diff --git a/CMakeLists.txt b/CMakeLists.txt
index e4442d2549..61f5e63098 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -25,12 +25,18 @@ message(STATUS "CXX compiler: ${CMAKE_CXX_COMPILER}, version: "
message(STATUS "C compiler: ${CMAKE_C_COMPILER}, version: "
"${CMAKE_C_COMPILER_ID} ${CMAKE_C_COMPILER_VERSION}")
if(WIN32)
+ set(CMAKE_SUPPRESS_REGENERATION ON)
set(CMAKE_STATIC_LIBRARY_PREFIX lib)
add_definitions("/DGOOGLE_GLOG_DLL_DECL=")
set(CMAKE_C_FLAGS_DEBUG "${CMAKE_C_FLAGS_DEBUG} /bigobj /MTd")
set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} /bigobj /MT")
set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} /bigobj /MTd")
set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /bigobj /MT")
+ add_compile_options(/wd4068 /wd4129 /wd4244 /wd4267 /wd4297 /wd4530 /wd4577 /wd4819 /wd4838)
+ set(PADDLE_LINK_FLAGS "/IGNORE:4006 /IGNORE:4098 /IGNORE:4217 /IGNORE:4221")
+ set(CMAKE_STATIC_LINKER_FLAGS "${CMAKE_STATIC_LINKER_FLAGS} ${PADDLE_LINK_FLAGS}")
+ set(CMAKE_SHARED_LINKER_FLAGS "${CMAKE_SHARED_LINKER_FLAGS} ${PADDLE_LINK_FLAGS}")
+ set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${PADDLE_LINK_FLAGS}")
endif(WIN32)
find_package(CUDA QUIET)
diff --git a/README.md b/README.md
index 32a302cc54..68421cf177 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,6 @@
# PaddlePaddle
+English | [简体中文](./README_cn.md)
[](https://travis-ci.org/PaddlePaddle/Paddle)
[](http://paddlepaddle.org/documentation/docs/en/1.2/getstarted/index_en.html)
@@ -7,7 +8,6 @@
[](https://github.com/PaddlePaddle/Paddle/releases)
[](LICENSE)
-
Welcome to the PaddlePaddle GitHub.
PaddlePaddle (PArallel Distributed Deep LEarning) is an easy-to-use,
@@ -18,16 +18,6 @@ learning to many products at Baidu.
Our vision is to enable deep learning for everyone via PaddlePaddle.
Please refer to our [release announcement](https://github.com/PaddlePaddle/Paddle/releases) to track the latest feature of PaddlePaddle.
-
-欢迎来到 PaddlePaddle GitHub
-
-PaddlePaddle (PArallel Distributed Deep LEarning) 是一个简单易用、高效灵活、可扩展的深度学习平台，最初由百度科学家和工程师共同开发，目的是将深度学习技术应用到百度的众多产品中。
-
-我们的愿景是让每个人都能通过PaddlePaddle接触深度学习
-
-跟进PaddlePaddle最新特性请参考我们的[版本说明](https://github.com/PaddlePaddle/Paddle/releases)
-
-
### Latest PaddlePaddle Release: [Fluid 1.2.0](https://github.com/PaddlePaddle/Paddle/tree/release/1.2)
### Install Latest Stable Release:
```
@@ -43,23 +33,6 @@ pip install paddlepaddle-gpu==1.2.0.post85
# For installation on other platform, refer to http://paddlepaddle.org/
```
-
-### PaddlePaddle最新版本: [Fluid 1.2.0](https://github.com/PaddlePaddle/Paddle/tree/release/1.2)
-### 安装最新稳定版本:
-```
-# Linux CPU
-pip install paddlepaddle
-# Linux GPU cuda9cudnn7
-pip install paddlepaddle-gpu
-# Linux GPU cuda8cudnn7
-pip install paddlepaddle-gpu==1.2.0.post87
-# Linux GPU cuda8cudnn5
-pip install paddlepaddle-gpu==1.2.0.post85
-
-# 其他平台上的安装指引请参考 http://paddlepaddle.org/
-```
-
-
## Features
- **Flexibility**
@@ -100,38 +73,10 @@ pip install paddlepaddle-gpu==1.2.0.post85
Baidu and it has achieved a significant impact. We hope you can also explore
the capability of PaddlePaddle to make an impact on your product.
-## 特点
-
-- **灵活性**
-
- PaddlePaddle支持丰富的神经网络架构和优化算法。易于配置复杂模型，例如带有注意力机制或复杂记忆连接的神经网络机器翻译模型。
-
-- **高效性**
-
- 为了高效使用异步计算资源，PaddlePaddle对框架的不同层进行优化，包括计算、存储、架构和通信。下面是一些样例：
-
- - 通过SSE/AVX 内置函数、BLAS库(例如MKL、OpenBLAS、cuBLAS)或定制的CPU/GPU内核优化数学操作。
- - 通过MKL-DNN库优化CNN网络
- - 高度优化循环网络，无需执行 `padding` 操作即可处理 **变长** 序列
- - 针对高维稀疏数据模型，优化了局部和分布式训练。
-
-
-- **稳定性**
-
- 有了 PaddlePaddle，使得利用各种CPU/GPU和机器来加速训练变得简单。PaddlePaddle 通过优化通信可以实现巨大吞吐量和快速执行。
-
-- **连接产品**
-
- 另外，PaddlePaddle 的设计也易于部署。在百度，PaddlePaddle 已经部署到含有巨大用户量的产品和服务上，包括广告点击率（CTR）预测、大规模图像分类、光学字符识别（OCR）、搜索排序，计算机病毒检测、推荐系统等等。PaddlePaddle广泛应用于百度产品中，产生了非常重要的影响。我们希望您也能探索 PaddlePaddle 的能力，为您的产品创造新的影响力和效果。
-
## Installation
It is recommended to read [this doc](http://paddlepaddle.org/documentation/docs/zh/1.2/beginners_guide/install/index_cn.html) on our website.
-## 安装
-
-推荐阅读官网上的[安装说明](http://paddlepaddle.org/documentation/docs/zh/1.2/beginners_guide/install/index_cn.html)
-
## Documentation
We provide [English](http://paddlepaddle.org/documentation/docs/en/1.2/getstarted/index_en.html) and
@@ -153,37 +98,9 @@ We provide [English](http://paddlepaddle.org/documentation/docs/en/1.2/getstarte
We appreciate your contributions!
-## 文档
-
-我们提供[英文](http://paddlepaddle.org/documentation/docs/en/1.2/getstarted/index_en.html)和
-[中文](http://paddlepaddle.org/documentation/docs/zh/1.2/beginners_guide/index.html) 文档
-
-- [深度学习101](https://github.com/PaddlePaddle/book)
-
- 或许您想从这个在线交互式书籍开始，可以在Jupyter Notebook中运行
-
-- [分布式训练](http://paddlepaddle.org/documentation/docs/zh/1.2/user_guides/howto/training/cluster_howto.html)
-
- 可以在MPI集群上运行分布式训练任务
-
-- [Python API](http://paddlepaddle.org/documentation/docs/zh/1.2/api_cn/index_cn.html)
-
- 新的API支持代码更少更简洁的程序
-
-- [贡献方式](http://paddlepaddle.org/documentation/docs/zh/1.2/advanced_usage/development/contribute_to_paddle/index_cn.html)
-
- 欢迎您的贡献!
-
## Ask Questions
You are welcome to submit questions and bug reports as [Github Issues](https://github.com/PaddlePaddle/Paddle/issues).
-## 答疑
-
-欢迎您将问题和bug报告以[Github Issues](https://github.com/PaddlePaddle/Paddle/issues)的形式提交
-
## Copyright and License
PaddlePaddle is provided under the [Apache-2.0 license](LICENSE).
-
-## 版权和许可证
-PaddlePaddle由[Apache-2.0 license](LICENSE)提供
diff --git a/README_cn.md b/README_cn.md
new file mode 100644
index 0000000000..dfb55b17ca
--- /dev/null
+++ b/README_cn.md
@@ -0,0 +1,88 @@
+# PaddlePaddle
+
+[English](./README.md) | 简体中文
+
+[](https://travis-ci.org/PaddlePaddle/Paddle)
+[](http://paddlepaddle.org/documentation/docs/en/1.2/getstarted/index_en.html)
+[](http://paddlepaddle.org/documentation/docs/zh/1.2/beginners_guide/index.html)
+[](https://github.com/PaddlePaddle/Paddle/releases)
+[](LICENSE)
+
+欢迎来到 PaddlePaddle GitHub
+
+PaddlePaddle (PArallel Distributed Deep LEarning) 是一个简单易用、高效灵活、可扩展的深度学习平台，最初由百度科学家和工程师共同开发，目的是将深度学习技术应用到百度的众多产品中。
+
+我们的愿景是让每个人都能通过PaddlePaddle接触深度学习
+
+跟进PaddlePaddle最新特性请参考我们的[版本说明](https://github.com/PaddlePaddle/Paddle/releases)
+
+### PaddlePaddle最新版本: [Fluid 1.2.0](https://github.com/PaddlePaddle/Paddle/tree/release/1.2)
+### 安装最新稳定版本:
+```
+# Linux CPU
+pip install paddlepaddle
+# Linux GPU cuda9cudnn7
+pip install paddlepaddle-gpu
+# Linux GPU cuda8cudnn7
+pip install paddlepaddle-gpu==1.2.0.post87
+# Linux GPU cuda8cudnn5
+pip install paddlepaddle-gpu==1.2.0.post85
+
+# 其他平台上的安装指引请参考 http://paddlepaddle.org/
+```
+
+## 特性
+
+- **灵活性**
+
+ PaddlePaddle支持丰富的神经网络架构和优化算法。易于配置复杂模型，例如带有注意力机制或复杂记忆连接的神经网络机器翻译模型。
+
+- **高效性**
+
+ 为了高效使用异步计算资源，PaddlePaddle对框架的不同层进行优化，包括计算、存储、架构和通信。下面是一些样例：
+
+ - 通过SSE/AVX 内置函数、BLAS库(例如MKL、OpenBLAS、cuBLAS)或定制的CPU/GPU内核优化数学操作。
+ - 通过MKL-DNN库优化CNN网络
+ - 高度优化循环网络，无需执行 `padding` 操作即可处理 **变长** 序列
+ - 针对高维稀疏数据模型，优化了局部和分布式训练。
+
+
+- **稳定性**
+
+ 有了 PaddlePaddle，使得利用各种CPU/GPU和机器来加速训练变得简单。PaddlePaddle 通过优化通信可以实现巨大吞吐量和快速执行。
+
+- **与产品相连**
+
+ 另外，PaddlePaddle 的设计也易于部署。在百度，PaddlePaddle 已经部署到含有巨大用户量的产品和服务上，包括广告点击率（CTR）预测、大规模图像分类、光学字符识别（OCR）、搜索排序，计算机病毒检测、推荐系统等等。PaddlePaddle广泛应用于百度产品中，产生了非常重要的影响。我们希望您也能探索 PaddlePaddle 的能力，为您的产品创造新的影响力和效果。
+
+## 安装
+
+推荐阅读官网上的[安装说明](http://paddlepaddle.org/documentation/docs/zh/1.2/beginners_guide/install/index_cn.html)
+
+## 文档
+
+我们提供[英文](http://paddlepaddle.org/documentation/docs/en/1.2/getstarted/index_en.html)和
+[中文](http://paddlepaddle.org/documentation/docs/zh/1.2/beginners_guide/index.html) 文档
+
+- [深度学习101](https://github.com/PaddlePaddle/book)
+
+ 或许您想从这个在线交互式书籍开始，可以在Jupyter Notebook中运行
+
+- [分布式训练](http://paddlepaddle.org/documentation/docs/zh/1.2/user_guides/howto/training/cluster_howto.html)
+
+ 可以在MPI集群上运行分布式训练任务
+
+- [Python API](http://paddlepaddle.org/documentation/docs/zh/1.2/api_cn/index_cn.html)
+
+ 新的API支持代码更少更简洁的程序
+
+- [贡献方式](http://paddlepaddle.org/documentation/docs/zh/1.2/advanced_usage/development/contribute_to_paddle/index_cn.html)
+
+ 欢迎您的贡献!
+
+## 答疑
+
+欢迎您将问题和bug报告以[Github Issues](https://github.com/PaddlePaddle/Paddle/issues)的形式提交
+
+## 版权和许可证
+PaddlePaddle由[Apache-2.0 license](LICENSE)提供
diff --git a/cmake/configure.cmake b/cmake/configure.cmake
index 076e839120..b0f54bf49a 100644
--- a/cmake/configure.cmake
+++ b/cmake/configure.cmake
@@ -152,7 +152,12 @@ endif()
if (WITH_MKLML AND MKLML_IOMP_LIB)
message(STATUS "Enable Intel OpenMP with ${MKLML_IOMP_LIB}")
- set(OPENMP_FLAGS "-fopenmp")
+ if(WIN32)
+ # openmp not support well for now on windows
+ set(OPENMP_FLAGS "")
+ else(WIN32)
+ set(OPENMP_FLAGS "-fopenmp")
+ endif(WIN32)
set(CMAKE_C_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
set(CMAKE_CXX_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OPENMP_FLAGS}")
diff --git a/cmake/cuda.cmake b/cmake/cuda.cmake
index ea46f6418e..ef4192ecc9 100644
--- a/cmake/cuda.cmake
+++ b/cmake/cuda.cmake
@@ -203,25 +203,26 @@ list(APPEND CUDA_NVCC_FLAGS "-w")
list(APPEND CUDA_NVCC_FLAGS "--expt-relaxed-constexpr")
if (NOT WIN32)
-if(CMAKE_BUILD_TYPE STREQUAL "Debug")
- list(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_DEBUG})
-elseif(CMAKE_BUILD_TYPE STREQUAL "Release")
- list(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_RELEASE})
-elseif(CMAKE_BUILD_TYPE STREQUAL "RelWithDebInfo")
- list(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_RELWITHDEBINFO})
-elseif(CMAKE_BUILD_TYPE STREQUAL "MinSizeRel")
- # nvcc 9 does not support -Os. Use Release flags instead
- list(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_RELEASE})
-endif()
+ if(CMAKE_BUILD_TYPE STREQUAL "Debug")
+ list(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_DEBUG})
+ elseif(CMAKE_BUILD_TYPE STREQUAL "Release")
+ list(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_RELEASE})
+ elseif(CMAKE_BUILD_TYPE STREQUAL "RelWithDebInfo")
+ list(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_RELWITHDEBINFO})
+ elseif(CMAKE_BUILD_TYPE STREQUAL "MinSizeRel")
+ # nvcc 9 does not support -Os. Use Release flags instead
+ list(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_RELEASE})
+ endif()
else(NOT WIN32)
-list(APPEND CUDA_NVCC_FLAGS "--compiler-options;/bigobj")
-if(CMAKE_BUILD_TYPE STREQUAL "Debug")
- list(APPEND CUDA_NVCC_FLAGS "-g -G")
- # match the cl's _ITERATOR_DEBUG_LEVEL
- list(APPEND CUDA_NVCC_FLAGS "-D_DEBUG")
-elseif(CMAKE_BUILD_TYPE STREQUAL "Release")
- list(APPEND CUDA_NVCC_FLAGS "-O3 -DNDEBUG")
-else()
+ list(APPEND CUDA_NVCC_FLAGS "-Xcompiler \"/wd 4244 /wd 4267 /wd 4819\"")
+ list(APPEND CUDA_NVCC_FLAGS "--compiler-options;/bigobj")
+ if(CMAKE_BUILD_TYPE STREQUAL "Debug")
+ list(APPEND CUDA_NVCC_FLAGS "-g -G")
+ # match the cl's _ITERATOR_DEBUG_LEVEL
+ list(APPEND CUDA_NVCC_FLAGS "-D_DEBUG")
+ elseif(CMAKE_BUILD_TYPE STREQUAL "Release")
+ list(APPEND CUDA_NVCC_FLAGS "-O3 -DNDEBUG")
+ else()
message(FATAL "Windows only support Release or Debug build now. Please set visual studio build type to Release/Debug, x64 build.")
endif()
endif(NOT WIN32)
diff --git a/cmake/external/glog.cmake b/cmake/external/glog.cmake
index 7a6a452388..d3a4d69d3a 100644
--- a/cmake/external/glog.cmake
+++ b/cmake/external/glog.cmake
@@ -20,8 +20,10 @@ SET(GLOG_INCLUDE_DIR "${GLOG_INSTALL_DIR}/include" CACHE PATH "glog include dire
IF(WIN32)
SET(GLOG_LIBRARIES "${GLOG_INSTALL_DIR}/lib/libglog.lib" CACHE FILEPATH "glog library." FORCE)
+ SET(GLOG_CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /wd4267 /wd4530")
ELSE(WIN32)
SET(GLOG_LIBRARIES "${GLOG_INSTALL_DIR}/lib/libglog.a" CACHE FILEPATH "glog library." FORCE)
+ SET(GLOG_CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS})
ENDIF(WIN32)
INCLUDE_DIRECTORIES(${GLOG_INCLUDE_DIR})
@@ -39,7 +41,7 @@ ExternalProject_Add(
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
- -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
+ -DCMAKE_CXX_FLAGS=${GLOG_CMAKE_CXX_FLAGS}
-DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
-DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
diff --git a/cmake/external/mkldnn.cmake b/cmake/external/mkldnn.cmake
index 6a7be73f09..92fe76d05c 100644
--- a/cmake/external/mkldnn.cmake
+++ b/cmake/external/mkldnn.cmake
@@ -49,6 +49,8 @@ IF(NOT WIN32)
SET(MKLDNN_FLAG "${MKLDNN_FLAG} -Wno-unused-result -Wno-unused-value")
SET(MKLDNN_CFLAG "${CMAKE_C_FLAGS} ${MKLDNN_FLAG}")
SET(MKLDNN_CXXFLAG "${CMAKE_CXX_FLAGS} ${MKLDNN_FLAG}")
+ELSE()
+ SET(MKLDNN_CXXFLAG "${CMAKE_CXX_FLAGS} /EHsc")
ENDIF(NOT WIN32)
ExternalProject_Add(
@@ -61,7 +63,6 @@ ExternalProject_Add(
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
CMAKE_ARGS -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
- CMAKE_ARGS -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
CMAKE_ARGS -DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
CMAKE_ARGS -DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
CMAKE_ARGS -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
diff --git a/cmake/external/snappy.cmake b/cmake/external/snappy.cmake
index 27d075336d..1e01057aa6 100644
--- a/cmake/external/snappy.cmake
+++ b/cmake/external/snappy.cmake
@@ -20,6 +20,12 @@ 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)
+if(WIN32)
+ SET(SNAPPY_CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /wd4244 /wd4267")
+else()
+ SET(SNAPPY_CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS})
+endif()
+
ExternalProject_Add(
extern_snappy
GIT_REPOSITORY "https://github.com/google/snappy"
@@ -31,7 +37,7 @@ ExternalProject_Add(
-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
-DCMAKE_C_FLAGS_DEBUG=${CMAKE_C_FLAGS_DEBUG}
-DCMAKE_C_FLAGS_RELEASE=${CMAKE_C_FLAGS_RELEASE}
- -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
+ -DCMAKE_CXX_FLAGS=${SNAPPY_CMAKE_CXX_FLAGS}
-DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
-DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
-DCMAKE_INSTALL_PREFIX=${SNAPPY_INSTALL_DIR}
diff --git a/cmake/flags.cmake b/cmake/flags.cmake
index 9e6c47f016..36b533aa4f 100644
--- a/cmake/flags.cmake
+++ b/cmake/flags.cmake
@@ -21,7 +21,7 @@ function(CheckCompilerCXX11Flag)
if (${CMAKE_CXX_COMPILER_VERSION} VERSION_LESS 3.3)
message(FATAL_ERROR "Unsupported Clang version. Clang >= 3.3 required.")
endif()
- endif()
+ endif()
endif()
endfunction()
@@ -147,12 +147,7 @@ set(GPU_COMMON_FLAGS
-Wno-error=unused-function # Warnings in Numpy Header.
-Wno-error=array-bounds # Warnings in Eigen::array
)
-
-else(NOT WIN32)
-set(COMMON_FLAGS
- "/w") #disable all warnings.
-set(GPU_COMMON_FLAGS
- "/w") #disable all warnings
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -m64")
endif(NOT WIN32)
if (APPLE)
@@ -193,8 +188,7 @@ safe_set_static_flag()
CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO
CMAKE_C_FLAGS CMAKE_C_FLAGS_DEBUG CMAKE_C_FLAGS_RELEASE
CMAKE_C_FLAGS_MINSIZEREL CMAKE_C_FLAGS_RELWITHDEBINFO)
- if(${flag_var} MATCHES "/W3")
- string(REGEX REPLACE "/W3" "/w" ${flag_var} "${${flag_var}}")
- endif(${flag_var} MATCHES "/W3")
+ string(REGEX REPLACE "(^| )/W[0-9]( |$)" " " ${flag_var} "${${flag_var}}")
+ set(flag_var "${flag_var} /w")
endforeach(flag_var)
endif(WIN32)
diff --git a/cmake/version.cmake b/cmake/version.cmake
index ac10bdf067..dd57d4ab99 100644
--- a/cmake/version.cmake
+++ b/cmake/version.cmake
@@ -31,8 +31,23 @@ while ("${PADDLE_VERSION}" STREQUAL "")
set(tmp_version "${GIT_TAG_NAME}~1")
endif()
else()
- # otherwise, we always set PADDLE_VERSION to 0.0.0 to represent latest
- set(PADDLE_VERSION "0.0.0")
+ execute_process(
+ COMMAND ${GIT_EXECUTABLE} describe --exact-match --tags ${tmp_version}
+ WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}
+ OUTPUT_VARIABLE GIT_EXACT_TAG_NAME
+ RESULT_VARIABLE GIT_EXACT_TAG_RESULT
+ ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
+ if (NOT ${GIT_EXACT_TAG_NAME})
+ # Check if current branch is tag branch
+ if (${GIT_EXACT_TAG_NAME} MATCHES "v${TAG_VERSION_REGEX}")
+ string(REPLACE "v" "" PADDLE_VERSION ${GIT_EXACT_TAG_NAME})
+ else()
+ set(PADDLE_VERSION "0.0.0")
+ endif()
+ else()
+ # otherwise, we always set PADDLE_VERSION to 0.0.0 to represent latest
+ set(PADDLE_VERSION "0.0.0")
+ endif()
endif()
else()
set(PADDLE_VERSION "0.0.0")
diff --git a/paddle/fluid/API.spec b/paddle/fluid/API.spec
index 84b4677777..df961be911 100644
--- a/paddle/fluid/API.spec
+++ b/paddle/fluid/API.spec
@@ -8,13 +8,13 @@ paddle.fluid.Program.parse_from_string ArgSpec(args=['binary_str'], varargs=None
paddle.fluid.Program.to_string ArgSpec(args=['self', 'throw_on_error', 'with_details'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.default_startup_program ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
paddle.fluid.default_main_program ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
-paddle.fluid.program_guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
-paddle.fluid.name_scope ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.program_guard ArgSpec(args=['main_program', 'startup_program'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.name_scope ArgSpec(args=['prefix'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.Executor.__init__ ArgSpec(args=['self', 'place'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Executor.close ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Executor.run ArgSpec(args=['self', 'program', 'feed', 'fetch_list', 'feed_var_name', 'fetch_var_name', 'scope', 'return_numpy', 'use_program_cache'], varargs=None, keywords=None, defaults=(None, None, None, 'feed', 'fetch', None, True, False))
paddle.fluid.global_scope ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
-paddle.fluid.scope_guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.scope_guard ArgSpec(args=['scope'], varargs=None, keywords=None, defaults=None)
paddle.fluid.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
paddle.fluid.DistributeTranspiler.get_pserver_programs ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
@@ -66,7 +66,7 @@ paddle.fluid.initializer.XavierInitializer.__init__ ArgSpec(args=['self', 'unifo
paddle.fluid.initializer.BilinearInitializer.__init__ ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.initializer.MSRAInitializer.__init__ ArgSpec(args=['self', 'uniform', 'fan_in', 'seed'], varargs=None, keywords=None, defaults=(True, None, 0))
paddle.fluid.initializer.force_init_on_cpu ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
-paddle.fluid.initializer.init_on_cpu ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.initializer.init_on_cpu ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
paddle.fluid.initializer.NumpyArrayInitializer.__init__ ArgSpec(args=['self', 'value'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.fc ArgSpec(args=['input', 'size', 'num_flatten_dims', 'param_attr', 'bias_attr', 'act', 'is_test', 'name'], varargs=None, keywords=None, defaults=(1, None, None, None, False, None))
paddle.fluid.layers.embedding ArgSpec(args=['input', 'size', 'is_sparse', 'is_distributed', 'padding_idx', 'param_attr', 'dtype'], varargs=None, keywords=None, defaults=(False, False, None, None, 'float32'))
@@ -229,7 +229,7 @@ paddle.fluid.layers.random_data_generator ArgSpec(args=['low', 'high', 'shapes',
paddle.fluid.layers.py_reader ArgSpec(args=['capacity', 'shapes', 'dtypes', 'lod_levels', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, None, True))
paddle.fluid.layers.create_py_reader_by_data ArgSpec(args=['capacity', 'feed_list', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, True))
paddle.fluid.layers.Preprocessor.__init__ ArgSpec(args=['self', 'reader', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.Preprocessor.block ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.layers.Preprocessor.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.Preprocessor.inputs ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.Preprocessor.outputs ArgSpec(args=['self'], varargs='outs', keywords=None, defaults=None)
paddle.fluid.layers.load ArgSpec(args=['out', 'file_path', 'load_as_fp16'], varargs=None, keywords=None, defaults=(None,))
@@ -270,7 +270,7 @@ paddle.fluid.layers.IfElse.input ArgSpec(args=['self', 'x'], varargs=None, keywo
paddle.fluid.layers.IfElse.output ArgSpec(args=['self'], varargs='outs', keywords=None, defaults=None)
paddle.fluid.layers.IfElse.true_block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.DynamicRNN.__init__ ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.DynamicRNN.block ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.layers.DynamicRNN.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.DynamicRNN.memory ArgSpec(args=['self', 'init', 'shape', 'value', 'need_reorder', 'dtype'], varargs=None, keywords=None, defaults=(None, None, 0.0, False, 'float32'))
paddle.fluid.layers.DynamicRNN.output ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None)
paddle.fluid.layers.DynamicRNN.static_input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None)
@@ -325,6 +325,7 @@ paddle.fluid.layers.iou_similarity ArgSpec(args=['x', 'y', 'name'], varargs=None
paddle.fluid.layers.box_coder ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name', 'axis'], varargs=None, keywords=None, defaults=('encode_center_size', True, None, 0))
paddle.fluid.layers.polygon_box_transform ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.yolov3_loss ArgSpec(args=['x', 'gtbox', 'gtlabel', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample_ratio', 'name'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.layers.box_clip ArgSpec(args=['input', 'im_info', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.multiclass_nms ArgSpec(args=['bboxes', 'scores', 'score_threshold', 'nms_top_k', 'keep_top_k', 'nms_threshold', 'normalized', 'nms_eta', 'background_label', 'name'], varargs=None, keywords=None, defaults=(0.3, True, 1.0, 0, None))
paddle.fluid.layers.accuracy ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None))
paddle.fluid.layers.auc ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1))
@@ -345,12 +346,12 @@ paddle.fluid.contrib.StateCell.set_state ArgSpec(args=['self', 'state_name', 'st
paddle.fluid.contrib.StateCell.state_updater ArgSpec(args=['self', 'updater'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.StateCell.update_states ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.TrainingDecoder.__init__ ArgSpec(args=['self', 'state_cell', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.contrib.TrainingDecoder.block ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.contrib.TrainingDecoder.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.TrainingDecoder.output ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None)
paddle.fluid.contrib.TrainingDecoder.static_input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.TrainingDecoder.step_input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.BeamSearchDecoder.__init__ ArgSpec(args=['self', 'state_cell', 'init_ids', 'init_scores', 'target_dict_dim', 'word_dim', 'input_var_dict', 'topk_size', 'sparse_emb', 'max_len', 'beam_size', 'end_id', 'name'], varargs=None, keywords=None, defaults=({}, 50, True, 100, 1, 1, None))
-paddle.fluid.contrib.BeamSearchDecoder.block ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.contrib.BeamSearchDecoder.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.BeamSearchDecoder.decode ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.BeamSearchDecoder.early_stop ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.BeamSearchDecoder.read_array ArgSpec(args=['self', 'init', 'is_ids', 'is_scores'], varargs=None, keywords=None, defaults=(False, False))
@@ -455,7 +456,7 @@ paddle.fluid.optimizer.AdadeltaOptimizer.apply_gradients ArgSpec(args=['self', '
paddle.fluid.optimizer.AdadeltaOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.optimizer.AdadeltaOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.ModelAverage.__init__ ArgSpec(args=['self', 'average_window_rate', 'min_average_window', 'max_average_window', 'regularization', 'name'], varargs=None, keywords=None, defaults=(10000, 10000, None, None))
-paddle.fluid.optimizer.ModelAverage.apply ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.optimizer.ModelAverage.apply ArgSpec(args=['self', 'executor', 'need_restore'], varargs=None, keywords=None, defaults=(True,))
paddle.fluid.optimizer.ModelAverage.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
paddle.fluid.optimizer.ModelAverage.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.optimizer.ModelAverage.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
@@ -490,14 +491,14 @@ paddle.fluid.clip.ErrorClipByValue.__init__ ArgSpec(args=['self', 'max', 'min'],
paddle.fluid.clip.GradientClipByValue.__init__ ArgSpec(args=['self', 'max', 'min'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.clip.GradientClipByNorm.__init__ ArgSpec(args=['self', 'clip_norm'], varargs=None, keywords=None, defaults=None)
paddle.fluid.clip.GradientClipByGlobalNorm.__init__ ArgSpec(args=['self', 'clip_norm', 'group_name'], varargs=None, keywords=None, defaults=('default_group',))
-paddle.fluid.profiler.cuda_profiler ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.profiler.cuda_profiler ArgSpec(args=['output_file', 'output_mode', 'config'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.profiler.reset_profiler ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
-paddle.fluid.profiler.profiler ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.profiler.profiler ArgSpec(args=['state', 'sorted_key', 'profile_path'], varargs=None, keywords=None, defaults=(None, '/tmp/profile'))
paddle.fluid.profiler.start_profiler ArgSpec(args=['state'], varargs=None, keywords=None, defaults=None)
paddle.fluid.profiler.stop_profiler ArgSpec(args=['sorted_key', 'profile_path'], varargs=None, keywords=None, defaults=(None, '/tmp/profile'))
paddle.fluid.unique_name.generate ArgSpec(args=['key'], varargs=None, keywords=None, defaults=None)
paddle.fluid.unique_name.switch ArgSpec(args=['new_generator'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.unique_name.guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
+paddle.fluid.unique_name.guard ArgSpec(args=['new_generator'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.recordio_writer.convert_reader_to_recordio_file ArgSpec(args=['filename', 'reader_creator', 'feeder', 'compressor', 'max_num_records', 'feed_order'], varargs=None, keywords=None, defaults=(Compressor.Snappy, 1000, None))
paddle.fluid.recordio_writer.convert_reader_to_recordio_files ArgSpec(args=['filename', 'batch_per_file', 'reader_creator', 'feeder', 'compressor', 'max_num_records', 'feed_order'], varargs=None, keywords=None, defaults=(Compressor.Snappy, 1000, None))
paddle.fluid.Scope Scope() -> paddle.fluid.core._Scope
diff --git a/paddle/fluid/framework/CMakeLists.txt b/paddle/fluid/framework/CMakeLists.txt
index 66f11dedba..7ddf1ab44f 100644
--- a/paddle/fluid/framework/CMakeLists.txt
+++ b/paddle/fluid/framework/CMakeLists.txt
@@ -128,7 +128,7 @@ cc_test(version_test SRCS version_test.cc DEPS version)
cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS shape_inference op_info operator glog version)
-cc_library(op_registry SRCS op_registry.cc DEPS op_proto_maker op_info operator glog proto_desc)
+cc_library(op_registry SRCS op_registry.cc DEPS op_proto_maker op_info operator glog proto_desc memory_optimize_helper)
nv_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry)
py_proto_compile(framework_py_proto SRCS framework.proto data_feed.proto)
@@ -158,18 +158,19 @@ cc_library(variable_helper SRCS variable_helper.cc DEPS lod_tensor)
cc_library(naive_executor SRCS naive_executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass variable_helper)
-if(WITH_DISTRIBUTE)
- cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog
- lod_rank_table feed_fetch_method sendrecvop_rpc ${GLOB_DISTRIBUTE_DEPS} graph_to_program_pass variable_helper)
+if(WITH_NGRAPH)
+ set(NGRAPH_EXE_DEPS ngraph_engine)
+else()
+ set(NGRAPH_EXE_DEPS)
+endif()
- set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
- set_source_files_properties(executor.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
+if(WITH_DISTRIBUTE)
+ cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog
+ lod_rank_table feed_fetch_method sendrecvop_rpc ${GLOB_DISTRIBUTE_DEPS} graph_to_program_pass variable_helper ${NGRAPH_EXE_DEPS})
+ set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
+ set_source_files_properties(executor.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
else()
- if (WITH_NGRAPH)
- cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass variable_helper ngraph_engine)
- else ()
- cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass variable_helper)
- endif()
+ cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass variable_helper ${NGRAPH_EXE_DEPS})
cc_test(test_naive_executor SRCS naive_executor_test.cc DEPS naive_executor elementwise_add_op)
endif()
@@ -192,6 +193,7 @@ 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)
cc_test(var_type_inference_test SRCS var_type_inference_test.cc DEPS op_registry
proto_desc)
+cc_test(inplace_op_inference_test SRCS inplace_op_inference_test.cc DEPS op_registry proto_desc op_info memory_optimize_helper)
cc_library(selected_rows SRCS selected_rows.cc DEPS tensor)
cc_test(selected_rows_test SRCS selected_rows_test.cc DEPS selected_rows)
diff --git a/paddle/fluid/framework/details/CMakeLists.txt b/paddle/fluid/framework/details/CMakeLists.txt
index d5966ad5a9..e88084424b 100644
--- a/paddle/fluid/framework/details/CMakeLists.txt
+++ b/paddle/fluid/framework/details/CMakeLists.txt
@@ -50,10 +50,10 @@ cc_library(data_balance_op_handle SRCS data_balance_op_handle.cc DEPS op_handle_
cc_library(gather_op_handle SRCS gather_op_handle.cc DEPS op_handle_base scope ddim memory variable_visitor)
cc_library(fuse_vars_op_handle SRCS fuse_vars_op_handle.cc DEPS op_handle_base scope)
-cc_library(memory_optimize_pass SRCS analysis_var_pass.cc memory_reuse_types.cc DEPS graph graph_helper pass)
+cc_library(memory_optimize_helper SRCS memory_optimize_helper.cc DEPS graph graph_helper)
+cc_library(memory_optimize_pass SRCS memory_optimize_pass.cc DEPS memory_optimize_helper pass)
+cc_library(inplace_op_pass SRCS inplace_op_pass.cc DEPS memory_optimize_pass op_info)
cc_library(modify_op_lock_and_record_event_pass SRCS modify_op_lock_and_record_event_pass.cc DEPS computation_op_handle op_graph_view multi_devices_helper)
-cc_library(memory_early_delete_pass SRCS memory_early_delete_pass.cc DEPS memory_optimize_pass computation_op_handle scale_loss_grad_op_handle rpc_op_handle
- all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle graph graph_helper pass)
cc_library(reference_count_pass_helper SRCS reference_count_pass_helper.cc DEPS garbage_collector computation_op_handle)
cc_library(eager_deletion_op_handle SRCS eager_deletion_op_handle.cc DEPS lod_tensor selected_rows reference_count_pass_helper)
cc_library(eager_deletion_pass SRCS eager_deletion_pass.cc DEPS computation_op_handle eager_deletion_op_handle graph graph_helper pass)
@@ -65,13 +65,11 @@ cc_library(all_reduce_deps_pass SRCS all_reduce_deps_pass.cc DEPS graph graph_he
cc_library(multi_devices_graph_pass SRCS multi_devices_graph_pass.cc DEPS multi_devices_helper computation_op_handle
scale_loss_grad_op_handle rpc_op_handle all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle fused_broadcast_op_handle)
-set(SSA_GRAPH_EXECUTOR_DEPS graph framework_proto sequential_execution_pass modify_op_lock_and_record_event_pass all_reduce_deps_pass reference_count_pass eager_deletion_pass memory_optimize_pass memory_early_delete_pass)
+set(SSA_GRAPH_EXECUTOR_DEPS graph framework_proto sequential_execution_pass modify_op_lock_and_record_event_pass all_reduce_deps_pass reference_count_pass eager_deletion_pass memory_optimize_pass inplace_op_pass)
if (WITH_GPU)
list(APPEND SSA_GRAPH_EXECUTOR_DEPS reference_count_pass)
endif()
-cc_test(memory_reuse_types_test SRCS memory_reuse_types_test.cc memory_reuse_types.cc DEPS framework_proto graph)
-cc_test(analysis_var_pass_test SRCS analysis_var_pass_test.cc analysis_var_pass.cc memory_reuse_types.cc DEPS framework_proto graph graph_helper op_registry pass)
-
+cc_test(memory_optimize_helper_test SRCS memory_optimize_helper_test.cc memory_optimize_helper.cc DEPS framework_proto graph graph_helper op_registry)
cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS ${SSA_GRAPH_EXECUTOR_DEPS})
cc_library(threaded_ssa_graph_executor SRCS threaded_ssa_graph_executor.cc DEPS fetch_op_handle ssa_graph_executor scope
diff --git a/paddle/fluid/framework/details/analysis_var_pass.cc b/paddle/fluid/framework/details/analysis_var_pass.cc
deleted file mode 100644
index 223b9da3cf..0000000000
--- a/paddle/fluid/framework/details/analysis_var_pass.cc
+++ /dev/null
@@ -1,656 +0,0 @@
-// 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/details/analysis_var_pass.h"
-#include <algorithm>
-#include <atomic>
-#include <deque>
-#include <fstream>
-#include <iostream>
-#include <iterator>
-#include <memory>
-#include <queue>
-#include <sstream>
-#include <string>
-#include <type_traits>
-#include <vector>
-#include "gflags/gflags.h"
-#include "paddle/fluid/framework/data_type.h"
-#include "paddle/fluid/framework/ir/graph.h"
-#include "paddle/fluid/framework/ir/graph_helper.h"
-
-DEFINE_bool(enable_subgraph_optimize, false,
- "SubGraph also reuse global graph variables, it will reduce the "
- "memory occupation"
- "but a higher risk of memory reuse error. default disabled.");
-DEFINE_string(memory_optimize_debug, "",
- "debug the operator output variable when do the variable reuse."
- "memory reuse pass."
- "only for debug, default disabled.");
-
-namespace paddle {
-namespace framework {
-namespace details {
-
-static inline bool IsSameDesc(OpDesc* op1, OpDesc* op2) {
- return op1->Type() == op2->Type() && op1->Inputs() == op2->Inputs() &&
- op1->Outputs() == op2->Outputs();
-}
-
-template <typename Container, typename Callback>
-class FilterVariableImpl {
- public:
- void operator()(const Container& nodes, Callback callback) {
- for (auto* node : nodes) {
- callback(node);
- }
- }
-};
-
-// filter var node for op->inputs/outputs
-template <typename Callback>
-class FilterVariableImpl<std::vector<ir::Node*>, Callback> {
- public:
- void operator()(const std::vector<ir::Node*>& nodes, Callback callback) {
- for (auto* var : nodes) {
- if (var->IsVar() && !var->IsCtrlVar()) {
- callback(var);
- }
- }
- }
-};
-
-template <typename Container, typename Callback>
-void FilterVariables(const Container& nodes, Callback callback) {
- FilterVariableImpl<Container, Callback>()(nodes, callback);
-}
-
-std::unique_ptr<ir::Graph> AnalysisVarPass::ApplyImpl(
- std::unique_ptr<ir::Graph> graph) const {
- auto nodes = graph->Nodes();
- auto subblock_vars = GetSubBlockVars(nodes);
- skip_set_.insert(subblock_vars.begin(), subblock_vars.end());
-
- cfg_.reset(new details::ControlFlowGraph(*graph));
- cfg_->LiveVariableAnalysis();
- InitSSAGraphNodes();
-
- int reuse_id = 0;
- for (size_t idx = 0; idx < cfg_->Ops().size(); ++idx) {
- auto& op = cfg_->Ops()[idx];
- auto* op_desc = op->Op();
- // some op in graph has no op desc
- if (op_desc == nullptr) continue;
- if (OpHasSubBlock(op_desc)) {
- if (FLAGS_enable_subgraph_optimize) {
- SubGraphOptimize(op_desc);
- } else {
- VLOG(3) << op->Name()
- << " has subblock, but disable subgraph optimize. skipped.";
- continue;
- }
- }
-
- for (auto& var : op->outputs) {
- if (NodeCanReused(var) && cfg_->Use(op).count(var->Name()) == 0) {
- ir::Node* cache = pool_.NodeMatch(var);
- if (var->Name() == FLAGS_memory_optimize_debug) {
- VLOG(3) << "start match var " << DebugString(var) << " of op "
- << op->Name();
- VLOG(3) << pool_.ToString();
- VLOG(3) << "matched in pool : "
- << ((cache == nullptr) ? "False" : "True");
- }
- if (cache != nullptr) {
- if (var->Name() == cache->Name()) {
- VLOG(3) << "The same cache variable is cascade reused."
- << var->Name() << " is re-filled to the pool after"
- << "the reused op is finished. Current op can not "
- << "replace it again. Skip this candidate.";
- continue;
- }
-
- int node_idx_in_pool = pool_.GetIndex(cache);
- VLOG(3) << string::Sprintf(
- "!!! %s, %s => %s, cache idx %d, pool size %d",
- std::to_string(reuse_id++), DebugString(var), DebugString(cache),
- node_idx_in_pool, static_cast<int>(pool_.size()));
- // update CFG Graph on the fly.
- // reused var maybe re-fill into the pool
- cfg_->RenameVarInCFGGraph(var->Name(), cache->Name(), idx);
- // NOTE(dzhwinter): we need to both update the ProgramDesc
- // and IR Graph. because op_desc/var_desc is used in CreateOp,
- // CreateVar when running happens. But IR Graph
- // define the dependence relationship between nodes.
- RenameVarInGraphDesc(var->Name(), cache->Name(), idx);
- RenameVarInGraphNode(var->Name(), cache->Name(), idx, graph.get());
-
- pool_.Erase(cache);
- }
- }
- }
- // fill the pool
- for (auto var : cfg_->LiveIn(op)) {
- if (cfg_->LiveOut(op).count(var) == 0) {
- ir::Node* var_node = cfg_->GetNodeFromVarName(var, op);
- if (var_node == nullptr) continue;
- if (NodeCanReused(var_node) && !pool_.Has(var_node)) {
- pool_.Insert(var_node, op);
- }
- }
- }
- }
- graph->ResolveHazard(var_nodes_);
-
- // For early delete pass. use GraphNodePool load the unlived vars.
- // 1. find all deps op for each unlived var in memory pool.
- for (auto& op : graph->Nodes()) {
- for (auto& var : op->inputs) {
- if (pool_.Has(var)) {
- pool_.Insert(var, op);
- }
- }
- }
- // 2. convert ir node based memory pool to graph node
- // because Node* maybe released bettwen passes.
- auto& graph_pool = graph->Get<GraphNodePool>(kGraphNodePool);
- for (auto it = pool_.begin(); it != pool_.end(); ++it) {
- std::unordered_set<OpDesc*> descs;
- for (auto& op : it->second) {
- PADDLE_ENFORCE(op->IsOp());
- descs.insert(op->Op());
- }
- graph_pool.push_back(std::make_pair(it->first->Name(), descs));
- }
-
- return graph;
-}
-
-void AnalysisVarPass::SubGraphOptimize(OpDesc* op_desc) const {
- // conditional block, while op and their grad op
- auto* sub_block_desc =
- AttrReader(op_desc->GetAttrMap()).Get<BlockDesc*>("sub_block");
-
- // create a mirror block to construct an IR Graph.
- ProgramDesc prog;
- auto* copy_block = prog.MutableBlock(0);
- for (auto* op : sub_block_desc->AllOps()) {
- auto* copy_op = copy_block->AppendOp();
- copy_op->CopyFrom(*op);
- copy_op->Flush();
- }
-
- for (auto* var : sub_block_desc->AllVars()) {
- auto* copy_var = copy_block->Var(var->Name());
- copy_var->SetDataType(var->GetDataType());
- // only lod tensor can be reused. So ignore the multiple dims case.
- copy_var->SetType(var->GetType());
- copy_var->SetShape(var->GetShape());
- copy_var->SetPersistable(var->Persistable());
- }
-
- ir::Graph sub_graph(prog);
- std::unordered_set<ir::Node*> sub_graph_all_ops;
- FilterVariables(sub_graph.Nodes(), [&](ir::Node* var) {
- // sub_graph_all_ops.emplace(var);
- if (var->IsVar() && !var->IsCtrlVar()) {
- sub_graph_all_ops.emplace(var);
- }
- });
- int sub_reuse_id = 0;
- // subgraph nodes is unordered, reuse need to follow the desc order.
- // find the right op node through the descs
- for (auto* sub_op_desc : sub_block_desc->AllOps()) {
- ir::Node* sub_op = nullptr;
- for (auto* node : sub_graph_all_ops) {
- if (node->Op() == sub_op_desc) {
- sub_op = node;
- break;
- }
- }
- PADDLE_ENFORCE(sub_op != nullptr);
- for (auto* var : sub_op->outputs) {
- if (NodeCanReused(var)) {
- ir::Node* cache = pool_.NodeMatch(var);
- if (cache != nullptr) {
- if (var->Var()->GetDataType() != cache->Var()->GetDataType()) {
- continue;
- }
- int node_idx_in_pool = pool_.GetIndex(cache);
- VLOG(3) << string::Sprintf(
- "!!! %s, %s => %s, cache idx %d, pool size %d",
- std::to_string(sub_reuse_id++), DebugString(var),
- DebugString(cache), node_idx_in_pool,
- static_cast<int>(pool_.size()));
- // NOTE(dzh): subblock is not in IR graph. Modify the block_desc
- // immediately to make the subblock variable reuse strategy take
- // effect. Because it is a single op in graph. No need to
- // update the ir nodes.
- sub_op_desc->Rename(var->Name(), cache->Name());
- if (sub_op_desc->Block()->HasVar(var->Name())) {
- sub_op_desc->Block()->RemoveVar(var->Name());
- }
- }
- }
- }
- }
-}
-
-std::unordered_set<std::string> AnalysisVarPass::GetSubBlockVars(
- const std::unordered_set<ir::Node*>& nodes) const {
- std::unordered_set<std::string> vars;
- for (auto& op : nodes) {
- if (!op->IsOp() || op->Op() == nullptr) continue;
- auto* op_desc = op->Op();
- if (OpHasSubBlock(op_desc)) {
- auto inputs = op_desc->InputArgumentNames();
- auto outputs = op_desc->OutputArgumentNames();
- vars.insert(inputs.begin(), inputs.end());
- vars.insert(outputs.begin(), outputs.end());
- }
- }
- return vars;
-}
-
-void AnalysisVarPass::RenameVarInGraphDesc(const std::string& var,
- const std::string& cache_var,
- size_t idx) const {
- for (size_t i = idx; i < cfg_->Ops().size(); ++i) {
- auto* op = cfg_->Ops()[i];
- PADDLE_ENFORCE(op->IsOp() && op->Op());
- auto* op_desc = op->Op();
- op_desc->RenameInput(var, cache_var);
- op_desc->RenameOutput(var, cache_var);
- if (op_desc->Block()->HasVar(var)) op_desc->Block()->RemoveVar(var);
- op_desc->Flush();
- }
-}
-
-void AnalysisVarPass::InitSSAGraphNodes() const {
- std::unordered_map<std::string, std::unordered_set<ir::Node*>> all_vars;
- if (var_nodes_.empty()) {
- for (auto* op : cfg_->Ops()) {
- for (auto* node : op->inputs) {
- if (all_vars[node->Name()].count(node) == 0) {
- all_vars[node->Name()].emplace(node);
- var_nodes_[node->Name()].emplace_back(node);
- }
- }
- for (auto* node : op->outputs) {
- if (all_vars[node->Name()].count(node) == 0) {
- all_vars[node->Name()].emplace(node);
- var_nodes_[node->Name()].emplace_back(node);
- }
- }
- }
- }
-}
-
-void AnalysisVarPass::RenameVarInGraphNode(const std::string& var,
- const std::string& cache_var,
- size_t idx, ir::Graph* graph) const {
- // if replace happens, we need to create a newer version cache_var
- // but use the same dims/data_type with var.
- PADDLE_ENFORCE(var_nodes_[var].size() >= 1 &&
- var_nodes_[var].at(0)->Var() != nullptr);
- std::unique_ptr<VarDesc> var_desc(new VarDesc(*var_nodes_[var].at(0)->Var()));
- var_desc->SetName(cache_var);
-
- for (size_t i = idx; i < cfg_->Ops().size(); ++i) {
- auto* op = cfg_->Ops()[i];
-
- // redirect the input to the latest version of cache_var
- for (auto* node : op->inputs) {
- if (node->Name() == var) {
- ir::Node* cache_node = graph->CreateVarNode(var_desc.get());
- var_nodes_[cache_var].emplace_back(cache_node);
-
- // swap node to cache_node
- cache_node->outputs.insert(cache_node->outputs.end(),
- node->outputs.begin(), node->outputs.end());
- PADDLE_ENFORCE(node->inputs.size() == 1 && node->inputs[0]->IsOp());
- auto* prev_op = node->inputs[0];
- std::replace(prev_op->outputs.begin(), prev_op->outputs.end(), node,
- cache_node);
- cache_node->inputs.emplace_back(prev_op);
- for (auto* next_op : node->outputs) {
- std::replace(next_op->inputs.begin(), next_op->inputs.end(), node,
- cache_node);
- }
- }
- }
-
- // if we need to rename the output,
- // always create a newer version of cache_var
- for (auto* node : op->outputs) {
- if (node->Name() == var) {
- ir::Node* cache_node = graph->CreateVarNode(var_desc.get());
- var_nodes_[cache_var].emplace_back(cache_node);
-
- // swap node to cache node
- cache_node->outputs.insert(cache_node->outputs.end(),
- node->outputs.begin(), node->outputs.end());
- cache_node->inputs.emplace_back(op);
- std::replace(op->outputs.begin(), op->outputs.end(), node, cache_node);
- for (auto* next_op : node->outputs) {
- std::replace(next_op->inputs.begin(), next_op->inputs.end(), node,
- cache_node);
- }
- }
- }
- }
-
- // release node of unused var in graph
- for (auto* node : var_nodes_[var]) {
- graph->RemoveNode(node);
- }
- var_nodes_.at(var).clear();
-}
-
-bool AnalysisVarPass::NodeCanReused(ir::Node* node) const {
- if (!node->IsVar() || node->IsCtrlVar()) return false;
- auto* desc = node->Var();
- auto type = desc->GetType();
- if (desc->Persistable() || type != proto::VarType::LOD_TENSOR ||
- desc->GetShape().empty()) {
- return false;
- }
- // vars can be @EMPTY@, @LR_DECAY_REUSE_ID@. For example, while_grad
- std::string name = node->Name();
- if (!name.empty() && name[0] == '@' && name[name.size() - 1] == '@')
- return false;
- if (skip_set_.count(name)) return false;
- for (auto* op : node->inputs) {
- if (op->Op()->HasAttr("force_cpu")) {
- // op output force generated in cpu, can not be reused.
- return framework::AttrReader(op->Op()->GetAttrMap())
- .Get<bool>("force_cpu") == 0;
- }
- }
- return true;
-}
-
-bool AnalysisVarPass::OpHasSubBlock(OpDesc* desc) const {
- const AttributeMap& attrs = desc->GetAttrMap();
- for (auto& attr : attrs) {
- if (attr.second.type() == typeid(BlockDesc*) || // NOLINT
- attr.second.type() == typeid(std::vector<BlockDesc*>)) // NOLINT
- return true;
- }
- return false;
-}
-
-std::vector<ir::Node*> SortOpLikeDescOrder(const ir::Graph& graph) {
- PADDLE_ENFORCE(graph.Has(kAllOpDescs),
- "Graph has no attribute of kAllOpDescs.");
- // 1. get op desc order
- auto& op_descs = graph.Get<const std::vector<OpDesc*>>(kAllOpDescs);
-
- // 2. topology sort order
- auto nodes = graph.Nodes();
- std::deque<ir::Node*> ops;
- FilterVariables(nodes, [&](ir::Node* op) {
- if (op->IsOp() && op->Op() != nullptr) {
- ops.emplace_back(op);
- }
- });
- std::unordered_map<ir::Node*, size_t> op_deps;
- std::list<ir::Node*> ready_ops;
- std::unordered_map<ir::Node*, std::unordered_set<ir::Node*>> pending_ops;
-
- for (auto* op : ops) {
- std::unordered_set<ir::Node*> preceding_op;
- for (auto* in : op->inputs) {
- if (in->inputs.empty()) continue;
- PADDLE_ENFORCE(in->inputs.size() == 1 && in->inputs[0]->IsOp());
- preceding_op.emplace(in->inputs[0]);
- pending_ops[in->inputs[0]].emplace(op);
- }
- op_deps[op] = preceding_op.size();
- if (preceding_op.empty()) {
- ready_ops.emplace_back(op);
- }
- }
-
- // 3. generated op list based desc order and the topology order
- std::vector<ir::Node*> ret;
- std::list<OpDesc*> op_descs_list(op_descs.begin(), op_descs.end());
-
- auto update_by_found_node = [&](ir::Node* found_node) {
- for (auto* pending_op : pending_ops[found_node]) {
- if (--op_deps[pending_op] == 0) {
- ready_ops.emplace_back(pending_op);
- }
- }
- ready_ops.remove(found_node);
- ret.emplace_back(found_node);
- };
-
- while (!ready_ops.empty()) {
- bool all_of_ready_op_unmatched = true;
- for (auto it = op_descs_list.begin(); it != op_descs_list.end();) {
- auto op_desc = *it;
- ir::Node* found_node = nullptr;
- for (auto* op : ready_ops) {
- if (IsSameDesc(op->Op(), op_desc)) {
- found_node = op;
- break;
- }
- }
-
- // 3.1 op desc deleted by other pass
- if (found_node == nullptr) {
- ++it;
- continue;
- } else {
- all_of_ready_op_unmatched = false;
- it = op_descs_list.erase(it);
- }
- update_by_found_node(found_node);
- }
-
- // 3.2 op descs are added by other pass
- // preceding op non empty means some new op descs are
- // created, but not contained in return node list.
