diff --git a/CMakeLists.txt b/CMakeLists.txt index 0ec65bac84b0b0d89123473a8941f80c90f1b339..1e11f86d0ee836f65e69c8398fb26c3b6a1070f6 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -36,6 +36,7 @@ include(simd) ################################ Configurations ####################################### option(WITH_GPU "Compile PaddlePaddle with NVIDIA GPU" ${CUDA_FOUND}) +option(WITH_AMD_GPU "Compile PaddlePaddle with AMD GPU" OFF) option(WITH_AVX "Compile PaddlePaddle with AVX intrinsics" ${AVX_FOUND}) option(WITH_MKL "Compile PaddlePaddle with MKL support." ${AVX_FOUND}) option(WITH_DSO "Compile PaddlePaddle with dynamic linked CUDA" ON) @@ -180,6 +181,11 @@ if(WITH_GPU) include(cuda) endif(WITH_GPU) +if(WITH_AMD_GPU) + find_package(HIP) + include(hip) +endif(WITH_AMD_GPU) + if(WITH_MKLML) list(APPEND EXTERNAL_LIBS ${MKLML_IOMP_LIB}) endif() diff --git a/benchmark/cluster/vgg16/vgg16_fluid.py b/benchmark/cluster/vgg16/vgg16_fluid.py index 786f224608f7d41c438411de0e09fedbcf2264b8..8b29227cfab2a36d5b9f6d17b837b33da8d2a92e 100644 --- a/benchmark/cluster/vgg16/vgg16_fluid.py +++ b/benchmark/cluster/vgg16/vgg16_fluid.py @@ -18,12 +18,13 @@ import sys import time import numpy as np import paddle.v2 as paddle -import paddle.v2.fluid as fluid -import paddle.v2.fluid.core as core -import paddle.v2.fluid.profiler as profiler +import paddle.fluid as fluid +import paddle.fluid.core as core +import paddle.fluid.profiler as profiler import argparse import functools import os +from paddle.fluid import debuger def str2bool(v): @@ -182,28 +183,27 @@ def main(): start_time = time.time() num_samples = 0 train_pass_acc.reset() - with profiler.profiler("CPU", 'total') as prof: - for batch_id, data in enumerate(train_reader()): - ts = time.time() - img_data = np.array( - map(lambda x: x[0].reshape(data_shape), data)).astype( - "float32") - y_data = np.array(map(lambda x: x[1], data)).astype("int64") - y_data = y_data.reshape([-1, 1]) - - loss, acc, b_size = exe.run( - trainer_prog, - feed={"pixel": img_data, - "label": y_data}, - fetch_list=[avg_cost, batch_acc, batch_size]) - iters += 1 - num_samples += len(data) - train_pass_acc.add(value=acc, weight=b_size) - print( - "Pass = %d, Iters = %d, Loss = %f, Accuracy = %f, Speed = %.2f img/s" - % (pass_id, iters, loss, acc, - len(data) / (time.time() - ts)) - ) # The accuracy is the accumulation of batches, but not the current batch. + for batch_id, data in enumerate(train_reader()): + ts = time.time() + img_data = np.array( + map(lambda x: x[0].reshape(data_shape), data)).astype( + "float32") + y_data = np.array(map(lambda x: x[1], data)).astype("int64") + y_data = y_data.reshape([-1, 1]) + + loss, acc, b_size = exe.run( + trainer_prog, + feed={"pixel": img_data, + "label": y_data}, + fetch_list=[avg_cost, batch_acc, batch_size]) + iters += 1 + num_samples += len(data) + train_pass_acc.add(value=acc, weight=b_size) + print( + "Pass = %d, Iters = %d, Loss = %f, Accuracy = %f, Speed = %.2f img/s" + % (pass_id, iters, loss, acc, + len(data) / (time.time() - ts)) + ) # The accuracy is the accumulation of batches, but not the current batch. pass_elapsed = time.time() - start_time pass_train_acc = train_pass_acc.eval() @@ -254,9 +254,7 @@ def main(): pserver_prog = t.get_pserver_program(current_endpoint) pserver_startup = t.get_startup_program(current_endpoint, pserver_prog) - print("starting server side startup") exe.run(pserver_startup) - print("starting parameter server...") exe.run(pserver_prog) elif training_role == "TRAINER": # Parameter initialization diff --git a/benchmark/cluster/vgg16/vgg16_tf.py b/benchmark/cluster/vgg16/vgg16_tf.py index 996df0e314b867ea8de618dfd3977f490fbe8372..2d220478acae46566760209dbc012cff316946aa 100644 --- a/benchmark/cluster/vgg16/vgg16_tf.py +++ b/benchmark/cluster/vgg16/vgg16_tf.py @@ -292,14 +292,18 @@ def run_benchmark(cluster_spec, server): return np.mean(test_accs) config = tf.ConfigProto( - intra_op_parallelism_threads=1, inter_op_parallelism_threads=1) + intra_op_parallelism_threads=1, + inter_op_parallelism_threads=1, + log_device_placement=True) config.gpu_options.allow_growth = True hooks = [tf.train.StopAtStepHook(last_step=1000000)] with tf.train.MonitoredTrainingSession( - master=server.target, is_chief=(args.task_index == 0), - hooks=hooks) as sess: + master=server.target, + is_chief=(args.task_index == 0), + hooks=hooks, + config=config) as sess: iters, num_samples, start_time = 0, 0, 0.0 for pass_id in range(args.num_passes): # train diff --git a/cmake/configure.cmake b/cmake/configure.cmake index 0f76f55270592c5625a9624b33f4c0f82efdc627..f726405c4773994f6ca6509e5218750805b03995 100644 --- a/cmake/configure.cmake +++ b/cmake/configure.cmake @@ -57,11 +57,7 @@ if(NOT WITH_GOLANG) add_definitions(-DPADDLE_WITHOUT_GOLANG) endif(NOT WITH_GOLANG) -if(NOT WITH_GPU) - add_definitions(-DHPPL_STUB_FUNC) - - list(APPEND CMAKE_CXX_SOURCE_FILE_EXTENSIONS cu) -else() +if(WITH_GPU) add_definitions(-DPADDLE_WITH_CUDA) FIND_PACKAGE(CUDA REQUIRED) @@ -84,7 +80,14 @@ else() # Include cuda and cudnn include_directories(${CUDNN_INCLUDE_DIR}) include_directories(${CUDA_TOOLKIT_INCLUDE}) -endif(NOT WITH_GPU) +elseif(WITH_AMD_GPU) + add_definitions(-DPADDLE_WITH_HIP) + set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -D__HIP_PLATFORM_HCC__") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -D__HIP_PLATFORM_HCC__") +else() + add_definitions(-DHPPL_STUB_FUNC) + list(APPEND CMAKE_CXX_SOURCE_FILE_EXTENSIONS cu) +endif() if (WITH_MKLML AND MKLML_IOMP_LIB) message(STATUS "Enable Intel OpenMP with ${MKLML_IOMP_LIB}") diff --git a/cmake/external/boost.cmake b/cmake/external/boost.cmake index d9cd264b49d546c35a2c57a82ead83ea654b60ae..10662fc96704685f030a5d76c6857d4bc20a63d9 100644 --- a/cmake/external/boost.cmake +++ b/cmake/external/boost.cmake @@ -24,7 +24,7 @@ set(BOOST_PROJECT "extern_boost") # So we use 1.41.0 here. set(BOOST_VER "1.41.0") set(BOOST_TAR "boost_1_41_0") -set(BOOST_URL "http://paddlepaddledeps.s3-website-us-west-1.amazonaws.com/${BOOST_TAR}.tar.gz") +set(BOOST_URL "http://paddlepaddledeps.bj.bcebos.com/${BOOST_TAR}.tar.gz") set(BOOST_SOURCES_DIR ${THIRD_PARTY_PATH}/boost) set(BOOST_DOWNLOAD_DIR "${BOOST_SOURCES_DIR}/src/${BOOST_PROJECT}") set(BOOST_INCLUDE_DIR "${BOOST_DOWNLOAD_DIR}/${BOOST_TAR}" CACHE PATH "boost include directory." FORCE) diff --git a/cmake/external/eigen.cmake b/cmake/external/eigen.cmake index 6a701e076c95372f903a09d35d4208ee73bd584c..73d70c34dce8bedd9e62519c207e5be3dcf7dba3 100644 --- a/cmake/external/eigen.cmake +++ b/cmake/external/eigen.cmake @@ -4,18 +4,33 @@ SET(EIGEN_SOURCE_DIR ${THIRD_PARTY_PATH}/eigen3) SET(EIGEN_INCLUDE_DIR ${EIGEN_SOURCE_DIR}/src/extern_eigen3) INCLUDE_DIRECTORIES(${EIGEN_INCLUDE_DIR}) -ExternalProject_Add( - extern_eigen3 - ${EXTERNAL_PROJECT_LOG_ARGS} - GIT_REPOSITORY "https://github.com/RLovelett/eigen.git" - GIT_TAG 70661066beef694cadf6c304d0d07e0758825c10 - PREFIX ${EIGEN_SOURCE_DIR} - UPDATE_COMMAND "" - CONFIGURE_COMMAND "" - BUILD_COMMAND "" - INSTALL_COMMAND "" - TEST_COMMAND "" -) +if(WITH_AMD_GPU) + ExternalProject_Add( + extern_eigen3 + ${EXTERNAL_PROJECT_LOG_ARGS} + GIT_REPOSITORY "https://github.com/sabreshao/hipeigen.git" + GIT_TAG 0cba03ff9f8f9f70bbd92ac5857b031aa8fed6f9 + PREFIX ${EIGEN_SOURCE_DIR} + UPDATE_COMMAND "" + CONFIGURE_COMMAND "" + BUILD_COMMAND "" + INSTALL_COMMAND "" + TEST_COMMAND "" + ) +else() + ExternalProject_Add( + extern_eigen3 + ${EXTERNAL_PROJECT_LOG_ARGS} + GIT_REPOSITORY "https://github.com/RLovelett/eigen.git" + GIT_TAG 70661066beef694cadf6c304d0d07e0758825c10 + PREFIX ${EIGEN_SOURCE_DIR} + UPDATE_COMMAND "" + CONFIGURE_COMMAND "" + BUILD_COMMAND "" + INSTALL_COMMAND "" + TEST_COMMAND "" + ) +endif() if (${CMAKE_VERSION} VERSION_LESS "3.3.0") set(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/eigen3_dummy.c) diff --git a/cmake/generic.cmake b/cmake/generic.cmake index 471e3929069d0d28105404b4f0f6baa303faf0e0..c749c97f13649fe8432091414b56f7d0ea8ace8b 100644 --- a/cmake/generic.cmake +++ b/cmake/generic.cmake @@ -317,6 +317,82 @@ function(nv_test TARGET_NAME) endif() endfunction(nv_test) +function(hip_library TARGET_NAME) + if (WITH_AMD_GPU) + set(options STATIC static SHARED shared) + set(oneValueArgs "") + set(multiValueArgs SRCS DEPS) + cmake_parse_arguments(hip_library "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) + set(_sources ${hip_library_SRCS}) + HIP_PREPARE_TARGET_COMMANDS(${TARGET_NAME} OBJ _generated_files _source_files ${_sources} HIPCC_OPTIONS ${_hipcc_options} HCC_OPTIONS ${_hcc_options} NVCC_OPTIONS ${_nvcc_options}) + if(_source_files) + list(REMOVE_ITEM _sources ${_source_files}) + endif() + if(hip_library_SRCS) + if (hip_library_SHARED OR hip_library_shared) # build *.so + add_library(${TARGET_NAME} SHARED ${_cmake_options} ${_generated_files} ${_sources}) + set_target_properties(${TARGET_NAME} PROPERTIES LINKER_LANGUAGE HIP) + else() + add_library(${TARGET_NAME} STATIC ${_cmake_options} ${_generated_files} ${_sources}) + set_target_properties(${TARGET_NAME} PROPERTIES LINKER_LANGUAGE CXX) + target_link_libraries(${TARGET_NAME} /opt/rocm/hip/lib/libhip_hcc.so /opt/rocm/hip/lib/libhip_device.a) + find_fluid_modules(${TARGET_NAME}) + endif() + if (hip_library_DEPS) + add_dependencies(${TARGET_NAME} ${hip_library_DEPS}) + target_link_libraries(${TARGET_NAME} ${hip_library_DEPS}) + endif() + # cpplint code style + foreach(source_file ${hip_library_SRCS}) + string(REGEX REPLACE "\\.[^.]*$" "" source ${source_file}) + if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${source}.h) + list(APPEND hip_library_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/${source}.h) + endif() + endforeach() + add_style_check_target(${TARGET_NAME} ${hip_library_SRCS} ${hip_library_HEADERS}) + else(hip_library_SRCS) + if (hip_library_DEPS) + merge_static_libs(${TARGET_NAME} ${hip_library_DEPS}) + else() + message(FATAL "Please specify source file or library in nv_library.") + endif() + endif(hip_library_SRCS) + endif() +endfunction(hip_library) + +function(hip_binary TARGET_NAME) + if (WITH_AMD_GPU) + set(options "") + set(oneValueArgs "") + set(multiValueArgs SRCS DEPS) + cmake_parse_arguments(hip_binary "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) + hip_add_executable(${TARGET_NAME} ${hip_binary_SRCS}) + if(hip_binary_DEPS) + target_link_libraries(${TARGET_NAME} ${hip_binary_DEPS}) + add_dependencies(${TARGET_NAME} ${hip_binary_DEPS}) + endif() + endif() +endfunction(hip_binary) + +function(hip_test TARGET_NAME) + if (WITH_AMD_GPU AND WITH_TESTING) + set(options "") + set(oneValueArgs "") + set(multiValueArgs SRCS DEPS) + cmake_parse_arguments(hip_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) + set(_sources ${hip_test_SRCS}) + HIP_PREPARE_TARGET_COMMANDS(${TARGET_NAME} OBJ _generated_files _source_files ${_sources} HIPCC_OPTIONS ${_hipcc_options} HCC_OPTIONS ${_hcc_options} NVCC_OPTIONS ${_nvcc_options}) + if(_source_files) + list(REMOVE_ITEM _sources ${_source_files}) + endif() + add_executable(${TARGET_NAME} ${_cmake_options} ${_generated_files} ${_sources}) + set_target_properties(${TARGET_NAME} PROPERTIES LINKER_LANGUAGE HIP) + target_link_libraries(${TARGET_NAME} ${hip_test_DEPS} paddle_gtest_main paddle_memory gtest gflags) + add_dependencies(${TARGET_NAME} ${hip_test_DEPS} paddle_gtest_main paddle_memory gtest gflags) + add_test(${TARGET_NAME} ${TARGET_NAME}) + endif() +endfunction(hip_test) + function(go_library TARGET_NAME) set(options STATIC static SHARED shared) set(oneValueArgs "") diff --git a/cmake/hip.cmake b/cmake/hip.cmake new file mode 100644 index 0000000000000000000000000000000000000000..bfe491bd6b7602959d3dd60bd06c67993593cc9b --- /dev/null +++ b/cmake/hip.cmake @@ -0,0 +1,43 @@ +if(NOT WITH_AMD_GPU) + return() +endif() + +include_directories("/opt/rocm/include") +include_directories("/opt/rocm/hipblas/include") +include_directories("/opt/rocm/hiprand/include") +include_directories("/opt/rocm/rocrand/include") +include_directories("/opt/rocm/rccl/include") +include_directories("/opt/rocm/thrust") + +list(APPEND EXTERNAL_LIBS "-L/opt/rocm/lib/ -lhip_hcc") + +set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -fPIC -DPADDLE_WITH_HIP -std=c++14" ) + +if(WITH_DSO) + set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DPADDLE_USE_DSO") +endif(WITH_DSO) + +if(WITH_DOUBLE) + set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DPADDLE_TYPE_DOUBLE") +endif(WITH_DOUBLE) + +if(WITH_TESTING) + set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DPADDLE_WITH_TESTING") +endif(WITH_TESTING) + +if(CMAKE_BUILD_TYPE STREQUAL "Debug") + list(APPEND HIP_HCC_FLAGS ${CMAKE_CXX_FLAGS_DEBUG}) +elseif(CMAKE_BUILD_TYPE STREQUAL "RelWithDebInfo") + list(APPEND HIP_HCC_FLAGS ${CMAKE_CXX_FLAGS_RELWITHDEBINFO}) +elseif(CMAKE_BUILD_TYPE STREQUAL "MinSizeRel") + list(APPEND HIP_HCC_FLAGS ${CMAKE_CXX_FLAGS_MINSIZEREL}) +endif() + +if("x${HCC_HOME}" STREQUAL "x") + set(HCC_HOME "/opt/rocm/hcc") +endif() + +set(CMAKE_HIP_LINK_EXECUTABLE "${HIP_HIPCC_CMAKE_LINKER_HELPER} ${HCC_HOME} -o ") +set(CMAKE_HIP_CREATE_SHARED_LIBRARY "${HIP_HIPCC_CMAKE_LINKER_HELPER} ${HCC_HOME} -o -shared") +set(CMAKE_HIP_CREATE_SHARED_MODULE "${HIP_HIPCC_CMAKE_LINKER_HELPER} ${HCC_HOME} -o -shared") + diff --git a/doc/CMakeLists.txt b/doc/CMakeLists.txt index da67701ec1af57df742dce105990cffa40f45d7c..a9b27933a5307aabeaf150aeb859e869197229f5 100644 --- a/doc/CMakeLists.txt +++ b/doc/CMakeLists.txt @@ -1 +1,2 @@ add_subdirectory(v2) +add_subdirectory(fluid) diff --git a/doc/fluid/CMakeLists.txt