diff --git a/.travis.yml b/.travis.yml index 0705baa1aca8b480b2a774076bd91fb9df401a53..162bebba091d84b295f929527de9804e65df5a65 100644 --- a/.travis.yml +++ b/.travis.yml @@ -25,9 +25,9 @@ addons: packages: - gcc-4.8 - g++-4.8 + - gfortran-4.8 - git - build-essential - - libatlas-base-dev - python - python-pip - python2.7-dev diff --git a/CMakeLists.txt b/CMakeLists.txt index c80c5c03c618d978818dfb938a67037b4f67ffe8..e991a9a0ea0cf2a3d4f5f1e900bfc38e703aaf39 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -30,7 +30,7 @@ include(simd) ################################ Configurations ####################################### option(WITH_GPU "Compile PaddlePaddle with NVIDIA GPU" ${CUDA_FOUND}) option(WITH_AVX "Compile PaddlePaddle with AVX intrinsics" ${AVX_FOUND}) -option(WITH_DSO "Compile PaddlePaddle with dynamic linked CUDA" OFF) +option(WITH_DSO "Compile PaddlePaddle with dynamic linked CUDA" ON) option(WITH_TESTING "Compile PaddlePaddle with unit testing" ON) option(WITH_SWIG_PY "Compile PaddlePaddle with inference api" ON) option(WITH_STYLE_CHECK "Compile PaddlePaddle with style check" ON) diff --git a/cmake/cblas.cmake b/cmake/cblas.cmake index 4e1ae7dc81231943c4bf3db4d4ac6f073f4fd1c4..26306f9849100d4463dde267acae5392cc81d7ac 100644 --- a/cmake/cblas.cmake +++ b/cmake/cblas.cmake @@ -16,7 +16,7 @@ set(CBLAS_FOUND OFF) ## Find MKL First. -set(MKL_ROOT $ENV{MKL_ROOT} CACHE PATH "Folder contains MKL") +set(MKL_ROOT $ENV{MKLROOT} CACHE PATH "Folder contains MKL") find_path(MKL_INCLUDE_DIR mkl.h PATHS ${MKL_ROOT}/include) diff --git a/cmake/external/openblas.cmake b/cmake/external/openblas.cmake index 0e8c29c831c823f701d8eecd954d3b120085e495..29d17691db9f4575bae4372c61a0e1964e163fc9 100644 --- a/cmake/external/openblas.cmake +++ b/cmake/external/openblas.cmake @@ -15,7 +15,6 @@ INCLUDE(cblas) IF(NOT ${CBLAS_FOUND}) - MESSAGE(FATAL_ERROR "Please install OpenBlas, MKL or ATLAS.") INCLUDE(ExternalProject) SET(CBLAS_SOURCES_DIR ${THIRD_PARTY_PATH}/openblas) @@ -28,20 +27,40 @@ IF(NOT ${CBLAS_FOUND}) SET(CBLAS_LIBRARIES "${CBLAS_INSTALL_DIR}/lib/libopenblas.a" CACHE FILEPATH "openblas library" FORCE) ENDIF(WIN32) + IF(CMAKE_COMPILER_IS_GNUCC) + ENABLE_LANGUAGE(Fortran) + LIST(APPEND CBLAS_LIBRARIES gfortran pthread) + ENDIF(CMAKE_COMPILER_IS_GNUCC) + + IF(NOT CMAKE_Fortran_COMPILER) + MESSAGE(FATAL_ERROR "To build lapack in libopenblas, " + "you need to set gfortran compiler: cmake .. -DCMAKE_Fortran_COMPILER=...") + ENDIF(NOT CMAKE_Fortran_COMPILER) + ExternalProject_Add( openblas ${EXTERNAL_PROJECT_LOG_ARGS} - URL "https://github.com/xianyi/OpenBLAS/archive/v0.2.19.tar.gz" + GIT_REPOSITORY https://github.com/xianyi/OpenBLAS.git + GIT_TAG v0.2.19 PREFIX ${CBLAS_SOURCES_DIR} INSTALL_DIR ${CBLAS_INSTALL_DIR} BUILD_IN_SOURCE 1 - CONFIGURE_COMMAND "" - BUILD_COMMAND make CC=${CMAKE_C_COMPILER} FC=${CMAKE_Fortran_COMPILER} - INSTALL_COMMAND make install PREFIX= + BUILD_COMMAND ${CMAKE_MAKE_PROGRAM} FC=${CMAKE_Fortran_COMPILER} CC=${CMAKE_C_COMPILER} HOSTCC=${CMAKE_C_COMPILER} NO_SHARED=1 libs netlib + INSTALL_COMMAND ${CMAKE_MAKE_PROGRAM} install NO_SHARED=1 PREFIX= UPDATE_COMMAND "" + CONFIGURE_COMMAND "" + ) + + ExternalProject_Add_Step( + openblas lapacke_install + COMMAND ${CMAKE_COMMAND} -E copy "${CBLAS_SOURCES_DIR}/src/openblas/lapack-netlib/LAPACKE/include/lapacke_mangling_with_flags.h" "${CBLAS_INSTALL_DIR}/include/lapacke_mangling.h" + COMMAND ${CMAKE_COMMAND} -E copy "${CBLAS_SOURCES_DIR}/src/openblas/lapack-netlib/LAPACKE/include/lapacke.h" "${CBLAS_INSTALL_DIR}/include/lapacke.h" + COMMAND ${CMAKE_COMMAND} -E copy "${CBLAS_SOURCES_DIR}/src/openblas/lapack-netlib/LAPACKE/include/lapacke_config.h" "${CBLAS_INSTALL_DIR}/include/lapacke_config.h" + COMMAND ${CMAKE_COMMAND} -E copy "${CBLAS_SOURCES_DIR}/src/openblas/lapack-netlib/LAPACKE/include/lapacke_utils.h" "${CBLAS_INSTALL_DIR}/include/lapacke_utils.h" + DEPENDEES install ) LIST(APPEND external_project_dependencies openblas) -ENDIF() +ENDIF(NOT ${CBLAS_FOUND}) INCLUDE_DIRECTORIES(${CBLAS_INC_DIR}) diff --git a/cmake/external/protobuf.cmake b/cmake/external/protobuf.cmake index c0cf2719f9a7b3ae6be5cefffa3dbd2c3f712e82..613614c0e3d42fac4147f78edbc1bd6d62847419 100644 --- a/cmake/external/protobuf.cmake +++ b/cmake/external/protobuf.cmake @@ -54,6 +54,7 @@ ExternalProject_Add( CONFIGURE_COMMAND ${CMAKE_COMMAND} ${PROTOBUF_SOURCES_DIR}/src/protobuf/cmake -Dprotobuf_BUILD_TESTS=OFF + -DZLIB_ROOT:FILEPATH=${ZLIB_ROOT} -DCMAKE_POSITION_INDEPENDENT_CODE=ON -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=${PROTOBUF_INSTALL_DIR} diff --git a/cmake/external/python.cmake b/cmake/external/python.cmake index 29247d5c3d474acaa5c65e450780f00b8885ee78..209e679f2cb2178423bf20dec73a0bccef199fcb 100644 --- a/cmake/external/python.cmake +++ b/cmake/external/python.cmake @@ -31,6 +31,7 @@ IF(PYTHONLIBS_FOUND AND PYTHONINTERP_FOUND) "please use pip to upgrade protobuf.") ENDIF(${PY_GOOGLE.PROTOBUF_VERSION} VERSION_LESS "3.0.0") ELSE(PYTHONLIBS_FOUND AND PYTHONINTERP_FOUND) + MESSAGE(FATAL_ERROR "Please install python 2.7 before building PaddlePaddle.") ##################################### PYTHON ######################################## SET(PYTHON_SOURCES_DIR ${THIRD_PARTY_PATH}/python) SET(PYTHON_INSTALL_DIR ${THIRD_PARTY_PATH}/install/python) diff --git a/cmake/external/swig.cmake b/cmake/external/swig.cmake index 63e8bd25462e50e2f78908899938468c989b3ac3..744c766ee7b067058b2cb4aa7f7b761cbb9778d4 100644 --- a/cmake/external/swig.cmake +++ b/cmake/external/swig.cmake @@ -38,14 +38,6 @@ IF(NOT SWIG_FOUND) SET(SWIG_DIR ${SWIG_SOURCES_DIR} CACHE FILEPATH "SWIG Directory" FORCE) SET(SWIG_EXECUTABLE ${SWIG_SOURCES_DIR}/swig.exe CACHE FILEPATH "SWIG Executable" FORCE) ELSE(WIN32) - # From PCRE configure - ExternalProject_Add(pcre - ${EXTERNAL_PROJECT_LOG_ARGS} - GIT_REPOSITORY https://github.com/svn2github/pcre.git - PREFIX ${SWIG_SOURCES_DIR}/pcre - CMAKE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${SWIG_INSTALL_DIR}/pcre - ) - # swig uses bison find it by cmake and pass it down FIND_PACKAGE(BISON) @@ -54,16 +46,11 @@ IF(NOT SWIG_FOUND) GIT_REPOSITORY https://github.com/swig/swig.git GIT_TAG rel-3.0.10 PREFIX ${SWIG_SOURCES_DIR} - CONFIGURE_COMMAND cd ${SWIG_SOURCES_DIR}/src/swig && ./autogen.sh - CONFIGURE_COMMAND cd ${SWIG_SOURCES_DIR}/src/swig && - env "PCRE_LIBS=${SWIG_INSTALL_DIR}/pcre/lib/libpcre.a ${SWIG_INSTALL_DIR}/pcre/lib/libpcrecpp.a ${SWIG_INSTALL_DIR}/pcre/lib/libpcreposix.a" - ./configure - --prefix=${SWIG_INSTALL_DIR} - --with-pcre-prefix=${SWIG_INSTALL_DIR}/pcre - BUILD_COMMAND cd ${SWIG_SOURCES_DIR}/src/swig && make - INSTALL_COMMAND cd ${SWIG_SOURCES_DIR}/src/swig && make install - UPDATE_COMMAND "" - DEPENDS pcre + CONFIGURE_COMMAND cd && ./autogen.sh && ./configure + --prefix=${SWIG_INSTALL_DIR} --without-pcre + BUILD_COMMAND cd && make + INSTALL_COMMAND cd && make install + UPDATE_COMMAND "" ) SET(SWIG_DIR ${SWIG_INSTALL_DIR}/share/swig/${SWIG_TARGET_VERSION}) diff --git a/cmake/flags.cmake b/cmake/flags.cmake index 0d1ef5cd8449bd31b4cfa4619f27bce7c1f55ebb..b76852fc6c50e80633c8294fb2724b83f15293a7 100644 --- a/cmake/flags.cmake +++ b/cmake/flags.cmake @@ -96,6 +96,7 @@ set(COMMON_FLAGS -Wno-unused-parameter -Wno-unused-function -Wno-error=literal-suffix + -Wno-error=sign-compare -Wno-error=unused-local-typedefs) set(GPU_COMMON_FLAGS @@ -105,6 +106,7 @@ set(GPU_COMMON_FLAGS -Wdelete-non-virtual-dtor -Wno-unused-parameter -Wno-unused-function + -Wno-error=sign-compare -Wno-error=literal-suffix -Wno-error=unused-local-typedefs -Wno-error=unused-function # Warnings in Numpy Header. diff --git a/cmake/system.cmake b/cmake/system.cmake index 788db404ebfb6facbaedf2910186f3b1afe775c1..ab124a89dcdc1cc5b865f6a15c1693b2f72ea39a 100644 --- a/cmake/system.cmake +++ b/cmake/system.cmake @@ -21,6 +21,7 @@ ELSE(WIN32) SET(MACOS_VERSION ${VERSION}) SET(HOST_SYSTEM "macosx") ELSE(APPLE) + IF(EXISTS "/etc/issue") FILE(READ "/etc/issue" LINUX_ISSUE) IF(LINUX_ISSUE MATCHES "CentOS") @@ -31,6 +32,14 @@ ELSE(WIN32) SET(HOST_SYSTEM "ubuntu") ENDIF() ENDIF(EXISTS "/etc/issue") + + IF(EXISTS "/etc/redhat-release") + FILE(READ "/etc/redhat-release" LINUX_ISSUE) + IF(LINUX_ISSUE MATCHES "CentOS") + SET(HOST_SYSTEM "centos") + ENDIF() + ENDIF(EXISTS "/etc/redhat-release") + ENDIF(APPLE) ENDIF(WIN32) @@ -47,7 +56,7 @@ SET(EXTERNAL_PROJECT_LOG_ARGS LOG_DOWNLOAD 0 # Wrap download in script to log output LOG_UPDATE 1 # Wrap update in script to log output LOG_CONFIGURE 1 # Wrap configure in script to log output - LOG_BUILD 1 # Wrap build in script to log output + LOG_BUILD 0 # Wrap build in script to log output LOG_TEST 1 # Wrap test in script to log output - LOG_INSTALL 1 # Wrap install in script to log output + LOG_INSTALL 0 # Wrap install in script to log output ) diff --git a/cmake/util.cmake b/cmake/util.cmake index 7da52bb758a3ab28db85f7c6c4d3a99b141e6342..24ad5c815ca20d9b6b317b1be4d2dc93a9e06fba 100644 --- a/cmake/util.cmake +++ b/cmake/util.cmake @@ -108,6 +108,7 @@ function(link_paddle_exe TARGET_NAME) endif() if(WITH_GPU) + target_link_libraries(${TARGET_NAME} ${CUDA_CUDART_LIBRARY}) if(NOT WITH_DSO OR WITH_METRIC) target_link_libraries(${TARGET_NAME} ${CUDNN_LIBRARY} diff --git a/demo/image_classification/train.sh b/demo/image_classification/train.sh index 6fc11caf1c75192242482c2e85f8167eb9fba4ec..e45bd47ad5925c6674d628a70a7ad7c4d5d5c173 100755 --- a/demo/image_classification/train.sh +++ b/demo/image_classification/train.sh @@ -27,5 +27,6 @@ paddle train \ --num_passes=300 \ --save_dir=$output \ 2>&1 | tee $log +paddle usage -l $log -e $? -n "image_classification_train" >/dev/null 2>&1 python -m paddle.utils.plotcurve -i $log > plot.png diff --git a/demo/introduction/train.sh b/demo/introduction/train.sh index b7bbb90ddd287e3e312a490b53924ae76fb20d2c..2ce6446d7c943ffc9bea8da43d153539f6f9f15f 100755 --- a/demo/introduction/train.sh +++ b/demo/introduction/train.sh @@ -19,3 +19,4 @@ paddle train \ --save_dir=./output \ --num_passes=30 \ 2>&1 |tee 'train.log' +paddle usage -l "train.log" -e $? -n "introduction" >/dev/null 2>&1 diff --git a/demo/mnist/train.sh b/demo/mnist/train.sh index da90cd749a02976633d0f0d6e4352d8a85c7cdef..ca2b1ad9eb960685b95b0f294a9b929e1a4acab1 100755 --- a/demo/mnist/train.sh +++ b/demo/mnist/train.sh @@ -27,5 +27,6 @@ paddle train \ --num_passes=100 \ --save_dir=$output \ 2>&1 | tee $log +paddle usage -l $log -e $? -n "mnist_train" >/dev/null 2>&1 python -m paddle.utils.plotcurve -i $log > plot.png diff --git a/demo/quick_start/cluster/cluster_train.sh b/demo/quick_start/cluster/cluster_train.sh index aac9b89b14b98ac8e2db7def19e5f06c01682493..a7b1f01064b29cf6abc4cd6b706ee466a6d6da36 100755 --- a/demo/quick_start/cluster/cluster_train.sh +++ b/demo/quick_start/cluster/cluster_train.sh @@ -25,6 +25,7 @@ log_file="$bin_dir/train.log" pushd "$home_dir" cfg=trainer_config.lr.py paddle train \ + --start_pserver=false \ --config=$cfg \ --save_dir=${model_dir} \ --trainer_count=4 \ diff --git a/demo/quick_start/predict.sh b/demo/quick_start/predict.sh index f02e5038e92790c7f1ddcd84a09c6d9a02f84ac4..e47c2dd01fb5c919203964e298018e6dc2bd366e 100755 --- a/demo/quick_start/predict.sh +++ b/demo/quick_start/predict.sh @@ -26,5 +26,7 @@ paddle train \ --init_model_path=$model \ --config_args=is_predict=1 \ --predict_output_dir=. \ +2>&1 | tee 'predict.log' +paddle usage -l 'predict.log' -e $? -n "quick_start_predict_${cfg}" >/dev/null 2>&1 mv rank-00000 result.txt diff --git a/demo/quick_start/train.sh b/demo/quick_start/train.sh index e3595fce7519297058e1eeb66487692267ddcfcc..01697fed48054be8ad98a01d4cbb5029e6a1ead0 100755 --- a/demo/quick_start/train.sh +++ b/demo/quick_start/train.sh @@ -31,3 +31,4 @@ paddle train \ --show_parameter_stats_period=100 \ --test_all_data_in_one_period=1 \ 2>&1 | tee 'train.log' +paddle usage -l "train.log" -e $? -n "quick_start_${cfg}" >/dev/null 2>&1 diff --git a/demo/recommendation/run.sh b/demo/recommendation/run.sh index e341d1cc7a3267bef9db916719b2e4b1981e31bc..22aef556082ba429e9ca7c6dd3ec72699b9dbcf4 100755 --- a/demo/recommendation/run.sh +++ b/demo/recommendation/run.sh @@ -22,3 +22,4 @@ paddle train \ --log_period=100 \ --dot_period=1 \ --num_passes=50 2>&1 | tee 'log.txt' +paddle usage -l log.txt -e $? -n "recommendation" >/dev/null 2>&1 diff --git a/demo/semantic_role_labeling/test.sh b/demo/semantic_role_labeling/test.sh index 11d9d6a19c1b17ad1b7540ee7a03017f85dd821e..095bbff2ea42627a13d8ebab436f5a05abc09743 100755 --- a/demo/semantic_role_labeling/test.sh +++ b/demo/semantic_role_labeling/test.sh @@ -38,3 +38,4 @@ paddle train \ --config_args=is_test=1 \ --test_all_data_in_one_period=1 \ 2>&1 | tee 'test.log' +paddle usage -l test.log -e $? -n "semantic_role_labeling_test" >/dev/null 2>&1 diff --git a/demo/semantic_role_labeling/train.sh b/demo/semantic_role_labeling/train.sh index 9354e72f46dc4dfc46138a04c330933d404c6cb8..eee14010d7b04a1b824f39090fa82fc532085e0d 100755 --- a/demo/semantic_role_labeling/train.sh +++ b/demo/semantic_role_labeling/train.sh @@ -27,3 +27,4 @@ paddle train \ --load_missing_parameter_strategy=rand \ --test_all_data_in_one_period=1 \ 2>&1 | tee 'train.log' +paddle usage -l train.log -e $? -n "semantic_role_labeling_train" >/dev/null 2>&1 diff --git a/demo/sentiment/test.sh b/demo/sentiment/test.sh index 8af827c3388c8df88a872bd87d121a4f9631c3ff..85c4f3ccfc3ede23fcf701769b9701ecbf57c789 100755 --- a/demo/sentiment/test.sh +++ b/demo/sentiment/test.sh @@ -37,3 +37,4 @@ paddle train --config=$net_conf \ --trainer_count=4 \ --config_args=is_test=1 \ 2>&1 | tee 'test.log' +paddle usage -l test.log -e $? -n "sentiment_test" >/dev/null 2>&1 diff --git a/demo/sentiment/train.sh b/demo/sentiment/train.sh index 5ce8bf4b997d962b9b61593cec0954d76c4874bc..14620f733bf03444e5ba3b3b792dfbed6146ecde 100755 --- a/demo/sentiment/train.sh +++ b/demo/sentiment/train.sh @@ -27,3 +27,4 @@ paddle train --config=$config \ --show_parameter_stats_period=100 \ --test_all_data_in_one_period=1 \ 2>&1 | tee 'train.log' +paddle usage -l train.log -e $? -n "sentiment_train" >/dev/null 2>&1 diff --git a/demo/seqToseq/paraphrase/train.sh b/demo/seqToseq/paraphrase/train.sh index 33a42f6eff2b0414c466d5f78c89989a6a517eb9..9bb6dbdb1d4c5e35bfb31855e0331f0250a69a20 100755 --- a/demo/seqToseq/paraphrase/train.sh +++ b/demo/seqToseq/paraphrase/train.sh @@ -27,3 +27,4 @@ paddle train \ --log_period=10 \ --dot_period=5 \ 2>&1 | tee 'paraphrase/train.log' +paddle usage -l 'paraphrase/train.log' -e $? -n "seqToseq_paraphrase_train" >/dev/null 2>&1 diff --git a/demo/seqToseq/translation/gen.sh b/demo/seqToseq/translation/gen.sh index a700ae213473dfe7c5b77156de15775b8fe9a9f0..64b78f5e9654e7b206740f92e224e0164108c9f1 100755 --- a/demo/seqToseq/translation/gen.sh +++ b/demo/seqToseq/translation/gen.sh @@ -24,3 +24,4 @@ paddle train \ --test_pass=12 \ --trainer_count=1 \ 2>&1 | tee 'translation/gen.log' +paddle usage -l 'translation/gen.log' -e $? -n "seqToseq_translation_gen" >/dev/null 2>&1 diff --git a/demo/seqToseq/translation/train.sh b/demo/seqToseq/translation/train.sh index bdece693e5c407c89bc172c461bac7f9b20560d3..b0ec9854b118cbb9ed39d6bed0cdd845403926a4 100755 --- a/demo/seqToseq/translation/train.sh +++ b/demo/seqToseq/translation/train.sh @@ -25,3 +25,4 @@ paddle train \ --log_period=10 \ --dot_period=5 \ 2>&1 | tee 'translation/train.log' +paddle usage -l 'translation/train.log' -e $? -n "seqToseq_translation_train" >/dev/null 2>&1 diff --git a/demo/sequence_tagging/train.sh b/demo/sequence_tagging/train.sh index 9a706b98d8686101ba21b513644bdd791062ec26..37e196c84200dc26ccb523076a81dbc393b1280f 100755 --- a/demo/sequence_tagging/train.sh +++ b/demo/sequence_tagging/train.sh @@ -7,4 +7,6 @@ paddle train \ --dot_period=10 \ --log_period=1000 \ --test_period=0 \ - --num_passes=10 + --num_passes=10 \ +2>&1 | tee 'train.log' +paddle usage -l 'train.log' -e $? -n "sequence_tagging_train" >/dev/null 2>&1 diff --git a/demo/sequence_tagging/train_linear.sh b/demo/sequence_tagging/train_linear.sh index 597b5afea9c63a8e209b69b6a40e74556e27ac31..ad6e2d8ee7f813c69f9dd250c6f7bbb4403a0ed5 100755 --- a/demo/sequence_tagging/train_linear.sh +++ b/demo/sequence_tagging/train_linear.sh @@ -7,3 +7,5 @@ paddle train \ --log_period=10000 \ --test_period=0 \ --num_passes=10 +2>&1 | tee 'train_linear.log' +paddle usage -l 'train_linear.log' -e $? -n "sequence_tagging_train_linear" >/dev/null 2>&1 diff --git a/demo/traffic_prediction/predict.sh b/demo/traffic_prediction/predict.sh index cec35dce11d1c146a9e878ebab81abe904d6136c..2dbd5e8805dd97d35c7d58917f8ec6b5033bda03 100755 --- a/demo/traffic_prediction/predict.sh +++ b/demo/traffic_prediction/predict.sh @@ -25,6 +25,6 @@ paddle train \ --config_args=is_predict=1 \ --predict_output_dir=. -python gen_result.py > result.txt +python gen_result.py > result.csv rm -rf rank-00000 diff --git a/doc/faq/index_cn.rst b/doc/faq/index_cn.rst index 7d425a05d46131d84ba895d0fefc3a592a9a36e1..6d5367177da2af6276698f94f86664a5b506dca2 100644 --- a/doc/faq/index_cn.rst +++ b/doc/faq/index_cn.rst @@ -286,22 +286,3 @@ PaddlePaddle的参数使用名字 :code:`name` 作为参数的ID，相同名字 .. code-block:: bash paddle train --use_gpu=true --trainer_count=2 --gpu_id=2 - -12. 编译源码提示warp-ctc/include/ctc.h 找不到的情况 ---------------------------------------------------- - -目前Paddle使用\ :code:`git submodule`\ 来引用一些第三方模块。简单的\ -:code:`git clone`\ 命令不能得到第三方模块的代码。需要使用\: - -.. code-block:: bash - - git clone --recursive https://github.com/PaddlePaddle/Paddle.git - -来获取所有源码。对于已经clone的git版本库，可以在Paddle的源码目录中执行\: - -.. code-block:: bash - - git submodule init - git submodule update - -来获得所有第三方模块。 diff --git a/doc/getstarted/build_and_install/build_from_source_en.md b/doc/getstarted/build_and_install/build_from_source_en.md index aaa07d49d3148266db27670a98c2b27db4dc0a8f..d9d54bff3096cb3520409971dbd1b2e179ac8be1 100644 --- a/doc/getstarted/build_and_install/build_from_source_en.md +++ b/doc/getstarted/build_and_install/build_from_source_en.md @@ -4,6 +4,8 @@ Installing from Sources * [1. Download and Setup](#download) * [2. Requirements](#requirements) * [3. Build on Ubuntu](#ubuntu) +* [4. Build on Centos](#centos) + ## Download and Setup You can download PaddlePaddle from the [github source](https://github.com/PaddlePaddle/Paddle). @@ -11,32 +13,22 @@ You can download PaddlePaddle from the [github source](https://github.com/Paddle ```bash git clone https://github.com/PaddlePaddle/Paddle paddle cd paddle -git submodule update --init --recursive -``` - -If you already have a local PaddlePaddle repo and have not initialized the submodule, your local submodule folder will be empty. You can simply run the last line of the above codes in your PaddlePaddle home directory to initialize your submodule folder. - -If you have already initialized your submodule and you would like to sync with the upstream submodule repo, you can run the following command ``` -git submodule update --remote -``` - ## Requirements To compile the source code, your computer must be equipped with the following dependencies. -- **Compiler**: GCC >= 4.8 or Clang >= 3.3 (AppleClang >= 5.1) -- **CMake**: version >= 2.8 +- **Compiler**: GCC >= 4.8 or Clang >= 3.3 (AppleClang >= 5.1) and gfortran compiler +- **CMake**: CMake >= 3.0 (at least CMake 3.4 on Mac OS X) - **BLAS**: MKL, OpenBlas or ATLAS -- **Protocol Buffers**: version >= 2.4, **Note: 3.x is not supported** -- **Python**: only python 2.7 is supported currently +- **Python**: only support Python 2.7 **Note:** For CUDA 7.0 and CUDA 7.5, GCC 5.0 and up are not supported! For CUDA 8.0, GCC versions later than 5.3 are not supported! ### Options -PaddlePaddle supports some build options. To enable it, first you need to install the related libraries. +PaddlePaddle supports some build options. @@ -47,12 +39,21 @@ PaddlePaddle supports some build options. To enable it, first you need to instal - - - - - - + + + + + + + + + + + + + + +
WITH_GPUCompile with GPU mode.