- // these new op desc may depend on each other.
- std::list<ir::Node*> prev_ready_ops(ready_ops);
- if (all_of_ready_op_unmatched) {
- for (auto op : prev_ready_ops) {
- update_by_found_node(op);
- }
- }
- }
-
- PADDLE_ENFORCE(std::all_of(
- op_deps.begin(), op_deps.end(),
- [&](const std::pair<ir::Node*, size_t>& p) { return p.second == 0; }));
-
- return ret;
-}
-
-ControlFlowGraph::ControlFlowGraph(const ir::Graph& graph) {
- ops_ = SortOpLikeDescOrder(graph);
- ConnectNodes();
-}
-
-void ControlFlowGraph::BuildCFGGraph() {
- // FIXME(dzh): same effect with ConnectNodes, but use the control
- // link to build dependency graph, it goes wrong in transformer.
- for (ir::Node* op : ops_) {
- for (auto& input_var : op->inputs) {
- if (!input_var->inputs.empty()) {
- PADDLE_ENFORCE(
- input_var->inputs.size() == 1 && input_var->inputs[0]->IsOp(),
- "Preceding Op Node of Var Node must be unique");
- auto* pred_op = input_var->inputs[0];
- if (pred_op->Op() != nullptr) {
- predecessors_[op].insert(pred_op);
- successors_[pred_op].insert(op);
- }
- }
- if (input_var->IsVar() && !input_var->IsCtrlVar()) {
- uses_[op].insert(input_var->Name());
- }
- }
- for (auto& output_var : op->outputs) {
- // output var may be used by many op
- for (auto* succ_op : output_var->outputs) {
- if (succ_op->Op() != nullptr) {
- successors_[op].insert(succ_op);
- predecessors_[succ_op].insert(op);
- }
- }
- if (output_var->IsVar() && !output_var->IsCtrlVar()) {
- defs_[op].insert(output_var->Name());
- }
- }
- }
-}
-
-void ControlFlowGraph::ConnectNodes() {
- for (size_t i = 0; i < ops_.size(); ++i) {
- auto& op = ops_[i];
- try {
- auto& next_op = ops_.at(i + 1);
- successors_[op].insert(next_op);
- predecessors_[next_op].insert(op);
- } catch (...) {
- // do nothing
- }
-
- FilterVariables(op->inputs,
- [&](ir::Node* var) { uses_[op].emplace(var->Name()); });
-
- FilterVariables(op->outputs,
- [&](ir::Node* var) { defs_[op].emplace(var->Name()); });
- }
-}
-
-void ControlFlowGraph::LiveVariableAnalysis() {
- // NOTE(dzh): variable liveless analysis (a.k.a reversed_ops algorithm)
- // compute the liveness of for each variable though reversed_ops algorithm.
- // It iterates the operators from end to begin, compute the live in/live out
- // variable set for each op, then the diff between in/out will be used for
- // the variable reuse. For detail refer to
- // http://www.cs.cornell.edu/courses/cs4120/2013fa/lectures/lec26-fa13.pdf
- std::list<ir::Node*> work_list(ops_.rbegin(), ops_.rend());
- while (!work_list.empty()) {
- ir::Node* op = work_list.front();
- work_list.pop_front();
- // get the live_in calculated before. Empty if first.
- auto prev_live_in = std::move(live_in_[op]);
- for (auto& s : successors_[op]) {
- for (auto& var : live_in_[s]) {
- live_out_[op].insert(var);
- }
- }
- for (auto& var : uses_[op]) {
- live_in_[op].insert(var);
- }
- for (auto& var : live_out_[op]) {
- live_in_[op].insert(var);
- }
- for (auto& var : defs_[op]) {
- live_in_[op].erase(var);
- }
-
- // If the live_in is not changed, then the liveness analysis of
- // predecessors is completed.
- //
- // Otherwise, recalculate the predecessors liveness
- if (live_in_[op] != prev_live_in) {
- for (auto& pre : predecessors_[op]) {
- work_list.push_back(pre);
- }
- }
- }
-}
-
-void ControlFlowGraph::RenameVarInCFGGraph(const std::string& old_node,
- const std::string& new_node,
- int begin_idx) {
- // update graph from begin idx to the end
- for (size_t i = begin_idx; i != ops_.size(); ++i) {
- auto* op = ops_[i];
- if (uses_[op].find(old_node) != uses_[op].end()) {
- uses_[op].erase(old_node);
- uses_[op].insert(new_node);
- }
- if (defs_[op].find(old_node) != defs_[op].end()) {
- defs_[op].erase(old_node);
- defs_[op].insert(new_node);
- }
- if (live_in_[op].find(old_node) != live_in_[op].end()) {
- live_in_[op].erase(old_node);
- live_in_[op].insert(new_node);
- }
- if (live_out_[op].find(old_node) != live_out_[op].end()) {
- live_out_[op].erase(old_node);
- live_out_[op].insert(new_node);
- }
- }
-}
-
-const std::set<std::string> ControlFlowGraph::LiveIn(ir::Node* op) const {
- auto it = live_in_.find(op);
- PADDLE_ENFORCE(
- it != live_in_.end(),
- string::Sprintf("Expect %s in live_in, but Not Found.", op->Name()));
- return it->second;
-}
-
-const std::set<std::string> ControlFlowGraph::LiveOut(ir::Node* op) const {
- auto it = live_out_.find(op);
- PADDLE_ENFORCE(
- it != live_out_.end(),
- string::Sprintf("Expect %s in live_out, but Not Found.", op->Name()));
- return it->second;
-}
-
-const std::set<std::string> ControlFlowGraph::Use(ir::Node* op) const {
- auto it = uses_.find(op);
- PADDLE_ENFORCE(
- it != uses_.end(),
- string::Sprintf("Expect %s in live_out, but Not Found.", op->Name()));
- return it->second;
-}
-
-const std::vector<ir::Node*> ControlFlowGraph::Ops() const { return ops_; }
-
-std::vector<ir::Node*>& ControlFlowGraph::Ops() { return ops_; }
-
-ir::Node* ControlFlowGraph::GetNodeFromVarName(const std::string& name,
- ir::Node* op) const {
- // in ssa-graph, different version nodes have same name,
- // this function get the latest version var before target op
- // It may return nullptr, such as data node.
- ir::Node* found_node = nullptr;
- for (auto* node : ops_) {
- if (node == op) break;
- for (auto& output : node->outputs) {
- if (output->Name() == name) {
- found_node = output;
- }
- }
- }
- return found_node;
-}
-
-} // namespace details
-} // namespace framework
-} // namespace paddle
-
-REGISTER_PASS(analysis_var_pass, paddle::framework::details::AnalysisVarPass)
- .RequireGraphAttr(paddle::framework::details::kGraphNodePool)
- .RequireGraphAttr(paddle::framework::details::kAllOpDescs);
diff --git a/paddle/fluid/framework/details/analysis_var_pass.h b/paddle/fluid/framework/details/analysis_var_pass.h
deleted file mode 100644
index 144204beaf..0000000000
--- a/paddle/fluid/framework/details/analysis_var_pass.h
+++ /dev/null
@@ -1,120 +0,0 @@
-// 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 <algorithm>
-#include <list>
-#include <map>
-#include <memory>
-#include <set>
-#include <string>
-#include <unordered_map>
-#include <utility>
-#include <vector>
-
-#include "paddle/fluid/framework/data_type.h"
-#include "paddle/fluid/framework/details/memory_reuse_types.h"
-#include "paddle/fluid/framework/ir/graph.h"
-#include "paddle/fluid/framework/ir/pass.h"
-
-namespace paddle {
-namespace framework {
-namespace details {
-constexpr char kAllOpDescs[] = "all_op_descs";
-
-std::vector<ir::Node*> SortOpLikeDescOrder(const ir::Graph& graph);
-// sort op in bfs order
-std::vector<ir::Node*> BFSSortGraphOps(const ir::Graph& graph);
-
-class ControlFlowGraph;
-
-class AnalysisVarPass : public ir::Pass {
- protected:
- std::unique_ptr<ir::Graph> ApplyImpl(
- std::unique_ptr<ir::Graph> graph) const override;
-
- private:
- // fill the variable map(var_nodes) by version.
- void InitSSAGraphNodes() const;
- // update program descs
- void RenameVarInGraphDesc(const std::string& var,
- const std::string& cache_var, size_t idx) const;
- // update ir nodes
- void RenameVarInGraphNode(const std::string& var,
- const std::string& cache_var, size_t idx,
- ir::Graph* graph) const;
-
- void SubGraphOptimize(OpDesc* op_desc) const;
- // valid a tensor can be reuse or not
- bool NodeCanReused(ir::Node* node) const;
- // scan subblock and collect the output/input variables.
- std::unordered_set<std::string> GetSubBlockVars(
- const std::unordered_set<ir::Node*>&) const;
- // check op has subblock or not
- bool OpHasSubBlock(OpDesc* desc) const;
-
- private:
- // Reuse Node Pool, Owned.
- mutable OrderedNodePairPool pool_;
- // controlflow Graph
- mutable std::unique_ptr<ControlFlowGraph> cfg_;
- // skip set
- mutable std::unordered_set<std::string> skip_set_;
- // var nodes
- mutable std::map<std::string, std::vector<ir::Node*>> var_nodes_;
-};
-
-class ControlFlowGraph {
- public:
- ControlFlowGraph() = default;
- // For IR Graph in parallelexecutor
- explicit ControlFlowGraph(const ir::Graph& graph);
-
- void LiveVariableAnalysis();
-
- void RenameVarInCFGGraph(const std::string& old_node,
- const std::string& new_node, int begin_idx);
-
- const std::set<std::string> LiveIn(ir::Node* op) const;
- const std::set<std::string> LiveOut(ir::Node* op) const;
- const std::set<std::string> Use(ir::Node* op) const;
- const std::vector<ir::Node*> Ops() const;
- std::vector<ir::Node*>& Ops();
-
- // for ssa-graph nodes
- ir::Node* GetNodeFromVarName(const std::string& name, ir::Node* op) const;
-
- private:
- void BuildCFGGraph();
- void ConnectNodes();
- using NodeListMap = std::unordered_map<ir::Node*, std::set<ir::Node*>>;
- using VarSetMap = std::map<ir::Node*, std::set<std::string>>;
- // successors ops use the output variables.
- NodeListMap successors_;
- // predecessors ops generated input variables.
- NodeListMap predecessors_;
- // variables lived before run current op.
- VarSetMap live_in_;
- // variables lived after run current op.
- VarSetMap live_out_;
- VarSetMap uses_; // op inputs
- VarSetMap defs_; // op outputs
-
- std::vector<ir::Node*> ops_; // op sequence by topology sort
-};
-
-} // namespace details
-} // namespace framework
-} // namespace paddle
diff --git a/paddle/fluid/framework/details/build_strategy.cc b/paddle/fluid/framework/details/build_strategy.cc
index ce5731a1f4..f8030c53f7 100644
--- a/paddle/fluid/framework/details/build_strategy.cc
+++ b/paddle/fluid/framework/details/build_strategy.cc
@@ -17,7 +17,7 @@ limitations under the License. */
#include <glog/logging.h>
#include <memory>
-#include "paddle/fluid/framework/details/memory_reuse_types.h"
+#include "paddle/fluid/framework/details/memory_optimize_helper.h"
#include "paddle/fluid/framework/details/multi_devices_graph_pass.h"
#include "paddle/fluid/framework/details/multi_devices_graph_print_pass.h"
#include "paddle/fluid/framework/details/reduce_op_handle.h"
@@ -47,6 +47,22 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
AppendPass("sequential_execution_pass");
}
+ // Add op fusion.
+ if (strategy.fuse_relu_depthwise_conv_) {
+ AppendPass("fuse_relu_depthwise_conv_pass");
+ }
+
+ // NOTE(dzhwinter): A note for automatical inplace.
+ // 1. modify program desc passes should put
+ // before inplace pass.
+ // 2. manually configured inplace should put
+ // before inplace_pass
+
+ // Add automatically inplace.
+ if (strategy_.enable_inplace_) {
+ AppendPass("inplace_pass");
+ }
+
// Add a graph viz pass to record a graph.
if (!strategy_.debug_graphviz_path_.empty()) {
auto viz_pass = AppendPass("graph_viz_pass");
@@ -55,10 +71,6 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
viz_pass->Set<std::string>("graph_viz_path", new std::string(graph_path));
}
- // Add op fusion.
- if (strategy.fuse_relu_depthwise_conv_) {
- AppendPass("fuse_relu_depthwise_conv_pass");
- }
if (strategy.fuse_elewise_add_act_ops_) {
auto fuse_elewise_add_act_pass = AppendPass("fuse_elewise_add_act_pass");
// Add a graph viz pass to record a graph.
@@ -88,7 +100,7 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
// A side-effect of that, memory optimize cannot forsee the fetched vars
// , so fetchlist should be set persistable before call the Run interface.
if (strategy.memory_optimize_) {
- auto analysis_var_pass = AppendPass("analysis_var_pass");
+ auto memory_optimize_pass = AppendPass("memory_optimize_pass");
}
AppendMultiDevPass(strategy);
@@ -186,14 +198,14 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
pass->Erase("nccl_ctxs");
pass->SetNotOwned<platform::NCCLContextMap>("nccl_ctxs", nctx);
#endif
-
- } else if (pass->Type() == "analysis_var_pass") {
+ } else if (pass->Type() == "memory_optimize_pass") {
+ if (graph->Has(kAllOpDescs)) {
+ graph->Erase(kAllOpDescs);
+ }
const std::vector<OpDesc *> *all_op_descs =
new std::vector<OpDesc *>(main_program.Block(0).AllOps());
graph->Set<const std::vector<OpDesc *>>(kAllOpDescs,
all_op_descs); // take ownership
- graph->Set<GraphNodePool>(kGraphNodePool,
- new GraphNodePool); // take ownership
pass->Erase(kAllOpDescs);
pass->SetNotOwned<const std::vector<OpDesc *>>(kAllOpDescs, all_op_descs);
@@ -214,6 +226,13 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
pass->Set<const std::vector<OpDesc *>>(
kAllOpDescs,
new std::vector<OpDesc *>(main_program.Block(0).AllOps()));
+ } else if (pass->Type() == "inplace_pass") {
+ if (graph->Has(kAllOpDescs)) {
+ graph->Erase(kAllOpDescs);
+ }
+ graph->Set<const std::vector<OpDesc *>>(
+ kAllOpDescs,
+ new std::vector<OpDesc *>(main_program.Block(0).AllOps()));
} else if (pass->Type() == "fuse_relu_depthwise_conv_pass") {
if (!use_cuda) {
LOG(WARNING) << "fuse_relu_depthwise_conv_pass is only supported on "
@@ -239,9 +258,10 @@ USE_PASS(allreduce_mode_multi_devices_pass);
USE_PASS(dist_multi_devices_pass);
USE_PASS(multi_devices_check_pass);
USE_PASS(multi_devices_print_pass);
-USE_PASS(analysis_var_pass);
+USE_PASS(memory_optimize_pass);
USE_PASS(sequential_execution_pass);
USE_PASS(all_reduce_deps_pass);
USE_PASS(modify_op_lock_and_record_event_pass);
+USE_PASS(inplace_pass);
USE_PASS(lock_free_optimize_pass);
USE_PASS(graph_to_program_pass);
diff --git a/paddle/fluid/framework/details/build_strategy.h b/paddle/fluid/framework/details/build_strategy.h
index cd24a31759..e62e3edcef 100644
--- a/paddle/fluid/framework/details/build_strategy.h
+++ b/paddle/fluid/framework/details/build_strategy.h
@@ -77,8 +77,10 @@ struct BuildStrategy {
bool fuse_relu_depthwise_conv_{false};
bool memory_optimize_{false};
-
- bool memory_early_delete_{false};
+ // TODO(dzhwinter):
+ // make enable_inplace, memory_optimize_
+ // memory_early_delete_ true by default
+ bool enable_inplace_{false};
bool enable_sequential_execution_{false};
diff --git a/paddle/fluid/framework/details/computation_op_handle.h b/paddle/fluid/framework/details/computation_op_handle.h
index 601ae4f8c6..1e3dbb1e44 100644
--- a/paddle/fluid/framework/details/computation_op_handle.h
+++ b/paddle/fluid/framework/details/computation_op_handle.h
@@ -26,7 +26,7 @@
namespace paddle {
namespace framework {
namespace details {
-struct ComputationOpHandle : public OpHandleBase {
+class ComputationOpHandle : public OpHandleBase {
public:
ComputationOpHandle(ir::Node *node, Scope *scope, platform::Place place,
size_t scope_idx);
diff --git a/paddle/fluid/framework/details/fused_broadcast_op_handle_test.cc b/paddle/fluid/framework/details/fused_broadcast_op_handle_test.cc
index be0d941c4f..6d53dac5c0 100644
--- a/paddle/fluid/framework/details/fused_broadcast_op_handle_test.cc
+++ b/paddle/fluid/framework/details/fused_broadcast_op_handle_test.cc
@@ -34,8 +34,8 @@ struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
->Var(details::kLocalExecScopeName)
->GetMutable<Scope*>() = &local_scope;
for (size_t j = 0; j < input_scope_idxes.size(); ++j) {
- local_scope.Var("out_var" + j);
- if (i == j) local_scope.Var("in_var" + j);
+ local_scope.Var("out_var" + std::to_string(j));
+ if (i == j) local_scope.Var("in_var" + std::to_string(j));
}
param_scopes_.emplace_back(&local_scope);
}
@@ -62,20 +62,21 @@ struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
// add input var handle
- nodes_.emplace_back(
- ir::CreateNodeForTest("in_node" + i, ir::Node::Type::kVariable));
- VarHandle* in_var_handle =
- new VarHandle(nodes_.back().get(), 1, input_scope_idxes[i],
- "in_var" + i, place_list_[input_scope_idxes[i]]);
+ nodes_.emplace_back(ir::CreateNodeForTest("in_node" + std::to_string(i),
+ ir::Node::Type::kVariable));
+ VarHandle* in_var_handle = new VarHandle(
+ nodes_.back().get(), 1, input_scope_idxes[i],
+ "in_var" + std::to_string(i), place_list_[input_scope_idxes[i]]);
vars_.emplace_back(in_var_handle);
op_handle_->AddInput(in_var_handle);
// add output var handle
for (size_t j = 0; j < place_list_.size(); ++j) {
- nodes_.emplace_back(
- ir::CreateNodeForTest("out_node" + i, ir::Node::Type::kVariable));
- VarHandle* out_var_handle = new VarHandle(
- nodes_.back().get(), 2, j, "out_var" + i, place_list_[j]);
+ nodes_.emplace_back(ir::CreateNodeForTest(
+ "out_node" + std::to_string(i), ir::Node::Type::kVariable));
+ VarHandle* out_var_handle =
+ new VarHandle(nodes_.back().get(), 2, j,
+ "out_var" + std::to_string(i), place_list_[j]);
vars_.emplace_back(out_var_handle);
op_handle_->AddOutput(out_var_handle);
}
@@ -86,7 +87,7 @@ struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
std::vector<std::vector<float>> send_vec;
f::LoD lod{{0, 10, 20}};
for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
- const std::string varname("in_var" + i);
+ const std::string varname("in_var" + std::to_string(i));
float val_scalar = static_cast<float>(i);
send_vec.push_back(
InitLoDTensor(varname, input_scope_idxes[i], lod, val_scalar));
@@ -96,7 +97,7 @@ struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
WaitAll();
for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
- const std::string& varname("out_var" + i);
+ const std::string& varname("out_var" + std::to_string(i));
for (size_t j = 0; j < place_list_.size(); ++j) {
LoDTensorEqual(varname, send_vec[i], lod, param_scopes_[j]);
}
@@ -109,7 +110,7 @@ struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
2, 4, 6, 3, 1, 1, 1, 1, 3, 7};
int height = static_cast<int>(kDims[0] * 2);
for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
- const std::string varname("in_var" + i);
+ const std::string varname("in_var" + std::to_string(i));
float val_scalar = static_cast<float>(i);
send_vector.push_back(InitSelectedRows(varname, input_scope_idxes[i],
rows, height, val_scalar));
@@ -119,7 +120,7 @@ struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
WaitAll();
for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
- const std::string& varname("out_var" + i);
+ const std::string& varname("out_var" + std::to_string(i));
for (size_t j = 0; j < place_list_.size(); ++j) {
SelectedRowsEqual(varname, input_scope_idxes[i], send_vector[i], rows,
height);
diff --git a/paddle/fluid/framework/details/graph_test_base.h b/paddle/fluid/framework/details/graph_test_base.h
new file mode 100644
index 0000000000..126959bcd8
--- /dev/null
+++ b/paddle/fluid/framework/details/graph_test_base.h
@@ -0,0 +1,80 @@
+// Copyright (c) 2019 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 <iostream>
+#include <iterator>
+#include <string>
+#include "glog/logging.h"
+#include "gtest/gtest.h"
+#include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/graph_helper.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/program_desc.h"
+
+namespace paddle {
+namespace framework {
+
+class DummyOp : public OperatorBase {
+ public:
+ DummyOp(const std::string& type, const VariableNameMap& inputs,
+ const VariableNameMap& outputs, const AttributeMap& attrs)
+ : OperatorBase(type, inputs, outputs, attrs) {}
+
+ private:
+ void RunImpl(const Scope& scope,
+ const platform::Place& place) const override {}
+};
+
+class SumOpMaker : public OpProtoAndCheckerMaker {
+ public:
+ void Make() {
+ AddInput("X", "").AsDuplicable();
+ AddOutput("Out", "");
+ AddComment("");
+ }
+};
+
+class AssignOpMaker : public OpProtoAndCheckerMaker {
+ public:
+ void Make() {
+ AddInput("X", "").AsDuplicable();
+ AddOutput("Out", "");
+ AddComment("");
+ }
+};
+
+class SplitOpMaker : public OpProtoAndCheckerMaker {
+ public:
+ void Make() {
+ AddInput("X", "");
+ AddOutput("Out", "").AsDuplicable();
+ AddComment("");
+ }
+};
+
+class DummyVarTypeInference : public VarTypeInference {
+ public:
+ void operator()(const OpDesc& op_desc, BlockDesc* block) const override {
+ auto& inputs = op_desc.Input("X");
+ auto type = block->Var(inputs.front())->GetType();
+ auto out_var_name = op_desc.Output("Out").front();
+ block->Var(out_var_name)->SetType(type);
+ }
+};
+
+} // namespace framework
+} // namespace paddle
diff --git a/paddle/fluid/framework/details/inplace_op_pass.cc b/paddle/fluid/framework/details/inplace_op_pass.cc
new file mode 100644
index 0000000000..b0c5968499
--- /dev/null
+++ b/paddle/fluid/framework/details/inplace_op_pass.cc
@@ -0,0 +1,432 @@
+// 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/details/inplace_op_pass.h"
+#include <algorithm>
+#include <deque>
+#include <iterator>
+#include <stack>
+#include <string>
+#include <unordered_map>
+#include <unordered_set>
+#include <vector>
+#include "paddle/fluid/framework/details/memory_optimize_pass.h"
+#include "paddle/fluid/framework/ir/graph_helper.h"
+#include "paddle/fluid/framework/op_info.h"
+
+// NOTE(dzhwinter): inplace means one op output variable reuse the input space.
+// By our design, one operator only can read its input(const Variable),
+// write its output(non-const Variable). If one operator is inplaced, means
+// user have chance to write the space before reading happens.
+// Especially when some optimize code writing style is applied.
+//
+//
+// /* wrong case in operator */
+// /*In this case, a larger allocation is allocated, input content is lost*/
+// const Tensor* in = ctx.Input<Tensor>("In")
+// Tensor* out = ctx.Output<Tensor>("Out");
+// auto* out_ptr = out->mutable_data<T>(ctx.GetPlace());
+// out_ptr[0] = 0; // input contect is overwrited.
+
+// NOTE(dzhwinter):
+// Only for backward compacity and stable. if enable_inplace_whitelist is turn
+// on.
+// only the ops in whitelist will be use inplace strategy.
+// if not, all the op will be inplaced if it registered with InplaceClass
+DEFINE_bool(
+ enable_inplace_whitelist, false,
+ "If this option turns on, only these op in whitelist can be inplaced."
+ "If it turns off, all of the running op can be candidate of inplaced op."
+ "Such as scale, elementwise_add"
+ "By default, it's turned on");
+
+DECLARE_string(memory_optimize_debug);
+
+// clang-format off
+const std::string kInplacedOpWhiteList[] = { // NOLINT
+ "sigmoid",
+ "exp",
+ "relu",
+ "tanh",
+ "sqrt",
+ "ceil",
+ "floor",
+ "reciprocal",
+ "relu6",
+ "soft_relu",
+ "hard_sigmoid",
+ "batch_norm",
+ "batch_norm_grad",
+ "sum",
+ "sum_grad",
+ "scale",
+ "reshape",
+ "elementwise_add",
+ "elementwise_add_grad",
+};
+// clang-format on
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+static inline ir::Node* GetNextCascadeInplacedVar(ir::Node* var) {
+ // if next op is inplaced, then return the output var
+ // otherwise return nullptr
+ PADDLE_ENFORCE(var && var->IsVar() && !var->IsCtrlVar());
+ ir::Node* inplaced_var = nullptr;
+ for (auto* next_op : var->outputs) {
+ for (auto* output : next_op->outputs) {
+ if (output->IsVar() && !output->IsCtrlVar() &&
+ output->Name() == var->Name()) {
+ inplaced_var = output;
+ }
+ }
+ }
+ return inplaced_var;
+}
+
+static inline ir::Node* GetPrevCascadeInplacedVar(ir::Node* var) {
+ PADDLE_ENFORCE(var && var->IsVar() && !var->IsCtrlVar());
+ if (var->inputs.empty()) return nullptr;
+ auto* prev_op = var->inputs.at(0);
+ auto input_it = std::find_if(prev_op->inputs.begin(), prev_op->inputs.end(),
+ [&](ir::Node* node) {
+ if (node->IsVar() && !node->IsCtrlVar() &&
+ node->Name() == var->Name()) {
+ return true;
+ } else {
+ return false;
+ }
+ });
+ return input_it == prev_op->inputs.end() ? nullptr : *input_it;
+}
+
+InplacePass::InplacePass() : Pass() {
+ if (FLAGS_enable_inplace_whitelist) {
+ for (auto& s : kInplacedOpWhiteList) {
+ whitelist_.emplace(s);
+ }
+ }
+}
+
+void InplacePass::InitSSAGraphNodes() const {
+ std::unordered_map<std::string, std::unordered_set<ir::Node*>> all_vars;
+ for (auto* op : view_.AllOps()) {
+ for (auto* node : op->inputs) {
+ if (!node->IsVar() || node->IsCtrlVar()) continue;
+ if (all_vars[node->Name()].count(node) == 0) {
+ all_vars[node->Name()].emplace(node);
+ var_nodes_[node->Name()].emplace_back(node);
+ }
+ }
+ for (auto* node : op->outputs) {
+ if (!node->IsVar() || node->IsCtrlVar()) continue;
+ if (all_vars[node->Name()].count(node) == 0) {
+ all_vars[node->Name()].emplace(node);
+ var_nodes_[node->Name()].emplace_back(node);
+ }
+ }
+ }
+}
+
+std::unique_ptr<ir::Graph> InplacePass::ApplyImpl(
+ std::unique_ptr<ir::Graph> graph) const {
+ var_nodes_.clear();
+ view_.Build(graph.get());
+ InitSSAGraphNodes();
+
+ for (auto* op : view_.AllOps()) {
+ if (FLAGS_enable_inplace_whitelist && !whitelist_.count(op->Name()))
+ continue;
+ TryInplaceOpInputOutput(op, graph.get());
+ }
+ graph->ResolveHazard(var_nodes_);
+
+ return graph;
+}
+
+void InplacePass::InplaceModifyDesc(const std::string& var,
+ const std::string& cache_var,
+ const size_t& idx) const {
+ for (size_t i = idx; i < view_.AllOps().size(); ++i) {
+ ir::Node* op = view_.AllOps()[i];
+ PADDLE_ENFORCE(op->IsOp() && op->Op());
+ auto* op_desc = op->Op();
+ op_desc->RenameInput(var, cache_var);
+ op_desc->RenameOutput(var, cache_var);
+ if (op_desc->Block()->HasVar(var)) op_desc->Block()->RemoveVar(var);
+ op_desc->Flush();
+ }
+}
+
+const NodeSwapQueue InplacePass::TryInplaceModifyVar(
+ const std::string& var, const std::string& cache_var, const size_t& idx,
+ ir::Graph* graph) const {
+ PADDLE_ENFORCE(var_nodes_[var].size() >= 1 &&
+ var_nodes_[var].at(0)->Var() != nullptr);
+ std::unique_ptr<VarDesc> var_desc(new VarDesc(*var_nodes_[var].at(0)->Var()));
+ var_desc->SetName(cache_var);
+
+ NodeSwapQueue swap_nodes;
+
+ for (size_t i = idx; i < view_.AllOps().size(); ++i) {
+ auto* op = view_.AllOps()[i];
+
+ // redirect the input to the latest version of cache_var
+ for (auto* node : op->inputs) {
+ if (node->Name() == var) {
+ ir::Node* cache_node = graph->CreateVarNode(var_desc.get());
+
+ // swap node to cache_node
+ cache_node->outputs.insert(cache_node->outputs.end(),
+ node->outputs.begin(), node->outputs.end());
+ PADDLE_ENFORCE(node->inputs.size() == 1 && node->inputs[0]->IsOp());
+ auto* prev_op = node->inputs[0];
+ std::replace(prev_op->outputs.begin(), prev_op->outputs.end(), node,
+ cache_node);
+ cache_node->inputs.emplace_back(prev_op);
+ for (auto* next_op : node->outputs) {
+ std::replace(next_op->inputs.begin(), next_op->inputs.end(), node,
+ cache_node);
+ }
+
+ swap_nodes.emplace_back(std::make_pair(node, cache_node));
+ }
+ }
+
+ // if we need to rename the output,
+ // always create a newer version of cache_var
+ for (auto* node : op->outputs) {
+ if (node->Name() == var) {
+ ir::Node* cache_node = graph->CreateVarNode(var_desc.get());
+ // swap node to cache node
+ cache_node->outputs.insert(cache_node->outputs.end(),
+ node->outputs.begin(), node->outputs.end());
+ cache_node->inputs.emplace_back(op);
+ std::replace(op->outputs.begin(), op->outputs.end(), node, cache_node);
+ for (auto* next_op : node->outputs) {
+ std::replace(next_op->inputs.begin(), next_op->inputs.end(), node,
+ cache_node);
+ }
+
+ swap_nodes.emplace_back(std::make_pair(node, cache_node));
+ }
+ }
+ }
+
+ return swap_nodes;
+}
+
+void InplacePass::CommitModify(const NodeSwapQueue& swap_nodes,
+ ir::Graph* graph) const {
+ for (auto& pair : swap_nodes) {
+ auto *node = pair.first, *cache_node = pair.second;
+ const std::string var = node->Name(), cache_var = cache_node->Name();
+ var_nodes_[cache_var].emplace_back(cache_node);
+ graph->RemoveNode(node);
+ auto& nodes = var_nodes_.at(var);
+ // release unused var in graph. Because python side memory optimize
+ // may reused the var in same name, so we only clear the var node
+ // after current inplaced index.
+ nodes.erase(std::remove(nodes.begin(), nodes.end(), node), nodes.end());
+ }
+}
+
+void InplacePass::WithdrawModify(const NodeSwapQueue& nodes,
+ ir::Graph* graph) const {
+ for (auto& pair : nodes) {
+ auto *node = pair.first, *cache_node = pair.second;
+ const std::string var = node->Name(), cache_var = cache_node->Name();
+ auto* prev_op = node->inputs[0];
+ std::replace(prev_op->outputs.begin(), prev_op->outputs.end(), cache_node,
+ node);
+ for (auto* next_op : node->outputs) {
+ std::replace(next_op->inputs.begin(), next_op->inputs.end(), cache_node,
+ node);
+ }
+ graph->RemoveNode(cache_node);
+ }
+}
+
+void InplacePass::TryInplaceOpInputOutput(ir::Node* op,
+ ir::Graph* graph) const {
+ VLOG(4) << "Try to inplace op " << op->Name();
+ PADDLE_ENFORCE(op->Op() != nullptr && op->Op()->Block() != nullptr,
+ "op_desc is nullptr");
+ // some pre-requirments need to meet if the op want to inplaced.
+
+ auto* op_desc = op->Op();
+ auto& infer_inplace =
+ OpInfoMap::Instance().Get(op_desc->Type()).infer_inplace_;
+
+ // 1. infer_inplace_ is registered.
+ if (!static_cast<bool>(infer_inplace)) return;
+ PADDLE_ENFORCE(static_cast<bool>(infer_inplace),
+ "%s's infer_inplace has not been registered", op_desc->Type());
+
+ auto* block = op_desc->Block();
+ auto in_to_outs = infer_inplace(*op_desc, block);
+
+ auto& all_ops = view_.AllOps();
+ auto cursor = std::find(all_ops.begin(), all_ops.end(), op);
+ size_t idx = std::distance(all_ops.begin(), cursor);
+
+ for (auto& pair : in_to_outs) {
+ auto& in_var_name = pair.first;
+ auto& out_var_name = pair.second;
+ auto* in_node = view_.GetNodeByName(in_var_name, op->inputs);
+ auto* out_node = view_.GetNodeByName(out_var_name, op->outputs);
+
+ // 2. there is no external pending op on the input node
+ if (view_.PendingOpsOnVar(in_node).size() > 1) {
+ VLOG(4) << string::Sprintf(
+ "Skiped pair %s => %s. %s input has external dependency."
+ "inplace such pair will overwrite the memory.",
+ out_var_name, in_var_name, op->Name());
+ continue;
+ }
+
+ // 3. if output has been memory optimize by python(fluid.memory_optmize()).
+ // this candidate can not be inplaced. Will be deprecated in the future.
+ if (view_.InSkipSet(out_node->Name())) {
+ VLOG(4) << string::Sprintf(
+ "Skiped %s => %s reused previous memory block in python memory "
+ "optmize,"
+ "it inplace may generate a circle",
+ out_var_name, in_var_name, op->Name());
+ continue;
+ }
+
+ // Debug Interface. Which would be skipped by the pass.
+ if (out_node->Name() == FLAGS_memory_optimize_debug) {
+ VLOG(3) << "Skiped var by force. FLAGS_memory_optimize_debug="
+ << out_node->Name();
+ continue;
+ }
+
+ // NOTE(dzhwinter):
+ // two stage commit of inplaced process. if after inplace happens generate a
+ // circle,
+ // then withdraw the changes. Otherwise, safely add the node.
+ auto swap_nodes =
+ TryInplaceModifyVar(out_var_name, in_var_name, idx, graph);
+
+ if (!ir::HasCircle(*graph)) {
+ VLOG(3) << string::Sprintf("!!! %s, %s => %s inplaced", op->Name(),
+ out_var_name, in_var_name);
+ InplaceModifyDesc(out_var_name, in_var_name, idx);
+ CommitModify(swap_nodes, graph);
+ } else {
+ VLOG(3) << string::Sprintf(
+ "Skiped pair %s => %s, inplace will generate a circle. withdraw %s",
+ out_var_name, in_var_name, op->Name());
+ WithdrawModify(swap_nodes, graph);
+ }
+ }
+}
+
+ir::Node* GraphView::GetNodeByName(const std::string& name,
+ const std::vector<ir::Node*>& nodes) const {
+ // nodes should be op->inputs/outputs
+ // node in same node do have different name.
+ std::unordered_set<std::string> nodes_in_op;
+ bool has_dup_node =
+ std::all_of(nodes.begin(), nodes.end(), [&nodes_in_op](ir::Node* node) {
+ if (!node->IsVar() || node->IsCtrlVar() || node->Var() == nullptr) {
+ if (nodes_in_op.count(node->Name())) return true;
+ nodes_in_op.emplace(node->Name());
+ }
+ return false;
+ });
+ PADDLE_ENFORCE(has_dup_node == false, "nodes has same name!");
+ ir::Node* node = nullptr;
+ for (auto* it : nodes) {
+ if (!it->IsVar() || it->IsCtrlVar() || it->Var() == nullptr) continue;
+ if (it->Name() == name) {
+ node = it;
+ break;
+ }
+ }
+ PADDLE_ENFORCE(node != nullptr,
+ string::Sprintf("Not found var %s in nodes!", name));
+ return node;
+}
+
+std::vector<ir::Node*> GraphView::PendingOpsOnVar(ir::Node* node) {
+ // get the pending ops depends on same var node.
+ // because node also maybe a inplaced variable, so need to backtrack all the
+ // previous inplaced vars.
+ std::vector<ir::Node*> pending_ops;
+ ir::Node* p = node;
+ while (p != nullptr) {
+ pending_ops.insert(pending_ops.end(), p->outputs.begin(), p->outputs.end());
+ p = GetPrevCascadeInplacedVar(p);
+ }
+ return pending_ops;
+}
+
+void GraphView::Build(ir::Graph* g) {
+ // track the var nodes in correct order.
+ // Because we insert some new created node. Which may have data race between
+ // nodes.
+ // resolve data harzards depends on the var nodes in right order.
+ ops_ = SortOpLikeDescOrder(*g);
+
+ // 1. track the nodes which reused previous node in Python memory optimize.
+ // these node can not be inplaced, otherwise may generate a circle in graph.
+ std::unordered_set<std::string> all_vars;
+ for (auto& node : g->Nodes()) {
+ if (node->IsVar()) continue;
+ for (auto& out : node->outputs) {
+ if (out->IsCtrlVar() || out->Var() == nullptr) continue;
+ if (all_vars.count(out->Name())) {
+ dup_nodes_.emplace(out->Name());
+ } else {
+ all_vars.emplace(out->Name());
+ }
+ }
+ }
+
+ // 2. track the nodes which used by parameter server.
+ // these node can not be inplaced, otherwise trainer
+ // pserver can not find each other name.
+ auto update_skip_set = [&](ir::Node* node) {
+ for (auto& in : node->inputs) {
+ if (in->IsVar() && in->Var() != nullptr) dup_nodes_.emplace(in->Name());
+ }
+ for (auto& out : node->outputs) {
+ if (out->IsVar() && out->Var() != nullptr)
+ dup_nodes_.emplace(out->Name());
+ }
+ };
+ for (auto& node : g->Nodes()) {
+ if (!node->IsOp()) continue;
+ if (node->Name() == "send") update_skip_set(node);
+ if (node->Name() == "recv") update_skip_set(node);
+ if (node->Name() == "prefetch") update_skip_set(node);
+ }
+}
+
+const std::vector<ir::Node*>& GraphView::AllOps() { return ops_; }
+
+bool GraphView::InSkipSet(const std::string& var) const {
+ return dup_nodes_.count(var);
+}
+
+} // namespace details
+} // namespace framework
+} // namespace paddle
+
+REGISTER_PASS(inplace_pass, paddle::framework::details::InplacePass);
diff --git a/paddle/fluid/framework/details/inplace_op_pass.h b/paddle/fluid/framework/details/inplace_op_pass.h
new file mode 100644
index 0000000000..7be7f31185
--- /dev/null
+++ b/paddle/fluid/framework/details/inplace_op_pass.h
@@ -0,0 +1,94 @@
+// 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 abtain 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 <map>
+#include <string>
+#include <unordered_map>
+#include <unordered_set>
+#include <utility>
+#include <vector>
+#include "paddle/fluid/framework/details/memory_optimize_helper.h"
+#include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/pass.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+class GraphView {
+ public:
+ GraphView() = default;
+
+ void Build(ir::Graph* g);
+
+ const std::vector<ir::Node*>& AllOps();
+
+ ir::Node* GetNodeByName(const std::string& name,
+ const std::vector<ir::Node*>& nodes) const;
+
+ std::vector<ir::Node*> PendingOpsOnVar(ir::Node* var);
+
+ // Will Deperated in the future.
+ // NOTE(dzhwinter) :
+ // 1. Python memory optimize will reuse
+ // memory based var name, so different op output may
+ // have the same variable name. enable inplace on such node
+ // will generate a circle in ssa graph.
+ // 2. DistributeTranspiler will use unique name to
+ // map the parameter and gradient, must be skipped.
+ bool InSkipSet(const std::string& var) const;
+
+ private:
+ std::vector<ir::Node*> ops_;
+ std::unordered_set<std::string> dup_nodes_; // mem opt affect nodes
+ std::map<ir::Node*, std::unordered_set<ir::Node*>> adj_list_;
+};
+
+// swap pairs in sequence
+typedef std::vector<std::pair<ir::Node*, ir::Node*>> NodeSwapQueue;
+class InplacePass : public ir::Pass {
+ public:
+ InplacePass();
+
+ protected:
+ std::unique_ptr<ir::Graph> ApplyImpl(
+ std::unique_ptr<ir::Graph> graph) const override;
+
+ void InitSSAGraphNodes() const;
+
+ private:
+ const NodeSwapQueue TryInplaceModifyVar(const std::string& var,
+ const std::string& cache_var,
+ const size_t& idx,
+ ir::Graph* graph) const;
+
+ void CommitModify(const NodeSwapQueue&, ir::Graph* graph) const;
+
+ void WithdrawModify(const NodeSwapQueue& nodes, ir::Graph* graph) const;
+
+ void InplaceModifyDesc(const std::string& in_var, const std::string& out_var,
+ const size_t& idx) const;
+
+ void TryInplaceOpInputOutput(ir::Node* op, ir::Graph* graph) const;
+
+ mutable std::map<std::string, std::vector<ir::Node*>> var_nodes_;
+
+ mutable std::unordered_set<std::string> whitelist_;
+ mutable GraphView view_;
+};
+
+} // namespace details
+} // namespace framework
+} // namespace paddle
diff --git a/paddle/fluid/framework/details/memory_early_delete_pass.cc b/paddle/fluid/framework/details/memory_early_delete_pass.cc
deleted file mode 100644
index 5906b7d57c..0000000000
--- a/paddle/fluid/framework/details/memory_early_delete_pass.cc
+++ /dev/null
@@ -1,117 +0,0 @@
-// 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/details/memory_early_delete_pass.h"
-#include <queue>
-#include <string>
-#include <vector>
-#include "paddle/fluid/framework/details/memory_reuse_types.h"
-#include "paddle/fluid/framework/details/multi_devices_helper.h"
-#include "paddle/fluid/framework/details/reference_count_pass_helper.h"
-#include "paddle/fluid/framework/ir/graph_helper.h"
-
-namespace paddle {
-namespace framework {
-namespace details {
-
-static ComputationOpHandle* FindNextComputationOpHandle(VarHandle* var_in) {
- std::queue<VarHandleBase*> queue;
- queue.push(var_in);
- do {
- auto* var = queue.front();
- queue.pop();
- for (auto* op : var->PendingOps()) {
- auto* compute_op = dynamic_cast<ComputationOpHandle*>(op);
- if (compute_op != nullptr && compute_op->GetPlace() == var_in->place()) {
- return compute_op;
- }
- for (auto* out_var : op->Outputs()) {
- queue.push(out_var);
- }
- }
- } while (!queue.empty());
- return nullptr;
-}
-
-std::unique_ptr<ir::Graph> MemoryEarlyDeletePass::ApplyImpl(
- std::unique_ptr<ir::Graph> graph) const {
- auto& graph_pool = Get<GraphNodePool>(kGraphNodePool);
- auto& gcs = Get<GarbageCollectorMap>(kGarbageCollector);
-
- std::unordered_map<std::string, std::unordered_set<OpDesc*>> unlived_vars;
- unlived_vars.reserve(graph_pool.size());
- for (auto& pair : graph_pool) {
- unlived_vars.insert(std::make_pair(pair.first, pair.second));
- }
-
- auto compare_and_insert_early_delete_op = [&](
- OpHandleBase* op, const std::vector<VarHandleBase*>& vars) {
- if (unlived_vars.empty()) return;
- // unlived vars can be deleted after the last used op has finished.
- auto* compute_op = dynamic_cast<ComputationOpHandle*>(op);
- const auto& places = Get<std::vector<platform::Place>>(kAllPlaces);
- for (auto& var : vars) {
- auto* var_handle = dynamic_cast<VarHandle*>(var);
- auto var_name = var->Node()->Name();
- auto& var_place = var_handle->place();
- if (unlived_vars.count(var_name) == 0) continue;
- if (!unlived_vars[var_name].empty()) {
- if (compute_op != nullptr &&
- unlived_vars[var_name].count(compute_op->Node()->Op()) != 0) {
- unlived_vars[var_name].erase(compute_op->Node()->Op());
- }
- continue;
- }
-
- if (var_handle == nullptr || !var_handle->Node()->IsVar() ||
- var_handle->Node()->IsCtrlVar())
- continue;
-
- // shameless copyed from reference count pass.
- if (compute_op == nullptr) {
- // use next computation op scope
- compute_op = FindNextComputationOpHandle(var_handle);
- }
- auto* early_delete_node =
- graph->CreateEmptyNode("early_delete", ir::Node::Type::kOperation);
- GarbageCollector* gc = gcs.at(places[compute_op->GetScopeIdx()]).get();
- auto* early_delete_handle = new EarlyDeleteOpHandle(
- early_delete_node, compute_op->GetScope(), var_place, {var_name}, gc);
- if (compute_op->Outputs().empty()) {
- auto* dep_var = new DummyVarHandle(graph->CreateControlDepVar());
- compute_op->AddOutput(dep_var);
- graph->Get<GraphDepVars>(kGraphDepVars).emplace(dep_var);
- }
- early_delete_handle->AddInput(compute_op->Outputs().front());
- VLOG(5) << "Add early delete op " << var_name << " to Operator"
- << compute_op->Name();
- }
- };
-
- auto all_ops = ir::FilterByNodeWrapper<OpHandleBase>(*graph);
- for (auto& op : all_ops) {
- compare_and_insert_early_delete_op(op, op->Inputs());
- compare_and_insert_early_delete_op(op, op->Outputs());
- }
- return graph;
-}
-
-} // namespace details
-} // namespace framework
-} // namespace paddle
-
-REGISTER_PASS(memory_early_delete_pass,
- paddle::framework::details::MemoryEarlyDeletePass)
- .RequireGraphAttr(paddle::framework::details::kGraphNodePool)
- .RequireGraphAttr(paddle::framework::details::kGarbageCollector);
diff --git a/paddle/fluid/framework/details/memory_optimize_helper.cc b/paddle/fluid/framework/details/memory_optimize_helper.cc
new file mode 100644
index 0000000000..6345ba3359
--- /dev/null
+++ b/paddle/fluid/framework/details/memory_optimize_helper.cc
@@ -0,0 +1,474 @@
+// 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/details/memory_optimize_helper.h"
+#include <deque>
+#include <functional>
+#include <iostream>
+#include <numeric>
+#include <sstream>
+#include <string>
+#include "paddle/fluid/framework/var_desc.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+using paddle::framework::VarDesc;
+
+std::vector<ir::Node*> SortOpLikeDescOrder(const ir::Graph& graph) {
+ PADDLE_ENFORCE(graph.Has(kAllOpDescs),
+ "Graph has no attribute of kAllOpDescs.");
+ // 1. get op desc order
+ auto& op_descs = graph.Get<const std::vector<OpDesc*>>(kAllOpDescs);
+
+ // 2. topology sort order
+ auto nodes = graph.Nodes();
+ std::deque<ir::Node*> ops;
+ FilterVariables(nodes, [&](ir::Node* op) {
+ if (op->IsOp() && op->Op() != nullptr) {
+ ops.emplace_back(op);
+ }
+ });
+ std::unordered_map<ir::Node*, size_t> op_deps;
+ std::list<ir::Node*> ready_ops;
+ std::unordered_map<ir::Node*, std::unordered_set<ir::Node*>> pending_ops;
+
+ for (auto* op : ops) {
+ std::unordered_set<ir::Node*> preceding_op;
+ for (auto* in : op->inputs) {
+ if (in->inputs.empty()) continue;
+ PADDLE_ENFORCE(in->inputs.size() == 1 && in->inputs[0]->IsOp());
+ preceding_op.emplace(in->inputs[0]);
+ pending_ops[in->inputs[0]].emplace(op);
+ }
+ op_deps[op] = preceding_op.size();
+ if (preceding_op.empty()) {
+ ready_ops.emplace_back(op);
+ }
+ }
+
+ // 3. generated op list based desc order and the topology order
+ std::vector<ir::Node*> ret;
+ std::list<OpDesc*> op_descs_list(op_descs.begin(), op_descs.end());
+
+ auto update_by_found_node = [&](ir::Node* found_node) {
+ for (auto* pending_op : pending_ops[found_node]) {
+ if (--op_deps[pending_op] == 0) {
+ ready_ops.emplace_back(pending_op);
+ }
+ }
+ ready_ops.remove(found_node);
+ ret.emplace_back(found_node);
+ };
+
+ while (!ready_ops.empty()) {
+ bool all_of_ready_op_unmatched = true;
+ for (auto it = op_descs_list.begin(); it != op_descs_list.end();) {
+ auto op_desc = *it;
+ ir::Node* found_node = nullptr;
+ for (auto* op : ready_ops) {
+ if (IsSameDesc(op->Op(), op_desc)) {
+ found_node = op;
+ break;
+ }
+ }
+
+ // 3.1 op desc deleted by other pass
+ if (found_node == nullptr) {
+ ++it;
+ continue;
+ } else {
+ all_of_ready_op_unmatched = false;
+ it = op_descs_list.erase(it);
+ }
+ update_by_found_node(found_node);
+ }
+
+ // 3.2 op descs are added by other pass
+ // preceding op non empty means some new op descs are
+ // created, but not contained in return node list.
+ // these new op desc may depend on each other.
+ std::list<ir::Node*> prev_ready_ops(ready_ops);
+ if (all_of_ready_op_unmatched) {
+ for (auto op : prev_ready_ops) {
+ update_by_found_node(op);
+ }
+ }
+ }
+
+ PADDLE_ENFORCE(std::all_of(
+ op_deps.begin(), op_deps.end(),
+ [&](const std::pair<ir::Node*, size_t>& p) { return p.second == 0; }));
+
+ return ret;
+}
+
+size_t NodeSize(const VarDesc& node) {
+ auto shape = node.GetShape();
+ int size =
+ std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>());
+ size_t type_size = SizeOfType(node.GetDataType());
+ return type_size * std::abs(size);
+}
+
+size_t NodeSize(ir::Node* n) {
+ auto* desc = FindVarDescInBlock(n);
+ return NodeSize(*desc);
+}
+
+std::string DebugStringImpl(VarDesc* var) {
+ std::stringstream ss;
+ ss << var->Name();
+ ss << "[";
+ try {
+ auto shape = var->GetShape();
+ for (size_t i = 0; i < shape.size(); ++i) {
+ if (i != shape.size() - 1) {
+ ss << shape[i] << ",";
+ } else {
+ ss << shape[i];
+ }
+ }
+ ss << "]";
+ } catch (...) {
+ ss << "Var has no VarDesc !!! Name:" << var->Name();
+ }
+ return ss.str();
+}
+
+std::string DebugString(ir::Node* var) {
+ return DebugStringImpl(FindVarDescInBlock(var));
+}
+
+// NOTE(dzh): based ir node, if a large node has been reused
+// by a small size node, then next time it appear in pool, it will
+// have the small size. Find the original node shap from blockdesc.