b/doc/fluid/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..cc999f5a8d70a2239ea3b130e9da172d5f681c65 --- /dev/null +++ b/doc/fluid/CMakeLists.txt @@ -0,0 +1,49 @@ +if(NOT DEFINED SPHINX_THEME) + set(SPHINX_THEME default) +endif() + +if(NOT DEFINED SPHINX_THEME_DIR) + set(SPHINX_THEME_DIR) +endif() + +# configured documentation tools and intermediate build results +set(BINARY_BUILD_DIR_EN "${CMAKE_CURRENT_BINARY_DIR}/en/_build") + +# Sphinx cache with pickled ReST documents +set(SPHINX_CACHE_DIR_EN "${CMAKE_CURRENT_BINARY_DIR}/en/_doctrees") + +# HTML output director +set(SPHINX_HTML_DIR_EN "${CMAKE_CURRENT_BINARY_DIR}/en/html") + +configure_file( + "${CMAKE_CURRENT_SOURCE_DIR}/../templates/conf.py.en.in" + "${BINARY_BUILD_DIR_EN}/conf.py" + @ONLY) + +sphinx_add_target(paddle_fluid_docs + html + ${BINARY_BUILD_DIR_EN} + ${SPHINX_CACHE_DIR_EN} + ${CMAKE_CURRENT_SOURCE_DIR} + ${SPHINX_HTML_DIR_EN}) + +# configured documentation tools and intermediate build results +set(BINARY_BUILD_DIR_CN "${CMAKE_CURRENT_BINARY_DIR}/cn/_build") + +# Sphinx cache with pickled ReST documents +set(SPHINX_CACHE_DIR_CN "${CMAKE_CURRENT_BINARY_DIR}/cn/_doctrees") + +# HTML output directory +set(SPHINX_HTML_DIR_CN "${CMAKE_CURRENT_BINARY_DIR}/cn/html") + +configure_file( + "${CMAKE_CURRENT_SOURCE_DIR}/../templates/conf.py.cn.in" + "${BINARY_BUILD_DIR_CN}/conf.py" + @ONLY) + +sphinx_add_target(paddle_fluid_docs_cn + html + ${BINARY_BUILD_DIR_CN} + ${SPHINX_CACHE_DIR_CN} + ${CMAKE_CURRENT_SOURCE_DIR} + ${SPHINX_HTML_DIR_CN}) diff --git a/doc/fluid/build_and_install/index_cn.rst b/doc/fluid/build_and_install/index_cn.rst new file mode 100644 index 0000000000000000000000000000000000000000..9276236f9fd511bde3570a8c88b437119911d60a --- /dev/null +++ b/doc/fluid/build_and_install/index_cn.rst @@ -0,0 +1,2 @@ +安装与使用 +------------ diff --git a/doc/fluid/build_and_install/index_en.rst b/doc/fluid/build_and_install/index_en.rst new file mode 100644 index 0000000000000000000000000000000000000000..cc1e61a58a026a0f5c3b106875a8a86dc9cba613 --- /dev/null +++ b/doc/fluid/build_and_install/index_en.rst @@ -0,0 +1,2 @@ +Build and Install +------------ diff --git a/doc/fluid/design/dynamic_rnn/rnn.md b/doc/fluid/design/dynamic_rnn/rnn.md index 2f4854793fa1f0b02e4dc17b51a48a972be61c06..6f414e5549b149bc88fb252085ff56dbb06730f8 100644 --- a/doc/fluid/design/dynamic_rnn/rnn.md +++ b/doc/fluid/design/dynamic_rnn/rnn.md @@ -5,7 +5,7 @@ This document describes the RNN (Recurrent Neural Network) operator and how it i ## RNN Algorithm Implementation

- +

The above diagram shows an RNN unrolled into a full network. @@ -22,7 +22,7 @@ There are several important concepts here: There could be local variables defined in each step-net. PaddlePaddle runtime realizes these variables in *step-scopes* which are created for each step.

-
+
Figure 2 illustrates the RNN's data flow

@@ -49,7 +49,7 @@ or copy the memory value of the previous step to the current ex-memory variable. ### Usage in Python -For more information on Block, please refer to the [design doc](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/block.md). +For more information on Block, please refer to the [design doc](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/fluid/design/concepts/block.md). We can define an RNN's step-net using a Block: @@ -93,7 +93,7 @@ For example, we could have a 2-level RNN, where the top level corresponds to par The following figure illustrates feeding in text into the lower level, one sentence at a step, and the feeding in step outputs to the top level. The final top level output is about the whole text.

- +

```python @@ -149,5 +149,5 @@ If the `output_all_steps` is set to False, it will only output the final time st

- +

diff --git a/doc/fluid/design/index_cn.rst b/doc/fluid/design/index_cn.rst new file mode 100644 index 0000000000000000000000000000000000000000..f1887be6901653d4263d711d78b626d2abfd45c9 --- /dev/null +++ b/doc/fluid/design/index_cn.rst @@ -0,0 +1,2 @@ +设计思想 +------------ diff --git a/doc/fluid/design/index_en.rst b/doc/fluid/design/index_en.rst new file mode 100644 index 0000000000000000000000000000000000000000..18a4b4122f6e3f0096676f34ffea8a80aa9b6696 --- /dev/null +++ b/doc/fluid/design/index_en.rst @@ -0,0 +1,2 @@ +Design +------------ diff --git a/doc/fluid/dev/index_cn.rst b/doc/fluid/dev/index_cn.rst new file mode 100644 index 0000000000000000000000000000000000000000..e1edf079fa0f85eb7f6709fd945fffae88625d01 --- /dev/null +++ b/doc/fluid/dev/index_cn.rst @@ -0,0 +1,2 @@ +开发标准 +------------ diff --git a/doc/fluid/dev/index_en.rst b/doc/fluid/dev/index_en.rst new file mode 100644 index 0000000000000000000000000000000000000000..faf9dfcd315fddc4774c3717b41086fa6c6bf85a --- /dev/null +++ b/doc/fluid/dev/index_en.rst @@ -0,0 +1,4 @@ +Development +------------ + +This is Development page diff --git a/doc/fluid/faq/index_cn.rst b/doc/fluid/faq/index_cn.rst new file mode 100644 index 0000000000000000000000000000000000000000..395c1109891b5a00eab6f0b44d855658def7fdd6 --- /dev/null +++ b/doc/fluid/faq/index_cn.rst @@ -0,0 +1,2 @@ +FAQ +------------ diff --git a/doc/fluid/faq/index_en.rst b/doc/fluid/faq/index_en.rst new file mode 100644 index 0000000000000000000000000000000000000000..395c1109891b5a00eab6f0b44d855658def7fdd6 --- /dev/null +++ b/doc/fluid/faq/index_en.rst @@ -0,0 +1,2 @@ +FAQ +------------ diff --git a/doc/fluid/getstarted/index_cn.rst b/doc/fluid/getstarted/index_cn.rst new file mode 100644 index 0000000000000000000000000000000000000000..c4d8525f23ee18cb7f41ab2f0d148fc1dcc852b2 --- /dev/null +++ b/doc/fluid/getstarted/index_cn.rst @@ -0,0 +1,4 @@ +新手入门 +------------ + +新手入门 diff --git a/doc/fluid/getstarted/index_en.rst b/doc/fluid/getstarted/index_en.rst new file mode 100644 index 0000000000000000000000000000000000000000..a4efd05e2fd94ac0e2cbbc8603e6b0261b7e787f --- /dev/null +++ b/doc/fluid/getstarted/index_en.rst @@ -0,0 +1,4 @@ +GET STARTED +------------ + +This is get started page diff --git a/doc/fluid/howto/cluster/fluid_cluster_train_cn.md b/doc/fluid/howto/cluster/fluid_cluster_train_cn.md new file mode 100644 index 0000000000000000000000000000000000000000..1b6f767869aaa800c122c8e7a06a1413e48e10e0 --- /dev/null +++ b/doc/fluid/howto/cluster/fluid_cluster_train_cn.md @@ -0,0 +1,145 @@ +# Fluid 分布式版本使用指南 +本篇文章将说明如何在PaddlePaddle Fluid版本下进行分布式训练的配置和执行，以及将单机训练脚本改造成支持集群训练的版本 + +## 准备工作 +* 可用的集群 + + 包含一个或多个计算节点的集群，每一个节点都能够执行PaddlePaddle的训练任务且拥有唯一的IP地址，集群内的所有计算节点可以通过网络相互通信。 +* 安装PaddlePaddle Fluid with Distribution版本 + + 所有的计算节点上均需要按照分布式版本的PaddlePaddle, 在用于GPU等设备的机器上还需要额外安装好相应的驱动程序和CUDA的库。 + + **注意：**当前对外提供的PaddlePaddle版本并不支持分布式，需要通过源码重新编译。编译和安装方法参见[编译和安装指南](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/build_and_install/index_en.html)。 + cmake编译命令中需要将WITH_DISTRIBUTE设置为ON，下面是一个cmake编译指令示例： +``` bash +cmake .. -DWITH_DOC=OFF -DWITH_GPU=OFF -DWITH_DISTRIBUTE=ON -DWITH_SWIG_PY=ON -DWITH_PYTHON=ON +``` + +## 更新训练脚本 +这里，我们以[Deep Learing 101](http://www.paddlepaddle.org/docs/develop/book/01.fit_a_line/index.html)课程中的第一章 fit a line 为例，描述如何将单机训练脚本改造成支持集群训练的版本。 +### 单机训练脚本示例 +```python +import paddle.v2 as paddle +import paddle.fluid as fluid + +x = fluid.layers.data(name='x', shape=[13], dtype='float32') +y_predict = fluid.layers.fc(input=x, size=1, act=None) +y = fluid.layers.data(name='y', shape=[1], dtype='float32') + +cost = fluid.layers.square_error_cost(input=y_predict, label=y) +avg_cost = fluid.layers.mean(x=cost) + +sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001) +sgd_optimizer.minimize(avg_cost) + +BATCH_SIZE = 20 + +train_reader = paddle.batch( + paddle.reader.shuffle( + paddle.dataset.uci_housing.train(), buf_size=500), + batch_size=BATCH_SIZE) + +place = fluid.CPUPlace() +feeder = fluid.DataFeeder(place=place, feed_list=[x, y]) +exe = fluid.Executor(place) + +exe.run(fluid.default_startup_program()) + +PASS_NUM = 100 +for pass_id in range(PASS_NUM): + fluid.io.save_persistables(exe, "./fit_a_line.model/") + fluid.io.load_persistables(exe, "./fit_a_line.model/") + for data in train_reader(): + avg_loss_value, = exe.run(fluid.default_main_program(), + feed=feeder.feed(data), + fetch_list=[avg_cost]) + + if avg_loss_value[0] < 10.0: + exit(0) # if avg cost less than 10.0, we think our code is good. +exit(1) +``` + +我们创建了一个简单的全连接神经网络程序，并且通过Fluid的Executor执行了100次迭代,现在我们需要将该单机版本的程序更新为分布式版本的程序。 +### 介绍Parameter Server +在非分布式版本的训练脚本中，只存在Trainer一种角色，它不仅处理常规的计算任务，也处理参数相关的计算、保存和优化任务。在分布式版本的训练过程中，由于存在多个Trainer节点进行同样的数据计算任务，因此需要有一个中心化的节点来统一处理参数相关的保存和分配。在PaddlePaddle中，我们称这样的节点为[Parameter Server](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/fluid/design/dist_train/parameter_server.md) + +**因此，在分布式的Fluid环境中，我们有两个角色需要创建，分别是Parameter Server和Trainer。** + +### 分布式训练 +Fliud专门提供了工具[Distributed Transpiler](https://github.com/PaddlePaddle/Paddle/blob/ba65d54d9d3b41cd3c5171b00f476d4e60133ddb/doc/fluid/design/dist_train/distributed_architecture.md#distributed-transpiler)用于将单机版的训练程序转换为分布式版本的训练程序。工具背后的理念是找出程序的优化算子和梯度参数，将他们分隔为两部分，通过send/recv 操作算子进行连接,优化算子和梯度参数可以在优化器的minimize函数的返回值中获取到。 +```python +optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost) +``` +将Distributed Transpiler、优化算子和梯度函数放在一个代码中如下： +```python +... #define the program, cost, and create sgd optimizer + +optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost) #get optimize OPs and gradient parameters + +t = fluid.DistributeTranspiler() # create the transpiler instance +# slice the program into 2 pieces with optimizer_ops and gradient parameters list, as well as pserver_endpoints, which is a comma separated list of [IP:PORT] and number of trainers +t.transpile(optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2) + +... #create executor + +# in pserver, run this +#current_endpoint here means current pserver IP:PORT you wish to run on +pserver_prog = t.get_pserver_program(current_endpoint) +pserver_startup = t.get_startup_program(current_endpoint, pserver_prog) +exe.run(pserver_startup) +exe.run(pserver_prog) + +# in trainer, run this +... # define data reader +exe.run(fluid.default_startup_program()) +for pass_id in range(100): + for data in train_reader(): + exe.run(t.get_trainer_program()) +``` +### 分布式训练脚本运行说明 +分布式任务的运行需要将表格中说明的多个参数进行赋值: + +| 参数名 | 值类型 | 说明 | 示例 | +|:-------------|:------|:---------------------------------------|:-------------| +| trainer_id | int | 当前训练节点的ID，训练节点ID编号为0 - n-1， n为trainers的值 | 0/1/2/3 | +| pservers | str | parameter server 列表 | 127.0.0.1:6710,127.0.0.1:6711 | +| trainers | int | 训练节点的总个数，>0的数字 | 4 | +| server_endpoint | str | 当前所起的服务节点的IP:PORT | 127.0.0.1:8789 | +| training_role | str | 节点角色， TRAINER/PSERVER | PSERVER | + +**注意：** ```training_role```是用来区分当前所起服务的角色的，用于训练程序中，用户可根据需要自行定义，其他参数为fluid.DistributeTranspiler的transpile函数所需要，需要在调用函数前进行定义，样例如下： + +```python +t = fluid.DistributeTranspiler() +t.transpile( + optimize_ops, + params_grads, + trainer_id, + pservers=pserver, + trainers=trainers) +if training_role == "PSERVER": + pserver_prog = t.get_pserver_program(server_endpoint) + pserver_startup = t.get_startup_program(server_endpoint, pserver_prog) +``` + +### Demo +完整的demo代码位于Fluid的test目录下的[book](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/tests/book/test_fit_a_line.py)中。 + +第一步，进入demo代码所在目录： +```bash +cd /paddle/python/paddle/fluid/tests/book +``` + +第二步，启动Parameter Server： +```bash +PADDLE_INIT_PORT=6174 PADDLE_INIT_PSERVERS=192.168.1.2 TRAINERS=2 POD_IP=192.168.1.2 PADDLE_INIT_TRAINER_ID=1 TRAINING_ROLE=PSERVER python test_fit_a_line.py +``` +执行命令后请等待出现提示： ```Server listening on 192.168.1.2:6174 ```, 表示Paramter Server已经正常启动。 + +第三步，启动Trainer： +```bash +PADDLE_INIT_PORT=6174 PADDLE_INIT_PSERVERS=192.168.1.3 TRAINERS=2 POD_IP=192.168.1.3 PADDLE_INIT_TRAINER_ID=1 TRAINING_ROLE=TRAINER python test_fit_a_line.py +``` +由于我们定义的Trainer的数量是2个，因此需要在另外一个计算节点上再启动一个Trainer。 + +现在我们就启动了一个包含一个Parameter Server和两个Trainer的分布式训练任务。 diff --git a/doc/fluid/howto/index_cn.rst b/doc/fluid/howto/index_cn.rst new file mode 100644 index 0000000000000000000000000000000000000000..a92abad0c56a4fd821f9a6b9f4f5909504c8aaf1 --- /dev/null +++ b/doc/fluid/howto/index_cn.rst @@ -0,0 +1,2 @@ +进阶使用 +------------ diff --git a/doc/fluid/howto/index_en.rst b/doc/fluid/howto/index_en.rst new file mode 100644 index 0000000000000000000000000000000000000000..06036bdce554a96443ea1fa47c15f7670ea6089d --- /dev/null +++ b/doc/fluid/howto/index_en.rst @@ -0,0 +1,4 @@ +HOW TO +------------ + +This is how to page diff --git a/doc/fluid/index_cn.rst b/doc/fluid/index_cn.rst new file mode 100644 index 0000000000000000000000000000000000000000..be3bed4393a7346d4f2a53e2c7409ee7165fb5b6 --- /dev/null +++ b/doc/fluid/index_cn.rst @@ -0,0 +1,12 @@ + PaddlePaddle Fluid +========================== + +.. toctree:: + :maxdepth: 1 + + getstarted/index_cn.rst + design/index_cn.rst + build_and_install/index_cn.rst + howto/index_cn.rst + dev/index_cn.rst + faq/index_cn.rst diff --git a/doc/fluid/index_en.rst b/doc/fluid/index_en.rst new file mode 100644 index 0000000000000000000000000000000000000000..87c831420a57b4b9ce77ecf44f7f4d0feec833a6 --- /dev/null +++ b/doc/fluid/index_en.rst @@ -0,0 +1,12 @@ + PaddlePaddle Fluid +========================== + +.. toctree:: + :maxdepth: 1 + + getstarted/index_en.rst + design/index_en.rst + build_and_install/index_en.rst + howto/index_en.rst + dev/index_en.rst + faq/index_en.rst diff --git a/doc/v2/dev/write_docs_cn.rst b/doc/v2/dev/write_docs_cn.rst index a055bb04c0c093c9159290067e5ccbd2525cd519..23615f8830e99633676c83ec5d28139a732c623c 100644 --- a/doc/v2/dev/write_docs_cn.rst +++ b/doc/v2/dev/write_docs_cn.rst @@ -2,13 +2,14 @@ 如何贡献文档 ############# -PaddlePaddle的文档包括中英文两个部分。