _logger.info("Finish {} multi process to download datas".format(
+        multi_processes))
+
+    local_downloads = []
+    for data in need_download:
+        data_name = os.path.basename(data)
+        re_path = os.path.relpath(os.path.dirname(data), hdfs_path)
+        local_re_path = os.path.join(local_path, re_path, data_name)
+        local_downloads.append(local_re_path)
+
+    return local_downloads
+
+
+if __name__ == "__main__":
+    hadoop_home = "/home/client/hadoop-client/hadoop/"
+
+    configs = {
+        "fs.default.name": "hdfs://xxx.hadoop.com:54310",
+        "hadoop.job.ugi": "hello,hello123"
+    }
+
+    client = HDFSClient(hadoop_home, configs)
+
+    client.ls("/user/com/train-25")
+    files = client.lsr("/user/com/train-25/models")
+
+    downloads = multi_download(
+        client,
+        "/user/com/train-25/model",
+        "/home/xx/data1",
+        1,
+        5,
+        multi_processes=5)
+
+    multi_upload(client, "/user/com/train-25/model", "/home/xx/data1")

python/paddle/fluid/layers/detection.py

@@ -1029,6 +1029,7 @@ def density_prior_box(input,
                       clip=False,
                       steps=[0.0, 0.0],
                       offset=0.5,
+                      flatten_to_2d=False,
                       name=None):
     """
     **Density Prior Box Operator**
@@ -1065,22 +1066,24 @@ def density_prior_box(input,
             height/weight of the input will be automatically calculated.
             Default: [0., 0.]
        offset(float): Prior boxes center offset. Default: 0.5
+       flatten_to_2d(bool): Whether to flatten output prior boxes and variance
+           to 2D shape, the second dim is 4. Default: False.
        name(str): Name of the density prior box op. Default: None.
 
     Returns:
         tuple: A tuple with two Variable (boxes, variances)
 
         boxes: the output density prior boxes of PriorBox.
-        The layout is [H, W, num_priors, 4].
+            The layout is [H, W, num_priors, 4] when flatten_to_2d is False.
+            The layout is [H * W * num_priors, 4] when flatten_to_2d is True.
             H is the height of input, W is the width of input,
-        num_priors is the total
-        box count of each position of input.
+            num_priors is the total box count of each position of input.
 
         variances: the expanded variances of PriorBox.
-        The layout is [H, W, num_priors, 4].
+            The layout is [H, W, num_priors, 4] when flatten_to_2d is False.
+            The layout is [H * W * num_priors, 4] when flatten_to_2d is True.
             H is the height of input, W is the width of input
-        num_priors is the total
-        box count of each position of input
+            num_priors is the total box count of each position of input.
 
 
     Examples:
@@ -1089,14 +1092,11 @@ def density_prior_box(input,
             box, var = fluid.layers.density_prior_box(
                 input=conv1,
                 image=images,
-                min_sizes=[100.],
-                max_sizes=[200.],
-                aspect_ratios=[1.0, 1.0 / 2.0, 2.0],
-                densities=[3, 4],
-                fixed_sizes=[50., 60.],
-                fixed_ratios=[1.0, 3.0, 1.0 / 3.0],
-                flip=True,
-                clip=True)
+                densities=[4, 2, 1],
+                fixed_sizes=[32.0, 64.0, 128.0],
+                fixed_ratios=[1.],
+                clip=True,
+                flatten_to_2d=True)
     """
     helper = LayerHelper("density_prior_box", **locals())
     dtype = helper.input_dtype()
@@ -1127,14 +1127,11 @@ def density_prior_box(input,
         'step_w': steps[0],
         'step_h': steps[1],
         'offset': offset,
+        'densities': densities,
+        'fixed_sizes': fixed_sizes,
+        'fixed_ratios': fixed_ratios,
+        'flatten_to_2d': flatten_to_2d,
     }
-    if densities is not None and len(densities) > 0:
-        attrs['densities'] = densities
-    if fixed_sizes is not None and len(fixed_sizes) > 0:
-        attrs['fixed_sizes'] = fixed_sizes
-    if fixed_ratios is not None and len(fixed_ratios) > 0:
-        attrs['fixed_ratios'] = fixed_ratios
-
     box = helper.create_variable_for_type_inference(dtype)
     var = helper.create_variable_for_type_inference(dtype)
     helper.append_op(

python/paddle/fluid/layers/io.py

@@ -347,10 +347,8 @@ def _copy_reader_create_op_(block, op):
     return new_op
 
 
-if os.name != 'nt':
-
-    @templatedoc(op_type='create_recordio_file_reader')
-    def open_recordio_file(filename,
+@templatedoc(op_type='create_recordio_file_reader')
+def open_recordio_file(filename,
                        shapes,
                        lod_levels,
                        dtypes,
@@ -406,8 +404,8 @@ if os.name != 'nt':
 
     startup_var.desc.set_dtypes(dtypes)
     startup_var.persistable = True
-        main_prog_var = _copy_reader_var_(
-            default_main_program().current_block(), startup_var)
+    main_prog_var = _copy_reader_var_(default_main_program().current_block(),
+                                      startup_var)
 
     if pass_num > 1:
         main_prog_var = multi_pass(reader=main_prog_var, pass_num=pass_num)

python/paddle/fluid/layers/nn.py

@@ -85,6 +85,7 @@ __all__ = [
     'row_conv',
     'multiplex',
     'layer_norm',
+    'group_norm',
     'softmax_with_cross_entropy',
     'smooth_l1',
     'one_hot',
@@ -343,10 +344,8 @@ def embedding(input,
     return tmp
 
 
-if os.name != 'nt':
-
-    @templatedoc(op_type="lstm")
-    def dynamic_lstm(input,
+@templatedoc(op_type="lstm")
+def dynamic_lstm(input,
                  size,
                  h_0=None,
                  c_0=None,
@@ -963,10 +962,8 @@ def linear_chain_crf(input, label, param_attr=None):
     return log_likelihood
 
 
-if os.name != 'nt':
-
-    @templatedoc()
-    def crf_decoding(input, param_attr, label=None):
+@templatedoc()
+def crf_decoding(input, param_attr, label=None):
     """
     ${comment}
 