+VarDesc* FindVarDescInBlock(ir::Node* n) {
+ PADDLE_ENFORCE(n->IsVar() && !n->IsCtrlVar() && n->inputs.size() == 1);
+ BlockDesc* block = n->inputs[0]->Op()->Block();
+ PADDLE_ENFORCE(block->HasVar(n->Name()),
+ string::Sprintf("Block do not has var %s", n->Name()));
+ return block->FindVar(n->Name());
+}
+
+struct NodeComparator {
+ bool operator()(ir::Node* lhs, ir::Node* rhs) const {
+ auto* lhs_desc = FindVarDescInBlock(lhs);
+ auto* rhs_desc = FindVarDescInBlock(rhs);
+ auto lhs_shape = lhs_desc->GetShape();
+ auto rhs_shape = rhs_desc->GetShape();
+ if ((lhs_shape[0] == -1 && rhs_shape[0] == -1) ||
+ (lhs_shape[0] != -1 && rhs_shape[0] != -1)) {
+ return NodeSize(lhs) <= NodeSize(rhs);
+ } else {
+ return false;
+ }
+ }
+};
+
+void OrderedSet::Insert(ir::Node* var) {
+ PADDLE_ENFORCE(var->IsVar() && !var->IsCtrlVar());
+ if (mark_table_.count(var->Name()) != 0) {
+ mark_table_[var->Name()]->emplace_back(var);
+ return;
+ }
+
+ auto* var_desc = FindVarDescInBlock(var);
+ auto var_shape = var_desc->GetShape();
+ int batch_size = static_cast<int>(var_shape[0]);
+
+ NodeComparator functor;
+ Iter it = nodes_.begin();
+ while (it != nodes_.end()) {
+ auto& prev = it->front();
+ auto* cache_desc = FindVarDescInBlock(prev);
+ int cache_batch_size = cache_desc->GetShape()[0];
+ if ((cache_batch_size == -1 && batch_size == -1) ||
+ (cache_batch_size != -1 && batch_size != -1)) {
+ if (functor(prev, var)) {
+ ++it;
+ } else {
+ break;
+ }
+ } else if (cache_batch_size == -1 && batch_size != -1) {
+ ++it;
+ } else if (cache_batch_size != -1 && batch_size == -1) {
+ break;
+ }
+ }
+
+ it = nodes_.insert(it, {var});
+ mark_table_[var->Name()] = it;
+}
+
+int OrderedSet::GetNodeIndexInPool(ir::Node* var) {
+ return std::distance(nodes_.begin(), mark_table_[var->Name()]);
+}
+
+ir::Node* OrderedSet::FindBestFitNode(ir::Node* var) const {
+ ir::Node* found_node = nullptr;
+ NodeComparator functor;
+
+ for (auto it = nodes_.begin(); it != nodes_.end(); ++it) {
+ auto& candidate = it->front();
+ if (functor(var, candidate)) {
+ found_node = candidate;
+ break;
+ }
+ }
+ return found_node;
+}
+
+bool OrderedSet::Has(ir::Node* var) const {
+ if (mark_table_.count(var->Name())) {
+ auto& node_in_samename = mark_table_.at(var->Name());
+ auto iter =
+ std::find_if(node_in_samename->begin(), node_in_samename->end(),
+ [&](ir::Node* n) { return n->Name() == var->Name(); });
+ return iter != node_in_samename->end();
+ }
+ return false;
+}
+
+void OrderedSet::Erase(ir::Node* var) {
+ PADDLE_ENFORCE(mark_table_.count(var->Name()));
+ nodes_.erase(mark_table_[var->Name()]);
+ mark_table_.erase(var->Name());
+}
+
+std::string OrderedSet::ToString() const {
+ std::stringstream ss;
+ for (auto it = nodes_.begin(); it != nodes_.end(); ++it) {
+ for (auto& node : *it) {
+ ss << DebugString(node) << " ";
+ }
+ }
+ return ss.str();
+}
+
+bool NodeCanReused(ir::Node* node) {
+ // valid the node is a var node
+ if (node == nullptr || !node->IsVar() || node->IsCtrlVar()) return false;
+
+ bool flag = true;
+ // op output force generated in cpu, can not be reused.
+ for (auto* op : node->inputs) {
+ if (op->Op()->HasAttr("force_cpu")) {
+ flag &= framework::AttrReader(op->Op()->GetAttrMap())
+ .Get<bool>("force_cpu") == 0;
+ }
+ }
+ // var desc validation.
+ flag &= NodeCanReused(*node->Var());
+ return flag;
+}
+
+bool NodeCanReused(const VarDesc& node) {
+ auto type = node.GetType();
+ if (!(type == proto::VarType::LOD_TENSOR ||
+ type == proto::VarType::SELECTED_ROWS ||
+ type == proto::VarType::LOD_TENSOR_ARRAY)) {
+ return false;
+ }
+ if (node.Persistable() || node.GetShape().empty()) {
+ return false;
+ }
+ // vars can be @EMPTY@, @LR_DECAY_REUSE_ID@. For example, while_grad
+ std::string name = node.Name();
+ if (!name.empty() && name[0] == '@' && name[name.size() - 1] == '@')
+ return false;
+ return true;
+}
+
+bool OpHasSubBlock(OpDesc* desc) {
+ const AttributeMap& attrs = desc->GetAttrMap();
+ for (auto& attr : attrs) {
+ if (attr.second.type() == typeid(BlockDesc*) || // NOLINT
+ attr.second.type() == typeid(std::vector<BlockDesc*>)) // NOLINT
+ return true;
+ }
+ return false;
+}
+
+ControlFlowGraph::ControlFlowGraph(const ir::Graph& graph) {
+ ops_ = SortOpLikeDescOrder(graph);
+ ConnectNodes();
+}
+
+void ControlFlowGraph::BuildCFGGraph() {
+ // FIXME(dzh): same effect with ConnectNodes, but use the control
+ // link to build dependency graph, it goes wrong in transformer.
+ for (ir::Node* op : ops_) {
+ for (auto& input_var : op->inputs) {
+ if (!input_var->inputs.empty()) {
+ PADDLE_ENFORCE(
+ input_var->inputs.size() == 1 && input_var->inputs[0]->IsOp(),
+ "Preceding Op Node of Var Node must be unique");
+ auto* pred_op = input_var->inputs[0];
+ if (pred_op->Op() != nullptr) {
+ predecessors_[op].insert(pred_op);
+ successors_[pred_op].insert(op);
+ }
+ }
+ if (input_var->IsVar() && !input_var->IsCtrlVar()) {
+ uses_[op].insert(input_var->Name());
+ }
+ }
+ for (auto& output_var : op->outputs) {
+ // output var may be used by many op
+ for (auto* succ_op : output_var->outputs) {
+ if (succ_op->Op() != nullptr) {
+ successors_[op].insert(succ_op);
+ predecessors_[succ_op].insert(op);
+ }
+ }
+ if (output_var->IsVar() && !output_var->IsCtrlVar()) {
+ defs_[op].insert(output_var->Name());
+ }
+ }
+ }
+}
+
+void ControlFlowGraph::ConnectNodes() {
+ for (size_t i = 0; i < ops_.size(); ++i) {
+ auto& op = ops_[i];
+ try {
+ auto& next_op = ops_.at(i + 1);
+ successors_[op].insert(next_op);
+ predecessors_[next_op].insert(op);
+ } catch (...) {
+ // do nothing
+ }
+
+ FilterVariables(op->inputs,
+ [&](ir::Node* var) { uses_[op].emplace(var->Name()); });
+
+ FilterVariables(op->outputs,
+ [&](ir::Node* var) { defs_[op].emplace(var->Name()); });
+ }
+}
+
+void ControlFlowGraph::LiveVariableAnalysis() {
+ // NOTE(dzh): variable liveless analysis (a.k.a reversed_ops algorithm)
+ // compute the liveness of for each variable though reversed_ops algorithm.
+ // It iterates the operators from end to begin, compute the live in/live out
+ // variable set for each op, then the diff between in/out will be used for
+ // the variable reuse. For detail refer to
+ // http://www.cs.cornell.edu/courses/cs4120/2013fa/lectures/lec26-fa13.pdf
+ std::list<ir::Node*> work_list(ops_.rbegin(), ops_.rend());
+ while (!work_list.empty()) {
+ ir::Node* op = work_list.front();
+ work_list.pop_front();
+ // get the live_in calculated before. Empty if first.
+ auto prev_live_in = std::move(live_in_[op]);
+ for (auto& s : successors_[op]) {
+ for (auto& var : live_in_[s]) {
+ live_out_[op].insert(var);
+ }
+ }
+ for (auto& var : uses_[op]) {
+ live_in_[op].insert(var);
+ }
+ for (auto& var : live_out_[op]) {
+ live_in_[op].insert(var);
+ }
+ for (auto& var : defs_[op]) {
+ live_in_[op].erase(var);
+ }
+
+ // If the live_in is not changed, then the liveness analysis of
+ // predecessors is completed.
+ //
+ // Otherwise, recalculate the predecessors liveness
+ if (live_in_[op] != prev_live_in) {
+ for (auto& pre : predecessors_[op]) {
+ work_list.push_back(pre);
+ }
+ }
+ }
+}
+
+void ControlFlowGraph::RenameVarInCFGGraph(const std::string& old_node,
+ const std::string& new_node,
+ int begin_idx) {
+ // update graph from begin idx to the end
+ for (size_t i = begin_idx; i != ops_.size(); ++i) {
+ auto* op = ops_[i];
+ if (uses_[op].find(old_node) != uses_[op].end()) {
+ uses_[op].erase(old_node);
+ uses_[op].insert(new_node);
+ }
+ if (defs_[op].find(old_node) != defs_[op].end()) {
+ defs_[op].erase(old_node);
+ defs_[op].insert(new_node);
+ }
+ if (live_in_[op].find(old_node) != live_in_[op].end()) {
+ live_in_[op].erase(old_node);
+ live_in_[op].insert(new_node);
+ }
+ if (live_out_[op].find(old_node) != live_out_[op].end()) {
+ live_out_[op].erase(old_node);
+ live_out_[op].insert(new_node);
+ }
+ }
+}
+
+const std::set<std::string> ControlFlowGraph::LiveIn(ir::Node* op) const {
+ auto it = live_in_.find(op);
+ PADDLE_ENFORCE(
+ it != live_in_.end(),
+ string::Sprintf("Expect %s in live_in, but Not Found.", op->Name()));
+ return it->second;
+}
+
+const std::set<std::string> ControlFlowGraph::LiveOut(ir::Node* op) const {
+ auto it = live_out_.find(op);
+ PADDLE_ENFORCE(
+ it != live_out_.end(),
+ string::Sprintf("Expect %s in live_out, but Not Found.", op->Name()));
+ return it->second;
+}
+
+const std::set<std::string> ControlFlowGraph::Use(ir::Node* op) const {
+ auto it = uses_.find(op);
+ PADDLE_ENFORCE(
+ it != uses_.end(),
+ string::Sprintf("Expect %s in live_out, but Not Found.", op->Name()));
+ return it->second;
+}
+
+const std::vector<ir::Node*> ControlFlowGraph::Ops() const { return ops_; }
+
+std::vector<ir::Node*>& ControlFlowGraph::Ops() { return ops_; }
+
+ir::Node* ControlFlowGraph::GetNodeByName(const std::string& name,
+ ir::Node* op) const {
+ // in ssa-graph, different version nodes have same name,
+ // this function get the latest version var before target op
+ // It may return nullptr, such as data node.
+ ir::Node* found_node = nullptr;
+ for (auto* node : ops_) {
+ if (node == op) break;
+ for (auto& output : node->outputs) {
+ if (output->Name() == name) {
+ found_node = output;
+ }
+ }
+ }
+ return found_node;
+}
+
+} // namespace details
+} // namespace framework
+} // namespace paddle
diff --git a/paddle/fluid/framework/details/memory_optimize_helper.h b/paddle/fluid/framework/details/memory_optimize_helper.h
new file mode 100644
index 0000000000..0bfaf827fe
--- /dev/null
+++ b/paddle/fluid/framework/details/memory_optimize_helper.h
@@ -0,0 +1,182 @@
+// 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 <algorithm>
+#include <iostream>
+#include <iterator>
+#include <list>
+#include <map>
+#include <set>
+#include <string>
+#include <utility>
+#include <vector>
+#include "paddle/fluid/framework/data_type.h"
+#include "paddle/fluid/framework/ir/graph.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+constexpr char kAllOpDescs[] = "all_op_descs";
+
+std::vector<ir::Node*> SortOpLikeDescOrder(const ir::Graph& graph);
+
+// NOTE(dzh): A ordered set for node reuse in memory optimize.
+// the orderedset sort node in ascend order(by node bytes size).
+// in fluid, -1 means the batch_size, which is determined in runtime.
+// So the reuse happens between nodes who's batch_size both are -1
+// simultaneously or not.
+//
+// sort rule:
+// rule 0 : smaller node ranking in front.
+// rule 1 : batch_size equal -1 ranking in the front than the node not.
+//
+// For example,
+// node0[-1, 1] node1[-1, 1, 1], node2[1,1], node3[1,1024], ..
+
+class OrderedSet {
+ public:
+ // nodes with same name exists in pool.
+ using NodeVector = std::vector<ir::Node*>;
+ using Iter = typename std::list<NodeVector>::iterator;
+ using ConstIter = typename std::list<NodeVector>::const_iterator;
+
+ void Insert(ir::Node* var);
+ void Erase(ir::Node* var);
+ bool Has(ir::Node* var) const;
+ void Clear() {
+ mark_table_.clear();
+ nodes_.clear();
+ }
+ // find the bestfit shape node block with var.
+ ir::Node* FindBestFitNode(ir::Node* var) const;
+ // map store non-const iterator, can not promise const
+ int GetNodeIndexInPool(ir::Node* var);
+ // pool all node to string
+ std::string ToString() const;
+
+ Iter begin() { return nodes_.begin(); }
+ Iter end() { return nodes_.end(); }
+ ConstIter begin() const { return nodes_.begin(); }
+ ConstIter end() const { return nodes_.end(); }
+
+ size_t size() const { return nodes_.size(); }
+
+ private:
+ // for searching.
+ std::unordered_map<std::string, Iter> mark_table_;
+ // node pool
+ std::list<NodeVector> nodes_;
+};
+
+class ControlFlowGraph {
+ public:
+ ControlFlowGraph() = default;
+ // IR Graph
+ explicit ControlFlowGraph(const ir::Graph& graph);
+
+ void LiveVariableAnalysis();
+
+ void RenameVarInCFGGraph(const std::string& old_node,
+ const std::string& new_node, int begin_idx);
+
+ const std::set<std::string> LiveIn(ir::Node* op) const;
+ const std::set<std::string> LiveOut(ir::Node* op) const;
+ const std::set<std::string> Use(ir::Node* op) const;
+ const std::vector<ir::Node*> Ops() const;
+ std::vector<ir::Node*>& Ops();
+
+ // for ssa-graph nodes
+ ir::Node* GetNodeByName(const std::string& name, ir::Node* op) const;
+
+ private:
+ void BuildCFGGraph();
+ void ConnectNodes();
+
+ using NodeListMap = std::unordered_map<ir::Node*, std::set<ir::Node*>>;
+ using VarSetMap = std::map<ir::Node*, std::set<std::string>>;
+ // successors ops use the output variables.
+ NodeListMap successors_;
+ // predecessors ops generated input variables.
+ NodeListMap predecessors_;
+ // variables lived before run current op.
+ VarSetMap live_in_;
+ // variables lived after run current op.
+ VarSetMap live_out_;
+ VarSetMap uses_; // op inputs
+ VarSetMap defs_; // op outputs
+
+ std::vector<ir::Node*> ops_; // op sequence by topology sort
+};
+
+// valid a tensor can be reuse or not
+bool NodeCanReused(ir::Node* node);
+
+// valid a tensor can be reuse or not.
+bool NodeCanReused(const VarDesc& node);
+
+// check op has subblock or not
+bool OpHasSubBlock(OpDesc* desc);
+
+// node memory size in bytes
+size_t NodeSize(ir::Node* n);
+
+// node memory size in bytes
+size_t NodeSize(const VarDesc&);
+
+std::string DebugString(ir::Node* var);
+
+// NOTE(dzhwinter)
+// after node reuse, the replaced node shape is
+// different with its VarDesc. So need to find the
+// correct VarDesc in Block.
+VarDesc* FindVarDescInBlock(ir::Node* n);
+
+static inline bool IsSameDesc(OpDesc* op1, OpDesc* op2) {
+ return op1->Type() == op2->Type() && op1->Inputs() == op2->Inputs() &&
+ op1->Outputs() == op2->Outputs();
+}
+
+template <typename Container, typename Callback>
+class FilterVariableImpl {
+ public:
+ void operator()(const Container& nodes, Callback callback) {
+ for (auto* node : nodes) {
+ callback(node);
+ }
+ }
+};
+
+// filter var node for op->inputs/outputs
+template <typename Callback>
+class FilterVariableImpl<std::vector<ir::Node*>, Callback> {
+ public:
+ void operator()(const std::vector<ir::Node*>& nodes, Callback callback) {
+ for (auto* var : nodes) {
+ if (var->IsVar() && !var->IsCtrlVar()) {
+ callback(var);
+ }
+ }
+ }
+};
+
+template <typename Container, typename Callback>
+void FilterVariables(const Container& nodes, Callback callback) {
+ FilterVariableImpl<Container, Callback>()(nodes, callback);
+}
+
+} // namespace details
+} // namespace framework
+} // namespace paddle
diff --git a/paddle/fluid/framework/details/analysis_var_pass_test.cc b/paddle/fluid/framework/details/memory_optimize_helper_test.cc
similarity index 82%
rename from paddle/fluid/framework/details/analysis_var_pass_test.cc
rename to paddle/fluid/framework/details/memory_optimize_helper_test.cc
index 9bc4fd33f7..5c13dda9e5 100644
--- a/paddle/fluid/framework/details/analysis_var_pass_test.cc
+++ b/paddle/fluid/framework/details/memory_optimize_helper_test.cc
@@ -12,12 +12,18 @@
// See the License for the specific language governing permissions and
// limitations under the License.
-#include "paddle/fluid/framework/details/analysis_var_pass.h"
+#include "paddle/fluid/framework/details/memory_optimize_helper.h"
#include <algorithm>
#include <iostream>
#include <iterator>
+#include <memory>
+#include <sstream>
+#include <string>
+#include <utility>
+#include <vector>
#include "glog/logging.h"
#include "gtest/gtest.h"
+#include "paddle/fluid/framework/details/graph_test_base.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/op_registry.h"
@@ -26,46 +32,82 @@
namespace paddle {
namespace framework {
+namespace details {
-class DummyOp : public OperatorBase {
- public:
- DummyOp(const std::string& type, const VariableNameMap& inputs,
- const VariableNameMap& outputs, const AttributeMap& attrs)
- : OperatorBase(type, inputs, outputs, attrs) {}
-
- private:
- void RunImpl(const Scope& scope,
- const platform::Place& place) const override {}
-};
-
-class SumOpMaker : public OpProtoAndCheckerMaker {
- public:
- void Make() {
- AddInput("X", "").AsDuplicable();
- AddOutput("Out", "");
- AddComment("");
+TEST(OrderedSet, Normal) {
+ OrderedSet pool;
+ std::vector<std::unique_ptr<ir::Node>> nodes;
+
+ // clang-format off
+ std::vector<std::vector<int64_t>> shapes = {{-1, 10},
+ {-1, 20},
+ {1, 2},
+ {5, 2},
+ {10, 20},
+ {-1, 2, 5},
+ {-1, 1, 5},
+ {-1, 1}};
+ // clang-format on
+ const int COUNT = shapes.size();
+ ProgramDesc prog;
+ BlockDesc* block_desc = prog.MutableBlock(0);
+ auto* op_desc = block_desc->AppendOp();
+ op_desc->SetType("dummy");
+ std::unique_ptr<ir::Node> op = ir::CreateNodeForTest(op_desc);
+
+ for (int i = 0; i < COUNT; ++i) {
+ auto desc = block_desc->Var(std::to_string(i));
+ desc->SetShape(shapes[i]);
+ std::unique_ptr<ir::Node> node = ir::CreateNodeForTest(desc);
+ node->inputs.emplace_back(op.get());
+ nodes.emplace_back(std::move(node));
}
-};
-
-class AssignOpMaker : public OpProtoAndCheckerMaker {
- public:
- void Make() {
- AddInput("X", "").AsDuplicable();
- AddOutput("Out", "");
- AddComment("");
+
+ // Insert
+ for (auto& node : nodes) {
+ pool.Insert(node.get());
}
-};
-
-class DummyVarTypeInference : public VarTypeInference {
- public:
- void operator()(const OpDesc& op_desc, BlockDesc* block) const override {
- auto& inputs = op_desc.Input("X");
- auto type = block->Var(inputs.front())->GetType();
- auto out_var_name = op_desc.Output("Out").front();
- block->Var(out_var_name)->SetType(type);
+
+ // Has/size
+ ASSERT_EQ(pool.size(), shapes.size());
+ for (auto& node : nodes) {
+ ASSERT_TRUE(pool.Has(node.get()));
}
-};
+ // assert its order and interface.
+ std::cout << pool.ToString() << std::endl;
+ pool.Erase(nodes.front().get());
+ std::cout << pool.ToString() << std::endl;
+
+ ASSERT_EQ(pool.size(), static_cast<size_t>(COUNT - 1));
+ ASSERT_EQ(pool.GetNodeIndexInPool(nodes.back().get()), 0);
+
+ {
+ auto v1 = block_desc->Var("11");
+ v1->SetShape({-1, 256, 56, 56});
+ std::unique_ptr<ir::Node> node1 = ir::CreateNodeForTest(v1);
+ node1->inputs.emplace_back(op.get());
+ auto* cache = pool.FindBestFitNode(node1.get());
+ ASSERT_EQ(cache, nullptr);
+ }
+ {
+ auto v2 = block_desc->Var("12");
+ v2->SetShape({-1, 2, 5});
+ std::unique_ptr<ir::Node> node1 = ir::CreateNodeForTest(v2);
+ node1->inputs.emplace_back(op.get());
+ auto* cache = pool.FindBestFitNode(node1.get());
+ ASSERT_EQ(pool.GetNodeIndexInPool(cache), 2); // match 6:[-1,2,5]
+ }
+ {
+ auto v3 = block_desc->Var("13");
+ v3->SetShape({2, 5});
+ std::unique_ptr<ir::Node> node1 = ir::CreateNodeForTest(v3);
+ node1->inputs.emplace_back(op.get());
+ auto* cache = pool.FindBestFitNode(node1.get());
+ ASSERT_EQ(pool.GetNodeIndexInPool(cache), 5); // match 4:[5,2]
+ }
+}
+} // namespace details
} // namespace framework
} // namespace paddle
@@ -102,11 +144,6 @@ namespace paddle {
namespace framework {
namespace details {
-static inline bool IsSameDesc(OpDesc* op1, OpDesc* op2) {
- return op1->Type() == op2->Type() && op1->Inputs() == op2->Inputs() &&
- op1->Outputs() == op2->Outputs();
-}
-
inline static ProgramDesc FillProgramDesc() {
ProgramDesc prog;
prog.MutableBlock(0)->Var("a")->SetType(proto::VarType::LOD_TENSOR);
@@ -141,15 +178,6 @@ inline static ProgramDesc FillProgramDesc() {
return prog;
}
-template <typename Container>
-inline static std::string DebugString(const Container& c) {
- std::stringstream ss;
- for (auto& item : c) {
- ss << item << " ";
- }
- return ss.str();
-}
-
TEST(CFGGraph, IRGraph) {
// prepare ir graph
auto prog = FillProgramDesc();
diff --git a/paddle/fluid/framework/details/memory_optimize_pass.cc b/paddle/fluid/framework/details/memory_optimize_pass.cc
new file mode 100644
index 0000000000..41e4a834df
--- /dev/null
+++ b/paddle/fluid/framework/details/memory_optimize_pass.cc
@@ -0,0 +1,327 @@
+// 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/details/memory_optimize_pass.h"
+#include <algorithm>
+#include <atomic>
+#include <deque>
+#include <fstream>
+#include <iostream>
+#include <iterator>
+#include <memory>
+#include <queue>
+#include <sstream>
+#include <string>
+#include <type_traits>
+#include <vector>
+#include "gflags/gflags.h"
+#include "paddle/fluid/framework/data_type.h"
+#include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/graph_helper.h"
+
+DEFINE_bool(enable_subgraph_optimize, false,
+ "SubGraph also reuse global graph variables, it will reduce the "
+ "memory occupation"
+ "but a higher risk of memory reuse error. default disabled.");
+DEFINE_string(memory_optimize_debug, "",
+ "debug the operator output variable when do the variable reuse."
+ "memory reuse pass."
+ "only for debug, default disabled.");
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+std::unique_ptr<ir::Graph> MemoryOptimizePass::ApplyImpl(
+ std::unique_ptr<ir::Graph> graph) const {
+ auto nodes = graph->Nodes();
+ CollectSkipVarsSet(nodes);
+
+ cfg_.reset(new details::ControlFlowGraph(*graph));
+ cfg_->LiveVariableAnalysis();
+ InitSSAGraphNodes();
+
+ int reuse_id = 0;
+ for (size_t idx = 0; idx < cfg_->Ops().size(); ++idx) {
+ auto& op = cfg_->Ops()[idx];
+ auto* op_desc = op->Op();
+ // some op in graph has no op desc
+ if (op_desc == nullptr) continue;
+ if (OpHasSubBlock(op_desc)) {
+ if (FLAGS_enable_subgraph_optimize) {
+ SubGraphOptimize(op_desc);
+ } else {
+ VLOG(3) << op->Name()
+ << " has subblock, but disable subgraph optimize. skipped.";
+ continue;
+ }
+ }
+
+ for (auto& var : op->outputs) {
+ if (!NodeCanReused(var) || cfg_->Use(op).count(var->Name()) == 0 ||
+ skip_set_.count(var->Name()))
+ continue;
+ ir::Node* cache = pool_.FindBestFitNode(var);
+
+ if (var->Name() == FLAGS_memory_optimize_debug) {
+ VLOG(3) << "start match var " << DebugString(var) << " of op "
+ << op->Name();
+ VLOG(3) << pool_.ToString();
+ VLOG(3) << "matched in pool : "
+ << ((cache == nullptr) ? "False" : "True");
+ }
+
+ if (cache == nullptr) continue;
+ if (var->Name() == cache->Name()) {
+ VLOG(3) << "The same cache variable is cascade reused." << var->Name()
+ << " is re-filled to the pool after"
+ << "the reused op is finished. Current op can not "
+ << "replace it again. Skip this candidate.";
+ continue;
+
+ int node_idx_in_pool = pool_.GetNodeIndexInPool(cache);
+ VLOG(3) << string::Sprintf(
+ "!!! %s, %s => %s, cache idx %d, pool size %d",
+ std::to_string(reuse_id++), DebugString(var), DebugString(cache),
+ node_idx_in_pool, static_cast<int>(pool_.size()));
+
+ // update CFG Graph on the fly.
+ // reused var maybe re-fill into the pool
+ cfg_->RenameVarInCFGGraph(var->Name(), cache->Name(), idx);
+ // NOTE(dzhwinter): we need to both update the ProgramDesc
+ // and IR Graph. because op_desc/var_desc is used in CreateOp,
+ // CreateVar when running happens. But IR Graph
+ // define the dependence relationship between nodes.
+ RenameVarInGraphDesc(var->Name(), cache->Name(), idx);
+ RenameVarInGraphNode(var->Name(), cache->Name(), idx, graph.get());
+
+ pool_.Erase(cache);
+ }
+
+ // fill the pool
+ std::unordered_set<std::string> unlived_vars;
+ for (auto var : cfg_->LiveIn(op)) {
+ if (cfg_->LiveOut(op).count(var) == 0) {
+ unlived_vars.emplace(var);
+ }
+ }
+ for (auto var : unlived_vars) {
+ ir::Node* var_node = cfg_->GetNodeByName(var, op);
+ if (NodeCanReused(var_node) && !pool_.Has(var_node)) {
+ pool_.Insert(var_node);
+ }
+ }
+ }
+ }
+ graph->ResolveHazard(var_nodes_);
+
+ return graph;
+}
+
+void MemoryOptimizePass::SubGraphOptimize(OpDesc* op_desc) const {
+ // conditional block, while op and their grad op
+ auto* sub_block_desc =
+ AttrReader(op_desc->GetAttrMap()).Get<BlockDesc*>("sub_block");
+
+ // create a mirror block to construct an IR Graph.
+ ProgramDesc prog;
+ auto* copy_block = prog.MutableBlock(0);
+ for (auto* op : sub_block_desc->AllOps()) {
+ auto* copy_op = copy_block->AppendOp();
+ copy_op->CopyFrom(*op);
+ copy_op->Flush();
+ }
+
+ for (auto* var : sub_block_desc->AllVars()) {
+ auto* copy_var = copy_block->Var(var->Name());
+ copy_var->SetDataType(var->GetDataType());
+ // only lod tensor can be reused. So ignore the multiple dims case.
+ copy_var->SetType(var->GetType());
+ copy_var->SetShape(var->GetShape());
+ copy_var->SetPersistable(var->Persistable());
+ }
+
+ ir::Graph sub_graph(prog);
+ std::unordered_set<ir::Node*> sub_graph_all_ops;
+ FilterVariables(sub_graph.Nodes(), [&](ir::Node* var) {
+ // sub_graph_all_ops.emplace(var);
+ if (var->IsVar() && !var->IsCtrlVar()) {
+ sub_graph_all_ops.emplace(var);
+ }
+ });
+ int sub_reuse_id = 0;
+ // subgraph nodes is unordered, reuse need to follow the desc order.
+ // find the right op node through the descs
+ for (auto* sub_op_desc : sub_block_desc->AllOps()) {
+ ir::Node* sub_op = nullptr;
+ for (auto* node : sub_graph_all_ops) {
+ if (node->Op() == sub_op_desc) {
+ sub_op = node;
+ break;
+ }
+ }
+ PADDLE_ENFORCE(sub_op != nullptr);
+ for (auto* var : sub_op->outputs) {
+ if (NodeCanReused(var)) {
+ ir::Node* cache = pool_.FindBestFitNode(var);
+ if (cache != nullptr) {
+ if (var->Var()->GetDataType() != cache->Var()->GetDataType()) {
+ continue;
+ }
+ int node_idx_in_pool = pool_.GetNodeIndexInPool(cache);
+ VLOG(3) << string::Sprintf(
+ "!!! %s, %s => %s, cache idx %d, pool size %d",
+ std::to_string(sub_reuse_id++), DebugString(var),
+ DebugString(cache), node_idx_in_pool,
+ static_cast<int>(pool_.size()));
+ // NOTE(dzh): subblock is not in IR graph. Modify the block_desc
+ // immediately to make the subblock variable reuse strategy take
+ // effect. Because it is a single op in graph. No need to
+ // update the ir nodes.
+ sub_op_desc->Rename(var->Name(), cache->Name());
+ if (sub_op_desc->Block()->HasVar(var->Name())) {
+ sub_op_desc->Block()->RemoveVar(var->Name());
+ }
+ }
+ }
+ }
+ }
+}
+
+void MemoryOptimizePass::CollectSkipVarsSet(
+ const std::unordered_set<ir::Node*>& nodes) const {
+ auto update_skip_set = [&](OpDesc* op_desc) {
+ auto inputs = op_desc->InputArgumentNames();
+ auto outputs = op_desc->OutputArgumentNames();
+ skip_set_.insert(inputs.begin(), inputs.end());
+ skip_set_.insert(outputs.begin(), outputs.end());
+ };
+ for (auto& op : nodes) {
+ if (!op->IsOp() || op->Op() == nullptr) continue;
+ auto* op_desc = op->Op();
+ // NOTE(dzhwinter):
+ // current block can not reuse next level block vars.
+ if (OpHasSubBlock(op_desc)) update_skip_set(op_desc);
+ // NOTE(dzhwinter):
+ // distributed ops input/output name need to
+ // keep same bettwen trainer/pserver
+ if (op_desc->Type() == "send") update_skip_set(op_desc);
+ if (op_desc->Type() == "recv") update_skip_set(op_desc);
+ if (op_desc->Type() == "prefetch") update_skip_set(op_desc);
+ }
+}
+
+void MemoryOptimizePass::RenameVarInGraphDesc(const std::string& var,
+ const std::string& cache_var,
+ size_t idx) const {
+ for (size_t i = idx; i < cfg_->Ops().size(); ++i) {
+ auto* op = cfg_->Ops()[i];
+ PADDLE_ENFORCE(op->IsOp() && op->Op());
+ auto* op_desc = op->Op();
+ op_desc->RenameInput(var, cache_var);
+ op_desc->RenameOutput(var, cache_var);
+ if (op_desc->Block()->HasVar(var)) op_desc->Block()->RemoveVar(var);
+ op_desc->Flush();
+ }
+}
+
+void MemoryOptimizePass::InitSSAGraphNodes() const {
+ std::unordered_map<std::string, std::unordered_set<ir::Node*>> all_vars;
+ if (var_nodes_.empty()) {
+ for (auto* op : cfg_->Ops()) {
+ for (auto* node : op->inputs) {
+ if (all_vars[node->Name()].count(node) == 0) {
+ all_vars[node->Name()].emplace(node);
+ var_nodes_[node->Name()].emplace_back(node);
+ }
+ }
+ for (auto* node : op->outputs) {
+ if (all_vars[node->Name()].count(node) == 0) {
+ all_vars[node->Name()].emplace(node);
+ var_nodes_[node->Name()].emplace_back(node);
+ }
+ }
+ }
+ }
+}
+
+void MemoryOptimizePass::RenameVarInGraphNode(const std::string& var,
+ const std::string& cache_var,
+ size_t idx,
+ ir::Graph* graph) const {
+ // if replace happens, we need to create a newer version cache_var
+ // but use the same dims/data_type with var.
+ PADDLE_ENFORCE(var_nodes_[var].size() >= 1 &&
+ var_nodes_[var].at(0)->Var() != nullptr);
+ std::unique_ptr<VarDesc> var_desc(new VarDesc(*var_nodes_[var].at(0)->Var()));
+ var_desc->SetName(cache_var);
+
+ for (size_t i = idx; i < cfg_->Ops().size(); ++i) {
+ auto* op = cfg_->Ops()[i];
+
+ // redirect the input to the latest version of cache_var
+ for (auto* node : op->inputs) {
+ if (node->Name() == var) {
+ ir::Node* cache_node = graph->CreateVarNode(var_desc.get());
+ var_nodes_[cache_var].emplace_back(cache_node);
+
+ // swap node to cache_node
+ cache_node->outputs.insert(cache_node->outputs.end(),
+ node->outputs.begin(), node->outputs.end());
+ PADDLE_ENFORCE(node->inputs.size() == 1 && node->inputs[0]->IsOp());
+ auto* prev_op = node->inputs[0];
+ std::replace(prev_op->outputs.begin(), prev_op->outputs.end(), node,
+ cache_node);
+ cache_node->inputs.emplace_back(prev_op);
+ for (auto* next_op : node->outputs) {
+ std::replace(next_op->inputs.begin(), next_op->inputs.end(), node,
+ cache_node);
+ }
+ }
+ }
+
+ // if we need to rename the output,
+ // always create a newer version of cache_var
+ for (auto* node : op->outputs) {
+ if (node->Name() == var) {
+ ir::Node* cache_node = graph->CreateVarNode(var_desc.get());
+ var_nodes_[cache_var].emplace_back(cache_node);
+
+ // swap node to cache node
+ cache_node->outputs.insert(cache_node->outputs.end(),
+ node->outputs.begin(), node->outputs.end());
+ cache_node->inputs.emplace_back(op);
+ std::replace(op->outputs.begin(), op->outputs.end(), node, cache_node);
+ for (auto* next_op : node->outputs) {
+ std::replace(next_op->inputs.begin(), next_op->inputs.end(), node,
+ cache_node);
+ }
+ }
+ }
+ }
+
+ // release node of unused var in graph
+ for (auto* node : var_nodes_[var]) {
+ graph->RemoveNode(node);
+ }
+ var_nodes_.at(var).clear();
+}
+
+} // namespace details
+} // namespace framework
+} // namespace paddle
+
+REGISTER_PASS(memory_optimize_pass,
+ paddle::framework::details::MemoryOptimizePass)
+ .RequireGraphAttr(paddle::framework::details::kAllOpDescs);
diff --git a/paddle/fluid/framework/details/memory_optimize_pass.h b/paddle/fluid/framework/details/memory_optimize_pass.h
new file mode 100644
index 0000000000..593ffc10fc
--- /dev/null
+++ b/paddle/fluid/framework/details/memory_optimize_pass.h
@@ -0,0 +1,71 @@
+// 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 <algorithm>
+#include <list>
+#include <map>
+#include <memory>
+#include <set>
+#include <string>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+
+#include "paddle/fluid/framework/data_type.h"
+#include "paddle/fluid/framework/details/memory_optimize_helper.h"
+#include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/pass.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+class MemoryOptimizePass : public ir::Pass {
+ protected:
+ std::unique_ptr<ir::Graph> ApplyImpl(
+ std::unique_ptr<ir::Graph> graph) const override;
+ // fill the variable map(var_nodes) by version.
+ void InitSSAGraphNodes() const;
+
+ private:
+ // update program descs
+ void RenameVarInGraphDesc(const std::string& var,
+ const std::string& cache_var, size_t idx) const;
+ // update ir nodes
+ void RenameVarInGraphNode(const std::string& var,
+ const std::string& cache_var, size_t idx,
+ ir::Graph* graph) const;
+
+ void SubGraphOptimize(OpDesc* op_desc) const;
+ // 1. scan op with subblock and collect the output/input vars.
+ // while, while_grad, conditional_block
+ // 2. scan distributed ops and collect the output/input vars
+ void CollectSkipVarsSet(const std::unordered_set<ir::Node*>&) const;
+
+ private:
+ // Reuse Node Pool, Owned.
+ mutable OrderedSet pool_;
+ // controlflow Graph
+ mutable std::unique_ptr<ControlFlowGraph> cfg_;
+ // skip set
+ mutable std::unordered_set<std::string> skip_set_;
+ // var nodes
+ mutable std::map<std::string, std::vector<ir::Node*>> var_nodes_;
+};
+
+} // namespace details
+} // namespace framework
+} // namespace paddle
diff --git a/paddle/fluid/framework/details/memory_reuse_types.cc b/paddle/fluid/framework/details/memory_reuse_types.cc
deleted file mode 100644
index 2b9ff518b9..0000000000
--- a/paddle/fluid/framework/details/memory_reuse_types.cc
+++ /dev/null
@@ -1,155 +0,0 @@
-// 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/details/memory_reuse_types.h"
-#include <iostream>
-#include <sstream>
-#include <string>
-
-namespace paddle {
-namespace framework {
-namespace details {
-
-size_t NodeSizeInBytes(ir::Node* n) {
- auto* desc = FindVarDescInBlock(n);
- auto shape = desc->GetShape();
- size_t type_size = SizeOfType(desc->GetDataType());
- int size = 1;
- for (auto& s : shape) {
- size *= s;
- }
- return type_size * std::abs(size);
-}
-
-std::string DebugStringImpl(VarDesc* var) {
- std::stringstream ss;
- ss << var->Name();
- ss << "[";
- try {
- auto shape = var->GetShape();
- for (size_t i = 0; i < shape.size(); ++i) {
- if (i != shape.size() - 1) {
- ss << shape[i] << ",";
- } else {
- ss << shape[i];
- }
- }
- ss << "]";
- } catch (...) {
- ss << "Var has no VarDesc !!! Name:" << var->Name();
- }
- return ss.str();
-}
-
-std::string DebugString(ir::Node* var) {
- return DebugStringImpl(FindVarDescInBlock(var));
-}
-// return DebugString(var->Var()); }
-
-// NOTE(dzh): based ir node, if a large node has been reused
-// by a small size node, then next time it appear in pool, it will
-// have the small size. Find the original node shap from blockdesc.
-VarDesc* FindVarDescInBlock(ir::Node* n) {
- PADDLE_ENFORCE(n->IsVar() && !n->IsCtrlVar() && n->inputs.size() == 1);
- BlockDesc* block = n->inputs[0]->Op()->Block();
- PADDLE_ENFORCE(block->HasVar(n->Name()),
- string::Sprintf("Block do not has var %s", n->Name()));
- return block->FindVar(n->Name());
-}
-
-struct NodeComparator {
- bool operator()(ir::Node* lhs, ir::Node* rhs) const {
- auto* lhs_desc = FindVarDescInBlock(lhs);
- auto* rhs_desc = FindVarDescInBlock(rhs);
- auto lhs_shape = lhs_desc->GetShape();
- auto rhs_shape = rhs_desc->GetShape();
- if ((lhs_shape[0] == -1 && rhs_shape[0] == -1) ||
- (lhs_shape[0] != -1 && rhs_shape[0] != -1)) {
- return NodeSizeInBytes(lhs) <= NodeSizeInBytes(rhs);
- } else {
- return false;
- }
- }
-};
-
-void OrderedNodePairPool::Insert(ir::Node* var, ir::Node* op) {
- PADDLE_ENFORCE(var->IsVar() && !var->IsCtrlVar());
- PADDLE_ENFORCE(op->IsOp());
- if (mark_table_.count(var->Name()) != 0) {
- mark_table_[var->Name()]->second.insert(op);
- return;
- }
-
- auto* var_desc = FindVarDescInBlock(var);
- auto var_shape = var_desc->GetShape();
- int batch_size = static_cast<int>(var_shape[0]);
-
- NodeComparator compare_node;
- Iter it = nodes_.begin();
- while (it != nodes_.end()) {
- auto* cache_desc = FindVarDescInBlock(it->first);
- int cache_batch_size = cache_desc->GetShape()[0];
- if ((cache_batch_size == -1 && batch_size == -1) ||
- (cache_batch_size != -1 && batch_size != -1)) {
- if (compare_node(it->first, var)) {
- ++it;
- } else {
- break;
- }
- } else if (cache_batch_size == -1 && batch_size != -1) {
- ++it;
- } else if (cache_batch_size != -1 && batch_size == -1) {
- break;
- }
- }
-
- it =
- nodes_.insert(it, std::make_pair(var, std::unordered_set<ir::Node*>{op}));
- mark_table_[var->Name()] = it;
-}
-
-int OrderedNodePairPool::GetIndex(ir::Node* var) {
- return std::distance(nodes_.begin(), mark_table_[var->Name()]);
-}
-
-ir::Node* OrderedNodePairPool::NodeMatch(ir::Node* var) const {
- ir::Node* found_node = nullptr;
- NodeComparator compare_node;
-
- for (auto it = nodes_.begin(); it != nodes_.end(); ++it) {
- if (compare_node(var, it->first)) {
- found_node = it->first;
- break;
- }
- }
- return found_node;
-}
-
-void OrderedNodePairPool::Erase(ir::Node* var) {
- PADDLE_ENFORCE(mark_table_.count(var->Name()));
- nodes_.erase(mark_table_[var->Name()]);
- mark_table_.erase(var->Name());
-}
-
-std::string OrderedNodePairPool::ToString() const {
- std::stringstream ss;
- for (auto it = nodes_.begin(); it != nodes_.end(); ++it) {
- ss << DebugString(it->first) << " ";
- }
- return ss.str();
-}
-
-} // namespace details
-} // namespace framework
-} // namespace paddle
diff --git a/paddle/fluid/framework/details/memory_reuse_types.h b/paddle/fluid/framework/details/memory_reuse_types.h
deleted file mode 100644
index 9a9c1d948e..0000000000
--- a/paddle/fluid/framework/details/memory_reuse_types.h
+++ /dev/null
@@ -1,87 +0,0 @@
-// 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 <algorithm>
-#include <iostream>
-#include <iterator>
-#include <list>
-#include <string>
-#include <utility>
-#include <vector>
-#include "paddle/fluid/framework/data_type.h"
-#include "paddle/fluid/framework/ir/graph.h"
-
-namespace paddle {
-namespace framework {
-namespace details {
-
-constexpr char kFetchedVars[] = "fetched_vars";
-constexpr char kGraphNodePool[] = "graph_node_pool";
-
-// NOTE(dzh): Variable and the operators use the var.
-// for early delete pass.
-// Because analysis var pass build base on ir::Node, which maybe released
-// or modified between passes, so we use OpDesc* to mark ops.
-using GraphNodePool = std::vector<
- std::pair<std::string /*var node*/, std::unordered_set<OpDesc*> /* ops */>>;
-
-// NOTE(dzh): by default, it sort node in ascend order(by node bytes size).
-// in fluid, -1 means the batch_size is determined in runtime.
-// the node batch_size equal -1 always ranking in the front than the node not.
-// For example,
-// node0[-1, 1] node1[-1, 1, 1], node2[1,1], node3[1,1024], ..
-// O(1) insert, delete
-class OrderedNodePairPool {
- public:
- using NodePair = std::pair<ir::Node*, std::unordered_set<ir::Node*>>;
- using Iter = typename std::list<NodePair>::iterator;
- using ConstIter = typename std::list<NodePair>::const_iterator;
-
- void Insert(ir::Node* var, ir::Node* op);
-
- void Erase(ir::Node* var);
-
- bool Has(ir::Node* var) { return mark_table_.count(var->Name()); }
-
- ir::Node* NodeMatch(ir::Node* var) const;
- // map store non-const iterator, can not promise const
- int GetIndex(ir::Node* var);
- // pool all node to string
- std::string ToString() const;
-
- Iter begin() { return nodes_.begin(); }
- Iter end() { return nodes_.end(); }
- ConstIter begin() const { return nodes_.begin(); }
- ConstIter end() const { return nodes_.end(); }
- size_t size() const { return nodes_.size(); }
-
- private:
- // for searching.
- std::unordered_map<std::string, Iter> mark_table_;
- // node swap pairs. var -> ops dep var
- std::list<NodePair> nodes_;
-};
-
-// node memory size in bytes
-size_t NodeSizeInBytes(ir::Node* n);
-
-std::string DebugString(ir::Node* var);
-
-// std::string DebugString(VarDesc* var);
-VarDesc* FindVarDescInBlock(ir::Node* n);
-
-} // namespace details
-} // namespace framework
-} // namespace paddle
diff --git a/paddle/fluid/framework/details/memory_reuse_types_test.cc b/paddle/fluid/framework/details/memory_reuse_types_test.cc
deleted file mode 100644
index d2fabf5ce0..0000000000
--- a/paddle/fluid/framework/details/memory_reuse_types_test.cc
+++ /dev/null
@@ -1,99 +0,0 @@
-// 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/details/memory_reuse_types.h"
-#include <algorithm>
-#include <iostream>
-#include <memory>
-#include <sstream>
-#include <string>
-#include <utility>
-#include <vector>
-#include "glog/logging.h"
-#include "gtest/gtest.h"
-
-namespace paddle {
-namespace framework {
-namespace details {
-
-TEST(OrderedNodePairPool, Normal) {
- OrderedNodePairPool pool;
- std::vector<std::unique_ptr<ir::Node>> nodes;
-
- // clang-format off
- std::vector<std::vector<int64_t>> shapes = {{-1, 10},
- {-1, 20},
- {1, 2},
- {5, 2},
- {10, 20},
- {-1, 2, 5},
- {-1, 1, 5},
- {-1, 1}};
- // clang-format on
- const int COUNT = shapes.size();
- ProgramDesc prog;
- BlockDesc* block_desc = prog.MutableBlock(0);
- auto* op_desc = block_desc->AppendOp();
- op_desc->SetType("dummy");
- std::unique_ptr<ir::Node> op = ir::CreateNodeForTest(op_desc);
-
- for (int i = 0; i < COUNT; ++i) {
- auto desc = block_desc->Var(std::to_string(i));
- desc->SetShape(shapes[i]);
- std::unique_ptr<ir::Node> node = ir::CreateNodeForTest(desc);
- node->inputs.emplace_back(op.get());
- nodes.emplace_back(std::move(node));
- }
-
- for (auto& node : nodes) {
- pool.Insert(node.get(), op.get());
- }
-
- // assert its order and interface.
- std::cout << pool.ToString() << std::endl;
- pool.Erase(nodes.front().get());
- std::cout << pool.ToString() << std::endl;
-
- ASSERT_EQ(pool.size(), static_cast<size_t>(COUNT - 1));
- ASSERT_EQ(pool.GetIndex(nodes.back().get()), 0);
-
- {
- auto v1 = block_desc->Var("11");
- v1->SetShape({-1, 256, 56, 56});
- std::unique_ptr<ir::Node> node1 = ir::CreateNodeForTest(v1);
- node1->inputs.emplace_back(op.get());
- auto* cache = pool.NodeMatch(node1.get());
- ASSERT_EQ(cache, nullptr);
- }
- {
- auto v2 = block_desc->Var("12");
- v2->SetShape({-1, 2, 5});
- std::unique_ptr<ir::Node> node1 = ir::CreateNodeForTest(v2);
- node1->inputs.emplace_back(op.get());
- auto* cache = pool.NodeMatch(node1.get());
- ASSERT_EQ(pool.GetIndex(cache), 2); // match 6:[-1,2,5]
- }
- {
- auto v3 = block_desc->Var("13");
- v3->SetShape({2, 5});
- std::unique_ptr<ir::Node> node1 = ir::CreateNodeForTest(v3);
- node1->inputs.emplace_back(op.get());
- auto* cache = pool.NodeMatch(node1.get());
- ASSERT_EQ(pool.GetIndex(cache), 5); // match 4:[5,2]
- }
-}
-
-} // namespace details
-} // namespace framework
-} // namespace paddle
diff --git a/paddle/fluid/framework/details/op_registry.h b/paddle/fluid/framework/details/op_registry.h
index eea7e712f8..0901e59f97 100644
--- a/paddle/fluid/framework/details/op_registry.h
+++ b/paddle/fluid/framework/details/op_registry.h
@@ -18,6 +18,7 @@ limitations under the License. */
#include <tuple>
#include <vector>
#include "paddle/fluid/framework/grad_op_desc_maker.h"
+#include "paddle/fluid/framework/inplace_op_inference.h"
#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/op_proto_maker.h"
#include "paddle/fluid/framework/operator.h"
@@ -32,7 +33,8 @@ enum OpInfoFillType {
kOpProtoAndCheckerMaker = 1,
kGradOpDescMaker = 2,
kVarTypeInference = 3,
- kShapeInference = 4
+ kShapeInference = 4,
+ kInplaceOpInference = 5
};
template <typename T>
@@ -48,8 +50,11 @@ struct OpInfoFillTypeID {
? kVarTypeInference
: (std::is_base_of<InferShapeBase, T>::value
? kShapeInference
- : static_cast<OpInfoFillType>(
- -1)))));
+ : (std::is_base_of<
+ InplaceOpInference, T>::value
+ ? kInplaceOpInference
+ : static_cast<OpInfoFillType>(
+ -1))))));
}
};
@@ -139,6 +144,16 @@ struct OpInfoFiller<T, kShapeInference> {
}
};
+template <typename T>
+struct OpInfoFiller<T, kInplaceOpInference> {
+ void operator()(const char* op_type, OpInfo* info) const {
+ info->infer_inplace_ = [](const OpDesc& op_desc, BlockDesc* block) {
+ T infer;
+ return infer(op_desc, block);
+ };
+ }
+};
+
} // namespace details
} // namespace framework
diff --git a/paddle/fluid/framework/details/parallel_ssa_graph_executor.cc b/paddle/fluid/framework/details/parallel_ssa_graph_executor.cc
index 128aaa33a2..e8deb5bfc6 100644
--- a/paddle/fluid/framework/details/parallel_ssa_graph_executor.cc
+++ b/paddle/fluid/framework/details/parallel_ssa_graph_executor.cc
@@ -65,7 +65,7 @@ FeedFetchList ParallelSSAGraphExecutor::Run(
if (pool_) {
run_futures.emplace_back(pool_->enqueue(std::move(call)));
} else {
- fetch_data.emplace_back(std::move(call()));
+ fetch_data.emplace_back(call());
}
}
@@ -74,7 +74,7 @@ FeedFetchList ParallelSSAGraphExecutor::Run(
if (exception_holder_.IsCaught()) {
f.wait();
} else {
- fetch_data.emplace_back(std::move(f.get()));
+ fetch_data.emplace_back(f.get());
}
}
}
diff --git a/paddle/fluid/framework/details/sequential_execution_pass.cc b/paddle/fluid/framework/details/sequential_execution_pass.cc
index cc2c8bfef9..879fb29d59 100644
--- a/paddle/fluid/framework/details/sequential_execution_pass.cc
+++ b/paddle/fluid/framework/details/sequential_execution_pass.cc
@@ -17,6 +17,7 @@
#include <unordered_map>
#include <unordered_set>
#include <vector>
+#include "paddle/fluid/framework/details/memory_optimize_helper.h"
#include "paddle/fluid/framework/op_proto_maker.h"
namespace paddle {
diff --git a/paddle/fluid/framework/details/sequential_execution_pass.h b/paddle/fluid/framework/details/sequential_execution_pass.h
index a04c08bc2e..ea3034877f 100644
--- a/paddle/fluid/framework/details/sequential_execution_pass.h
+++ b/paddle/fluid/framework/details/sequential_execution_pass.h
@@ -21,8 +21,6 @@ namespace paddle {
namespace framework {
namespace details {
-constexpr char kAllOpDescs[] = "all_op_descs";
-
class SequentialExecutionPass : public ir::Pass {
protected:
std::unique_ptr<ir::Graph> ApplyImpl(
diff --git a/paddle/fluid/framework/inplace_op_inference.h b/paddle/fluid/framework/inplace_op_inference.h
new file mode 100644
index 0000000000..a3ccf677c9
--- /dev/null
+++ b/paddle/fluid/framework/inplace_op_inference.h
@@ -0,0 +1,115 @@
+// 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 <functional>
+#include <numeric>
+#include <string>
+#include <unordered_map>
+#include "glog/logging.h"
+#include "paddle/fluid/framework/block_desc.h"
+#include "paddle/fluid/framework/details/memory_optimize_helper.h"
+#include "paddle/fluid/framework/op_desc.h"
+#include "paddle/fluid/framework/type_defs.h"
+
+namespace paddle {
+namespace framework {
+
+/*
+ Inplace Inference for create In->Out pairs for inplaced operator.
+ If we specify a pair of corresponding names. For example, X->Out.
+ then Out will inplaced use X's memory. The base class will do
+ legality validation for both variables.
+*/
+class InplaceOpInference {
+ public:
+ virtual ~InplaceOpInference() {}
+ virtual std::unordered_map<std::string, std::string> operator()(
+ const OpDesc& op_desc, BlockDesc* block) const = 0;
+};
+
+class InplaceInToOut : public InplaceOpInference {
+ public:
+ std::unordered_map<std::string, std::string> operator()(
+ const OpDesc& op_desc, BlockDesc* block) const {
+ std::unordered_map<std::string, std::string> ret;
+ auto in_out_var_names_pair = this->Apply(op_desc, block);
+ for (auto& pair : in_out_var_names_pair) {
+ PADDLE_ENFORCE(!op_desc.Input(pair.first).empty(),
+ string::Sprintf("op %s do not have input of %s!",
+ op_desc.Type(), pair.first));
+ PADDLE_ENFORCE(!op_desc.Output(pair.second).empty(),
+ string::Sprintf("op %s do not have output of %s!",
+ op_desc.Type(), pair.second));
+ auto& in_name = op_desc.Input(pair.first).at(0);
+ auto& out_name = op_desc.Output(pair.second).at(0);
+
+ auto in = block->FindRecursiveOrCreateVar(in_name);
+ auto out = block->FindRecursiveOrCreateVar(out_name);
+ if (TryInplaceInputOutput(in, out)) ret.insert({in_name, out_name});
+ }
+ return ret;
+ }
+
+ protected:
+ virtual std::unordered_map<std::string, std::string> Apply(
+ const OpDesc& op_desc, BlockDesc* block) const = 0;
+
+ bool TryInplaceInputOutput(const VarDesc& in, const VarDesc& out) const {
+ return in.Name() != out.Name() && details::NodeCanReused(in) &&
+ details::NodeCanReused(out) &&
+ details::NodeSize(out) <= details::NodeSize(in);
+ }
+};
+
+/*
+ Inplace In and Out for operator only have an Input and an Output.