文档都是通过 ``cmake`` 驱动 ``sphinx`` 编译生成，也可以利用paddlepaddle.org工具来编译和预览文档。 +PaddlePaddle的文档包括中英文两个部分。文档都是通过 ``cmake`` 驱动 ``sphinx`` 编译生成的，PaddlePaddle.org工具可以帮助我们实现这一编译过程，并提供更好的预览效果。 如何构建文档 ============ PaddlePaddle的文档构建有两种方式，分别为使用paddlepaddle.org工具和不使用paddlepaddle.org工具，两种方式都有各自的优点，前者方便预览，后者方便开发者进行调试。这两种方式中又分别有使用docker和不使用docker的两种构建方法。 +我们建议使用PaddlePaddle.org工具来构建文档。 使用PaddlePaddle.org工具 ------------------------ @@ -31,7 +32,7 @@ PaddlePaddle.org工具可以配合Docker使用，需要在系统里先安装好D docker run -it -p 8000:8000 -v `pwd`:/var/content paddlepaddle/paddlepaddle.org:latest 注意: PaddlePaddle.org 会在 -v (volume) 指定的内容存储库运行命令 -之后再用网页连到http://localhost:8000就可以在网页上生成需要的文档 +之后再用网页连到 http://localhost:8000 就可以在网页上生成需要的文档 编译后的文件将被存储在工作目录 /.ppo_workspace/content。 如果不想使用Docker，你还可以通过运行Django框架直接激活工具的服务器。使用下面的命令来运行它。 @@ -56,7 +57,7 @@ PaddlePaddle.org工具可以配合Docker使用，需要在系统里先安装好D python manage.py runserver 工具服务器将读取环境变量 CONTENT_DIR 搜索代码库。请指定的PaddlePaddle工作目录给环境变量 CONTENT_DIR。 -之后再用网页连到http://localhost:8000就可以在网页上生成需要的文档。 +之后再用网页连到 http://localhost:8000 就可以在网页上生成需要的文档。 编译后的文件将被存储在工作目录 /.ppo_workspace/content。 想了解更多PaddlePaddle.org工具的详细信息，可以 `点击这里 `_ 。 @@ -96,7 +97,7 @@ PaddlePaddle.org工具可以配合Docker使用，需要在系统里先安装好D python -m SimpleHTTPServer 8088 -在浏览器中输入http://localhost:8088就可以看到编译生成的中/英文的文档页面和英文的API页面,下图为生成的英文文档首页示例。注意，示例中由于使用了sphinx的原始主题，所以页面的风格与官网并不一致，但这并不影响开发者进行调试。 +在浏览器中输入 http://localhost:8088 就可以看到编译生成的中/英文的文档页面和英文的API页面,下图为生成的英文文档首页示例。注意，示例中由于使用了sphinx的原始主题，所以页面的风格与官网并不一致，但这并不影响开发者进行调试。 .. image:: src/doc_en.png :align: center diff --git a/doc/v2/dev/write_docs_en.rst b/doc/v2/dev/write_docs_en.rst index f3408a84269aaeef19986c220454555fbbe30e23..15ff0d34ad622f100fe98d8738b830e47c35b41b 100644 --- a/doc/v2/dev/write_docs_en.rst +++ b/doc/v2/dev/write_docs_en.rst @@ -2,21 +2,20 @@ Contribute Documentation ######################## -PaddlePaddle supports English documentation ``doc`` and Chinese documentation ``doc_cn``. -Both are compiled by `cmake`_ and `sphinx`_ , the compiled documentations will be stored under ``doc`` and ``doc_cn`` directories. -When using the PaddlePaddle.org to compile documentations, the compiled documentations will be stored under a consolidated directory: .ppo_workspace/content +PaddlePaddle's documentation includes both Chinese and English versions. The documentation is built using the ``cmake`` command to drive the ``sphinx`` compiler. The PaddlePaddle.org tool helps us to implement this compilation process and provides better preview results. -How to Build Documentations -============ +How to build Documentation +=========================== -We recommend using PaddlePaddle.org tool to build documentation +PaddlePaddle's documentation is built in two ways: using the PaddlePaddle.org tool and without using it. Both methods have their own advantages. The former facilitates previewing, while the latter facilitates debugging by the developer. We could choose to build the documentation with Docker or without it in each of the above ways. +We recommend using PaddlePaddle.org tool to build documentation. -Use PaddlePaddle.org tool --------------- -This is the recommended method to build documentation. It can compile documentation and preview the documentation in a web browser. +Using PaddlePaddle.org tool +----------------------------- +This is the recommended method to build documentation, because it can automatically compile the documentation and preview the documentation directly in a web page. Note that, although you can preview the documentation in other ways, its style may not be consistent with the official website. Compiling with the PaddlePaddle.org tool produces a preview that will be consistent with the official website documentation style. -The tool uses Docker, please install it on your system. Please check Docker official website on how to install Docker. You may use the following commands to activate the tool +The PaddlePaddle.org tool can be used with Docker and Docker needs to be installed first. Please refer to `Docker's official website `_ on how to install Docker. After installing Docker, you may use the following commands to activate the tool .. code-block:: bash @@ -32,8 +31,8 @@ The tool uses Docker, please install it on your system. Please check Docker offi # Please specify the working directory through -v docker run -it -p 8000:8000 -v `pwd`:/var/content paddlepaddle/paddlepaddle.org:latest -Note: PaddlePaddle.org will read the content repos specified in the -v (volume) flag of the docker run command -Use a web browser and navigate to http://localhost:8000, click the buttons to compile the documentation +Note: PaddlePaddle.org will read the content repos specified in the -v (volume) flag of the docker run commands +Use a web browser and navigate to http://localhost:8000. Click the buttons to compile the documentation. The compiled documentations will be stored in /.ppo_workspace/content @@ -58,19 +57,62 @@ If you don't wish to use Docker, you can also activate the tool through Django. pip install -r requirements.txt python manage.py runserver -Use a web browser and navigate to http://localhost:8000, click the buttons to compile the documentation +Specify the PaddlePaddle working directory for the environment variable CONTENT_DIR so that the tool could find where the working directory is. + +Use a web browser and navigate to http://localhost:8000. Click the buttons to compile the documentation The compiled documentations will be stored in /.ppo_workspace/content -If you want to learn more on the PaddlePaddle.org, please `click here `_ 。 +Please `click here `_ for more information about the PaddlePaddle.org tool. + + +Manually Building the Documentation +------------------------------------- + +Build PaddlePaddle's documentation with Docker，you need to install Docker first. Please refer to `Docker's official website `_ on how to install Docker. After Docker is installed, you could use the scripts in the source directory to build the documentation. + +[TBD] + +If you do not wish to use Docker, you can also use the following commands to directly build the PaddlePaddle documentation. + +.. code-block:: bash + + mkdir paddle + cd paddle + git clone https://github.com/PaddlePaddle/Paddle.git + mkdir -p build + cd build + cmake .. -DCMAKE_BUILD_TYPE=Release -DWITH_GPU=OFF -DWITH_MKL=OFF -DWITH_DOC=ON + + # If you only need to build documents, use the following commands + make -j $processors gen_proto_py + make -j $processors paddle_docs paddle_docs_cn + + # If you only need to build APIs, use the following commands + make -j $processors gen_proto_py framework_py_proto + make -j $processors copy_paddle_pybind + make -j $processors paddle_api_docs + +$processors indicates