@@ -992,11 +989,9 @@ if os.name != 'nt':
         dtype=helper.input_dtype())
     helper.append_op(
         type='crf_decoding',
-            inputs={
-                "Emission": [input],
+        inputs={"Emission": [input],
                 "Transition": transition,
-                "Label": label
-            },
+                "Label": label},
         outputs={"ViterbiPath": [viterbi_path]})
 
     return viterbi_path
@@ -2139,11 +2134,16 @@ def pool2d(input,
                           input tensor is NCHW, where N is batch size, C is
                           the number of channels, H is the height of the
                           feature, and W is the width of the feature.
-        pool_size (int): The side length of pooling windows. All pooling
-                         windows are squares with pool_size on a side.
+        pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
+            it must contain two integers, (pool_size_Height, pool_size_Width).
+            Otherwise, the pool kernel size will be a square of an int.
         pool_type: ${pooling_type_comment}
-        pool_stride (int): stride of the pooling layer.
-        pool_padding (int): padding size.
+        pool_stride (int|list|tuple): The pool stride size. If pool stride size is a tuple or list,
+            it must contain two integers, (pool_stride_Height, pool_stride_Width).
+            Otherwise, the pool stride size will be a square of an int.
+        pool_padding (int|list|tuple): The pool padding size. If pool padding size is a tuple,
+            it must contain two integers, (pool_padding_on_Height, pool_padding_on_Width).
+            Otherwise, the pool padding size will be a square of an int.
         global_pooling (bool): ${global_pooling_comment}
         use_cudnn (bool): ${use_cudnn_comment}
         ceil_mode (bool): ${ceil_mode_comment}
@@ -2553,6 +2553,84 @@ def layer_norm(input,
     return helper.append_activation(layer_norm_out)
 
 
+@templatedoc()
+def group_norm(input,
+               groups,
+               epsilon=1e-05,
+               param_attr=None,
+               bias_attr=None,
+               act=None,
+               data_layout='NCHW',
+               name=None):
+    """
+    **Group Normalization Layer**
+
+    Refer to `Group Normalization <https://arxiv.org/abs/1803.08494>`
+
+    Args:
+        input(Variable): The input tensor variable.
+        groups(int): The number of groups that divided from channels.
+        epsilon(float): The small value added to the variance to prevent
+            division by zero.
+        param_attr(ParamAttr|None): The parameter attribute for the learnable
+            scale :math:`g`. If it is set to False, no scale will be added to the output units.
+            If it is set to None, the bias is initialized one. Default: None.
+        bias_attr(ParamAttr|None): The parameter attribute for the learnable
+            bias :math:`b`. If it is set to False, no bias will be added to the output units.
+            If it is set to None, the bias is initialized zero. Default: None.
+        act(str): Activation to be applied to the output of group normalizaiton.
+        data_layout(string|NCHW): Only NCHW is supported.
+        name (str): The name of this layer. It is optional.
+
+    Returns:
+        Variable: A tensor variable which is the result after applying group normalization on the input.
+
+    Examples:
+
+        >>> data = fluid.layers.data(name='data', shape=[8, 32, 32],
+        >>>                          dtype='float32')
+        >>> x = fluid.layers.group_norm(input=data, groups=4)
+    """
+    helper = LayerHelper('group_norm', **locals())
+    dtype = helper.input_dtype()
+
+    # create intput and parameters
+    inputs = {'X': input}
+    input_shape = input.shape
+    if data_layout != 'NCHW':
+        raise ValueError("unsupported data layout:" + data_layout)
+    param_shape = [input_shape[1]]
+    if param_attr:
+        scale = helper.create_parameter(
+            attr=helper.param_attr,
+            shape=param_shape,
+            dtype=dtype,
+            default_initializer=Constant(1.0))
+        inputs['Scale'] = scale
+    if bias_attr:
+        bias = helper.create_parameter(
+            attr=helper.bias_attr, shape=param_shape, dtype=dtype, is_bias=True)
+        inputs['Bias'] = bias
+
+    # create output
+    mean_out = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
+    variance_out = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
+    group_norm_out = helper.create_tmp_variable(dtype)
+
+    helper.append_op(
+        type="group_norm",
+        inputs=inputs,
+        outputs={
+            "Y": group_norm_out,
+            "Mean": mean_out,
+            "Variance": variance_out,
+        },
+        attrs={"epsilon": epsilon,
+               "groups": groups})
+
+    return helper.append_activation(group_norm_out)
+
+
 def conv2d_transpose(input,
                      num_filters,
                      output_size=None,
@@ -5593,14 +5671,8 @@ def label_smooth(label,
     return smooth_label
 
 
-if os.name != 'nt':
-
-    @templatedoc()
-    def roi_pool(input,
-                 rois,
-                 pooled_height=1,
-                 pooled_width=1,
-                 spatial_scale=1.0):
+@templatedoc()
+def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
     """
     ${comment}
 
@@ -5788,7 +5860,7 @@ def image_resize(input,
     Examples:
         .. code-block:: python
 
-            out = fluid.layers.image_resize(input, out_shape=[12, 12])
+            out = fluid.layers.image_resize(input, out_shape=[12, 12], resample="NEAREST")
     """
     resample_methods = {
         'BILINEAR': 'bilinear',
@@ -5891,6 +5963,11 @@ def resize_bilinear(input,
 
     Returns:
         ${out_comment}.
+
+    Examples:
+        .. code-block:: python
+
+            out = fluid.layers.resize_bilinear(input, out_shape=[12, 12])
     """
 
     return image_resize(input, out_shape, scale, name, 'BILINEAR', actual_shape)
@@ -5937,6 +6014,11 @@ def resize_nearest(input,
 
     Returns:
         ${out_comment}.
+
+    Examples:
+        .. code-block:: python
+
+            out = fluid.layers.resize_nearest(input, out_shape=[12, 12])
     """
 
     return image_resize(input, out_shape, scale, name, 'NEAREST', actual_shape)
@@ -7692,6 +7774,15 @@ def logical_and(x, y, out=None, name=None):
 
     Returns:
         out(${out_type}): ${out_comment}
+
+    Examples:
+        .. code-block:: python
+
+            left = fluid.layers.data(
+                name='left', shape=[1], dtype='int32')
+            right = fluid.layers.data(
+                name='right', shape=[1], dtype='int32')
+            result = fluid.layers.logical_and(x=left, y=right)
     """
 
     return _logical_op(
@@ -7711,6 +7802,15 @@ def logical_or(x, y, out=None, name=None):
 