WITH_DOUBLECompile with double precision floating-point, default: single precision.
WITH_TESTINGCompile with gtest for PaddlePaddle's unit testing.
WITH_DOC Compile to generate PaddlePaddle's docs, default: disabled (OFF).
WITH_SWIG_PYCompile with python predict API, default: disabled (OFF).
WITH_STYLE_CHECKCompile with code style check, default: enabled (ON).
WITH_GPUCompile PaddlePaddle with NVIDIA GPU
WITH_AVXCompile PaddlePaddle with AVX intrinsics
WITH_DSOCompile PaddlePaddle with dynamic linked CUDA
WITH_TESTINGCompile PaddlePaddle with unit testing
WITH_SWIG_PYCompile PaddlePaddle with inference api
WITH_STYLE_CHECKCompile PaddlePaddle with style check
WITH_PYTHONCompile PaddlePaddle with python interpreter
WITH_DOUBLECompile PaddlePaddle with double precision
WITH_RDMACompile PaddlePaddle with RDMA support
WITH_TIMERCompile PaddlePaddle with stats timer
WITH_PROFILERCompile PaddlePaddle with GPU profiler
WITH_DOCCompile PaddlePaddle with documentation
ON_COVERALLSCompile PaddlePaddle with code coverage
COVERALLS_UPLOADPackage code coverage data to coveralls
ON_TRAVISExclude special unit test on Travis CI
@@ -64,18 +65,16 @@ PaddlePaddle supports some build options. To enable it, first you need to instal As a simple example, consider the following: -1. **Python Dependencies(optional)** +1. **BLAS Dependencies(optional)** - To compile PaddlePaddle with python predict API, make sure swig installed and set `-DWITH_SWIG_PY=ON` as follows: + CMake will search BLAS libraries from system. If not found, OpenBLAS will be downloaded, built and installed automatically. + To utilize preinstalled BLAS， you can simply specify MKL, OpenBLAS or ATLAS via `MKL_ROOT`, `OPENBLAS_ROOT` or `ATLAS_ROOT`. ```bash - # install swig on ubuntu - sudo apt-get install swig - # install swig on Mac OS X - brew install swig - - # active swig in cmake - cmake .. -DWITH_SWIG_PY=ON + # specify MKL + cmake .. -DMKL_ROOT= + # or specify OpenBLAS + cmake .. -DOPENBLAS_ROOT= ``` 2. **Doc Dependencies(optional)** @@ -99,24 +98,21 @@ As a simple example, consider the following: ### Install Dependencies -- **CPU Dependencies** +- **Paddle Dependencies** ```bash # necessary sudo apt-get update - sudo apt-get install -y g++ make cmake swig build-essential libatlas-base-dev python python-pip libpython-dev m4 libprotobuf-dev protobuf-compiler python-protobuf python-numpy git - # optional - sudo apt-get install libgoogle-glog-dev - sudo apt-get install libgflags-dev - sudo apt-get install libgtest-dev - sudo pip install wheel - pushd /usr/src/gtest - cmake . - make - sudo cp *.a /usr/lib - popd + sudo apt-get install -y git curl gcc g++ gfortran make build-essential automake + sudo apt-get install -y python python-pip python-numpy libpython-dev bison + sudo pip install 'protobuf==3.1.0.post1' + + # install cmake 3.4 + curl -sSL https://cmake.org/files/v3.4/cmake-3.4.1.tar.gz | tar -xz && \ + cd cmake-3.4.1 && ./bootstrap && make -j4 && sudo make install && \ + cd .. && rm -rf cmake-3.4.1 ``` - + - **GPU Dependencies (optional)** To build GPU version, you will need the following installed: @@ -149,51 +145,78 @@ As usual, the best option is to create build folder under paddle project directo ```bash mkdir build && cd build -cmake .. +``` + +Finally, you can build and install PaddlePaddle: + +```bash +# you can add build option here, such as: +cmake .. -DCMAKE_INSTALL_PREFIX= +# please use sudo make install, if you want to install PaddlePaddle into the system +make -j `nproc` && make install +# set PaddlePaddle installation path in ~/.bashrc +export PATH=/bin:$PATH +# install PaddlePaddle Python modules. +sudo pip install /opt/paddle/share/wheels/*.whl ``` +## Build on Centos 7 -CMake first check PaddlePaddle's dependencies in system default path. After installing some optional -libraries, corresponding build option will be set automatically (for instance, glog, gtest and gflags). -If still not found, you can manually set it based on CMake error information from your screen. +### Install Dependencies -As a simple example, consider the following: +- **CPU Dependencies** -- **Only CPU with swig** + ```bash + # necessary + sudo yum update + sudo yum install -y epel-release + sudo yum install -y make cmake3 python-devel python-pip gcc-gfortran swig git + sudo pip install wheel numpy + sudo pip install 'protobuf>=3.0.0' + ``` + +- **GPU Dependencies (optional)** - ```bash - cmake .. -DWITH_GPU=OFF -DWITH_SWIG_PY=ON - ``` -- **GPU with swig** + To build GPU version, you will need the following installed: - ```bash - cmake .. -DWITH_GPU=ON -DWITH_SWIG_PY=ON - ``` + 1. a CUDA-capable GPU + 2. A supported version of Linux with a gcc compiler and toolchain + 3. NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads) + 4. NVIDIA cuDNN Library (availabel at https://developer.nvidia.com/cudnn) + + The CUDA development environment relies on tight integration with the host development environment, + including the host compiler and C runtime libraries, and is therefore only supported on + distribution versions that have been qualified for this CUDA Toolkit release. + + After downloading cuDNN library, issue the following commands: + + ```bash + sudo tar -xzf cudnn-7.5-linux-x64-v5.1.tgz -C /usr/local + sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn* + ``` + Then you need to set LD\_LIBRARY\_PATH, PATH environment variables in ~/.bashrc. -- **GPU with doc and swig** + ```bash + export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH + export PATH=/usr/local/cuda/bin:$PATH + ``` + +### Build and Install + +As usual, the best option is to create build folder under paddle project directory. - ```bash - cmake .. -DWITH_GPU=ON -DWITH_DOC=ON -DWITH_SWIG_PY=ON - ``` +```bash +mkdir build && cd build +``` -Finally, you can build PaddlePaddle: +Finally, you can build and install PaddlePaddle: ```bash # you can add build option here, such as: -cmake .. -DWITH_GPU=ON -DCMAKE_INSTALL_PREFIX= -DWITH_SWIG_PY=ON +cmake3 .. -DCMAKE_INSTALL_PREFIX= # please use sudo make install, if you want to install PaddlePaddle into the system make -j `nproc` && make install # set PaddlePaddle installation path in ~/.bashrc export PATH=/bin:$PATH -``` - -If you set `WITH_SWIG_PY=ON`, related python dependencies also need to be installed. -Otherwise, PaddlePaddle will automatically install python dependencies -at first time when user run paddle commands, such as `paddle version`, `paddle train`. -It may require sudo privileges: - -```bash -# you can run +# install PaddlePaddle Python modules. sudo pip install /opt/paddle/share/wheels/*.whl -# or just run -sudo paddle version ``` diff --git a/doc/getstarted/build_and_install/cmake/build_from_source_cn.rst b/doc/getstarted/build_and_install/cmake/build_from_source_cn.rst index 3a52c8723bbccd70dd89e8913092d92813925f90..be0c1ffa451b2901ec06621dd4d886f800b4562e 100644 --- a/doc/getstarted/build_and_install/cmake/build_from_source_cn.rst +++ b/doc/getstarted/build_and_install/cmake/build_from_source_cn.rst @@ -40,4 +40,4 @@ PaddePaddle通过编译时指定路径来实现引用各种BLAS/CUDA/Cudnn库。 cmake .. -DMKL_ROOT=/opt/mkl/ -DCUDNN_ROOT=/opt/cudnnv5 -注意：这几个编译选项的设置，只在第一次cmake的时候有效。如果之后想要重新设置，推荐清理整个编译目录（``rm -rf``）后，再指定。 \ No newline at end of file +注意：这几个编译选项的设置，只在第一次cmake的时候有效。如果之后想要重新设置，推荐清理整个编译目录（``rm -rf``）后，再指定。 diff --git a/doc/getstarted/build_and_install/docker_install_en.rst b/doc/getstarted/build_and_install/docker_install_en.rst index 34279a29b2e4c84aa5039f2e5ab2c6ed9a06da2f..51a1a11674d98781d04137ff14bf8debe3277318 100644 --- a/doc/getstarted/build_and_install/docker_install_en.rst +++ b/doc/getstarted/build_and_install/docker_install_en.rst @@ -16,23 +16,13 @@ Developers can work on PaddlePaddle using Docker. This allows developers to work on different platforms -- Linux, Mac OS X, and Windows -- in a consistent way. -The general development workflow with Docker and Bazel is as follows: +The general development workflow with Docker and CMake is as follows: 1. Get the source code of Paddle: .. code-block:: bash - git clone --recursive https://github.com/PaddlePaddle/Paddle.git - - - Here **git clone --recursive is required** as we have a submodule `warp-ctc `_. - - If you have used :code:`git clone https://github.com/PaddlePaddle/Paddle` and find that the directory :code:`warp-ctc` is - empty, please use the following command to get the submodule. - - .. code-block:: bash - - git submodule update --init --recursive + git clone https://github.com/PaddlePaddle/Paddle.git 2. Build a development Docker image :code:`paddle:dev` from the source @@ -162,7 +152,6 @@ source code: cd ~ git clone https://github.com/PaddlePaddle/Paddle.git cd Paddle - git submodule update --init --recursive docker build --build-arg WITH_AVX=OFF -t paddle:cpu-noavx -f paddle/scripts/docker/Dockerfile . docker build --build-arg WITH_AVX=OFF -t paddle:gpu-noavx -f paddle/scripts/docker/Dockerfile.gpu . diff --git a/doc/howto/deep_model/rnn/hierarchical_layer_cn.rst b/doc/howto/deep_model/rnn/hierarchical_layer_cn.rst index 943b1d4bb84646d9f60de7790be166a83d10b1e0..4b328fc9d38bc5dfec35d5e0f0d46136aeeb41bc 100644 --- a/doc/howto/deep_model/rnn/hierarchical_layer_cn.rst +++ b/doc/howto/deep_model/rnn/hierarchical_layer_cn.rst @@ -32,7 +32,7 @@ pooling_layer 的使用示例如下，详细见 :ref:`api_trainer_config_helpers - `pooling_type` 目前支持两种，分别是：MaxPooling()和AvgPooling()。 -- `agg_level=AggregateLevel.TIMESTEP` 时（默认值）： +- `agg_level=AggregateLevel.EACH_TIMESTEP` 时（默认值）： - 作用：双层序列经过运算变成一个0层序列，或单层序列经过运算变成一个0层序列 - 输入：一个双层序列，或一个单层序列 @@ -54,7 +54,7 @@ last_seq 的使用示例如下（ :ref:`api_trainer_config_helpers_layers_first_ last = last_seq(input=layer, agg_level=AggregateLevel.EACH_SEQUENCE) -- `agg_level=AggregateLevel.TIMESTEP` 时（默认值）： +- `agg_level=AggregateLevel.EACH_TIMESTEP` 时（默认值）： - 作用：一个双层序列经过运算变成一个0层序列，或一个单层序列经过运算变成一个0层序列 - 输入：一个双层序列或一个单层序列 diff --git a/doc/howto/dev/contribute_to_paddle_cn.md b/doc/howto/dev/contribute_to_paddle_cn.md index e0a63f5a14c7b2e8953aa21739668ee2a9ebeff1..ee1b3213eaed3bfd94e449997dff9848b8fb4228 100644 --- a/doc/howto/dev/contribute_to_paddle_cn.md +++ b/doc/howto/dev/contribute_to_paddle_cn.md @@ -33,7 +33,6 @@ cd Paddle git checkout -b develop # 创建 develop 分支 git remote add upstream https://github.com/PaddlePaddle/Paddle.git # 添加 upstream 到 baidu/Paddle git pull upstream develop # 更新 upstream -git submodule update --init --recursive ``` 然后你可以通过做一个本地开发分支开始开发 diff --git a/doc/howto/dev/contribute_to_paddle_en.md b/doc/howto/dev/contribute_to_paddle_en.md index e578f6fce8b94180da7d5de041a0e17b1d59f6ea..9b0d3e83c0dc264650eda73e6801c60a75439b4a 100644 --- a/doc/howto/dev/contribute_to_paddle_en.md +++ b/doc/howto/dev/contribute_to_paddle_en.md @@ -38,7 +38,6 @@ cd Paddle git checkout -b develop # create develop branch. git remote add upstream https://github.com/PaddlePaddle/Paddle.git # add upstream to baidu/Paddle git pull upstream develop # update to upstream -git submodule update --init --recursive ``` Then you can start to develop by making a local developement branch diff --git a/doc/howto/usage/k8s/k8s_aws_en.md b/doc/howto/usage/k8s/k8s_aws_en.md index b04bfba590de42956dfe99256cde325b24adbfab..a6422b9be00e210a6a305260585520acd72fb2f1 100644 --- a/doc/howto/usage/k8s/k8s_aws_en.md +++ b/doc/howto/usage/k8s/k8s_aws_en.md @@ -2,25 +2,16 @@ ## Create AWS Account and IAM Account -To use AWS, we need to sign up an AWS account on Amazon's Web site. -An AWS account allows us to login to the AWS Console Web interface to -create IAM users and user groups. Usually, we create a user group with -privileges required to run PaddlePaddle, and we create users for -those who are going to run PaddlePaddle and add these users into the -group. IAM users can identify themselves using password and tokens, -where passwords allows users to log in to the AWS Console, and tokens -make it easy for users to submit and inspect jobs from the command -line. +Under each AWS account, we can create multiple [IAM](http://docs.aws.amazon.com/IAM/latest/UserGuide/introduction.html) users. This allows us to grant some privileges to each IAM user and to create/operate AWS clusters as an IAM user. To sign up an AWS account, please follow [this guide](http://docs.aws.amazon.com/lambda/latest/dg/setting-up.html). -To create users and user groups under an AWS account, please +To create IAM users and user groups under an AWS account, please follow [this guide](http://docs.aws.amazon.com/IAM/latest/UserGuide/id_users_create.html). -Please be aware that this tutorial needs the following privileges in -the user group: +Please be aware that this tutorial needs the following privileges for the user in IAM: - AmazonEC2FullAccess - AmazonS3FullAccess @@ -31,14 +22,7 @@ the user group: - IAMUserSSHKeys - IAMFullAccess - NetworkAdministrator - - -By the time we write this tutorial, we noticed that Chinese AWS users -might suffer from authentication problems when running this tutorial. -Our solution is that we create a VM instance with the default Amazon -AMI and in the same zone as our cluster runs, so we can SSH to this VM -instance as a tunneling server and control our cluster and jobs from -it. +- AWSKeyManagementServicePowerUser ## PaddlePaddle on AWS @@ -46,9 +30,11 @@ it. Here we will show you step by step on how to run PaddlePaddle training on AWS cluster. -###Download kube-aws and kubectl +### Download kube-aws and kubectl + +#### kube-aws -####kube-aws +[kube-aws](https://github.com/coreos/kube-aws) is a CLI tool to automate cluster deployment to AWS. Import the CoreOS Application Signing Public Key: @@ -63,7 +49,7 @@ gpg2 --fingerprint FC8A365E ``` The correct key fingerprint is `18AD 5014 C99E F7E3 BA5F 6CE9 50BD D3E0 FC8A 365E` -Go to the [releases](https://github.com/coreos/kube-aws/releases) and download the latest release tarball and detached signature (.sig) for your architecture. +We can download `kube-aws` from its [release page](https://github.com/coreos/kube-aws/releases). In this tutorial, we use version 0.9.1 Validate the tarball's GPG signature: @@ -88,24 +74,30 @@ mv ${PLATFORM}/kube-aws /usr/local/bin ``` -####kubectl +#### kubectl -Go to the [releases](https://github.com/kubernetes/kubernetes/releases) and download the latest release tarball. +[kubectl](https://kubernetes.io/docs/user-guide/kubectl-overview/) is a command line interface for running commands against Kubernetes clusters. -Extract the tarball and then concate the kubernetes binaries directory into PATH: +Download `kubectl` from the Kubernetes release artifact site with the `curl` tool. ``` -export PATH=/platforms/linux/amd64:$PATH +# OS X +curl -O https://storage.googleapis.com/kubernetes-release/release/"$(curl -s https://storage.googleapis.com/kubernetes-release/release/stable.txt)"/bin/darwin/amd64/kubectl +# Linux +curl -O https://storage.googleapis.com/kubernetes-release/release/"$(curl -s https://storage.googleapis.com/kubernetes-release/release/stable.txt)"/bin/linux/amd64/kubectl ``` -User credentials and security tokens will be generated later in user directory, not in `~/.kube/config`, they will be necessary to use the CLI or the HTTP Basic Auth. +Make the kubectl binary executable and move it to your PATH (e.g. `/usr/local/bin`): +``` +chmod +x ./kubectl +sudo mv ./kubectl /usr/local/bin/kubectl +``` -###Configure AWS Credentials - -First check out [this](http://docs.aws.amazon.com/cli/latest/userguide/installing.html) for installing the AWS command line interface, if you use ec2 instance with default amazon AMI, the cli tool has already been installed on your machine. +### Configure AWS Credentials +First check out [this](http://docs.aws.amazon.com/cli/latest/userguide/installing.html) for installing the AWS command line interface. And then configure your AWS account information: @@ -115,44 +107,49 @@ aws configure ``` -Fill in the required fields (You can get your AWS aceess key id and AWS secrete access key by following [this](http://docs.aws.amazon.com/cli/latest/userguide/cli-chap-getting-started.html) instruction): +Fill in the required fields: ``` AWS Access Key ID: YOUR_ACCESS_KEY_ID AWS Secrete Access Key: YOUR_SECRETE_ACCESS_KEY -Default region name: us-west-2 +Default region name: us-west-1 Default output format: json - ``` -Test that your credentials work by describing any instances you may already have running on your account: +`YOUR_ACCESS_KEY_ID`, and `YOUR_SECRETE_ACCESS_KEY` is the IAM key and secret from [Create AWS Account and IAM Account](#create-aws-account-and-iam-account) + +Verify that your credentials work by describing any instances you may already have running on your account: ``` aws ec2 describe-instances ``` -###Define Cluster Parameters +### Define Cluster Parameters -####EC2 key pair +#### EC2 key pair The keypair that will authenticate SSH access to your EC2 instances. The public half of this key pair will be configured on each CoreOS node. -After creating a key pair, you will use the name you gave the keys to configure the cluster. Key pairs are only available to EC2 instances in the same region. More info in the [EC2 Keypair docs](http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-key-pairs.html). +Follow [EC2 Keypair docs](http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-key-pairs.html) to create a EC2 key pair + +After creating a key pair, you will use the key pair name to configure the cluster. -####KMS key +Key pairs are only available to EC2 instances in the same region. We are using us-west-1 in our tutorial, so make sure to creat key pairs in that region (N. California). + +#### KMS key Amazon KMS keys are used to encrypt and decrypt cluster TLS assets. If you already have a KMS Key that you would like to use, you can skip creating a new key and provide the Arn string for your existing key. You can create a KMS key in the AWS console, or with the aws command line tool: ``` -$ aws kms --region=us-west-2 create-key --description="kube-aws assets" +aws kms --region=us-west-1 create-key --description="kube-aws assets" { "KeyMetadata": { "CreationDate": 1458235139.724, "KeyState": "Enabled", - "Arn": "arn:aws:kms:us-west-2:xxxxxxxxx:key/xxxxxxxxxxxxxxxxxxx", + "Arn": "arn:aws:kms:us-west-1:aaaaaaaaaaaaa:key/xxxxxxxxxxxxxxxxxxx", "AWSAccountId": "xxxxxxxxxxxxx", "Enabled": true, "KeyUsage": "ENCRYPT_DECRYPT", @@ -162,11 +159,13 @@ $ aws kms --region=us-west-2 create-key --description="kube-aws assets" } ``` -You will use the `KeyMetadata.Arn` string to identify your KMS key in the init step. +We will need to use the value of `Arn` later. And then you need to add several inline policies in your user permission. -kms inline policy: +Go to IAM user page, click on `Add inline policy` button, and then select `Custom Policy` + +paste into following inline policies: ``` { @@ -180,18 +179,10 @@ kms inline policy: "kms:Encrypt" ], "Resource": [ - "arn:aws:kms:*:xxxxxxxxx:key/*" + "arn:aws:kms:*:AWS_ACCOUNT_ID:key/*" ] - } - ] -} -``` -cloudformation inline policy: - -``` -"Version": "2012-10-17", - "Statement": [ - { + }, + { "Sid": "Stmt1482205746000", "Effect": "Allow", "Action": [ @@ -200,26 +191,43 @@ cloudformation inline policy: "cloudformation:DeleteStack", "cloudformation:DescribeStacks", "cloudformation:DescribeStackResource", - "cloudformation:GetTemplate" + "cloudformation:GetTemplate", + "cloudformation:DescribeStackEvents" ], "Resource": [ - "arn:aws:cloudformation:us-west-2:xxxxxxxxx:stack/YOUR_CLUSTER_NAME/*" + "arn:aws:cloudformation:us-west-1:AWS_ACCOUNT_ID:stack/MY_CLUSTER_NAME/*" ] } ] } ``` +`AWS_ACCOUNT_ID`: You can get it from following command line: + +``` +aws sts get-caller-identity --output text --query Account +``` -####External DNS name +`MY_CLUSTER_NAME`: Pick a MY_CLUSTER_NAME that you like, you will use it later as well. -When the cluster is created, the controller will expose the TLS-secured API on a public IP address. You will need to create an A record for the external DNS hostname you want to point to this IP address. You can find the API external IP address after the cluster is created by invoking kube-aws status. +#### External DNS name -####S3 bucket +When the cluster is created, the controller will expose the TLS-secured API on a DNS name. + +The A record of that DNS name needs to be point to the cluster ip address. + +We will need to use DNS name later in tutorial. If you don't already own one, you can choose any DNS name (e.g., `paddle`) and modify `/etc/hosts` to associate cluster ip with that DNS name. + +#### S3 bucket You need to create an S3 bucket before startup the Kubernetes cluster. -####Initialize an asset directory +There are some bugs in aws cli in creating S3 bucket, so let's use the [Web console](https://console.aws.amazon.com/s3/home?region=us-west-1). + +Click on `Create Bucket`, fill in a unique BUCKET_NAME, and make sure region is us-west-1 (Northern California). + + +#### Initialize an asset directory Create a directory on your local machine to hold the generated assets: @@ -231,29 +239,44 @@ $ cd my-cluster Initialize the cluster CloudFormation stack with the KMS Arn, key pair name, and DNS name from the previous step: ``` -$ kube-aws init \ ---cluster-name=my-cluster-name \ ---external-dns-name=my-cluster-endpoint \ +kube-aws init \ +--cluster-name=MY_CLUSTER_NAME \ +--external-dns-name=MY_EXTERNAL_DNS_NAME \ --region=us-west-1 \ ---availability-zone=us-west-1c \ ---key-name=key-pair-name \ ---kms-key-arn="arn:aws:kms:us-west-2:xxxxxxxxxx:key/xxxxxxxxxxxxxxxxxxx" +--availability-zone=us-west-1a \ +--key-name=KEY_PAIR_NAME \ +--kms-key-arn="arn:aws:kms:us-west-1:xxxxxxxxxx:key/xxxxxxxxxxxxxxxxxxx" ``` +`MY_CLUSTER_NAME`: the one you picked in [KMS key](#kms-key) + +`MY_EXTERNAL_DNS_NAME`: see [External DNS name](#external-dns-name) + +`KEY_PAIR_NAME`: see [EC2 key pair](#ec2-key-pair) + +`--kms-key-arn`: the "Arn" in [KMS key](#kms-key) + +Here `us-west-1a` is used for parameter `--availability-zone`, but supported availability zone varies among AWS accounts. + +Please check if `us-west-1a` is supported by `aws ec2 --region us-west-1 describe-availability-zones`, if not switch to other supported availability zone. (e.g., `us-west-1a`, or `us-west-1b`) + +Note: please don't use `us-west-1c`. Subnets can currently only be created in the following availability zones: us-west-1b, us-west-1a. + There will now be a cluster.yaml file in the asset directory. This is the main configuration file for your cluster. -####Render contents of the asset directory + +#### Render contents of the asset directory In the simplest case, you can have kube-aws generate both your TLS identities and certificate authority for you. ``` -$ kube-aws render credentials --generate-ca +kube-aws render credentials --generate-ca ``` The next command generates the default set of cluster assets in your asset directory. ``` -sh $ kube-aws render stack +kube-aws render stack ``` Here's what the directory structure looks like: @@ -285,47 +308,62 @@ $ tree These assets (templates and credentials) are used to create, update and interact with your Kubernetes cluster. -###Kubernetes Cluster Start Up +### Kubernetes Cluster Start Up -####Create the instances defined in the CloudFormation template +#### Create the instances defined in the CloudFormation template -Now for the exciting part, creating your cluster: +Now let's create your cluster (choose any PREFIX for the command below): ``` -$ kube-aws up --s3-uri s3:/// +kube-aws up --s3-uri s3://BUCKET_NAME/PREFIX ``` -####Configure DNS +`BUCKET_NAME`: the bucket name that you used in [S3 bucket](#s3-bucket) -You can invoke `kube-aws status` to get the cluster API endpoint after cluster creation, if necessary. This command can take a while. And then dig the load balancer hostname to get the ip address, use this ip to setup an A record for your external dns name. -####Access the cluster +#### Configure DNS -Once the API server is running, you should see: +You can invoke `kube-aws status` to get the cluster API endpoint after cluster creation. ``` -$ kubectl --kubeconfig=kubeconfig get nodes -NAME STATUS AGE -ip-10-0-0-xxx.us-west-1.compute.internal Ready 5m -ip-10-0-0-xxx.us-west-1.compute.internal Ready 5m -ip-10-0-0-xx.us-west-1.compute.internal Ready,SchedulingDisabled 5m +$ kube-aws status +Cluster Name: paddle-cluster +Controller DNS Name: paddle-cl-ElbAPISe-EEOI3EZPR86C-531251350.us-west-1.elb.amazonaws.com ``` +Use command `dig` to check the load balancer hostname to get the ip address. -###Setup PaddlePaddle Environment on AWS +``` +$ dig paddle-cl-ElbAPISe-EEOI3EZPR86C-531251350.us-west-1.elb.amazonaws.com -Now, we've created a cluster with following network capability: +;; QUESTION SECTION: +;paddle-cl-ElbAPISe-EEOI3EZPR86C-531251350.us-west-1.elb.amazonaws.com. IN A -1. All Kubernetes nodes can communicate with each other. +;; ANSWER SECTION: +paddle-cl-ElbAPISe-EEOI3EZPR86C-531251350.us-west-1.elb.amazonaws.com. 59 IN A 54.241.164.52 +paddle-cl-ElbAPISe-EEOI3EZPR86C-531251350.us-west-1.elb.amazonaws.com. 59 IN A 54.67.102.112 +``` -1. All Docker containers on Kubernetes nodes can communicate with each other. +In the above output, both ip `54.241.164.52`, `54.67.102.112` will work. -1. All Kubernetes nodes can communicate with all Docker containers on Kubernetes nodes. +If you own a DNS name, set the A record to any of the above ip. Otherwise you can edit `/etc/hosts` to associate ip with the DNS name. -1. All other traffic loads from outside of Kubernetes nodes cannot reach to the Docker containers on Kubernetes nodes except for creating the services for containers. +#### Access the cluster +Once the API server is running, you should see: -For sharing the training data across all the Kubernetes nodes, we use EFS (Elastic File System) in AWS. Ceph might be a better solution, but it requires high version of Linux kernel that might not be stable enough at this moment. We haven't automated the EFS setup at this moment, so please do the following steps: +``` +$ kubectl --kubeconfig=kubeconfig get nodes +NAME STATUS AGE +ip-10-0-0-xxx.us-west-1.compute.internal Ready 5m +ip-10-0-0-xxx.us-west-1.compute.internal Ready 5m +ip-10-0-0-xx.us-west-1.compute.internal Ready,SchedulingDisabled 5m +``` + + +### Setup Elastic File System for Cluster + +Training data is usually served on a distributed filesystem, we use Elastic File System (EFS) on AWS. Ceph might be a better solution, but it requires high version of Linux kernel that might not be stable enough at this moment. We haven't automated the EFS setup at this moment, so please do the following steps: 1. Make sure you added AmazonElasticFileSystemFullAccess policy in your group. @@ -342,57 +380,71 @@ For sharing the training data across all the Kubernetes nodes, we use EFS (Elast
![](src/efs_mount.png)
-Before starting the training, you should place your user config and divided training data onto EFS. When the training start, each task will copy related files from EFS into container, and it will also write the training results back onto EFS, we will show you how to place the data later in this article. +We will place user config and divided training data onto EFS. Training task will cache related files by copying them from EFS into container. It will also write the training results back onto EFS. We will show you how to place the data later in this article. + + + +### Core Concepts of PaddlePaddle Training on AWS + +Now we've already setup a 3 nodes distributed Kubernetes cluster, and on each node we've attached the EFS volume. In this training demo, we will create three Kubernetes pods and schedule them on three nodes. Each pod contains a PaddlePaddle container. When container gets created, it will start parameter server (pserver) and trainer process, load the training data from EFS volume and start the distributed training task. +#### Distributed Training Job +A distributed training job is represented by a [kubernetes job](https://kubernetes.io/docs/user-guide/jobs/#what-is-a-job). -###Core Concept of PaddlePaddle Training on AWS +Each Kuberentes job is described by a job config file, which specifies the information like the number of pods in the job and environment variables. -Now we've already setup a 3 nodes distributed Kubernetes cluster, and on each node we've attached the EFS volume, in this training demo, we will create three Kubernetes pod and scheduling them on 3 node. Each pod contains a PaddlePaddle container. When container gets created, it will start pserver and trainer process, load the training data from EFS volume and start the distributed training task. +In a distributed training job, we would: -####Use Kubernetes Job +1. upload the partitioned training data and configuration file onto EFS volume, and +1. create and submit the Kubernetes job config to the Kubernetes cluster to start the training job. -We use Kubernetes job to represent one time of distributed training. After the job get finished, Kubernetes will destroy job container and release all related resources. +#### Parameter Servers and Trainers -We can write a yaml file to describe the Kubernetes job. The file contains lots of configuration information, for example PaddlePaddle's node number, `paddle pserver` open port number, the network card info etc., these information are passed into container for processes to use as environment variables. +There are two roles in a PaddlePaddle cluster: `parameter server` and `trainer`. Each parameter server process maintains a shard of the global model. Each trainer has its local copy of the model, and uses its local data to update the model. During the training process, trainers send model updates to parameter servers, parameter servers are responsible for aggregating these updates, so that trainers can synchronize their local copy with the global model. -In one time of distributed training, user will confirm the PaddlePaddle node number first. And then upload the pre-divided training data and configuration file onth EFS volume. And then create the Kubernetes job yaml file; submit to the Kubernetes cluster to start the training job. +
![Model is partitioned into two shards. Managed by two parameter servers respectively.](src/pserver_and_trainer.png)
-####Create PaddlePaddle Node +In order to communicate with pserver, trainer needs to know the ip address of each pserver. In kubernetes it's better to use a service discovery mechanism (e.g., DNS hostname) rather than static ip address, since any pserver's pod may be killed and a new pod could be schduled onto another node of different ip address. We will improve paddlepaddle's service discovery ability. For now we will use static ip. -After Kubernetes master gets the request, it will parse the yaml file and create several pods (defined by PaddlePaddle's node number), Kubernetes will allocate these pods onto cluster's node. A pod represents a PaddlePaddle node, when pod is successfully allocated onto one physical/virtual machine, Kubernetes will startup the container in the pod, and this container will use the environment variables in yaml file and start up `paddle pserver` and `paddle trainer` processes. +Parameter server and trainer are packaged into a same docker image. They will run once pod is scheduled by kubernetes job. +#### Trainer ID -####Start up Training +Each trainer process requires a trainer ID, a zero-based index value, passed in as a command-line parameter. The trainer process thus reads the data partition indexed by this ID. -After container gets started, it starts up the distributed training by using scripts. We know `paddle train` process need to know other node's ip address and it's own trainer_id, since PaddlePaddle currently don't have the ability to do the service discovery, so in the start up script, each node will use job pod's name to query all to pod info from Kubernetes apiserver (apiserver's endpoint is an environment variable in container by default). +#### Training -With pod information, we can assign each pod a unique trainer_id. Here we sort all the pods by pod's ip, and assign the index to each PaddlePaddle node as it's trainer_id. The workflow of starting up the script is as follows: +The entry-point of a container is a Python script. As it runs in a pod, it can see some environment variables pre-defined by Kubernetes. This includes one that gives the job's identity, which can be used in a remote call to the Kubernetes apiserver that lists all pods in the job. -1. Query the api server to get pod information, and assign the trainer_id by sorting the ip. +We rank each pod by sorting them by their ips. The rank of each pod could be the "pod ID". Because we run one trainer and one parameter server in each pod, we can use this "pod ID" as the trainer ID. A detailed workflow of the entry-point script is as follows: + +1. Query the api server to get pod information, and assign the `trainer_id` by sorting the ip. 1. Copy the training data from EFS sharing volume into container. -1. Parse the `paddle pserver` and 'paddle trainer' startup parameters from environment variables, and then start up the processes. -1. PaddlePaddle will automatically write the result onto the PaddlePaddle node with trainer_id:0, we set the output path to be the EFS volume to save the result data. +1. Parse the `paddle pserver` and `paddle trainer` startup parameters from environment variables, and then start up the processes. +1. Trainer with `train_id` 0 will automatically write results onto EFS volume. -###Start PaddlePaddle Training Demo on AWS +### Start PaddlePaddle Training Demo on AWS Now we'll start a PaddlePaddle training demo on AWS, steps are as follows: 1. Build PaddlePaddle Docker image. 1. Divide the training data file and upload it onto the EFS sharing volume. -1. Create the training job yaml file, and start up the job. +1. Create the training job config file, and start up the job. 1. Check the result after training. -####Build PaddlePaddle Docker Image +#### Build PaddlePaddle Docker Image -PaddlePaddle docker image need to provide the runtime environment for `paddle pserver` and `paddle train`, so the container use this image should have two main function: +PaddlePaddle docker image need to provide the runtime environment for `pserver` and `trainer`, so the container use this image should have two main function: 1. Copy the training data into container. -1. Generate the startup parameter for `paddle pserver` and `paddle train` process, and startup the training. +1. Generate the startup parameter for `pserver` and `trainer` process, and startup the training. + +We need to create a new image since official `paddledev/paddle:cpu-latest` only have PaddlePaddle binary, but lack of the above functionalities. -Since official `paddledev/paddle:cpu-latest` have already included the PaddlePaddle binary, but lack of the above functionalities, so we will create the startup script based on this image, to achieve the work above. the detailed Dockerfile is as follows: +Dockerfile for creating the new image is as follows: ``` FROM paddledev/paddle:cpu-latest @@ -481,7 +533,7 @@ And then push the built image onto docker registry. docker push your_repo/paddle:mypaddle ``` -####Upload Training Data File +#### Upload Training Data File Here we will use PaddlePaddle's official recommendation demo as the content for this training, we put the training data file into a directory named by job name, which located in EFS sharing volume, the tree structure for the directory looks like: @@ -498,10 +550,10 @@ efs └── recommendation ``` -The `paddle-cluster-job` directory is the job name for this training, this training includes 3 PaddlePaddle node, we store the pre-divided data under `paddle-cluster-job/data` directory, directory 0, 1, 2 each represent 3 nodes' trainer_id. the training data in in recommendation directory, the training results and logs will be in the output directory. +The `paddle-cluster-job` directory is the job name for this training, this training includes 3 PaddlePaddle node, we store the partitioned data under `paddle-cluster-job/data` directory, directory 0, 1, 2 each represent 3 nodes' trainer_id. the training data in in recommendation directory, the training results and logs will be in the output directory. -####Create Kubernetes Job +#### Create Kubernetes Job Kubernetes use yaml file to describe job details, and then use command line tool to create the job in Kubernetes cluster. @@ -583,7 +635,7 @@ After we execute the above command, Kubernetes will create 3 pods and then pull -####Check Training Results +#### Check Training Results During the training, we can see the logs and models on EFS sharing volume, the output directory contains the training results. (Caution: node_0, node_1, node_2 directories represents PaddlePaddle node and train_id, not the Kubernetes node) @@ -640,7 +692,7 @@ I1116 09:10:18.019836 50 ParameterClient2.cpp:122] pserver 5 192.168.129.71:7 It'll take around 8 hours to finish this PaddlePaddle recommendation training demo on three 2 core 8 GB EC2 machine (m3.large). -###Kubernetes Cluster Tear Down +### Kubernetes Cluster Tear Down If you want to tear down the whole Kubernetes cluster, make sure to *delete* the EFS volume first (otherwise, you will get stucked on following steps), and then use the following command: @@ -651,16 +703,3 @@ kube-aws destroy It's an async call, it might take 5 min to tear down the whole cluster. If you created any Kubernetes Services of type LoadBalancer, you must delete these first, as the CloudFormation cannot be fully destroyed if any externally-managed resources still exist. - - - -## For Experts with Kubernetes and AWS - -Sometimes we might need to create or manage the cluster on AWS manually with limited privileges, so here we will explain more on what’s going on with the Kubernetes setup script. - -### Some Presumptions - -* Instances run on CoreOS, the official IAM. -* Kubernetes node use instance storage, no EBS get mounted. Etcd is running on additional node. -* For networking, we use Flannel network at this moment, we will use Calico solution later on. -* When you create a service with Type=LoadBalancer, Kubernetes will create and ELB, and create a security group for the ELB. diff --git a/doc/howto/usage/k8s/src/pserver_and_trainer.png b/doc/howto/usage/k8s/src/pserver_and_trainer.png new file mode 100644 index 0000000000000000000000000000000000000000..f41fe48920590333ad332bb51eb18e03dc251541 Binary files /dev/null and b/doc/howto/usage/k8s/src/pserver_and_trainer.png differ diff --git a/paddle/api/paddle_api_config.py.in b/paddle/api/paddle_api_config.py.in index e11ee920362aed3ec79a2e62d447d7dde4a99248..82f45ba6ccec49eb190d1814a67a575f311689e8 100644 --- a/paddle/api/paddle_api_config.py.in +++ b/paddle/api/paddle_api_config.py.in @@ -13,5 +13,5 @@ GFLAGS_LIBRARIES="@GFLAGS_LIBRARIES@" GFLAGS_LOCATION="@GFLAGS_LOCATION@" CBLAS_LIBRARIES="@CBLAS_LIBRARIES@" -CUDA_LIBRARIES="@CUDA_cudart_shared_LIBRARY@" +CUDA_LIBRARIES="@CUDA_CUDART_LIBRARY@" WITH_COVERALLS="@ON_COVERALLS@" diff --git a/paddle/cuda/CMakeLists.txt b/paddle/cuda/CMakeLists.txt index 57fb89608f4bcf3e6829fe850a61c2a626adfbdc..a28ccd6f07cfd56b7f1978f67fdcf6e7e5fe6337 100755 --- a/paddle/cuda/CMakeLists.txt +++ b/paddle/cuda/CMakeLists.txt @@ -15,7 +15,6 @@ else() endif() set(CUDA_CXX_WITH_GPU_SOURCES - src/hl_cudart_wrap.cc src/hl_cuda_cublas.cc src/hl_cuda_cudnn.cc src/hl_cuda_device.cc) diff --git a/paddle/cuda/include/hl_dso_loader.h b/paddle/cuda/include/hl_dso_loader.h index 20c13f21e61a92b0635b686f6f724ae2b44518cc..276a07d3c735c771c851e8b4bd14c720f9ab6569 100644 --- a/paddle/cuda/include/hl_dso_loader.h +++ b/paddle/cuda/include/hl_dso_loader.h @@ -36,14 +36,6 @@ void GetCublasDsoHandle(void** dso_handle); */ void GetCudnnDsoHandle(void** dso_handle); -/** - * @brief load the DSO of CUDA Run Time - * - * @param **dso_handle dso handler - * - */ -void GetCudartDsoHandle(void** dso_handle); - /** * @brief load the DSO of CURAND * diff --git a/paddle/cuda/src/hl_cuda_device.cc b/paddle/cuda/src/hl_cuda_device.cc index a71eecba2736234dafaf6b67e5efac5358a30871..6dfb12e00b80db36ad2e53326b880c7d1ed59263 100644 --- a/paddle/cuda/src/hl_cuda_device.cc +++ b/paddle/cuda/src/hl_cuda_device.cc @@ -22,10 +22,9 @@ limitations under the License. */ #include #include #include -#include "hl_cuda.h" #include "hl_cuda.ph" -#include "hl_dso_loader.h" #include "hl_thread.ph" +#include "hl_dso_loader.h" #include "paddle/utils/Logging.h" // clang-format on @@ -77,78 +76,6 @@ CURAND_RAND_ROUTINE_EACH(DYNAMIC_LOAD_CURAND_WRAP) #undef CURAND_RAND_ROUTINE_EACH #undef DYNAMIC_LOAD_CURAND_WRAP -std::once_flag cudart_dso_flag; -void *cudart_dso_handle = nullptr; - -/** - * The following macro definition can generate structs - * (for each function) to dynamic load cuda routine - * via operator overloading. - * - * note: default dynamic linked libs - */ -#ifdef PADDLE_USE_DSO -#define DYNAMIC_LOAD_CUDART_WRAP(__name) \ - struct DynLoad__##__name { \ - template \ - auto operator()(Args... args) -> decltype(__name(args...)) { \ - using cudart_func = decltype(__name(args...)) (*)(Args...); \ - std::call_once(cudart_dso_flag, GetCudartDsoHandle, &cudart_dso_handle); \ - void *p_##__name = dlsym(cudart_dso_handle, #__name); \ - return reinterpret_cast(p_##__name)(args...); \ - } \ - } __name; /* struct DynLoad__##__name */ -#else -#define DYNAMIC_LOAD_CUDART_WRAP(__name) \ - struct DynLoad__##__name { \ - template \ - auto operator()(Args... args) -> decltype(__name(args...)) { \ - return __name(args...); \ - } \ - } __name; /* struct DynLoad__##__name */ -#endif - -/* include all needed cuda functions in HPPL */ -// clang-format off -#define CUDA_ROUTINE_EACH(__macro) \ - __macro(cudaMalloc) \ - __macro(cudaHostAlloc) \ - __macro(cudaFree) \ - __macro(cudaFreeHost) \ - __macro(cudaMemcpy) \ - __macro(cudaMemset) \ - __macro(cudaMemcpyAsync) \ - __macro(cudaSetDevice) \ - __macro(cudaGetDevice) \ - __macro(cudaGetDeviceCount) \ - __macro(cudaGetDeviceProperties) \ - __macro(cudaDeviceSynchronize) \ - __macro(cudaDeviceCanAccessPeer) \ - __macro(cudaDeviceEnablePeerAccess) \ - __macro(cudaStreamCreate) \ - __macro(cudaStreamDestroy) \ - __macro(cudaStreamSynchronize) \ - __macro(cudaStreamWaitEvent) \ - __macro(cudaEventCreate) \ - __macro(cudaEventRecord) \ - __macro(cudaEventQuery) \ - __macro(cudaEventDestroy) \ - __macro(cudaEventSynchronize) \ - __macro(cudaEventElapsedTime) \ - __macro(cudaSetDeviceFlags) \ - __macro(cudaGetLastError) \ - __macro(cudaFuncSetCacheConfig) \ - __macro(cudaRuntimeGetVersion) \ - __macro(cudaGetErrorString) \ - __macro(cudaProfilerStart) \ - __macro(cudaProfilerStop) -// clang-format on - -CUDA_ROUTINE_EACH(DYNAMIC_LOAD_CUDART_WRAP) - -#undef CUDA_ROUNTINE_EACH -#undef DYNAMIC_LOAD_CUDART_WRAP - } /* namespace dynload */ /** @@ -171,11 +98,11 @@ int g_cuda_lib_version = 0; * Check build-in cuda function using glog and it **does not** * support << operator for more details error info. */ -#define CHECK_CUDA(cudaFunc) \ - do { \ - cudaError_t cudaStat = cudaFunc; \ - CHECK_EQ(cudaSuccess, cudaStat) << "Cuda Error: " \ - << dynload::cudaGetErrorString(cudaStat); \ +#define CHECK_CUDA(cudaFunc) \ + do { \ + cudaError_t cudaStat = cudaFunc; \ + CHECK_EQ(cudaSuccess, cudaStat) << "Cuda Error: " \ + << cudaGetErrorString(cudaStat); \ } while (0) /** @@ -284,13 +211,13 @@ void hl_fini() { tmp_stream = (char *)t_device[dev]->stream; } for (int j = 0; j < NUMBER_OF_THREAD_STREAM; j++) { - CHECK_CUDA(dynload::cudaStreamDestroy(t_device[dev]->stream[j])); + CHECK_CUDA(cudaStreamDestroy(t_device[dev]->stream[j])); } /* free device memory */ hl_free_mem_device(t_device[dev]->gpu_mem); hl_free_mem_host(t_device[dev]->cpu_mem); - CHECK_CUDA(dynload::cudaEventDestroy(t_device[dev]->mem_event)); + CHECK_CUDA(cudaEventDestroy(t_device[dev]->mem_event)); } free(tmp); @@ -308,7 +235,7 @@ void hl_set_device(int device) { CHECK(device >= 0 && device < g_system_device_num && g_device[device]) << "Device: " << device << " is not specified in startup."; - CHECK_CUDA(dynload::cudaSetDevice(device)); + CHECK_CUDA(cudaSetDevice(device)); /* switch thread stream */ for (int i = 0; i < NUMBER_OF_GLOBAL_STREAM; i++) { @@ -336,7 +263,7 @@ void hl_set_device(int device) { int hl_get_device() { int device; - CHECK_CUDA(dynload::cudaGetDevice(&device)); + CHECK_CUDA(cudaGetDevice(&device)); return device; } @@ -344,7 +271,7 @@ void *hl_malloc_device(size_t size) { void *dest_d; CHECK(size) << __func__ << ": the size for device memory is 0, please check."; - CHECK_CUDA(dynload::cudaMalloc((void **)&dest_d, size)); + CHECK_CUDA(cudaMalloc((void **)&dest_d, size)); return dest_d; } @@ -352,7 +279,7 @@ void *hl_malloc_device(size_t size) { void hl_free_mem_device(void *dest_d) { CHECK_NOTNULL(dest_d); - cudaError_t err = dynload::cudaFree(dest_d); + cudaError_t err = cudaFree(dest_d); CHECK(cudaSuccess == err || cudaErrorCudartUnloading == err) << hl_get_device_error_string(); } @@ -361,8 +288,7 @@ void *hl_malloc_host(size_t size) { void *dest_h; CHECK(size) << __func__ << ": the size for device memory is 0, please check."; - CHECK_CUDA( - dynload::cudaHostAlloc((void **)&dest_h, size, cudaHostAllocDefault)); + CHECK_CUDA(cudaHostAlloc((void **)&dest_h, size, cudaHostAllocDefault)); return dest_h; } @@ -370,7 +296,7 @@ void *hl_malloc_host(size_t size) { void hl_free_mem_host(void *dest_h) { CHECK_NOTNULL(dest_h); - cudaError_t err = dynload::cudaFreeHost(dest_h); + cudaError_t err = cudaFreeHost(dest_h); CHECK(cudaSuccess == err || cudaErrorCudartUnloading == err) << hl_get_device_error_string(); } @@ -381,11 +307,11 @@ void hl_memcpy(void *dst, void *src, size_t size) { } CHECK_NOTNULL(dst); CHECK_NOTNULL(src); - CHECK_CUDA(dynload::cudaMemcpy(dst, src, size, cudaMemcpyDefault)); + CHECK_CUDA(cudaMemcpy(dst, src, size, cudaMemcpyDefault)); } void hl_memset_device(void *dest_d, int value, size_t size) { - CHECK_CUDA(dynload::cudaMemset(dest_d, value, size)); + CHECK_CUDA(cudaMemset(dest_d, value, size)); } void hl_memcpy_host2device(void *dest_d, void *src_h, size_t size) { @@ -394,7 +320,7 @@ void hl_memcpy_host2device(void *dest_d, void *src_h, size_t size) { } CHECK_NOTNULL(src_h); CHECK_NOTNULL(dest_d); - CHECK_CUDA(dynload::cudaMemcpy(dest_d, src_h, size, cudaMemcpyHostToDevice)); + CHECK_CUDA(cudaMemcpy(dest_d, src_h, size, cudaMemcpyHostToDevice)); } void hl_memcpy_device2host(void *dest_h, void *src_d, size_t size) { @@ -403,7 +329,7 @@ void hl_memcpy_device2host(void *dest_h, void *src_d, size_t size) { } CHECK_NOTNULL(dest_h); CHECK_NOTNULL(src_d); - CHECK_CUDA(dynload::cudaMemcpy(dest_h, src_d, size, cudaMemcpyDeviceToHost)); + CHECK_CUDA(cudaMemcpy(dest_h, src_d, size, cudaMemcpyDeviceToHost)); } void hl_memcpy_device2device(void *dest_d, void *src_d, size_t size) { @@ -412,8 +338,7 @@ void hl_memcpy_device2device(void *dest_d, void *src_d, size_t size) { } CHECK_NOTNULL(dest_d); CHECK_NOTNULL(src_d); - CHECK_CUDA( - dynload::cudaMemcpy(dest_d, src_d, size, cudaMemcpyDeviceToDevice)); + CHECK_CUDA(cudaMemcpy(dest_d, src_d, size, cudaMemcpyDeviceToDevice)); } void hl_memcpy_async(void *dst, void *src, size_t size, hl_stream_t stream) { @@ -427,8 +352,7 @@ void hl_memcpy_async(void *dst, void *src, size_t size, hl_stream_t stream) { CHECK_LT(stream, HPPL_STREAM_END); cu_stream = t_resource.stream[stream]; - CHECK_CUDA( - dynload::cudaMemcpyAsync(dst, src, size, cudaMemcpyDefault, cu_stream)); + CHECK_CUDA(cudaMemcpyAsync(dst, src, size, cudaMemcpyDefault, cu_stream)); } void hl_start() { @@ -439,8 +363,7 @@ void hl_start() { bool hl_device_can_access_peer(int device, int peerDevice) { int canAccessPeer; - CHECK_CUDA( - dynload::cudaDeviceCanAccessPeer(&canAccessPeer, device, peerDevice)); + CHECK_CUDA(cudaDeviceCanAccessPeer(&canAccessPeer, device, peerDevice)); if (canAccessPeer == 1) { return true; @@ -450,9 +373,9 @@ bool hl_device_can_access_peer(int device, int peerDevice) { } void hl_device_enable_peer_access(int peerDevice) { - cudaError_t err = dynload::cudaDeviceEnablePeerAccess(peerDevice, 0); + cudaError_t err = cudaDeviceEnablePeerAccess(peerDevice, 0); if (cudaErrorPeerAccessAlreadyEnabled == err) { - dynload::cudaGetLastError(); + cudaGetLastError(); } else { CHECK_CUDA(err); } @@ -463,9 +386,9 @@ void hl_create_global_resources(hl_device_prop device_prop) { int device = device_prop->device; global_device_resources device_res = device_prop->device_resources; - CHECK_CUDA(dynload::cudaSetDevice(device)); + CHECK_CUDA(cudaSetDevice(device)); /* device properties */ - CHECK_CUDA(dynload::cudaGetDeviceProperties(&cu_prop, device)); + CHECK_CUDA(cudaGetDeviceProperties(&cu_prop, device)); device_prop->major = cu_prop.major; device_prop->minor = cu_prop.minor; @@ -474,7 +397,7 @@ void hl_create_global_resources(hl_device_prop device_prop) { /* create device stream */ for (int j = 0; j < NUMBER_OF_GLOBAL_STREAM; j++) { - CHECK_CUDA(dynload::cudaStreamCreate(&device_res->stream[j])); + CHECK_CUDA(cudaStreamCreate(&device_res->stream[j])); } /* cublas init */ @@ -501,18 +424,18 @@ void hl_create_global_resources(hl_device_prop device_prop) { device_res->gen_mutex = (pthread_mutex_t *)(malloc(sizeof(pthread_mutex_t))); pthread_mutex_init(device_res->gen_mutex, NULL); - CHECK_CUDA(dynload::cudaRuntimeGetVersion(&g_cuda_lib_version)); + CHECK_CUDA(cudaRuntimeGetVersion(&g_cuda_lib_version)); } int hl_get_cuda_version() { return g_cuda_lib_version; } void hl_create_thread_resources(int device, thread_device_resources device_res) { - CHECK_CUDA(dynload::cudaSetDevice(device)); + CHECK_CUDA(cudaSetDevice(device)); /* create thread stream */ for (int j = 0; j < NUMBER_OF_THREAD_STREAM; j++) { - CHECK_CUDA(dynload::cudaStreamCreate(&device_res->stream[j])); + CHECK_CUDA(cudaStreamCreate(&device_res->stream[j])); } /* allocation device memory */ @@ -521,14 +444,14 @@ void hl_create_thread_resources(int device, /* allocation host memory */ device_res->cpu_mem = (real *)hl_malloc_host(HPPL_GPU_MEMORY_SIZE); - CHECK_CUDA(dynload::cudaEventCreate(&device_res->mem_event)); + CHECK_CUDA(cudaEventCreate(&device_res->mem_event)); } void hl_specify_devices_start(int *device, int number) { if (hl_start_flag) return; /* 1. get the number of devices */ - CHECK_CUDA(dynload::cudaGetDeviceCount(&g_system_device_num)); + CHECK_CUDA(cudaGetDeviceCount(&g_system_device_num)); CHECK_NE(g_system_device_num, 0) << "[Start failed] there is no GPU device"; if (device == NULL) { number = g_system_device_num; @@ -640,7 +563,7 @@ void hl_stream_synchronize(hl_stream_t stream) { << ": the parameter stream is error."; cu_stream = t_resource.stream[stream]; - CHECK_CUDA(dynload::cudaStreamSynchronize(cu_stream)); + CHECK_CUDA(cudaStreamSynchronize(cu_stream)); } void hl_create_event(hl_event_t *event) { @@ -649,7 +572,7 @@ void hl_create_event(hl_event_t *event) { struct _hl_event_st *st_event = (struct _hl_event_st *)malloc(sizeof(struct _hl_event_st)); - CHECK_CUDA(dynload::cudaEventCreate(&st_event->cu_event)); + CHECK_CUDA(cudaEventCreate(&st_event->cu_event)); *event = st_event; } @@ -659,8 +582,7 @@ float hl_event_elapsed_time(hl_event_t start, hl_event_t end) { CHECK_NOTNULL(start); CHECK_NOTNULL(end); - CHECK_CUDA( - dynload::cudaEventElapsedTime(&time, start->cu_event, end->cu_event)); + CHECK_CUDA(cudaEventElapsedTime(&time, start->cu_event, end->cu_event)); return time; } @@ -672,7 +594,7 @@ void hl_stream_record_event(hl_stream_t stream, hl_event_t event) { << ": the parameter stream is error."; cu_stream = t_resource.stream[stream]; - CHECK_CUDA(dynload::cudaEventRecord(event->cu_event, cu_stream)); + CHECK_CUDA(cudaEventRecord(event->cu_event, cu_stream)); } void hl_stream_wait_event(hl_stream_t stream, hl_event_t event) { @@ -683,12 +605,12 @@ void hl_stream_wait_event(hl_stream_t stream, hl_event_t event) { << ": the parameter stream is error."; cu_stream = t_resource.stream[stream]; - CHECK_CUDA(dynload::cudaStreamWaitEvent(cu_stream, event->cu_event, 0)); + CHECK_CUDA(cudaStreamWaitEvent(cu_stream, event->cu_event, 0)); } void hl_destroy_event(hl_event_t event) { CHECK_NOTNULL(event); - CHECK_CUDA(dynload::cudaEventDestroy(event->cu_event)); + CHECK_CUDA(cudaEventDestroy(event->cu_event)); free(event); event = NULL; @@ -696,7 +618,7 @@ void hl_destroy_event(hl_event_t event) { void hl_event_synchronize(hl_event_t event) { CHECK_NOTNULL(event); - CHECK_CUDA(dynload::cudaEventSynchronize(event->cu_event)); + CHECK_CUDA(cudaEventSynchronize(event->cu_event)); } void hl_get_device_name(char *name, int len, int device) { @@ -725,24 +647,24 @@ void hl_get_device_compute_capability(int *major, int *minor, int device) { *minor = g_device[device]->minor; } -int hl_get_device_last_error() { return (int)dynload::cudaGetLastError(); } +int hl_get_device_last_error() { return (int)cudaGetLastError(); } const char *hl_get_device_error_string() { - cudaError_t err = dynload::cudaGetLastError(); - return dynload::cudaGetErrorString(err); + cudaError_t err = cudaGetLastError(); + return cudaGetErrorString(err); } const char *hl_get_device_error_string(size_t err) { - return dynload::cudaGetErrorString((cudaError_t)err); + return cudaGetErrorString((cudaError_t)err); } -void hl_device_synchronize() { CHECK_CUDA(dynload::cudaDeviceSynchronize()); } +void hl_device_synchronize() { CHECK_CUDA(cudaDeviceSynchronize()); } void hl_set_device_flags_block() { - CHECK_CUDA(dynload::cudaSetDeviceFlags(cudaDeviceScheduleBlockingSync)); + CHECK_CUDA(cudaSetDeviceFlags(cudaDeviceScheduleBlockingSync)); } bool hl_cuda_event_is_ready(hl_event_t event) { - cudaError_t err = dynload::cudaEventQuery(event->cu_event); + cudaError_t err = cudaEventQuery(event->cu_event); CHECK(cudaSuccess == err || cudaErrorNotReady == err); if (cudaErrorNotReady == err) { @@ -751,6 +673,6 @@ bool hl_cuda_event_is_ready(hl_event_t event) { return true; } -void hl_profiler_start() { CHECK_CUDA(dynload::cudaProfilerStart()); } +void hl_profiler_start() { CHECK_CUDA(cudaProfilerStart()); } -void hl_profiler_end() { CHECK_CUDA(dynload::cudaProfilerStop()); } +void hl_profiler_end() { CHECK_CUDA(cudaProfilerStop()); } diff --git a/paddle/cuda/src/hl_cudart_wrap.cc b/paddle/cuda/src/hl_cudart_wrap.cc deleted file mode 100644 index ecc03a729dde2f2b4f8f004234a47d9272997a50..0000000000000000000000000000000000000000 --- a/paddle/cuda/src/hl_cudart_wrap.cc +++ /dev/null @@ -1,200 +0,0 @@ -/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. */ - -#ifdef PADDLE_USE_DSO - -#include -#include -#include "hl_dso_loader.h" - -/** - * cudart wrapper: for dynamic load libcudart.so. - * When nvcc compile cuda kernels, it will insert - * some build-in runtime routines, which must be - * provided by us if PADDLE_USE_DSO is true. If - * PADDLE_USE_DSO is false, all of them must be - * ignored to avoid multiple definitions. - */ -namespace dynload { - -extern std::once_flag cudart_dso_flag; -extern void *cudart_dso_handle; - -/** - * The following macro definition can generate structs - * (for each function) to dynamic load cuda routine - * via operator overloading. - **/ -#define DYNAMIC_LOAD_CUDART_WRAP(__name, __type) \ - struct DynLoad__##__name { \ - template \ - __type operator()(Args... args) { \ - typedef __type (*cudartFunc)(Args...); \ - std::call_once(cudart_dso_flag, GetCudartDsoHandle, &cudart_dso_handle); \ - void *p_##__name = dlsym(cudart_dso_handle, #__name); \ - return reinterpret_cast(p_##__name)(args...); \ - } \ - } __name; /* struct DynLoad__##__name */ - -/* include all needed cuda functions in HPPL */ -// clang-format off -#define CUDA_ROUTINE_EACH(__macro) \ - __macro(cudaLaunch, cudaError_t) \ - __macro(cudaSetupArgument, cudaError_t) \ - __macro(cudaConfigureCall, cudaError_t) \ - __macro(__cudaRegisterFatBinary, void**) \ - __macro(__cudaUnregisterFatBinary, void) \ - __macro(__cudaRegisterFunction, void) \ - __macro(__cudaRegisterVar, void) \ - __macro(__cudaRegisterManagedVar, void) \ - __macro(__cudaInitModule, char) \ - __macro(__cudaRegisterTexture, void) \ - __macro(__cudaRegisterSurface, void) -// clang-format on - -CUDA_ROUTINE_EACH(DYNAMIC_LOAD_CUDART_WRAP) - -#if CUDART_VERSION >= 7000 -DYNAMIC_LOAD_CUDART_WRAP(cudaLaunchKernel, cudaError_t) -#endif - -#undef CUDA_ROUNTINE_EACH - -} /* namespace dynload */ - -#if CUDART_VERSION >= 7000 -__host__ cudaError_t CUDARTAPI cudaLaunchKernel(const void *func, - dim3 gridDim, - dim3 blockDim, - void **args, - size_t sharedMem, - cudaStream_t stream) { - return dynload::cudaLaunchKernel( - func, gridDim, blockDim, args, sharedMem, stream); -} -#endif /* CUDART_VERSION >= 7000 */ - -__host__ cudaError_t CUDARTAPI cudaLaunch(const void *func) { - return dynload::cudaLaunch(func); -} - -__host__ cudaError_t CUDARTAPI cudaSetupArgument(const void *arg, - size_t size, - size_t offset) { - return dynload::cudaSetupArgument(arg, size, offset); -} - -__host__ cudaError_t CUDARTAPI cudaConfigureCall(dim3 gridDim, - dim3 blockDim, - size_t sharedMem, - cudaStream_t stream) { - return dynload::cudaConfigureCall(gridDim, blockDim, sharedMem, stream); -} - -extern "C" { - -void **CUDARTAPI __cudaRegisterFatBinary(void *fatCubin) { - return dynload::__cudaRegisterFatBinary(fatCubin); -} - -void CUDARTAPI __cudaUnregisterFatBinary(void **fatCubinHandle) { - return dynload::__cudaUnregisterFatBinary(fatCubinHandle); -} - -void CUDARTAPI __cudaRegisterFunction(void **fatCubinHandle, - const char *hostFun, - char *deviceFun, - const char *deviceName, - int thread_limit, - uint3 *tid, - uint3 *bid, - dim3 *bDim, - dim3 *gDim, - int *wSize) { - return dynload::__cudaRegisterFunction(fatCubinHandle, - hostFun, - deviceFun, - deviceName, - thread_limit, - tid, - bid, - bDim, - gDim, - wSize); -} - -void CUDARTAPI __cudaRegisterVar(void **fatCubinHandle, - char *hostVar, - char *deviceAddress, - const char *deviceName, - int ext, - int size, - int constant, - int global) { - return dynload::__cudaRegisterVar(fatCubinHandle, - hostVar, - deviceAddress, - deviceName, - ext, - size, - constant, - global); -} - -extern void CUDARTAPI __cudaRegisterManagedVar(void **fatCubinHandle, - void **hostVarPtrAddress, - char *deviceAddress, - const char *deviceName, - int ext, - int size, - int constant, - int global) { - return dynload::__cudaRegisterManagedVar(fatCubinHandle, - hostVarPtrAddress, - deviceAddress, - deviceName, - ext, - size, - constant, - global); -} - -char CUDARTAPI __cudaInitModule(void **fatCubinHandle) { - return dynload::__cudaInitModule(fatCubinHandle); -} - -void CUDARTAPI __cudaRegisterTexture(void **fatCubinHandle, - const struct textureReference *hostVar, - const void **deviceAddress, - const char *deviceName, - int dim, - int norm, - int ext) { - return dynload::__cudaRegisterTexture( - fatCubinHandle, hostVar, deviceAddress, deviceName, dim, norm, ext); -} - -void CUDARTAPI __cudaRegisterSurface(void **fatCubinHandle, - const struct surfaceReference *hostVar, - const void **deviceAddress, - const char *deviceName, - int dim, - int ext) { - return dynload::__cudaRegisterSurface( - fatCubinHandle, hostVar, deviceAddress, deviceName, dim, ext); -} - -} /* extern "C" */ - -#endif diff --git a/paddle/cuda/src/hl_dso_loader.cc b/paddle/cuda/src/hl_dso_loader.cc index c92909de534a875028d6d4784b02f08648c85a9a..53164dd27c7c5f5254e743b6fcf1d7b6fc895e31 100644 --- a/paddle/cuda/src/hl_dso_loader.cc +++ b/paddle/cuda/src/hl_dso_loader.cc @@ -25,10 +25,8 @@ DEFINE_string(cudnn_dir, DEFINE_string(cuda_dir, "", "Specify path for loading cuda library, such as libcublas, " - "libcurand. For instance, /usr/local/cuda/lib64. (Note: " - "libcudart can not be specified by cuda_dir, since some " - "build-in function in cudart already ran before main entry). " - "If default, dlopen will search cuda from LD_LIBRARY_PATH"); + "libcurand. For instance, /usr/local/cuda/lib64. If default, " + "dlopen will search cuda from LD_LIBRARY_PATH"); DEFINE_string(warpctc_dir, "", "Specify path for loading libwarpctc.so."); @@ -147,14 +145,6 @@ void GetCudnnDsoHandle(void** dso_handle) { #endif } -void GetCudartDsoHandle(void** dso_handle) { -#if defined(__APPLE__) || defined(__OSX__) - GetDsoHandleFromSearchPath("", "libcudart.dylib", dso_handle); -#else - GetDsoHandleFromSearchPath("", "libcudart.so", dso_handle); -#endif -} - void GetCurandDsoHandle(void** dso_handle) { #if defined(__APPLE__) || defined(__OSX__) GetDsoHandleFromSearchPath(FLAGS_cuda_dir, "libcurand.dylib", dso_handle); diff --git a/paddle/function/BufferArg.cpp b/paddle/function/BufferArg.cpp index 65c6f303041d830812fb2d99503b2b2166145f4a..5d595deb12c6c8ea419dd1f31b3c131a2f6a587a 100644 --- a/paddle/function/BufferArg.cpp +++ b/paddle/function/BufferArg.cpp @@ -15,17 +15,32 @@ limitations under the License. */ #include #include "BufferArg.h" +#include "paddle/math/SparseMatrix.h" namespace paddle { const SequenceArg& BufferArg::sequence() const { - // CHECK_EQ(bufferType_, TENSOR_SEQUENCE_DATA); + CHECK_EQ(bufferType_, TENSOR_SEQUENCE_DATA); return dynamic_cast(*this); } const SparseMatrixArg& BufferArg::sparse() const { - // CHECK_EQ(bufferType_, TENSOR_SPARSE); + CHECK_EQ(bufferType_, TENSOR_SPARSE); return dynamic_cast(*this); } +SparseMatrixArg::SparseMatrixArg(const CpuSparseMatrix& sparse, ArgType argType) + : BufferArg(sparse, argType), + row_(reinterpret_cast(sparse.getRows()), VALUE_TYPE_INT32), + col_(reinterpret_cast(sparse.getCols()), VALUE_TYPE_INT32) { + bufferType_ = TENSOR_SPARSE; +} + +SparseMatrixArg::SparseMatrixArg(const GpuSparseMatrix& sparse, ArgType argType) + : BufferArg(sparse, argType), + row_(reinterpret_cast(sparse.getRows()), VALUE_TYPE_INT32), + col_(reinterpret_cast(sparse.getCols()), VALUE_TYPE_INT32) { + bufferType_ = TENSOR_SPARSE; +} + } // namespace paddle diff --git a/paddle/function/BufferArg.h b/paddle/function/BufferArg.h index 9649913fa8d9bf82b67fc2ac97ae9f30e7029528..84209265ce7634121e3e4dde609cd787093c45ec 100644 --- a/paddle/function/BufferArg.h +++ b/paddle/function/BufferArg.h @@ -18,17 +18,16 @@ limitations under the License. */ #include "TensorShape.h" #include "TensorType.h" -#include "paddle/math/CpuSparseMatrix.h" #include "paddle/math/Matrix.h" -#include "paddle/math/SparseMatrix.h" namespace paddle { enum BufferType { - TENSOR_NORMAL = 0, - TENSOR_SEQUENCE_ID = 1, - TENSOR_SEQUENCE_DATA = 2, - TENSOR_SPARSE = 3 + TENSOR_UNKNOWN = 0, + TENSOR_NORMAL = 1, + TENSOR_SEQUENCE_ID = 2, + TENSOR_SEQUENCE_DATA = 3, + TENSOR_SPARSE = 4 }; enum SparseDataType { @@ -41,7 +40,6 @@ enum SparseDataFormat { SPARSE_CSR_FORMAT = 0, SPARSE_CSC_FORMAT = 1 }; class BufferArg; class SequenceArg; class SparseMatrixArg; -typedef std::shared_ptr BufferArgPtr; /** * \brief BufferArg used as the argument type of Function. @@ -52,6 +50,11 @@ typedef std::shared_ptr BufferArgPtr; * 3. SequenceArg for a Buffer of sequence data. * 4. SparseMatrixArg for a Buffer of sparse matrix. * + * Buffer shape + * For most buffers, the first dimension `shape()[0]` represents + * the size of the mini-batch. + * + * Buffer argType * There is an ArgType property for the BufferArg used as Function Output. * Whether the result of the Function calculation is assigned to the * output Buffer or added to the output Buffer is determined by the @@ -73,6 +76,14 @@ public: ArgType getArgType() const { return argType_; } public: + BufferArg(ValueType valueType, + const TensorShape& shape, + ArgType argType = UNSPECIFIED) + : buf_(nullptr), + valueType_(valueType), + shape_(shape), + argType_(argType) {} + BufferArg(void* buf, ValueType valueType, const TensorShape& shape, @@ -88,6 +99,7 @@ public: valueType_(DataType::value), shape_(2), argType_(argType) { + bufferType_ = TENSOR_NORMAL; shape_.setDim(0, matrix.getHeight()); shape_.setDim(1, matrix.getWidth()); } @@ -100,6 +112,7 @@ public: valueType_(DataType::value), shape_(shape), argType_(argType) { + bufferType_ = TENSOR_NORMAL; CHECK_EQ(matrix.getElementCnt(), shape.getElements()); } @@ -109,6 +122,7 @@ public: valueType_(DataType::value), shape_(1), argType_(argType) { + bufferType_ = TENSOR_NORMAL; shape_.setDim(0, vector.getSize()); } @@ -118,6 +132,7 @@ public: valueType_(VALUE_TYPE_INT32), shape_(1), argType_(argType) { + bufferType_ = TENSOR_NORMAL; shape_.setDim(0, vector.getSize()); } @@ -152,6 +167,8 @@ public: ValueType valueType() const { return valueType_; } BufferType bufferType() const { return bufferType_; } const TensorShape& shape() const { return shape_; } + bool isSparse() const { return (TENSOR_SPARSE == bufferType_); } + bool isSequenceArg() const { return TENSOR_SEQUENCE_DATA == bufferType_; } const SequenceArg& sequence() const; const SparseMatrixArg& sparse() const; @@ -160,8 +177,8 @@ protected: void* buf_; ValueType valueType_; TensorShape shape_; - BufferType bufferType_; - ArgType argType_ = UNSPECIFIED; + BufferType