+ For example, activation op.
+ */
+class SingleOpInplaceInToOut : public InplaceInToOut {
+ protected:
+ std::unordered_map<std::string, std::string> Apply(
+ const OpDesc& op_desc, BlockDesc* block) const override {
+ PADDLE_ENFORCE(!op_desc.InputNames().empty(),
+ "Op inputs must not be empty");
+ PADDLE_ENFORCE(!op_desc.OutputNames().empty(),
+ "Op outputs must not be empty");
+ auto x_name = op_desc.InputNames().at(0);
+ auto out_name = op_desc.OutputNames().at(0);
+ return std::unordered_map<std::string, std::string>{{x_name, out_name}};
+ }
+};
+
+/*
+ Gradient op. Inplace output use it's Input.
+ For example, Input@Grad->Input reuse strategy.
+ */
+class GradOpInplaceInToOut : public InplaceInToOut {
+ protected:
+ std::unordered_map<std::string, std::string> Apply(
+ const OpDesc& op_desc, BlockDesc* block) const override {
+ std::unordered_map<std::string, std::string> ret;
+ std::unordered_set<std::string> output_names(op_desc.OutputNames().begin(),
+ op_desc.OutputNames().end());
+ for (auto& input_name : op_desc.InputNames()) {
+ if (output_names.count(GradVarName(input_name))) {
+ ret.insert({input_name, GradVarName(input_name)});
+ }
+ }
+ return ret;
+ }
+};
+
+} // namespace framework
+} // namespace paddle
diff --git a/paddle/fluid/framework/inplace_op_inference_test.cc b/paddle/fluid/framework/inplace_op_inference_test.cc
new file mode 100644
index 0000000000..3e4d715c6f
--- /dev/null
+++ b/paddle/fluid/framework/inplace_op_inference_test.cc
@@ -0,0 +1,288 @@
+/* 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 <iterator>
+#include <string>
+#include "gtest/gtest.h"
+#include "paddle/fluid/framework/op_info.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/framework/program_desc.h"
+#include "paddle/fluid/framework/var_type_inference.h"
+
+namespace paddle {
+namespace framework {
+
+class NOP : public OperatorBase {
+ public:
+ NOP(const std::string& type, const VariableNameMap& inputs,
+ const VariableNameMap& outputs, const AttributeMap& attrs)
+ : OperatorBase(type, inputs, outputs, attrs) {}
+
+ private:
+ void RunImpl(const Scope& scope,
+ const platform::Place& place) const override {}
+};
+
+class SingleOpMaker : public OpProtoAndCheckerMaker {
+ public:
+ void Make() {
+ AddInput("X", "").AsDuplicable();
+ AddOutput("Out", "");
+ AddComment("");
+ }
+};
+
+class SingleGradOpMaker : public framework::SingleGradOpDescMaker {
+ public:
+ using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+ std::unique_ptr<framework::OpDesc> Apply() const override {
+ auto* op = new framework::OpDesc();
+ op->SetType("single_op_grad");
+ op->SetInput("Out", OutputGrad("Out"));
+ op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+ return std::unique_ptr<OpDesc>(op);
+ }
+};
+
+class SingleOpShapeInference : public framework::InferShapeBase {
+ public:
+ void operator()(framework::InferShapeContext* ctx) const override {
+ ctx->HasInput("X");
+ ctx->HasOutput("Out");
+ ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
+ }
+};
+
+class SingleGradOpShapeInference : public framework::InferShapeBase {
+ public:
+ void operator()(framework::InferShapeContext* ctx) const override {
+ ctx->HasInput(framework::GradVarName("Out"));
+ ctx->HasOutput(framework::GradVarName("X"));
+ ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("Out"));
+ }
+};
+
+class MultiOutOpMaker : public OpProtoAndCheckerMaker {
+ public:
+ void Make() {
+ AddInput("X", "").AsDuplicable();
+ AddInput("Y", "").AsDuplicable();
+ AddInput("Z", "").AsDuplicable();
+ AddOutput("Out", "");
+ AddOutput("YOut", "");
+ AddOutput("ZOut", "");
+ AddOutput("NotReuseOut", "");
+ AddComment("");
+ }
+};
+
+class MultiOutShapeInference : public framework::InferShapeBase {
+ public:
+ void operator()(framework::InferShapeContext* ctx) const override {
+ ctx->ShareDim("X", "Out");
+ ctx->ShareDim("Y", "YOut");
+ ctx->ShareDim("Z", "ZOut");
+ }
+};
+
+class MultiGradOpMaker : public framework::SingleGradOpDescMaker {
+ public:
+ using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+ std::unique_ptr<framework::OpDesc> Apply() const override {
+ auto* op = new framework::OpDesc();
+ op->SetType("multi_out_grad");
+ op->SetInput("X", Input("X"));
+ op->SetOutput(framework::GradVarName("Y"), OutputGrad("YOut"));
+ op->SetOutput(framework::GradVarName("X"), OutputGrad("Out"));
+ op->SetOutput(framework::GradVarName("Z"), OutputGrad("ZOut"));
+ return std::unique_ptr<framework::OpDesc>(op);
+ }
+};
+
+class MultiOutGradShapeInference : public framework::InferShapeBase {
+ public:
+ void operator()(framework::InferShapeContext* ctx) const override {
+ ctx->SetOutputDim(framework::GradVarName("Y"),
+ ctx->GetInputDim(framework::GradVarName("YOut")));
+ ctx->SetOutputDim(framework::GradVarName("X"),
+ ctx->GetInputDim(framework::GradVarName("Out")));
+ ctx->SetOutputDim(framework::GradVarName("Z"),
+ ctx->GetInputDim(framework::GradVarName("ZOut")));
+ }
+};
+
+class MultiOutInplaceInToOut : public framework::InplaceInToOut {
+ public:
+ using framework::InplaceInToOut::InplaceInToOut;
+
+ protected:
+ std::unordered_map<std::string, std::string> Apply(
+ const OpDesc& op_desc, BlockDesc* block) const override {
+ return std::unordered_map<std::string, std::string>{
+ {"X", "Out"}, {"Y", "YOut"}, {"Z", "ZOut"},
+ };
+ }
+};
+
+class MultiOutGradInplaceInToOut : public framework::InplaceInToOut {
+ public:
+ using framework::InplaceInToOut::InplaceInToOut;
+
+ protected:
+ std::unordered_map<std::string, std::string> Apply(
+ const OpDesc& op_desc, BlockDesc* block) const override {
+ return std::unordered_map<std::string, std::string>{
+ {framework::GradVarName("YOut"), framework::GradVarName("Y")},
+ {framework::GradVarName("Out"), framework::GradVarName("X")},
+ {framework::GradVarName("ZOut"), framework::GradVarName("Z")},
+ };
+ }
+};
+
+} // namespace framework
+} // namespace paddle
+
+namespace f = paddle::framework;
+REGISTER_OPERATOR(single_op, f::NOP, f::SingleOpMaker, f::SingleGradOpMaker,
+ f::SingleOpInplaceInToOut, f::SingleOpShapeInference);
+REGISTER_OPERATOR(single_op_grad, f::NOP, f::SingleOpInplaceInToOut,
+ f::SingleGradOpShapeInference);
+REGISTER_OPERATOR(multi_out_op, f::NOP, f::MultiOutOpMaker, f::MultiGradOpMaker,
+ f::MultiOutInplaceInToOut, f::MultiOutShapeInference);
+REGISTER_OPERATOR(multi_out_grad, f::NOP, f::MultiOutGradInplaceInToOut,
+ f::MultiOutGradShapeInference);
+
+namespace paddle {
+namespace framework {
+
+TEST(InferInplace, SingleOpInplaceInToOut) {
+ ProgramDesc prog;
+ auto* op = prog.MutableBlock(0)->AppendOp();
+ op->SetType("single_op");
+ op->SetInput("X", {"test2_a", "test2_b", "test2_c"});
+ op->SetOutput("Out", {"test2_out"});
+
+ prog.MutableBlock(0)->Var("test2_a")->SetType(proto::VarType::LOD_TENSOR);
+ prog.MutableBlock(0)->Var("test2_a")->SetShape({32, 64});
+ prog.MutableBlock(0)->Var("test2_b")->SetType(proto::VarType::LOD_TENSOR);
+ prog.MutableBlock(0)->Var("test2_c")->SetType(proto::VarType::LOD_TENSOR);
+ prog.MutableBlock(0)->Var("test2_out");
+ prog.MutableBlock(0)->Var("test2_out")->SetShape({32, 16});
+
+ auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
+ auto in_to_outs = infer_inplace(*op, op->Block());
+ EXPECT_EQ(in_to_outs.size(), 1ul);
+ auto it = in_to_outs.begin();
+ EXPECT_EQ(it->first, "test2_a");
+ EXPECT_EQ(it->second, "test2_out");
+}
+
+TEST(InferInplace, SingleGradOpInplaceInToOut) {
+ ProgramDesc prog;
+ auto* op = prog.MutableBlock(0)->AppendOp();
+ op->SetType("single_op_grad");
+ op->SetInput(GradVarName("Out"), {"test2_out"});
+ op->SetOutput(GradVarName("X"), {"test2_a", "test2_b", "test2_c"});
+
+ prog.MutableBlock(0)->Var("test2_a")->SetType(proto::VarType::LOD_TENSOR);
+ prog.MutableBlock(0)->Var("test2_a")->SetShape({32, 16});
+ prog.MutableBlock(0)->Var("test2_b")->SetType(proto::VarType::LOD_TENSOR);
+ prog.MutableBlock(0)->Var("test2_c")->SetType(proto::VarType::LOD_TENSOR);
+ prog.MutableBlock(0)->Var("test2_out");
+ prog.MutableBlock(0)->Var("test2_out")->SetShape({32, 16});
+
+ auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
+ auto in_to_outs = infer_inplace(*op, op->Block());
+ EXPECT_EQ(in_to_outs.size(), 1ul);
+ auto it = in_to_outs.begin();
+ EXPECT_EQ(it->first, "test2_out");
+ EXPECT_EQ(it->second, "test2_a");
+}
+
+TEST(InferInplace, MultiOutInplaceInToOut) {
+ ProgramDesc prog;
+ auto* op = prog.MutableBlock(0)->AppendOp();
+ op->SetType("multi_out_op");
+ op->SetInput("X", {"a0", "a1"});
+ op->SetInput("Y", {"b0"});
+ op->SetInput("Z", {"c0", "c1"});
+ op->SetOutput("Out", {"o0"});
+ op->SetOutput("YOut", {"y0"});
+ op->SetOutput("ZOut", {"z0"});
+
+ prog.MutableBlock(0)->Var("a0")->SetType(proto::VarType::LOD_TENSOR);
+ prog.MutableBlock(0)->Var("b0")->SetType(proto::VarType::LOD_TENSOR);
+ prog.MutableBlock(0)->Var("c0")->SetType(proto::VarType::LOD_TENSOR);
+ prog.MutableBlock(0)->Var("c1")->SetType(proto::VarType::LOD_TENSOR);
+ prog.MutableBlock(0)->Var("o0");
+ prog.MutableBlock(0)->Var("y0");
+ prog.MutableBlock(0)->Var("z0");
+ prog.MutableBlock(0)->Var("a0")->SetShape({32, 16});
+ prog.MutableBlock(0)->Var("b0")->SetShape({32, 16});
+ prog.MutableBlock(0)->Var("c0")->SetShape({32, 16});
+ prog.MutableBlock(0)->Var("o0")->SetShape({32, 16});
+ prog.MutableBlock(0)->Var("y0")->SetShape({32, 16});
+ prog.MutableBlock(0)->Var("z0")->SetShape({32, 16});
+
+ auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
+ auto in_to_outs = infer_inplace(*op, op->Block());
+ EXPECT_EQ(in_to_outs.size(), 3ul);
+ std::unordered_map<std::string, std::string> expects = {
+ {"a0", "o0"}, {"b0", "y0"}, {"c0", "z0"},
+ };
+ EXPECT_TRUE(expects == in_to_outs);
+}
+
+TEST(InferInplace, MultiGradInplaceInToOut) {
+ ProgramDesc prog;
+ auto* op = prog.MutableBlock(0)->AppendOp();
+ op->SetType("multi_out_grad");
+ op->SetInput(GradVarName("Out"), {"o0"});
+ op->SetInput(GradVarName("YOut"), {"y0"});
+ op->SetInput(GradVarName("ZOut"), {"z0"});
+ op->SetOutput(GradVarName("X"), {"a0", "a1"});
+ op->SetOutput(GradVarName("Y"), {"b0"});
+ op->SetOutput(GradVarName("Z"), {"c0", "c1"});
+
+ prog.MutableBlock(0)->Var("a0")->SetType(proto::VarType::LOD_TENSOR);
+ prog.MutableBlock(0)->Var("b0")->SetType(proto::VarType::LOD_TENSOR);
+ prog.MutableBlock(0)->Var("c0")->SetType(proto::VarType::LOD_TENSOR);
+ prog.MutableBlock(0)->Var("c1")->SetType(proto::VarType::LOD_TENSOR);
+ prog.MutableBlock(0)->Var("o0");
+ prog.MutableBlock(0)->Var("y0");
+ prog.MutableBlock(0)->Var("z0");
+ prog.MutableBlock(0)->Var("a0")->SetShape({32, 16});
+ prog.MutableBlock(0)->Var("b0")->SetShape({32, 16});
+ prog.MutableBlock(0)->Var("c0")->SetShape({32, 16});
+ prog.MutableBlock(0)->Var("o0")->SetShape({32, 16});
+ prog.MutableBlock(0)->Var("y0")->SetShape({32, 16});
+ prog.MutableBlock(0)->Var("z0")->SetShape({32, 16});
+
+ auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
+ auto in_to_outs = infer_inplace(*op, op->Block());
+
+ EXPECT_EQ(in_to_outs.size(), 3ul);
+ std::unordered_map<std::string, std::string> expects = {
+ {"o0", "a0"}, {"y0", "b0"}, {"z0", "c0"},
+ };
+ EXPECT_TRUE(expects == in_to_outs);
+}
+
+} // namespace framework
+} // namespace paddle
diff --git a/paddle/fluid/framework/ir/CMakeLists.txt b/paddle/fluid/framework/ir/CMakeLists.txt
index 914bcce775..07c2c970d4 100644
--- a/paddle/fluid/framework/ir/CMakeLists.txt
+++ b/paddle/fluid/framework/ir/CMakeLists.txt
@@ -65,6 +65,7 @@ pass_library(conv_elementwise_add2_act_fuse_pass inference)
pass_library(conv_elementwise_add_fuse_pass inference)
pass_library(conv_affine_channel_fuse_pass inference)
pass_library(transpose_flatten_concat_fuse_pass inference)
+pass_library(identity_scale_op_clean_pass base)
# There may be many transpose-flatten structures in a model, and the output of
# these structures will be used as inputs to the concat Op. This pattern will
diff --git a/paddle/fluid/framework/ir/graph.cc b/paddle/fluid/framework/ir/graph.cc
index 3eb5bdba3b..4b5c846f32 100644
--- a/paddle/fluid/framework/ir/graph.cc
+++ b/paddle/fluid/framework/ir/graph.cc
@@ -76,7 +76,7 @@ std::map<std::string, std::vector<ir::Node *>> Graph::InitFromProgram(
var->inputs.push_back(node);
}
}
- return std::move(var_nodes);
+ return var_nodes;
}
void Graph::ResolveHazard(
diff --git a/paddle/fluid/framework/ir/graph.h b/paddle/fluid/framework/ir/graph.h
index 8bb3c27bdd..feb3330176 100644
--- a/paddle/fluid/framework/ir/graph.h
+++ b/paddle/fluid/framework/ir/graph.h
@@ -141,7 +141,8 @@ class Graph {
ir::Node *CreateControlDepVar() {
// TODO(panyx0718): control var name should be really unique.
const std::string name = string::Sprintf(
- "%s@%llu", ir::Node::kControlDepVarName, node_set_.size());
+ "%s@%llu", static_cast<const char *>(ir::Node::kControlDepVarName),
+ num_node_created_);
auto *x = AddNode(new ir::Node(name, ir::Node::Type::kVariable));
x->SetId(num_node_created_++);
return x;
diff --git a/paddle/fluid/framework/ir/graph_helper.cc b/paddle/fluid/framework/ir/graph_helper.cc
index 8de93cf285..22d4c0a91c 100644
--- a/paddle/fluid/framework/ir/graph_helper.cc
+++ b/paddle/fluid/framework/ir/graph_helper.cc
@@ -52,16 +52,29 @@ bool HasCircleHelper(
ir::Node *node,
const std::map<ir::Node *, std::unordered_set<ir::Node *>> &adj_list,
std::unordered_set<ir::Node *> *visited,
- std::unordered_set<ir::Node *> *in_trace) {
+ std::unordered_set<ir::Node *> *in_trace,
+ std::vector<std::vector<ir::Node *>> *circles) {
if (visited->find(node) == visited->end()) {
visited->insert(node);
in_trace->insert(node);
for (ir::Node *in : adj_list.at(node)) {
if (visited->find(in) == visited->end() &&
- HasCircleHelper(in, adj_list, visited, in_trace)) {
+ HasCircleHelper(in, adj_list, visited, in_trace, circles)) {
return true;
} else if (in_trace->find(in) != in_trace->end()) {
+ if (circles != nullptr) {
+ std::vector<ir::Node *> circle;
+ circle.emplace_back(in);
+ ir::Node *p = in;
+ for (auto &adj : adj_list.at(p)) {
+ if (in_trace->count(adj)) {
+ circle.emplace_back(adj);
+ p = adj;
+ }
+ }
+ circles->emplace_back(circle);
+ }
return true;
}
}
@@ -71,11 +84,12 @@ bool HasCircleHelper(
}
bool HasCircleInternal(
- const std::map<ir::Node *, std::unordered_set<ir::Node *>> &adj_list) {
+ const std::map<ir::Node *, std::unordered_set<ir::Node *>> &adj_list,
+ std::vector<std::vector<ir::Node *>> *circles) {
std::unordered_set<ir::Node *> visited;
std::unordered_set<ir::Node *> in_trace;
for (auto &adj : adj_list) {
- if (HasCircleHelper(adj.first, adj_list, &visited, &in_trace)) {
+ if (HasCircleHelper(adj.first, adj_list, &visited, &in_trace, circles)) {
return true;
}
}
@@ -84,13 +98,18 @@ bool HasCircleInternal(
} // namespace
bool HasCircle(const Graph &graph) {
- return HasCircleInternal(BuildOperationAdjList(graph));
+ return HasCircleInternal(BuildOperationAdjList(graph), nullptr);
+}
+
+bool FindCircleSubGraph(const Graph &graph,
+ std::vector<std::vector<ir::Node *>> *circles) {
+ return HasCircleInternal(BuildOperationAdjList(graph), circles);
}
std::vector<ir::Node *> TopologySortOperations(const Graph &graph) {
std::map<ir::Node *, std::unordered_set<ir::Node *>> adj_list =
BuildOperationAdjList(graph);
- PADDLE_ENFORCE(!HasCircleInternal(adj_list));
+ PADDLE_ENFORCE(!HasCircleInternal(adj_list, nullptr));
std::unordered_set<ir::Node *> visited;
std::vector<ir::Node *> ret;
for (auto adj : adj_list) {
diff --git a/paddle/fluid/framework/ir/graph_helper.h b/paddle/fluid/framework/ir/graph_helper.h
index fba4936f2c..214de9ec7d 100644
--- a/paddle/fluid/framework/ir/graph_helper.h
+++ b/paddle/fluid/framework/ir/graph_helper.h
@@ -28,6 +28,11 @@ namespace ir {
// Test if the graph contains circle.
bool HasCircle(const Graph &graph);
+// Find All Circles for debugging,
+// store all subgraph in circles.
+bool FindCircleSubGraph(const Graph &graph,
+ std::vector<std::vector<ir::Node *>> *circles);
+
size_t GraphNum(const Graph &graph);
// Topology Sort the operations in the graph from inputs to outputs.
diff --git a/paddle/fluid/framework/ir/graph_helper_test.cc b/paddle/fluid/framework/ir/graph_helper_test.cc
index 260a73ae76..d8973d5aed 100644
--- a/paddle/fluid/framework/ir/graph_helper_test.cc
+++ b/paddle/fluid/framework/ir/graph_helper_test.cc
@@ -195,6 +195,17 @@ void BuildTwoGraphs(Graph* g) {
// v4->outputs.push_back(o5);
}
+TEST(GraphHelperTest, Circles) {
+ ProgramDesc prog;
+
+ Graph g(prog);
+ BuildCircleGraph(&g);
+
+ std::vector<std::vector<ir::Node*>> circles;
+ ASSERT_TRUE(FindCircleSubGraph(g, &circles));
+ ASSERT_EQ(circles.size(), 1UL);
+}
+
TEST(GraphHelperTest, GraphNum) {
ProgramDesc prog;
diff --git a/paddle/fluid/framework/ir/graph_pattern_detector.cc b/paddle/fluid/framework/ir/graph_pattern_detector.cc
index 6282ced1e4..9ea0729e1f 100644
--- a/paddle/fluid/framework/ir/graph_pattern_detector.cc
+++ b/paddle/fluid/framework/ir/graph_pattern_detector.cc
@@ -117,11 +117,6 @@ bool GraphPatternDetector::MarkPDNodesInGraph(const ir::Graph &graph) {
// return false;
}
}
- for (auto &item : pdnodes2nodes_) {
- for (auto &n : item.second) {
- GetMarkedNodes(const_cast<Graph *>(&graph)).insert(n);
- }
- }
VLOG(3) << pdnodes2nodes_.size() << " nodes marked";
return !pdnodes2nodes_.empty();
diff --git a/paddle/fluid/framework/ir/identity_scale_op_clean_pass.cc b/paddle/fluid/framework/ir/identity_scale_op_clean_pass.cc
new file mode 100644
index 0000000000..3b738aa159
--- /dev/null
+++ b/paddle/fluid/framework/ir/identity_scale_op_clean_pass.cc
@@ -0,0 +1,80 @@
+// Copyright (c) 2019 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/ir/identity_scale_op_clean_pass.h"
+#include <string>
+#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+std::unique_ptr<ir::Graph> IdentityScaleOpCleanPass::ApplyImpl(
+ std::unique_ptr<ir::Graph> graph) const {
+ FusePassBase::Init("identity_scale_op_clean", graph.get());
+
+ // pre_op -> scale_in -> scale_op -> scale_out
+ // ->
+ // pre_op -> scale_out
+ GraphPatternDetector detector;
+ auto pre_op = detector.mutable_pattern()->NewNode("pre_op")->assert_is_op();
+ auto scale_in = detector.mutable_pattern()
+ ->NewNode("scale_in")
+ ->assert_is_op_input("scale")
+ ->AsIntermediate();
+ auto scale_op = detector.mutable_pattern()
+ ->NewNode("scale_fuse")
+ ->assert_is_op("scale")
+ ->assert_op_attr<float>("scale", 1.)
+ ->assert_op_attr<float>("bias", 0.);
+ auto scale_out = detector.mutable_pattern()
+ ->NewNode("scale_out")
+ ->assert_is_op_output("scale");
+
+ pre_op->LinksTo({scale_in});
+ scale_op->LinksFrom({scale_in}).LinksTo({scale_out});
+
+ GraphPatternDetector::handle_t handler = [&](
+ const GraphPatternDetector::subgraph_t& subgraph, Graph* graph) {
+ Node* scale_op_var = subgraph.at(scale_op);
+ Node* scale_in_var = subgraph.at(scale_in);
+ Node* scale_out_var = subgraph.at(scale_out);
+ Node* pre_op_var = subgraph.at(pre_op);
+ // Link pre_op directly to scale_out
+ const std::string scale_in_name = scale_in_var->Name();
+ const std::string scale_out_name = scale_out_var->Name();
+ // Remove links in graph
+ GraphSafeRemoveNodes(graph, {scale_in_var, scale_op_var});
+ // Modify proto message
+ auto* pre_op_desc = pre_op_var->Op();
+ for (auto& parameter : *pre_op_desc->Proto()->mutable_outputs()) {
+ auto* arguments = parameter.mutable_arguments();
+ auto it = std::find(arguments->begin(), arguments->end(), scale_in_name);
+ PADDLE_ENFORCE(it != arguments->end());
+ *it = scale_out_name;
+ }
+
+ IR_NODE_LINK_TO(pre_op_var, scale_out_var);
+ };
+
+ detector(graph.get(), handler);
+ return graph;
+}
+
+} // namespace ir
+} // namespace framework
+} // namespace paddle
+
+REGISTER_PASS(identity_scale_op_clean_pass,
+ paddle::framework::ir::IdentityScaleOpCleanPass);
diff --git a/paddle/fluid/framework/details/memory_early_delete_pass.h b/paddle/fluid/framework/ir/identity_scale_op_clean_pass.h
similarity index 62%
rename from paddle/fluid/framework/details/memory_early_delete_pass.h
rename to paddle/fluid/framework/ir/identity_scale_op_clean_pass.h
index 8215aa1b2b..50a654d82f 100644
--- a/paddle/fluid/framework/details/memory_early_delete_pass.h
+++ b/paddle/fluid/framework/ir/identity_scale_op_clean_pass.h
@@ -1,4 +1,4 @@
-// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+// Copyright (c) 2019 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.
@@ -13,20 +13,21 @@
// limitations under the License.
#pragma once
-#include "paddle/fluid/framework/details/early_delete_op_handle.h"
-#include "paddle/fluid/framework/ir/graph.h"
-#include "paddle/fluid/framework/ir/pass.h"
+
+#include "paddle/fluid/framework/ir/fuse_pass_base.h"
namespace paddle {
namespace framework {
-namespace details {
+namespace ir {
-class MemoryEarlyDeletePass : public ir::Pass {
+class IdentityScaleOpCleanPass : public FusePassBase {
protected:
- std::unique_ptr<ir::Graph> ApplyImpl(
- std::unique_ptr<ir::Graph> graph) const override;
+ std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
+
+ private:
+ virtual ~IdentityScaleOpCleanPass() = default;
};
-} // namespace details
+} // namespace ir
} // namespace framework
} // namespace paddle
diff --git a/paddle/fluid/framework/ir/infer_clean_graph_pass.cc b/paddle/fluid/framework/ir/infer_clean_graph_pass.cc
index 7713ed1eab..6607c026a7 100644
--- a/paddle/fluid/framework/ir/infer_clean_graph_pass.cc
+++ b/paddle/fluid/framework/ir/infer_clean_graph_pass.cc
@@ -37,6 +37,7 @@ class InferCleanGraphPass : public FusePassBase {
std::unordered_set<const Node*> invalid_nodes;
int valid_op = 0;
for (auto* node : graph->Nodes()) {
+ PADDLE_ENFORCE_NOT_NULL(node);
if (is_valid_node(node)) {
invalid_nodes.insert(node);
} else if (node->IsOp()) {
diff --git a/paddle/fluid/framework/ir/seqpool_concat_fuse_pass_tester.cc b/paddle/fluid/framework/ir/seqpool_concat_fuse_pass_tester.cc
index 456a03192c..35d1d5129b 100644
--- a/paddle/fluid/framework/ir/seqpool_concat_fuse_pass_tester.cc
+++ b/paddle/fluid/framework/ir/seqpool_concat_fuse_pass_tester.cc
@@ -164,7 +164,7 @@ ProgramDesc BuildProgramDesc(int num_inputs_of_concat) {
};
std::vector<std::string> concat_inputs;
for (int i = 0; i < num_inputs_of_concat; ++i) {
- std::string prefix = "seqpool_op_" + i;
+ std::string prefix = "seqpool_op_" + std::to_string(i);
new_var(prefix + "in");
new_var(prefix + "out");
new_var(prefix + "out_unused");
diff --git a/paddle/fluid/framework/op_info.h b/paddle/fluid/framework/op_info.h
index 19e5c2c73e..4b55bd0703 100644
--- a/paddle/fluid/framework/op_info.h
+++ b/paddle/fluid/framework/op_info.h
@@ -38,6 +38,7 @@ struct OpInfo {
OpAttrChecker* checker_{nullptr};
InferVarTypeFN infer_var_type_;
InferShapeFN infer_shape_;
+ InferInplaceOpFN infer_inplace_;
bool HasOpProtoAndChecker() const {
return proto_ != nullptr && checker_ != nullptr;
diff --git a/paddle/fluid/framework/operator.cc b/paddle/fluid/framework/operator.cc
index 9d6c10ab9e..b22523e0f4 100644
--- a/paddle/fluid/framework/operator.cc
+++ b/paddle/fluid/framework/operator.cc
@@ -188,14 +188,14 @@ void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
VLOG(3) << place << " " << DebugStringEx(&scope);
} catch (platform::EnforceNotMet exception) {
if (Attrs().count("sub_block") != 0) {
- throw exception;
+ throw;
}
auto& callstack = Attr<std::vector<std::string>>(
OpProtoAndCheckerMaker::OpCreationCallstackAttrName());
if (callstack.empty()) {
- throw exception;
+ throw;
}
std::ostringstream sout;
sout << "Invoke operator " << Type() << " error.\n";
@@ -206,7 +206,7 @@ void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
sout << "C++ Callstacks: \n";
sout << exception.err_str_;
exception.err_str_ = sout.str();
- throw exception;
+ throw;
} catch (...) {
std::rethrow_exception(std::current_exception());
}
@@ -589,7 +589,7 @@ class RuntimeInferShapeContext : public InferShapeContext {
public:
RuntimeInferShapeContext(const OperatorBase& op, const Scope& scope,
const RuntimeContext& ctx)
- : op_(op), scope_(scope), ctx_(ctx) {}
+ : op_(op), ctx_(ctx) {}
bool HasInput(const std::string& name) const override {
// has only one input
@@ -881,7 +881,6 @@ class RuntimeInferShapeContext : public InferShapeContext {
}
const OperatorBase& op_;
- const Scope& scope_;
const RuntimeContext& ctx_;
};
diff --git a/paddle/fluid/framework/operator.h b/paddle/fluid/framework/operator.h
index 40d935a5ff..e33214b44b 100644
--- a/paddle/fluid/framework/operator.h
+++ b/paddle/fluid/framework/operator.h
@@ -222,12 +222,7 @@ class ExecutionContext {
if (it == ctx_.inputs.end()) {
return {};
}
- std::vector<const Variable*> res;
- res.reserve(it->second.size());
- std::transform(it->second.begin(), it->second.end(),
- std::back_inserter(res),
- [this](Variable* var) { return var; });
- return res;
+ return {it->second.begin(), it->second.end()};
}
std::vector<Variable*> MultiOutputVar(const std::string& name) const {
diff --git a/paddle/fluid/framework/parallel_executor.cc b/paddle/fluid/framework/parallel_executor.cc
index f61c9e3a91..ff7ef0cce2 100644
--- a/paddle/fluid/framework/parallel_executor.cc
+++ b/paddle/fluid/framework/parallel_executor.cc
@@ -171,14 +171,6 @@ std::unique_ptr<ir::Graph> ParallelExecutorPrivate::PrepareGCAndRefCnts(
eager_deletion_pass->SetNotOwned(details::kAllPlaces, &places_);
graph = eager_deletion_pass->Apply(std::move(graph));
VLOG(10) << "EagerDeletionPass Applied";
-
- if (build_strategy_.memory_early_delete_) {
- auto early_delete_pass =
- ir::PassRegistry::Instance().Get("memory_early_delete_pass");
- early_delete_pass->SetNotOwned(details::kGarbageCollector, &gcs_);
- graph = early_delete_pass->Apply(std::move(graph));
- }
- VLOG(10) << "MemoryEarlyDeletePass Applied.";
}
return graph;
@@ -288,6 +280,8 @@ ParallelExecutor::ParallelExecutor(
graphs.push_back(std::move(graph));
#endif
auto max_memory_size = GetEagerDeletionThreshold();
+ VLOG(10) << "Eager Deletion Threshold "
+ << static_cast<float>(max_memory_size) / (1 << 30);
if (max_memory_size >= 0) {
for (size_t i = 0; i < graphs.size(); ++i) {
graphs[i] = member_->PrepareGCAndRefCnts(
@@ -506,6 +500,5 @@ ParallelExecutor::~ParallelExecutor() {
} // namespace framework
} // namespace paddle
-USE_PASS(memory_early_delete_pass);
USE_PASS(reference_count_pass);
USE_PASS(eager_deletion_pass);
diff --git a/paddle/fluid/framework/scope.cc b/paddle/fluid/framework/scope.cc
index 9536185609..87f0f307d3 100644
--- a/paddle/fluid/framework/scope.cc
+++ b/paddle/fluid/framework/scope.cc
@@ -22,11 +22,7 @@ limitations under the License. */
#include "paddle/fluid/framework/threadpool.h"
#include "paddle/fluid/string/printf.h"
-DEFINE_bool(benchmark, false,
- "Doing memory benchmark. It will make deleting scope synchronized, "
- "and add some memory usage logs."
- "Default cuda is asynchronous device, set to True will"
- "force op run in synchronous mode.");
+DECLARE_bool(benchmark);
DEFINE_bool(
eager_delete_scope, true,
diff --git a/paddle/fluid/framework/type_defs.h b/paddle/fluid/framework/type_defs.h
index 938e2024c3..d02c699b97 100644
--- a/paddle/fluid/framework/type_defs.h
+++ b/paddle/fluid/framework/type_defs.h
@@ -57,5 +57,8 @@ using InferVarTypeFN =
using InferShapeFN = std::function<void(InferShapeContext*)>;
+using InplacePair = std::unordered_map<std::string, std::string>;
+using InferInplaceOpFN = std::function<InplacePair(const OpDesc&, BlockDesc*)>;
+
} // namespace framework
} // namespace paddle
diff --git a/paddle/fluid/imperative/CMakeLists.txt b/paddle/fluid/imperative/CMakeLists.txt
index 5db4221199..ec8dedd605 100644
--- a/paddle/fluid/imperative/CMakeLists.txt
+++ b/paddle/fluid/imperative/CMakeLists.txt
@@ -1,5 +1,5 @@
if(WITH_PYTHON)
-cc_library(layer SRCS layer.cc DEPS proto_desc operator device_context blas)
-cc_library(tracer SRCS tracer.cc DEPS proto_desc device_context)
+cc_library(layer SRCS layer.cc DEPS proto_desc operator device_context blas pybind)
+cc_library(tracer SRCS tracer.cc DEPS proto_desc device_context pybind)
cc_library(engine SRCS engine.cc)
endif()
diff --git a/paddle/fluid/inference/CMakeLists.txt b/paddle/fluid/inference/CMakeLists.txt
index 11484a6473..157862016e 100644
--- a/paddle/fluid/inference/CMakeLists.txt
+++ b/paddle/fluid/inference/CMakeLists.txt
@@ -58,12 +58,13 @@ if(WIN32)
sep_library(paddle_fluid_shared SHARED SRCS ${SHARED_INFERENCE_SRCS}
DEPS ${fluid_modules} paddle_fluid_api reset_tensor_array
analysis_config paddle_pass_builder)
- target_link_libraries(paddle_fluid_shared shlwapi)
else(WIN32)
cc_library(paddle_fluid_shared SHARED SRCS ${SHARED_INFERENCE_SRCS}
DEPS ${fluid_modules} paddle_fluid_api reset_tensor_array
analysis_config paddle_pass_builder)
endif()
+get_property(os_dependency_modules GLOBAL PROPERTY OS_DEPENDENCY_MODULES)
+target_link_libraries(paddle_fluid_shared ${os_dependency_modules})
set_target_properties(paddle_fluid_shared PROPERTIES OUTPUT_NAME paddle_fluid)
if(NOT APPLE AND NOT WIN32)
diff --git a/paddle/fluid/inference/analysis/ir_pass_manager.cc b/paddle/fluid/inference/analysis/ir_pass_manager.cc
index fe3c841186..8d5ee36ae6 100644
--- a/paddle/fluid/inference/analysis/ir_pass_manager.cc
+++ b/paddle/fluid/inference/analysis/ir_pass_manager.cc
@@ -83,7 +83,6 @@ void IRPassManager::CreatePasses(Argument *argument,
new std::string(GetOrCreateModelOptCacheDir(model_opt_cache_dir)));
}
- // graph_ = pass->Apply(std::move(graph_));
pre_pass = pass_name;
passes_.emplace_back(std::move(pass));
@@ -97,11 +96,12 @@ std::unique_ptr<Graph> IRPassManager::Apply(std::unique_ptr<Graph> graph) {
PADDLE_ENFORCE(graph.get());
// Apply all the passes
for (const auto &pass : passes_) {
- if (pass->Type() == "graph_viz_pass") continue;
- PrettyLogEndl(Style::H2(), "--- Running IR pass [%s]", pass->Type());
+ if (pass->Type() != "graph_viz_pass") {
+ PrettyLogEndl(Style::H2(), "--- Running IR pass [%s]", pass->Type());
+ }
graph = pass->Apply(std::move(graph));
}
- return std::move(graph);
+ return graph;
}
framework::proto::ProgramDesc IRPassManager::AcquireProgram(
diff --git a/paddle/fluid/inference/analysis/ir_passes/CMakeLists.txt b/paddle/fluid/inference/analysis/ir_passes/CMakeLists.txt
index eb6e1768a2..410a90132a 100644
--- a/paddle/fluid/inference/analysis/ir_passes/CMakeLists.txt
+++ b/paddle/fluid/inference/analysis/ir_passes/CMakeLists.txt
@@ -1,4 +1,7 @@
cc_library(subgraph_detector SRCS subgraph_detector.cc DEPS proto_desc)
+if(WITH_TESTING)
+ add_dependencies(subgraph_detector gtest)
+endif()
if (WITH_GPU AND TENSORRT_FOUND)
cc_library(tensorrt_subgraph_pass SRCS tensorrt_subgraph_pass.cc DEPS subgraph_detector tensorrt_op_teller)
diff --git a/paddle/fluid/inference/analysis/passes/memory_optimize_pass.cc b/paddle/fluid/inference/analysis/passes/memory_optimize_pass.cc
index 3d1be9196f..4b0a9d9b1c 100644
--- a/paddle/fluid/inference/analysis/passes/memory_optimize_pass.cc
+++ b/paddle/fluid/inference/analysis/passes/memory_optimize_pass.cc
@@ -18,6 +18,7 @@
#include <limits>
#include <map>
#include <string>
+#include <type_traits>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/ir/graph_helper.h"
@@ -168,7 +169,11 @@ bool FindSuitableTensorToReuse(
if (!cluster->count(candidate)) continue;
size_t space = space_table.at(candidate);
- size_t space_diff = std::abs<size_t>(space - space_required);
+ PADDLE_ENFORCE(
+ space <= std::numeric_limits<std::make_signed<size_t>::type>::max(),
+ "space overload");
+ size_t space_diff =
+ std::abs((std::make_signed<size_t>::type)space - space_required);
if (space_diff < best_fit.second) {
best_fit.first = candidate;
best_fit.second = space_diff;
diff --git a/paddle/fluid/inference/api/CMakeLists.txt b/paddle/fluid/inference/api/CMakeLists.txt
index ad0af4005a..85755fc471 100644
--- a/paddle/fluid/inference/api/CMakeLists.txt
+++ b/paddle/fluid/inference/api/CMakeLists.txt
@@ -52,8 +52,8 @@ cc_test(test_analysis_predictor SRCS analysis_predictor_tester.cc DEPS analysis_
if (WITH_ANAKIN AND WITH_MKL) # only needed in CI
# compile the libinference_anakin_api.a and anakin.so.
- cc_library(inference_anakin_api SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber mklml zero_copy_tensor_dummy)
- cc_library(inference_anakin_api_shared SHARED SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber zero_copy_tensor_dummy)
+ cc_library(inference_anakin_api SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber mklml zero_copy_tensor_dummy device_context)
+ cc_library(inference_anakin_api_shared SHARED SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber zero_copy_tensor_dummy device_context)
function(anakin_target target_name)
target_compile_options(${target_name} BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
endfunction()
diff --git a/paddle/fluid/inference/api/analysis_config.cc b/paddle/fluid/inference/api/analysis_config.cc
index eecab238a8..e92273b4dd 100644
--- a/paddle/fluid/inference/api/analysis_config.cc
+++ b/paddle/fluid/inference/api/analysis_config.cc
@@ -318,4 +318,9 @@ NativeConfig AnalysisConfig::ToNativeConfig() const {
return config;
}
+void AnalysisConfig::SwitchIrDebug(int x) {
+ ir_debug_ = x;
+ Update();
+}
+
} // namespace paddle
diff --git a/paddle/fluid/inference/api/analysis_predictor.cc b/paddle/fluid/inference/api/analysis_predictor.cc
index 14d6ba8c56..712e010db4 100644
--- a/paddle/fluid/inference/api/analysis_predictor.cc
+++ b/paddle/fluid/inference/api/analysis_predictor.cc
@@ -58,7 +58,8 @@ namespace {
bool IsPersistable(const framework::VarDesc *var) {
if (var->Persistable() &&
var->GetType() != framework::proto::VarType::FEED_MINIBATCH &&
- var->GetType() != framework::proto::VarType::FETCH_LIST) {
+ var->GetType() != framework::proto::VarType::FETCH_LIST &&
+ var->GetType() != framework::proto::VarType::RAW) {
return true;
}
return false;
@@ -420,7 +421,7 @@ std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
if (!dynamic_cast<AnalysisPredictor *>(predictor.get())->Init(nullptr)) {
return nullptr;
}
- return std::move(predictor);
+ return predictor;
}
void AnalysisPredictor::PrepareFeedFetch() {
diff --git a/paddle/fluid/inference/api/analysis_predictor_tester.cc b/paddle/fluid/inference/api/analysis_predictor_tester.cc
index 6d11b46108..002ba90e40 100644
--- a/paddle/fluid/inference/api/analysis_predictor_tester.cc
+++ b/paddle/fluid/inference/api/analysis_predictor_tester.cc
@@ -196,7 +196,7 @@ TEST(AnalysisPredictor, memory_optim) {
AnalysisConfig config(FLAGS_dirname);
config.DisableGpu();
config.EnableMemoryOptim(true);
- config.pass_builder()->TurnOnDebug();
+ config.SwitchIrDebug();
auto native_predictor =
CreatePaddlePredictor<NativeConfig>(config.ToNativeConfig());
diff --git a/paddle/fluid/inference/api/paddle_analysis_config.h b/paddle/fluid/inference/api/paddle_analysis_config.h
index 9d9ed6a39d..47361b3279 100644
--- a/paddle/fluid/inference/api/paddle_analysis_config.h
+++ b/paddle/fluid/inference/api/paddle_analysis_config.h
@@ -140,9 +140,12 @@ struct AnalysisConfig {
*/
bool tensorrt_engine_enabled() const { return use_tensorrt_; }
- /** Control whther to debug IR graph analysis phase.
+ /** \brief Control whether to debug IR graph analysis phase.
+ *
+ * This will generate DOT files for visualizing the computation graph after
+ * each analysis pass applied.
*/
- void SwitchIrDebug(int x = true) { ir_debug_ = x; }
+ void SwitchIrDebug(int x = true);
/** Turn on MKLDNN.
*/
diff --git a/paddle/fluid/inference/api/paddle_pass_builder.h b/paddle/fluid/inference/api/paddle_pass_builder.h
index 391932a1ee..aa353f12ca 100644
--- a/paddle/fluid/inference/api/paddle_pass_builder.h
+++ b/paddle/fluid/inference/api/paddle_pass_builder.h
@@ -117,6 +117,7 @@ class CpuPassStrategy : public PassStrategy {
"conv_bn_fuse_pass", //
"conv_eltwiseadd_bn_fuse_pass", //
"is_test_pass", //
+ "identity_scale_op_clean_pass", //
});
use_gpu_ = false;
}
@@ -155,6 +156,7 @@ class GpuPassStrategy : public PassStrategy {
GpuPassStrategy() : PassStrategy({}) {
passes_.assign({
"infer_clean_graph_pass", //
+ "identity_scale_op_clean_pass", //
"conv_affine_channel_fuse_pass", //
"conv_eltwiseadd_affine_channel_fuse_pass", //
"conv_bn_fuse_pass", //
diff --git a/paddle/fluid/inference/tests/api/analyzer_seq_pool1_tester.cc b/paddle/fluid/inference/tests/api/analyzer_seq_pool1_tester.cc
index 8be2a6d79b..dd953e0dcc 100644
--- a/paddle/fluid/inference/tests/api/analyzer_seq_pool1_tester.cc
+++ b/paddle/fluid/inference/tests/api/analyzer_seq_pool1_tester.cc
@@ -142,7 +142,7 @@ void SetConfig(AnalysisConfig *cfg, bool use_mkldnn = false) {
cfg->SetModel(FLAGS_infer_model + "/model", FLAGS_infer_model + "/params");
cfg->DisableGpu();
cfg->SwitchSpecifyInputNames();
- cfg->pass_builder()->TurnOnDebug();
+ cfg->SwitchIrDebug();
cfg->SetCpuMathLibraryNumThreads(FLAGS_paddle_num_threads);
if (use_mkldnn) {
cfg->EnableMKLDNN();
diff --git a/paddle/fluid/inference/tests/api/analyzer_text_classification_tester.cc b/paddle/fluid/inference/tests/api/analyzer_text_classification_tester.cc
index 2db297e200..2003be8201 100644
--- a/paddle/fluid/inference/tests/api/analyzer_text_classification_tester.cc
+++ b/paddle/fluid/inference/tests/api/analyzer_text_classification_tester.cc
@@ -69,7 +69,7 @@ void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
TEST(Analyzer_Text_Classification, profile) {
AnalysisConfig cfg;
SetConfig(&cfg);
- cfg.pass_builder()->TurnOnDebug();
+ cfg.SwitchIrDebug();
std::vector<PaddleTensor> outputs;
std::vector<std::vector<PaddleTensor>> input_slots_all;
diff --git a/paddle/fluid/inference/utils/benchmark_tester.cc b/paddle/fluid/inference/utils/benchmark_tester.cc
index 80763160df..0c48c2db9b 100644
--- a/paddle/fluid/inference/utils/benchmark_tester.cc
+++ b/paddle/fluid/inference/utils/benchmark_tester.cc
@@ -34,6 +34,6 @@ TEST(Benchmark, PersistToFile) {
benchmark.SetLatency(220);
benchmark.PersistToFile("1.log");
- benchmark.PersistToFile("1.log");
- benchmark.PersistToFile("1.log");
+ benchmark.PersistToFile("2.log");
+ benchmark.PersistToFile("3.log");
}
diff --git a/paddle/fluid/memory/allocation/allocator_facade.cc b/paddle/fluid/memory/allocation/allocator_facade.cc
index 794d729bdc..ea0b729dc6 100644
--- a/paddle/fluid/memory/allocation/allocator_facade.cc
+++ b/paddle/fluid/memory/allocation/allocator_facade.cc
@@ -83,7 +83,7 @@ class ChunkedAllocator : public Allocator {
VLOG(1) << "Create AutoIncrementAllocator with chunk_size "
<< max_chunk_size_ << " and capacity " << capacity;
default_allocator_ = std::make_shared<AutoIncrementAllocator>(
- [this] { return std::move(CreateAllocatorWithChunk()); }, capacity);
+ [this] { return CreateAllocatorWithChunk(); }, capacity);
}
}
diff --git a/paddle/fluid/memory/allocation/legacy_allocator.cc b/paddle/fluid/memory/allocation/legacy_allocator.cc
index 8759ec8096..e983ae327d 100644
--- a/paddle/fluid/memory/allocation/legacy_allocator.cc
+++ b/paddle/fluid/memory/allocation/legacy_allocator.cc
@@ -36,6 +36,7 @@ DEFINE_bool(init_allocated_mem, false,
"that initializing the allocated memory with a small value "
"during unit testing.");
DECLARE_double(fraction_of_gpu_memory_to_use);
+DECLARE_bool(benchmark);
namespace paddle {
namespace memory {
@@ -59,11 +60,6 @@ size_t memory_usage(const platform::Place &p);
using BuddyAllocator = detail::BuddyAllocator;
-std::unordered_map</*device id*/ int,
- std::pair</*current memory usage*/ uint64_t,
- /*peak memory usage*/ uint64_t>>
- gpu_mem_info;
-
BuddyAllocator *GetCPUBuddyAllocator() {
// We tried thread_local for inference::RNN1 model, but that not works much
// for multi-thread test.
@@ -144,6 +140,8 @@ BuddyAllocator *GetGPUBuddyAllocator(int gpu_id) {
devices = platform::GetSelectedDevices();
int gpu_num = devices.size();
+ allocation::GPUMemMonitor.Initialize(devices.size());
+
a_arr = new BuddyAllocator *[gpu_num];
for (size_t i = 0; i < devices.size(); ++i) {
int dev_id = devices[i];
@@ -190,25 +188,19 @@ void *Alloc<platform::CUDAPlace>(const platform::CUDAPlace &place,
platform::SetDeviceId(place.device);
size_t avail, total;
platform::GpuMemoryUsage(&avail, &total);
- LOG(WARNING) << "Cannot allocate " << string::HumanReadableSize(size)
- << " in GPU " << place.device << ", available "
- << string::HumanReadableSize(avail);
- LOG(WARNING) << "total " << total;
- LOG(WARNING) << "GpuMinChunkSize "
- << string::HumanReadableSize(
- buddy_allocator->GetMinChunkSize());
- LOG(WARNING) << "GpuMaxChunkSize "
- << string::HumanReadableSize(
- buddy_allocator->GetMaxChunkSize());
- LOG(WARNING) << "GPU memory used: "
- << string::HumanReadableSize(Used<platform::CUDAPlace>(place));
+ LOG(FATAL) << "Cannot allocate " << string::HumanReadableSize(size)
+ << " in GPU " << place.device << ", available "
+ << string::HumanReadableSize(avail) << "total " << total
+ << "GpuMinChunkSize "
+ << string::HumanReadableSize(buddy_allocator->GetMinChunkSize())
+ << "GpuMaxChunkSize "
+ << string::HumanReadableSize(buddy_allocator->GetMaxChunkSize())
+ << "GPU memory used: "
+ << string::HumanReadableSize(Used<platform::CUDAPlace>(place));
platform::SetDeviceId(cur_dev);
} else {
- gpu_mem_info[place.device].first += size;
- if (gpu_mem_info[place.device].first > gpu_mem_info[place.device].second) {
- gpu_mem_info[place.device].second = gpu_mem_info[place.device].first;
- VLOG(3) << "device: " << place.device << " peak memory usage : "
- << (gpu_mem_info[place.device].second >> 20) << " MiB";
+ if (FLAGS_benchmark) {
+ allocation::GPUMemMonitor.Add(place.device, size);
}
if (FLAGS_init_allocated_mem) {
cudaMemset(ptr, 0xEF, size);
@@ -225,7 +217,9 @@ void Free<platform::CUDAPlace>(const platform::CUDAPlace &place, void *p,
size_t size) {
#ifdef PADDLE_WITH_CUDA
GetGPUBuddyAllocator(place.device)->Free(p);
- gpu_mem_info[place.device].first -= size;
+ if (FLAGS_benchmark) {
+ allocation::GPUMemMonitor.Minus(place.device, size);
+ }
#else
PADDLE_THROW("'CUDAPlace' is not supported in CPU only device.");
#endif
@@ -264,7 +258,7 @@ void *Alloc<platform::CUDAPinnedPlace>(const platform::CUDAPinnedPlace &place,
void *ptr = buddy_allocator->Alloc(size);
if (ptr == nullptr) {
- LOG(WARNING) << "cudaMallocHost Cannot allocate " << size
+ LOG(WARNING) << "cudaHostAlloc Cannot allocate " << size
<< " bytes in CUDAPinnedPlace";
}
if (FLAGS_init_allocated_mem) {
@@ -335,6 +329,8 @@ size_t Usage::operator()(const platform::CUDAPinnedPlace &cuda_pinned) const {
namespace allocation {
+LegacyMemMonitor GPUMemMonitor;
+
Allocation *LegacyAllocator::AllocateImpl(size_t size, Allocator::Attr attr) {
void *ptr = boost::apply_visitor(legacy::AllocVisitor(size), place_);
return new Allocation(ptr, size, place_);
@@ -346,6 +342,63 @@ void LegacyAllocator::Free(Allocation *allocation) {
allocation->place());
delete allocation;
}
+
+bool MemInfo::Add(const size_t &size) {
+ std::lock_guard<std::mutex> lock(mutex_);
+ usage_ += size;
+ bool peak_point = usage_ > peak_usage_;
+ if (peak_point) peak_usage_ = usage_;
+ return peak_point;
+}
+
+void MemInfo::Minus(const size_t &size) {
+ std::lock_guard<std::mutex> lock(mutex_);
+ usage_ -= size;
+}
+
+uint64_t MemInfo::GetPeakUsage() { return peak_usage_; }
+
+LegacyMemMonitor::~LegacyMemMonitor() {
+ for (auto &item : gpu_mem_info_) delete item.second;
+}
+
+void LegacyMemMonitor::Initialize(const int &device_num) {
+ for (auto i = 0; i < device_num; ++i) {
+ gpu_mem_info_[i] = new MemInfo();
+ }
+}
+
+void LegacyMemMonitor::Add(const int &device, const size_t &size) {
+ if (gpu_mem_info_[device]->Add(size)) {
+ VLOG(3) << "#LegacyMemMonitor# device: " << device
+ << " peak memory usage : "
+ << (gpu_mem_info_[device]->GetPeakUsage() >> 20) << " MiB";
+ }
+}
+
+void LegacyMemMonitor::Minus(const int &device, const size_t &size) {
+ gpu_mem_info_[device]->Minus(size);
+}
+
+uint64_t LegacyMemMonitor::GetMemUsage(const int &device) {
+ return gpu_mem_info_.find(device) == gpu_mem_info_.end()
+ ? 0
+ : gpu_mem_info_[device]->GetPeakUsage();
+}
+
+void LegacyMemMonitor::PrintMemUsage() {
+ std::vector<int> devices;
+ for (const auto &item : gpu_mem_info_) {
+ devices.emplace_back(item.first);
+ }
+ std::sort(devices.begin(), devices.end());
+ for (const auto &device : devices) {
+ std::cout << "Device : " << device << " Peak Memory Usage : "
+ << (gpu_mem_info_[device]->GetPeakUsage() >> 20) << " MiB"
+ << std::endl;
+ }
+}
+
} // namespace allocation
} // namespace memory
} // namespace paddle
diff --git a/paddle/fluid/memory/allocation/legacy_allocator.h b/paddle/fluid/memory/allocation/legacy_allocator.h
index 503a7a685c..ccbc8c70d8 100644
--- a/paddle/fluid/memory/allocation/legacy_allocator.h
+++ b/paddle/fluid/memory/allocation/legacy_allocator.h
@@ -13,12 +13,59 @@
// limitations under the License.