     Returns:
         out(${out_type}): ${out_comment}
+
+    Examples:
+        .. code-block:: python
+
+            left = fluid.layers.data(
+                name='left', shape=[1], dtype='int32')
+            right = fluid.layers.data(
+                name='right', shape=[1], dtype='int32')
+            result = fluid.layers.logical_or(x=left, y=right)
     """
 
     return _logical_op(
@@ -7730,6 +7830,15 @@ def logical_xor(x, y, out=None, name=None):
 
     Returns:
         out(${out_type}): ${out_comment}
+
+    Examples:
+        .. code-block:: python
+
+            left = fluid.layers.data(
+                name='left', shape=[1], dtype='int32')
+            right = fluid.layers.data(
+                name='right', shape=[1], dtype='int32')
+            result = fluid.layers.logical_xor(x=left, y=right)
     """
 
     return _logical_op(
@@ -7748,6 +7857,13 @@ def logical_not(x, out=None, name=None):
 
     Returns:
         out(${out_type}): ${out_comment}
+
+    Examples:
+        .. code-block:: python
+
+            left = fluid.layers.data(
+                name='left', shape=[1], dtype='int32')
+            result = fluid.layers.logical_not(x=left)
     """
 
     return _logical_op(
@@ -7767,6 +7883,13 @@ def clip(x, min, max, name=None):
 
     Returns:
         out(${out_type}): ${out_comment}
+
+    Examples:
+        .. code-block:: python
+
+            input = fluid.layers.data(
+                name='data', shape=[1], dtype='float32')
+            reward = fluid.layers.clip(x=input, min=-1.0, max=1.0)
     """
 
     helper = LayerHelper("clip", **locals())
@@ -7799,6 +7922,13 @@ def clip_by_norm(x, max_norm, name=None):
 
     Returns:
         out(${out_type}): ${out_comment}
+
+    Examples:
+        .. code-block:: python
+
+            input = fluid.layers.data(
+                name='data', shape=[1], dtype='float32')
+            reward = fluid.layers.clip_by_norm(x=input, max_norm=1.0)
     """
 
     helper = LayerHelper("clip_by_norm", **locals())

python/paddle/fluid/layers/ops.py

@@ -100,12 +100,12 @@ Examples:
     >>> result = fluid.layers.hard_shrink(x=data, threshold=0.3)
 """
 
-if os.name != 'nt':
-    __all__ += ['cumsum']
+__all__ += ['cumsum']
 
-    _cum_sum_ = generate_layer_fn('cumsum')
+_cum_sum_ = generate_layer_fn('cumsum')
 
-    def cumsum(x, axis=None, exclusive=None, reverse=None):
+
+def cumsum(x, axis=None, exclusive=None, reverse=None):
     locals_var = locals().keys()
     kwargs = dict()
     for name in locals_var:
@@ -114,12 +114,13 @@ if os.name != 'nt':
             kwargs[name] = val
     return _cum_sum_(**kwargs)
 
-    cumsum.__doc__ = _cum_sum_.__doc__ + """
-    Examples:
+
+cumsum.__doc__ = _cum_sum_.__doc__ + """
+Examples:
 
     >>> data = fluid.layers.data(name="input", shape=[32, 784])
     >>> result = fluid.layers.cumsum(data, axis=0)
-    """
+"""
 
 __all__ += ['thresholded_relu']
 

python/paddle/fluid/tests/test_detection.py

@@ -112,6 +112,8 @@ class TestDetection(unittest.TestCase):
 
 class TestPriorBox(unittest.TestCase):
     def test_prior_box(self):
+        program = Program()
+        with program_guard(program):
             data_shape = [3, 224, 224]
             images = fluid.layers.data(
                 name='pixel', shape=data_shape, dtype='float32')
@@ -130,6 +132,8 @@ class TestPriorBox(unittest.TestCase):
 
 class TestDensityPriorBox(unittest.TestCase):
     def test_density_prior_box(self):
+        program = Program()
+        with program_guard(program):
             data_shape = [3, 224, 224]
             images = fluid.layers.data(
                 name='pixel', shape=data_shape, dtype='float32')
@@ -143,7 +147,7 @@ class TestDensityPriorBox(unittest.TestCase):
                 clip=True)
             assert len(box.shape) == 4
             assert box.shape == var.shape
-        assert box.shape[3] == 4
+            assert box.shape[-1] == 4
 
 
 class TestAnchorGenerator(unittest.TestCase):

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -23,6 +23,12 @@ if(NOT WITH_DISTRIBUTE)
     LIST(REMOVE_ITEM TEST_OPS test_dist_text_classification)
 endif(NOT WITH_DISTRIBUTE)
 
+if (NOT ${WITH_GPU})
+    LIST(REMOVE_ITEM TEST_OPS test_conv2d_fusion_op)
+elseif(${CUDNN_MAJOR_VERSION} VERSION_LESS 7)
+    LIST(REMOVE_ITEM TEST_OPS test_conv2d_fusion_op)
+endif()
+
 list(REMOVE_ITEM TEST_OPS test_seq_concat_op) # FIXME(helin): https://github.com/PaddlePaddle/Paddle/issues/8290
 list(REMOVE_ITEM TEST_OPS test_modified_huber_loss_op) # FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/5184
 list(REMOVE_ITEM TEST_OPS test_lstm_unit_op) # # FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/5185
@@ -45,6 +51,10 @@ if(APPLE)
     list(REMOVE_ITEM TEST_OPS test_dist_se_resnext)
     list(REMOVE_ITEM TEST_OPS test_fuse_elewise_add_act_pass)
 endif()
+if(NOT WITH_MKLML)
+    # this op is not support on openblas
+    list(REMOVE_ITEM TEST_OPS test_fusion_seqexpand_concat_fc_op)
+endif()
 
 function(py_test_modules TARGET_NAME)
   if(WITH_TESTING)
@@ -71,10 +81,12 @@ list(REMOVE_ITEM TEST_OPS test_dist_se_resnext)
 list(REMOVE_ITEM TEST_OPS test_dist_transformer)
 list(REMOVE_ITEM TEST_OPS test_parallel_executor_transformer)
 list(REMOVE_ITEM TEST_OPS test_image_classification_resnet)
+list(REMOVE_ITEM TEST_OPS test_interpolate_op)
 foreach(TEST_OP ${TEST_OPS})
     py_test_modules(${TEST_OP} MODULES ${TEST_OP})
 endforeach(TEST_OP)
 py_test_modules(test_warpctc_op MODULES test_warpctc_op ENVS FLAGS_warpctc_dir=${WARPCTC_LIB_DIR} SERIAL)
+py_test_modules(test_interpolate_op MODULES test_interpolate_op SERIAL)
 if(WITH_DISTRIBUTE)
     py_test_modules(test_dist_train MODULES test_dist_train SERIAL)
     set_tests_properties(test_listen_and_serv_op PROPERTIES TIMEOUT 20)