bufferType_{TENSOR_UNKNOWN}; + ArgType argType_{UNSPECIFIED}; // leading dimensions. The size is dims_.size() // Dims lds_; }; @@ -172,15 +189,24 @@ protected: // if a < b then value_.buf_[a] < value_.buf_[b] class SequenceIdArg : public BufferArg { public: + SequenceIdArg(const TensorShape& shape, ArgType argType = UNSPECIFIED) + : BufferArg(VALUE_TYPE_INT32, shape, argType) { + CHECK_EQ(shape_.ndims(), (size_t)1); + CHECK_GT(shape_[0], 1); + numSeqs_ = shape_[0] - 1; + } + SequenceIdArg(void* buf, const TensorShape& shape, ArgType argType = UNSPECIFIED) : BufferArg(buf, VALUE_TYPE_INT32, shape, argType) { + bufferType_ = TENSOR_SEQUENCE_ID; CHECK_EQ(shape_.ndims(), (size_t)1); numSeqs_ = shape_[0] - 1; } SequenceIdArg(const IVector& vector) : BufferArg(vector) { + bufferType_ = TENSOR_SEQUENCE_ID; numSeqs_ = shape_[0] - 1; } @@ -192,26 +218,41 @@ private: size_t numSeqs_; }; -// sequence data +// sequences data +// For mini-batch calculate, +// one batch can contain more than one sequence of data. +// SequenceArg can be used to represent sequences that contain multiple +// unequal lengths. class SequenceArg : public BufferArg { public: + SequenceArg(ValueType valueType, + const TensorShape& shape, + ArgType argType = UNSPECIFIED) + : BufferArg(valueType, shape, argType), startPositions_(TensorShape()) {} + SequenceArg(void* buf, ValueType valueType, const TensorShape& shape, const SequenceIdArg& startPositions, ArgType argType = UNSPECIFIED) : BufferArg(buf, valueType, shape, argType), - startPositions_(startPositions) {} + startPositions_(startPositions) { + bufferType_ = TENSOR_SEQUENCE_DATA; + } SequenceArg(const Matrix& matrix, const IVector& vector, ArgType argType = UNSPECIFIED) - : BufferArg(matrix, argType), startPositions_(vector) {} + : BufferArg(matrix, argType), startPositions_(vector) { + bufferType_ = TENSOR_SEQUENCE_DATA; + } ~SequenceArg() {} void* getIdBuf() const { return startPositions_.data(); } size_t numSeqs() const { return startPositions_.numSeqs(); } + SequenceIdArg& getSequenceId() { return startPositions_; } + const SequenceIdArg& getSequenceId() const { return startPositions_; } private: SequenceIdArg startPositions_; @@ -237,6 +278,7 @@ public: nnz_(nnz), format_(format), type_(type) { + bufferType_ = TENSOR_SPARSE; CHECK((valueType == VALUE_TYPE_FLOAT) || (valueType == VALUE_TYPE_DOUBLE)); CHECK_EQ(shape_.ndims(), (size_t)2); CHECK_EQ(row_.shape().ndims(), (size_t)1); @@ -248,15 +290,9 @@ public: } } - SparseMatrixArg(const CpuSparseMatrix& sparse, ArgType argType = UNSPECIFIED) - : BufferArg(sparse, argType), - row_(reinterpret_cast(sparse.getRows()), VALUE_TYPE_INT32), - col_(reinterpret_cast(sparse.getCols()), VALUE_TYPE_INT32) {} + SparseMatrixArg(const CpuSparseMatrix& sparse, ArgType argType = UNSPECIFIED); - SparseMatrixArg(const GpuSparseMatrix& sparse, ArgType argType = UNSPECIFIED) - : BufferArg(sparse, argType), - row_(reinterpret_cast(sparse.getRows()), VALUE_TYPE_INT32), - col_(reinterpret_cast(sparse.getCols()), VALUE_TYPE_INT32) {} + SparseMatrixArg(const GpuSparseMatrix& sparse, ArgType argType = UNSPECIFIED); ~SparseMatrixArg() {} diff --git a/paddle/function/BufferArgTest.cpp b/paddle/function/BufferArgTest.cpp index a9ee3ab079e339b86a9db8602c41e419df9dc544..1744f377808f137dcda4a28acce336dc22be3d01 100644 --- a/paddle/function/BufferArgTest.cpp +++ b/paddle/function/BufferArgTest.cpp @@ -14,7 +14,6 @@ limitations under the License. */ #include "BufferArg.h" #include -#include "Function.h" #include "paddle/math/MemoryHandle.h" namespace paddle { @@ -36,55 +35,4 @@ TEST(BufferTest, SequenceIdArg) { EXPECT_EQ(buffer.numSeqs(), 9); } -TEST(BufferTest, asArgument) { - MatrixPtr matrix = Matrix::create(100, 200); - VectorPtr vector = Vector::create(100, false); - CpuSparseMatrix sparse(200, 300, 50); - - // prepare arguments - BufferArgs argments; - argments.addArg(*matrix); - argments.addArg(*vector); - argments.addArg(sparse); - - // function - auto function = [=](const BufferArgs& inputs) { - EXPECT_EQ(inputs.size(), 3); - - // check inputs[0] - EXPECT_EQ(inputs[0].shape().ndims(), 2); - EXPECT_EQ(inputs[0].shape()[0], 100); - EXPECT_EQ(inputs[0].shape()[1], 200); - EXPECT_EQ(inputs[0].data(), matrix->getData()); - - EXPECT_EQ(inputs[0].matrix().getHeight(), - matrix->getHeight()); - EXPECT_EQ(inputs[0].matrix().getWidth(), - matrix->getWidth()); - EXPECT_EQ(inputs[0].matrix().getData(), matrix->getData()); - - // check inputs[1] - EXPECT_EQ(inputs[1].shape().ndims(), 1); - EXPECT_EQ(inputs[1].shape()[0], 100); - EXPECT_EQ(inputs[1].data(), vector->getData()); - CpuVector inVector = inputs[1].vector(); - EXPECT_EQ(inVector.getSize(), vector->getSize()); - EXPECT_EQ(inVector.getData(), vector->getData()); - - // check inputs[2] - EXPECT_EQ(inputs[2].shape().ndims(), 2); - EXPECT_EQ(inputs[2].shape()[0], 200); - EXPECT_EQ(inputs[2].shape()[1], 300); - EXPECT_EQ(inputs[2].data(), sparse.getData()); - // CHECK_EQ(inputs[2].sparse().nnz(), 50); - // CHECK_EQ(inputs[2].sparse().dataFormat(), SPARSE_CSR_FORMAT); - // CHECK_EQ(inputs[2].sparse().dataType(), SPARSE_FLOAT_VALUE); - EXPECT_EQ(inputs[2].sparse().getRowBuf(), sparse.getRows()); - EXPECT_EQ(inputs[2].sparse().getColBuf(), sparse.getCols()); - }; - - // call function - function(argments); -} - } // namespace paddle diff --git a/paddle/function/CMakeLists.txt b/paddle/function/CMakeLists.txt index 75a2acc55ec3d33687f96d2b0398e52b69e8680d..a5cf16cb568ee9bafd15a8c9737d933b6fbbd12b 100644 --- a/paddle/function/CMakeLists.txt +++ b/paddle/function/CMakeLists.txt @@ -19,12 +19,12 @@ if(WITH_TESTING) # TODO: # file(GLOB test_files . *OpTest.cpp) # add_executable(${test_bin} EXCLUDE_FROM_ALL ${test_files}) - # add_simple_unittest(CrossMapNormalOpTest) + add_simple_unittest(CrossMapNormalOpTest) add_simple_unittest(TensorShapeTest) add_simple_unittest(TensorTypeTest) add_simple_unittest(BufferArgTest) add_simple_unittest(FunctionTest) - # add_simple_unittest(ContextProjectionOpTest) + add_simple_unittest(ContextProjectionOpTest) endif() endif() diff --git a/paddle/function/ContextProjectionOp.cpp b/paddle/function/ContextProjectionOp.cpp index cb448562ebb37022f727ee65024f06f69d63e9cb..6cd4e4abee8fccf3a4745b0bfc6701df4ddfa5c0 100644 --- a/paddle/function/ContextProjectionOp.cpp +++ b/paddle/function/ContextProjectionOp.cpp @@ -17,7 +17,10 @@ limitations under the License. */ #include "paddle/math/Vector.h" namespace paddle { - +/** + * Context Projection Forward with CPU Matrix Device. + * + */ template <> void ContextProjectionForward(CpuMatrix& out_mat, const CpuMatrix& input_mat, @@ -70,10 +73,30 @@ void ContextProjectionForward(CpuMatrix& out_mat, } /** - * \param inputs[0] input value. - * \param inputs[1] input weight. - * \param inputs[2] input sequence. - * \param outputs[0] output value. + * Paddle Function for Context Projection Forward. + * Calculate the output layer value sequence after context projection. + * + * What is Context Projection for a sequence? + * For example, assumed input (x) has 4 words and the dimension of each word + * representation is 2. If we use zero to pad instead of learned weight to pad, + * and the context_lenth is 3, the output (y) is: + * + * @code + * x = [a1, a2; + * b1, b2; + * c1, c2; + * d1, d2] + * y = [0, 0, a1, a2, b1, b2; + * a1, a2, b1, b2, c1, c2; + * b1, b2, c1, c2, d1, d2; + * c1, c2, d1, d2, 0, 0] + * @endcode + * + * \param outputs[0].matrix output layer value, n * (d * l) + * \param outputs[0].vector start position sequence, n * 1 + * \param inputs[0].matrix input layer value, n * d + * \param inputs[0].vector start position sequence, n * 1 + * \param inputs[1].matrix input layer weight, pad * d */ template class ContextProjectionForwardFunc : public FunctionBase { @@ -85,28 +108,37 @@ public: } void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { - CHECK_EQ((size_t)3, inputs.size()); + CHECK(1 == inputs.size() || 2 == inputs.size()); CHECK_EQ((size_t)1, outputs.size()); + CHECK(inputs[0].isSequenceArg() && outputs[0].isSequenceArg()) + << "SequenceArg required here"; + const auto val_seqs = dynamic_cast(inputs[0]); + auto out_seq = dynamic_cast(outputs[0]); - CHECK(outputs[0].data() && inputs[0].data() && inputs[2].data()); - CHECK_EQ(outputs[0].shape().ndims(), (size_t)2); - CHECK_EQ(inputs[0].shape().ndims(), (size_t)2); - CHECK_EQ(inputs[1].shape().ndims(), (size_t)2); - CHECK_EQ(inputs[2].shape().ndims(), (size_t)1); + CHECK(out_seq.data() && val_seqs.data() && val_seqs.getSequenceId().data()); + CHECK_EQ(out_seq.shape().ndims(), (size_t)2); + CHECK_EQ(val_seqs.shape().ndims(), (size_t)2); + CHECK_EQ(val_seqs.getSequenceId().shape().ndims(), (size_t)1); + if (2 == inputs.size()) { + CHECK_EQ(inputs[1].shape().ndims(), (size_t)2); + } /// dim of output = dim of input * context_length - CHECK_EQ(outputs[0].shape()[1], inputs[0].shape()[1] * context_length_); - /// dim of input == dim of weight - CHECK_EQ(inputs[0].shape()[1], inputs[1].shape()[1]); + CHECK_EQ(out_seq.shape()[1], val_seqs.shape()[1] * context_length_); /// input and output has the same batch_size - CHECK_EQ(inputs[0].shape()[0], outputs[0].shape()[0]); + CHECK_EQ(val_seqs.shape()[0], out_seq.shape()[0]); + /// dim of input == dim of weight + if (2 == inputs.size()) { + CHECK_EQ(val_seqs.shape()[1], inputs[1].shape()[1]); + } - CHECK_EQ(outputs[0].getArgType(), ADD_TO); - auto out_mat = outputs[0].matrix(); - auto in_mat = inputs[0].matrix(); - auto w_mat = !inputs[1].data() - ? typename Tensor::Matrix(nullptr, 0, 0) - : inputs[1].matrix(); - auto seq_vec = inputs[2].vector(); + CHECK_EQ(out_seq.getArgType(), ADD_TO); + auto out_mat = out_seq.matrix(); + const auto in_mat = val_seqs.matrix(); + const auto w_mat = + (2 == inputs.size()) + ? inputs[1].matrix() + : typename Tensor::Matrix(nullptr, 0, 0); + const auto seq_vec = val_seqs.getSequenceId().vector(); ContextProjectionForward(out_mat, in_mat, w_mat, @@ -122,8 +154,12 @@ private: size_t begin_pad_; }; +/** + * Context Projection Backward with CPU Matrix Device. + * + */ template <> -void ContextProjectionBackward(CpuMatrix& out_grad_mat, +void ContextProjectionBackward(const CpuMatrix& out_grad_mat, CpuMatrix& in_grad_mat, CpuMatrix& w_grad_mat, const CpuIVector& seq_vec, @@ -146,7 +182,8 @@ void ContextProjectionBackward(CpuMatrix& out_grad_mat, int64_t pad_size = std::min(starts[i] - begin, starts[i + 1] - starts[i]); if (is_padding && w_grad_mat) { - MatrixPtr mat = out_grad_mat.subMatrix(starts[i], pad_size); + MatrixPtr mat = const_cast(out_grad_mat) + .subMatrix(starts[i], pad_size); MatrixPtr sub = w_grad_mat.subMatrix(j, pad_size); sub->addAtOffset(*mat, j * input_dim); } @@ -157,8 +194,8 @@ void ContextProjectionBackward(CpuMatrix& out_grad_mat, int64_t pad_size = std::min(end - starts[i + 1], starts[i + 1] - starts[i]); if (is_padding && w_grad_mat) { - MatrixPtr mat = - out_grad_mat.subMatrix(starts[i + 1] - pad_size, pad_size); + MatrixPtr mat = const_cast(out_grad_mat) + .subMatrix(starts[i + 1] - pad_size, pad_size); MatrixPtr sub = w_grad_mat.subMatrix( begin_pad + context_start + j - pad_size, pad_size); sub->addAtOffset(*mat, j * input_dim); @@ -169,17 +206,22 @@ void ContextProjectionBackward(CpuMatrix& out_grad_mat, if (end <= begin) continue; if (!in_grad_mat) continue; MatrixPtr src = in_grad_mat.subMatrix(begin, end - begin); - MatrixPtr dst = out_grad_mat.subMatrix(dst_begin, dst_end - dst_begin); + MatrixPtr dst = const_cast(out_grad_mat) + .subMatrix(dst_begin, dst_end - dst_begin); src->addAtOffset(*dst, j * input_dim); } } } /** - * \param inputs[0] input grad. - * \param inputs[1] weight grad. - * \param inputs[2] input sequence. - * \param outputs[0] output value. + * Context Projection Backward Function. + * Update the weight gradient and input layer gradient with backprop + * + * \param inputs[0].matrix output layer grad, n * (d * l) + * \param inputs[0].vector start position sequence, n * 1 + * \param outputs[0].matrix input layer grad, n * d + * \param outputs[0].vector start position sequence, n * 1 + * \param outputs[1] weight grad, pad * d */ template class ContextProjectionBackwardFunc : public FunctionBase { @@ -193,32 +235,36 @@ public: } void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { - CHECK_EQ((size_t)3, inputs.size()); - CHECK_EQ((size_t)1, outputs.size()); + CHECK_EQ((size_t)1, inputs.size()); + CHECK_EQ((size_t)2, outputs.size()); + CHECK(inputs[0].isSequenceArg() && outputs[0].isSequenceArg()) + << "SequenceArg required here"; + const auto in_seq = dynamic_cast(inputs[0]); + auto out_seq = dynamic_cast(outputs[0]); + CHECK(in_seq.data() && in_seq.getSequenceId().data()); + CHECK_EQ(in_seq.shape().ndims(), (size_t)2); + CHECK_EQ(in_seq.getSequenceId().shape().ndims(), (size_t)1); + CHECK_EQ(out_seq.shape().ndims(), (size_t)2); + CHECK_EQ(out_seq.getSequenceId().shape().ndims(), (size_t)1); + CHECK_EQ(outputs[1].shape().ndims(), (size_t)2); - CHECK(outputs[0].data() && inputs[2].data()); - CHECK_EQ(outputs[0].shape().ndims(), (size_t)2); - CHECK_EQ(inputs[0].shape().ndims(), (size_t)2); - CHECK_EQ(inputs[1].shape().ndims(), (size_t)2); - CHECK_EQ(inputs[2].shape().ndims(), (size_t)1); + /// dim of input grad == dim of weight + CHECK_EQ(out_seq.shape()[1], outputs[1].shape()[1]); + /// input and output grad has the same batch_size + CHECK_EQ(out_seq.shape()[0], in_seq.shape()[0]); + /// dim of output grad = dim of input grad * context_length + CHECK_EQ(in_seq.shape()[1], out_seq.shape()[1] * context_length_); + CHECK_EQ(out_seq.getArgType(), ADD_TO); + CHECK_EQ(outputs[1].getArgType(), ADD_TO); - /// dim of input == dim of weight - CHECK_EQ(inputs[0].shape()[1], inputs[1].shape()[1]); - /// input and output has the same batch_size - CHECK_EQ(inputs[0].shape()[0], outputs[0].shape()[0]); - /// dim of output = dim of input * context_length - CHECK_EQ(outputs[0].shape()[1], inputs[0].shape()[1] * context_length_); - - CHECK_EQ(outputs[0].getArgType(), ADD_TO); - - auto out_grad_mat = outputs[0].matrix(); + const auto seq_vec = in_seq.getSequenceId().vector(); + const auto out_grad_mat = in_seq.matrix(); auto in_grad_mat = - !inputs[0].data() ? typename Tensor::Matrix(nullptr, 0, 0) - : inputs[0].matrix(); - auto w_grad_mat = !inputs[1].data() + !out_seq.data() ? typename Tensor::Matrix(nullptr, 0, 0) + : out_seq.matrix(); + auto w_grad_mat = !outputs[1].data() ? typename Tensor::Matrix(nullptr, 0, 0) - : inputs[1].matrix(); - auto seq_vec = inputs[2].vector(); + : outputs[1].matrix(); ContextProjectionBackward(out_grad_mat, in_grad_mat, w_grad_mat, @@ -238,11 +284,16 @@ private: size_t total_pad_; }; -#if 0 /** - * \param inputs[0] input grad. - * \param inputs[1] input sequence. - * \param outputs[0] output grad. + * Context Projection Backward Data Function + * Update input layer grad + * input: sequence of output layer grad + * output: sequence of input layer grad + * + * \param outputs[0].matrix input layer grad, n * d + * \param outputs[0].vector start position sequence, n * 1 + * \param inputs[0].matrix output layer grad, n * (d * l) + * \param inputs[0].vector start positon sequence, n * 1 */ template class ContextProjectionBackwardDataFunc : public FunctionBase { @@ -252,32 +303,30 @@ public: context_start_ = config.get("context_start"); } - void calc(const Arguments& inputs, - const Arguments& outputs, - const Arguments& inouts) override { - CHECK_EQ(2, static_cast(inputs.size())); + void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { + CHECK_EQ(1, static_cast(inputs.size())); CHECK_EQ(1, static_cast(outputs.size())); - CHECK_EQ(0, static_cast(inouts.size())); - CHECK(inputs[0].getData() && outputs[0].getData() && inputs[1].getData()); - CHECK_EQ(static_cast(outputs[0].dims_.size()), 2); - CHECK_EQ(static_cast(inputs[0].dims_.size()), 2); - CHECK_EQ(static_cast(inputs[1].dims_.size()), 1); - CHECK_EQ(outputs[0].dims_[1], inputs[0].dims_[1] * context_length_); + CHECK(inputs[0].isSequenceArg() && outputs[0].isSequenceArg()) + << "SequenceArg required here"; + const auto in_seq = dynamic_cast(inputs[0]); + const auto out_seq = dynamic_cast(outputs[0]); + + CHECK(in_seq.data() && out_seq.data() && in_seq.getSequenceId().data()); + CHECK_EQ(static_cast(out_seq.shape().ndims()), 2); + CHECK_EQ(static_cast(in_seq.shape().ndims()), 2); + CHECK_EQ(static_cast(in_seq.getSequenceId().shape().ndims()), 1); + /// output layer grad dim == input layer grad dim * context_length_ + CHECK_EQ(in_seq.shape().ndims(), out_seq.shape().ndims() * context_length_); /// input and output has the same batch_size - CHECK_EQ(inputs[0].dims_[0], outputs[0].dims_[0]); + CHECK_EQ(in_seq.shape()[0], out_seq.shape()[0]); + CHECK_EQ(outputs[0].getArgType(), ASSIGN_TO); - auto out_grad_mat = std::make_shared::type>( - outputs[0].getData(), outputs[0].dims_[0], outputs[0].dims_[1]); - const auto in_grad_mat = std::make_shared::type>( - inputs[0].getData(), inputs[0].dims_[0], inputs[0].dims_[1]); - typename SequenceT::type seq_vec( - inputs[1].dims_[0], reinterpret_cast(inputs[1].getData())); + const auto out_grad_mat = in_seq.matrix(); + const auto seq_vec = in_seq.getSequenceId().vector(); + auto in_grad_mat = out_seq.matrix(); - ContextProjectionBackwardData(out_grad_mat.get(), - in_grad_mat.get(), - seq_vec, - context_length_, - context_start_); + ContextProjectionBackwardData( + out_grad_mat, in_grad_mat, seq_vec, context_length_, context_start_); } private: @@ -286,9 +335,14 @@ private: }; /** - * \param inputs[0] weight grad. - * \param inputs[1] input sequence. - * \param outputs[0] output grad. + * Context Projection Backward Weight Function + * Update weight grad by backprop + * input: sequence of output layer grad + * output: weight grad + * + * \param outputs[0] weight grad, pad * d + * \param inputs[0].matrix output layer grad, n * (d * l) + * \param inputs[0].vecotr start positon sequence, n * 1 */ template class ContextProjectionBackwardWeightFunc : public FunctionBase { @@ -300,28 +354,25 @@ public: total_pad_ = config.get("total_pad"); } - void calc(const Arguments& inputs, - const Arguments& outputs, - const Arguments& inouts) override { - CHECK_EQ(2, static_cast(inputs.size())); + void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { + CHECK_EQ(1, static_cast(inputs.size())); CHECK_EQ(1, static_cast(outputs.size())); - CHECK_EQ(0, static_cast(inouts.size())); - - CHECK(inputs[0].getData() && outputs[0].getData() && inputs[1].getData()); - CHECK_EQ(static_cast(outputs[0].dims_.size()), 2); - CHECK_EQ(static_cast(inputs[0].dims_.size()), 2); - CHECK_EQ(static_cast(inputs[1].dims_.size()), 1); - CHECK_EQ(outputs[0].dims_[1], inputs[0].dims_[1] * context_length_); - - auto out_grad_mat = std::make_shared::type>( - outputs[0].getData(), outputs[0].dims_[0], outputs[0].dims_[1]); - auto w_grad_mat = std::make_shared::type>( - inputs[0].getData(), inputs[0].dims_[0], inputs[0].dims_[1]); - typename SequenceT::type seq_vec( - inputs[1].dims_[0], reinterpret_cast(inputs[1].getData())); + CHECK(inputs[0].isSequenceArg()) << "SequenceArg required here"; + const auto in_seq = dynamic_cast(inputs[0]); + CHECK(in_seq.data() && in_seq.getSequenceId().data() && outputs[0].data()); + CHECK_EQ(static_cast(outputs[0].shape().ndims()), 2); + CHECK_EQ(static_cast(in_seq.shape().ndims()), 2); + CHECK_EQ(static_cast(in_seq.getSequenceId().shape().ndims()), 1); + CHECK_EQ(in_seq.shape()[0], outputs[0].shape()[0]); + /// output layer grad dim == weight dim * context_length_ + CHECK_EQ(in_seq.shape()[1], outputs[0].shape()[1] * context_length_); + CHECK_EQ(outputs[0].getArgType(), ADD_TO); - ContextProjectionBackwardWeight(out_grad_mat.get(), - w_grad_mat.get(), + const auto seq_vec = in_seq.getSequenceId().vector(); + const auto out_grad_mat = in_seq.matrix(); + auto w_grad_mat = outputs[0].matrix(); + ContextProjectionBackwardWeight(out_grad_mat, + w_grad_mat, seq_vec, context_length_, context_start_, @@ -335,7 +386,6 @@ private: size_t begin_pad_; size_t total_pad_; }; -#endif REGISTER_TYPED_FUNC(ContextProjectionForward, CPU, @@ -350,7 +400,6 @@ REGISTER_TYPED_FUNC(ContextProjectionForward, REGISTER_TYPED_FUNC(ContextProjectionBackward, GPU, ContextProjectionBackwardFunc); -#if 0 REGISTER_TYPED_FUNC(ContextProjectionBackwardData, GPU, ContextProjectionBackwardDataFunc); @@ -358,5 +407,4 @@ REGISTER_TYPED_FUNC(ContextProjectionBackwardWeight, GPU, ContextProjectionBackwardWeightFunc); #endif -#endif } // namespace paddle diff --git a/paddle/function/ContextProjectionOp.h b/paddle/function/ContextProjectionOp.h index a558df5e072f2f4dcc5c45afa385b3cf88872d26..2bdd47e4e9b02483c2c5af82bf00c4e55d68f93e 100644 --- a/paddle/function/ContextProjectionOp.h +++ b/paddle/function/ContextProjectionOp.h @@ -21,14 +21,14 @@ namespace paddle { /** * \brief Context Projection Forward. * - * \param[out] outputs output data. - * \param[in] input input data. - * \param[in] weight input weight. - * \param[in] sequence input data. - * \param[in] context_length consecutive rows for concatenation. - * \param[in] context_start context start position. - * \param[in] begin_pad begining pad position. - * \param[in] is_padding whether padding 0 or not. + * \param[in/out] outputs output data. + * \param[in] input input data. + * \param[in] weight input weight. + * \param[in] sequence input data. + * \param[in] context_length consecutive rows for concatenation. + * \param[in] context_start context start position. + * \param[in] begin_pad begining pad position. + * \param[in] is_padding whether padding 0 or not. * */ template @@ -56,7 +56,7 @@ void ContextProjectionForward( */ template void ContextProjectionBackward( - typename Tensor::Matrix& out_grad, + const typename Tensor::Matrix& out_grad, typename Tensor::Matrix& in_grad, typename Tensor::Matrix& w_grad, const typename Tensor::Vector& seq_vec, @@ -68,7 +68,7 @@ void ContextProjectionBackward( template void ContextProjectionBackwardData( - typename Tensor::Matrix& out_grad, + const typename Tensor::Matrix& out_grad, typename Tensor::Matrix& in_grad, const typename Tensor::Vector& sequence, size_t context_length, @@ -76,7 +76,7 @@ void ContextProjectionBackwardData( template void ContextProjectionBackwardWeight( - typename Tensor::Matrix& out_grad, + const typename Tensor::Matrix& out_grad, typename Tensor::Matrix& w_grad, const typename Tensor::Vector& seq_vec, size_t context_length, diff --git a/paddle/function/ContextProjectionOpGpu.cu b/paddle/function/ContextProjectionOpGpu.cu index 6a4a01a6510416fc1f945305203f55ece7a28f11..1a5b4042402df3081a493962a5e080d72b7f40b2 100644 --- a/paddle/function/ContextProjectionOpGpu.cu +++ b/paddle/function/ContextProjectionOpGpu.cu @@ -138,10 +138,10 @@ void ContextProjectionForward(GpuMatrix& output, begin_pad); } -__global__ void KeContextProjectionBackwardData(real* out_grad, +__global__ void KeContextProjectionBackwardData(const real* out_grad, const int* sequence, real* in_grad, - int input_dim, + size_t input_dim, int context_length, int context_start) { int idx = threadIdx.x; @@ -152,7 +152,8 @@ __global__ void KeContextProjectionBackwardData(real* out_grad, real value = 0; int instances = seq_end - seq_start + context_length - 1; - out_grad += seq_start * input_dim * context_length; + auto out = const_cast(out_grad); + out += seq_start * input_dim * context_length; in_grad += seq_start * input_dim; for (int k = 0; k <= input_dim / block_size; k++) { if (idx < input_dim) { @@ -169,7 +170,7 @@ __global__ void KeContextProjectionBackwardData(real* out_grad, int outx = (i - context_length) < 0 ? i : (context_length - 1); int outy = (i - context_length) < 0 ? 