#pragma once
+#include <algorithm>
+#include <mutex> // NOLINT
+#include <unordered_map>
+#include <utility>
+#include <vector>
#include "paddle/fluid/memory/allocation/allocator.h"
#include "paddle/fluid/platform/place.h"
namespace paddle {
namespace memory {
namespace allocation {
+class MemInfo {
+ public:
+ MemInfo() : usage_(0), peak_usage_(0) {}
+ MemInfo(const MemInfo &) = delete;
+ MemInfo &operator=(const MemInfo &) = delete;
+
+ // return a flag to indicate current operation will create a peak point or not
+ bool Add(const size_t &);
+ void Minus(const size_t &);
+
+ uint64_t GetPeakUsage();
+
+ private:
+ /* current memory usage*/
+ uint64_t usage_;
+ uint64_t peak_usage_;
+ std::mutex mutex_;
+};
+
+class LegacyMemMonitor {
+ public:
+ // used to store the GPU memory usage of each devices
+ using MemUsage = std::unordered_map</*device id*/ int,
+ /*mem usage info node*/ MemInfo *>;
+
+ MemUsage GetMemUsageInfo() { return gpu_mem_info_; }
+ ~LegacyMemMonitor();
+
+ void Initialize(const int &);
+ void Add(const int &, const size_t &);
+ void Minus(const int &, const size_t &);
+
+ uint64_t GetMemUsage(const int &);
+
+ void PrintMemUsage();
+
+ protected:
+ MemUsage gpu_mem_info_;
+};
+
+extern LegacyMemMonitor GPUMemMonitor;
+
class LegacyAllocatorPrivate;
class LegacyAllocator : public Allocator {
public:
diff --git a/paddle/fluid/memory/allocation/pinned_allocator.cc b/paddle/fluid/memory/allocation/pinned_allocator.cc
index 6ac3aefdd1..de81d12cca 100644
--- a/paddle/fluid/memory/allocation/pinned_allocator.cc
+++ b/paddle/fluid/memory/allocation/pinned_allocator.cc
@@ -32,7 +32,7 @@ Allocation *CPUPinnedAllocator::AllocateImpl(size_t size,
// "CPUPinnedAllocator should be used for Cross-Device Communication");
void *ptr;
- PADDLE_ENFORCE(cudaMallocHost(&ptr, size));
+ PADDLE_ENFORCE(cudaHostAlloc(&ptr, size, cudaHostAllocPortable));
return new CPUPinnedAllocation(ptr, size);
}
} // namespace allocation
diff --git a/paddle/fluid/memory/allocation/pinned_allocator.h b/paddle/fluid/memory/allocation/pinned_allocator.h
index 26d12dd91c..42d0938f2a 100644
--- a/paddle/fluid/memory/allocation/pinned_allocator.h
+++ b/paddle/fluid/memory/allocation/pinned_allocator.h
@@ -19,7 +19,7 @@ namespace paddle {
namespace memory {
namespace allocation {
-// Allocator uses `cudaMallocHost`
+// Allocator uses `cudaHostAlloc`
class CPUPinnedAllocation : public Allocation {
public:
CPUPinnedAllocation(void *ptr, size_t size)
diff --git a/paddle/fluid/memory/detail/system_allocator.cc b/paddle/fluid/memory/detail/system_allocator.cc
index 3e8fb83e9d..197d1c2f21 100644
--- a/paddle/fluid/memory/detail/system_allocator.cc
+++ b/paddle/fluid/memory/detail/system_allocator.cc
@@ -173,14 +173,14 @@ void* CUDAPinnedAllocator::Alloc(size_t* index, size_t size) {
void* p;
// PINNED memory is visible to all CUDA contexts.
- cudaError_t result = cudaMallocHost(&p, size);
+ cudaError_t result = cudaHostAlloc(&p, size, cudaHostAllocPortable);
if (result == cudaSuccess) {
*index = 1; // PINNED memory
cuda_pinnd_alloc_size_ += size;
return p;
} else {
- LOG(WARNING) << "cudaMallocHost failed.";
+ LOG(WARNING) << "cudaHostAlloc failed.";
return nullptr;
}
diff --git a/paddle/fluid/operators/activation_op.cc b/paddle/fluid/operators/activation_op.cc
index 7ec9d2fed5..65efe2966c 100644
--- a/paddle/fluid/operators/activation_op.cc
+++ b/paddle/fluid/operators/activation_op.cc
@@ -37,7 +37,7 @@ using paddle::framework::Tensor;
"(bool, default false) Set to true for inference only, false " \
"for training. Some layers may run faster when this is true.") \
.SetDefault(false); \
- AddComment(#OP_COMMENT); \
+ AddComment(OP_COMMENT); \
} \
}
@@ -124,7 +124,7 @@ class ActivationOpGrad : public framework::OperatorWithKernel {
UNUSED constexpr char SigmoidDoc[] = R"DOC(
Sigmoid Activation Operator
-$$out = \frac{1}{1 + e^{-x}}$$
+$$out = \\frac{1}{1 + e^{-x}}$$
)DOC";
@@ -187,14 +187,14 @@ $out = |x|$
UNUSED constexpr char CeilDoc[] = R"DOC(
Ceil Activation Operator.
-$out = ceil(x)$
+$out = \left \lceil x \right \rceil$
)DOC";
UNUSED constexpr char FloorDoc[] = R"DOC(
Floor Activation Operator.
-$out = floor(x)$
+$out = \left \lfloor x \right \rfloor$
)DOC";
@@ -252,7 +252,7 @@ $out = \ln(1 + e^{x})$
UNUSED constexpr char SoftsignDoc[] = R"DOC(
Softsign Activation Operator.
-$$out = \frac{x}{1 + |x|}$$
+$$out = \\frac{x}{1 + \|x\|}$$
)DOC";
@@ -547,12 +547,14 @@ namespace ops = paddle::operators;
__macro(Swish, swish); \
__macro(ThresholdedRelu, thresholded_relu);
-#define REGISTER_INPLACE_ACTIVATION_OP(OP_NAME, KERNEL_TYPE) \
- REGISTER_OPERATOR(KERNEL_TYPE, ::paddle::operators::ActivationOp, \
- ::paddle::operators::OP_NAME##OpMaker, \
- ::paddle::operators::ActivationOpInferVarType, \
- ::paddle::operators::OP_NAME##GradMaker); \
- REGISTER_OPERATOR(KERNEL_TYPE##_grad, ::paddle::operators::ActivationOpGrad)
+#define REGISTER_INPLACE_ACTIVATION_OP(OP_NAME, KERNEL_TYPE) \
+ REGISTER_OPERATOR(KERNEL_TYPE, ::paddle::operators::ActivationOp, \
+ ::paddle::operators::OP_NAME##OpMaker, \
+ ::paddle::operators::ActivationOpInferVarType, \
+ ::paddle::operators::OP_NAME##GradMaker, \
+ ::paddle::framework::SingleOpInplaceInToOut); \
+ REGISTER_OPERATOR(KERNEL_TYPE##_grad, ::paddle::operators::ActivationOpGrad, \
+ ::paddle::framework::SingleOpInplaceInToOut)
#define REGISTER_ACTIVATION_OP(OP_NAME, KERNEL_TYPE) \
REGISTER_OPERATOR(KERNEL_TYPE, ::paddle::operators::ActivationOp, \
diff --git a/paddle/fluid/operators/batch_norm_op.cc b/paddle/fluid/operators/batch_norm_op.cc
index 8b672e09b2..feac412538 100644
--- a/paddle/fluid/operators/batch_norm_op.cc
+++ b/paddle/fluid/operators/batch_norm_op.cc
@@ -589,8 +589,10 @@ class BatchNormGradMaker : public framework::SingleGradOpDescMaker {
op->SetInput("SavedVariance", Output("SavedVariance"));
// used when setting use_global_stats True during training
- op->SetInput("Mean", Output("MeanOut"));
- op->SetInput("Variance", Output("VarianceOut"));
+ if (boost::get<bool>(GetAttr("use_global_stats"))) {
+ op->SetInput("Mean", Output("MeanOut"));
+ op->SetInput("Variance", Output("VarianceOut"));
+ }
op->SetAttrMap(Attrs());
@@ -602,13 +604,48 @@ class BatchNormGradMaker : public framework::SingleGradOpDescMaker {
}
};
+class BatchNormInplaceInToOut : public framework::InplaceInToOut {
+ public:
+ using InplaceInToOut::InplaceInToOut;
+
+ protected:
+ std::unordered_map<std::string, std::string> Apply(
+ const framework::OpDesc &op_desc,
+ framework::BlockDesc *block) const override {
+ std::unordered_map<std::string, std::string> inplace_in_to_out = {
+ {"Mean", "MeanOut"}, {"Variance", "VarianceOut"}, {"X", "Y"},
+ };
+ return inplace_in_to_out;
+ }
+};
+
+class BatchNormGradInplaceInToOut : public framework::InplaceInToOut {
+ public:
+ using InplaceInToOut::InplaceInToOut;
+
+ protected:
+ std::unordered_map<std::string, std::string> Apply(
+ const framework::OpDesc &op_desc,
+ framework::BlockDesc *block) const override {
+ std::unordered_map<std::string, std::string> inplace_in_to_out = {
+ // Scale, Bias, SavedMean, SavedVariance shape is [batch_size, C]
+ {framework::GradVarName("Y"), framework::GradVarName("X")},
+ {"SavedMean", framework::GradVarName("Scale")},
+ {"SavedVariance", framework::GradVarName("Bias")},
+ };
+ return inplace_in_to_out;
+ }
+};
+
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(batch_norm, ops::BatchNormOp, ops::BatchNormOpMaker,
- ops::BatchNormOpInferVarType, ops::BatchNormGradMaker);
-REGISTER_OPERATOR(batch_norm_grad, ops::BatchNormGradOp);
+ ops::BatchNormOpInferVarType, ops::BatchNormGradMaker,
+ ops::BatchNormInplaceInToOut);
+REGISTER_OPERATOR(batch_norm_grad, ops::BatchNormGradOp,
+ ops::BatchNormGradInplaceInToOut);
REGISTER_OP_CPU_KERNEL(
batch_norm, ops::BatchNormKernel<paddle::platform::CPUDeviceContext, float>,
diff --git a/paddle/fluid/operators/detection/CMakeLists.txt b/paddle/fluid/operators/detection/CMakeLists.txt
index cace42bc1b..f6fbe97565 100644
--- a/paddle/fluid/operators/detection/CMakeLists.txt
+++ b/paddle/fluid/operators/detection/CMakeLists.txt
@@ -31,6 +31,7 @@ detection_library(polygon_box_transform_op SRCS polygon_box_transform_op.cc
polygon_box_transform_op.cu)
detection_library(rpn_target_assign_op SRCS rpn_target_assign_op.cc)
detection_library(generate_proposal_labels_op SRCS generate_proposal_labels_op.cc)
+detection_library(box_clip_op SRCS box_clip_op.cc box_clip_op.cu)
detection_library(yolov3_loss_op SRCS yolov3_loss_op.cc)
if(WITH_GPU)
diff --git a/paddle/fluid/operators/detection/bbox_util.h b/paddle/fluid/operators/detection/bbox_util.h
index b99edb5bf0..a7bc3e0272 100644
--- a/paddle/fluid/operators/detection/bbox_util.h
+++ b/paddle/fluid/operators/detection/bbox_util.h
@@ -99,5 +99,29 @@ void BboxOverlaps(const framework::Tensor& r_boxes,
}
}
+template <class T>
+void ClipTiledBoxes(const platform::DeviceContext& ctx,
+ const framework::Tensor& im_info,
+ const framework::Tensor& input_boxes,
+ framework::Tensor* out) {
+ T* out_data = out->mutable_data<T>(ctx.GetPlace());
+ const T* im_info_data = im_info.data<T>();
+ const T* input_boxes_data = input_boxes.data<T>();
+ T zero(0);
+ T im_w = round(im_info_data[1] / im_info_data[2]);
+ T im_h = round(im_info_data[0] / im_info_data[2]);
+ for (int64_t i = 0; i < input_boxes.numel(); ++i) {
+ if (i % 4 == 0) {
+ out_data[i] = std::max(std::min(input_boxes_data[i], im_w - 1), zero);
+ } else if (i % 4 == 1) {
+ out_data[i] = std::max(std::min(input_boxes_data[i], im_h - 1), zero);
+ } else if (i % 4 == 2) {
+ out_data[i] = std::max(std::min(input_boxes_data[i], im_w - 1), zero);
+ } else {
+ out_data[i] = std::max(std::min(input_boxes_data[i], im_h - 1), zero);
+ }
+ }
+}
+
} // namespace operators
} // namespace paddle
diff --git a/paddle/fluid/operators/detection/box_clip_op.cc b/paddle/fluid/operators/detection/box_clip_op.cc
new file mode 100644
index 0000000000..3aa766559a
--- /dev/null
+++ b/paddle/fluid/operators/detection/box_clip_op.cc
@@ -0,0 +1,86 @@
+/* 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/box_clip_op.h"
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+class BoxClipOp : public framework::OperatorWithKernel {
+ public:
+ using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+ void InferShape(framework::InferShapeContext* ctx) const override {
+ PADDLE_ENFORCE(ctx->HasInput("Input"),
+ "Input(Input) of BoxClipOp should not be null.");
+ PADDLE_ENFORCE(ctx->HasInput("ImInfo"),
+ "Input(ImInfo) of BoxClipOp should not be null.");
+
+ auto input_box_dims = ctx->GetInputDim("Input");
+ auto im_info_dims = ctx->GetInputDim("ImInfo");
+
+ if (ctx->IsRuntime()) {
+ auto input_box_size = input_box_dims.size();
+ PADDLE_ENFORCE_EQ(input_box_dims[input_box_size - 1], 4,
+ "The last dimension of Input must be 4");
+ PADDLE_ENFORCE_EQ(im_info_dims.size(), 2,
+ "The rank of Input(Input) in BoxClipOp must be 2");
+ PADDLE_ENFORCE_EQ(im_info_dims[1], 3,
+ "The last dimension of ImInfo must be 3");
+ }
+ ctx->ShareDim("Input", /*->*/ "Output");
+ ctx->ShareLoD("Input", /*->*/ "Output");
+ }
+};
+
+class BoxClipOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+ void Make() override {
+ AddInput("Input",
+ "(LoDTensor) "
+ "Input is a LoDTensor with shape [..., 4] holds 4 points"
+ "in last dimension in format [xmin, ymin, xmax, ymax]");
+ AddInput("ImInfo",
+ "(Tensor) Information for image reshape is in shape (N, 3), "
+ "in format (height, width, im_scale)");
+ AddOutput("Output",
+ "(LoDTensor) "
+ "Output is a LoDTensor with the same shape as Input"
+ "and it is the result after clip");
+ AddComment(R"DOC(
+This operator clips input boxes to original input images.
+
+For each input box, The formula is given as follows:
+
+ $$xmin = \max(\min(xmin, im_w - 1), 0)$$
+ $$ymin = \max(\min(ymin, im_h - 1), 0)$$
+ $$xmax = \max(\min(xmax, im_w - 1), 0)$$
+ $$ymax = \max(\min(ymax, im_h - 1), 0)$$
+
+where im_w and im_h are computed from ImInfo, the formula is given as follows:
+
+ $$im_w = \round(width / im_scale)$$
+ $$im_h = \round(height / im_scale)$$
+)DOC");
+ }
+};
+
+} // namespace operators
+} // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(box_clip, ops::BoxClipOp, ops::BoxClipOpMaker,
+ paddle::framework::EmptyGradOpMaker);
+REGISTER_OP_CPU_KERNEL(
+ box_clip, ops::BoxClipKernel<paddle::platform::CPUDeviceContext, float>,
+ ops::BoxClipKernel<paddle::platform::CPUDeviceContext, double>);
diff --git a/paddle/fluid/operators/detection/box_clip_op.cu b/paddle/fluid/operators/detection/box_clip_op.cu
new file mode 100644
index 0000000000..b727da5f7b
--- /dev/null
+++ b/paddle/fluid/operators/detection/box_clip_op.cu
@@ -0,0 +1,74 @@
+/* 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 <algorithm>
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/detection/box_clip_op.h"
+#include "paddle/fluid/operators/math/math_function.h"
+#include "paddle/fluid/platform/cuda_primitives.h"
+#include "paddle/fluid/platform/hostdevice.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using LoDTenso = framework::LoDTensor;
+
+static constexpr int ImInfoSize = 3;
+
+template <typename T, int BlockSize>
+static __global__ void GPUBoxClip(const T *input, const size_t *lod,
+ const size_t width, const T *im_info,
+ T *output) {
+ T im_w = round(im_info[blockIdx.x * ImInfoSize + 1] /
+ im_info[blockIdx.x * ImInfoSize + 2]);
+ T im_h = round(im_info[blockIdx.x * ImInfoSize] /
+ im_info[blockIdx.x * ImInfoSize + 2]);
+ for (int i = threadIdx.x; i < (lod[blockIdx.x + 1] - lod[blockIdx.x]) * width;
+ i += BlockSize) {
+ int idx = lod[blockIdx.x] * width + i;
+ T im_size = (idx % 2 == 0) ? im_w : im_h;
+ output[idx] = max(min(input[idx], im_size - 1), T(0.));
+ }
+}
+
+template <typename DeviceContext, typename T>
+class GPUBoxClipKernel : public framework::OpKernel<T> {
+ public:
+ void Compute(const framework::ExecutionContext &context) const override {
+ PADDLE_ENFORCE(platform::is_gpu_place(context.GetPlace()),
+ "This kernel only runs on GPU device.");
+ auto *input = context.Input<LoDTensor>("Input");
+ auto *im_info = context.Input<Tensor>("ImInfo");
+ auto *output = context.Output<LoDTensor>("Output");
+ const int64_t num = input->dims()[0];
+ const int64_t bbox_width = input->numel() / num;
+ auto lod = input->lod();
+ framework::LoD abs_offset_lod = framework::ToAbsOffset(lod);
+ auto &dev_ctx = context.template device_context<DeviceContext>();
+ auto stream = dev_ctx.stream();
+ const size_t batch_size = lod.back().size() - 1;
+ T *output_data = output->mutable_data<T>(dev_ctx.GetPlace());
+ GPUBoxClip<T, 512><<<batch_size, 512, 0, stream>>>(
+ input->data<T>(), abs_offset_lod[0].CUDAMutableData(dev_ctx.GetPlace()),
+ bbox_width, im_info->data<T>(), output_data);
+ }
+};
+
+} // namespace operators
+} // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(
+ box_clip, ops::GPUBoxClipKernel<paddle::platform::CUDADeviceContext, float>,
+ ops::GPUBoxClipKernel<paddle::platform::CUDADeviceContext, double>);
diff --git a/paddle/fluid/operators/detection/box_clip_op.h b/paddle/fluid/operators/detection/box_clip_op.h
new file mode 100644
index 0000000000..74e1f88f8d
--- /dev/null
+++ b/paddle/fluid/operators/detection/box_clip_op.h
@@ -0,0 +1,50 @@
+/* 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 <string>
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/detection/bbox_util.h"
+#include "paddle/fluid/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+
+template <typename DeviceContext, typename T>
+class BoxClipKernel : public framework::OpKernel<T> {
+ public:
+ void Compute(const framework::ExecutionContext& context) const override {
+ auto* input_box = context.Input<LoDTensor>("Input");
+ auto* im_info = context.Input<LoDTensor>("ImInfo");
+ auto* output_box = context.Output<LoDTensor>("Output");
+ auto& dev_ctx =
+ context.template device_context<platform::CPUDeviceContext>();
+ output_box->mutable_data<T>(context.GetPlace());
+ if (input_box->lod().size()) {
+ PADDLE_ENFORCE_EQ(input_box->lod().size(), 1UL,
+ "Only support 1 level of LoD.");
+ }
+ auto box_lod = input_box->lod().back();
+ int64_t n = static_cast<int64_t>(box_lod.size() - 1);
+ for (int i = 0; i < n; ++i) {
+ Tensor im_info_slice = im_info->Slice(i, i + 1);
+ Tensor box_slice = input_box->Slice(box_lod[i], box_lod[i + 1]);
+ Tensor output_slice = output_box->Slice(box_lod[i], box_lod[i + 1]);
+ ClipTiledBoxes<T>(dev_ctx, im_info_slice, box_slice, &output_slice);
+ }
+ }
+};
+
+} // namespace operators
+} // namespace paddle
diff --git a/paddle/fluid/operators/detection/box_coder_op.cc b/paddle/fluid/operators/detection/box_coder_op.cc
index fdcff62e1f..0a51d50e06 100644
--- a/paddle/fluid/operators/detection/box_coder_op.cc
+++ b/paddle/fluid/operators/detection/box_coder_op.cc
@@ -38,20 +38,12 @@ class BoxCoderOp : public framework::OperatorWithKernel {
"The shape of PriorBox is [N, 4]");
if (ctx->HasInput("PriorBoxVar")) {
auto prior_box_var_dims = ctx->GetInputDim("PriorBoxVar");
- PADDLE_ENFORCE(
- prior_box_var_dims.size() == 1 || prior_box_var_dims.size() == 2,
- "Input(PriorBoxVar) of BoxCoderOp should be 1 or 2.");
- if (prior_box_var_dims.size() == 1) {
- PADDLE_ENFORCE_EQ(
- prior_box_var_dims[0], 4,
- "The 1st dimension of Input(PriorBoxVar) should be 4"
- "when the rank is 1.");
- } else {
- PADDLE_ENFORCE_EQ(
- prior_box_dims, prior_box_var_dims,
- "The dimension of Input(PriorBoxVar) should be equal to"
- "the dimension of Input(PriorBox when the rank is 2.)");
- }
+ PADDLE_ENFORCE(prior_box_var_dims.size() == 2,
+ "Input(PriorBoxVar) of BoxCoderOp should be 2.");
+ PADDLE_ENFORCE_EQ(
+ prior_box_dims, prior_box_var_dims,
+ "The dimension of Input(PriorBoxVar) should be equal to"
+ "the dimension of Input(PriorBox) when the rank is 2.");
}
}
diff --git a/paddle/fluid/operators/detection/box_coder_op.cu b/paddle/fluid/operators/detection/box_coder_op.cu
index e078af3eb4..19a5bb90fa 100644
--- a/paddle/fluid/operators/detection/box_coder_op.cu
+++ b/paddle/fluid/operators/detection/box_coder_op.cu
@@ -56,10 +56,7 @@ __global__ void EncodeCenterSizeKernel(
output[idx * len + 2] = log(fabs(target_box_width / prior_box_width));
output[idx * len + 3] = log(fabs(target_box_height / prior_box_height));
if (prior_box_var_data) {
- int prior_var_offset = 0;
- if (prior_box_var_size == 2) {
- prior_var_offset = col_idx * len;
- }
+ int prior_var_offset = col_idx * len;
output[idx * len] /= prior_box_var_data[prior_var_offset];
output[idx * len + 1] /= prior_box_var_data[prior_var_offset + 1];
output[idx * len + 2] /= prior_box_var_data[prior_var_offset + 2];
@@ -99,10 +96,7 @@ __global__ void DecodeCenterSizeKernel(
T box_var_x = T(1), box_var_y = T(1);
T box_var_w = T(1), box_var_h = T(1);
if (prior_box_var_data) {
- int prior_var_offset = 0;
- if (prior_box_var_size == 2) {
- prior_var_offset = axis == 0 ? col_idx * len : row_idx * len;
- }
+ int prior_var_offset = axis == 0 ? col_idx * len : row_idx * len;
box_var_x = prior_box_var_data[prior_var_offset];
box_var_y = prior_box_var_data[prior_var_offset + 1];
box_var_w = prior_box_var_data[prior_var_offset + 2];
diff --git a/paddle/fluid/operators/detection/box_coder_op.h b/paddle/fluid/operators/detection/box_coder_op.h
index a0b1faf7bd..6d406f8196 100644
--- a/paddle/fluid/operators/detection/box_coder_op.h
+++ b/paddle/fluid/operators/detection/box_coder_op.h
@@ -79,10 +79,7 @@ class BoxCoderKernel : public framework::OpKernel<T> {
output[offset + 3] =
std::log(std::fabs(target_box_height / prior_box_height));
if (prior_box_var) {
- int prior_var_offset = 0;
- if (prior_box_var->dims().size() == 2) {
- prior_var_offset = j * len;
- }
+ int prior_var_offset = j * len;
output[offset] /= prior_box_var_data[prior_var_offset];
output[offset + 1] /= prior_box_var_data[prior_var_offset + 1];
output[offset + 2] /= prior_box_var_data[prior_var_offset + 2];
@@ -95,11 +92,12 @@ class BoxCoderKernel : public framework::OpKernel<T> {
}
}
}
+ template <int axis, int var_size>
void DecodeCenterSize(const framework::Tensor* target_box,
const framework::Tensor* prior_box,
const framework::Tensor* prior_box_var,
- const bool normalized, const int axis,
- const std::vector<float> variance, T* output) const {
+ const bool normalized, std::vector<float> variance,
+ T* output) const {
int64_t row = target_box->dims()[0];
int64_t col = target_box->dims()[1];
int64_t len = target_box->dims()[2];
@@ -107,19 +105,17 @@ class BoxCoderKernel : public framework::OpKernel<T> {
auto* target_box_data = target_box->data<T>();
auto* prior_box_data = prior_box->data<T>();
const T* prior_box_var_data = nullptr;
- if (prior_box_var) prior_box_var_data = prior_box_var->data<T>();
+ if (var_size == 2) prior_box_var_data = prior_box_var->data<T>();
int prior_box_offset = 0;
+ T var_data[4] = {1., 1., 1., 1.};
+ T* var_ptr = var_data;
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for collapse(2)
#endif
for (int64_t i = 0; i < row; ++i) {
for (int64_t j = 0; j < col; ++j) {
size_t offset = i * col * len + j * len;
- if (axis == 0) {
- prior_box_offset = j * len;
- } else if (axis == 1) {
- prior_box_offset = i * len;
- }
+ prior_box_offset = axis == 0 ? j * len : i * len;
T prior_box_width = prior_box_data[prior_box_offset + 2] -
prior_box_data[prior_box_offset] +
(normalized == false);
@@ -133,26 +129,18 @@ class BoxCoderKernel : public framework::OpKernel<T> {
T target_box_center_x = 0, target_box_center_y = 0;
T target_box_width = 0, target_box_height = 0;
- T box_var_x = T(1), box_var_y = T(1);
- T box_var_w = T(1), box_var_h = T(1);
- if (prior_box_var) {
- int prior_var_offset = 0;
- if (prior_box_var->dims().size() == 2) {
- if (axis == 0)
- prior_var_offset = j * len;
- else if (axis == 1)
- prior_var_offset = i * len;
- }
- box_var_x = prior_box_var_data[prior_var_offset];
- box_var_y = prior_box_var_data[prior_var_offset + 1];
- box_var_w = prior_box_var_data[prior_var_offset + 2];
- box_var_h = prior_box_var_data[prior_var_offset + 3];
- } else if (!(variance.empty())) {
- box_var_x = static_cast<T>(variance[0]);
- box_var_y = static_cast<T>(variance[1]);
- box_var_w = static_cast<T>(variance[2]);
- box_var_h = static_cast<T>(variance[3]);
+ int prior_var_offset = axis == 0 ? j * len : i * len;
+ if (var_size == 2) {
+ std::memcpy(var_ptr, prior_box_var_data + prior_var_offset,
+ 4 * sizeof(T));
+ } else if (var_size == 1) {
+ var_ptr = reinterpret_cast<T*>(variance.data());
}
+ T box_var_x = *var_ptr;
+ T box_var_y = *(var_ptr + 1);
+ T box_var_w = *(var_ptr + 2);
+ T box_var_h = *(var_ptr + 3);
+
target_box_center_x =
box_var_x * target_box_data[offset] * prior_box_width +
prior_box_center_x;
@@ -211,8 +199,31 @@ class BoxCoderKernel : public framework::OpKernel<T> {
EncodeCenterSize(target_box, prior_box, prior_box_var, normalized,
variance, output);
} else if (code_type == BoxCodeType::kDecodeCenterSize) {
- DecodeCenterSize(target_box, prior_box, prior_box_var, normalized, axis,
- variance, output);
+ if (prior_box_var) {
+ if (axis == 0) {
+ DecodeCenterSize<0, 2>(target_box, prior_box, prior_box_var,
+ normalized, variance, output);
+ } else {
+ DecodeCenterSize<1, 2>(target_box, prior_box, prior_box_var,
+ normalized, variance, output);
+ }
+ } else if (!(variance.empty())) {
+ if (axis == 0) {
+ DecodeCenterSize<0, 1>(target_box, prior_box, prior_box_var,
+ normalized, variance, output);
+ } else {
+ DecodeCenterSize<1, 1>(target_box, prior_box, prior_box_var,
+ normalized, variance, output);
+ }
+ } else {
+ if (axis == 0) {
+ DecodeCenterSize<0, 0>(target_box, prior_box, prior_box_var,
+ normalized, variance, output);
+ } else {
+ DecodeCenterSize<1, 0>(target_box, prior_box, prior_box_var,
+ normalized, variance, output);
+ }
+ }
}
}
};
diff --git a/paddle/fluid/operators/detection/density_prior_box_op.h b/paddle/fluid/operators/detection/density_prior_box_op.h
index ed2f5df80c..3591681fc3 100644
--- a/paddle/fluid/operators/detection/density_prior_box_op.h
+++ b/paddle/fluid/operators/detection/density_prior_box_op.h
@@ -52,6 +52,10 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
step_height = step_h;
}
int num_priors = 0;
+
+#ifdef PADDLE_WITH_MKLML
+#pragma omp parallel for reduction(+ : num_priors)
+#endif
for (size_t i = 0; i < densities.size(); ++i) {
num_priors += (fixed_ratios.size()) * (pow(densities[i], 2));
}
@@ -64,6 +68,17 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
auto e_boxes = framework::EigenTensor<T, 4>::From(*boxes).setConstant(0.0);
int step_average = static_cast<int>((step_width + step_height) * 0.5);
+ std::vector<float> sqrt_fixed_ratios;
+#ifdef PADDLE_WITH_MKLML
+#pragma omp parallel for
+#endif
+ for (int i = 0; i < fixed_ratios.size(); i++) {
+ sqrt_fixed_ratios.push_back(sqrt(fixed_ratios[i]));
+ }
+
+#ifdef PADDLE_WITH_MKLML
+#pragma omp parallel for collapse(2)
+#endif
for (int h = 0; h < feature_height; ++h) {
for (int w = 0; w < feature_width; ++w) {
T center_x = (w + offset) * step_width;
@@ -73,34 +88,25 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
for (size_t s = 0; s < fixed_sizes.size(); ++s) {
auto fixed_size = fixed_sizes[s];
int density = densities[s];
+ int shift = step_average / density;
// Generate density prior boxes with fixed ratios.
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);
+ float box_width_ratio = fixed_size * sqrt_fixed_ratios[r];
+ float box_height_ratio = fixed_size / sqrt_fixed_ratios[r];
+ float density_center_x = center_x - step_average / 2. + shift / 2.;
+ float density_center_y = center_y - step_average / 2. + shift / 2.;
for (int di = 0; di < density; ++di) {
for (int dj = 0; dj < density; ++dj) {
- float center_x_temp =
- center_x - step_average / 2. + shift / 2. + dj * shift;
- float center_y_temp =
- center_y - step_average / 2. + shift / 2. + di * shift;
- e_boxes(h, w, idx, 0) =
- (center_x_temp - box_width_ratio / 2.) / img_width >= 0
- ? (center_x_temp - box_width_ratio / 2.) / img_width
- : 0;
- e_boxes(h, w, idx, 1) =
- (center_y_temp - box_height_ratio / 2.) / img_height >= 0
- ? (center_y_temp - box_height_ratio / 2.) / img_height
- : 0;
- e_boxes(h, w, idx, 2) =
- (center_x_temp + box_width_ratio / 2.) / img_width <= 1
- ? (center_x_temp + box_width_ratio / 2.) / img_width
- : 1;
- e_boxes(h, w, idx, 3) =
- (center_y_temp + box_height_ratio / 2.) / img_height <= 1
- ? (center_y_temp + box_height_ratio / 2.) / img_height
- : 1;
+ float center_x_temp = density_center_x + dj * shift;
+ float center_y_temp = density_center_y + di * shift;
+ e_boxes(h, w, idx, 0) = std::max(
+ (center_x_temp - box_width_ratio / 2.) / img_width, 0.);
+ e_boxes(h, w, idx, 1) = std::max(
+ (center_y_temp - box_height_ratio / 2.) / img_height, 0.);
+ e_boxes(h, w, idx, 2) = std::min(
+ (center_x_temp + box_width_ratio / 2.) / img_width, 1.);
+ e_boxes(h, w, idx, 3) = std::min(
+ (center_y_temp + box_height_ratio / 2.) / img_height, 1.);
idx++;
}
}
@@ -131,8 +137,14 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
vars->Resize({box_num, static_cast<int>(variances.size())});
auto e_vars = framework::EigenMatrix<T, Eigen::RowMajor>::From(*vars);
-
- e_vars = var_et.broadcast(Eigen::DSizes<int, 2>(box_num, 1));
+#ifdef PADDLE_WITH_MKLML
+#pragma omp parallel for collapse(2)
+#endif
+ for (int i = 0; i < box_num; ++i) {
+ for (int j = 0; j < variances.size(); ++j) {
+ e_vars(i, j) = variances[j];
+ }
+ }
vars->Resize(var_dim);
boxes->Resize(box_dim);
diff --git a/paddle/fluid/operators/elementwise/elementwise_add_op.cc b/paddle/fluid/operators/elementwise/elementwise_add_op.cc
index 7e789cd8d9..c6c658236c 100644
--- a/paddle/fluid/operators/elementwise/elementwise_add_op.cc
+++ b/paddle/fluid/operators/elementwise/elementwise_add_op.cc
@@ -18,6 +18,7 @@ namespace ops = paddle::operators;
REGISTER_ELEMWISE_GRAD_MAKER(elementwise_add, Add);
REGISTER_ELEMWISE_EXPLICIT_OP(elementwise_add, "Add", "Out = X + Y", "Out",
"X");
+
REGISTER_OP_CPU_KERNEL(
elementwise_add,
ops::ElementwiseAddKernel<paddle::platform::CPUDeviceContext, float>,
diff --git a/paddle/fluid/operators/elementwise/elementwise_op.h b/paddle/fluid/operators/elementwise/elementwise_op.h
index fd2a98cb45..91e4415265 100644
--- a/paddle/fluid/operators/elementwise/elementwise_op.h
+++ b/paddle/fluid/operators/elementwise/elementwise_op.h
@@ -250,6 +250,37 @@ class ElemwiseGradKernel : public framework::OpKernel<T> {
}
};
+class ElementwiseOpInplace : public framework::InplaceInToOut {
+ public:
+ using framework::InplaceInToOut::InplaceInToOut;
+
+ protected:
+ std::unordered_map<std::string, std::string> Apply(
+ const framework::OpDesc &op_desc,
+ framework::BlockDesc *block) const override {
+ return std::unordered_map<std::string, std::string>{
+ {"X", "Out"},
+ };
+ }
+};
+
+class ElementwiseGradOpInplace : public framework::InplaceInToOut {
+ public:
+ using framework::InplaceInToOut::InplaceInToOut;
+
+ protected:
+ std::unordered_map<std::string, std::string> Apply(
+ const framework::OpDesc &op_desc,
+ framework::BlockDesc *block) const override {
+ std::unordered_map<std::string, std::string> ret;
+ if (block->HasVar(framework::GradVarName("X")) &&
+ block->HasVar(framework::GradVarName("Out"))) {
+ ret[framework::GradVarName("Out")] = framework::GradVarName("X");
+ }
+ return ret;
+ }
+};
+
} // namespace operators
} // namespace paddle
@@ -299,6 +330,8 @@ class ElemwiseGradKernel : public framework::OpKernel<T> {
REGISTER_OPERATOR(op_type, ::paddle::operators::ElementwiseOp, \
__ElemwiseOp##op_type##Maker__, \
::paddle::operators::ElementwiseOpInferVarType, \
- op_type##GradMaker); \
+ op_type##GradMaker, \
+ ::paddle::operators::ElementwiseOpInplace); \
REGISTER_OPERATOR(op_type##_grad, \
- ::paddle::operators::ElementwiseOpExplicitGrad)
+ ::paddle::operators::ElementwiseOpExplicitGrad, \
+ ::paddle::operators::ElementwiseGradOpInplace)
diff --git a/paddle/fluid/operators/flatten_op.cc b/paddle/fluid/operators/flatten_op.cc
index 8e80dc0e64..bb904166c4 100644
--- a/paddle/fluid/operators/flatten_op.cc
+++ b/paddle/fluid/operators/flatten_op.cc
@@ -267,6 +267,35 @@ class Flatten2GradOp : public framework::OperatorBase {
}
};
+class FlattenOpInplaceInToOut : public framework::InplaceInToOut {
+ public:
+ using InplaceInToOut::InplaceInToOut;
+
+ protected:
+ std::unordered_map<std::string, std::string> Apply(
+ const framework::OpDesc &op_desc,
+ framework::BlockDesc *block) const override {
+ std::unordered_map<std::string, std::string> inplace_in_to_out = {
+ {"X", "Out"},
+ };
+ return inplace_in_to_out;
+ }
+};
+
+class FlattenGradInplaceinToOut : public framework::InplaceInToOut {
+ using InplaceInToOut::InplaceInToOut;
+
+ protected:
+ std::unordered_map<std::string, std::string> Apply(
+ const framework::OpDesc &op_desc,
+ framework::BlockDesc *block) const override {
+ std::unordered_map<std::string, std::string> inplace_in_to_out = {
+ {framework::GradVarName("Out"), framework::GradVarName("X")},
+ };
+ return inplace_in_to_out;
+ }
+};
+
} // namespace operators
} // namespace paddle
@@ -275,10 +304,13 @@ USE_OP(reshape);
namespace ops = paddle::operators;
REGISTER_OPERATOR(flatten, ops::FlattenOp, ops::FlattenOpMaker,
ops::FlattenOpInferShape,
- paddle::framework::DefaultGradOpDescMaker<true>);
-REGISTER_OPERATOR(flatten_grad, ops::FlattenGradOp, ops::FlattenGradInferShape);
+ paddle::framework::DefaultGradOpDescMaker<true>,
+ ops::FlattenOpInplaceInToOut);
+REGISTER_OPERATOR(flatten_grad, ops::FlattenGradOp, ops::FlattenGradInferShape,
+ ops::FlattenGradInplaceinToOut);
REGISTER_OPERATOR(flatten2, ops::Flatten2Op, ops::Flatten2OpMaker,
- ops::Flatten2OpInferShape, ops::Flatten2GradOpMaker);
+ ops::Flatten2OpInferShape, ops::Flatten2GradOpMaker,
+ ops::FlattenOpInplaceInToOut);
REGISTER_OPERATOR(flatten2_grad, ops::Flatten2GradOp,
- ops::Flatten2GradInferShape);
+ ops::Flatten2GradInferShape, ops::FlattenGradInplaceinToOut);
diff --git a/paddle/fluid/operators/jit/benchmark.cc b/paddle/fluid/operators/jit/benchmark.cc
index 1b9360afce..97ddf223ae 100644
--- a/paddle/fluid/operators/jit/benchmark.cc
+++ b/paddle/fluid/operators/jit/benchmark.cc
@@ -93,6 +93,7 @@ std::vector<int> TestSizes() {
template <typename KernelTuples, typename... Args>
struct BenchFunc {
// return this function avg time
+ // TODO(TJ): clear cache every time
double operator()(const typename KernelTuples::func_type tgt, Args... args) {
for (int i = 0; i < FLAGS_burning; ++i) {
tgt(args...);
@@ -172,6 +173,9 @@ void BenchXYZNKernel() {
RandomVec<T>(d, y_data);
BenchAllImpls<KT, jit::XYZNTuples<T>, PlaceType>(d, x.data<T>(),
y.data<T>(), z_data, d);
+ // test inplace
+ BenchAllImpls<KT, jit::XYZNTuples<T>, PlaceType>(d, x.data<T>(), z_data,
+ z_data, d);
}
}
diff --git a/paddle/fluid/operators/jit/gen/act.h b/paddle/fluid/operators/jit/gen/act.h
index 68e66f9298..1664dfa906 100644
--- a/paddle/fluid/operators/jit/gen/act.h
+++ b/paddle/fluid/operators/jit/gen/act.h
@@ -63,7 +63,6 @@ class VActFunc : public JitCode {
public:
explicit VActFunc(size_t code_size, void* code_ptr)
: JitCode(code_size, code_ptr) {}
- virtual const char* name() const = 0;
virtual void genCode() = 0;
protected:
diff --git a/paddle/fluid/operators/jit/gen/blas.cc b/paddle/fluid/operators/jit/gen/blas.cc
index dee6c7b9d3..5da24c359e 100644
--- a/paddle/fluid/operators/jit/gen/blas.cc
+++ b/paddle/fluid/operators/jit/gen/blas.cc
@@ -155,7 +155,7 @@ class NCHW16CMulNCCreator : public JitCodeCreator<int> {
class name##Creator : public JitCodeCreator<int> { \
public: \
bool UseMe(const int& attr) const override { \
- return platform::MayIUse(platform::avx); \
+ return platform::MayIUse(platform::avx) && attr <= 1024; \
} \
size_t CodeSize(const int& d) const override { \
return 96 + d / YMM_FLOAT_BLOCK * 4 * 8; \
diff --git a/paddle/fluid/operators/jit/gen/blas.h b/paddle/fluid/operators/jit/gen/blas.h
index de6b33f467..e991139266 100644
--- a/paddle/fluid/operators/jit/gen/blas.h
+++ b/paddle/fluid/operators/jit/gen/blas.h
@@ -41,7 +41,7 @@ class VXXJitCode : public JitCode {
this->genCode();
}
- virtual const char* name() const {
+ virtual const char* name() const override {
std::string base = "VXXJitCode";
if (scalar_index_ == 1) {
base += "_Scalar";
@@ -61,6 +61,7 @@ class VXXJitCode : public JitCode {
base += "_Vec";
}
base += (with_relu_ ? "_Relu" : "");
+ base += "_D" + std::to_string(num_);
return base.c_str();
}
void genCode() override;
diff --git a/paddle/fluid/operators/jit/gen/hopv.h b/paddle/fluid/operators/jit/gen/hopv.h
index d3bc94b63d..c336fe73fe 100644
--- a/paddle/fluid/operators/jit/gen/hopv.h
+++ b/paddle/fluid/operators/jit/gen/hopv.h
@@ -35,7 +35,7 @@ class HOPVJitCode : public JitCode {
this->genCode();
}
- virtual const char* name() const {
+ virtual const char* name() const override {
std::string base = "VXXJitCode";
if (type_ == operand_type::MAX) {
base += "_MAX";
diff --git a/paddle/fluid/operators/jit/gen/jitcode.h b/paddle/fluid/operators/jit/gen/jitcode.h
index c388109604..91058f6cf6 100644
--- a/paddle/fluid/operators/jit/gen/jitcode.h
+++ b/paddle/fluid/operators/jit/gen/jitcode.h
@@ -68,7 +68,6 @@ class JitCode : public GenBase, public Xbyak::CodeGenerator {
(code_size % 4096 != 0 ? (code_size / 4096 + 1) * 4096 : code_size),
code_ptr) {}
- virtual const char* name() const = 0;
virtual void genCode() = 0;
size_t getSize() const override { return CodeGenerator::getSize(); }
diff --git a/paddle/fluid/operators/jit/gen/matmul.h b/paddle/fluid/operators/jit/gen/matmul.h
index 626baa8f73..7976e3112d 100644
--- a/paddle/fluid/operators/jit/gen/matmul.h
+++ b/paddle/fluid/operators/jit/gen/matmul.h
@@ -36,7 +36,7 @@ class MatMulJitCode : public JitCode {
this->genCode();
}
- virtual const char* name() const {
+ virtual const char* name() const override {
std::string base = "MatMulJitCode";
base = base + "_M" + std::to_string(m_) + "_N" + std::to_string(n_) + "_K" +
std::to_string(k_);
diff --git a/paddle/fluid/operators/jit/gen/seqpool.h b/paddle/fluid/operators/jit/gen/seqpool.h
index fcbbb3c84c..c464c2eac8 100644
--- a/paddle/fluid/operators/jit/gen/seqpool.h
+++ b/paddle/fluid/operators/jit/gen/seqpool.h
@@ -38,7 +38,7 @@ class SeqPoolJitCode : public JitCode {
this->genCode();
}
- virtual const char* name() const {
+ virtual const char* name() const override {
std::string base = "SeqPoolJitCode";
if (type_ == SeqPoolType::kSum) {
base += "_Sum";
diff --git a/paddle/fluid/operators/jit/gen_base.cc b/paddle/fluid/operators/jit/gen_base.cc
index 3cd5f6554b..f3603875ad 100644
--- a/paddle/fluid/operators/jit/gen_base.cc
+++ b/paddle/fluid/operators/jit/gen_base.cc
@@ -17,7 +17,13 @@
#include <iostream>
#include <sstream>
#include <vector>
+#include "paddle/fluid/memory/allocation/cpu_allocator.h" // for posix_memalign
#include "paddle/fluid/platform/cpu_info.h"
+#include "paddle/fluid/platform/enforce.h"
+
+#ifndef _WIN32
+#define posix_memalign_free free
+#endif
DEFINE_bool(dump_jitcode, false, "Whether to dump the jitcode to file");
@@ -40,6 +46,17 @@ void GenBase::dumpCode(const unsigned char* code) const {
}
}
+void* GenBase::operator new(size_t size) {
+ void* ptr;
+ constexpr size_t alignment = 32ul;
+ PADDLE_ENFORCE_EQ(posix_memalign(&ptr, alignment, size), 0,
+ "GenBase Alloc %ld error!", size);
+ PADDLE_ENFORCE(ptr, "Fail to allocate GenBase CPU memory: size = %d .", size);
+ return ptr;
+}
+
+void GenBase::operator delete(void* ptr) { posix_memalign_free(ptr); }
+
std::vector<int> packed_groups(int n, int k, int* block_out, int* rest_out) {
int block;
int max_num_regs;
diff --git a/paddle/fluid/operators/jit/gen_base.h b/paddle/fluid/operators/jit/gen_base.h
index d808a33247..0f85245ba9 100644
--- a/paddle/fluid/operators/jit/gen_base.h
+++ b/paddle/fluid/operators/jit/gen_base.h
@@ -42,6 +42,11 @@ class GenBase : public Kernel {
return reinterpret_cast<Func>(const_cast<unsigned char*>(code));
}
+ void* operator new(size_t size);
+ void operator delete(void* ptr);
+ void* operator new[](size_t size) { return operator new(size); }
+ void operator delete[](void* ptr) { operator delete(ptr); }
+
protected:
void dumpCode(const unsigned char* code) const;
};
diff --git a/paddle/fluid/operators/jit/helper.h b/paddle/fluid/operators/jit/helper.h
index bba3a13619..d5773d6594 100644
--- a/paddle/fluid/operators/jit/helper.h
+++ b/paddle/fluid/operators/jit/helper.h
@@ -118,26 +118,33 @@ typename KernelTuples::func_type Get(
return GetRefer<KT, KernelTuples>();
}
-template <KernelType KT, typename KernelTuples>
-class KernelFuncsCache {
+template <KernelType KT, typename KernelTuples, typename PlaceType>
+class KernelFuncs {
public:
- KernelFuncsCache() = default;
- static KernelFuncsCache& Instance() {
- static thread_local KernelFuncsCache<KT, KernelTuples> g_func_cache;
+ KernelFuncs() = default;
+ static KernelFuncs& Cache() {
+ static thread_local KernelFuncs<KT, KernelTuples, PlaceType> g_func_cache;
return g_func_cache;
}
bool Has(int key) const { return funcs_.find(key) != funcs_.end(); }
- typename KernelTuples::func_type At(int key) { return funcs_.at(key); }
-
void Insert(int key, typename KernelTuples::func_type func) {
funcs_.emplace(key, func);
}
+ typename KernelTuples::func_type At(int key) {
+ if (Has(key)) {
+ return funcs_.at(key);
+ }
+ auto func = Get<KT, KernelTuples, PlaceType>(key);
+ Insert(key, func);
+ return func;
+ }
+
private:
std::unordered_map<int, typename KernelTuples::func_type> funcs_;
- DISABLE_COPY_AND_ASSIGN(KernelFuncsCache);
+ DISABLE_COPY_AND_ASSIGN(KernelFuncs);
};
const char* to_string(KernelType kt);
diff --git a/paddle/fluid/operators/jit/more/mix/mix.cc b/paddle/fluid/operators/jit/more/mix/mix.cc
index 0f42ac158c..0036d1c238 100644
--- a/paddle/fluid/operators/jit/more/mix/mix.cc
+++ b/paddle/fluid/operators/jit/more/mix/mix.cc
@@ -49,49 +49,16 @@ void VTanh(const T* x, T* y, int n) {
}
void Softmax(const T* x, T* y, int n, int bs) {
- typename XRNTuples<T>::func_type compute_hmax{nullptr};
- typename XRNTuples<T>::func_type compute_hsum{nullptr};
- typename AXYNTuples<T>::func_type compute_vscal{nullptr};
- typename AXYNTuples<T>::func_type compute_vaddbias{nullptr};
- typename XYNTuples<T>::func_type compute_vexp{nullptr};
-
- if (!KernelFuncsCache<kHMax, XRNTuples<T>>::Instance().Has(n)) {
- compute_hmax = Get<kHMax, XRNTuples<T>, platform::CPUPlace>(n);
- KernelFuncsCache<kHMax, XRNTuples<T>>::Instance().Insert(n, compute_hmax);
- } else {
- compute_hmax = KernelFuncsCache<kHMax, XRNTuples<T>>::Instance().At(n);
- }
-
- if (!KernelFuncsCache<kHSum, XRNTuples<T>>::Instance().Has(n)) {
- compute_hsum = Get<kHSum, XRNTuples<T>, platform::CPUPlace>(n);
- KernelFuncsCache<kHSum, XRNTuples<T>>::Instance().Insert(n, compute_hsum);
- } else {
- compute_hsum = KernelFuncsCache<kHSum, XRNTuples<T>>::Instance().At(n);
- }
-
- if (!KernelFuncsCache<kVScal, AXYNTuples<T>>::Instance().Has(n)) {
- compute_vscal = Get<kVScal, AXYNTuples<T>, platform::CPUPlace>(n);
- KernelFuncsCache<kVScal, AXYNTuples<T>>::Instance().Insert(n,
- compute_vscal);
- } else {
- compute_vscal = KernelFuncsCache<kVScal, AXYNTuples<T>>::Instance().At(n);
- }
-
- if (!KernelFuncsCache<kVAddBias, AXYNTuples<T>>::Instance().Has(n)) {
- compute_vaddbias = Get<kVAddBias, AXYNTuples<T>, platform::CPUPlace>(n);
- KernelFuncsCache<kVAddBias, AXYNTuples<T>>::Instance().Insert(
- n, compute_vaddbias);
- } else {
- compute_vaddbias =
- KernelFuncsCache<kVAddBias, AXYNTuples<T>>::Instance().At(n);
- }
-
- if (!KernelFuncsCache<kVExp, XYNTuples<T>>::Instance().Has(n)) {
- compute_vexp = Get<KernelType::kVExp, XYNTuples<T>, platform::CPUPlace>(n);
- KernelFuncsCache<kVExp, XYNTuples<T>>::Instance().Insert(n, compute_vexp);
- } else {
- compute_vexp = KernelFuncsCache<kVExp, XYNTuples<T>>::Instance().At(n);
- }
+ auto compute_hmax =
+ KernelFuncs<kHMax, XRNTuples<T>, platform::CPUPlace>::Cache().At(n);
+ auto compute_hsum =
+ KernelFuncs<kHSum, XRNTuples<T>, platform::CPUPlace>::Cache().At(n);
+ auto compute_vscal =
+ KernelFuncs<kVScal, AXYNTuples<T>, platform::CPUPlace>::Cache().At(n);
+ auto compute_vaddbias =
+ KernelFuncs<kVAddBias, AXYNTuples<T>, platform::CPUPlace>::Cache().At(n);
+ auto compute_vexp =
+ KernelFuncs<kVExp, XYNTuples<T>, platform::CPUPlace>::Cache().At(n);
for (int i = 0; i < bs; ++i) {
T scalar;
diff --git a/paddle/fluid/operators/jit/more/mkl/mkl.cc b/paddle/fluid/operators/jit/more/mkl/mkl.cc
index c7d0215eda..4c999131ab 100644
--- a/paddle/fluid/operators/jit/more/mkl/mkl.cc
+++ b/paddle/fluid/operators/jit/more/mkl/mkl.cc
@@ -136,7 +136,7 @@ bool VMulKernel<float>::UseMe(const int& d) const {
template <>
bool VAddKernel<float>::UseMe(const int& d) const {
- return platform::MayIUse(platform::avx512f) && d > 512;
+ return platform::MayIUse(platform::avx) && d > 512;
}
template <>
diff --git a/paddle/fluid/operators/lookup_table_op.h b/paddle/fluid/operators/lookup_table_op.h
index a7d0fd4856..56c6e37ae3 100644
--- a/paddle/fluid/operators/lookup_table_op.h
+++ b/paddle/fluid/operators/lookup_table_op.h
@@ -129,6 +129,7 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
"must be either LoDTensor or SelectedRows");
}
+ int64_t padding_idx = context.Attr<int64_t>("padding_idx");
bool is_sparse = context.Attr<bool>("is_sparse");
// Since paddings are not trainable and fixed in forward, the gradient of
// paddings makes no sense and we don't deal with it in backward.