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

@@ -362,7 +362,9 @@ class OpTest(unittest.TestCase):
             else:
                 return []
         places = [fluid.CPUPlace()]
-        if core.is_compiled_with_cuda() and core.op_support_gpu(self.op_type):
+        cpu_only = self._cpu_only if hasattr(self, '_cpu_only') else False
+        if core.is_compiled_with_cuda() and core.op_support_gpu(self.op_type)\
+           and not cpu_only:
             places.append(core.CUDAPlace(0))
         return places
 
@@ -379,7 +381,7 @@ class OpTest(unittest.TestCase):
             outs.sort(key=len)
             checker(outs)
 
-    def __assert_is_close(self, numeric_grads, analytic_grads, names,
+    def _assert_is_close(self, numeric_grads, analytic_grads, names,
                          max_relative_error, msg_prefix):
 
         for a, b, name in six.moves.zip(numeric_grads, analytic_grads, names):
@@ -449,7 +451,7 @@ class OpTest(unittest.TestCase):
         analytic_grads = self._get_gradient(inputs_to_check, place,
                                             output_names, no_grad_set)
 
-        self.__assert_is_close(numeric_grads, analytic_grads, inputs_to_check,
+        self._assert_is_close(numeric_grads, analytic_grads, inputs_to_check,
                               max_relative_error,
                               "Gradient Check On %s" % str(place))
 

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

@@ -36,7 +36,8 @@ class TestDensityPriorBoxOp(OpTest):
             'offset': self.offset,
             'densities': self.densities,
             'fixed_sizes': self.fixed_sizes,
-            'fixed_ratios': self.fixed_ratios
+            'fixed_ratios': self.fixed_ratios,
+            'flatten_to_2d': self.flatten_to_2d
         }
         self.outputs = {'Boxes': self.out_boxes, 'Variances': self.out_var}
 
@@ -48,16 +49,17 @@ class TestDensityPriorBoxOp(OpTest):
         self.set_data()
 
     def set_density(self):
-        self.densities = []
-        self.fixed_sizes = []
-        self.fixed_ratios = []
+        self.densities = [4, 2, 1]
+        self.fixed_sizes = [32.0, 64.0, 128.0]
+        self.fixed_ratios = [1.0]
+        self.layer_w = 17
+        self.layer_h = 17
+        self.image_w = 533
+        self.image_h = 533
+        self.flatten_to_2d = False
 
     def init_test_params(self):
-        self.layer_w = 32
-        self.layer_h = 32
-
-        self.image_w = 40
-        self.image_h = 40
+        self.set_density()
 
         self.step_w = float(self.image_w) / float(self.layer_w)
         self.step_h = float(self.image_h) / float(self.layer_h)
@@ -69,8 +71,6 @@ class TestDensityPriorBoxOp(OpTest):
         self.variances = [0.1, 0.1, 0.2, 0.2]
         self.variances = np.array(self.variances, dtype=np.float).flatten()
 
-        self.set_density()
-
         self.clip = True
         self.num_priors = 0
         if len(self.fixed_sizes) > 0 and len(self.densities) > 0:
@@ -129,6 +129,9 @@ class TestDensityPriorBoxOp(OpTest):
                           (self.layer_h, self.layer_w, self.num_priors, 1))
         self.out_boxes = out_boxes.astype('float32')
         self.out_var = out_var.astype('float32')
+        if self.flatten_to_2d:
+            self.out_boxes = self.out_boxes.reshape((-1, 4))
+            self.out_var = self.out_var.reshape((-1, 4))
 