0 : (i - (context_length - 1)); real* output_r = - out_grad + outy * input_dim * context_length + outx * input_dim; + out + outy * input_dim * context_length + outx * input_dim; for (int j = outy; j < seq_end - seq_start; j++) { value += output_r[idx]; if (j - outy == outx) break; @@ -194,7 +195,7 @@ __global__ void KeContextProjectionBackwardData(real* out_grad, * @param[in] context_start context start. * */ -void hl_context_projection_backward_data(real* out_grad, +void hl_context_projection_backward_data(const real* out_grad, const int* sequence, real* input_grad, size_t num_sequences, @@ -216,7 +217,7 @@ void hl_context_projection_backward_data(real* out_grad, } template <> -void ContextProjectionBackwardData(GpuMatrix& out_grad, +void ContextProjectionBackwardData(const GpuMatrix& out_grad, GpuMatrix& in_grad, const GpuIVector& sequence, size_t context_length, @@ -231,7 +232,7 @@ void ContextProjectionBackwardData(GpuMatrix& out_grad, } template -__global__ void KeContextProjectionBackwardWeight(real* out_grad, +__global__ void KeContextProjectionBackwardWeight(const real* out_grad, const int* sequence, real* w_grad, int num_sequences, @@ -254,7 +255,8 @@ __global__ void KeContextProjectionBackwardWeight(real* out_grad, for (int seqId = idy; seqId < num_sequences; seqId += THREADS_Y) { int seq_start = sequence[seqId]; int seq_end = sequence[seqId+1]; - output_r = out_grad + seq_start * w_dim * context_length; + output_r = const_cast(out_grad) + + seq_start * w_dim * context_length; if (context_start < 0) { if (padId + context_start < 0) { @@ -318,7 +320,7 @@ __global__ void KeContextProjectionBackwardWeight(real* out_grad, * beginning. * */ -void hl_context_projection_backward_weight(real* out_grad, +void hl_context_projection_backward_weight(const real* out_grad, const int* sequence, real* w_grad, size_t num_sequences, @@ -346,7 +348,7 @@ void hl_context_projection_backward_weight(real* out_grad, template <> void ContextProjectionBackwardWeight( - GpuMatrix& out_grad, + const GpuMatrix& out_grad, GpuMatrix& w_grad, const GpuIVector& seq_vec, size_t context_length, @@ -365,7 +367,7 @@ void ContextProjectionBackwardWeight( } template <> -void ContextProjectionBackward(GpuMatrix& out_grad, +void ContextProjectionBackward(const GpuMatrix& out_grad, GpuMatrix& in_grad, GpuMatrix& w_grad, const GpuIVector& sequence, diff --git a/paddle/function/ContextProjectionOpTest.cpp b/paddle/function/ContextProjectionOpTest.cpp index 6223d2fd23ac3bbb4fbcf51d37d22feaf3b1330b..c9db2ff8008e0bb0fa04370fb7b3ecd7641d2062 100644 --- a/paddle/function/ContextProjectionOpTest.cpp +++ b/paddle/function/ContextProjectionOpTest.cpp @@ -56,22 +56,25 @@ void testMatrixProjectionForward(int context_start, cpu_out.randomizeUniform(); gpu_out.copyFrom(cpu_out); - compare.getCpuFunction()->calc( - {Tensor(cpu_in.getData(), Dims{batch_size, input_dim}), - Tensor(cpu_weight ? cpu_weight->getData() : nullptr, - Dims{pad, input_dim}), - Tensor(reinterpret_cast(cpu_seq->getData()), - Dims{cpu_seq->getSize()})}, - {Tensor(cpu_out.getData(), Dims{batch_size, input_dim * context_length})}, - {}); - compare.getGpuFunction()->calc( - {Tensor(gpu_in.getData(), Dims{batch_size, input_dim}), - Tensor(gpu_weight ? gpu_weight->getData() : nullptr, - Dims{pad, input_dim}), - Tensor(reinterpret_cast(gpu_seq->getData()), - Dims{gpu_seq->getSize()})}, - {Tensor(gpu_out.getData(), Dims{batch_size, input_dim * context_length})}, - {}); + BufferArgs cpu_inputs; + BufferArgs cpu_outputs; + cpu_inputs.addArg(cpu_in, *cpu_seq); + if (cpu_weight) { + cpu_inputs.addArg(*cpu_weight, *cpu_seq); + } + cpu_outputs.addArg(cpu_out, *cpu_seq, ADD_TO); + + compare.getCpuFunction()->calc(cpu_inputs, cpu_outputs); + + BufferArgs gpu_inputs; + BufferArgs gpu_outputs; + gpu_inputs.addArg(gpu_in, *gpu_seq); + if (gpu_weight) { + gpu_inputs.addArg(*gpu_weight, *gpu_seq); + } + gpu_outputs.addArg(gpu_out, *gpu_seq, ADD_TO); + + compare.getGpuFunction()->calc(gpu_inputs, gpu_outputs); autotest::TensorCheckEqual(cpu_out, gpu_out); } @@ -117,25 +120,23 @@ void testMatrixProjectionBackward(int context_start, gpu_w_grad->copyFrom(*cpu_w_grad); } - compare.getCpuFunction()->calc( - {Tensor(cpu_in_grad.getData(), Dims{batch_size, input_dim}), - Tensor(cpu_w_grad ? cpu_w_grad->getData() : nullptr, - Dims{pad, input_dim}), - Tensor(reinterpret_cast(cpu_seq->getData()), - Dims{cpu_seq->getSize()})}, - {Tensor(cpu_out_grad.getData(), - Dims{batch_size, input_dim * context_length})}, - {}); - - compare.getGpuFunction()->calc( - {Tensor(gpu_in_grad.getData(), Dims{batch_size, input_dim}), - Tensor(gpu_w_grad ? gpu_w_grad->getData() : nullptr, - Dims{pad, input_dim}), - Tensor(reinterpret_cast(gpu_seq->getData()), - Dims{gpu_seq->getSize()})}, - {Tensor(gpu_out_grad.getData(), - Dims{batch_size, input_dim * context_length})}, - {}); + BufferArgs cpu_inputs; + BufferArgs cpu_outputs; + cpu_inputs.addArg(cpu_out_grad, *cpu_seq); + cpu_outputs.addArg(cpu_in_grad, *cpu_seq, ADD_TO); + cpu_outputs.addArg( + cpu_w_grad ? *cpu_w_grad : CpuMatrix(nullptr, 0, input_dim), ADD_TO); + + compare.getCpuFunction()->calc(cpu_inputs, cpu_outputs); + + BufferArgs gpu_inputs; + BufferArgs gpu_outputs; + gpu_inputs.addArg(gpu_out_grad, *gpu_seq); + gpu_outputs.addArg(gpu_in_grad, *gpu_seq, ADD_TO); + gpu_outputs.addArg( + gpu_w_grad ? *gpu_w_grad : GpuMatrix(nullptr, 0, input_dim), ADD_TO); + + compare.getGpuFunction()->calc(gpu_inputs, gpu_outputs); autotest::TensorCheckErr(cpu_in_grad, gpu_in_grad); if (is_padding) { diff --git a/paddle/function/CrossMapNormalOp.cpp b/paddle/function/CrossMapNormalOp.cpp index 92980c503fdaaaa9ac600070197dba6ba4bfb7a4..8e7dc72524a7680a03ea6eb4770a3e25c09ad913 100644 --- a/paddle/function/CrossMapNormalOp.cpp +++ b/paddle/function/CrossMapNormalOp.cpp @@ -188,8 +188,13 @@ public: CHECK(inputs[0].shape() == inputs[3].shape()); CHECK(inputs[0].shape() == outputs[0].shape()); - // TODO(hedaoyuan): need support ASSIGN_TO mode. - CHECK_EQ(outputs[0].getArgType(), ADD_TO); + if (outputs[0].getArgType() != ADD_TO) { + // Currently, some algorithm implementations are ASSIGN_TO mode, + // if need to support the ADD_TO calculation, need to clear the output. + typename Tensor::Vector tmp( + outputs[0].shape().getElements(), outputs[0].data()); + tmp.zero(); + } size_t samples = inputs[0].shape()[0]; size_t channels = inputs[0].shape()[1]; diff --git a/paddle/function/CrossMapNormalOpTest.cpp b/paddle/function/CrossMapNormalOpTest.cpp index d65d9310affd7c9b7fee3118c79449870849c243..51f5da81bfc9ae870ac9949ba74da01a9449a04d 100644 --- a/paddle/function/CrossMapNormalOpTest.cpp +++ b/paddle/function/CrossMapNormalOpTest.cpp @@ -27,15 +27,19 @@ TEST(CrossMapNormal, real) { << " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW << " size=" << size; - FunctionCompare compare("CrossMapNormal", - FuncConfig() - .set("size", size) - .set("scale", (real)1.5) - .set("pow", (real)0.5)); - Dims dims{numSamples, channels, imgSizeH, imgSizeW}; - compare.cmpWithArg({Tensor(nullptr, dims)}, - {Tensor(nullptr, dims), Tensor(nullptr, dims)}, - {}); + // init Test object + FunctionCompare test("CrossMapNormal", + FuncConfig() + .set("size", size) + .set("scale", (real)1.5) + .set("pow", (real)0.5)); + // prepare input arguments + TensorShape shape{numSamples, channels, imgSizeH, imgSizeW}; + test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape)); + test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, shape)); + test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, shape)); + // run Function + test.run(); } } } @@ -53,18 +57,19 @@ TEST(CrossMapNormalGrad, real) { << " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW << " size=" << size; - FunctionCompare compare("CrossMapNormalGrad", - FuncConfig() - .set("size", size) - .set("scale", (real)1.5) - .set("pow", (real)0.5)); - Dims dims{numSamples, channels, imgSizeH, imgSizeW}; - compare.cmpWithArg({Tensor(nullptr, dims), - Tensor(nullptr, dims), - Tensor(nullptr, dims), - Tensor(nullptr, dims)}, - {Tensor(nullptr, dims)}, - {}); + FunctionCompare test("CrossMapNormalGrad", + FuncConfig() + .set("size", size) + .set("scale", (real)1.5) + .set("pow", (real)0.5)); + TensorShape shape{numSamples, channels, imgSizeH, imgSizeW}; + test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape)); + test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape)); + test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape)); + test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape)); + test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, shape)); + // run Function + test.run(); } } } diff --git a/paddle/function/Function.cpp b/paddle/function/Function.cpp index dbe3a4e9f608df6333a5637f2d962a555b04d7c3..f47d55a4ade97d76e0f1940a2234e34e20efade6 100644 --- a/paddle/function/Function.cpp +++ b/paddle/function/Function.cpp @@ -79,15 +79,25 @@ FuncConfig& FuncConfig::set(const std::string& key, bool v) { void BufferArgs::addArg(const Matrix& arg, const TensorShape& shape, ArgType argType) { - args_.push_back(std::make_shared(arg, shape, argType)); + _args_.push_back(new BufferArg(arg, shape, argType)); + addArg(*_args_.back()); } void BufferArgs::addArg(const CpuSparseMatrix& arg, ArgType argType) { - args_.push_back(std::make_shared(arg, argType)); + _args_.push_back(new SparseMatrixArg(arg, argType)); + addArg(*_args_.back()); } void BufferArgs::addArg(const GpuSparseMatrix& arg, ArgType argType) { - args_.push_back(std::make_shared(arg, argType)); + _args_.push_back(new SparseMatrixArg(arg, argType)); + addArg(*_args_.back()); +} + +void BufferArgs::addArg(const Matrix& matrix, + const IVector& vector, + ArgType argType) { + _args_.push_back(new SequenceArg(matrix, vector, argType)); + addArg(*_args_.back()); } ClassRegistrar FunctionBase::funcRegistrar_; diff --git a/paddle/function/Function.h b/paddle/function/Function.h index 249f8f9cfad58bf596e8cdce9188409b5690f969..9215c137eb8e85a9a03575104d7f89bbce441eba 100644 --- a/paddle/function/Function.h +++ b/paddle/function/Function.h @@ -50,19 +50,44 @@ protected: * Argument type for Function::calc(). * A BufferArgs contains a set of BufferArg, * because Function can have multiple inputs and outputs. + * + * addArg() with Matix object used to adapt Layer Argument. + * Will create a BufferArg object in addArg(), + * and free in destructor of BufferArgs. + * + * addArg() with BufferArg object, just save BufferArg object address, + * and the caller needs to guarantee the validity of the BufferArg object + * in the BufferArgs life time. */ class BufferArgs { public: BufferArgs() {} + + ~BufferArgs() { + for (auto arg : _args_) { + delete arg; + } + } + size_t size() const { return args_.size(); } // add argument into BufferArgs // Tensor can be Matrix, Vector, IVector. // For inputs, do not need argType. // For outputs, the argType needs to be specified as ASSIGN_TO or ADD_TO. - template - void addArg(const Tensor& arg, ArgType argType = UNSPECIFIED) { - args_.push_back(std::make_shared(arg, argType)); + void addArg(const Matrix& arg, ArgType argType = UNSPECIFIED) { + _args_.push_back(new BufferArg(arg, argType)); + addArg(*_args_.back()); + } + + void addArg(const Vector& arg, ArgType argType = UNSPECIFIED) { + _args_.push_back(new BufferArg(arg, argType)); + addArg(*_args_.back()); + } + + void addArg(const IVector& arg, ArgType argType = UNSPECIFIED) { + _args_.push_back(new BufferArg(arg, argType)); + addArg(*_args_.back()); } // Add arg into BufferArgs and reshape the arg. @@ -77,20 +102,37 @@ public: void addArg(const CpuSparseMatrix& arg, ArgType argType = UNSPECIFIED); void addArg(const GpuSparseMatrix& arg, ArgType argType = UNSPECIFIED); + void addArg(const Matrix& matrix, + const IVector& vector, + ArgType argType = UNSPECIFIED); + // get argument const BufferArg& operator[](size_t num) const { CHECK_LT(num, args_.size()); return *args_[num]; } + void addArg(BufferArg& arg) { args_.push_back(&arg); } + + void addArg(SequenceIdArg& arg) { args_.push_back(&arg); } + + void addArg(SequenceArg& arg) { args_.push_back(&arg); } + + void addArg(SparseMatrixArg& arg) { args_.push_back(&arg); } + private: - std::vector args_; + std::vector args_; + // The BufferArg object is constructed and freed by BufferArgs. + std::vector _args_; }; /** * \brief Base class for Function. * The basic Function implementation requires override init and calc interfaces. * + * The caller needs to ensure the validity of the arguments + * during Function execution. + * * Function inputs are readonly, Function outputs have two modes: ASSIGN_TO * and ADD_TO. * If output.getArgType() == ASSIGN_TO, this is assign mode, and the calculation diff --git a/paddle/function/FunctionTest.cpp b/paddle/function/FunctionTest.cpp index 7ce908320a6f6f764e8fdacc96432aca78d7b2df..fdf7e631e5ab8c67eb5cf906bd0af49740d60112 100644 --- a/paddle/function/FunctionTest.cpp +++ b/paddle/function/FunctionTest.cpp @@ -14,6 +14,7 @@ limitations under the License. */ #include "Function.h" #include +#include "paddle/math/SparseMatrix.h" namespace paddle { @@ -56,4 +57,110 @@ TEST(Function, BufferArgs) { Function(gpuArgments); } +/** + * Some tests case are used to check the consistency between the BufferArg type + * argument received by Function and the original type argument. + * + * Use Case: + * TEST() { + * Matrix matrix(...); + * CheckBufferArg lambda = [=](const BufferArg& arg) { + * // check matrix and arg are equivalent + * EXPECT_EQ(matrix, arg); + * } + * + * BufferArgs argments{matrix...}; + * std::vector checkFunc{lambda...}; + * testBufferArgs(argments, checkFunc); + * } + */ +typedef std::function CheckBufferArg; + +void testBufferArgs(const BufferArgs& inputs, + const std::vector& check) { + EXPECT_EQ(inputs.size(), check.size()); + for (size_t i = 0; i < inputs.size(); i++) { + check[i](inputs[i]); + } +} + +void testBufferArgs(const BufferArgs& inputs, const CheckBufferArg& check) { + EXPECT_EQ(inputs.size(), 1); + check(inputs[0]); +} + +TEST(Arguments, Matrix) { + MatrixPtr matrix = Matrix::create(100, 200); + CheckBufferArg check = [=](const BufferArg& arg) { + EXPECT_EQ(arg.shape().ndims(), 2); + EXPECT_EQ(arg.shape()[0], 100); + EXPECT_EQ(arg.shape()[1], 200); + EXPECT_EQ(arg.data(), matrix->getData()); + + EXPECT_EQ(arg.matrix().getHeight(), matrix->getHeight()); + EXPECT_EQ(arg.matrix().getWidth(), matrix->getWidth()); + EXPECT_EQ(arg.matrix().getData(), matrix->getData()); + }; + + BufferArgs argments; + argments.addArg(*matrix); + std::vector checkFunc; + checkFunc.push_back(check); + testBufferArgs(argments, checkFunc); +} + +TEST(Arguments, Vector) { + VectorPtr vector = Vector::create(100, false); + CheckBufferArg check = [=](const BufferArg& arg) { + EXPECT_EQ(arg.shape().ndims(), 1); + EXPECT_EQ(arg.shape()[0], 100); + EXPECT_EQ(arg.data(), vector->getData()); + + CpuVector inVector = arg.vector(); + EXPECT_EQ(inVector.getSize(), vector->getSize()); + EXPECT_EQ(inVector.getData(), vector->getData()); + }; + + BufferArgs argments; + argments.addArg(*vector); + std::vector checkFunc; + checkFunc.push_back(check); + testBufferArgs(argments, checkFunc); +} + +TEST(Arguments, CpuSparseMatrix) { + CpuSparseMatrix sparse(200, 300, 50); + CheckBufferArg check = [=](const BufferArg& arg) { + EXPECT_EQ(arg.shape().ndims(), 2); + EXPECT_EQ(arg.shape()[0], 200); + EXPECT_EQ(arg.shape()[1], 300); + EXPECT_EQ(arg.data(), sparse.getData()); + // CHECK_EQ(arg.sparse().nnz(), 50); + // CHECK_EQ(arg.sparse().dataFormat(), SPARSE_CSR_FORMAT); + // CHECK_EQ(arg.sparse().dataType(), SPARSE_FLOAT_VALUE); + EXPECT_EQ(arg.sparse().getRowBuf(), sparse.getRows()); + EXPECT_EQ(arg.sparse().getColBuf(), sparse.getCols()); + }; + + BufferArgs argments; + argments.addArg(sparse); + std::vector checkFunc; + checkFunc.push_back(check); + testBufferArgs(argments, checkFunc); +} + +TEST(Arguments, BufferArg) { + BufferArg arg(nullptr, VALUE_TYPE_FLOAT, {1, 2, 3}); + CheckBufferArg check = [=](const BufferArg& arg) { + EXPECT_EQ(arg.shape().ndims(), 3); + EXPECT_EQ(arg.shape()[0], 1); + EXPECT_EQ(arg.shape()[1], 2); + EXPECT_EQ(arg.shape()[2], 3); + }; + + BufferArgs argments; + argments.addArg(arg); + testBufferArgs(argments, check); +} + } // namespace paddle diff --git a/paddle/function/FunctionTest.h b/paddle/function/FunctionTest.h index 32131037f6de4a9f7a3ebf8f5773eccd65dc2cdb..24e7a36a43cfa8941535cb778aa1557ec5a0a6f4 100644 --- a/paddle/function/FunctionTest.h +++ b/paddle/function/FunctionTest.h @@ -15,95 +15,186 @@ limitations under the License. */ #include "Function.h" #include "paddle/math/Vector.h" #include "paddle/math/tests/TensorCheck.h" +#include "paddle/testing/TestUtil.h" namespace paddle { +typedef std::shared_ptr BufferArgPtr; + +/** + * \brief A class for comparing CPU and GPU implementations of Function. + * + * + * Use case: + * // Initializes a test object, the corresponding cpu and gpu Function + * // are constructed according to FunctionName and FuncConfig. + * FunctionCompare test(FunctionName, FuncConfig); + * // Prepare inputs and outputs arguments. + * // Here the input and output can not contain real data, + * // only contains the argument type and shape. + * test.addInputs(input1); + * test.addInputs(input2); + * test.addOutputs(output1); + * test.addOutputs(output2); + * // Run. + * // Will according to the type and shape of arguments(inputs_/outputs_), + * // automatic initialization cpu and gpu function required arguments + * // (cpuInputs_/cpuOutputs_/gpuInputs_/gpuOutputs_). + * // Call the CPU and GPU Function calculation results. + * // Compares CPU and GPU calculation results for consistency. + * test.run(); + */ class FunctionCompare { public: FunctionCompare(const std::string& name, const FuncConfig& config) - : cpu(FunctionBase::funcRegistrar_.createByType(name + "-CPU")), - gpu(FunctionBase::funcRegistrar_.createByType(name + "-GPU")) { - cpu->init(config); - gpu->init(config); + : cpuFunc_(FunctionBase::funcRegistrar_.createByType(name + "-CPU")), + gpuFunc_(FunctionBase::funcRegistrar_.createByType(name + "-GPU")) { + cpuFunc_->init(config); + gpuFunc_->init(config); + } + + ~FunctionCompare() {} + + // input need only contains shape, do not contains data. + void addInputs(const BufferArg& input) { + size_t size = + input.shape().getElements() * sizeOfValuType(input.valueType()); + cpuMemory_.emplace_back(std::make_shared(size)); + gpuMemory_.emplace_back(std::make_shared(size)); + + cpuInputs_.emplace_back(std::make_shared( + cpuMemory_.back()->getBuf(), input.valueType(), input.shape())); + gpuInputs_.emplace_back(std::make_shared( + gpuMemory_.back()->getBuf(), input.valueType(), input.shape())); + } + + // output need only contains shape, do not contains data. + void addOutputs(const BufferArg& output) { + size_t size = + output.shape().getElements() * sizeOfValuType(output.valueType()); + cpuMemory_.emplace_back(std::make_shared(size)); + gpuMemory_.emplace_back(std::make_shared(size)); + + cpuOutputs_.emplace_back( + std::make_shared(cpuMemory_.back()->getBuf(), + output.valueType(), + output.shape(), + ASSIGN_TO)); + gpuOutputs_.emplace_back( + std::make_shared(gpuMemory_.back()->getBuf(), + output.valueType(), + output.shape(), + ASSIGN_TO)); } - void cmpWithArg(const Arguments& inputs, - const Arguments& outputs, - const Arguments& inouts) { - // init cpu and gpu arguments - auto initArgs = [=]( - Arguments& cpuArgs, Arguments& gpuArgs, const Arguments& inArgs) { - for (const auto arg : inArgs) { - size_t size = sizeof(real); - for (const auto dim : arg.dims_) { - size *= dim; - } - if (arg.getData()) { - // todo(tianbing), waste unnecessary