@@ -187,10 +188,15 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
memset(d_table_data, 0, d_table->numel() * sizeof(T));
for (int64_t i = 0; i < ids->numel(); ++i) {
- PADDLE_ENFORCE_LT(ids_data[i], N);
- PADDLE_ENFORCE_GE(ids_data[i], 0);
- for (int j = 0; j < D; ++j) {
- d_table_data[ids_data[i] * D + j] += d_output_data[i * D + j];
+ if (padding_idx != kNoPadding && ids_data[i] == padding_idx) {
+ // the gradient of padding_idx should be 0, already done by memset, so
+ // do nothing.
+ } else {
+ PADDLE_ENFORCE_LT(ids_data[i], N);
+ PADDLE_ENFORCE_GE(ids_data[i], 0);
+ for (int j = 0; j < D; ++j) {
+ d_table_data[ids_data[i] * D + j] += d_output_data[i * D + j];
+ }
}
}
}
diff --git a/paddle/fluid/operators/math/CMakeLists.txt b/paddle/fluid/operators/math/CMakeLists.txt
index e20524012a..4b6eef18d8 100644
--- a/paddle/fluid/operators/math/CMakeLists.txt
+++ b/paddle/fluid/operators/math/CMakeLists.txt
@@ -37,7 +37,7 @@ math_library(concat_and_split)
math_library(context_project DEPS im2col math_function)
math_library(cross_entropy)
math_library(cos_sim_functor)
-math_library(depthwise_conv)
+math_library(depthwise_conv DEPS cub)
math_library(im2col)
math_library(sampler)
diff --git a/paddle/fluid/operators/math/fc_compute.h b/paddle/fluid/operators/math/fc_compute.h
index cddd0a18db..0ad57c51be 100644
--- a/paddle/fluid/operators/math/fc_compute.h
+++ b/paddle/fluid/operators/math/fc_compute.h
@@ -30,15 +30,17 @@ inline void FCCompute(const BlasT<DeviceContext, T>& blas, const int M,
return;
}
if (relu) {
- auto compute =
- jit::Get<jit::kVAddRelu, jit::XYZNTuples<T>, platform::CPUPlace>(N);
+ auto compute = jit::KernelFuncs<jit::kVAddRelu, jit::XYZNTuples<T>,
+ platform::CPUPlace>::Cache()
+ .At(N);
for (int i = 0; i < M; i++) {
T* dst = Y + i * N;
compute(B, dst, dst, N);
}
} else {
- auto compute =
- jit::Get<jit::kVAdd, jit::XYZNTuples<T>, platform::CPUPlace>(N);
+ auto compute = jit::KernelFuncs<jit::kVAdd, jit::XYZNTuples<T>,
+ platform::CPUPlace>::Cache()
+ .At(N);
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for
#endif
diff --git a/paddle/fluid/operators/math/softmax_impl.h b/paddle/fluid/operators/math/softmax_impl.h
index 1ff9ff684f..a1cb3f9728 100644
--- a/paddle/fluid/operators/math/softmax_impl.h
+++ b/paddle/fluid/operators/math/softmax_impl.h
@@ -82,8 +82,9 @@ class SoftmaxFunctor<DeviceContext, float, true, enable_if_CPU<DeviceContext>> {
const int kClassDim = 1;
// 2D data. Batch x C
auto compute_softmax =
- jit::Get<jit::kSoftmax, jit::SoftmaxTuples<float>, platform::CPUPlace>(
- in_dims[kClassDim]);
+ jit::KernelFuncs<jit::kSoftmax, jit::SoftmaxTuples<float>,
+ platform::CPUPlace>::Cache()
+ .At(in_dims[kClassDim]);
compute_softmax(in_data, out_data, in_dims[kClassDim], in_dims[kBatchDim]);
}
};
diff --git a/paddle/fluid/operators/ngraph/ngraph_bridge.cc b/paddle/fluid/operators/ngraph/ngraph_bridge.cc
index 13b168ce45..e8b92fc02a 100644
--- a/paddle/fluid/operators/ngraph/ngraph_bridge.cc
+++ b/paddle/fluid/operators/ngraph/ngraph_bridge.cc
@@ -31,6 +31,11 @@ std::map<std::string,
std::shared_ptr<std::unordered_map<
std::string, std::shared_ptr<ngraph::Node>>>)>>
NgraphBridge::NG_NODE_MAP = {
+ {"accuracy", NG_OPS::BuildAccuracyNode},
+ {"conv2d", NG_OPS::BuildConv2dNode},
+ {"conv2d_grad", NG_OPS::BuildConv2dGradNode},
+ {"batch_norm", NG_OPS::BuildBatchNormNode},
+ {"batch_norm_grad", NG_OPS::BuildBatchNormGradNode},
{"elementwise_add", NG_OPS::BuildElementwiseAddNode},
{"elementwise_add_grad", NG_OPS::BuildElementwiseAddGradNode},
{"fill_constant", NG_OPS::BuildFillConstantNode},
diff --git a/paddle/fluid/operators/ngraph/ngraph_engine_op.h b/paddle/fluid/operators/ngraph/ngraph_engine_op.h
index d2974298b0..2f194a9b87 100644
--- a/paddle/fluid/operators/ngraph/ngraph_engine_op.h
+++ b/paddle/fluid/operators/ngraph/ngraph_engine_op.h
@@ -35,7 +35,7 @@ class NgraphEngineOp : public framework::OperatorWithKernel {
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
framework::OpKernelType kt = framework::OpKernelType(
- framework::proto::VarType::FP32, ctx.GetPlace());
+ framework::proto::VarType::FP32, platform::CPUPlace());
return kt;
}
};
diff --git a/paddle/fluid/operators/ngraph/ngraph_ops.h b/paddle/fluid/operators/ngraph/ngraph_ops.h
index 4b7aa3393b..438a9c1be9 100644
--- a/paddle/fluid/operators/ngraph/ngraph_ops.h
+++ b/paddle/fluid/operators/ngraph/ngraph_ops.h
@@ -21,7 +21,10 @@ limitations under the License. */
#pragma once
-#include "ops/binary_unnary_op.h"
+#include "ops/accuracy_op.h"
+#include "ops/batch_norm_op.h"
+#include "ops/binary_unary_op.h"
+#include "ops/conv2d_op.h"
#include "ops/elementwise_add_op.h"
#include "ops/fill_constant_op.h"
#include "ops/mean_op.h"
diff --git a/paddle/fluid/operators/ngraph/ops/accuracy_op.h b/paddle/fluid/operators/ngraph/ops/accuracy_op.h
new file mode 100644
index 0000000000..bf37ce48d8
--- /dev/null
+++ b/paddle/fluid/operators/ngraph/ops/accuracy_op.h
@@ -0,0 +1,65 @@
+/*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 <string>
+#include <vector>
+#include "ngraph/ngraph.hpp"
+#include "paddle/fluid/platform/ngraph_helper.h"
+
+namespace paddle {
+namespace operators {
+namespace ngraphs {
+
+void BuildAccuracyNode(
+ const std::shared_ptr<framework::OperatorBase>& op,
+ std::shared_ptr<
+ std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+ ngb_node_map) {
+ auto indices = platform::GetInputNode(op, "Indices", ngb_node_map);
+ auto label = platform::GetInputNode(op, "Label", ngb_node_map);
+ auto inference = platform::GetInputNode(op, "Out", ngb_node_map);
+ auto inference_shape = inference->get_shape();
+ size_t num_samples = inference_shape.at(0);
+ size_t k = inference_shape.at(1);
+
+ std::shared_ptr<ngraph::Node> label_k = label;
+ if (k > 1) {
+ auto label_1d = std::make_shared<ngraph::op::Reshape>(
+ label, ngraph::AxisVector{0, 1}, ngraph::Shape{num_samples});
+ label_k = std::make_shared<ngraph::op::Broadcast>(label_1d, inference_shape,
+ ngraph::AxisSet{1});
+ }
+
+ auto node_equal = std::make_shared<ngraph::op::Equal>(indices, label_k);
+ auto node_eq_int =
+ std::make_shared<ngraph::op::Convert>(node_equal, ngraph::element::i64);
+ auto num_correct_0d =
+ std::make_shared<ngraph::op::Sum>(node_eq_int, ngraph::AxisSet{0, 1});
+ std::shared_ptr<ngraph::Node> num_correct =
+ platform::NgReshaper(num_correct_0d, ngraph::Shape{1});
+ std::shared_ptr<ngraph::Node> n_samples = ngraph::op::Constant::create(
+ ngraph::element::i64, ngraph::Shape{1}, {num_samples});
+ std::shared_ptr<ngraph::Node> accuracy = std::make_shared<ngraph::op::Divide>(
+ std::make_shared<ngraph::op::Convert>(num_correct, ngraph::element::f32),
+ std::make_shared<ngraph::op::Convert>(n_samples, ngraph::element::f32));
+
+ platform::SetOutputNode(op, "Accuracy", accuracy, ngb_node_map);
+ platform::SetOutputNode(op, "Correct", num_correct, ngb_node_map);
+ platform::SetOutputNode(op, "Total", n_samples, ngb_node_map);
+}
+} // namespace ngraphs
+} // namespace operators
+} // namespace paddle
diff --git a/paddle/fluid/operators/ngraph/ops/batch_norm_op.h b/paddle/fluid/operators/ngraph/ops/batch_norm_op.h
new file mode 100644
index 0000000000..2cdd029976
--- /dev/null
+++ b/paddle/fluid/operators/ngraph/ops/batch_norm_op.h
@@ -0,0 +1,150 @@
+/*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 <string>
+#include <vector>
+
+#include "ngraph/ngraph.hpp"
+#include "paddle/fluid/operators/ngraph/ops/elementwise_node.h"
+#include "paddle/fluid/operators/ngraph/ops/elementwise_scalar_op.h"
+#include "paddle/fluid/platform/ngraph_helper.h"
+
+namespace paddle {
+namespace operators {
+namespace ngraphs {
+
+void BuildBatchNormNode(
+ const std::shared_ptr<paddle::framework::OperatorBase>& op,
+ std::shared_ptr<
+ std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+ ngb_node_map) {
+ auto op_attrs = paddle::framework::AttrReader(op->Attrs());
+ auto& data_layout = op_attrs.Get<std::string>("data_layout");
+
+ auto bias = paddle::platform::GetInputNode(op, "Bias", ngb_node_map);
+ auto mean = paddle::platform::GetInputNode(op, "Mean", ngb_node_map);
+ auto variance = paddle::platform::GetInputNode(op, "Variance", ngb_node_map);
+ auto scale = paddle::platform::GetInputNode(op, "Scale", ngb_node_map);
+ auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
+
+ const bool is_test = op_attrs.Get<bool>("is_test");
+ const float epsilon = op_attrs.Get<float>("epsilon");
+ const float momentum = op_attrs.Get<float>("momentum");
+
+ if (data_layout == "NHWC") {
+ x = paddle::platform::Nhwc2Nchw(x);
+ }
+
+ std::shared_ptr<ngraph::Node> mean_out, saved_mean, saved_variance,
+ variance_out, y;
+
+ if (!is_test) {
+ auto BN = std::make_shared<ngraph::op::BatchNormTraining>(epsilon, scale,
+ bias, x);
+ y = std::make_shared<ngraph::op::GetOutputElement>(BN, 0);
+ saved_mean = std::make_shared<ngraph::op::GetOutputElement>(BN, 1);
+ saved_variance = std::make_shared<ngraph::op::GetOutputElement>(BN, 2);
+
+ mean_out = std::make_shared<ngraph::op::Add>(
+ paddle::operators::ngraphs::ElementwiseScalar<ngraph::op::Multiply>(
+ momentum, mean),
+ paddle::operators::ngraphs::ElementwiseScalar<ngraph::op::Multiply>(
+ 1. - momentum, saved_mean));
+ variance_out = std::make_shared<ngraph::op::Add>(
+ paddle::operators::ngraphs::ElementwiseScalar<ngraph::op::Multiply>(
+ momentum, variance),
+ paddle::operators::ngraphs::ElementwiseScalar<ngraph::op::Multiply>(
+ 1. - momentum, saved_variance));
+
+ if (data_layout == "NHWC") {
+ y = paddle::platform::Nchw2Nhwc(y);
+ }
+
+ paddle::platform::SetOutputNode(op, "MeanOut", mean_out, ngb_node_map);
+ paddle::platform::SetOutputNode(op, "VarianceOut", variance_out,
+ ngb_node_map);
+ paddle::platform::SetOutputNode(op, "SavedMean", saved_mean, ngb_node_map);
+ paddle::platform::SetOutputNode(op, "SavedVariance", saved_variance,
+ ngb_node_map);
+ paddle::platform::SetOutputNode(op, "Y", y, ngb_node_map);
+ } else {
+ y = std::make_shared<ngraph::op::BatchNormInference>(epsilon, scale, bias,
+ x, mean, variance);
+ paddle::platform::SetOutputNode(op, "Y", y, ngb_node_map);
+ }
+}
+
+void BuildBatchNormGradNode(
+ const std::shared_ptr<paddle::framework::OperatorBase>& op,
+ std::shared_ptr<
+ std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+ ngb_node_map) {
+ auto op_attrs = paddle::framework::AttrReader(op->Attrs());
+ auto& data_layout = op_attrs.Get<std::string>("data_layout");
+
+ auto bias = paddle::platform::GetInputNode(op, "Bias", ngb_node_map);
+ auto saved_mean =
+ paddle::platform::GetInputNode(op, "SavedMean", ngb_node_map);
+ auto saved_variance =
+ paddle::platform::GetInputNode(op, "SavedVariance", ngb_node_map);
+ auto scale = paddle::platform::GetInputNode(op, "Scale", ngb_node_map);
+ auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
+ auto dy = paddle::platform::GetInputNode(op, "Y@GRAD", ngb_node_map);
+ auto x_shape = x->get_shape();
+ auto dy_shape = dy->get_shape();
+
+ PADDLE_ENFORCE(x_shape.size() == 2 || x_shape.size() == 4,
+ "BN grap input size needs to be 2 or 4");
+ PADDLE_ENFORCE_EQ(x_shape.size(), dy_shape.size(),
+ "BN grap input and delta size needs to be equal");
+
+ if (x_shape.size() == 2) {
+ x = std::make_shared<ngraph::op::Reshape>(
+ x, ngraph::AxisVector{0, 1},
+ ngraph::Shape{x_shape.at(0), x_shape.at(1), 1, 1});
+ dy = std::make_shared<ngraph::op::Reshape>(
+ dy, ngraph::AxisVector{0, 1},
+ ngraph::Shape{dy_shape.at(0), dy_shape.at(1), 1, 1});
+ }
+
+ if (data_layout == "NHWC") {
+ x = paddle::platform::Nhwc2Nchw(dy);
+ dy = paddle::platform::Nhwc2Nchw(dy);
+ }
+ const float epsilon = op_attrs.Get<float>("epsilon");
+
+ auto bn_bprop = std::make_shared<ngraph::op::BatchNormTrainingBackprop>(
+ epsilon, scale, bias, x, saved_mean, saved_variance, dy);
+
+ std::shared_ptr<ngraph::Node> dx =
+ std::make_shared<ngraph::op::GetOutputElement>(bn_bprop, 0);
+ auto dscale = std::make_shared<ngraph::op::GetOutputElement>(bn_bprop, 1);
+ auto dbias = std::make_shared<ngraph::op::GetOutputElement>(bn_bprop, 2);
+ paddle::platform::SetOutputNode(op, "Bias@GRAD", dbias, ngb_node_map);
+ paddle::platform::SetOutputNode(op, "Scale@GRAD", dscale, ngb_node_map);
+ if (x_shape.size() == 2) {
+ paddle::platform::SetOutputNode(
+ op, "X@GRAD", paddle::platform::NgReshaper(dx, x_shape), ngb_node_map);
+ } else {
+ if (data_layout == "NHWC") {
+ dx = paddle::platform::Nchw2Nhwc(dx);
+ }
+ paddle::platform::SetOutputNode(op, "X@GRAD", dx, ngb_node_map);
+ }
+}
+} // namespace ngraphs
+} // namespace operators
+} // namespace paddle
diff --git a/paddle/fluid/operators/ngraph/ops/binary_unnary_op.h b/paddle/fluid/operators/ngraph/ops/binary_unary_op.h
similarity index 100%
rename from paddle/fluid/operators/ngraph/ops/binary_unnary_op.h
rename to paddle/fluid/operators/ngraph/ops/binary_unary_op.h
diff --git a/paddle/fluid/operators/ngraph/ops/conv2d_op.h b/paddle/fluid/operators/ngraph/ops/conv2d_op.h
new file mode 100644
index 0000000000..46fb2703f5
--- /dev/null
+++ b/paddle/fluid/operators/ngraph/ops/conv2d_op.h
@@ -0,0 +1,235 @@
+/* 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 <string>
+#include <vector>
+#include "ngraph/ngraph.hpp"
+#include "paddle/fluid/platform/ngraph_helper.h"
+
+namespace paddle {
+namespace operators {
+namespace ngraphs {
+
+std::shared_ptr<ngraph::Node> GroupedConvolution(
+ const std::shared_ptr<ngraph::Node>& data_batch,
+ const std::shared_ptr<ngraph::Node>& filters, const ngraph::Strides strides,
+ const ngraph::Strides dilations, const ngraph::CoordinateDiff& paddings,
+ size_t groups) {
+ auto& data_shape = data_batch->get_shape();
+ auto& filter_shape = filters->get_shape();
+ ngraph::NodeVector ng_slices;
+
+ for (size_t i = 0; i < groups; ++i) {
+ size_t channel_step = filter_shape.at(1);
+ const std::vector<size_t> lower_bound{0, i * channel_step, 0, 0};
+ const std::vector<size_t> upper_bound{data_shape.at(0),
+ (i + 1) * channel_step,
+ data_shape.at(2), data_shape.at(3)};
+ auto data_slice = std::make_shared<ngraph::op::Slice>(
+ data_batch, lower_bound, upper_bound);
+
+ size_t filter_step = filter_shape.at(0) / groups;
+ const std::vector<size_t> filter_lower_bound{i * filter_step, 0, 0, 0};
+ const std::vector<size_t> filter_upper_bound{
+ (i + 1) * filter_step, filter_shape.at(1), filter_shape.at(2),
+ filter_shape.at(3)};
+ auto filter_slice = std::make_shared<ngraph::op::Slice>(
+ filters, filter_lower_bound, filter_upper_bound);
+ auto ng_conv = std::make_shared<ngraph::op::Convolution>(
+ data_slice, filter_slice, strides, dilations, paddings, paddings);
+ ng_slices.push_back(ng_conv);
+ }
+
+ size_t concat_axis = 1;
+ return std::make_shared<ngraph::op::Concat>(ng_slices, concat_axis);
+}
+
+std::shared_ptr<ngraph::Node> GroupedGradConvolutionFilter(
+ const std::shared_ptr<ngraph::Node>& data_batch,
+ const std::shared_ptr<ngraph::Node>& filters,
+ const std::shared_ptr<ngraph::Node>& doutput, const ngraph::Strides strides,
+ const ngraph::Strides dilations, const ngraph::CoordinateDiff& paddings,
+ size_t groups) {
+ auto& data_shape = data_batch->get_shape();
+ auto& filter_shape = filters->get_shape();
+ auto& out_shape = doutput->get_shape();
+ ngraph::NodeVector ng_slices;
+
+ for (size_t i = 0; i < groups; ++i) {
+ size_t channel_step = filter_shape.at(1);
+ const std::vector<size_t> lower_bound{0, i * channel_step, 0, 0};
+ const std::vector<size_t> upper_bound{data_shape.at(0),
+ (i + 1) * channel_step,
+ data_shape.at(2), data_shape.at(3)};
+ auto data_slice = std::make_shared<ngraph::op::Slice>(
+ data_batch, lower_bound, upper_bound);
+
+ size_t filter_step = data_shape.at(0);
+
+ const std::vector<size_t> filter_lower_bound{i * filter_step, 0, 0, 0};
+ const std::vector<size_t> filter_upper_bound{
+ (i + 1) * filter_step, filter_shape.at(1), filter_shape.at(2),
+ filter_shape.at(3)};
+ auto filter_slice = std::make_shared<ngraph::op::Slice>(
+ filters, filter_lower_bound, filter_upper_bound);
+
+ const std::vector<size_t> olower_bound{0, i * filter_step, 0, 0};
+ const std::vector<size_t> oupper_bound{out_shape.at(0),
+ (i + 1) * filter_step,
+ out_shape.at(2), out_shape.at(3)};
+ auto out_slice = std::make_shared<ngraph::op::Slice>(doutput, olower_bound,
+ oupper_bound);
+
+ auto ng_conv = std::make_shared<ngraph::op::ConvolutionBackpropFilters>(
+ data_slice, filter_slice->get_shape(), out_slice, strides, dilations,
+ paddings, paddings, ngraph::Strides{1, 1});
+
+ ng_slices.push_back(ng_conv);
+ }
+
+ size_t concat_axis = 0;
+ return std::make_shared<ngraph::op::Concat>(ng_slices, concat_axis);
+}
+
+std::shared_ptr<ngraph::Node> GroupedGradConvolutionData(
+ const std::shared_ptr<ngraph::Node>& data_batch,
+ const std::shared_ptr<ngraph::Node>& filters,
+ const std::shared_ptr<ngraph::Node>& doutput, const ngraph::Strides strides,
+ const ngraph::Strides dilations, const ngraph::CoordinateDiff& paddings,
+ size_t groups) {
+ auto& data_shape = data_batch->get_shape();
+ auto& filter_shape = filters->get_shape();
+ auto& out_shape = doutput->get_shape();
+ ngraph::NodeVector ng_slices;
+
+ for (size_t i = 0; i < groups; ++i) {
+ size_t channel_step = filter_shape.at(1);
+ const std::vector<size_t> lower_bound{0, i * channel_step, 0, 0};
+ const std::vector<size_t> upper_bound{data_shape.at(0),
+ (i + 1) * channel_step,
+ data_shape.at(2), data_shape.at(3)};
+ auto data_slice = std::make_shared<ngraph::op::Slice>(
+ data_batch, lower_bound, upper_bound);
+
+ size_t filter_step = data_shape.at(0);
+
+ const std::vector<size_t> filter_lower_bound{i * filter_step, 0, 0, 0};
+ const std::vector<size_t> filter_upper_bound{
+ (i + 1) * filter_step, filter_shape.at(1), filter_shape.at(2),
+ filter_shape.at(3)};
+ auto filter_slice = std::make_shared<ngraph::op::Slice>(
+ filters, filter_lower_bound, filter_upper_bound);
+
+ const std::vector<size_t> olower_bound{0, i * filter_step, 0, 0};
+ const std::vector<size_t> oupper_bound{out_shape.at(0),
+ (i + 1) * filter_step,
+ out_shape.at(2), out_shape.at(3)};
+ auto out_slice = std::make_shared<ngraph::op::Slice>(doutput, olower_bound,
+ oupper_bound);
+
+ auto ng_conv = std::make_shared<ngraph::op::ConvolutionBackpropData>(
+ data_slice->get_shape(), filter_slice, out_slice, strides, dilations,
+ paddings, paddings, ngraph::Strides{1, 1});
+ ng_slices.push_back(ng_conv);
+ }
+
+ size_t concat_axis = 1;
+ return std::make_shared<ngraph::op::Concat>(ng_slices, concat_axis);
+}
+
+void BuildConv2dNode(
+ const std::shared_ptr<paddle::framework::OperatorBase>& op,
+ std::shared_ptr<
+ std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+ ngb_node_map) {
+ auto op_attrs = paddle::framework::AttrReader(op->Attrs());
+ auto filters = paddle::platform::GetInputNode(op, "Filter", ngb_node_map);
+ auto input = paddle::platform::GetInputNode(op, "Input", ngb_node_map);
+
+ std::vector<int> strides = op_attrs.Get<std::vector<int>>("strides");
+ std::vector<int> paddings = op_attrs.Get<std::vector<int>>("paddings");
+ std::vector<int> dilations = op_attrs.Get<std::vector<int>>("dilations");
+
+ const ngraph::Strides ng_strides{static_cast<size_t>(strides.at(0)),
+ static_cast<size_t>(strides.at(1))};
+ const ngraph::Strides ng_dilations{static_cast<size_t>(dilations.at(0)),
+ static_cast<size_t>(dilations.at(1))};
+ const ngraph::CoordinateDiff ng_paddings{
+ static_cast<std::ptrdiff_t>(paddings.at(0)),
+ static_cast<std::ptrdiff_t>(paddings.at(1))};
+
+ int groups = static_cast<size_t>(op_attrs.Get<int>("groups"));
+ PADDLE_ENFORCE_GE(groups, 1, "conv groups needs be no less than 1");
+
+ std::shared_ptr<ngraph::Node> result;
+ if (groups == 1) {
+ result = std::make_shared<ngraph::op::Convolution>(
+ input, filters, ng_strides, ng_dilations, ng_paddings, ng_paddings);
+ } else {
+ result = GroupedConvolution(input, filters, ng_strides, ng_dilations,
+ ng_paddings, groups);
+ }
+ paddle::platform::SetOutputNode(op, "Output", result, ngb_node_map);
+}
+
+void BuildConv2dGradNode(
+ const std::shared_ptr<paddle::framework::OperatorBase>& op,
+ std::shared_ptr<
+ std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+ ngb_node_map) {
+ auto op_attrs = paddle::framework::AttrReader(op->Attrs());
+ auto filter = paddle::platform::GetInputNode(op, "Filter", ngb_node_map);
+ auto input = paddle::platform::GetInputNode(op, "Input", ngb_node_map);
+ auto doutput =
+ paddle::platform::GetInputNode(op, "Output@GRAD", ngb_node_map);
+
+ int groups = op_attrs.Get<int>("groups");
+ std::vector<int> strides = op_attrs.Get<std::vector<int>>("strides");
+ std::vector<int> paddings = op_attrs.Get<std::vector<int>>("paddings");
+ std::vector<int> dilations = op_attrs.Get<std::vector<int>>("dilations");
+
+ const ngraph::Strides ng_strides{static_cast<size_t>(strides.at(0)),
+ static_cast<size_t>(strides.at(1))};
+ const ngraph::Strides ng_dilations{static_cast<size_t>(dilations.at(0)),
+ static_cast<size_t>(dilations.at(1))};
+ const ngraph::CoordinateDiff ng_paddings{
+ static_cast<std::ptrdiff_t>(paddings.at(0)),
+ static_cast<std::ptrdiff_t>(paddings.at(1))};
+
+ std::shared_ptr<ngraph::Node> dfilter;
+ std::shared_ptr<ngraph::Node> dinput;
+ if (groups == 1) {
+ dfilter = std::make_shared<ngraph::op::ConvolutionBackpropFilters>(
+ input, filter->get_shape(), doutput, ng_strides, ng_dilations,
+ ng_paddings, ng_paddings, ngraph::Strides{1, 1});
+
+ dinput = std::make_shared<ngraph::op::ConvolutionBackpropData>(
+ input->get_shape(), filter, doutput, ng_strides, ng_dilations,
+ ng_paddings, ng_paddings, ngraph::Strides{1, 1});
+
+ } else {
+ dfilter = GroupedGradConvolutionFilter(input, filter, doutput, ng_strides,
+ ng_dilations, ng_paddings, groups);
+ dinput = GroupedGradConvolutionData(input, filter, doutput, ng_strides,
+ ng_dilations, ng_paddings, groups);
+ }
+
+ paddle::platform::SetOutputNode(op, "Filter@GRAD", dfilter, ngb_node_map);
+ paddle::platform::SetOutputNode(op, "Input@GRAD", dinput, ngb_node_map);
+}
+} // namespace ngraphs
+} // namespace operators
+} // namespace paddle
diff --git a/paddle/fluid/operators/ngraph/ops/top_k_op.h b/paddle/fluid/operators/ngraph/ops/top_k_op.h
index ea66953a12..852ecd7139 100644
--- a/paddle/fluid/operators/ngraph/ops/top_k_op.h
+++ b/paddle/fluid/operators/ngraph/ops/top_k_op.h
@@ -36,11 +36,6 @@ void BuildTopKNode(
std::make_shared<ngraph::op::GetOutputElement>(top_k, 0);
std::shared_ptr<ngraph::Node> out =
std::make_shared<ngraph::op::GetOutputElement>(top_k, 1);
- auto dummy_out = paddle::platform::GetOutputNode(op, "Out", ngb_node_map);
- if (dummy_out && dummy_out->get_element_type() != out->get_element_type()) {
- out = std::make_shared<ngraph::op::Convert>(out,
- dummy_out->get_element_type());
- }
paddle::platform::SetOutputNode(op, "Indices", indices, ngb_node_map);
paddle::platform::SetOutputNode(op, "Out", out, ngb_node_map);
}
diff --git a/paddle/fluid/operators/norm_op.h b/paddle/fluid/operators/norm_op.h
index 6c95d3f3bf..f81cbc2c73 100644
--- a/paddle/fluid/operators/norm_op.h
+++ b/paddle/fluid/operators/norm_op.h
@@ -99,10 +99,10 @@ class NormGradKernel : public framework::OpKernel<T> {
auto dx_e = framework::EigenVector<T>::Flatten(*out_dx);
Eigen::DSizes<int, 3> shape(pre, n, post);
- Eigen::DSizes<int, 2> norm_shape(pre, post);
+ Eigen::DSizes<int, 3> rshape(pre, 1, post);
auto x = x_e.reshape(shape);
auto dy = dy_e.reshape(shape);
- auto norm = norm_e.reshape(norm_shape);
+ auto norm = norm_e.reshape(rshape);
auto dx = dx_e.reshape(shape);
framework::Tensor rsum;
@@ -111,7 +111,6 @@ class NormGradKernel : public framework::OpKernel<T> {
Eigen::DSizes<int, 1> rdim(1);
Eigen::DSizes<int, 3> bcast(1, n, 1);
- Eigen::DSizes<int, 3> rshape(pre, 1, post);
// dx = ( dy/sqrt(sum(x*x)) ) * [1 - x*sum(x) / (sum(x*x) + e)]
// = [dy - dy * x * sum(x) / (sum(x*x) + e)] / sqrt(sum(x*x))
diff --git a/paddle/fluid/operators/pool_op.cc b/paddle/fluid/operators/pool_op.cc
index 5399ae556e..fc3636e0b2 100644
--- a/paddle/fluid/operators/pool_op.cc
+++ b/paddle/fluid/operators/pool_op.cc
@@ -259,7 +259,7 @@ Example:
W_{out} = \\frac{(W_{in} - ksize[1] + 2 * paddings[1] + strides[1] - 1)}{strides[1]} + 1
$$
- For exclusive = true:
+ For exclusive = false:
$$
hstart = i * strides[0] - paddings[0]
hend = hstart + ksize[0]
@@ -267,7 +267,7 @@ Example:
wend = wstart + ksize[1]
Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{ksize[0] * ksize[1]}
$$
- For exclusive = false:
+ For exclusive = true:
$$
hstart = max(0, i * strides[0] - paddings[0])
hend = min(H, hstart + ksize[0])
@@ -403,7 +403,7 @@ Example:
H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1] + strides[1] -1)}{strides[1]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2] + strides[2] -1)}{strides[2]} + 1
$$
- For exclusive = true:
+ For exclusive = false:
$$
dstart = i * strides[0] - paddings[0]
dend = dstart + ksize[0]
@@ -413,7 +413,7 @@ Example:
wend = wstart + ksize[2]
Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{ksize[0] * ksize[1] * ksize[2]}
$$
- For exclusive = false:
+ For exclusive = true:
$$
dstart = max(0, i * strides[0] - paddings[0])
dend = min(D, dstart + ksize[0])
diff --git a/paddle/fluid/operators/random_crop_op.h b/paddle/fluid/operators/random_crop_op.h
index d68ba9d661..ee034b2705 100644
--- a/paddle/fluid/operators/random_crop_op.h
+++ b/paddle/fluid/operators/random_crop_op.h
@@ -121,7 +121,7 @@ struct RandomCropFunctor {
HOSTDEVICE void operator()(size_t ins_idx) {
typename Random<DeviceContext>::Engine engine(seed_);
engine.discard(ins_idx * (rank_ - num_batchsize_dims_));
- size_t offsets[9];
+ size_t offsets[9] = {};
for (int i = num_batchsize_dims_; i < rank_; ++i) {
typename Random<DeviceContext>::template UniformIntDist<size_t> dist(
0, x_dims_[i] - out_dims_[i]);
diff --git a/paddle/fluid/operators/reader/buffered_reader.cc b/paddle/fluid/operators/reader/buffered_reader.cc
index 26ff221dfa..defc29b91f 100644
--- a/paddle/fluid/operators/reader/buffered_reader.cc
+++ b/paddle/fluid/operators/reader/buffered_reader.cc
@@ -14,6 +14,7 @@
#include "paddle/fluid/operators/reader/buffered_reader.h"
#include <vector>
+#include "paddle/fluid/framework/data_type.h"
namespace paddle {
namespace operators {
@@ -24,6 +25,13 @@ BufferedReader::~BufferedReader() {
position_.front().wait();
position_.pop();
}
+#ifdef PADDLE_WITH_CUDA
+ if (platform::is_gpu_place(place_)) {
+ platform::SetDeviceId(boost::get<platform::CUDAPlace>(place_).device);
+ PADDLE_ENFORCE(cudaStreamDestroy(stream));
+ for (auto &event : events) PADDLE_ENFORCE(cudaEventDestroy(event));
+ }
+#endif
}
BufferedReader::BufferedReader(
@@ -33,6 +41,19 @@ BufferedReader::BufferedReader(
thread_pool_(1),
place_(place),
buffer_size_(buffer_size) {
+#ifdef PADDLE_WITH_CUDA
+ if (platform::is_gpu_place(place_)) {
+ platform::SetDeviceId(boost::get<platform::CUDAPlace>(place_).device);
+ compute_stream =
+ ((platform::CUDADeviceContext *)(platform::DeviceContextPool::Instance()
+ .Get(place_)))
+ ->stream();
+ events.resize(buffer_size);
+ for (auto &event : events)
+ PADDLE_ENFORCE(cudaEventCreateWithFlags(&event, cudaEventDisableTiming));
+ PADDLE_ENFORCE(cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking));
+ }
+#endif
cpu_buffer_.resize(buffer_size);
gpu_buffer_.resize(buffer_size);
ReadTillBufferFullAsync();
@@ -46,6 +67,12 @@ void BufferedReader::ReadTillBufferFullAsync() {
}
void BufferedReader::ReadAsync(size_t i) {
+#ifdef PADDLE_WITH_CUDA
+ if (platform::is_gpu_place(place_)) {
+ platform::SetDeviceId(boost::get<platform::CUDAPlace>(place_).device);
+ PADDLE_ENFORCE(cudaEventRecord(events[i], compute_stream));
+ }
+#endif
position_.emplace(thread_pool_.enqueue([this, i]() -> size_t {
TensorVec &cpu = cpu_buffer_[i];
reader_->ReadNext(&cpu);
@@ -54,14 +81,41 @@ void BufferedReader::ReadAsync(size_t i) {
return -1UL;
}
+#ifdef PADDLE_WITH_CUDA
+ // NOTE(liangdun): using async copy instead of TensorCopySync
+ // TensorCopySync would block other stream
if (platform::is_gpu_place(place_)) {
+ platform::SetDeviceId(boost::get<platform::CUDAPlace>(place_).device);
+ PADDLE_ENFORCE(cudaStreamWaitEvent(stream, events[i], 0));
TensorVec &gpu = gpu_buffer_[i];
gpu.resize(cpu.size());
for (size_t i = 0; i < cpu.size(); ++i) {
- framework::TensorCopySync(cpu[i], place_, &gpu[i]);
+ gpu[i].Resize(cpu[i].dims());
+ gpu[i].set_layout(cpu[i].layout());
+ auto cpu_place = cpu[i].place();
+ auto cpu_ptr = cpu[i].data<void>();
+ auto gpu_ptr = gpu[i].mutable_data(place_, cpu[i].type());
+ auto size =
+ cpu[i].numel() * paddle::framework::SizeOfType(cpu[i].type());
+ if (platform::is_cuda_pinned_place(cpu_place))
+ memory::Copy(boost::get<platform::CUDAPlace>(place_), gpu_ptr,
+ boost::get<platform::CUDAPinnedPlace>(cpu_place),
+ cpu_ptr, size, stream);
+ else if ((platform::is_gpu_place(cpu_place)))
+ memory::Copy(boost::get<platform::CUDAPlace>(place_), gpu_ptr,
+ boost::get<platform::CUDAPlace>(cpu_place), cpu_ptr,
+ size, stream);
+ else
+ // if cpu place is not pinned, async copy is slower than sync copy,
+ // so we use sync copy instead.
+ memory::Copy(boost::get<platform::CUDAPlace>(place_), gpu_ptr,
+ boost::get<platform::CPUPlace>(cpu_place), cpu_ptr, size,
+ 0);
gpu[i].set_lod(cpu[i].lod());
}
+ PADDLE_ENFORCE(cudaStreamSynchronize(stream));
}
+#endif
return i;
}));
}
diff --git a/paddle/fluid/operators/reader/buffered_reader.h b/paddle/fluid/operators/reader/buffered_reader.h
index cbe2bc1b5f..87680da01a 100644
--- a/paddle/fluid/operators/reader/buffered_reader.h
+++ b/paddle/fluid/operators/reader/buffered_reader.h
@@ -19,6 +19,9 @@
#include <vector>
#include "ThreadPool.h"
#include "paddle/fluid/framework/reader.h"
+#ifdef PADDLE_WITH_CUDA
+#include "paddle/fluid/platform/gpu_info.h"
+#endif
namespace paddle {
namespace operators {
@@ -59,6 +62,11 @@ class BufferedReader : public framework::DecoratedReader {
std::vector<TensorVec> cpu_buffer_;
std::vector<TensorVec> gpu_buffer_;
size_t prev_pos_{-1UL};
+#ifdef PADDLE_WITH_CUDA
+ cudaStream_t stream;
+ cudaStream_t compute_stream;
+ std::vector<cudaEvent_t> events;
+#endif
};
} // namespace reader
diff --git a/paddle/fluid/operators/reader/ctr_reader.cc b/paddle/fluid/operators/reader/ctr_reader.cc
index f08798794a..43a49de522 100644
--- a/paddle/fluid/operators/reader/ctr_reader.cc
+++ b/paddle/fluid/operators/reader/ctr_reader.cc
@@ -213,7 +213,7 @@ void ReadSvmData(const DataDesc& data_desc, std::shared_ptr<Reader> reader,
framework::LoD lod{lod_data};
lod_tensor.set_lod(lod);
int64_t* tensor_data = lod_tensor.mutable_data<int64_t>(
- framework::make_ddim({1, static_cast<int64_t>(batch_feasign.size())}),
+ framework::make_ddim({static_cast<int64_t>(batch_feasign.size()), 1}),
platform::CPUPlace());
memcpy(tensor_data, batch_feasign.data(),
batch_feasign.size() * sizeof(int64_t));
@@ -223,7 +223,7 @@ void ReadSvmData(const DataDesc& data_desc, std::shared_ptr<Reader> reader,
// insert label tensor
framework::LoDTensor label_tensor;
auto* label_tensor_data = label_tensor.mutable_data<int64_t>(
- framework::make_ddim({1, static_cast<int64_t>(batch_label.size())}),
+ framework::make_ddim({static_cast<int64_t>(batch_label.size()), 1}),
platform::CPUPlace());
memcpy(label_tensor_data, batch_label.data(),
batch_label.size() * sizeof(int64_t));
diff --git a/paddle/fluid/operators/reader/ctr_reader_test.cc b/paddle/fluid/operators/reader/ctr_reader_test.cc
index 9f3a254c84..6410439816 100644
--- a/paddle/fluid/operators/reader/ctr_reader_test.cc
+++ b/paddle/fluid/operators/reader/ctr_reader_test.cc
@@ -123,7 +123,7 @@ TEST(CTR_READER, read_data) {
std::vector<std::tuple<LoD, std::vector<int64_t>>> data_slot_6003{b1, b2, b3,
b4};
- std::vector<DDim> label_dims = {{1, 3}, {1, 3}, {1, 3}, {1, 1}};
+ std::vector<DDim> label_dims = {{3, 1}, {3, 1}, {3, 1}, {1, 1}};
LoDTensorBlockingQueueHolder queue_holder;
int capacity = 64;
diff --git a/paddle/fluid/operators/reduce_ops/CMakeLists.txt b/paddle/fluid/operators/reduce_ops/CMakeLists.txt
index 5fe4d15ae2..ebd07d90eb 100644
--- a/paddle/fluid/operators/reduce_ops/CMakeLists.txt
+++ b/paddle/fluid/operators/reduce_ops/CMakeLists.txt
@@ -1,5 +1,9 @@
include(operators)
-register_operators()
+if(WITH_GPU)
+ register_operators(DEPS cub)
+else()
+ register_operators()
+endif()
if(WITH_GPU)
file(GLOB OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "*.part.cu")
diff --git a/paddle/fluid/operators/reshape_op.cc b/paddle/fluid/operators/reshape_op.cc
index 32365d6a96..eda54f76b8 100644
--- a/paddle/fluid/operators/reshape_op.cc
+++ b/paddle/fluid/operators/reshape_op.cc
@@ -327,14 +327,45 @@ class Reshape2GradOp : public framework::OperatorWithKernel {
}
};
+class ReshapeOpInplaceInToOut : public framework::InplaceInToOut {
+ public:
+ using InplaceInToOut::InplaceInToOut;
+
+ protected:
+ std::unordered_map<std::string, std::string> Apply(
+ const framework::OpDesc &op_desc,
+ framework::BlockDesc *block) const override {
+ std::unordered_map<std::string, std::string> inplace_in_to_out = {
+ {"X", "Out"},
+ };
+ return inplace_in_to_out;
+ }
+};
+
+class ReshapeGradInplaceInToOut : public framework::InplaceInToOut {
+ using InplaceInToOut::InplaceInToOut;
+
+ protected:
+ std::unordered_map<std::string, std::string> Apply(
+ const framework::OpDesc &op_desc,
+ framework::BlockDesc *block) const override {
+ std::unordered_map<std::string, std::string> inplace_in_to_out = {
+ {framework::GradVarName("Out"), framework::GradVarName("X")},
+ };
+ return inplace_in_to_out;
+ }
+};
+
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OPERATOR(reshape, ops::ReshapeOp, ops::ReshapeOpMaker,
- paddle::framework::DefaultGradOpDescMaker<true>);
-REGISTER_OPERATOR(reshape_grad, ops::ReshapeGradOp);
+ paddle::framework::DefaultGradOpDescMaker<true>,
+ ops::ReshapeOpInplaceInToOut);
+REGISTER_OPERATOR(reshape_grad, ops::ReshapeGradOp,
+ ops::ReshapeGradInplaceInToOut);
REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape, float, ops::ReshapeKernel, double,
ops::ReshapeKernel, int, ops::ReshapeKernel,
int64_t, ops::ReshapeKernel);
@@ -344,8 +375,9 @@ REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape_grad, float, ops::ReshapeGradKernel,
ops::ReshapeGradKernel);
REGISTER_OPERATOR(reshape2, ops::Reshape2Op, ops::Reshape2OpMaker,
- ops::Reshape2GradMaker);
-REGISTER_OPERATOR(reshape2_grad, ops::Reshape2GradOp);
+ ops::Reshape2GradMaker, ops::ReshapeOpInplaceInToOut);
+REGISTER_OPERATOR(reshape2_grad, ops::Reshape2GradOp,
+ ops::ReshapeGradInplaceInToOut);
REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape2, float, ops::ReshapeKernel, double,
ops::ReshapeKernel, int, ops::ReshapeKernel,
int64_t, ops::ReshapeKernel);
diff --git a/paddle/fluid/operators/scale_op.cc b/paddle/fluid/operators/scale_op.cc
index 981969d2aa..4ea77ed30d 100644
--- a/paddle/fluid/operators/scale_op.cc
+++ b/paddle/fluid/operators/scale_op.cc
@@ -100,13 +100,14 @@ class ScaleGradMaker : public framework::SingleGradOpDescMaker {
}
};
+using ScaleOpInplace = framework::SingleOpInplaceInToOut;
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(scale, ops::ScaleOp, ops::ScaleOpMaker, ops::ScaleGradMaker,
- ops::ScaleOpVarTypeInference);
+ ops::ScaleOpVarTypeInference, ops::ScaleOpInplace);
REGISTER_OP_CPU_KERNEL(
scale, ops::ScaleKernel<paddle::platform::CPUDeviceContext, float>,
ops::ScaleKernel<paddle::platform::CPUDeviceContext, double>,
diff --git a/paddle/fluid/operators/softmax_op.cc b/paddle/fluid/operators/softmax_op.cc
index bc889a5a04..8fbf299a7c 100644
--- a/paddle/fluid/operators/softmax_op.cc
+++ b/paddle/fluid/operators/softmax_op.cc
@@ -198,6 +198,21 @@ class SoftmaxOpGradMaker : public framework::SingleGradOpDescMaker {
return std::unique_ptr<framework::OpDesc>(op);
}
};
+
+class SoftmaxInplaceInToOut : public framework::InplaceInToOut {
+ public:
+ using framework::InplaceInToOut::InplaceInToOut;
+
+ protected:
+ std::unordered_map<std::string, std::string> Apply(
+ const framework::OpDesc& op_desc,
+ framework::BlockDesc* block) const override {
+ return std::unordered_map<std::string, std::string>{
+ {"X", "Out"},
+ };
+ }
+};
+
} // namespace operators
} // namespace paddle
diff --git a/paddle/fluid/platform/CMakeLists.txt b/paddle/fluid/platform/CMakeLists.txt
index 1f51b5bab3..fbb2ac3fe8 100644
--- a/paddle/fluid/platform/CMakeLists.txt
+++ b/paddle/fluid/platform/CMakeLists.txt
@@ -1,4 +1,4 @@
-proto_library(profiler_proto SRCS profiler.proto DEPS framework_proto)
+proto_library(profiler_proto SRCS profiler.proto DEPS framework_proto simple_threadpool)
py_proto_compile(profiler_py_proto SRCS profiler.proto)
add_custom_target(profiler_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
@@ -36,7 +36,7 @@ cc_test(cpu_info_test SRCS cpu_info_test.cc DEPS cpu_info)
nv_library(gpu_info SRCS gpu_info.cc DEPS gflags glog enforce)
-cc_library(place SRCS place.cc DEPS enforce boost)
+cc_library(place SRCS place.cc DEPS enforce boost lib_any)
cc_test(place_test SRCS place_test.cc DEPS place glog gflags)
add_subdirectory(dynload)
diff --git a/paddle/fluid/platform/cuda_device_function.h b/paddle/fluid/platform/cuda_device_function.h
index 2ce8f141d3..31b6c38d61 100644
--- a/paddle/fluid/platform/cuda_device_function.h
+++ b/paddle/fluid/platform/cuda_device_function.h
@@ -53,10 +53,12 @@ inline static int RoundToPowerOfTwo(int dim) {
__VA_ARGS__; \
} break
-#define CUDA_LAUNCH_KERNEL_HELPER(...) \
- CUDA_LAUNCH_KERNEL_BASE(256, ##__VA_ARGS__); \
- CUDA_LAUNCH_KERNEL_BASE(128, ##__VA_ARGS__); \
- CUDA_LAUNCH_KERNEL_BASE(64, ##__VA_ARGS__); \
+#define CUDA_LAUNCH_KERNEL_HELPER(...) \
+ CUDA_LAUNCH_KERNEL_BASE(1024, ##__VA_ARGS__); \
+ CUDA_LAUNCH_KERNEL_BASE(512, ##__VA_ARGS__); \
+ CUDA_LAUNCH_KERNEL_BASE(256, ##__VA_ARGS__); \
+ CUDA_LAUNCH_KERNEL_BASE(128, ##__VA_ARGS__); \
+ CUDA_LAUNCH_KERNEL_BASE(64, ##__VA_ARGS__); \
CUDA_LAUNCH_KERNEL_BASE(32, ##__VA_ARGS__);
template <typename T>
diff --git a/paddle/fluid/platform/ngraph_helper.h b/paddle/fluid/platform/ngraph_helper.h
index c5b65d6636..5ee985ea71 100644
--- a/paddle/fluid/platform/ngraph_helper.h
+++ b/paddle/fluid/platform/ngraph_helper.h
@@ -23,6 +23,26 @@ limitations under the License. */
namespace paddle {
namespace platform {
+std::shared_ptr<ngraph::Node> Nhwc2Nchw(std::shared_ptr<ngraph::Node> in) {
+ auto in_shape = in->get_shape();
+ in_shape[0] = in->get_shape()[0];
+ in_shape[1] = in->get_shape()[3];
+ in_shape[2] = in->get_shape()[1];
+ in_shape[3] = in->get_shape()[2];
+ ngraph::AxisVector axis_vec = {0, 3, 1, 2};
+ return std::make_shared<ngraph::op::Reshape>(in, axis_vec, in_shape);
+}
+
+std::shared_ptr<ngraph::Node> Nchw2Nhwc(std::shared_ptr<ngraph::Node> in) {
+ auto in_shape = in->get_shape();
+ in_shape[0] = in->get_shape()[0];
+ in_shape[1] = in->get_shape()[2];
+ in_shape[2] = in->get_shape()[3];
+ in_shape[3] = in->get_shape()[1];
+ ngraph::AxisVector axis_vec = {0, 2, 3, 1};
+ return std::make_shared<ngraph::op::Reshape>(in, axis_vec, in_shape);
+}
+
ngraph::Shape FlattenTo2d(ngraph::Shape sh, int num) {
auto x1 = std::accumulate(std::begin(sh), std::begin(sh) + num, 1,
std::multiplies<size_t>());
@@ -43,13 +63,14 @@ std::shared_ptr<ngraph::Node> NgReshaper(std::shared_ptr<ngraph::Node> input,
std::shared_ptr<ngraph::Node> GetNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
- const std::string prm, const paddle::framework::VariableNameMap& var_map,
+ const std::string name, const paddle::framework::VariableNameMap& var_map,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
- auto& var_names = var_map.at(prm);
+ auto& var_names = var_map.at(name);
PADDLE_ENFORCE_EQ(var_names.size(), 1,
- "op %s prm %s expects one associated var", op->Type(), prm);
+ "op %s name %s expects one associated var", op->Type(),
+ name);
if (ngb_node_map->find(var_names[0]) != ngb_node_map->end()) {
return (*ngb_node_map)[var_names[0]];
} else {
@@ -59,43 +80,53 @@ std::shared_ptr<ngraph::Node> GetNode(
std::shared_ptr<ngraph::Node> GetInputNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
- const std::string prm,
+ const std::string name,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
- return GetNode(op, prm, op->Inputs(), ngb_node_map);
+ return GetNode(op, name, op->Inputs(), ngb_node_map);
}
std::shared_ptr<ngraph::Node> GetOutputNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
- const std::string prm,
+ const std::string name,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
- return GetNode(op, prm, op->Outputs(), ngb_node_map);
+ return GetNode(op, name, op->Outputs(), ngb_node_map);
}
void SetOutputNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
- const std::string prm, std::shared_ptr<ngraph::Node> node,
+ const std::string name, std::shared_ptr<ngraph::Node> node,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
- auto& var_names = op->Outputs().at(prm);
+ auto& var_names = op->Outputs().at(name);
if (var_names.size() == 1) {
+ /* */
+ auto dummy_out = GetOutputNode(op, name, ngb_node_map);
+ if (dummy_out && dummy_out->get_shape() != node->get_shape()) {
+ node = NgReshaper(node, dummy_out->get_shape());
+ }
+ if (dummy_out &&
+ dummy_out->get_element_type() != node->get_element_type()) {
+ node = std::make_shared<ngraph::op::Convert>(
+ node, dummy_out->get_element_type());
+ }
(*ngb_node_map)[var_names[0]] = node;
} else if (var_names.size() == 0) {
(*ngb_node_map)[""] = node;
} else {
- PADDLE_THROW("prm %s has more than 1 var_names.", prm);
+ PADDLE_THROW("name %s has more than 1 var_names.", name);
}
}
bool HasOutput(const std::shared_ptr<paddle::framework::OperatorBase>& op,
- const std::string prm) {
+ const std::string name) {
auto& outputs = op->Outputs();
- if (outputs.find(prm) == outputs.end()) return false;
- return outputs.at(prm).size() > 0;
+ if (outputs.find(name) == outputs.end()) return false;
+ return outputs.at(name).size() > 0;
}
inline void GetMidDims(const ngraph::Shape& x_shape,
diff --git a/paddle/fluid/platform/place.cc b/paddle/fluid/platform/place.cc
index 655ce8485d..60b2d83f15 100644
--- a/paddle/fluid/platform/place.cc
+++ b/paddle/fluid/platform/place.cc
@@ -14,6 +14,12 @@ limitations under the License. */
#include "paddle/fluid/platform/place.h"
+DEFINE_bool(benchmark, false,
+ "Doing memory benchmark. It will make deleting scope synchronized, "
+ "and add some memory usage logs."