 
 class TestDensityPriorBox(TestDensityPriorBoxOp):
@@ -136,6 +139,11 @@ class TestDensityPriorBox(TestDensityPriorBoxOp):
         self.densities = [3, 4]
         self.fixed_sizes = [1.0, 2.0]
         self.fixed_ratios = [1.0]
+        self.layer_w = 32
+        self.layer_h = 32
+        self.image_w = 40
+        self.image_h = 40
+        self.flatten_to_2d = True
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/unittests/test_elementwise_mul_mkldnn_op.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.
+
+from __future__ import print_function
+import unittest
+import numpy as np
+from op_test import OpTest
+import paddle.fluid.core as core
+from paddle.fluid.op import Operator
+from test_elementwise_mul_op import *
+
+
+class TestElementwiseMulMKLDNNOp_BroadcastNCHW16c(ElementwiseMulOp):
+    def init_input_output(self):
+        x = np.random.rand(1, 16, 2, 2).astype(self.dtype)
+        self.x = x.transpose(0, 2, 3, 1).reshape(1, 16, 2, 2)
+        self.y = np.random.rand(1, 16).astype(self.dtype)
+
+        self.out = x * self.y.reshape(1, 16, 1, 1)
+        self.out = self.out.transpose(0, 2, 3, 1).reshape(1, 16, 2, 2)
+
+    def setUp(self):
+        super(TestElementwiseMulMKLDNNOp_BroadcastNCHW16c, self).setUp()
+        self.attrs["x_data_format"] = "nchw16c"
+        self.attrs["y_data_format"] = "nc"
+        self._cpu_only = True
+
+    def init_kernel_type(self):
+        self.use_mkldnn = True
+
+    def init_axis(self):
+        self.axis = 0
+
+    def test_check_grad_normal(self):
+        pass
+
+    def test_check_grad_ingore_x(self):
+        pass
+
+    def test_check_grad_ingore_y(self):
+        pass
+
+
+@unittest.skip(
+    "Not implemented yet.")  # TODO(mgallus): enable when implemented.
+class TestElementwiseMulMKLDNNOp_BroadcastNCHW8c(ElementwiseMulOp):
+    def init_input_output(self):
+        x = np.random.rand(1, 8, 2, 2).astype(self.dtype)
+        self.x = x.transpose(0, 2, 3, 1).reshape(1, 8, 2, 2)
+        self.y = np.random.rand(1, 8).astype(self.dtype)
+
+        self.out = x * self.y.reshape(1, 8, 1, 1)
+        self.out = self.out.transpose(0, 2, 3, 1).reshape(1, 8, 2, 2)
+
+    def setUp(self):
+        super(TestElementwiseMulMKLDNNOp_BroadcastNCHW8c, self).setUp()
+        self.attrs["x_data_format"] = "nchw8c"
+        self.attrs["y_data_format"] = "nc"
+        self._cpu_only = True
+
+    def init_kernel_type(self):
+        self.use_mkldnn = True
+
+    def init_axis(self):
+        self.axis = 0
+
+    def test_check_grad_normal(self):
+        pass
+
+    def test_check_grad_ingore_x(self):
+        pass
+
+    def test_check_grad_ingore_y(self):
+        pass
+
+
+class TestElementwiseMulMKLDNNOp_FallbackNCHW(ElementwiseMulOp):
+    def init_input_output(self):
+        self.x = np.random.rand(1, 16, 2, 2).astype(self.dtype)
+        self.y = np.random.rand(1, 16).astype(self.dtype)
+
+        self.out = self.x * self.y.reshape(1, 16, 1, 1)
+
+    def init_kernel_type(self):
+        self.use_mkldnn = True
+
+    def init_axis(self):
+        self.axis = 0
+
+    def test_check_grad_normal(self):
+        pass
+
+    def test_check_grad_ingore_x(self):
+        pass
+
+    def test_check_grad_ingore_y(self):
+        pass
+
+
+class TestElementwiseMulMKLDNNOp_FallbackNCHW16C(ElementwiseMulOp):
+    def init_input_output(self):
+        x = np.random.rand(1, 16, 2, 2).astype(self.dtype)
+        self.x = x.transpose(0, 2, 3, 1).reshape(1, 16, 2, 2)
+        y = np.random.rand(1, 16, 2, 2).astype(self.dtype)
+        self.y = y.transpose(0, 2, 3, 1).reshape(1, 16, 2, 2)
+
+        self.out = self.x * self.y
+
+    def setUp(self):
+        super(TestElementwiseMulMKLDNNOp_FallbackNCHW16C, self).setUp()
+        self.attrs["x_data_format"] = "nchw16c"
+        self.attrs["y_data_format"] = "nchw16c"
+        self._cpu_only = True
+
+    def init_kernel_type(self):
+        self.use_mkldnn = True
+
+    def init_axis(self):
+        self.axis = 0
+
+    def test_check_grad_normal(self):
+        pass
+
+    def test_check_grad_ingore_x(self):
+        pass
+
+    def test_check_grad_ingore_y(self):
+        pass
+
+
+class TestElementwiseMulMKLDNNOp_FallbackNoReorders(ElementwiseMulOp):
+    def init_input_output(self):
+        x = np.random.rand(1, 16, 2, 2).astype(self.dtype)
+        self.x = x.transpose(0, 2, 3, 1).reshape(1, 16, 2, 2)
+        y = np.random.rand(1, 16, 2, 2).astype(self.dtype)
+        self.y = y.transpose(0, 2, 3, 1).reshape(1, 16, 2, 2)
+
+        self.out = self.x * self.y
+
+    def setUp(self):
+        super(TestElementwiseMulMKLDNNOp_FallbackNoReorders, self).setUp()
+        self.attrs["x_data_format"] = "nchw16c"
+        self.attrs["y_data_format"] = "nchw16c"
+        self._cpu_only = True
+
+    def init_kernel_type(self):
+        self.use_mkldnn = True
+
+    def init_axis(self):
+        self.axis = 0
+
+    def test_check_grad_normal(self):
+        pass
+
+    def test_check_grad_ingore_x(self):
+        pass
+
+    def test_check_grad_ingore_y(self):
+        pass
+
+
+class TestElementwiseMulMKLDNNOp_FallbackWithReorder1(ElementwiseMulOp):
+    def init_input_output(self):
+        self.x = np.random.rand(1, 16, 2, 2).astype(self.dtype)
+        y = np.random.rand(1, 16, 2, 2).astype(self.dtype)
+        self.y = y.transpose(0, 2, 3, 1).reshape(1, 16, 2, 2)
+
+        self.out = self.x * y
+
+    def setUp(self):
+        super(TestElementwiseMulMKLDNNOp_FallbackWithReorder1, self).setUp()
+        self.attrs["x_data_format"] = "nchw"
+        self.attrs["y_data_format"] = "nchw16c"
+        self._cpu_only = True
+
+    def init_kernel_type(self):
+        self.use_mkldnn = True
+
+    def init_axis(self):
+        self.axis = 0
+
+    def test_check_grad_normal(self):
+        pass
+
+    def test_check_grad_ingore_x(self):
+        pass
+
+    def test_check_grad_ingore_y(self):
+        pass
+
+
+class TestElementwiseMulMKLDNNOp_FallbackWithReorder2(ElementwiseMulOp):
+    def init_input_output(self):
+        self.y = np.random.rand(1, 16, 2, 2).astype(self.dtype)
+        x = np.random.rand(1, 16, 2, 2).astype(self.dtype)
+        self.x = x.transpose(0, 2, 3, 1).reshape(1, 16, 2, 2)
+
+        self.out = x * self.y
+
+    def setUp(self):
+        super(TestElementwiseMulMKLDNNOp_FallbackWithReorder2, self).setUp()
+        self.attrs["x_data_format"] = "nchw16c"
+        self.attrs["y_data_format"] = "nchw"
+        self._cpu_only = True
+
+    def init_kernel_type(self):
+        self.use_mkldnn = True
+
+    def init_axis(self):
+        self.axis = 0
+
+    def test_check_grad_normal(self):
+        pass
+
+    def test_check_grad_ingore_x(self):
+        pass
+
+    def test_check_grad_ingore_y(self):
+        pass
+
+
+class TestElementwiseMulMKLDNNOp_FallbackNoReorders2(ElementwiseMulOp):
+    def init_input_output(self):
+        self.x = np.random.rand(1, 16).astype(self.dtype)
+        self.y = np.random.rand(1, 16).astype(self.dtype)
+
+        self.out = self.x * self.y
+
+    def setUp(self):
+        super(TestElementwiseMulMKLDNNOp_FallbackNoReorders2, self).setUp()
+        self.attrs["x_data_format"] = "nc"
+        self.attrs["y_data_format"] = "nc"
+        self._cpu_only = True
+
+    def init_kernel_type(self):
+        self.use_mkldnn = True
+
+    def init_axis(self):
+        self.axis = 0
+
+    def test_check_grad_normal(self):
+        pass
+
+    def test_check_grad_ingore_x(self):
+        pass
+
+    def test_check_grad_ingore_y(self):
+        pass
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -21,13 +21,24 @@ from paddle.fluid.op import Operator
 