mem here - cpuMemory.emplace_back(std::make_shared(size)); - gpuMemory.emplace_back(std::make_shared(size)); - cpuArgs.emplace_back(Tensor((real*)arg.getData(), arg.dims_)); - gpuArgs.emplace_back(Tensor((real*)arg.getData(), arg.dims_)); - // already init outside - } else { - cpuMemory.emplace_back(std::make_shared(size)); - gpuMemory.emplace_back(std::make_shared(size)); - cpuArgs.emplace_back( - Tensor((real*)cpuMemory.back()->getBuf(), arg.dims_)); - gpuArgs.emplace_back( - Tensor((real*)gpuMemory.back()->getBuf(), arg.dims_)); - // will use an api to refactor this code. - CpuVector cpuVector(size / sizeof(real), - (real*)cpuArgs.back().getData()); - GpuVector gpuVector(size / sizeof(real), - (real*)gpuArgs.back().getData()); - cpuVector.uniform(0.001, 1); - gpuVector.copyFrom(cpuVector); - } + void addInputs(const SequenceArg& input) { + size_t batchSize = input.shape()[0]; + size_t numSeqs = batchSize / 10 + 1; + + size_t sizeId = (numSeqs + 1) * sizeOfValuType(VALUE_TYPE_INT32); + cpuMemory_.emplace_back(std::make_shared(sizeId)); + gpuMemory_.emplace_back(std::make_shared(sizeId)); + + TensorShape seqsId({numSeqs + 1}); + // void* cpuBuffer = cpuMemory_.back()->getBuf(); + // void* gpuBuffer = gpuMemory_.back()->getBuf(); + + size_t size = + input.shape().getElements() * sizeOfValuType(input.valueType()); + cpuMemory_.emplace_back(std::make_shared(size)); + gpuMemory_.emplace_back(std::make_shared(size)); + + // TODO: need be implemented. + } + + void run() { + // prepare cpu/gpu arguments + initInputs(); + + // function calculate + auto callFunction = [](FunctionBase* function, + std::vector& inputs, + std::vector& outputs) { + BufferArgs inArgs; + BufferArgs outArgs; + for (auto arg : inputs) { + inArgs.addArg(*arg); } + for (auto arg : outputs) { + outArgs.addArg(*arg); + } + function->calc(inArgs, outArgs); }; - initArgs(cpuInputs, gpuInputs, inputs); - initArgs(cpuOutputs, gpuOutputs, outputs); - initArgs(cpuInouts, gpuInouts, inouts); - // function calculate - cpu->calc(cpuInputs, cpuOutputs, cpuInouts); - gpu->calc(gpuInputs, gpuOutputs, gpuInouts); + callFunction(cpuFunc_.get(), cpuInputs_, cpuOutputs_); + callFunction(gpuFunc_.get(), gpuInputs_, gpuOutputs_); // check outputs and inouts - auto checkArgs = [=](const Arguments& cpuArgs, const Arguments& gpuArgs) { - for (size_t i = 0; i < cpuArgs.size(); i++) { - auto cpu = cpuArgs[i]; - auto gpu = gpuArgs[i]; - size_t size = 1; - for (auto dim : cpu.dims_) { - size *= dim; - } - CpuVector cpuVector(size, (real*)cpu.getData()); - GpuVector gpuVector(size, (real*)gpu.getData()); - - autotest::TensorCheckErr(cpuVector, gpuVector); - } - }; - checkArgs(cpuOutputs, gpuOutputs); - checkArgs(cpuInouts, gpuInouts); + compareOutputs(); + } + + std::shared_ptr getCpuFunction() const { return cpuFunc_; } + + std::shared_ptr getGpuFunction() const { return gpuFunc_; } + +protected: + void initInputs() { + for (size_t i = 0; i < cpuInputs_.size(); i++) { + initArg(*cpuInputs_[i]); + + // TODO: Need a BufferCopy used to copy from one BufferArg to another. + CpuVector cpuVector(cpuInputs_[i]->shape().getElements(), + (real*)cpuInputs_[i]->data()); + GpuVector gpuVector(gpuInputs_[i]->shape().getElements(), + (real*)gpuInputs_[i]->data()); + + gpuVector.copyFrom(cpuVector); + } + } + + void compareOutputs() { + for (size_t i = 0; i < cpuOutputs_.size(); i++) { + // TODO, Need a BufferCheck used to compare the two buffers. + auto cpu = cpuOutputs_[i]; + auto gpu = gpuOutputs_[i]; + CpuVector cpuVector(cpu->shape().getElements(), (real*)cpu->data()); + GpuVector gpuVector(cpu->shape().getElements(), (real*)gpu->data()); + + autotest::TensorCheckErr(cpuVector, gpuVector); + } } - std::shared_ptr getCpuFunction() const { return cpu; } + // only init cpu argument, gpu argument copy from cpu argument. + void initArg(BufferArg& arg) { + CpuVector vector(arg.shape().getElements(), (real*)arg.data()); + vector.uniform(0.001, 1); + } - std::shared_ptr getGpuFunction() const { return gpu; } + void initArg(SequenceIdArg& arg, size_t batchSize) { + size_t numSeqs = arg.numSeqs(); + int* buf = reinterpret_cast(arg.data()); + int pos = 0; + size_t maxLen = 2 * batchSize / numSeqs; + for (int i = 0; i < (int)numSeqs; ++i) { + int len = uniformRandom( + std::min(maxLen, batchSize - pos - numSeqs + i)) + + 1; + buf[i] = pos; + pos += len; + VLOG(1) << " len=" << len; + } + buf[numSeqs] = batchSize; + } protected: - std::shared_ptr cpu; - std::shared_ptr gpu; - std::vector cpuMemory; - std::vector gpuMemory; - Arguments cpuInputs; - Arguments cpuOutputs; - Arguments cpuInouts; - Arguments gpuInputs; - Arguments gpuOutputs; - Arguments gpuInouts; + std::shared_ptr cpuFunc_; + std::shared_ptr gpuFunc_; + std::vector cpuMemory_; + std::vector gpuMemory_; + std::vector cpuInputs_; + std::vector cpuOutputs_; + std::vector gpuInputs_; + std::vector gpuOutputs_; }; } // namespace paddle diff --git a/paddle/gserver/layers/ContextProjection.cpp b/paddle/gserver/layers/ContextProjection.cpp index ebcc87cbf48a3c34a4e625e67f872fed69cdf44f..d7042af1c25e7432e5b1efbb89cd8fd3f63fb4ae 100644 --- a/paddle/gserver/layers/ContextProjection.cpp +++ b/paddle/gserver/layers/ContextProjection.cpp @@ -118,16 +118,15 @@ void ContextProjection::forward() { /// first use state_, otherwise use weight_(padding false === w nullptr) auto w_ptr = state_ ? state_.get() : is_padding ? weight_->getW().get() : nullptr; - auto start_pos = in_->sequenceStartPositions; - + const auto start_pos = in_->sequenceStartPositions->getVector(useGpu_); BufferArgs inputs; BufferArgs outputs; - inputs.addArg(*in_->value); - inputs.addArg(CpuMatrix(w_ptr ? w_ptr->getData() : nullptr, - w_ptr ? w_ptr->getHeight() : 0, - input_dim)); - inputs.addArg(*in_->sequenceStartPositions->getVector(useGpu_)); - outputs.addArg(*out_->value, ADD_TO); + inputs.addArg(*in_->value, *start_pos); + if (w_ptr) { + inputs.addArg(CpuMatrix(w_ptr->getData(), w_ptr->getHeight(), input_dim), + *start_pos); + } + outputs.addArg(*out_->value, *start_pos, ADD_TO); forward_[0]->calc(inputs, outputs); if (state_ && config_.context_start() < 0) { @@ -166,13 +165,16 @@ void ContextProjection::backward(const UpdateCallback& callback) { BufferArgs inputs; BufferArgs outputs; - inputs.addArg(CpuMatrix( - in_->grad ? in_->grad->getData() : nullptr, batch_size, input_dim)); - inputs.addArg(CpuMatrix(w_ptr ? w_ptr->getData() : nullptr, - w_ptr ? w_ptr->getHeight() : 0, - input_dim)); - inputs.addArg(*in_->sequenceStartPositions->getVector(useGpu_)); - outputs.addArg(*out_->grad, ADD_TO); + inputs.addArg(*out_->grad, *in_->sequenceStartPositions->getVector(useGpu_)); + outputs.addArg( + CpuMatrix( + in_->grad ? in_->grad->getData() : nullptr, batch_size, input_dim), + *in_->sequenceStartPositions->getVector(useGpu_), + ADD_TO); + outputs.addArg(CpuMatrix(w_ptr ? w_ptr->getData() : nullptr, + w_ptr ? w_ptr->getHeight() : 0, + input_dim), + ADD_TO); backward_[0]->calc(inputs, outputs); if (config_.trainable_padding()) { diff --git a/paddle/math/RowBuffer.h b/paddle/math/RowBuffer.h new file mode 100644 index 0000000000000000000000000000000000000000..dbb829c4e24a659e4a97c0a3ba4c5c78b68815d3 --- /dev/null +++ b/paddle/math/RowBuffer.h @@ -0,0 +1,135 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once +#include +#include "MemoryHandle.h" +#include "paddle/utils/Util.h" + +namespace paddle { + +/** + * @brief The RowBuffer class + * Represent the SparseRow Matrix Data. + * + * If not set memory handler, then the data could be auto growth. + */ +class RowBuffer { +public: + /** + * @brief RowBuffer create a auto-growth row buffer. The row length is width. + * @param width the length of each row, a.k.a matrix width. + */ + explicit RowBuffer(size_t width) : width_(width) {} + + /** + * @brief RowBuffer create a row buffer, which cannot be auto-growth. + * @param mem the pre-allocated memory. + * @param width the length of each row, a.k.a matrix width. + */ + RowBuffer(const CpuMemHandlePtr& mem, size_t width) + : preallocatedBuf_(mem), width_(width) {} + + /** + * @brief resize resize the buffer with rowCount + * @param rowCnt number of row. matrix height. + */ + inline void resize(int rowCnt) { + if (preallocatedBuf_) { + CHECK(preallocatedBuf_->getSize() >= rowCnt * width_ * sizeof(real)); + } else { + rowStore_.resize(rowCnt * width_); + } + } + + /** + * @brief get a row buffer with row index. + * @param row the index of row. + * @return row buffer. + */ + inline real* get(int row) const { + if (preallocatedBuf_) { + CHECK_LE((row + 1) * width_ * sizeof(real), preallocatedBuf_->getSize()); + return reinterpret_cast(preallocatedBuf_->getBuf()) + row * width_; + } else { + CHECK_LE((row + 1) * width_, rowStore_.size()); + return const_cast(rowStore_.data() + row * width_); + } + } + + /** + * @brief get a row buffer with row index. If row index is larger than local + * buffer, the size of local buffer will grow. + * @param row the index of row. + * @return row buffer. + */ + inline real* getWithAutoGrowth(int row) { + if (preallocatedBuf_) { + return get(row); + } else { + if ((rowStore_.size() <= row * width_)) { + rowStore_.resize((row + 1) * width_); + } + return rowStore_.data() + row * width_; + } + } + + /** + * @return raw data buffer. + */ + inline real* data() { + if (preallocatedBuf_) { + return reinterpret_cast(preallocatedBuf_->getBuf()); + } else { + return rowStore_.data(); + } + } + + /** + * @brief clear local buffer. It only affect auto-growth buffer. + */ + inline void clear() { rowStore_.clear(); } + + /** + * @brief get current number of rows. + * @return number of rows. + */ + inline size_t getRowCount() const { + if (preallocatedBuf_) { + return preallocatedBuf_->getSize() / sizeof(real) / width_; + } else { + return rowStore_.size() / width_; + } + } + + /** + * @brief get is this buffer can automatically grow or not. + * @return ture if can automacitally grow. + */ + inline bool isAutoGrowth() const { return !preallocatedBuf_; } + + /** + * @brief return the width of matrix. a.k.a length of row. + * @return width of matrix + */ + inline size_t getWidth() const { return width_; } + +private: + //! TODO(yuyang18): Add resize method to CpuMemHandlePtr, then we can get rid + //! of std::vector here. + CpuMemHandlePtr preallocatedBuf_; + std::vector> rowStore_; + size_t width_; +}; +} // namespace paddle diff --git a/paddle/math/SparseRowMatrix.h b/paddle/math/SparseRowMatrix.h index 778a9bd845661849261b52dcbeb519809d0c6306..c05fc98ff9fe739688ed3c21466fb29b70e36854 100644 --- a/paddle/math/SparseRowMatrix.h +++ b/paddle/math/SparseRowMatrix.h @@ -18,6 +18,7 @@ limitations under the License. */ #include #include #include "Matrix.h" +#include "RowBuffer.h" #include "paddle/utils/Util.h" DECLARE_bool(allow_inefficient_sparse_update); @@ -45,12 +46,9 @@ public: IndexDictPtr indexDictHandle = nullptr, bool trans = false) : CpuMatrix(nullptr, height, width, trans), - storeMat_(dataHandle, - dataHandle ? dataHandle->getSize() / sizeof(real) / width : 0, - width, - trans), indexDictHandle_(indexDictHandle) { init(height, width); + buf_.reset(new RowBuffer(dataHandle, width)); } virtual ~SparseRowCpuMatrix() {} @@ -71,25 +69,16 @@ public: * * @param row row id in local storage */ - real* getLocalRow(size_t row) { - if (storeMat_.getData()) return storeMat_.rowBuf(row); - if (rowStore_.size() <= row * width_) { - rowStore_.resize((row + 1) * width_); - } - return rowStore_.data() + row * width_; - } + real* getLocalRow(size_t row) { return buf_->getWithAutoGrowth(row); } /** - * reserve the storage for rows according to current size of indexDictHandle. + * reserve the storage for rows according to current size of + * indexDictHandle. * * This is only used when SparseRowCpuMatrix is constructed with * indexDictHandle. */ - void reserveStore() { - if (!storeMat_.getData() && !localIndices_->empty()) { - rowStore_.resize(localIndices_->size() * width_); - } - } + void reserveStore() { buf_->resize(localIndices_->size()); } // row is the row id in the original matrix virtual real* getRowBuf(size_t row) { return getRow(row); } @@ -117,7 +106,8 @@ public: * * If L1 decay set use L1, else if L2 set use L2, otherwise no decay atall. * - * t0 is a int vector used by L1/L2 decay, size = height of parameter matrix, + * t0 is a int vector used by L1/L2 decay, size = height of parameter + * matrix, * store the time that each weight row last updated. * * Time is batchId, currentTime is current batchId. @@ -176,8 +166,7 @@ public: protected: template void apply(Func f) { - real* data = storeMat_.getData() ? storeMat_.getData() : rowStore_.data(); - f(data, localIndices_->size() * width_); + f(buf_->data(), localIndices_->size() * width_); } void init(size_t height, size_t width); @@ -188,25 +177,24 @@ protected: globalIndices_[id] = kUnusedId_; } localIndices_->clear(); - rowStore_.clear(); + buf_->clear(); } inline void checkStoreSize() { - if (storeMat_.getData()) { - CHECK_LE(localIndices_->size(), storeMat_.getHeight()); - } else if (!FLAGS_allow_inefficient_sparse_update) { - if (localIndices_->size() > 0.5 * height_) { + if (buf_->isAutoGrowth()) { + if (buf_->getRowCount() > 0.5 * height_) { LOG(WARNING) << "There are more than 0.5*height (" << localIndices_->size() << ") rows are used for sparse " << "update, which is not efficient. Considering not use " << "sparse_update or set --allow_inefficient_sparse_update=true"; } + } else { + CHECK_LE(localIndices_->size(), buf_->getRowCount()); } } - CpuMatrix storeMat_; - std::vector> rowStore_; + std::unique_ptr buf_; IndexDictPtr indexDictHandle_; std::vector* localIndices_; // =&indexDictHandle_->localIndices unsigned int* globalIndices_; // =indexDictHandle_->globalIndices.data(); diff --git a/paddle/math/tests/CMakeLists.txt b/paddle/math/tests/CMakeLists.txt index 06fc10bae7232fb1278e89e8d9cbdf477fc27b60..ceb96b2e250d8e04ffb2b1d8c77ad498dca91cf3 100644 --- a/paddle/math/tests/CMakeLists.txt +++ b/paddle/math/tests/CMakeLists.txt @@ -4,6 +4,7 @@ add_simple_unittest(test_ExecViaCpu) add_simple_unittest(test_SIMDFunctions) add_simple_unittest(test_TrainingAlgorithm) add_simple_unittest(test_SparseMatrix) +add_simple_unittest(test_RowBuffer) # TODO(yuyang18): Refactor TestUtil.cpp. Remove this cross module reference. add_unittest(test_matrixCompare diff --git a/paddle/math/tests/test_RowBuffer.cpp b/paddle/math/tests/test_RowBuffer.cpp new file mode 100644 index 0000000000000000000000000000000000000000..5f66f22ef73dcff1868c1a3e03139a680b1ce2b5 --- /dev/null +++ b/paddle/math/tests/test_RowBuffer.cpp @@ -0,0 +1,65 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include +#include "paddle/math/RowBuffer.h" + +TEST(RowBuffer, testAutoGrow) { + paddle::RowBuffer buf(128); + ASSERT_EQ(128, buf.getWidth()); + ASSERT_TRUE(buf.isAutoGrowth()); + buf.resize(2); + ASSERT_EQ(2, buf.getRowCount()); + for (size_t i = 0; i < buf.getWidth() * 2; ++i) { + buf.data()[i] = i; + } + for (size_t i = 0; i < buf.getRowCount(); ++i) { + for (size_t j = 0; j < buf.getWidth(); ++j) { + ASSERT_NEAR(i * buf.getWidth() + j, buf.get(i)[j], 1e-5); + } + } + + auto data = buf.getWithAutoGrowth(2); + for (size_t i = 0; i < buf.getWidth(); ++i) { + data[i] = i; + } + + ASSERT_EQ(3, buf.getRowCount()); + for (size_t i = 0; i < buf.getRowCount() - 1; ++i) { + for (size_t j = 0; j < buf.getWidth(); ++j) { + ASSERT_NEAR(i * buf.getWidth() + j, buf.get(i)[j], 1e-5); + } + } + for (size_t i = 0; i < buf.getWidth(); ++i) { + ASSERT_NEAR(i, buf.get(2)[i], 1e-5); + } +} + +TEST(RowBuffer, testWithMemBuf) { + paddle::CpuMemHandlePtr mem = + std::make_shared(128 * 2 * sizeof(real)); + paddle::RowBuffer buf(mem, 128); + ASSERT_TRUE(!buf.isAutoGrowth()); + ASSERT_EQ(2, buf.getRowCount()); + for (size_t i = 0; i < buf.getWidth() * 2; ++i) { + buf.data()[i] = i; + } + for (size_t i = 0; i < buf.getRowCount(); ++i) { + for (size_t j = 0; j < buf.getWidth(); ++j) { + ASSERT_NEAR(i * buf.getWidth() + j, buf.getWithAutoGrowth(i)[j], 1e-5); + } + } + + ASSERT_DEATH_IF_SUPPORTED(buf.getWithAutoGrowth(3), ".*"); +} diff --git a/paddle/pserver/CMakeLists.txt b/paddle/pserver/CMakeLists.txt index 1c1e1964b8d3fd83c801f3988760a72dfc032e7f..b7f85ea1a6dfda2a37c315ba15c6ca1979cf4131 100644 --- a/paddle/pserver/CMakeLists.txt +++ b/paddle/pserver/CMakeLists.txt @@ -24,13 +24,15 @@ set(PSERVER_SOURCES BaseClient.cpp ParameterClient2.cpp ParameterServer2.cpp - SparseParameterDistribution.cpp) + SparseParameterDistribution.cpp + ParameterServerController.cpp) set(PSERVER_HEADERS BaseClient.h ParameterClient2.h ParameterServer2.h - SparseParameterDistribution.h) + SparseParameterDistribution.h + ParameterServerController.h) add_library(paddle_pserver STATIC ${PSERVER_SOURCES}) diff --git a/paddle/pserver/ParameterServer2Main.cpp b/paddle/pserver/ParameterServer2Main.cpp index ffc521f2c143d95ff07c3825e0a746cb31743d9b..845a2c27e242cfbe31679fea6eae13d2b400ec81 100644 --- a/paddle/pserver/ParameterServer2Main.cpp +++ b/paddle/pserver/ParameterServer2Main.cpp @@ -13,66 +13,17 @@ See the License for the specific language governing permissions and limitations under the License. */ #include -#include "paddle/utils/StringUtil.h" -#include "paddle/utils/Util.h" - -#include "ParameterServer2.h" -#include "RDMANetwork.h" -#include "paddle/utils/Flags.h" +#include "ParameterServerController.h" using namespace paddle; // NOLINT int main(int argc, char** argv) { initMain(argc, argv); - std::vector devices; - std::vector> pservers; - - // round robin to loadbalance RDMA server ENGINE - int rdmaCpu = 0; - int onlineCpus = rdma::numCpus(); - int numPorts = FLAGS_ports_num + FLAGS_ports_num_for_sparse; - if (FLAGS_nics.empty()) { - pservers.resize(numPorts); - for (int i = 0; i < numPorts; ++i) { - if (FLAGS_rdma_tcp == "rdma") { - pservers[i].reset( - new ParameterServer2(std::string(), FLAGS_port + i, rdmaCpu++)); - rdmaCpu = rdmaCpu % onlineCpus; - } else { - pservers[i].reset(new ParameterServer2(std::string(), FLAGS_port + i)); - } - CHECK(pservers[i]->init()) << "Fail to initialize parameter server" - << FLAGS_port + i; - LOG(INFO) << "pserver started : " << FLAGS_port + i; - pservers[i]->start(); - } - } else { - str::split(FLAGS_nics, ',', &devices); - pservers.resize(devices.size() * numPorts); - for (int i = 0; i < numPorts; ++i) { - for (size_t j = 0; j < devices.size(); ++j) { - if (FLAGS_rdma_tcp == "rdma") { - pservers[i * devices.size() + j].reset(new ParameterServer2( - getIpAddr(devices[j]), FLAGS_port + i, rdmaCpu++)); - rdmaCpu = rdmaCpu % onlineCpus; - } else { - pservers[i * devices.size() + j].reset( - new ParameterServer2(getIpAddr(devices[j]), FLAGS_port + i)); - } - CHECK(pservers[i * devices.size() + j]->init()) - << "Fail to initialize parameter server" << devices[j] - << FLAGS_port + i; - LOG(INFO) << "pserver started : " << devices[j] << ":" - << FLAGS_port + i; - pservers[i * devices.size() + j]->start(); - } - } - } - - for (auto& pserver : pservers) { - pserver->join(); - } + std::unique_ptr parameterServerPtr( + paddle::ParameterServerController::createFromGflags()); + parameterServerPtr->start(); + parameterServerPtr->wait(); return 0; } diff --git a/paddle/pserver/ParameterServerController.cpp b/paddle/pserver/ParameterServerController.cpp new file mode 100644 index 0000000000000000000000000000000000000000..1d11a2e1acbc0f091901f3854ca99490d89ebe36 --- /dev/null +++ b/paddle/pserver/ParameterServerController.cpp @@ -0,0 +1,102 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "ParameterServerController.h" + +namespace paddle { + +ParameterServerController::ParameterServerController( + const ParameterServerConfig& config) { + // round robin to load balance RDMA server ENGINE + std::vector devices; + int rdmaCpu = 0; + int onlineCpus = rdma::numCpus(); + int numPorts = config.ports_num() + config.ports_num_for_sparse(); + + if (config.nics().empty()) { + parameterServers_.resize(numPorts); + for (int i = 0; i < numPorts; ++i) { + if (config.rdma_tcp() == "rdma") { + parameterServers_[i].reset( + new ParameterServer2(std::string(), config.port() + i, rdmaCpu++)); + rdmaCpu = rdmaCpu % onlineCpus; + } else { + parameterServers_[i].reset( + new ParameterServer2(std::string(), config.port() + i)); + } + CHECK(parameterServers_[i]->init()) << "Fail to initialize parameter " + "server on port " + << config.port() + i; + } + } else { + str::split(config.nics(), ',', &devices); + parameterServers_.resize(devices.size() * numPorts); + for (int i = 0; i < numPorts; ++i) { + for (size_t j = 0; j < devices.size(); ++j) { + if (config.rdma_tcp() == "rdma") { + parameterServers_[i * devices.size() + j].reset(new ParameterServer2( + getIpAddr(devices[j]), config.port() + i, rdmaCpu++)); + rdmaCpu = rdmaCpu % onlineCpus; + } else { + parameterServers_[i * devices.size() + j].reset( + new ParameterServer2(getIpAddr(devices[j]), config.port() + i)); + } + CHECK(parameterServers_[i * devices.size() + j]->init()) + << "Fail to initialize parameter server with device " << devices[j] + << config.port() + i; + } + } + } +} + +ParameterServerController::~ParameterServerController() { this->wait(); } + +ParameterServerController* ParameterServerController::createFromGflags() { + ParameterServerConfig config; + + config.set_nics(FLAGS_nics); + config.set_rdma_tcp(FLAGS_rdma_tcp); + config.set_port(FLAGS_port); + config.set_ports_num(FLAGS_ports_num); + config.set_ports_num_for_sparse(FLAGS_ports_num_for_sparse); + + return create(config); +} + +ParameterServerController* ParameterServerController::create( + const ParameterServerConfig& config) { + return new ParameterServerController(config); +} + +void ParameterServerController::start() { + LOG(INFO) << "number of parameterServer instances: " + << parameterServers_.size(); + int i = 0; + for (const auto& parameterServer : parameterServers_) { + LOG(INFO) << "Starting parameterServer[" << i << "]"; + parameterServer->start(); + i++; + } +} + +void ParameterServerController::wait() { + int i = 0; + for (const auto& parameterServer : parameterServers_) { + LOG(INFO) << "Waiting parameterServer[" << i << "]"; + parameterServer->join(); + i++; + } +} + +} // namespace paddle diff --git a/paddle/pserver/ParameterServerController.h b/paddle/pserver/ParameterServerController.h new file mode 100644 index 0000000000000000000000000000000000000000..fe9bb0b4d02339d0d31d5bc2942932e1f876098b --- /dev/null +++ b/paddle/pserver/ParameterServerController.h @@ -0,0 +1,74 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once + +#include "ParameterServer2.h" +#include "ParameterServerConfig.pb.h" +#include "RDMANetwork.h" +#include "paddle/utils/StringUtil.h" + +namespace paddle { + +/** + * @brief ParameterServerController is used for create, init and manage multi + * parameter server instances. The num of the instances is decided by port + * num(the ports number for parameter send) and network devices configured + * by gflags or proto. + */ +class ParameterServerController final { +public: + DISABLE_COPY(ParameterServerController); + + /** + * @brief Ctor, Create a ParameterServerController from ParameterServerConfig. + */ + explicit ParameterServerController(const ParameterServerConfig& config); + + /** + * @brief Dtor. + */ + ~ParameterServerController(); + + /** + * @brief create ParameterServerController from gflags, this is used for + * compatibility with the old usage of configuration by gflags. + */ + static ParameterServerController* createFromGflags(); + + /** + * @brief create ParameterServerController with ParameterServerConfig, remove + * gflags from ParameterServer. Init all ParameterServer2 instances according + * to + * the config. + */ + static ParameterServerController* create(const ParameterServerConfig& config); + + /** + * @brief start all ParameterServer2 instances in this + * ParameterServerController. + */ + void start(); + + /** + * @brief join and wait for all ParameterServer2 instances thread in this + * ParameterServerController. + */ + void wait(); + +private: + std::vector> parameterServers_; +}; + +} // namespace paddle diff --git a/paddle/py_paddle/dataprovider_converter.py b/paddle/py_paddle/dataprovider_converter.py index 981d10afda2671be9e8f0da1a4bee755f7aa9d61..21d1cb75f4d40e6ed011b33c6366c9d31c0fcc7c 100644 --- a/paddle/py_paddle/dataprovider_converter.py +++ b/paddle/py_paddle/dataprovider_converter.py @@ -34,6 +34,10 @@ class IScanner(object): class DenseScanner(IScanner): + """ + :type __mat__: numpy.ndarray + """ + def __init__(self, input_type, pos): IScanner.