+ "Default cuda is asynchronous device, set to True will"
+ "force op run in synchronous mode.");
+
namespace paddle {
namespace platform {
diff --git a/paddle/fluid/pybind/CMakeLists.txt b/paddle/fluid/pybind/CMakeLists.txt
index 803ea6b260..4ac5b83c56 100644
--- a/paddle/fluid/pybind/CMakeLists.txt
+++ b/paddle/fluid/pybind/CMakeLists.txt
@@ -26,5 +26,5 @@ if(WITH_PYTHON)
get_property (os_dependency_modules GLOBAL PROPERTY OS_DEPENDENCY_MODULES)
target_link_libraries(paddle_pybind ${os_dependency_modules})
- cc_test(tensor_py_test SRCS tensor_py_test.cc DEPS python)
+ cc_test(tensor_py_test SRCS tensor_py_test.cc DEPS python pybind)
endif(WITH_PYTHON)
diff --git a/paddle/fluid/pybind/inference_api.cc b/paddle/fluid/pybind/inference_api.cc
index 39e47be606..7db2bb451b 100644
--- a/paddle/fluid/pybind/inference_api.cc
+++ b/paddle/fluid/pybind/inference_api.cc
@@ -74,12 +74,12 @@ void BindPaddleBuf(py::module *m) {
.def(py::init([](std::vector<float> &data) {
auto buf = PaddleBuf(data.size() * sizeof(float));
std::memcpy(buf.data(), static_cast<void *>(data.data()), buf.length());
- return std::move(buf);
+ return buf;
}))
.def(py::init([](std::vector<int64_t> &data) {
auto buf = PaddleBuf(data.size() * sizeof(int64_t));
std::memcpy(buf.data(), static_cast<void *>(data.data()), buf.length());
- return std::move(buf);
+ return buf;
}))
.def("resize", &PaddleBuf::Resize)
.def("reset",
diff --git a/paddle/fluid/pybind/pybind.cc b/paddle/fluid/pybind/pybind.cc
index adbe1dcf4d..a4a01ad647 100644
--- a/paddle/fluid/pybind/pybind.cc
+++ b/paddle/fluid/pybind/pybind.cc
@@ -37,6 +37,7 @@ limitations under the License. */
#include "paddle/fluid/framework/version.h"
#include "paddle/fluid/imperative/layer.h"
#include "paddle/fluid/memory/allocation/allocator_strategy.h"
+#include "paddle/fluid/memory/allocation/legacy_allocator.h"
#include "paddle/fluid/operators/activation_op.h"
#include "paddle/fluid/operators/py_func_op.h"
#include "paddle/fluid/operators/reader/lod_tensor_blocking_queue.h"
@@ -127,6 +128,13 @@ PYBIND11_MODULE(core, m) {
m.add_object("_cleanup",
py::capsule([]() { ScopePool::Instance().Clear(); }));
+ m.def("get_mem_usage", [](int device) {
+ return memory::allocation::GPUMemMonitor.GetMemUsage(device);
+ });
+
+ m.def("print_mem_usage",
+ []() { return memory::allocation::GPUMemMonitor.PrintMemUsage(); });
+
py::class_<imperative::VarBase>(m, "VarBase", R"DOC()DOC")
// .def(py::init<>())
.def(py::init<bool>(), py::arg("stop_gradient") = false)
@@ -287,6 +295,7 @@ PYBIND11_MODULE(core, m) {
.def("_get_float_element", TensorGetElement<float>)
.def("_set_double_element", TensorSetElement<double>)
.def("_get_double_element", TensorGetElement<double>)
+ .def("_place", [](Tensor &self) { return self.place(); })
.def("_dtype", [](Tensor &self) { return self.type(); });
py::class_<LoDTensor, Tensor>(m, "LoDTensor", R"DOC(
@@ -665,6 +674,12 @@ All parameter, weight, gradient are variables in Paddle.
py::class_<platform::Place>(m, "Place")
.def(py::init<>())
+ .def("is_gpu_place",
+ [](platform::Place &self) { return platform::is_gpu_place(self); })
+ .def("gpu_device_id",
+ [](platform::Place &self) {
+ return boost::get<platform::CUDAPlace>(self).device;
+ })
.def("set_place",
[](platform::Place &self, const platform::CPUPlace &cpu_place) {
self = cpu_place;
@@ -1082,9 +1097,9 @@ All parameter, weight, gradient are variables in Paddle.
[](const BuildStrategy &self) { return self.is_distribution_; },
[](BuildStrategy &self, bool b) { self.is_distribution_ = b; })
.def_property(
- "memory_early_delete",
- [](const BuildStrategy &self) { return self.memory_early_delete_; },
- [](BuildStrategy &self, bool b) { self.memory_early_delete_ = b; })
+ "enable_inplace",
+ [](const BuildStrategy &self) { return self.enable_inplace_; },
+ [](BuildStrategy &self, bool b) { self.enable_inplace_ = b; })
.def("_finalize_strategy_and_create_passes",
[](BuildStrategy &self) -> std::shared_ptr<ir::PassBuilder> {
return self.CreatePassesFromStrategy(true);
diff --git a/paddle/scripts/fast_install.sh b/paddle/scripts/fast_install.sh
new file mode 100644
index 0000000000..b960d0f00a
--- /dev/null
+++ b/paddle/scripts/fast_install.sh
@@ -0,0 +1,923 @@
+#!/bin/bash
+
+path='http://paddlepaddle.org/download?url='
+#release_version=`curl -s https://pypi.org/project/paddlepaddle/|grep -E "/project/paddlepaddle/"|grep "release"|awk -F '/' '{print $(NF-1)}'|head -1`
+release_version=1.2.0
+python_list=(
+"27"
+"35"
+"36"
+"37"
+)
+
+
+function use_cpu(){
+ while true
+ do
+ read -p "是否安装CPU版本的PaddlePaddle？(y/n)" cpu_option
+ cpu_option=`echo $cpu_option | tr 'A-Z' 'a-z'`
+ if [[ "$cpu_option" == "" || "$cpu_option" == "n" ]];then
+ echo "退出安装中..."
+ exit
+ else
+ GPU='cpu'
+ echo "将为您安装CPU版本的PaddlePaddle"
+ break
+ fi
+ done
+}
+
+function checkLinuxCUDNN(){
+ echo
+ read -n1 -p "请按回车键进行下一步..."
+ echo
+ while true
+ do
+ version_file='/usr/local/cuda/include/cudnn.h'
+ if [ -f "$version_file" ];then
+ CUDNN=`cat $version_file | grep CUDNN_MAJOR |awk 'NR==1{print $NF}'`
+ fi
+ if [ "$CUDNN" == "" ];then
+ version_file=`sudo find /usr -name "cudnn.h"|head -1`
+ if [ "$version_file" != "" ];then
+ CUDNN=`cat ${version_file} | grep CUDNN_MAJOR -A 2|awk 'NR==1{print $NF}'`
+ else
+ echo "检测结果：未在常规路径下找到cuda/include/cudnn.h文件"
+ while true
+ do
+ read -p "请核实cudnn.h位置，并在此输入路径（请注意，路径需要输入到“cudnn.h”这一级）:" cudnn_version
+ echo
+ if [ "$cudnn_version" == "" ] || [ ! -f "$cudnn_version" ];then
+ read -p "仍未找到cuDNN，输入y将安装CPU版本的PaddlePaddle，输入n可重新录入cuDNN路径，请输入（y/n）" cpu_option
+ echo
+ cpu_option=`echo $cpu_option | tr 'A-Z' 'a-z'`
+ if [ "$cpu_option" == "y" -o "$cpu_option" == "" ];then
+ GPU='cpu'
+ break
+ else
+ echo "请重新输入"
+ echo
+ fi
+ else
+ CUDNN=`cat $cudnn_version | grep CUDNN_MAJOR |awk 'NR==1{print $NF}'`
+ echo "检测结果：找到cudnn.h"
+ break
+ fi
+ done
+ if [ "$GPU" == "cpu" ];then
+ break
+ fi
+ fi
+ fi
+ if [ "$CUDA" == "9" -a "$CUDNN" != "7" ];then
+ echo
+ echo "目前CUDA9下仅支持cuDNN7，暂不支持您机器上的CUDNN${CUDNN}。您可以访问NVIDIA官网下载适合版本的CUDNN，请ctrl+c退出安装进程。按回车键将为您安装CPU版本的PaddlePaddle"
+ echo
+ use_cpu()
+ if [ "$GPU"=="cpu" ];then
+ break
+ fi
+ fi
+
+ if [ "$CUDNN" == 5 ] || [ "$CUDNN" == 7 ];then
+ echo
+ echo "您的CUDNN版本是: CUDNN$CUDNN"
+ break
+ else
+ echo
+ read -n1 -p "目前支持的CUDNN版本为5和7,暂不支持您机器上的CUDNN${CUDNN}，将为您安装CPU版本的PaddlePaddle,请按回车键开始安装"
+ echo
+ use_cpu
+ if [ "$GPU"=="cpu" ];then
+ break
+ fi
+ fi
+ done
+}
+
+function checkLinuxCUDA(){
+ while true
+ do
+ CUDA=`echo ${CUDA_VERSION}|awk -F "[ .]" '{print $1}'`
+ if [ "$CUDA" == "" ];then
+ if [ -f "/usr/local/cuda/version.txt" ];then
+ CUDA=`cat /usr/local/cuda/version.txt | grep 'CUDA Version'|awk -F '[ .]' '{print $3}'`
+ tmp_cuda=$CUDA
+ fi
+ if [ -f "/usr/local/cuda8/version.txt" ];then
+ CUDA=`cat /usr/local/cuda8/version.txt | grep 'CUDA Version'|awk -F '[ .]' '{print $3}'`
+ tmp_cuda8=$CUDA
+ fi
+ if [ -f "/usr/local/cuda9/version.txt" ];then
+ CUDA=`cat /usr/local/cuda9/version.txt | grep 'CUDA Version'|awk -F '[ .]' '{print $3}'`
+ tmp_cuda9=$CUDA
+ fi
+ fi
+
+ if [ "$tmp_cuda" != "" ];then
+ echo "检测结果：找到CUDA $tmp_cuda"
+ fi
+ if [ "$tmp_cudai8" != "" ];then
+ echo "检测结果：找到CUDA $tmp_cuda8"
+ fi
+ if [ "$tmp_cuda9" != "" ];then
+ echo "检测结果：找到CUDA $tmp_cuda9"
+ fi
+
+ if [ "$CUDA" == "" ];then
+ echo "检测结果：没有在常规路径下找到cuda/version.txt文件"
+ while true
+ do
+ read -p "请输入cuda/version.txt的路径:" cuda_version
+ if [ "$cuda_version" == "" || ! -f "$cuda_version" ];then
+ read -p "仍未找到CUDA，输入y将安装CPU版本的PaddlePaddle，输入n可重新录入CUDA路径，请输入（y/n）" cpu_option
+ cpu_option=`echo $cpu_option | tr 'A-Z' 'a-z'`
+ if [ "$cpu_option" == "y" || "$cpu_option" == "" ];then
+ GPU='cpu'
+ break
+ else
+ echo "重新输入..."
+ fi
+ else
+ CUDA=`cat $cuda_version | grep 'CUDA Version'|awk -F '[ .]' '{print $3}'`
+ if [ "$CUDA" == "" ];then
+ echo "未能在version.txt中找到CUDA相关信息"
+ else
+ break
+ fi
+ fi
+ done
+ if [ "$GPU" == "cpu" ];then
+ break
+ fi
+ fi
+
+ if [ "$CUDA" == "8" ] || [ "$CUDA" == "9" ];then
+ echo "您的CUDA版本是${CUDA}"
+ break
+ else
+ echo "目前支持CUDA8/9，暂不支持您的CUDA${CUDA}，将为您安装CPU版本的PaddlePaddle"
+ echo
+ use_cpu
+ fi
+
+ if [ "$GPU" == "cpu" ];then
+ break
+ fi
+ done
+}
+
+function checkLinuxMathLibrary(){
+ while true
+ do
+ if [ "$AVX" == "" ];then
+ echo "正在检测您环境中是否存在AVX指令集..."
+ echo
+ echo "检测结果：您电脑上没有AVX指令集，目前针对无AVX指令集的环境，我们仅提供支持mkl数学库的PaddlePaddle，将为您安装此版本的PaddlePaddle"
+ math='mkl'
+ break
+ elif [ "$GPU" == "gpu" ];then
+ math='mkl'
+ echo "检测到您的机器上配备GPU，推荐您使用mkl数学库"
+ break
+ else
+ read -p "请输入您希望使用的数学库：
+ 1：openblas 一个高性能多核 BLAS 库
+ 2：mkl（推荐） 英特尔数学核心函数库
+ => 请输入数字1或2。如输入其他字符或直接回车，将会默认选择【 2. mkl 】 。请在这里输入并回车：" math
+ if [ "$math" == "" ];then
+ math="mkl"
+ echo "您选择了数字【2】"
+ break
+ fi
+ if [ "$math" == "1" ];then
+ math=openblas
+ echo "您选择了数字【1】"
+ break
+ elif [ "$math" == "2" ];then
+ math=mkl
+ echo "您选择了数字【2】"
+ break
+ fi
+ echo "输入错误，请再次输入"
+ fi
+ done
+}
+
+function checkLinuxPaddleVersion(){
+ read -n1 -p "请按回车键继续..."
+ while true
+ do
+ read -p "
+ 1. 开发版：对应Github上develop分支，如您需要开发、或希望使用PaddlePaddle最新功能，请选用此版本
+ 2. 稳定版（推荐）：如您无特殊开发需求，建议使用此版本，目前最新的版本号为 ${release_version}
+ => 请输入数字1或2。如输入其他字符或直接回车，将会默认选择【 2. 稳定版 】 。请在这里输入并回车：" paddle_version
+ if [ "$paddle_version" == "" ];then
+ paddle_version="release-${release_version}"
+ echo "您选择了数字【2】，为您安装release-${release_version}"
+ break
+ fi
+ if [ "$paddle_version" == "1" ];then
+ echo "您选择了数字【1】，将为您安装开发版"
+ break
+ elif [ "$paddle_version" == "2" ];then
+ echo "您选择了数字【2】，为您安装release-${release_version}"
+ break
+ fi
+ echo "输入错误，请再次输入"
+ done
+}
+
+function checkLinuxPip(){
+ while true
+ do
+ echo "请输入您要使用的pip目录（您可以另起终端，并使用which pip来查看）："
+ read -p "" pip_path
+ if [ "$pip_path" == "" -o ! -f "$pip_path" ];then
+ echo "检测结果：pip不存在,请重新输入"
+ continue
+ fi
+ python_version=`$pip_path --version|awk -F "[ |)]" '{print $6}'|sed 's#\.##g'`
+ if [ "$python_version" == "27" ];then
+ uncode=`python -c "import pip._internal;print(pip._internal.pep425tags.get_supported())"|grep "cp27mu"`
+ if [[ "$uncode" == "" ]];then
+ uncode=
+ else
+ uncode=u
+ fi
+ fi
+ if [ "$python_version" == "" ];then
+ echo "检测结果：pip不存在,请重新输入"
+ else
+ version_list=`echo "${python_list[@]}" | grep "$python_version" `
+ if [ "$version_list" != "" ];then
+ echo "检测结果：找到python${python_version}版本"
+ break
+ else
+ echo "检测结果：找不到可用的 pip, 我们只支持Python27/35/36/37及其对应的pip, 请重新输入， 或使用ctrl + c退出 "
+ fi
+ fi
+ done
+}
+
+function checkLinuxAVX(){
+ while true
+ do
+ if [[ "$AVX" != "" ]];then
+ AVX="avx"
+ break
+ else
+ if [ "$CUDA" == "8" -a "$CUDNN" == "7" ] || [ "$GPU" == "cpu" ];then
+ AVX="noavx"
+ break
+ else
+ echo "Step 6. 检测是否有avx"
+ echo
+ echo "检测结果：未能找到avx，我们仅提供CPU版本或配置为CUDA8 cuDNN7的GPU版本的安装包"
+ break
+ fi
+ fi
+ done
+}
+
+function PipLinuxInstall(){
+ wheel_cpu_release="http://paddle-wheel.bj.bcebos.com/${release_version}-${GPU}-${AVX}-${math}/paddlepaddle-${release_version}-cp${python_version}-cp${python_version}m${uncode}-linux_x86_64.whl"
+ wheel_gpu_release="http://paddle-wheel.bj.bcebos.com/${release_version}-gpu-cuda${CUDA}-cudnn${CUDNN}-${AVX}-${math}/paddlepaddle_gpu-${release_version}.post${CUDA}${CUDNN}-cp${python_version}-cp${python_version}m${uncode}-linux_x86_64.whl"
+ wheel_gpu_release_noavx="http://paddle-wheel.bj.bcebos.com/${release_version}-gpu-cuda${CUDA}-cudnn${CUDNN}-${AVX}-${math}/paddlepaddle_gpu-${release_version}-cp${python_version}-cp${python_version}m${uncode}-linux_x86_64.whl"
+ wheel_cpu_develop="http://paddle-wheel.bj.bcebos.com/latest-cpu-${AVX}-${math}/paddlepaddle-latest-cp${python_version}-cp${python_version}m${uncode}-linux_x86_64.whl"
+ wheel_gpu_develop="http://paddle-wheel.bj.bcebos.com/latest-gpu-cuda${CUDA}-cudnn${CUDNN}-${AVX}-${math}/paddlepaddle_gpu-latest-cp${python_version}-cp${python_version}m${uncode}-linux_x86_64.whl"
+
+
+ if [[ "$paddle_version" == "2" ]];then
+ if [[ "$GPU" == "gpu" ]];then
+ if [[ ${AVX} == "avx" ]];then
+ rm -rf `echo $wheel_gpu_release|awk -F '/' '{print $NF}'`
+ wget -q $wheel_gpu_release
+ if [ "$?" == "0" ];then
+ $pip_path install --user -i https://mirrors.aliyun.com/pypi/simple --trusted-host=mirrors.aliyun.com $wheel_gpu_release
+ else
+ echo "paddlepaddle whl包下载失败"
+ exit 1
+ fi
+ else
+ rm -rf `echo $wheel_gpu_release_novax|awk -F '/' '{print $NF}'`
+ wget -q $wheel_gpu_release_novax
+ if [ "$?" == "0" ];then
+ $pip_path install --user -i https://mirrors.aliyun.com/pypi/simple --trusted-host=mirrors.aliyun.com $wheel_gpu_release_noavx
+ else
+ echo "paddlepaddle whl包下载失败"
+ exit 1
+ fi
+ fi
+ else
+ rm -rf `echo $wheel_cpu_release|awk -F '/' '{print $NF}'`
+ wget -q $wheel_cpu_release
+ if [ "$?" == "0" ];then
+ $pip_path install --user -i https://mirrors.aliyun.com/pypi/simple --trusted-host=mirrors.aliyun.com $wheel_cpu_release
+ else
+ echo "paddlepaddle whl包下载失败"
+ exit 1
+ fi
+ fi
+ else
+ if [[ "$GPU" == "gpu" ]];then
+ rm -rf `echo $wheel_gpu_develop|awk -F '/' '{print $NF}'`
+ wget -q $wheel_gpu_develop
+ if [ "$?" == "0" ];then
+ $pip_path install --user -i https://mirrors.aliyun.com/pypi/simple --trusted-host=mirrors.aliyun.com $wheel_gpu_develop
+ else
+ echo "paddlepaddle whl包下载失败"
+ exit 1
+ fi
+ else
+ rm -rf `echo $wheel_cpu_develop|awk -F '/' '{print $NF}'`
+ wget -q $wheel_cpu_develop
+ if [ "$?" == "0" ];then
+ $pip_path install --user -i https://mirrors.aliyun.com/pypi/simple --trusted-host=mirrors.aliyun.com $wheel_cpu_develop
+ else
+ echo "paddlepaddle whl包下载失败"
+ exit 1
+ fi
+ fi
+ fi
+}
+
+
+function checkLinuxGPU(){
+ read -n1 -p "即将检测您的机器是否含GPU，请按回车键继续..."
+ echo
+ AVX=`cat /proc/cpuinfo |grep avx|tail -1|grep avx`
+ which nvidia-smi >/dev/null 2>&1
+ if [ "$?" != "0" ];then
+ GPU='cpu'
+ echo "未在机器上找到GPU，或PaddlePaddle暂不支持此型号的GPU"
+ else
+ GPU='gpu'
+ echo "已在您的机器上找到GPU，即将确认CUDA和CUDNN版本..."
+ echo
+ fi
+ if [ "$GPU" == 'gpu' ];then
+ checkLinuxCUDA
+ checkLinuxCUDNN
+ fi
+}
+
+function linux(){
+gpu_list=(
+"GeForce 410M"
+"GeForce 610M"
+"GeForce 705M"
+"GeForce 710M"
+"GeForce 800M"
+"GeForce 820M"
+"GeForce 830M"
+"GeForce 840M"
+"GeForce 910M"
+"GeForce 920M"
+"GeForce 930M"
+"GeForce 940M"
+"GeForce GT 415M"
+"GeForce GT 420M"
+"GeForce GT 430"
+"GeForce GT 435M"
+"GeForce GT 440"
+"GeForce GT 445M"
+"GeForce GT 520"
+"GeForce GT 520M"
+"GeForce GT 520MX"
+"GeForce GT 525M"
+"GeForce GT 540M"
+"GeForce GT 550M"
+"GeForce GT 555M"
+"GeForce GT 610"
+"GeForce GT 620"
+"GeForce GT 620M"
+"GeForce GT 625M"
+"GeForce GT 630"
+"GeForce GT 630M"
+"GeForce GT 635M"
+"GeForce GT 640"
+"GeForce GT 640 (GDDR5)"
+"GeForce GT 640M"
+"GeForce GT 640M LE"
+"GeForce GT 645M"
+"GeForce GT 650M"
+"GeForce GT 705"
+"GeForce GT 720"
+"GeForce GT 720M"
+"GeForce GT 730"
+"GeForce GT 730M"
+"GeForce GT 735M"
+"GeForce GT 740"
+"GeForce GT 740M"
+"GeForce GT 745M"
+"GeForce GT 750M"
+"GeForce GTS 450"
+"GeForce GTX 1050"
+"GeForce GTX 1060"
+"GeForce GTX 1070"
+"GeForce GTX 1080"
+"GeForce GTX 1080 Ti"
+"GeForce GTX 460"
+"GeForce GTX 460M"
+"GeForce GTX 465"
+"GeForce GTX 470"
+"GeForce GTX 470M"
+"GeForce GTX 480"
+"GeForce GTX 480M"
+"GeForce GTX 485M"
+"GeForce GTX 550 Ti"
+"GeForce GTX 560M"
+"GeForce GTX 560 Ti"
+"GeForce GTX 570"
+"GeForce GTX 570M"
+"GeForce GTX 580"
+"GeForce GTX 580M"
+"GeForce GTX 590"
+"GeForce GTX 650"
+"GeForce GTX 650 Ti"
+"GeForce GTX 650 Ti BOOST"
+"GeForce GTX 660"
+"GeForce GTX 660M"
+"GeForce GTX 660 Ti"
+"GeForce GTX 670"
+"GeForce GTX 670M"
+"GeForce GTX 670MX"
+"GeForce GTX 675M"
+"GeForce GTX 675MX"
+"GeForce GTX 680"
+"GeForce GTX 680M"
+"GeForce GTX 680MX"
+"GeForce GTX 690"
+"GeForce GTX 750"
+"GeForce GTX 750 Ti"
+"GeForce GTX 760"
+"GeForce GTX 760M"
+"GeForce GTX 765M"
+"GeForce GTX 770"
+"GeForce GTX 770M"
+"GeForce GTX 780"
+"GeForce GTX 780M"
+"GeForce GTX 780 Ti"
+"GeForce GTX 850M"
+"GeForce GTX 860M"
+"GeForce GTX 870M"
+"GeForce GTX 880M"
+"GeForce GTX 950"
+"GeForce GTX 950M"
+"GeForce GTX 960"
+"GeForce GTX 960M"
+"GeForce GTX 965M"
+"GeForce GTX 970"
+"GeForce GTX 970M"
+"GeForce GTX 980"
+"GeForce GTX 980M"
+"GeForce GTX 980 Ti"
+"GeForce GTX TITAN"
+"GeForce GTX TITAN Black"
+"GeForce GTX TITAN X"
+"GeForce GTX TITAN Z"
+"Jetson TK1"
+"Jetson TX1"
+"Jetson TX2"
+"Mobile Products"
+"NVIDIA NVS 310"
+"NVIDIA NVS 315"
+"NVIDIA NVS 510"
+"NVIDIA NVS 810"
+"NVIDIA TITAN V"
+"NVIDIA TITAN X"
+"NVIDIA TITAN Xp"
+"NVS 4200M"
+"NVS 5200M"
+"NVS 5400M"
+"Quadro 410"
+"Quadro GP100"
+"Quadro K1100M"
+"Quadro K1200"
+"Quadro K2000"
+"Quadro K2000D"
+"Quadro K2100M"
+"Quadro K2200"
+"Quadro K2200M"
+"Quadro K3100M"
+"Quadro K4000"
+"Quadro K4100M"
+"Quadro K420"
+"Quadro K4200"
+"Quadro K4200M"
+"Quadro K5000"
+"Quadro K500M"
+"Quadro K5100M"
+"Quadro K510M"
+"Quadro K5200"
+"Quadro K5200M"
+"Quadro K600"
+"Quadro K6000"
+"Quadro K6000M"
+"Quadro K610M"
+"Quadro K620"
+"Quadro K620M"
+"Quadro M1000M"
+"Quadro M1200"
+"Quadro M2000"
+"Quadro M2000M"
+"Quadro M2200"
+"Quadro M3000M"
+"Quadro M4000"
+"Quadro M4000M"
+"Quadro M5000"
+"Quadro M5000M"
+"Quadro M500M"
+"Quadro M520"
+"Quadro M5500M"
+"Quadro M6000"
+"Quadro M6000 24GB"
+"Quadro M600M"
+"Quadro M620"
+"Quadro Mobile Products"
+"Quadro P1000"
+"Quadro P2000"
+"Quadro P3000"
+"Quadro P400"
+"Quadro P4000"
+"Quadro P5000"
+"Quadro P600"
+"Quadro P6000"
+"Quadro Plex 7000"
+"Tegra K1"
+"Tegra X1"
+"Tesla C2050/C2070"
+"Tesla C2075"
+"Tesla Data Center Products"
+"Tesla K10"
+"Tesla K20"
+"Tesla K40"
+"Tesla K80"
+"Tesla M40"
+"Tesla M60"
+"Tesla P100"
+"Tesla P4"
+"Tesla P40"
+"Tesla V100")
+
+ echo "Step 2. 检测GPU型号和CUDA/cuDNN版本"
+ echo
+ checkLinuxGPU
+ echo
+ echo "Step 3. 检测数学库"
+ echo
+ checkLinuxMathLibrary
+ echo
+ echo "Step 4. 选择要安装的PaddlePaddle版本"
+ echo
+ checkLinuxPaddleVersion
+ echo
+ echo "Step 5. 检测pip版本"
+ echo
+ checkLinuxPip
+ echo
+ checkLinuxAVX
+ echo "*********************2. 开始安装*****************************"
+ PipLinuxInstall
+}
+
+function checkMacPython2(){
+ while true
+ do
+ read -p "
+ => 未能在常规路径下找到Python2，请使用ctrl+c命令退出安装程序，并使用brew或pypi.org下载安装Python2（注意Python版本不能低于2.7.15）
+ 如希望自定义Python路径，请输入路径：" python_root
+ echo
+ python_version=`$python_root --version 2>&1 1>&1`
+ if [ $? == "0" ];then
+ :
+ else
+ python_version=""
+ fi
+ check_python=`echo $python_version | grep "Python 2"`
+ if [ "$python_version" == "" ] || [ "$python_root" == "/usr/bin/python" -a "$python_version" == "Python 2.7.10" ] ;then
+ python_version=""
+ elif [ -n "$check_python" ];then
+ while true
+ do
+ read -p "
+ => 在您的环境中找到 $python_version, 确认使用此版本请输入y；如您希望自定义Python路径请输入n。请在这里输入（y/n）并回车: " use_python
+ echo
+ use_python=`echo $use_python | tr 'A-Z' 'a-z'`
+ if [ "$use_python" == "y" ]||[ "$use_python" == "" ];then
+ use_python="y"
+ break
+ elif [ "$use_python" == "n" ];then
+ python_root=""
+ break
+ else
+ echo "输入错误，请重新输入(y/n)"
+ fi
+ done
+ if [ "$use_python" == "y" ];then
+ break
+ fi
+ else
+ echo "您输入Python的不是Python2"
+ python_version=""
+ fi
+ done
+}
+
+function checkMacPython3(){
+ while true
+ do
+ read -p "
+ => 未能在常规路径下找到Python3，请使用ctrl+c命令退出安装程序，并使用brew或pypi.org下载Python3
+ 如希望自定义Python路径，请输入路径：" python_root
+ python_version=`$python_root --version 2>&1 1>&1`
+ if [ $? == "0" ];then
+ :
+ else
+ python_version=""
+ fi
+ check_python=`echo $python_version | grep "Python 3"`
+ if [ "$python_version" == "" ] || [ "$python_root" == "/usr/bin/python" -a "$python_version" == "Python 2.7.10" ] ;then
+ python_version=""
+ elif [ -n "$check_python" ] ;then
+ while true
+ do
+ read -p "
+ => 在您的环境中找到 $python_version, 确认使用此版本请输入y；如您希望自定义Python路径请输入n。请在这里输入（y/n）并回车: " use_python
+ echo
+ use_python=`echo $use_python | tr 'A-Z' 'a-z'`
+ if [ "$use_python" == "y" ]||[ "$use_python" == "" ];then
+ use_python="y"
+ break
+ elif [ "$use_python" == "n" ];then
+ python_root=""
+ break
+ else
+ echo "输入错误，请重新输入(y/n)"
+ fi
+ done
+ if [ "$use_python" == "y" ];then
+ break
+ fi
+ else
+ echo "您输入Python的不是Python3"
+ python_version=""
+ fi
+ done
+}
+
+function checkMacPaddleVersion(){
+ while true
+ do
+ read -n1 -p "Step 2. 选择PaddlePaddle的版本，请按回车键继续..."
+ echo
+ read -p "
+ 1. 开发版：对应Github上develop分支，如您需要开发、或希望使用PaddlePaddle最新功能，请选用此版本
+ 2. 稳定版（推荐）：如您无特殊开发需求，建议使用此版本，目前最新的版本号为 ${release_version}
+
+ => 请输入数字1或2。如输入其他字符或直接回车，将会默认选择【 2. 稳定版 】 。请在这里输入并回车：" paddle_version
+ if [ "$paddle_version" == "1" ]||[ "$paddle_version" == "2" ];then
+ echo
+ echo "您选择了数字【"$paddle_version" 】"
+ echo
+ break
+ else
+ paddle_version="2"
+ echo
+ echo "您选择了数字【2】"
+ echo
+ break
+ fi
+ done
+}
+
+function checkMacPythonVersion(){
+ while true
+ do
+ read -n1 -p "Step 3. 选择Python版本，请按回车键继续..."
+ read -p "
+ 2. 使用python 2.x
+ 3. 使用python 3.x
+
+ => 请输入数字2或3。如输入其他字符或直接回车，将会默认使用【Python 2 】。请在这里输入并回车：" python_V
+ echo
+ if [ "$python_V" == "" ];then
+ python_V="2"
+ fi
+ echo "您选择了数字【"$python_V"】，正在寻找符合您要求的Python版本，请按回车键继续..."
+ echo
+ if [ "$python_V" == "2" ];then
+ python_root=`which python2.7`
+ if [ "$python_root" == "" ];then
+ python_root=`which python`
+ fi
+ python_version=`$python_root --version 2>&1 1>&1`
+ if [ $? == "0" ];then
+ :
+ else
+ python_version=""
+ fi
+ if [ "$python_root" == "" ]||[ "$python_root" == "/usr/bin/python" -a "$python_version" == "Python 2.7.10" ]||[ "$python_root" == "/usr/bin/python2.7" -a "$python_version" == "Python 2.7.10" ];then
+ checkMacPython2
+ fi
+ while true
+ do
+ read -p "
+ => 在您的环境中找到 $python_version, 确认使用此版本请输入y；如您希望自定义Python路径请输入n。请在这里输入（y/n）并回车：" use_python
+ echo
+ use_python=`echo $use_python | tr 'A-Z' 'a-z'`
+ if [ "$use_python" == "y" ]||[ "$use_python" == "" ];then
+ break
+ elif [ "$use_python" == "n" ];then
+ python_root=""
+ checkMacPython2
+ break
+ else
+ echo "输入错误，请重新输入(y/n)"
+ fi
+ done
+
+ elif [ "$python_V" == "3" ];then
+ python_root=`which python3`
+ python_version=`$python_root --version 2>&1 1>&1`
+ if [ $? == "0" ];then
+ :
+ else
+ python_version=""
+ fi
+ if [ "$python_root" == "" ]||[ "$python_root" == "/usr/bin/python" -a "$python_version" == "Python 2.7.10" ];then
+ checkMacPython3
+ fi
+ while true
+ do
+ read -p "
+ => 在您的环境中找到 $python_version, 确认使用此版本请输入y；如您希望自定义Python路径请输入n。请在这里输入（y/n）并回车：" use_python
+ echo
+ use_python=`echo $use_python | tr 'A-Z' 'a-z'`
+ if [ "$use_python" == "y" ]||[ "$use_python" == "" ];then
+ break
+ elif [ "$use_python" == "n" ];then
+ checkMacPython3
+ break
+ else
+ echo "输入错误，请重新输入(y/n)"
+ fi
+ done
+ else
+ :
+ fi
+
+
+ if [ "$python_V" == "2" ]||[ "$python_V" == "3" ];then
+ python_brief_version=`$python_root -m pip -V |awk -F "[ |)]" '{print $6}'|sed 's#\.##g'`
+ if [[ $python_brief_version == "27" ]];then
+ uncode=`python -c "import pip._internal;print(pip._internal.pep425tags.get_supported())"|grep "cp27"`
+ if [[ $uncode == "" ]];then
+ uncode="mu"
+ else
+ uncode="m"
+ fi
+ fi
+ version_list=`echo "${python_list[@]}" | grep "$python_brief_version" `
+ if [ "$version_list" != "" ];then
+ break
+ else
+ echo "未找到可用的pip或pip3。PaddlePaddle目前支持：Python2.7/3.5/3.6/3.7及其对应的pip, 请重新输入，或使用ctrl + c退出"
+ fi
+ else
+ echo "输入错误，请重新输入"
+ fi
+ done
+}
+
+function checkMacAVX(){
+ read -n1 -p "Step 4. 检测您的Mac是否支持AVX指令集，请按回车键继续..."
+ echo
+ if [[ $AVX != "" ]];then
+ AVX="avx"
+ echo "检测结果：支持"
+ else
+ read -n1 -p "检测结果：不支持。非常抱歉，PaddlePaddle在Mac系统暂不提供no_avx类型的安装包，您可以选择在Linux系统中安装no_avx版的PaddlePaddle, 请按回车键退出..."
+ exit
+ fi
+ echo
+}
+
+function checkMacGPU(){
+ read -n1 -p "Step 5. 选择CPU/GPU版本，请按回车键继续..."
+ echo
+ if [[ $GPU != "" ]];then
+ echo "MacOS环境下，暂未提供GPU版本的PaddlePaddle安装包，将为您安装CPU版本的PaddlePaddle"
+ else
+ echo "MacOS环境下，暂未提供GPU版本的PaddlePaddle安装包，将为您安装CPU版本的PaddlePaddle"
+ GPU=cpu
+ fi
+ echo
+}
+
+function macos() {
+ path='http://paddlepaddle.org/download?url='
+ AVX=`sysctl -a | grep cpu | grep AVX1.0 | tail -1 | grep AVX`
+
+ while true
+ do
+ checkMacPaddleVersion
+ checkMacPythonVersion
+ checkMacAVX
+ checkMacGPU
+
+
+ echo "*********************2. 开始安装*****************************"
+ echo
+ read -n1 -p "即将为您下载并安装PaddlePaddle，请按回车键继续..."
+ echo
+ if [[ $paddle_version == "2" ]];then
+ $python_root -m pip install paddlepaddle
+ if [ $? == "0" ];then
+ echo "安装成功，可以使用: ${python_root} 来启动安装了PaddlePaddle的Python解释器"
+ break
+ else
+ rm $whl_cpu_release
+ echo "未能正常安装PaddlePaddle，请尝试更换您输入的python路径，或者ctrl + c退出后请检查您使用的python对应的pip或pip源是否可用"
+ echo""
+ echo "=========================================================================================="
+ echo""
+ exit 1
+ fi
+ else
+ if [ -f $whl_cpu_develop ];then
+ $python_root -m pip install $whl_cpu_develop
+ if [ $? == "0" ];then
+ rm -rf $whl_cpu_develop
+ echo "安装成功！小提示：可以使用: ${python_root} 来启动安装了PaddlePaddle的Python解释器"
+ break
+ else
+ echo "未能正常安装PaddlePaddle，请尝试更换您输入的python路径，或者ctrl + c退出后请检查您使用的python对应的pip或pip源是否可用"
+ echo""
+ echo "=========================================================================================="
+ echo""
+ exit 1
+ fi
+ else
+ wget ${path}$whl_cpu_develop -O $whl_cpu_develop
+ if [ $? == "0" ];then
+ $python_root -m pip install $whl_cpu_develop
+ if [ $? == "0" ];then
+ rm $wheel_cpu_develop
+ echo "安装成功，可以使用: ${python_root} 来启动安装了PaddlePaddle的Python解释器"
+ break
+ else
+ rm $whl_cpu_release
+ echo "未能正常安装PaddlePaddle，请尝试更换您输入的python路径，或者ctrl + c退出后请检查您使用的python对应的pip或pip源是否可用"
+ echo""
+ echo "=========================================================================================="
+ echo""
+ exit 1
+ fi
+ else
+ rm $whl_cpu_develop
+ echo "未能正常安装PaddlePaddle，请检查您的网络 或者确认您是否安装有 wget，或者ctrl + c退出后反馈至https://github.com/PaddlePaddle/Paddle/issues"
+ echo""
+ echo "=========================================================================================="
+ echo""
+ exit 1
+ fi
+ fi
+ fi
+ done
+}
+
+function main() {
+ echo "*********************************"
+ echo "欢迎使用PaddlePaddle快速安装脚本"
+ echo "*********************************"
+ echo
+ echo "如果您在安装过程中遇到任何问题，请在https://github.com/PaddlePaddle/Paddle/issues反馈，我们的工作人员将会帮您答疑解惑"
+ echo
+ echo "本安装包将帮助您在Linux或Mac系统下安装PaddlePaddle，包括 1）安装前的准备和 2）开始安装 两部分"
+ echo
+ read -n1 -p "请按回车键进行下一步..."
+ echo
+ echo
+ echo "*********************1. 安装前的准备*****************************"
+ echo
+ echo "Step 1. 正在检测您的操作系统信息..."
+ echo
+ SYSTEM=`uname -s`
+ if [ "$SYSTEM" == "Darwin" ];then
+ echo "您的系统为：MAC OSX"
+ echo
+ macos
+ else
+ echo "您的系统为：Linux"
+ echo
+ OS=`cat /etc/issue|awk 'NR==1 {print $1}'`
+ if [ $OS == "\S" ] || [ "$OS" == "CentOS" ] || [ $OS == "Ubuntu" ];then
+ linux
+ else
+ echo "您的系统不在本安装包的支持范围，如您需要在windows环境下安装PaddlePaddle，请您参考PaddlePaddle官网的windows安装文档"
+ fi
+ fi
+}
+main
diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt
index 4cdf96efbd..bcc997ff45 100644
--- a/python/CMakeLists.txt
+++ b/python/CMakeLists.txt
@@ -54,7 +54,7 @@ ELSE(WIN32)
DEPENDS copy_paddle_pybind ${FLUID_CORE} framework_py_proto profiler_py_proto ${PY_FILES} ${external_project_dependencies} ${COPY_PADDLE_MASTER})
ENDIF()
-set(paddle_python_deps ${PADDLE_PYTHON_BUILD_DIR}/.timestamp ${MKL_DEPENDS})
+set(paddle_python_deps ${PADDLE_PYTHON_BUILD_DIR}/.timestamp ${MKL_DEPENDS} ${external_project_dependencies})
add_custom_target(paddle_python ALL DEPENDS ${paddle_python_deps})
set(PADDLE_PYTHON_PACKAGE_DIR ${CMAKE_CURRENT_BINARY_DIR}/dist/)
diff --git a/python/paddle/__init__.py b/python/paddle/__init__.py
index 53746afdb2..fe2ae67ec6 100644
--- a/python/paddle/__init__.py
+++ b/python/paddle/__init__.py
@@ -25,4 +25,5 @@ import paddle.reader
import paddle.dataset
import paddle.batch
import paddle.compat
+import paddle.distributed
batch = batch.batch
diff --git a/python/paddle/distributed/__init__.py b/python/paddle/distributed/__init__.py
new file mode 100644
index 0000000000..d0c32e2609
--- /dev/null
+++ b/python/paddle/distributed/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2019 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.
diff --git a/tools/run_mp.py b/python/paddle/distributed/launch.py
similarity index 83%
rename from tools/run_mp.py
rename to python/paddle/distributed/launch.py
index 2485400ab8..03c4078775 100644
--- a/tools/run_mp.py
+++ b/python/paddle/distributed/launch.py
@@ -37,7 +37,7 @@ default_envs = {
GPUS = 8
-def start_procs(gpus, cmd, log_dir):
+def start_procs(gpus, entrypoint, entrypoint_args, log_dir):
procs = []
log_fns = []
os.system("mkdir -p %s" % log_dir)
@@ -73,12 +73,11 @@ def start_procs(gpus, cmd, log_dir):
"PADDLE_TRAINER_ENDPOINTS": all_nodes_devices_endpoints
})
- print("starting process ", i, cmd, curr_env)
+ print("starting process ", i, entrypoint, entrypoint_args, curr_env)
fn = open("%s/workerlog.%d" % (log_dir, i), "w")
log_fns.append(fn)
- procs.append(
- subprocess.Popen(
- cmd.strip().split(" "), stdout=fn, stderr=fn, env=curr_env))
+ cmd = [sys.executable, "-u", entrypoint] + entrypoint_args
+ procs.append(subprocess.Popen(cmd, stdout=fn, stderr=fn, env=curr_env))
for i in range(gpus):
try:
@@ -89,7 +88,8 @@ def start_procs(gpus, cmd, log_dir):
pass
-def main():
+def parse_args():
+
parser = argparse.ArgumentParser(
description='''start paddle training using multi-process mode.
NOTE: your train program ***must*** run as distributed nccl2 mode,
@@ -108,21 +108,27 @@ POD_IP (current node ip address, not needed for local training)
type=int,
default=8,
help='start number of processes for every gpu')
- parser.add_argument(
- '--cmd',
- type=str,
- default="",
- help='command to run for each process, e.g. python train.py --lr 0.1')
parser.add_argument(
'--log_dir',
type=str,
default="mylog",
help='directory to put logs per process.')
- args = parser.parse_args()
- if args.cmd == "":
- parser.print_help()
- exit(0)
- start_procs(args.gpus, args.cmd, args.log_dir)
+ parser.add_argument(
+ 'entrypoint_script',
+ type=str,
+ help="The entrypoint script to be launched in parallel,"
+ "followed by all the arguments for each process,"
+ "e.g. train.py --lr 0.1")
+ parser.add_argument('entrypoint_args', nargs=argparse.REMAINDER)
+ return parser.parse_args()
+
+
+def main():
+ args = parse_args()
+
+ # launch multiple training process
+ start_procs(args.gpus, args.entrypoint_script, args.entrypoint_args,
+ args.log_dir)
if __name__ == "__main__":
diff --git a/python/paddle/fluid/__init__.py b/python/paddle/fluid/__init__.py
index 564882bd2a..aa1f85734d 100644
--- a/python/paddle/fluid/__init__.py
+++ b/python/paddle/fluid/__init__.py
@@ -158,9 +158,9 @@ def __bootstrap__():
'enable_cublas_tensor_op_math', 'conv_workspace_size_limit',
'cudnn_exhaustive_search', 'memory_optimize_debug', 'selected_gpus',
'sync_nccl_allreduce', 'limit_of_tmp_allocation',
- 'times_excess_than_required_tmp_allocation'
+ 'times_excess_than_required_tmp_allocation',
+ 'enable_inplace_whitelist'
]
-
core.init_gflags([sys.argv[0]] +
["--tryfromenv=" + ",".join(read_env_flags)])
core.init_glog(sys.argv[0])
diff --git a/python/paddle/fluid/compiler.py b/python/paddle/fluid/compiler.py
index a35a4c5983..ef02429428 100644
--- a/python/paddle/fluid/compiler.py
+++ b/python/paddle/fluid/compiler.py
@@ -174,6 +174,11 @@ class CompiledProgram(object):
self._exec_strategy.num_threads = cpu_num * 2
trainers_endpoints = self._program._trainers_endpoints
+
+ # FIXME(dzhwinter): enable_inplace should be after memory_optimize
+ # if turn on python memory optimize, turn off the inplace_pass.
+ self._build_strategy.enable_inplace = False if self._program._is_mem_optimized else True
+
if self._build_strategy.num_trainers > 1 and trainers_endpoints:
assert self._build_strategy.num_trainers == len(
trainers_endpoints), "num_trainers == len(end_points)"
diff --git a/python/paddle/fluid/contrib/decoder/beam_search_decoder.py b/python/paddle/fluid/contrib/decoder/beam_search_decoder.py
index f2b7ac8375..5854cadb58 100644
--- a/python/paddle/fluid/contrib/decoder/beam_search_decoder.py
+++ b/python/paddle/fluid/contrib/decoder/beam_search_decoder.py
@@ -22,7 +22,7 @@ This API is still under active development and may change drastically.
from __future__ import print_function
-import contextlib
+from ...wrapped_decorator import signature_safe_contextmanager
import numpy as np
import six
@@ -419,7 +419,7 @@ class TrainingDecoder(object):
self._state_cell = state_cell
self._state_cell._enter_decoder(self)
- @contextlib.contextmanager
+ @signature_safe_contextmanager
def block(self):
"""
Define the behavior of the decoder for each RNN time step.
@@ -613,7 +613,7 @@ class BeamSearchDecoder(object):
self._word_dim = word_dim
self._input_var_dict = input_var_dict
- @contextlib.contextmanager
+ @signature_safe_contextmanager
def block(self):
"""
Define the behavior of the decoder for each RNN time step.
diff --git a/python/paddle/fluid/contrib/inferencer.py b/python/paddle/fluid/contrib/inferencer.py
index b8d5f4ffea..4f37129234 100644
--- a/python/paddle/fluid/contrib/inferencer.py
+++ b/python/paddle/fluid/contrib/inferencer.py
@@ -14,7 +14,7 @@
from __future__ import print_function
-import contextlib
+from ..wrapped_decorator import signature_safe_contextmanager
from .. import core
@@ -105,7 +105,7 @@ class Inferencer(object):
return results
- @contextlib.contextmanager
+ @signature_safe_contextmanager
def _prog_and_scope_guard(self):
with framework.program_guard(main_program=self.inference_program):
with executor.scope_guard(self.scope):
diff --git a/python/paddle/fluid/contrib/trainer.py b/python/paddle/fluid/contrib/trainer.py
index 8569e486f9..d27b808438 100644
--- a/python/paddle/fluid/contrib/trainer.py
+++ b/python/paddle/fluid/contrib/trainer.py
@@ -14,7 +14,7 @@
from __future__ import print_function
-import contextlib
+from ..wrapped_decorator import signature_safe_contextmanager
import os
import errno
import shutil
@@ -453,7 +453,7 @@ class Trainer(object):
io.save_inference_model(param_path, feeded_var_names, target_vars,
exe)
- @contextlib.contextmanager
+ @signature_safe_contextmanager
def _prog_and_scope_guard(self):
with framework.program_guard(
main_program=self.train_program,
diff --git a/python/paddle/fluid/executor.py b/python/paddle/fluid/executor.py
index d3ff14a179..8815911eae 100644
--- a/python/paddle/fluid/executor.py
+++ b/python/paddle/fluid/executor.py
@@ -17,7 +17,7 @@ from __future__ import print_function
import os
import multiprocessing
import numpy as np
-import contextlib
+from .wrapped_decorator import signature_safe_contextmanager
import six
from .framework import Program, default_main_program, Variable
from . import core
@@ -49,7 +49,7 @@ def _switch_scope(scope):
return ex
-@contextlib.contextmanager
+@signature_safe_contextmanager
def scope_guard(scope):
"""
Change the global/default scope instance by Python `with` statement. All
diff --git a/python/paddle/fluid/framework.py b/python/paddle/fluid/framework.py
index dcb20704fe..ef304b1110 100644
--- a/python/paddle/fluid/framework.py
+++ b/python/paddle/fluid/framework.py
@@ -18,6 +18,7 @@ import collections
from collections import defaultdict
from collections import Iterable
import contextlib
+from .wrapped_decorator import signature_safe_contextmanager
import os
import re
import traceback
@@ -112,7 +113,7 @@ class NameScope(object):
_name_scope = NameScope()
-@contextlib.contextmanager
+@signature_safe_contextmanager
def name_scope(prefix=None):
"""
Generate hierarchical name prefix for the operators.
@@ -1922,6 +1923,19 @@ class Program(object):
self._trainers_endpoints = []
# the distributed lookup table names
self._distributed_lookup_table = None
+ # @deprecated(the python memory optimize transpiler is deprecated)
+ # whether the program is optimized by memory_optimize_transpiler
+ self.__is_mem_optimized = False
+
+ @property
+ def _is_mem_optimized(self):
+ # if the program is optimized, operator input/outputs
+ # maybe same, which conflict with save_inference_model.
+ return self.__is_mem_optimized
+
+ @_is_mem_optimized.setter
+ def _is_mem_optimized(self, target):
+ self.__is_mem_optimized = target
@property
def op_role(self):
@@ -1941,7 +1955,7 @@ class Program(object):
return self._current_role
@op_role.setter
- def set_op_role(self, role):
+ def op_role(self, role):
self._current_role = role
@property
@@ -1959,7 +1973,7 @@ class Program(object):
def set_op_role_var(self, var_name):
self._op_role_var = [var_name]
- @contextlib.contextmanager
+ @signature_safe_contextmanager
def _optimized_guard(self, param_and_grads):
"""
A with guard to set :code:`Optimization` :code:`OpRole` and
@@ -1989,7 +2003,7 @@ class Program(object):
self._op_role_var = tmp_var
self._current_role = tmp_role
- @contextlib.contextmanager
+ @signature_safe_contextmanager
def _lr_schedule_guard(self, is_with_opt=False):
"""
A with guard to set :code:`LRSched` :code:`OpRole` and
@@ -2643,7 +2657,7 @@ def switch_startup_program(program):
return prev_program
-@contextlib.contextmanager
+@signature_safe_contextmanager
def program_guard(main_program, startup_program=None):
"""
Change the global main program and startup program with `with` statement.
@@ -2708,7 +2722,7 @@ def _get_var(name, program=None):
return program.global_block().var(name)
-@contextlib.contextmanager
+@signature_safe_contextmanager
def _imperative_guard(tracer):
global _imperative_tracer_
tmp_trace = _imperative_tracer_
@@ -2719,7 +2733,7 @@ def _imperative_guard(tracer):
_imperative_tracer_ = tmp_trace
-@contextlib.contextmanager
+@signature_safe_contextmanager
def _imperative_place_guard(place):
global _imperative_current_expected_place_
tmp_place = _imperative_current_expected_place_
diff --git a/python/paddle/fluid/imperative/base.py b/python/paddle/fluid/imperative/base.py
index ff3984b11f..d4525233cc 100644
--- a/python/paddle/fluid/imperative/base.py
+++ b/python/paddle/fluid/imperative/base.py
@@ -11,7 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
-import contextlib
+from ..wrapped_decorator import signature_safe_contextmanager
import numpy as np
from paddle.fluid import core
@@ -24,7 +24,7 @@ def enabled():
return framework._in_imperative_mode()
-@contextlib.contextmanager
+@signature_safe_contextmanager
def guard(place=None):
train = framework.Program()
startup = framework.Program()
diff --git a/python/paddle/fluid/initializer.py b/python/paddle/fluid/initializer.py
index 5be21ff7f7..e8341be286 100644
--- a/python/paddle/fluid/initializer.py
+++ b/python/paddle/fluid/initializer.py
@@ -16,7 +16,7 @@ from __future__ import print_function
from . import framework
import numpy as np
-import contextlib
+from .wrapped_decorator import signature_safe_contextmanager
from .core import VarDesc
from . import unique_name
@@ -49,7 +49,7 @@ def force_init_on_cpu():
return _force_init_on_cpu_
-@contextlib.contextmanager
+@signature_safe_contextmanager
def init_on_cpu():
"""
Force the variable to be inited on CPU.
diff --git a/python/paddle/fluid/io.py b/python/paddle/fluid/io.py
index 6b1d4cc34f..a2abbf36c0 100644
--- a/python/paddle/fluid/io.py
+++ b/python/paddle/fluid/io.py
@@ -16,14 +16,16 @@ from __future__ import print_function
import os
import errno
+import warnings
import time
import shutil
import six
from functools import reduce
+from paddle.fluid import layers
from paddle.fluid.executor import Executor
from paddle.fluid.evaluator import Evaluator
-from paddle.fluid.framework import Program, Parameter, default_main_program, default_startup_program, Variable
+from paddle.fluid.framework import Program, Parameter, default_main_program, default_startup_program, Variable, program_guard
from . import core
__all__ = [
@@ -930,6 +932,24 @@ def save_inference_model(dirname,
if main_program is None:
main_program = default_main_program()
+ if main_program._is_mem_optimized:
+ warnings.warn(
+ "save_inference_model must put before you call memory_optimize. \
+ the memory_optimize will modify the original program, \
+ is not suitable for saving inference model \
+ we save the original program as inference model.",
+ RuntimeWarning)
+
+ # fix the bug that the activation op's output as target will be pruned.