 
 class ElementwiseMulOp(OpTest):
+    def init_kernel_type(self):
+        self.use_mkldnn = False
+
     def setUp(self):
         self.op_type = "elementwise_mul"
+        self.dtype = np.float32
+        self.axis = -1
+        self.init_dtype()
+        self.init_input_output()
+        self.init_kernel_type()
+        self.init_axis()
+
         self.inputs = {
-            'X': np.random.uniform(0.1, 1, [13, 17]).astype("float64"),
-            'Y': np.random.uniform(0.1, 1, [13, 17]).astype("float64")
+            'X': OpTest.np_dtype_to_fluid_dtype(self.x),
+            'Y': OpTest.np_dtype_to_fluid_dtype(self.y)
         }
-        self.outputs = {'Out': np.multiply(self.inputs['X'], self.inputs['Y'])}
+        self.outputs = {'Out': self.out}
+        self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
 
     def test_check_output(self):
         self.check_output()
@@ -41,6 +52,17 @@ class ElementwiseMulOp(OpTest):
     def test_check_grad_ingore_y(self):
         self.check_grad(['X'], 'Out', no_grad_set=set('Y'))
 
+    def init_input_output(self):
+        self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
+        self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
+        self.out = np.multiply(self.x, self.y)
+
+    def init_dtype(self):
+        pass
+
+    def init_axis(self):
+        pass
+
 
 class TestElementwiseMulOp_scalar(ElementwiseMulOp):
     def setUp(self):
@@ -63,17 +85,13 @@ class TestElementwiseMulOp_Vector(ElementwiseMulOp):
 
 
 class TestElementwiseMulOp_broadcast_0(ElementwiseMulOp):
-    def setUp(self):
-        self.op_type = "elementwise_mul"
-        self.inputs = {
-            'X': np.random.rand(2, 3, 4).astype(np.float64),
-            'Y': np.random.rand(2).astype(np.float64)
-        }
+    def init_input_output(self):
+        self.x = np.random.rand(2, 3, 4).astype(self.dtype)
+        self.y = np.random.rand(2).astype(self.dtype)
+        self.out = self.x * self.y.reshape(2, 1, 1)
 
-        self.attrs = {'axis': 0}
-        self.outputs = {
-            'Out': self.inputs['X'] * self.inputs['Y'].reshape(2, 1, 1)
-        }
+    def init_axis(self):
+        self.axis = 0
 
 
 class TestElementwiseMulOp_broadcast_1(ElementwiseMulOp):

python/paddle/fluid/tests/unittests/test_group_norm_op.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.
+
+from __future__ import print_function
+import unittest
+import numpy as np
+
+from operator import mul
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+from op_test import OpTest
+
+from testsuite import create_op
+
+
+def group_norm_naive(x, scale, bias, epsilon, groups):
+    N, C, H, W = x.shape
+    G = groups
+    x = x.reshape((N * G, -1))
+    mean = np.mean(x, axis=1, keepdims=True)
+    var = np.var(x, axis=1, keepdims=True)
+    output = (x - mean) / np.sqrt(var + epsilon)
+    output = output.reshape((N, C, H, W)) * scale.reshape(
+        (-1, 1, 1)) + bias.reshape((-1, 1, 1))
+    return output, mean.reshape((N, G)), var.reshape((N, G))
+
+
+class TestGroupNormOp(OpTest):
+    def setUp(self):
+        self.op_type = "group_norm"
+        self.data_format = "NCHW"
+        self.dtype = np.float32
+        self.shape = (2, 4, 3, 3)
+        self.attrs = {'epsilon': 1e-5, 'groups': 2}
+        self.compare_between_place = False
+        self.init_test_case()
+
+        input = np.random.random(self.shape).astype(self.dtype)
+        scale = np.random.random([self.shape[1]]).astype(self.dtype)
+        bias = np.random.random([self.shape[1]]).astype(self.dtype)
+        output, mean, var = group_norm_naive(
+            input, scale, bias, self.attrs['epsilon'], self.attrs['groups'])
+
+        self.inputs = {
+            'X': OpTest.np_dtype_to_fluid_dtype(input),
+            'Scale': OpTest.np_dtype_to_fluid_dtype(scale),
+            'Bias': OpTest.np_dtype_to_fluid_dtype(bias)
+        }
+        self.outputs = {'Y': output, 'Mean': mean, 'Variance': var}
+
+    def test_check_output(self):
+        atol = 1e-4
+        place = core.CPUPlace()
+        self.check_output_with_place(place, atol=atol)
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+            self.check_output_with_place(place, atol=atol)
+
+    def do_compare_between_place(self):
+        if not core.is_compiled_with_cuda(): return
+        place = core.CPUPlace()
+        place2 = core.CUDAPlace(0)
+        self.scope = core.Scope()
+        op_inputs = self.inputs if hasattr(self, "inputs") else dict()
+        op_outputs = self.outputs if hasattr(self, "outputs") else dict()
+        op_attrs = self.attrs if hasattr(self, "attrs") else dict()
+        self.op = create_op(self.scope, self.op_type, op_inputs, op_outputs,
+                            op_attrs)
+        inputs_to_check = set(['X', 'Scale', 'Bias'])
+        output_names = 'Y'
+        cpu_grads = self._get_gradient(inputs_to_check, place, output_names,
+                                       None)
+        gpu_grads = self._get_gradient(inputs_to_check, place2, output_names,
+                                       None)
+        self._assert_is_close(cpu_grads, gpu_grads, inputs_to_check, 0.005,
+                              "Gradient Check On %s" % str(place))
+
+    def test_check_grad(self):
+        if self.compare_between_place:
+            self.do_compare_between_place()
+            return
+        place = core.CPUPlace()
+        self.check_grad_with_place(
+            place, set(['X', 'Scale', 'Bias']), 'Y', max_relative_error=0.01)
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+            self.check_grad_with_place(
+                place,
+                set(['X', 'Scale', 'Bias']),
+                'Y',
+                max_relative_error=0.01)
+
+    def init_test_case(self):
+        pass
+
+
+class TestGroupNormOp1(TestGroupNormOp):
+    def init_test_case(self):
+        self.attrs['groups'] = 1
+
+
+class TestGroupNormOp2(TestGroupNormOp):
+    def init_test_case(self):
+        self.attrs['groups'] = 4
+
+
+class TestGroupNormOpBigEps1(TestGroupNormOp):
+    def init_test_case(self):
+        self.attrs['groups'] = 1
+        self.attrs['epsilon'] = 0.5
+
+
+class TestGroupNormOpBigEps2(TestGroupNormOp):
+    def init_test_case(self):
+        self.attrs['groups'] = 4
+        self.attrs['epsilon'] = 0.5
+
+
+class TestGroupNormOpBigEps3(TestGroupNormOp):
+    def init_test_case(self):
+        self.attrs['epsilon'] = 0.5
+
+
+class TestGroupNormOpLargeData(TestGroupNormOp):
+    def init_test_case(self):
+        self.shape = (2, 32, 64, 64)
+        self.attrs['groups'] = 8
+        self.compare_between_place = True
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -202,6 +202,17 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(layers.sequence_unpad(x=x, length=length))
         print(str(program))
 