__init__(self, input_type, pos) self.__mat__ = None @@ -47,6 +51,8 @@ class DenseScanner(IScanner): def finish_scan(self, argument): assert isinstance(argument, swig_paddle.Arguments) assert isinstance(self.input_type, dp2.InputType) + if self.__mat__.dtype != numpy.float32: + self.__mat__ = self.__mat__.astype(numpy.float32) m = swig_paddle.Matrix.createDenseFromNumpy(self.__mat__, True, False) argument.setSlotValue(self.pos, m) diff --git a/paddle/scripts/CMakeLists.txt b/paddle/scripts/CMakeLists.txt index 1bae396a18688cd53e164774df07660ccc2451d7..66a46e1883a49d491f0cb3056a7039407d72e337 100644 --- a/paddle/scripts/CMakeLists.txt +++ b/paddle/scripts/CMakeLists.txt @@ -2,8 +2,16 @@ configure_file(submit_local.sh.in submit_local.sh @ONLY) - install(FILES ${CMAKE_CURRENT_BINARY_DIR}/submit_local.sh DESTINATION bin PERMISSIONS OWNER_EXECUTE OWNER_WRITE OWNER_READ GROUP_EXECUTE GROUP_READ WORLD_EXECUTE WORLD_READ RENAME paddle) + +configure_file(tools/usage_stat/usage.sh + usage.sh + @ONLY) + +install(FILES ${CMAKE_CURRENT_BINARY_DIR}/usage.sh DESTINATION opt/paddle/bin + PERMISSIONS OWNER_EXECUTE OWNER_WRITE OWNER_READ + GROUP_EXECUTE GROUP_READ WORLD_EXECUTE WORLD_READ + RENAME paddle_usage) diff --git a/paddle/scripts/docker/Dockerfile b/paddle/scripts/docker/Dockerfile index 1522be023f6de32f86fc8a367867bbe2f1c9aeb6..79c4efbed0b8562e6f50b27d6ac297da0929de79 100644 --- a/paddle/scripts/docker/Dockerfile +++ b/paddle/scripts/docker/Dockerfile @@ -4,28 +4,32 @@ MAINTAINER PaddlePaddle Authors ARG DEBIAN_FRONTEND=noninteractive ARG UBUNTU_MIRROR RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ubuntu.com#${UBUNTU_MIRROR}#g' /etc/apt/sources.list; fi' -RUN apt-get update \ - && apt-get install -y cmake libprotobuf-dev protobuf-compiler git \ - libgoogle-glog-dev libgflags-dev libgtest-dev \ - libatlas-dev libatlas3-base g++ m4 python-pip \ - python-protobuf python-numpy python-dev swig openssh-server \ - wget unzip python-matplotlib tar xz-utils bzip2 gzip coreutils \ - sed grep graphviz libjpeg-dev zlib1g-dev doxygen \ - clang-3.8 llvm-3.8 libclang-3.8-dev \ - && apt-get clean -y -RUN cd /usr/src/gtest && cmake . && make && cp *.a /usr/lib -RUN pip install -U BeautifulSoup docopt PyYAML pillow \ - sphinx sphinx_rtd_theme recommonmark jupyter + +RUN apt-get update && \ + apt-get install -y git python-pip python-dev openssh-server bison && \ + apt-get install -y wget unzip tar xz-utils bzip2 gzip coreutils && \ + apt-get install -y curl sed grep graphviz libjpeg-dev zlib1g-dev && \ + apt-get install -y python-numpy python-matplotlib gcc g++ gfortran && \ + apt-get install -y automake clang-3.8 llvm-3.8 libclang-3.8-dev && \ + apt-get clean -y + +RUN pip install --upgrade pip && \ + pip install 'protobuf==3.1.0.post1' && \ + pip install -U wheel pillow BeautifulSoup && \ + pip install -U docopt PyYAML sphinx && \ + pip install -U sphinx_rtd_theme recommonmark jupyter + +RUN curl -sSL https://cmake.org/files/v3.4/cmake-3.4.1.tar.gz | tar -xz && \ + cd cmake-3.4.1 && ./bootstrap && make -j4 && make install && \ + cd .. && rm -rf cmake-3.4.1 ARG WITH_AVX ARG WITH_DOC -ARG WITH_SWIG_PY ARG WITH_STYLE_CHECK ENV WITH_GPU=OFF ENV WITH_AVX=${WITH_AVX:-ON} ENV WITH_DOC=${WITH_DOC:-ON} -ENV WITH_SWIG_PY=${WITH_SWIG_PY:-ON} ENV WITH_STYLE_CHECK=${WITH_STYLE_CHECK:-OFF} RUN mkdir /paddle diff --git a/paddle/scripts/docker/Dockerfile.gpu b/paddle/scripts/docker/Dockerfile.gpu index 09f07043e2172319de257cc952fb81ba53ce89a5..6c1c2225d1a304f234a940584c6c33502eaabbb8 100644 --- a/paddle/scripts/docker/Dockerfile.gpu +++ b/paddle/scripts/docker/Dockerfile.gpu @@ -4,28 +4,32 @@ MAINTAINER PaddlePaddle Authors ARG DEBIAN_FRONTEND=noninteractive ARG UBUNTU_MIRROR RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ubuntu.com#${UBUNTU_MIRROR}#g' /etc/apt/sources.list; fi' -RUN apt-get update \ - && apt-get install -y cmake libprotobuf-dev protobuf-compiler git \ - libgoogle-glog-dev libgflags-dev libgtest-dev \ - libatlas-dev libatlas3-base g++ m4 python-pip \ - python-protobuf python-numpy python-dev swig openssh-server \ - wget unzip python-matplotlib tar xz-utils bzip2 gzip coreutils \ - sed grep graphviz libjpeg-dev zlib1g-dev doxygen \ - clang-3.8 llvm-3.8 libclang-3.8-dev \ - && apt-get clean -y -RUN cd /usr/src/gtest && cmake . && make && cp *.a /usr/lib -RUN pip install -U BeautifulSoup docopt PyYAML pillow \ - sphinx sphinx_rtd_theme recommonmark jupyter + +RUN apt-get update && \ + apt-get install -y git python-pip python-dev openssh-server bison && \ + apt-get install -y wget unzip tar xz-utils bzip2 gzip coreutils && \ + apt-get install -y curl sed grep graphviz libjpeg-dev zlib1g-dev && \ + apt-get install -y python-numpy python-matplotlib gcc g++ gfortran && \ + apt-get install -y automake clang-3.8 llvm-3.8 libclang-3.8-dev && \ + apt-get clean -y + +RUN pip install --upgrade pip && \ + pip install 'protobuf==3.1.0.post1' && \ + pip install -U wheel pillow BeautifulSoup && \ + pip install -U docopt PyYAML sphinx && \ + pip install -U sphinx_rtd_theme recommonmark jupyter + +RUN curl -sSL https://cmake.org/files/v3.4/cmake-3.4.1.tar.gz | tar -xz && \ + cd cmake-3.4.1 && ./bootstrap && make -j4 && make install && \ + cd .. && rm -rf cmake-3.4.1 ARG WITH_AVX ARG WITH_DOC -ARG WITH_SWIG_PY ARG WITH_STYLE_CHECK ENV WITH_GPU=ON ENV WITH_AVX=${WITH_AVX:-ON} ENV WITH_DOC=${WITH_DOC:-ON} -ENV WITH_SWIG_PY=${WITH_SWIG_PY:-ON} ENV WITH_STYLE_CHECK=${WITH_STYLE_CHECK:-OFF} RUN mkdir /paddle diff --git a/paddle/scripts/submit_local.sh.in b/paddle/scripts/submit_local.sh.in index 283fd34a6d8a2268f3800ec69920e128ac75e7dc..f29d32f0d947dc7cde6112160e4f79ce8113505f 100644 --- a/paddle/scripts/submit_local.sh.in +++ b/paddle/scripts/submit_local.sh.in @@ -122,6 +122,9 @@ case "$1" in "make_diagram") python -m paddle.utils.make_model_diagram ${@:2} ;; + "usage") + $MYDIR/../opt/paddle/bin/paddle_usage ${@:2} + ;; "version") version ;; diff --git a/paddle/scripts/tools/usage_stat/usage.sh b/paddle/scripts/tools/usage_stat/usage.sh new file mode 100755 index 0000000000000000000000000000000000000000..7dbd1f58842f50ea1df0e2476c4a493569b1dda9 --- /dev/null +++ b/paddle/scripts/tools/usage_stat/usage.sh @@ -0,0 +1,168 @@ +#!/bin/bash + +ARGPARSE=`getopt -o u:vin:l:e: --long git-user:,help,dry-run,task-name:,log-file:,exit-code: -- "$@"` +KEEP_ANONYMOUS="A_USER_DOES_NOT_TELL_US" +# paddle config home dir, same as paddle +PADDLE_CONF_HOME="$HOME/.config/paddle" +# api url, mirror url(s) will be append later +PD_URLS="http://api.paddlepaddle.org/version" + +usage() +{ + echo "Usage: `basename $0` [options]" + echo "Options:" + echo " -e, --exit-code=EXIT_CODE The train/predict process's exit code" + echo " -l, --log-file=LOG_FILE_PATH Read which log file to get the duration of process" + echo " -n, --task-name=TASK_NAME The name of demo or example" + echo " -u, --git-user=GITHUB_USER provide contact info, like username or email" + echo " -v, -i Verbose output and interact with user when necessary" + echo " --help display this help message" +} + +eval set -- "${ARGPARSE}" +while true; do + case "$1" in + -l|--log-file) + log_file=$2 + shift 2 + ;; + -e|--exit-code) + exit_code=$2 + shift 2 + ;; + -u|--git-user) + github_user=$2 + shift 2 + ;; + -n|--task-name) + task=$2 + shift 2 + ;; + -v|-i) + v=1 + shift + ;; + --dry-run) + dry_run=1 + shift + ;; + --) + shift + break + ;; + --help) + usage + exit 0 + ;; + *) + echo "Invalid option $1" + usage + exit 1 + ;; + esac +done + +# parse the log_file to get the time costs +if [ -s "${log_file}" ]; then + duration=`awk 'BEGIN{day=0;last_sec=0;min_sec=0;max_sec=0;} + {if(index($2,":")==3){ + t=substr($2,1,8); + sec=day*86400+substr(t,1,2)*3600+substr(t,4,2)*60+substr(t,7,2); + if(secsec){min_sec=sec;} + if(max_sec==0 || max_sec/dev/null` + git_url=`git config --get remote.origin.url 2>/dev/null` + if [ "`echo ${git_url} | cut -b 1-19`" = "https://github.com/" ]; then + # under a git url, like https://github.com/user_xxx/proj_yyy.git + if [ "${v}" = "1" ]; then echo " from github url..."; fi + github_user=`echo ${git_url} | cut -d "/" -f 4` + if [ "${github_user}" = "PaddlePaddle" ]; then + github_user= + fi + fi + if [ -n "${git_username}" -a -z "${github_user}" ]; then + if [ "${v}" = "1" ]; then echo " from global git username..."; fi + github_user=${git_username} + fi + fi +fi +# allow user to set the user name, if it's not found +if [ -z "${github_user}" -a "${v}" = "1" ]; then + read -p "Please input your github username or email, or just return to keep this feedback anonymous:" + github_user=${REPLY} + if [ -z "${github_user}" ]; then + # empty input, consider as one anonymous user + github_user="${KEEP_ANONYMOUS}" + fi +fi +if [ -n "${github_user}" -a -z "${dry_run}" ]; then + # valid user and not in dry-run mode, then save to cache + mkdir -p ${PADDLE_CONF_HOME} + echo "${github_user}" >${PADDLE_CONF_HOME}/github_user +fi +if [ "${v}" = "1" ]; then echo "username: ${github_user}"; fi +if [ "${github_user}" = "${KEEP_ANONYMOUS}" ]; then + # anonymous user should keep the var empty. + github_user= +fi + +# read local paddle version +paddle_version=`paddle version | grep PaddlePaddle | head -n1 | cut -d " " -f 2 | cut -d "," -f 1` +if [ "${v}" = "1" ]; then echo "version:${paddle_version}"; fi + +# read local system time +system_time=`date "+%Y%m%d%H%M%S"` +if [ "${v}" = "1" ]; then echo "system time:${system_time}"; fi + +# make empty job_name as default value. +if [ -z "${task}" ]; then + task="(unknown_task)" +fi +if [ "${v}" = "1" ]; then echo "task: ${task}"; fi + +# concat the curl command +params="content={\"data_type\":\"usage\",\ +\"system_time\":${system_time},\"paddle_version\":\"${paddle_version}\",\ +\"github_user\":\"${github_user}\",\"job_name\":\"${task}\",\ +\"duration\":${duration},\"exit_code\":\"${exit_code}\"\ +}&type=1" +curl_cmd_prefix="curl -m 5 -X POST -d ${params}\ + -b ${PADDLE_CONF_HOME}/paddle.cookie -c ${PADDLE_CONF_HOME}/paddle.cookie " + +if [ "${dry_run}" = "1" ]; then + first_url=`echo ${PD_URLS} | cut -d " " -f 1` + echo "(dry-run mode)curl command: ${curl_cmd_prefix} ${first_url}" + exit 0 +else + for u in ${PD_URLS}; do + curl_cmd="${curl_cmd_prefix} ${u}" + if [ "${v}" = "1" ]; then echo "run: ${curl_cmd}"; fi + ${curl_cmd} >/dev/null 2>&1 + if [ $? -eq 0 ]; then + if [ "${v}" = "1" ]; then echo "upload OK!"; fi + exit 0 + else + if [ "${v}" = "1" ]; then echo "upload failed...try next"; fi + fi + done + if [ "${v}" = "1" ]; then echo "all urls tried but all failed...exit"; fi + exit 1 +fi diff --git a/paddle/scripts/travis/before_install.osx.sh b/paddle/scripts/travis/before_install.osx.sh index 7036f971fdd7bac68b67c7b5a92e50c352e214c1..80f031a74e7052d183b5ef21d432476ff1cce722 100755 --- a/paddle/scripts/travis/before_install.osx.sh +++ b/paddle/scripts/travis/before_install.osx.sh @@ -1,6 +1,4 @@ #!/bin/bash brew update brew tap homebrew/science -brew install python -sudo pip install --upgrade protobuf -brew install swig openblas md5sha1sum protobuf +brew install openblas swig md5sha1sum diff --git a/paddle/scripts/travis/build_and_test.sh b/paddle/scripts/travis/build_and_test.sh index fd3aeb02b21d659f783702905117fc838b93eafd..5e6350b57458594163f23cca41a546d7bd9b1eda 100755 --- a/paddle/scripts/travis/build_and_test.sh +++ b/paddle/scripts/travis/build_and_test.sh @@ -6,7 +6,7 @@ if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then export PYTHONPATH=/opt/python/2.7.12/lib/python2.7/site-packages export PYTHONHOME=/opt/python/2.7.12 export PATH=/opt/python/2.7.12/bin:${PATH} - cmake .. -DON_TRAVIS=ON -DON_COVERALLS=ON -DCOVERALLS_UPLOAD=ON ${EXTRA_CMAKE_OPTS} + cmake .. -DCMAKE_Fortran_COMPILER=/usr/bin/gfortran-4.8 -DON_TRAVIS=ON -DON_COVERALLS=ON -DCOVERALLS_UPLOAD=ON ${EXTRA_CMAKE_OPTS} NRPOC=`nproc` make -j $NPROC make coveralls diff --git a/paddle/scripts/travis/docs.sh b/paddle/scripts/travis/docs.sh index bdafb145bcd4e5990f382bb890f804687c474f7c..6b43cad20b76e9abeb3cb10a726d3d8e3da5f8e2 100755 --- a/paddle/scripts/travis/docs.sh +++ b/paddle/scripts/travis/docs.sh @@ -4,7 +4,7 @@ source ./common.sh # Compile Documentation only. -cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_DOC=ON ${EXTRA_CMAKE_OPTS} +cmake .. -DCMAKE_BUILD_TYPE=Debug -DCMAKE_Fortran_COMPILER=/usr/bin/gfortran-4.8 -DWITH_GPU=OFF -DWITH_DOC=ON ${EXTRA_CMAKE_OPTS} make paddle_docs paddle_docs_cn # check websites for broken links diff --git a/paddle/trainer/TrainerMain.cpp b/paddle/trainer/TrainerMain.cpp index e2fbd21e14afa7c89b82999b08bf91c1de182906..c5c1d484e5f85c774fd4b8f1d4a8d46abfa2f547 100644 --- a/paddle/trainer/TrainerMain.cpp +++ b/paddle/trainer/TrainerMain.cpp @@ -12,14 +12,12 @@ 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/pserver/ParameterServer2.h" -#include "paddle/utils/Common.h" +#include +#include "paddle/pserver/ParameterServerController.h" #include "paddle/utils/PythonUtil.h" -#include "paddle/utils/StringUtil.h" #include "ParamUtil.h" #include "Trainer.h" -#include "paddle/pserver/RDMANetwork.h" DEFINE_bool(start_pserver, false, "Whether to start pserver"); DECLARE_int32(gpu_id); @@ -38,54 +36,11 @@ int main(int argc, char** argv) { initMain(argc, argv); initPython(argc, argv); - std::vector> pservers; - std::vector devices; - + std::unique_ptr parameterServerPtr(nullptr); if (FLAGS_start_pserver) { - // round robin to loadbalance RDMA server ENGINE - int rdmaCpu = 0; - int onlineCpus = rdma::numCpus(); - int numPorts = FLAGS_ports_num + FLAGS_ports_num_for_sparse; - if (FLAGS_nics.empty()) { - pservers.resize(numPorts); - for (int i = 0; i < numPorts; ++i) { - if (FLAGS_rdma_tcp == "rdma") { - pservers[i].reset( - new ParameterServer2(std::string(), FLAGS_port + i, rdmaCpu++)); - rdmaCpu = rdmaCpu % onlineCpus; - } else { - pservers[i].reset( - new ParameterServer2(std::string(), FLAGS_port + i)); - } - - CHECK(pservers[i]->init()) << "Fail to initialize parameter server" - << FLAGS_port + i; - LOG(INFO) << "pserver started : " << FLAGS_port + i; - pservers[i]->start(); - } - } else { - str::split(FLAGS_nics, ',', &devices); - pservers.resize(devices.size() * numPorts); - for (int i = 0; i < numPorts; ++i) { - for (size_t j = 0; j < devices.size(); ++j) { - if (FLAGS_rdma_tcp == "rdma") { - pservers[i * devices.size() + j].reset(new ParameterServer2( - getIpAddr(devices[j]), FLAGS_port + i, rdmaCpu++)); - rdmaCpu = rdmaCpu % onlineCpus; - } else { - pservers[i * devices.size() + j].reset( - new ParameterServer2(getIpAddr(devices[j]), FLAGS_port + i)); - } - - CHECK(pservers[i * devices.size() + j]->init()) - << "Fail to initialize parameter server" << devices[j] - << FLAGS_port + i; - LOG(INFO) << "pserver started : " << devices[j] << ":" - << FLAGS_port + i; - pservers[i * devices.size() + j]->start(); - } - } - } + parameterServerPtr.reset( + paddle::ParameterServerController::createFromGflags()); + parameterServerPtr->start(); } Trainer trainer; auto config = TrainerConfigHelper::createFromFlags(); diff --git a/proto/CMakeLists.txt b/proto/CMakeLists.txt index e854b2b427e550ec491dacf931cc2d2cce7ba6c2..62d5b9e38b21ee82d1e78c3bde5aa5df7e4a33ee 100644 --- a/proto/CMakeLists.txt +++ b/proto/CMakeLists.txt @@ -4,7 +4,8 @@ set(proto_filenames ModelConfig.proto ParameterConfig.proto ParameterService.proto - TrainerConfig.proto) + TrainerConfig.proto + ParameterServerConfig.proto) set(PROTO_GEN) set(PROTO_GEN_PY) diff --git a/proto/ParameterServerConfig.proto b/proto/ParameterServerConfig.proto new file mode 100644 index 0000000000000000000000000000000000000000..3068bba8b10d89b432b41076dc6eb3ebc40b3883 --- /dev/null +++ b/proto/ParameterServerConfig.proto @@ -0,0 +1,50 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ +syntax = "proto2"; + +package paddle; + + +/** + * Configuration structure for ParameterClient2. + */ +message ParameterClientConfig { + required int32 trainer_id = 1; +} + +/** + * Configuration structure for ParameterServer2. + */ +message ParameterServerConfig { + // The ports number for parameter send, + // increment based on default port number + required int32 ports_num = 1 [default = 1]; + // The ports number for parameter send, + // increment based on default (port + ports_num + required int32 ports_num_for_sparse = 2 [default = 0]; + // network device name for pservers + required string nics = 3 [default = "xgbe0,xgbe1"]; + required string rdma_tcp = 4 [default = "tcp"]; + // Listening port for pserver + required int32 port = 5 [default = 20134]; + // number of gradient servers + required int32 num_gradient_servers = 6 [default = 1]; + // number of threads for sync op exec + required int32 pserver_num_threads = 7 [default = 1]; + // control config_.async_lagged_grad_discard_ratio() min value + required double async_lagged_ratio_min = 8 [default = 1.0]; + // if async_lagged_grad_discard_ratio is not set in trainer_config.conf + // use it as defalut value + required double async_lagged_ratio_default = 9 [default = 1.5]; +} \ No newline at end of file diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index 674b5ac58b6febd914cb36c75356d8aa70a908b1..8e9c40877bac59378fb352b5bd2448c5ae3f5d29 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -2628,7 +2628,7 @@ class AverageLayer(LayerBase): @config_layer('cos') class CosSimLayer(LayerBase): - def __init__(self, name, inputs, cos_scale=5, device=None): + def __init__(self, name, inputs, cos_scale=1, device=None): super(CosSimLayer, self).__init__( name, 'cos', 1, inputs=inputs, device=device) config_assert(len(self.inputs) == 2, 'CosSimLayer must have 2 inputs') diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 9b6e5774bc82dc05e14a2565fa9cce98764adf04..23c0e9174ff958cbca8963de12cb39bac0b2583d 100644 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -1673,7 +1673,7 @@ def trans_layer(input, name=None, layer_attr=None): @wrap_name_default() @layer_support() -def cos_sim(a, b, scale=5, size=1, name=None, layer_attr=None): +def cos_sim(a, b, scale=1, size=1, name=None, layer_attr=None): """ Cosine Similarity Layer. The cosine similarity equation is here. diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_ntm_layers.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_ntm_layers.protostr index b30bbb2a4e24d74ebe1d6c8eda8be5aa09217f6d..c1bfdf1b19c61d096c25af061c6fbb3bbfc50265 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_ntm_layers.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_ntm_layers.protostr @@ -79,7 +79,7 @@ layers { inputs { input_layer_name: "b" } - cos_scale: 5 + cos_scale: 1 } layers { name: "__cos_sim_1__" @@ -92,7 +92,7 @@ layers { inputs { input_layer_name: "c" } - cos_scale: 5 + cos_scale: 1 } layers { name: "__sum_to_one_norm_layer_0__"