+ # will affect the inference performance.
+ # TODO(Superjomn) add an IR pass to remove 1-scale op.
+ with program_guard(main_program):
+ uniq_target_vars = []
+ for var in target_vars:
+ if isinstance(var, Variable):
+ var1 = layers.scale(var, 1.)
+ uniq_target_vars.append(var1)
+ target_vars = uniq_target_vars
# when a pserver and a trainer running on the same machine, mkdir may conflict
try:
diff --git a/python/paddle/fluid/layer_helper.py b/python/paddle/fluid/layer_helper.py
index a172141b3a..7d1636774c 100644
--- a/python/paddle/fluid/layer_helper.py
+++ b/python/paddle/fluid/layer_helper.py
@@ -302,7 +302,8 @@ class LayerHelper(object):
if default_initializer is None and attr.initializer is None:
if isinstance(dtype, core.VarDesc.VarType):
if dtype != core.VarDesc.VarType.FP32 and \
- dtype != core.VarDesc.VarType.FP64:
+ dtype != core.VarDesc.VarType.FP64 and \
+ dtype != core.VarDesc.VarType.FP16:
raise TypeError(
"Can not create parameter with default initializer when dtype is not float type. Set default_initializer to fit the parameter dtype!"
)
diff --git a/python/paddle/fluid/layers/control_flow.py b/python/paddle/fluid/layers/control_flow.py
index a7494aacea..3a6753b01f 100644
--- a/python/paddle/fluid/layers/control_flow.py
+++ b/python/paddle/fluid/layers/control_flow.py
@@ -13,7 +13,7 @@
# limitations under the License.
from __future__ import print_function
-import contextlib
+from ..wrapped_decorator import signature_safe_contextmanager
from .layer_function_generator import autodoc, templatedoc
from .tensor import assign, fill_constant
@@ -1532,7 +1532,7 @@ class DynamicRNN(object):
outputs={'Out': [x_reordered]})
return shrink_memory(x_reordered, self.step_idx, self.lod_rank_table)
- @contextlib.contextmanager
+ @signature_safe_contextmanager
def block(self):
"""
The block for user to define operators in RNN. See the class docstring
diff --git a/python/paddle/fluid/layers/detection.py b/python/paddle/fluid/layers/detection.py
index 0602d7a194..3b43ae0b9c 100644
--- a/python/paddle/fluid/layers/detection.py
+++ b/python/paddle/fluid/layers/detection.py
@@ -49,6 +49,7 @@ __all__ = [
'box_coder',
'polygon_box_transform',
'yolov3_loss',
+ 'box_clip',
'multiclass_nms',
]
@@ -396,10 +397,10 @@ def box_coder(prior_box,
input is image feature map, they are close to
the origin of the coordinate system. [xmax, ymax]
is the right bottom coordinate of the anchor box.
- prior_box_var(Variable|list): prior_box_var supports two types of input.
- One is variable with shape [M, 4] holds M group.
- The other one is list consist of 4 elements
- shared by all boxes.
+ prior_box_var(Variable|list|None): prior_box_var supports two types
+ of input. One is variable with shape [M, 4]
+ holds M group. The other one is list consist of
+ 4 elements shared by all boxes.
target_box(Variable): This input can be a 2-D LoDTensor with shape
[N, 4] when code_type is 'encode_center_size'.
This input also can be a 3-D Tensor with shape
@@ -2055,6 +2056,54 @@ def generate_proposals(scores,
return rpn_rois, rpn_roi_probs
+def box_clip(input, im_info, name=None):
+ """
+ Clip the box into the size given by im_info
+ For each input box, The formula is given as follows:
+
+ .. code-block:: text
+
+ xmin = max(min(xmin, im_w - 1), 0)
+ ymin = max(min(ymin, im_h - 1), 0)
+ xmax = max(min(xmax, im_w - 1), 0)
+ ymax = max(min(ymax, im_h - 1), 0)
+
+ where im_w and im_h are computed from im_info:
+
+ .. code-block:: text
+
+ im_h = round(height / scale)
+ im_w = round(weight / scale)
+
+ Args:
+ input(variable): The input box, the last dimension is 4.
+ im_info(variable): The information of image with shape [N, 3] with
+ layout (height, width, scale). height and width
+ is the input size and scale is the ratio of input
+ size and original size.
+ name (str): The name of this layer. It is optional.
+
+ Returns:
+ Variable: The cliped tensor variable.
+
+ Examples:
+ .. code-block:: python
+
+ boxes = fluid.layers.data(
+ name='data', shape=[8, 4], dtype='float32', lod_level=1)
+ im_info = fluid.layers.data(name='im_info', shape=[3])
+ out = fluid.layers.box_clip(
+ input=boxes, im_info=im_info, inplace=True)
+ """
+
+ helper = LayerHelper("box_clip", **locals())
+ output = helper.create_variable_for_type_inference(dtype=input.dtype)
+ inputs = {"Input": input, "ImInfo": im_info}
+ helper.append_op(type="box_clip", inputs=inputs, outputs={"Output": output})
+
+ return output
+
+
def multiclass_nms(bboxes,
scores,
score_threshold,
@@ -2132,9 +2181,11 @@ def multiclass_nms(bboxes,
(After version 1.3, when no boxes detected, the lod is changed
from {0} to {1})
+
Examples:
.. code-block:: python
+
boxes = fluid.layers.data(name='bboxes', shape=[81, 4],
dtype='float32', lod_level=1)
scores = fluid.layers.data(name='scores', shape=[81],
diff --git a/python/paddle/fluid/layers/io.py b/python/paddle/fluid/layers/io.py
index 1762bd3e34..b88be66906 100644
--- a/python/paddle/fluid/layers/io.py
+++ b/python/paddle/fluid/layers/io.py
@@ -13,7 +13,7 @@
# limitations under the License.
from __future__ import print_function
-import contextlib
+from ..wrapped_decorator import signature_safe_contextmanager
import multiprocessing
import os
import six
@@ -1116,7 +1116,7 @@ class Preprocessor(object):
def _is_completed(self):
return self.sub_block and self.source_var_names and self.sink_var_names
- @contextlib.contextmanager
+ @signature_safe_contextmanager
def block(self):
self.status = Preprocessor.IN_SUB_BLOCK
self.sub_block = self.main_prog._create_block()
diff --git a/python/paddle/fluid/optimizer.py b/python/paddle/fluid/optimizer.py
index e0e781a322..fbd04f1eb4 100644
--- a/python/paddle/fluid/optimizer.py
+++ b/python/paddle/fluid/optimizer.py
@@ -15,7 +15,7 @@
from __future__ import print_function
from collections import defaultdict
-from contextlib import contextmanager
+from .wrapped_decorator import signature_safe_contextmanager
from paddle.fluid.framework import Program, Variable, name_scope, default_main_program
from paddle.fluid.distribute_lookup_table import find_distributed_lookup_table
@@ -1610,7 +1610,7 @@ class ModelAverage(Optimizer):
},
stop_gradient=True)
- @contextmanager
+ @signature_safe_contextmanager
def apply(self, executor, need_restore=True):
"""Apply average values to parameters of current model.
"""
diff --git a/python/paddle/fluid/parallel_executor.py b/python/paddle/fluid/parallel_executor.py
index a07ff6ac69..22212ae9a2 100644
--- a/python/paddle/fluid/parallel_executor.py
+++ b/python/paddle/fluid/parallel_executor.py
@@ -146,6 +146,10 @@ class ParallelExecutor(object):
# step4: get main_program, scope, local_scopes
main = main_program if main_program \
else framework.default_main_program()
+ # FIXME(dzhwinter): enable_inplace should be after memory_optimize
+ # if turn on python memory optimize, turn off the inplace_pass.
+ if build_strategy.enable_inplace is None:
+ build_strategy.enable_inplace = False if main._is_mem_optimized else True
scope = scope if scope is not None else executor.global_scope()
if share_vars_from and not isinstance(share_vars_from,
diff --git a/python/paddle/fluid/profiler.py b/python/paddle/fluid/profiler.py
index e05885f5f5..d5670dbc82 100644
--- a/python/paddle/fluid/profiler.py
+++ b/python/paddle/fluid/profiler.py
@@ -15,7 +15,7 @@
from __future__ import print_function
from . import core
-from contextlib import contextmanager
+from .wrapped_decorator import signature_safe_contextmanager
import os
import six
@@ -35,7 +35,7 @@ NVPROF_CONFIG = [
]
-@contextmanager
+@signature_safe_contextmanager
def cuda_profiler(output_file, output_mode=None, config=None):
"""The CUDA profiler.
This fuctions is used to profile CUDA program by CUDA runtime application
@@ -217,7 +217,7 @@ def stop_profiler(sorted_key=None, profile_path='/tmp/profile'):
core.disable_profiler(key_map[sorted_key], profile_path)
-@contextmanager
+@signature_safe_contextmanager
def profiler(state, sorted_key=None, profile_path='/tmp/profile'):
"""The profiler interface.
Different from cuda_profiler, this profiler can be used to profile both CPU
diff --git a/python/paddle/fluid/recordio_writer.py b/python/paddle/fluid/recordio_writer.py
index 076a942cdd..aa581f23a1 100644
--- a/python/paddle/fluid/recordio_writer.py
+++ b/python/paddle/fluid/recordio_writer.py
@@ -15,14 +15,14 @@
from __future__ import print_function
import os
-import contextlib
+from .wrapped_decorator import signature_safe_contextmanager
from . import core
__all__ = [
'convert_reader_to_recordio_file', 'convert_reader_to_recordio_files'
]
-@contextlib.contextmanager
+@signature_safe_contextmanager
def create_recordio_writer(filename,
compressor=core.RecordIOWriter.Compressor.Snappy,
max_num_records=1000):
diff --git a/python/paddle/fluid/tests/test_detection.py b/python/paddle/fluid/tests/test_detection.py
index 77dfa1cb51..0d39a139ee 100644
--- a/python/paddle/fluid/tests/test_detection.py
+++ b/python/paddle/fluid/tests/test_detection.py
@@ -482,6 +482,17 @@ class TestYoloDetection(unittest.TestCase):
self.assertIsNotNone(loss)
+class TestBoxClip(unittest.TestCase):
+ def test_box_clip(self):
+ program = Program()
+ with program_guard(program):
+ input_box = layers.data(
+ name='input_box', shape=[7, 4], dtype='float32', lod_level=1)
+ im_info = layers.data(name='im_info', shape=[3], dtype='float32')
+ out = layers.box_clip(input_box, im_info)
+ self.assertIsNotNone(out)
+
+
class TestMulticlassNMS(unittest.TestCase):
def test_multiclass_nms(self):
program = Program()
diff --git a/python/paddle/fluid/tests/unittests/CMakeLists.txt b/python/paddle/fluid/tests/unittests/CMakeLists.txt
index 699181d01d..4b26bacce9 100644
--- a/python/paddle/fluid/tests/unittests/CMakeLists.txt
+++ b/python/paddle/fluid/tests/unittests/CMakeLists.txt
@@ -110,6 +110,10 @@ py_test_modules(test_parallel_executor_transformer MODULES test_parallel_executo
if(NOT APPLE)
py_test_modules(test_image_classification_resnet MODULES test_image_classification_resnet SERIAL)
endif()
+if(CMAKE_BUILD_TYPE STREQUAL "Debug")
+ # change the timeout from 600 to 900, because in debug mode, this test need more time.
+ set_tests_properties(test_image_classification_resnet PROPERTIES TIMEOUT 900)
+endif()
if (WITH_NGRAPH)
add_subdirectory(ngraph)
diff --git a/python/paddle/fluid/tests/unittests/ngraph/test_accuracy_ngraph_op.py b/python/paddle/fluid/tests/unittests/ngraph/test_accuracy_ngraph_op.py
new file mode 100644
index 0000000000..84b9198dbf
--- /dev/null
+++ b/python/paddle/fluid/tests/unittests/ngraph/test_accuracy_ngraph_op.py
@@ -0,0 +1,53 @@
+# 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 paddle.fluid.tests.unittests.op_test import OpTest
+
+
+class TestNGRAPHAccuracyOp(OpTest):
+ def setUp(self):
+ self.op_type = "accuracy"
+ self.dtype = np.float32
+ self.init_dtype()
+ n = 128
+ infer = np.random.random((n, 1)).astype(self.dtype)
+ indices = np.random.randint(0, 2, (n, 1))
+ label = np.random.randint(0, 2, (n, 1))
+ self.inputs = {'Out': infer, 'Indices': indices, "Label": label}
+ num_correct = 0
+ for rowid in range(n):
+ for ele in indices[rowid]:
+ if ele == label[rowid]:
+ num_correct += 1
+ break
+ self.outputs = {
+ 'Accuracy': np.array([num_correct / float(n)]).astype(self.dtype),
+ 'Correct': np.array([num_correct]).astype("int64"),
+ 'Total': np.array([n]).astype("int64")
+ }
+ self._cpu_only = True
+
+ def init_dtype(self):
+ pass
+
+ def test_check_output(self):
+ self.check_output()
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/python/paddle/fluid/tests/unittests/ngraph/test_batch_norm_ngraph_op.py b/python/paddle/fluid/tests/unittests/ngraph/test_batch_norm_ngraph_op.py
new file mode 100644
index 0000000000..511173af5e
--- /dev/null
+++ b/python/paddle/fluid/tests/unittests/ngraph/test_batch_norm_ngraph_op.py
@@ -0,0 +1,37 @@
+# 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
+from paddle.fluid.tests.unittests.test_batch_norm_op import TestBatchNormOpTraining, TestBatchNormOpInference
+
+
+class TestNGRAPHBatchNormOpTraining(TestBatchNormOpTraining):
+ def init_kernel_type(self):
+ super(TestNGRAPHBatchNormOpTraining, self).init_kernel_type()
+
+
+class TestNGRAPHBatchNormOpInference(TestBatchNormOpInference):
+ def init_kernel_type(self):
+ super(TestNGRAPHBatchNormOpInference, self).init_kernel_type()
+
+
+class TestNGRAPHBatchNormOpWithReluInference(TestBatchNormOpInference):
+ def init_kernel_type(self):
+ super(TestNGRAPHBatchNormOpWithReluInference, self).init_kernel_type()
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/python/paddle/fluid/tests/unittests/ngraph/test_conv2d_ngraph_op.py b/python/paddle/fluid/tests/unittests/ngraph/test_conv2d_ngraph_op.py
new file mode 100644
index 0000000000..dbc8557b4e
--- /dev/null
+++ b/python/paddle/fluid/tests/unittests/ngraph/test_conv2d_ngraph_op.py
@@ -0,0 +1,76 @@
+# 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
+from paddle.fluid.tests.unittests.test_conv2d_op import TestConv2dOp, TestWithPad, TestWithStride, TestWithGroup, TestWith1x1, TestWithInput1x1Filter1x1
+
+
+class TestNGRAPH(TestConv2dOp):
+ def setUp(self):
+ super(TestNGRAPH, self).setUp()
+ self._cpu_only = True
+
+ def init_kernel_type(self):
+ super(TestNGRAPH, self).init_kernel_type()
+
+
+class TestNGRAPHWithPad(TestWithPad):
+ def setUp(self):
+ super(TestNGRAPHWithPad, self).setUp()
+ self._cpu_only = True
+
+ def init_kernel_type(self):
+ super(TestNGRAPHWithPad, self).init_kernel_type()
+
+
+class TestNGRAPHWithStride(TestWithStride):
+ def setUp(self):
+ super(TestNGRAPHWithStride, self).setUp()
+ self._cpu_only = True
+
+ def init_kernel_type(self):
+ super(TestNGRAPHWithStride, self).init_kernel_type()
+
+
+class TestNGRAPHWithGroup(TestWithGroup):
+ def setUp(self):
+ super(TestNGRAPHWithGroup, self).setUp()
+ self._cpu_only = True
+
+ def init_kernel_type(self):
+ super(TestNGRAPHWithGroup, self).init_kernel_type()
+
+
+class TestNGRAPHWith1x1(TestWith1x1):
+ def setUp(self):
+ super(TestNGRAPHWith1x1, self).setUp()
+ self._cpu_only = True
+
+ def init_kernel_type(self):
+ super(TestNGRAPHWith1x1, self).init_kernel_type()
+
+
+class TestNGRAPHWithInput1x1Filter1x1(TestWithInput1x1Filter1x1):
+ def setUp(self):
+ super(TestNGRAPHWithInput1x1Filter1x1, self).setUp()
+ self._cpu_only = True
+
+ def init_kernel_type(self):
+ super(TestNGRAPHWithInput1x1Filter1x1, self).init_kernel_type()
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/python/paddle/fluid/tests/unittests/ngraph/test_elementwise_add_ngraph_op.py b/python/paddle/fluid/tests/unittests/ngraph/test_elementwise_add_ngraph_op.py
index 67722db89b..67f749bfee 100644
--- a/python/paddle/fluid/tests/unittests/ngraph/test_elementwise_add_ngraph_op.py
+++ b/python/paddle/fluid/tests/unittests/ngraph/test_elementwise_add_ngraph_op.py
@@ -14,73 +14,16 @@
from __future__ import print_function
import unittest
-from paddle.fluid.tests.unittests.test_elementwise_add_op import *
+from paddle.fluid.tests.unittests.test_elementwise_add_op import TestElementwiseAddOp
class TestNGRAPHElementwiseAddOp(TestElementwiseAddOp):
- def init_input_output(self):
- super(TestNGRAPHElementwiseAddOp, self).init_input_output()
-
-
-class TestNGRAPHElementwiseAddOp_scalar(TestElementwiseAddOp_scalar):
- def init_input_output(self):
- super(TestNGRAPHElementwiseAddOp_scalar, self).init_input_output()
-
-
-class TestNGRAPHElementwiseAddOp_scalar2(TestElementwiseAddOp_scalar2):
- def init_input_output(self):
- super(TestNGRAPHElementwiseAddOp_scalar2, self).init_input_output()
-
-
-class TestNGRAPHElementwiseAddOp_Vector(TestElementwiseAddOp_Vector):
- def init_input_output(self):
- super(TestNGRAPHElementwiseAddOp_Vector, self).init_input_output()
-
-
-class TesNGRAPHtElementwiseAddOp_broadcast_0(TestElementwiseAddOp_broadcast_0):
- def init_input_output(self):
- super(TesNGRAPHtElementwiseAddOp_broadcast_0, self).init_input_output()
-
-
-class TestNGRAPHElementwiseAddOp_broadcast_1(TestElementwiseAddOp_broadcast_1):
- def init_input_output(self):
- super(TestNGRAPHElementwiseAddOp_broadcast_1, self).init_input_output()
+ def setUp(self):
+ super(TestNGRAPHElementwiseAddOp, self).setUp()
+ self._cpu_only = True
-
-class TestNGRAPHElementwiseAddOp_broadcast_2(TestElementwiseAddOp_broadcast_2):
def init_input_output(self):
- super(TestNGRAPHElementwiseAddOp_broadcast_2, self).init_input_output()
-
-
-class TestNGRAPHElementwiseAddOp_broadcast_3(TestElementwiseAddOp_broadcast_3):
- def init_input_output(self):
- super(TestNGRAPHElementwiseAddOp_broadcast_3, self).init_input_output()
-
-
-class TestNGRAPHElementwiseAddOp_broadcast_4(TestElementwiseAddOp_broadcast_4):
- def init_input_output(self):
- super(TestNGRAPHElementwiseAddOp_broadcast_4, self).init_input_output()
-
-
-class TestNGRAPHElementwiseAddOp_rowwise_add_0(
- TestElementwiseAddOp_rowwise_add_0):
- def init_input_output(self):
- super(TestNGRAPHElementwiseAddOp_rowwise_add_0,
- self).init_input_output()
-
-
-class TestNGRAPHElementwiseAddOp_rowwise_add_1(
- TestElementwiseAddOp_rowwise_add_1):
- def init_input_output(self):
- super(TestNGRAPHElementwiseAddOp_rowwise_add_1,
- self).init_input_output()
-
-
-class TestNGRAPHElementwiseAddOp_channelwise_add(
- TestElementwiseAddOp_channelwise_add):
- def init_input_output(self):
- super(TestNGRAPHElementwiseAddOp_channelwise_add,
- self).init_input_output()
+ super(TestNGRAPHElementwiseAddOp, self).init_input_output()
if __name__ == '__main__':
diff --git a/python/paddle/fluid/tests/unittests/ngraph/test_mean_ngraph_op.py b/python/paddle/fluid/tests/unittests/ngraph/test_mean_ngraph_op.py
index 5535427ea8..11881ac6e5 100644
--- a/python/paddle/fluid/tests/unittests/ngraph/test_mean_ngraph_op.py
+++ b/python/paddle/fluid/tests/unittests/ngraph/test_mean_ngraph_op.py
@@ -14,17 +14,13 @@
from __future__ import print_function
import unittest
-from paddle.fluid.tests.unittests.test_mean_op import TestMeanOp, TestFP16MeanOp
+from paddle.fluid.tests.unittests.test_mean_op import TestMeanOp
class TestNGRAPHMeanOp(TestMeanOp):
def setUp(self):
super(TestNGRAPHMeanOp, self).setUp()
-
-
-class TestNGRAPHFP16MeanOp(TestFP16MeanOp):
- def setUp(self):
- super(TestNGRAPHFP16MeanOp, self).setUp()
+ self._cpu_only = True
if __name__ == "__main__":
diff --git a/python/paddle/fluid/tests/unittests/ngraph/test_mul_ngraph_op.py b/python/paddle/fluid/tests/unittests/ngraph/test_mul_ngraph_op.py
index 6aba62f7c0..a916c8d450 100644
--- a/python/paddle/fluid/tests/unittests/ngraph/test_mul_ngraph_op.py
+++ b/python/paddle/fluid/tests/unittests/ngraph/test_mul_ngraph_op.py
@@ -15,27 +15,38 @@
from __future__ import print_function
import unittest
-from paddle.fluid.tests.unittests.test_mul_op import TestMulOp, TestMulOp2, TestFP16MulOp1, TestFP16MulOp2
+import numpy as np
+from paddle.fluid.tests.unittests.op_test import OpTest
+
+
+class TestNGRAPHMulOp(OpTest):
+ def setUp(self):
+ self.op_type = "mul"
+ self.dtype = np.float32
+ self.init_dtype_type()
+ self.inputs = {
+ 'X': np.random.random((2, 4)).astype(self.dtype),
+ 'Y': np.random.random((4, 4)).astype(self.dtype)
+ }
+ self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
+ self._cpu_only = True
-
-class TestNGRAPHMulOp(TestMulOp):
def init_dtype_type(self):
pass
+ def test_check_output(self):
+ self.check_output()
-class TestNGRAPHMulOp2(TestMulOp2):
- def init_dtype_type(self):
- pass
+ def test_check_grad_normal(self):
+ self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5)
+ def test_check_grad_ingore_x(self):
+ self.check_grad(
+ ['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X"))
-class TestNGRAPHFP16MulOp1(TestFP16MulOp1):
- def init_dtype_type(self):
- pass
-
-
-class TestNGRAPHFP16MulOp2(TestFP16MulOp2):
- def init_dtype_type(self):
- pass
+ def test_check_grad_ingore_y(self):
+ self.check_grad(
+ ['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
if __name__ == "__main__":
diff --git a/python/paddle/fluid/tests/unittests/ngraph/test_pool2d_ngraph_op.py b/python/paddle/fluid/tests/unittests/ngraph/test_pool2d_ngraph_op.py
index 95e592e8ec..96a2b72d8a 100644
--- a/python/paddle/fluid/tests/unittests/ngraph/test_pool2d_ngraph_op.py
+++ b/python/paddle/fluid/tests/unittests/ngraph/test_pool2d_ngraph_op.py
@@ -14,35 +14,59 @@
from __future__ import print_function
-from paddle.fluid.tests.unittests.test_pool2d_op import *
+from paddle.fluid.tests.unittests.test_pool2d_op import TestPool2D_Op, TestCase1, TestCase2, TestCase3, TestCase4, TestCase5
class TestNGRAPHPool2D_Op(TestPool2D_Op):
+ def setUp(self):
+ super(TestNGRAPHPool2D_Op, self).setUp()
+ self._cpu_only = True
+
def init_test_case(self):
super(TestNGRAPHPool2D_Op, self).init_test_case()
class TestNGRAPHCase1(TestCase1):
+ def setUp(self):
+ super(TestNGRAPHCase1, self).setUp()
+ self._cpu_only = True
+
def init_test_case(self):
super(TestNGRAPHCase1, self).init_test_case()
class TestNGRAPHCase2(TestCase2):
+ def setUp(self):
+ super(TestNGRAPHCase2, self).setUp()
+ self._cpu_only = True
+
def init_test_case(self):
super(TestNGRAPHCase2, self).init_test_case()
class TestNGRAPHCase3(TestCase3):
+ def setUp(self):
+ super(TestNGRAPHCase3, self).setUp()
+ self._cpu_only = True
+
def init_pool_type(self):
super(TestNGRAPHCase3, self).init_pool_type()
class TestNGRAPHCase4(TestCase4):
+ def setUp(self):
+ super(TestNGRAPHCase4, self).setUp()
+ self._cpu_only = True
+
def init_pool_type(self):
super(TestNGRAPHCase4, self).init_pool_type()
class TestNGRAPHCase5(TestCase5):
+ def setUp(self):
+ super(TestNGRAPHCase5, self).setUp()
+ self._cpu_only = True
+
def init_pool_type(self):
super(TestNGRAPHCase5, self).init_pool_type()
diff --git a/python/paddle/fluid/tests/unittests/ngraph/test_scale_ngraph_op.py b/python/paddle/fluid/tests/unittests/ngraph/test_scale_ngraph_op.py
index b42a1f73fa..4da5ca4583 100644
--- a/python/paddle/fluid/tests/unittests/ngraph/test_scale_ngraph_op.py
+++ b/python/paddle/fluid/tests/unittests/ngraph/test_scale_ngraph_op.py
@@ -13,25 +13,23 @@
# limitations under the License.
from __future__ import print_function
import unittest
-from paddle.fluid.tests.unittests.test_scale_op import TestScaleOp, TestScaleOpSelectedRows, TestScaleFp16Op, TestScaleFp16OpSelectedRows
+from paddle.fluid.tests.unittests.test_scale_op import TestScaleOp, TestScaleOpSelectedRows
class TestNGRAPHScaleOp(TestScaleOp):
- def init_dtype_type(self):
- pass
+ def setUp(self):
+ super(TestNGRAPHScaleOp, self).setUp()
+ self._cpu_only = True
-
-class TestNGRAPHScaleOpSelectedRows(TestScaleOpSelectedRows):
def init_dtype_type(self):
pass
-class TestNGRAPHScaleFp16Op(TestScaleFp16Op):
- def init_dtype_type(self):
- pass
-
+class TestNGRAPHScaleOpSelectedRows(TestScaleOpSelectedRows):
+ def setUp(self):
+ super(TestNGRAPHScaleOpSelectedRows, self).setUp()
+ self._cpu_only = True
-class TestNGRAPHScaleFp16OpSelectedRows(TestScaleFp16OpSelectedRows):
def init_dtype_type(self):
pass
diff --git a/python/paddle/fluid/tests/unittests/ngraph/test_top_k_ngraph_op.py b/python/paddle/fluid/tests/unittests/ngraph/test_top_k_ngraph_op.py
index 3a0171087d..fa68df1adf 100644
--- a/python/paddle/fluid/tests/unittests/ngraph/test_top_k_ngraph_op.py
+++ b/python/paddle/fluid/tests/unittests/ngraph/test_top_k_ngraph_op.py
@@ -20,21 +20,25 @@ from paddle.fluid.tests.unittests.test_top_k_op import TestTopkOp, TestTopkOp3d,
class TestNGRAPHTopkOp(TestTopkOp):
def setUp(self):
super(TestNGRAPHTopkOp, self).setUp()
+ self._cpu_only = True
class TestNGRAPHTopkOp2(TestTopkOp2):
def setUp(self):
super(TestNGRAPHTopkOp2, self).setUp()
+ self._cpu_only = True
class TestNGRAPHTopkOp3(TestTopkOp3):
def setUp(self):
super(TestNGRAPHTopkOp3, self).setUp()
+ self._cpu_only = True
class TestNGRAPHTopkOp4(TestTopkOp4):
def setUp(self):
super(TestNGRAPHTopkOp4, self).setUp()
+ self._cpu_only = True
if __name__ == "__main__":
diff --git a/python/paddle/fluid/tests/unittests/parallel_executor_test_base.py b/python/paddle/fluid/tests/unittests/parallel_executor_test_base.py
index fdacd241f9..c429c8af7d 100644
--- a/python/paddle/fluid/tests/unittests/parallel_executor_test_base.py
+++ b/python/paddle/fluid/tests/unittests/parallel_executor_test_base.py
@@ -40,7 +40,8 @@ class TestParallelExecutorBase(unittest.TestCase):
seed=None,
use_parallel_executor=True,
use_reduce=False,
- use_ir_memory_optimize=False,
+ use_ir_memory_optimize=True,
+ enable_inplace=True,
fuse_elewise_add_act_ops=False,
fuse_relu_depthwise_conv=False,
optimizer=fluid.optimizer.Adam,
@@ -60,63 +61,65 @@ class TestParallelExecutorBase(unittest.TestCase):
main.random_seed = seed
loss = method(use_feed=feed_dict is not None)
-
if optimizer:
optimizer().minimize(loss)
if memory_opt:
fluid.memory_optimize(main)
- place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
- exe = fluid.Executor(place)
- exe.run(startup)
- exec_strategy = fluid.ExecutionStrategy()
- exec_strategy.allow_op_delay = allow_op_delay
- if use_fast_executor:
- exec_strategy.use_experimental_executor = True
- build_strategy = fluid.BuildStrategy()
- build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce \
- if use_reduce else fluid.BuildStrategy.ReduceStrategy.AllReduce
- build_strategy.fuse_elewise_add_act_ops = fuse_elewise_add_act_ops
- build_strategy.fuse_relu_depthwise_conv = fuse_relu_depthwise_conv
- build_strategy.memory_optimize = use_ir_memory_optimize
- build_strategy.enable_sequential_execution = enable_sequential_execution
- if use_cuda and core.is_compiled_with_cuda():
- build_strategy.remove_unnecessary_lock = True
- if use_parallel_executor:
- binary = compiler.CompiledProgram(main).with_data_parallel(
- loss_name=loss.name,
- build_strategy=build_strategy,
- exec_strategy=exec_strategy)
- else:
- binary = compiler.CompiledProgram(main)
-
- if batch_size is not None:
- batch_size *= fluid.core.get_cuda_device_count(
- ) if use_cuda else int(
- os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
- begin = time.time()
- first_loss, = run_executor(
- exe=exe, binary=binary, feed=feed_dict, fetch_list=[loss.name])
-
- for i in range(iter):
- run_executor(
- exe=exe, binary=binary, feed=feed_dict, fetch_list=[])
-
- last_loss, = run_executor(
- exe=exe, binary=binary, feed=feed_dict, fetch_list=[loss.name])
- end = time.time()
-
- if batch_size is not None:
- print("%.4f Instance per second" % (
- (batch_size * iter + 2) / (end - begin)))
-
- avg_last_loss_val = np.array(last_loss).mean()
- avg_first_loss_val = np.array(first_loss).mean()
- if math.isnan(float(avg_last_loss_val)) or math.isnan(
- float(avg_first_loss_val)):
- sys.exit("got NaN loss, training failed.")
-
- print(first_loss, last_loss)
- # self.assertGreater(first_loss[0], last_loss[0])
- return first_loss, last_loss
+ place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+ exe = fluid.Executor(place)
+ exe.run(startup)
+ exec_strategy = fluid.ExecutionStrategy()
+ exec_strategy.allow_op_delay = allow_op_delay
+ if use_fast_executor:
+ exec_strategy.use_experimental_executor = True
+ build_strategy = fluid.BuildStrategy()
+ build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce \
+ if use_reduce else fluid.BuildStrategy.ReduceStrategy.AllReduce
+ build_strategy.fuse_elewise_add_act_ops = fuse_elewise_add_act_ops
+ build_strategy.fuse_relu_depthwise_conv = fuse_relu_depthwise_conv
+ build_strategy.memory_optimize = use_ir_memory_optimize
+ # python memory optimization is conflict with inplace pass.
+ # Use ir graph memory optimization after inplace pass is the correct way.
+ build_strategy.enable_inplace = False if memory_opt else enable_inplace
+ build_strategy.enable_sequential_execution = enable_sequential_execution
+
+ if use_cuda and core.is_compiled_with_cuda():
+ build_strategy.remove_unnecessary_lock = True
+ if use_parallel_executor:
+ binary = compiler.CompiledProgram(main).with_data_parallel(
+ loss_name=loss.name,
+ build_strategy=build_strategy,
+ exec_strategy=exec_strategy)
+ else:
+ binary = compiler.CompiledProgram(main)
+
+ if batch_size is not None:
+ batch_size *= fluid.core.get_cuda_device_count(
+ ) if use_cuda else int(
+ os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
+ begin = time.time()
+ first_loss, = run_executor(
+ exe=exe, binary=binary, feed=feed_dict, fetch_list=[loss.name])
+
+ for i in range(iter):
+ run_executor(exe=exe, binary=binary, feed=feed_dict, fetch_list=[])
+
+ last_loss, = run_executor(
+ exe=exe, binary=binary, feed=feed_dict, fetch_list=[loss.name])
+ end = time.time()
+
+ if batch_size is not None:
+ print("%.4f Instance per second" % (
+ (batch_size * iter + 2) / (end - begin)))
+
+ avg_last_loss_val = np.array(last_loss).mean()
+ avg_first_loss_val = np.array(first_loss).mean()
+ if math.isnan(float(avg_last_loss_val)) or math.isnan(
+ float(avg_first_loss_val)):
+ sys.exit("got NaN loss, training failed.")
+
+ print(first_loss, last_loss)
+ # self.assertGreater(first_loss[0], last_loss[0])
+ return first_loss, last_loss
diff --git a/python/paddle/fluid/tests/unittests/test_box_clip_op.py b/python/paddle/fluid/tests/unittests/test_box_clip_op.py
new file mode 100644
index 0000000000..b2b0598f31
--- /dev/null
+++ b/python/paddle/fluid/tests/unittests/test_box_clip_op.py
@@ -0,0 +1,70 @@
+# 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
+import sys
+import math
+from op_test import OpTest
+import copy
+
+
+def box_clip(input_box, im_info, output_box):
+ im_w = round(im_info[1] / im_info[2])
+ im_h = round(im_info[0] / im_info[2])
+ output_box[:, :, 0] = np.maximum(
+ np.minimum(input_box[:, :, 0], im_w - 1), 0)
+ output_box[:, :, 1] = np.maximum(
+ np.minimum(input_box[:, :, 1], im_h - 1), 0)
+ output_box[:, :, 2] = np.maximum(
+ np.minimum(input_box[:, :, 2], im_w - 1), 0)
+ output_box[:, :, 3] = np.maximum(
+ np.minimum(input_box[:, :, 3], im_h - 1), 0)
+
+
+def batch_box_clip(input_boxes, im_info, lod):
+ n = input_boxes.shape[0]
+ m = input_boxes.shape[1]
+ output_boxes = np.zeros((n, m, 4), dtype=np.float32)
+ cur_offset = 0
+ for i in range(len(lod)):
+ box_clip(input_boxes[cur_offset:(cur_offset + lod[i]), :, :],
+ im_info[i, :],
+ output_boxes[cur_offset:(cur_offset + lod[i]), :, :])
+ cur_offset += lod[i]
+ return output_boxes
+
+
+class TestBoxClipOp(OpTest):
+ def test_check_output(self):
+ self.check_output()
+
+ def setUp(self):
+ self.op_type = "box_clip"
+ lod = [[1, 2, 3]]
+ input_boxes = np.random.random((6, 10, 4)) * 5
+ im_info = np.array([[5, 8, 1.], [6, 6, 1.], [7, 5, 1.]])
+ output_boxes = batch_box_clip(input_boxes, im_info, lod[0])
+
+ self.inputs = {
+ 'Input': (input_boxes.astype('float32'), lod),
+ 'ImInfo': im_info.astype('float32'),
+ }
+ self.outputs = {'Output': output_boxes}
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/python/paddle/fluid/tests/unittests/test_box_coder_op.py b/python/paddle/fluid/tests/unittests/test_box_coder_op.py
index 6156268bf2..220bffebe8 100644
--- a/python/paddle/fluid/tests/unittests/test_box_coder_op.py
+++ b/python/paddle/fluid/tests/unittests/test_box_coder_op.py
@@ -34,7 +34,9 @@ def box_decoder(t_box, p_box, pb_v, output_box, norm, axis=0):
pb_y = pb_y.reshape(shape)
if pb_v.ndim == 2:
- pb_v = pb_v.reshape(1, pb_v.shape[0], pb_v.shape[1])
+ var_shape = (1, pb_v.shape[0], pb_v.shape[1]) if axis == 0 else (
+ pb_v.shape[0], 1, pb_v.shape[1])
+ pb_v = pb_v.reshape(var_shape)
if pb_v.ndim == 1:
tb_x = pb_v[0] * t_box[:, :, 0] * pb_w + pb_x
tb_y = pb_v[1] * t_box[:, :, 1] * pb_h + pb_y
@@ -125,33 +127,6 @@ class TestBoxCoderOp(OpTest):
self.outputs = {'OutputBox': output_box}
-class TestBoxCoderOpWithOneRankVar(OpTest):
- def test_check_output(self):
- self.check_output()
-
- def setUp(self):
- self.op_type = "box_coder"
- lod = [[1, 1, 1, 1, 1]]
- prior_box = np.random.random((81, 4)).astype('float32')
- prior_box_var = np.random.random((4)).astype('float32')
- target_box = np.random.random((20, 81, 4)).astype('float32')
- code_type = "DecodeCenterSize"
- box_normalized = False
- output_box = batch_box_coder(prior_box, prior_box_var, target_box,
- lod[0], code_type, box_normalized)
-
- self.inputs = {
- 'PriorBox': prior_box,
- 'PriorBoxVar': prior_box_var,
- 'TargetBox': target_box,
- }
- self.attrs = {
- 'code_type': 'decode_center_size',
- 'box_normalized': False
- }
- self.outputs = {'OutputBox': output_box}
-
-
class TestBoxCoderOpWithoutBoxVar(OpTest):
def test_check_output(self):
self.check_output()
@@ -210,7 +185,7 @@ class TestBoxCoderOpWithAxis(OpTest):
self.op_type = "box_coder"
lod = [[1, 1, 1, 1, 1]]
prior_box = np.random.random((30, 4)).astype('float32')
- prior_box_var = np.random.random((4)).astype('float32')
+ prior_box_var = np.random.random((30, 4)).astype('float32')
target_box = np.random.random((30, 81, 4)).astype('float32')
code_type = "DecodeCenterSize"
box_normalized = False
diff --git a/python/paddle/fluid/tests/unittests/test_eager_deletion_transformer.py b/python/paddle/fluid/tests/unittests/test_eager_deletion_transformer.py
index 754d5fd409..603c8e7488 100644
--- a/python/paddle/fluid/tests/unittests/test_eager_deletion_transformer.py
+++ b/python/paddle/fluid/tests/unittests/test_eager_deletion_transformer.py
@@ -16,12 +16,10 @@ import os
import unittest
os.environ['FLAGS_eager_delete_tensor_gb'] = "0.0"
-from test_parallel_executor_transformer import TestTransformer
-
-
-class EagerDeletionTestTransformer(TestTransformer):
- pass
+os.environ[
+ 'RECORDIO_FILENAME'] = '/tmp/eager_deletion_transformer.wmt16.recordio'
+from test_parallel_executor_transformer import TestTransformer
if __name__ == '__main__':
unittest.main()
diff --git a/python/paddle/fluid/tests/unittests/test_inference_model_io.py b/python/paddle/fluid/tests/unittests/test_inference_model_io.py
index 9962702f69..9c9f863307 100644
--- a/python/paddle/fluid/tests/unittests/test_inference_model_io.py
+++ b/python/paddle/fluid/tests/unittests/test_inference_model_io.py
@@ -25,6 +25,7 @@ import paddle.fluid.layers as layers
import paddle.fluid.optimizer as optimizer
from paddle.fluid.framework import Program, program_guard
from paddle.fluid.io import save_inference_model, load_inference_model
+from paddle.fluid.transpiler import memory_optimize
class TestBook(unittest.TestCase):
@@ -82,9 +83,36 @@ class TestBook(unittest.TestCase):
self.assertEqual(feed_var_names, ["x", "y"])
self.assertEqual(len(fetch_vars), 1)
- self.assertEqual(str(fetch_vars[0]), str(avg_cost))
+ print("fetch %s" % str(fetch_vars[0]))
+ self.assertTrue("scale" in str(fetch_vars[0]))
self.assertEqual(expected, actual)
+class TestSaveInferenceModel(unittest.TestCase):
+ def test_save_inference_model(self):
+ MODEL_DIR = "./tmp/inference_model2"
+ init_program = Program()
+ program = Program()
+
+ # fake program without feed/fetch
+ with program_guard(program, init_program):
+ x = layers.data(name='x', shape=[2], dtype='float32')
+ y = layers.data(name='y', shape=[1], dtype='float32')
+
+ y_predict = layers.fc(input=x, size=1, act=None)
+
+ cost = layers.square_error_cost(input=y_predict, label=y)
+ avg_cost = layers.mean(cost)
+
+ place = core.CPUPlace()
+ exe = executor.Executor(place)
+ exe.run(init_program, feed={}, fetch_list=[])
+
+ memory_optimize(program, print_log=True)
+ self.assertEqual(program._is_mem_optimized, True)
+ # will print warning message
+ save_inference_model(MODEL_DIR, ["x", "y"], [avg_cost], exe, program)
+
+
if __name__ == '__main__':
unittest.main()
diff --git a/python/paddle/fluid/tests/unittests/test_ir_inplace_pass.py b/python/paddle/fluid/tests/unittests/test_ir_inplace_pass.py
new file mode 100644
index 0000000000..4e196758ef
--- /dev/null
+++ b/python/paddle/fluid/tests/unittests/test_ir_inplace_pass.py
@@ -0,0 +1,76 @@
+# Copyright (c) 2019 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 os
+import unittest
+import numpy as np
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+from parallel_executor_test_base import TestParallelExecutorBase
+
+
+def fc_with_batchnorm(use_feed):
+ img = fluid.layers.data(name='image', shape=[784], dtype='float32')
+ label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+
+ hidden = img
+ for _ in range(3):
+ hidden = fluid.layers.fc(
+ hidden,
+ size=200,
+ act='tanh',
+ bias_attr=fluid.ParamAttr(
+ initializer=fluid.initializer.Constant(value=1.0)))
+
+ hidden = fluid.layers.batch_norm(input=hidden)
+ prediction = fluid.layers.fc(hidden, size=10, act='softmax')
+ loss = fluid.layers.cross_entropy(input=prediction, label=label)
+ loss = fluid.layers.mean(loss)
+ return loss
+
+
+class TestIrInplace(TestParallelExecutorBase):
+ @classmethod
+ def setUpClass(cls):
+ os.environ['CPU_NUM'] = str(4)
+
+ def _fc_with_batchnorm(self,
+ ir_memory_optimize,
+ enable_inplace,
+ memory_opt=False):
+
+ if not core.is_compiled_with_cuda():
+ return
+ np.random.seed(5)
+ img = np.random.random(size=[32, 784]).astype(np.float32)
+ label = np.ones(shape=[32, 1], dtype='int64')
+ self.check_network_convergence(
+ fc_with_batchnorm,
+ feed_dict={"image": img,
+ "label": label},
+ use_cuda=True,
+ memory_opt=memory_opt,
+ use_ir_memory_optimize=ir_memory_optimize,
+ enable_inplace=enable_inplace)
+
+ def test_fc_with_batchnorm(self, delta=1e-3):
+ loss00 = self._fc_with_batchnorm(False, False)
+ loss10 = self._fc_with_batchnorm(True, False)
+ loss01 = self._fc_with_batchnorm(False, True)
+ loss11 = self._fc_with_batchnorm(True, True)
+ self.assertAlmostEqual(loss00, loss10, delta=delta)
+ self.assertAlmostEqual(loss00, loss01, delta=delta)
+ self.assertAlmostEqual(loss00, loss11, delta=delta)
diff --git a/python/paddle/fluid/tests/unittests/test_parallel_executor_seresnext.py b/python/paddle/fluid/tests/unittests/test_parallel_executor_seresnext.py
index e7a56bb638..9548598d75 100644
--- a/python/paddle/fluid/tests/unittests/test_parallel_executor_seresnext.py
+++ b/python/paddle/fluid/tests/unittests/test_parallel_executor_seresnext.py
@@ -200,7 +200,7 @@ class TestResnet(TestParallelExecutorBase):
model,
use_cuda,
iter=20,
- delta2=1e-6):
+ delta2=1e-5):
if use_cuda and not core.is_compiled_with_cuda():
return
@@ -228,7 +228,7 @@ class TestResnet(TestParallelExecutorBase):
optimizer=optimizer)
for loss in zip(all_reduce_first_loss, reduce_first_loss):
- self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
+ self.assertAlmostEquals(loss[0], loss[1], delta=1e-5)
for loss in zip(all_reduce_last_loss, reduce_last_loss):
self.assertAlmostEquals(loss[0], loss[1], delta=delta2)
@@ -258,17 +258,17 @@ class TestResnet(TestParallelExecutorBase):
enable_sequential_execution=True)
for loss in zip(all_reduce_first_loss, all_reduce_first_loss_seq):
- self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
+ self.assertAlmostEquals(loss[0], loss[1], delta=1e-5)
for loss in zip(all_reduce_last_loss, all_reduce_last_loss_seq):
self.assertAlmostEquals(loss[0], loss[1], delta=delta2)
for loss in zip(reduce_first_loss, reduce_first_loss_seq):
- self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
+ self.assertAlmostEquals(loss[0], loss[1], delta=1e-5)
for loss in zip(reduce_last_loss, reduce_last_loss_seq):
self.assertAlmostEquals(loss[0], loss[1], delta=delta2)
for loss in zip(all_reduce_first_loss_seq, reduce_first_loss_seq):
- self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
+ self.assertAlmostEquals(loss[0], loss[1], delta=1e-5)
for loss in zip(all_reduce_last_loss_seq, reduce_last_loss_seq):
self.assertAlmostEquals(loss[0], loss[1], delta=delta2)
@@ -277,7 +277,7 @@ class TestResnet(TestParallelExecutorBase):
use_cuda=True,
use_reduce=False,
iter=20,
- delta2=1e-6):
+ delta2=1e-5):
if use_cuda and not core.is_compiled_with_cuda():
return
@@ -308,7 +308,7 @@ class TestResnet(TestParallelExecutorBase):
optimizer=optimizer)
self.assertAlmostEquals(
- np.mean(parallel_first_loss), single_first_loss[0], delta=1e-6)
+ np.mean(parallel_first_loss), single_first_loss[0], delta=1e-5)
self.assertAlmostEquals(
np.mean(parallel_last_loss), single_last_loss[0], delta=delta2)
diff --git a/python/paddle/fluid/tests/unittests/test_parallel_executor_transformer.py b/python/paddle/fluid/tests/unittests/test_parallel_executor_transformer.py
index 3827743908..aacc1c3ecd 100644
--- a/python/paddle/fluid/tests/unittests/test_parallel_executor_transformer.py
+++ b/python/paddle/fluid/tests/unittests/test_parallel_executor_transformer.py
@@ -24,7 +24,7 @@ import paddle.fluid.core as core
import paddle.dataset.wmt16 as wmt16
import os
-WMT16_RECORDIO_FILE = "/tmp/wmt16.recordio"
+WMT16_RECORDIO_FILE = os.environ.get('RECORDIO_FILENAME', '/tmp/wmt16.recordio')
class ModelHyperParams(object):
diff --git a/python/paddle/fluid/tests/unittests/test_peak_gpumem_monitor.py b/python/paddle/fluid/tests/unittests/test_peak_gpumem_monitor.py
new file mode 100644
index 0000000000..3673fd10c4
--- /dev/null
+++ b/python/paddle/fluid/tests/unittests/test_peak_gpumem_monitor.py
@@ -0,0 +1,59 @@
+# 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 os
+os.environ['FLAGS_benchmark'] = 'True'
+
+import numpy
+import paddle.fluid.core as core
+from paddle.fluid.executor import Executor
+from paddle.fluid.layers import mul, data
+
+
+class TestPeakMemoryMonitoring(unittest.TestCase):
+ def test_mul(self):
+
+ a = data(name='a', shape=[784], dtype='float32')
+ b = data(
+ name='b',
+ shape=[784, 100],
+ dtype='float32',
+ append_batch_size=False)
+ out = mul(x=a, y=b)
+
+ if core.is_compiled_with_cuda():
+ place = core.CUDAPlace(0)
+
+ a_np = numpy.random.random((100, 784)).astype('float32')
+ b_np = numpy.random.random((784, 100)).astype('float32')
+ self.assertEqual(0, core.get_mem_usage(0))
+ exe = Executor(place)
+ outs = exe.run(feed={'a': a_np, 'b': b_np}, fetch_list=[out])
+ out = outs[0]
+ #disable this assert since ctest will ignore the os.environ setting
+ #self.assertGreater(core.get_mem_usage(0), 0)
+
+ raised = False
+ try:
+ core.print_mem_usage()
+ except:
+ raised = True
+ self.assertFalse(raised, 'Exception raised')
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/python/paddle/fluid/tests/unittests/transformer_model.py b/python/paddle/fluid/tests/unittests/transformer_model.py
index 143d187edc..905b7d6fe7 100644
--- a/python/paddle/fluid/tests/unittests/transformer_model.py
+++ b/python/paddle/fluid/tests/unittests/transformer_model.py
@@ -17,6 +17,7 @@ from __future__ import print_function
from functools import partial
import numpy as np
+import os
import paddle.fluid as fluid
import paddle.fluid.layers as layers
from paddle.fluid.layers.io import open_recordio_file
@@ -408,7 +409,7 @@ def transformer(
trg_pad_idx,
pos_pad_idx, ):
file_obj = open_recordio_file(
- filename='/tmp/wmt16.recordio',
+ filename=os.environ.get('RECORDIO_FILENAME', '/tmp/wmt16.recordio'),
shapes=[
[batch_size * max_length, 1],
[batch_size * max_length, 1],
diff --git a/python/paddle/fluid/transpiler/memory_optimization_transpiler.py b/python/paddle/fluid/transpiler/memory_optimization_transpiler.py
index e5d48d3d19..52c1aea288 100755
--- a/python/paddle/fluid/transpiler/memory_optimization_transpiler.py
+++ b/python/paddle/fluid/transpiler/memory_optimization_transpiler.py
@@ -540,6 +540,7 @@ def memory_optimize(input_program,
if skip_opt_set is not None:
skip_opt_set = set(map(to_name_str, skip_opt_set))
cfgs = _get_cfgs(input_program)
+ input_program._is_mem_optimized = True
for cfg in cfgs:
cfg.memory_optimize(skip_opt_set=skip_opt_set, level=level)
@@ -559,5 +560,6 @@ def release_memory(input_program, skip_opt_set=None):
None
"""
cfgs = _get_cfgs(input_program)
+ input_program._is_mem_optimized = True
for cfg in cfgs:
cfg.release_memory(skip_opt_set=skip_opt_set)
diff --git a/python/paddle/fluid/unique_name.py b/python/paddle/fluid/unique_name.py
index b9957a699e..324257c13f 100644
--- a/python/paddle/fluid/unique_name.py
+++ b/python/paddle/fluid/unique_name.py
@@ -15,7 +15,7 @@
from __future__ import print_function
import collections
-import contextlib
+from .wrapped_decorator import signature_safe_contextmanager
import six
import sys
@@ -68,7 +68,7 @@ def switch(new_generator=None):
return old
-@contextlib.contextmanager
+@signature_safe_contextmanager
def guard(new_generator=None):
if isinstance(new_generator, six.string_types):
new_generator = UniqueNameGenerator(new_generator)
diff --git a/python/paddle/fluid/wrapped_decorator.py b/python/paddle/fluid/wrapped_decorator.py
new file mode 100644
index 0000000000..7e7dbff656
--- /dev/null
+++ b/python/paddle/fluid/wrapped_decorator.py
@@ -0,0 +1,30 @@
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import decorator
+import contextlib
+
+__all__ = ['wrap_decorator', 'signature_safe_contextmanager']
+
+
+def wrap_decorator(decorator_func):
+ @decorator.decorator
+ def __impl__(func, *args, **kwargs):
+ wrapped_func = decorator_func(func)
+ return wrapped_func(*args, **kwargs)
+
+ return __impl__
+
+
+signature_safe_contextmanager = wrap_decorator(contextlib.contextmanager)
diff --git a/python/requirements.txt b/python/requirements.txt
index 03d5e33e88..5a70f1aa3f 100644
--- a/python/requirements.txt
+++ b/python/requirements.txt
@@ -11,3 +11,4 @@ graphviz
six
funcsigs
pyyaml
+decorator
diff --git a/python/setup.py.in b/python/setup.py.in
index f93f0cd130..a7c1e91f9c 100644
--- a/python/setup.py.in
+++ b/python/setup.py.in
@@ -100,6 +100,7 @@ packages=['paddle',
'paddle.utils',
'paddle.dataset',
'paddle.reader',
+ 'paddle.distributed',
'paddle.fluid',
'paddle.fluid.imperative',
'paddle.fluid.proto',