+    def test_pool2d(self):
+        program = Program()
+        with program_guard(program):
+            x = layers.data(name='x', shape=[3, 224, 224], dtype='float32')
+            self.assertIsNotNone(
+                layers.pool2d(
+                    x,
+                    pool_size=[5, 3],
+                    pool_stride=[1, 2],
+                    pool_padding=(2, 1)))
+
     def test_lstm_unit(self):
         program = Program()
         with program_guard(program):

python/requirements.txt

 requests==2.9.2
-numpy>=1.12,<=1.14 #TODO:change to ">=1.12" when numpy fix bug in 1.15 and higher version
+numpy>=1.12
 protobuf==3.1
 recordio>=0.1.0
 matplotlib==2.2.3 # TODO: let python3 paddlepaddle package use latest matplotlib

tools/manylinux1/Dockerfile.x64

@@ -16,7 +16,7 @@ ENV PKG_CONFIG_PATH=/usr/local/lib/pkgconfig
 RUN yum install -y sqlite-devel zlib-devel openssl-devel pcre-devel vim tk-devel tkinter libtool xz graphviz
 COPY build_scripts /build_scripts
 RUN bash build_scripts/build.sh && \
-  bash build_scripts/install_nccl2.sh && rm -r build_scripts
+  bash build_scripts/install_nccl2.sh && rm -rf build_scripts
 
 ENV SSL_CERT_FILE=/opt/_internal/certs.pem
 
@@ -36,17 +36,21 @@ RUN cd /opt && wget -q --no-check-certificate https://github.com/google/protobuf
     tar xzf protobuf-cpp-3.1.0.tar.gz && \
     cd protobuf-3.1.0 && ./configure && make -j4 && make install && cd .. && rm -f protobuf-cpp-3.1.0.tar.gz
 
-RUN wget -O /root/requirements.txt https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/python/requirements.txt
+RUN wget https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/python/requirements.txt -O /root/requirements.txt
 
 RUN LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs4/lib:${LD_LIBRARY_PATH} /opt/python/cp27-cp27mu/bin/pip install -r /root/requirements.txt && \
     LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs2/lib:${LD_LIBRARY_PATH} /opt/python/cp27-cp27m/bin/pip install -r /root/requirements.txt && \
     LD_LIBRARY_PATH=/opt/_internal/cpython-3.5.1/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.5.1/bin/pip3 install -r /root/requirements.txt && \
+    LD_LIBRARY_PATH=/opt/_internal/cpython-3.6.0/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.6.0/bin/pip3 install -r /root/requirements.txt && \
+    LD_LIBRARY_PATH=/opt/_internal/cpython-3.7.0/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.7.0/bin/pip3 install -r /root/requirements.txt && \
     go get github.com/Masterminds/glide && \
     rm -rf /root/requirements.txt
 
 RUN LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs4/lib:${LD_LIBRARY_PATH} /opt/python/cp27-cp27mu/bin/pip install pre-commit 'ipython==5.3.0' opencv-python && \
     LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs2/lib:${LD_LIBRARY_PATH} /opt/python/cp27-cp27m/bin/pip install pre-commit 'ipython==5.3.0' opencv-python && \
-    LD_LIBRARY_PATH=/opt/_internal/cpython-3.5.1/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.5.1/bin/pip3 install pre-commit 'ipython==5.3.0' opencv-python
+    LD_LIBRARY_PATH=/opt/_internal/cpython-3.5.1/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.5.1/bin/pip3 install pre-commit 'ipython==5.3.0' opencv-python && \
+    LD_LIBRARY_PATH=/opt/_internal/cpython-3.6.0/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.6.0/bin/pip3 install pre-commit 'ipython==5.3.0' opencv-python && \
+    LD_LIBRARY_PATH=/opt/_internal/cpython-3.7.0/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.7.0/bin/pip3 install pre-commit 'ipython==5.3.0' opencv-python
 
 RUN wget -O /opt/swig-2.0.12.tar.gz https://cytranet.dl.sourceforge.net/project/swig/swig/swig-2.0.12/swig-2.0.12.tar.gz && \
     cd /opt && tar xzf swig-2.0.12.tar.gz && cd /opt/swig-2.0.12 && ./configure && make && make install && cd /opt && rm swig-2.0.12.tar.gz

tools/manylinux1/build_scripts/build.sh

@@ -9,12 +9,12 @@ set -ex
 # remove others to expedite build and reduce docker image size. The original
 # manylinux docker image project builds many python versions.
 # NOTE We added back 3.5.1, since auditwheel requires python 3.3+
-CPYTHON_VERSIONS="2.7.11 3.5.1"
+CPYTHON_VERSIONS="3.7.0 3.6.0 3.5.1 2.7.11"
 
 # openssl version to build, with expected sha256 hash of .tar.gz
 # archive
-OPENSSL_ROOT=openssl-1.0.2l
-OPENSSL_HASH=ce07195b659e75f4e1db43552860070061f156a98bb37b672b101ba6e3ddf30c
+OPENSSL_ROOT=openssl-1.1.0i
+OPENSSL_HASH=ebbfc844a8c8cc0ea5dc10b86c9ce97f401837f3fa08c17b2cdadc118253cf99
 EPEL_RPM_HASH=e5ed9ecf22d0c4279e92075a64c757ad2b38049bcf5c16c4f2b75d5f6860dc0d
 DEVTOOLS_HASH=a8ebeb4bed624700f727179e6ef771dafe47651131a00a78b342251415646acc
 PATCHELF_HASH=d9afdff4baeacfbc64861454f368b7f2c15c44d245293f7587bbf726bfe722fb
@@ -25,7 +25,7 @@ AUTOCONF_HASH=954bd69b391edc12d6a4a51a2dd1476543da5c6bbf05a95b59dc0dd6fd4c2969
 
 # Dependencies for compiling Python that we want to remove from
 # the final image after compiling Python
-PYTHON_COMPILE_DEPS="zlib-devel bzip2-devel ncurses-devel sqlite-devel readline-devel tk-devel gdbm-devel db4-devel libpcap-devel xz-devel"
+PYTHON_COMPILE_DEPS="zlib-devel bzip2-devel ncurses-devel sqlite-devel readline-devel tk-devel gdbm-devel db4-devel libpcap-devel xz-devel libffi-devel"
 
 # Libraries that are allowed as part of the manylinux1 profile
 MANYLINUX1_DEPS="glibc-devel libstdc++-devel glib2-devel libX11-devel libXext-devel libXrender-devel  mesa-libGL-devel libICE-devel libSM-devel ncurses-devel freetype-devel libpng-devel"
@@ -61,7 +61,7 @@ yum -y install bzip2 make git patch unzip bison yasm diffutils \
 
 wget -q https://cmake.org/files/v3.5/cmake-3.5.2.tar.gz && tar xzf cmake-3.5.2.tar.gz && \
 cd cmake-3.5.2 && ./bootstrap && \
-make -j4 && make install && cd .. && rm cmake-3.5.2.tar.gz
+make -j8 && make install && cd .. && rm cmake-3.5.2.tar.gz
 
 
 # Install newest autoconf
@@ -77,11 +77,13 @@ mkdir -p /opt/python
 build_cpythons $CPYTHON_VERSIONS
 
 PY35_BIN=/opt/python/cp35-cp35m/bin
+PY36_BIN=/opt/python/cp36-cp36m/bin
+PY37_BIN=/opt/python/cp37-cp37m/bin
 # NOTE Since our custom manylinux image builds pythons with shared
 # libpython, we need to add libpython's dir to LD_LIBRARY_PATH before running
 # python.
 ORIGINAL_LD_LIBRARY_PATH="${LD_LIBRARY_PATH}"
-LD_LIBRARY_PATH="${ORIGINAL_LD_LIBRARY_PATH}:$(dirname ${PY35_BIN})/lib"
+LD_LIBRARY_PATH="${ORIGINAL_LD_LIBRARY_PATH}:$(dirname ${PY35_BIN})/lib:$(dirname ${PY36_BIN})/lib:$(dirname ${PY37_BIN})/lib"
 
 # Our openssl doesn't know how to find the system CA trust store
 #   (https://github.com/pypa/manylinux/issues/53)
@@ -119,9 +121,8 @@ ln -s $PY35_BIN/auditwheel /usr/local/bin/auditwheel
 # final image
 yum -y erase wireless-tools gtk2 libX11 hicolor-icon-theme \
     avahi freetype bitstream-vera-fonts \
-    ${PYTHON_COMPILE_DEPS}  > /dev/null 2>&1
-yum -y install ${MANYLINUX1_DEPS}
-yum -y clean all > /dev/null 2>&1
+    ${PYTHON_COMPILE_DEPS}  > /dev/null 2>&1 || true
+yum -y install ${MANYLINUX1_DEPS} && yum -y clean all > /dev/null 2>&1 || true
 yum list installed
 # we don't need libpython*.a, and they're many megabytes
 find /opt/_internal -name '*.a' -print0 | xargs -0 rm -f

tools/manylinux1/build_scripts/build_utils.sh

@@ -50,11 +50,28 @@ function do_cpython_build {
     mkdir -p ${prefix}/lib
     # -Wformat added for https://bugs.python.org/issue17547 on Python 2.6
 
+    if [ $(lex_pyver $py_ver) -eq $(lex_pyver 3.6) ]; then
+        wget https://www.sqlite.org/2018/sqlite-autoconf-3250300.tar.gz
+        tar -zxf sqlite-autoconf-3250300.tar.gz
+        cd sqlite-autoconf-3250300
+        ./configure --prefix=/usr/local
+        make -j8 && make install
+        cd ../ && rm sqlite-autoconf-3250300.tar.gz
+    fi
+
     # NOTE --enable-shared for generating libpython shared library needed for
     # linking of some of the nupic.core test executables.
-    CFLAGS="-Wformat" ./configure --prefix=${prefix} --enable-shared $unicode_flags > /dev/null
-    make -j2 > /dev/null
-    make install > /dev/null
+    if [ $(lex_pyver $py_ver) -ge $(lex_pyver 3.7) ]; then
+        # NOTE python 3.7 should be installed via make altinstall rather than
+        # make install, and we should specify the location of ssl
+        CFLAGS="-Wformat" ./configure --prefix=${prefix} --with-openssl=/usr/local/ssl --enable-shared $unicode_flags > /dev/null
+        make -j8 > /dev/null
+        make altinstall > /dev/null
+    else
+        LD_LIBRARY_PATH=/usr/local/lib:${LD_LIBRARY_PATH} CFLAGS="-Wformat" ./configure --prefix=${prefix} --enable-shared $unicode_flags > /dev/null
+        LD_LIBRARY_PATH=/usr/local/lib:${LD_LIBRARY_PATH} make -j8 > /dev/null
+        LD_LIBRARY_PATH=/usr/local/lib:${LD_LIBRARY_PATH} make install > /dev/null
+    fi
     popd
     echo "ZZZ looking for libpython"
     find / -name 'libpython*.so*'
@@ -64,6 +81,9 @@ function do_cpython_build {
     if [ -e ${prefix}/bin/python3 ]; then
         ln -s python3 ${prefix}/bin/python
     fi
+    if [ -e ${prefix}/bin/python3.7 ]; then
+        ln -s python3.7 ${prefix}/bin/python
+    fi
     # NOTE Make libpython shared library visible to python calls below
     LD_LIBRARY_PATH="${prefix}/lib" ${prefix}/bin/python get-pip.py
     LD_LIBRARY_PATH="${prefix}/lib" ${prefix}/bin/pip install wheel