that as many processes as the CPU cores are started to compile in parallel. It should be set according to the number of CPU cores of your machine. + +After the compilation is complete, enter the ``doc/v2`` directory. If you chose to build documents, it will generate ``cn/html/`` and ``en/html`` subdirectories under this directory. If you chose to build APIs，it will generate``api/en/html`` subdirectory. Please enter these directories respectively and execute the following commands: + +.. code-block:: bash + + python -m SimpleHTTPServer 8088 + +Use a web browser and navigate to http://localhost:8000, you could see the compiled Chinese/English documents page and the English APIs page. The following figure is an example of the built English documents home page. Note that due to the sphinx's original theme used in the example, the style of the page is not consistent with the official website, but this does not affect the developer's debugging. -How to write Documentations -============ +.. image:: src/doc_en.png + :align: center + :scale: 60 % -PaddlePaddle uses `sphinx`_ to compile documentations，Please check sphinx official website for more detail. +How to write Documentation +=========================== +PaddlePaddle uses `sphinx`_ to compile documentation，Please check sphinx official website for more detail. How to update www.paddlepaddle.org -============================ +=================================== Please create PRs and submit them to github, please check `Contribute Code `_ 。 PaddlePaddle develop branch will update the documentation once the PR is merged. User may check latest `Chinese Docs `_ and diff --git a/doc/v2/faq/index_en.rst b/doc/v2/faq/index_en.rst index 57df868f760038b25fae30df7ab20a68875ad36a..3fa220792b252617848a1c76bc2be49928e35f64 100644 --- a/doc/v2/faq/index_en.rst +++ b/doc/v2/faq/index_en.rst @@ -1,7 +1,8 @@ FAQ ==== - +This document provides answers to some of the frequently asked questions about PaddlePaddle. If you have a question that is not covered here, please go to `PaddlePaddle Community `_ , to find an answer or submit new `issue `_ , we will reply in time. + .. toctree:: :maxdepth: 1 diff --git a/doc/v2/howto/capi/index_en.rst b/doc/v2/howto/capi/index_en.rst index 2cbbe362fd8e06abe9866d998f60fbb3458a80b5..4ec39c9d5223442cf6872edaf7befeb5053b538e 100644 --- a/doc/v2/howto/capi/index_en.rst +++ b/doc/v2/howto/capi/index_en.rst @@ -1,6 +1,23 @@ -C-API Prediction Library +C-API Inference Library ======================== +After we train a neural network, we use it to do inference. Inference is the process of preparing input data and propagating it through the model to produce the result. + +Compared with model training, prediction has the following features: + +#. Inference does not require backpropagation and parameter updates, as required during training. +#. Labels are not needed in prediction. +#. Most of the time, predictions need to be integrated with the user system. + +Therefore, the model prediction SDK needs to be designed separately and has the following features: + +#. The predictive SDK does not include backpropagation and parameter updates to reduce the size of the SDK. +#. The predictive SDK needs a simple user interface for ease of use. +#. Since the input data may have a variety of structures, the format of the input data is clearly and compactly packaged. +#. In order to be compatible with user's system, the SDK's interface must conform to the C-standard interface. + +PaddlePaddle provides C-API to solve the above problem. Following are the guidelines to use the C-API: + .. toctree:: :maxdepth: 1 diff --git a/doc/v2/howto/index_en.rst b/doc/v2/howto/index_en.rst index 2079be766f2d8e6d63ca11dccd98f80613309ceb..bf2320a169df149cc8d44612d975ecf64f8ea779 100644 --- a/doc/v2/howto/index_en.rst +++ b/doc/v2/howto/index_en.rst @@ -1,11 +1,37 @@ HOW TO -======= +======== + +PaddlePaddle provides the users the ability to flexibly set various command line parameters to control the model training and inference process. Please refer to the following instructions on using PaddlePaddle: + +.. toctree:: + :maxdepth: 1 + + cmd_parameter/index_cn.rst + +PaddlePaddle supports distributed training tasks on fabric clusters, MPI clusters, and Kubernetes clusters. For detailed configuration and usage instructions, refer to: + +.. toctree:: + :maxdepth: 1 + + cluster/index_cn.rst + +PaddlePaddle provides a C-API for inference. We provide the following guidelines for using the C-API: + +.. toctree:: + :maxdepth: 1 + + capi/index_cn.rst + +PaddlePaddle supports a variety of flexible and efficient recurrent neural networks. For details, please refer to： + +.. toctree:: + :maxdepth: 1 + + rnn/index_cn.rst + +How to use the built-in timing tool, nvprof, or nvvp to run performance analysis and tuning, please refer to： .. toctree:: :maxdepth: 1 - cmd_parameter/index_en.rst - cluster/index_en.rst - capi/index_en.rst - rnn/index_en.rst - optimization/gpu_profiling_en.rst + optimization/gpu_profiling_cn.rst diff --git a/paddle/fluid/framework/concurrency_test.cc b/paddle/fluid/framework/concurrency_test.cc index 25152054eb8452a9667bd65b4441665476c1d46d..e98e9d94bf71fe9ac226ab3ad7f587b37a5c6e33 100644 --- a/paddle/fluid/framework/concurrency_test.cc +++ b/paddle/fluid/framework/concurrency_test.cc @@ -150,8 +150,9 @@ void AddFibonacciSelect(Scope *scope, p::CPUPlace *place, ProgramDesc *program, // Select block AddOp("select", {{"X", {dataChanName, quitChanName}}, {"case_to_execute", {"caseToExecute"}}}, - {}, {{"sub_block", casesBlock}, - {"cases", std::vector{case0Config, case1Config}}}, + {{"Out", {}}}, + {{"sub_block", casesBlock}, + {"cases", std::vector{case0Config, case1Config}}}, whileBlock); scope->Var("stepScopes"); @@ -209,9 +210,8 @@ TEST(Concurrency, Go_Op) { executor.Run(program, &scope, 0, true, true); - // After we call executor.run, the Go operator should do a channel_send to set - // the - // "result" variable to 99 + // After we call executor.run, the Go operator should do a channel_send to + // set the "result" variable to 99. auto *finalData = tensor.data(); EXPECT_EQ(finalData[0], 99); } diff --git a/paddle/fluid/framework/executor.cc b/paddle/fluid/framework/executor.cc index a688115b11af164319458207b19e915e8eaf676a..0b171e1dcfa90c3ad8f5a9ace8a9342baaf76e61 100644 --- a/paddle/fluid/framework/executor.cc +++ b/paddle/fluid/framework/executor.cc @@ -185,7 +185,7 @@ void Executor::Run(const ProgramDesc& program, Scope* scope, std::map& feed_targets, std::map& fetch_targets, const std::string& feed_holder_name, - const std::string& fetch_holder_name) { + const std::string& fetch_holder_name, bool create_vars) { platform::RecordBlock b(kProgramId); bool has_feed_ops = has_feed_operators(program.Block(0), feed_targets, feed_holder_name); @@ -255,7 +255,7 @@ void Executor::Run(const ProgramDesc& program, Scope* scope, } } - Run(*copy_program, scope, 0, true, true); + Run(*copy_program, scope, 0, create_vars, create_vars); // obtain the data of fetch_targets from fetch_holder for (auto* op : global_block->AllOps()) { diff --git a/paddle/fluid/framework/executor.h b/paddle/fluid/framework/executor.h index fb29c70f1456eca7b46e779f737976f5f2da0682..d8dd82469af06a4c5c6a37d2249ee23413884a91 100644 --- a/paddle/fluid/framework/executor.h +++ b/paddle/fluid/framework/executor.h @@ -54,7 +54,8 @@ class Executor { std::map& feed_targets, std::map& fetch_targets, const std::string& feed_holder_name = "feed", - const std::string& fetch_holder_name = "fetch"); + const std::string& fetch_holder_name = "fetch", + bool create_vars = true); static std::unique_ptr Prepare( const ProgramDesc& program, int block_id); diff --git a/paddle/fluid/framework/mixed_vector.h b/paddle/fluid/framework/mixed_vector.h index 6a6fa538718837a958b7d82c37f583f62f4bf96e..d99a15547b77a0e0d71b14bd1c798cd1485720b0 100644 --- a/paddle/fluid/framework/mixed_vector.h +++ b/paddle/fluid/framework/mixed_vector.h @@ -176,7 +176,7 @@ class Vector { // resize the vector void resize(size_t size) { - if (size + 1 < capacity()) { + if (size + 1 <= capacity()) { size_ = size; } else { MutableCPU(); diff --git a/paddle/fluid/framework/mixed_vector_test.cu b/paddle/fluid/framework/mixed_vector_test.cu index 4bf78499f2fda2d2631e05ddcbbd0bc49498af1a..d57f82510833d6a0cea7009cf1f0b49543812f8d 100644 --- a/paddle/fluid/framework/mixed_vector_test.cu +++ b/paddle/fluid/framework/mixed_vector_test.cu @@ -104,3 +104,11 @@ TEST(mixed_vector, ForEach) { for (auto& v : tmp) { } } + +TEST(mixed_vector, Reserve) { + paddle::framework::Vector vec; + vec.reserve(1); + vec.push_back(0); + vec.push_back(0); + vec.push_back(0); +} diff --git a/paddle/fluid/operators/CMakeLists.txt b/paddle/fluid/operators/CMakeLists.txt index d30124d4a3b89b802a4abaae07a33b76526f163d..c0245379ac481d922ee936c75bfc6b63a81be5fd 100644 --- a/paddle/fluid/operators/CMakeLists.txt +++ b/paddle/fluid/operators/CMakeLists.txt @@ -12,6 +12,8 @@ function(op_library TARGET) set(OP_LIBRARY ${TARGET} ${OP_LIBRARY} PARENT_SCOPE) set(cc_srcs) set(cu_srcs) + set(hip_cu_srcs) + set(miopen_hip_cc_srcs) set(cu_cc_srcs) set(cudnn_cu_cc_srcs) set(CUDNN_FILE) @@ -36,10 +38,19 @@ function(op_library TARGET) if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${TARGET}.cu) list(APPEND cu_srcs ${TARGET}.cu) endif() + if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${TARGET}.hip.cu) + list(APPEND hip_cu_srcs ${TARGET}.hip.cu) + endif() string(REPLACE "_op" "_cudnn_op" CUDNN_FILE "${TARGET}") if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${CUDNN_FILE}.cu.cc) list(APPEND cudnn_cu_cc_srcs ${CUDNN_FILE}.cu.cc) endif() + if(WITH_AMD_GPU) + string(REPLACE "_op" "_miopen_op" MIOPEN_FILE "${TARGET}") + if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${MIOPEN_FILE}.hip.cc) + list(APPEND miopen_hip_cc_srcs ${MIOPEN_FILE}.hip.cc) + endif() + endif() if(WITH_MKLDNN) string(REPLACE "_op" "_mkldnn_op" MKLDNN_FILE "${TARGET}") if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${MKLDNN_FILE}.cc) @@ -48,10 +59,14 @@ function(op_library TARGET) endif() else() foreach(src ${op_library_SRCS}) - if (${src} MATCHES ".*\\.cu$") + if (${src} MATCHES ".*\\.hip.cu$") + list(APPEND hip_cu_srcs ${src}) + elseif (${src} MATCHES ".*\\.cu$") list(APPEND cu_srcs ${src}) elseif(${src} MATCHES ".*_cudnn_op.cu.cc$") list(APPEND cudnn_cu_cc_srcs ${src}) + elseif(WITH_AMD_GPU AND ${src} MATCHES ".*_miopen_op.hip.cc$") + list(APPEND miopen_hip_cc_srcs ${src}) elseif(WITH_MKLDNN AND ${src} MATCHES ".*_mkldnn_op.cc$") list(APPEND mkldnn_cc_srcs ${src}) elseif(${src} MATCHES ".*\\.cu.cc$") @@ -76,6 +91,9 @@ function(op_library TARGET) if (WITH_GPU) nv_library(${TARGET} SRCS ${cc_srcs} ${cu_cc_srcs} ${cudnn_cu_cc_srcs} ${mkldnn_cc_srcs} ${cu_srcs} DEPS ${op_library_DEPS} ${op_common_deps}) + elseif (WITH_AMD_GPU) + hip_library(${TARGET} SRCS ${cc_srcs} ${hip_cu_srcs} ${miopen_hip_cc_srcs} ${mkldnn_cc_srcs} DEPS ${op_library_DEPS} + ${op_common_deps}) else() cc_library(${TARGET} SRCS ${cc_srcs} ${mkldnn_cc_srcs} DEPS ${op_library_DEPS} ${op_common_deps}) @@ -88,7 +106,7 @@ function(op_library TARGET) endif() endforeach() - # The registration of USE_OP, please refer to paddle/framework/op_registry.h. + # The registration of USE_OP, please refer to paddle/fluid/framework/op_registry.h. # Note that it's enough to just adding one operator to pybind in a *_op.cc file. # And for detail pybind information, please see generated paddle/pybind/pybind.h. file(READ ${TARGET}.cc TARGET_CONTENT) @@ -114,7 +132,10 @@ function(op_library TARGET) list(LENGTH cu_srcs cu_srcs_len) list(LENGTH cu_cc_srcs cu_cc_srcs_len) list(LENGTH mkldnn_cc_srcs mkldnn_cc_srcs_len) - if (${pybind_flag} EQUAL 0 AND ${mkldnn_cc_srcs_len} EQUAL 0 AND ${cu_srcs_len} EQUAL 0 AND ${cu_cc_srcs_len} EQUAL 0) + list(LENGTH hip_cu_srcs hip_cu_srcs_len) + list(LENGTH miopen_hip_cc_srcs miopen_hip_cc_srcs_len) + if (${pybind_flag} EQUAL 0 AND ${mkldnn_cc_srcs_len} EQUAL 0 AND ${cu_srcs_len} EQUAL 0 AND ${cu_cc_srcs_len} EQUAL 0 AND + ${hip_cu_srcs_len} EQUAL 0 AND ${miopen_hip_cc_srcs_len} EQUAL 0) file(APPEND ${pybind_file} "USE_CPU_ONLY_OP(${TARGET});\n") set(pybind_flag 1) endif() @@ -125,6 +146,11 @@ function(op_library TARGET) file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(${TARGET}, CUDNN);\n") endif() + # pybind USE_OP_DEVICE_KERNEL for MIOPEN + if (WITH_AMD_GPU AND ${miopen_hip_cc_srcs_len} GREATER 0) + file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(${TARGET}, MIOPEN);\n") + endif() + # pybind USE_OP_DEVICE_KERNEL for MKLDNN if (WITH_MKLDNN AND ${mkldnn_cc_srcs_len} GREATER 0) file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(${TARGET}, MKLDNN);\n") diff --git a/paddle/fluid/operators/activation_op.cc b/paddle/fluid/operators/activation_op.cc index d74c47b981e51f12d99098818c71f3f6ec455d98..ec637658c03ad94624ee9a4f5def6a84387d293e 100644 --- a/paddle/fluid/operators/activation_op.cc +++ b/paddle/fluid/operators/activation_op.cc @@ -613,3 +613,14 @@ REGISTER_OP(swish, ops::ActivationOp, ops::SwishOpMaker, swish_grad, ops::grad_functor>); FOR_EACH_KERNEL_FUNCTOR(REGISTER_ACTIVATION_CPU_KERNEL); + +REGISTER_OP_CPU_KERNEL(relu, + ops::ActivationKernel>, + ops::ActivationKernel>); +REGISTER_OP_CPU_KERNEL( + relu_grad, ops::ActivationGradKernel>, + ops::ActivationGradKernel>); diff --git a/paddle/fluid/operators/activation_op.cu b/paddle/fluid/operators/activation_op.cu index b2633d017623c3a6a3bab2b416009d6d7c8fc1d4..7709a551dc155e1f3cd2a19a689999608f497beb 100644 --- a/paddle/fluid/operators/activation_op.cu +++ b/paddle/fluid/operators/activation_op.cu @@ -14,6 +14,7 @@ limitations under the License. */ #define EIGEN_USE_GPU #include "paddle/fluid/operators/activation_op.h" +#include "paddle/fluid/platform/float16.h" namespace ops = paddle::operators; @@ -31,3 +32,16 @@ namespace ops = paddle::operators; ops::grad_functor>); FOR_EACH_KERNEL_FUNCTOR(REGISTER_ACTIVATION_CUDA_KERNEL); + +REGISTER_OP_CUDA_KERNEL( + relu, ops::ActivationKernel>, + ops::ActivationKernel>, + ops::ActivationKernel>); +REGISTER_OP_CUDA_KERNEL( + relu_grad, ops::ActivationGradKernel>, + ops::ActivationGradKernel>); diff --git a/paddle/fluid/operators/activation_op.h b/paddle/fluid/operators/activation_op.h index 8f791a6ca81c13a92fd8adf0d1620203bd4cf7d6..b95e793586219b7c413d0c7adb835081874d9363 100644 --- a/paddle/fluid/operators/activation_op.h +++ b/paddle/fluid/operators/activation_op.h @@ -772,7 +772,6 @@ struct SwishGradFunctor : public BaseActivationFunctor { __macro(sigmoid, SigmoidFunctor, SigmoidGradFunctor); \ __macro(logsigmoid, LogSigmoidFunctor, LogSigmoidGradFunctor); \ __macro(exp, ExpFunctor, ExpGradFunctor); \ - __macro(relu, ReluFunctor, ReluGradFunctor); \ __macro(tanh, TanhFunctor, TanhGradFunctor); \ __macro(softshrink, SoftShrinkFunctor, SoftShrinkGradFunctor); \ __macro(sqrt, SqrtFunctor, SqrtGradFunctor); \ diff --git a/paddle/fluid/operators/average_accumulates_op.cc b/paddle/fluid/operators/average_accumulates_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..c95077fcbdb6b6c0da31f30b795dbe4d7d4fe6fe --- /dev/null +++ b/paddle/fluid/operators/average_accumulates_op.cc @@ -0,0 +1,216 @@ +/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/fluid/operators/average_accumulates_op.h" + +namespace paddle { +namespace operators { + +template <> +void GetAccumulators( + const framework::ExecutionContext& ctx, int64_t& num_updates_, + int64_t& num_accumulates_, int64_t& old_num_accumulates_) { + auto* in_old_num_accumulates = ctx.Input("in_old_num_accumulates"); + auto* in_num_accumulates = ctx.Input("in_num_accumulates"); + auto* in_num_updates = ctx.Input("in_num_updates"); + + old_num_accumulates_ = in_old_num_accumulates->data()[0]; + num_accumulates_ = in_num_accumulates->data()[0]; + num_updates_ = in_num_updates->data()[0]; +} + +template <> +void SetAccumulators( + const framework::ExecutionContext& ctx, int64_t num_updates_, + int64_t num_accumulates_, int64_t old_num_accumulates_) { + auto* out_old_num_accumulates = ctx.Output("out_old_num_accumulates"); + auto* out_num_accumulates = ctx.Output("out_num_accumulates"); + auto* out_num_updates = ctx.Output("out_num_updates"); + + out_old_num_accumulates->data()[0] = old_num_accumulates_; + out_num_accumulates->data()[0] = num_accumulates_; + out_num_updates->data()[0] = num_updates_; +} + +class AverageAccumulatesOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + void InferShape(framework::InferShapeContext* ctx) const override { + PADDLE_ENFORCE( + ctx->HasInput("param"), + "Input (param) of average_accumulates op should not be null."); + PADDLE_ENFORCE( + ctx->HasInput("in_sum_1"), + "Input (sum_1) of average_accumulates op should not be null."); + PADDLE_ENFORCE( + ctx->HasInput("in_sum_2"), + "Input (sum_2) of average_accumulates op should not be null."); + PADDLE_ENFORCE( + ctx->HasInput("in_sum_3"), + "Input (sum_3) of average_accumulates op should not be null."); + PADDLE_ENFORCE( + ctx->HasInput("in_num_accumulates"), + "Input (in_num_accumulates) of average_accumulates op should " + "not be null."); + PADDLE_ENFORCE(ctx->HasInput("in_old_num_accumulates"), + "Input (old_num_accumulates) of average_accumulates op " + "should not be null."); + PADDLE_ENFORCE( + ctx->HasInput("in_num_updates"), + "Input (num_updates) of average_accumulates op should not be null."); + + PADDLE_ENFORCE( + ctx->HasOutput("out_sum_1"), + "Output (sum_1) of average_accumulates op should not be null."); + PADDLE_ENFORCE( + ctx->HasOutput("out_sum_2"), + "Output (sum_2) of average_accumulates op should not be null."); + PADDLE_ENFORCE( + ctx->HasOutput("out_sum_3"), + "Output (sum_3) of average_accumulates op should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("out_num_accumulates"), + "Output (num_accumulates) of average_accumulates op should " + "not be null."); + PADDLE_ENFORCE(ctx->HasOutput("out_old_num_accumulates"), + "Output (old_num_accumulates) of average_accumulates op " + "should not be null."); + PADDLE_ENFORCE( + ctx->HasOutput("out_num_updates"), + "Output (num_updates) of average_accumulates op should not be null."); + + auto in_dim = ctx->GetInputDim("param"); + + ctx->SetOutputDim("out_sum_1", in_dim); + ctx->SetOutputDim("out_sum_2", in_dim); + ctx->SetOutputDim("out_sum_3", in_dim); + ctx->SetOutputDim("out_num_accumulates", {1}); + ctx->SetOutputDim("out_old_num_accumulates", {1}); + ctx->SetOutputDim("out_num_updates", {1}); + } + + protected: + framework::OpKernelType GetExpectedKernelType( + const framework::ExecutionContext& ctx) const override { + return framework::OpKernelType( + framework::ToDataType(ctx.Input("param")->type()), + ctx.GetPlace()); + } +}; + +class AverageAccumulatesOpMaker : public framework::OpProtoAndCheckerMaker { + public: + AverageAccumulatesOpMaker(OpProto* proto, OpAttrChecker* op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("param", "(Tensor), The parameter to be accumulated."); + AddInput("in_sum_1", + "(Tensor), A tensor used to store the parameter " + "sums with the same shape as input(param)."); + AddInput("in_sum_2", + "(Tensor), A auxiliary tensor to help " + "accumulating sums of parameter values with the same shape as " + "input(param). It is used to avoid loss of precision due to too " + "many sums."); + AddInput("in_sum_3", + "(Tensor), A auxiliary tensor to help " + "accumulating sums of parameter values with the same shape as " + "input(param)."); + AddInput("in_num_accumulates", + "(Tensor), The accumulating times of current window with " + "shape [1]."); + AddInput( + "in_old_num_accumulates", + "(Tensor), The accumulating times of previous window with " + "shape [1]."); + AddInput("in_num_updates", + "(Tensor), The total number of batches used by trainning " + "before this batch with shape [1]."); + + AddOutput("out_sum_1", + "(Tensor), A tensor used to store the " + "parameter sums with the same shape as input(param)."); + AddOutput("out_sum_2", + "(Tensor), A auxiliary tensor to help " + "accumulating sums of parameter values with the same shape as " + "input(param). It is used to avoid loss of precision due to too " + "many sums."); + AddOutput("out_sum_3", + "(Tensor), A auxiliary tensor to help " + "accumulating sums of parameter values with the same shape as " + "input(param)."); + AddOutput( + "out_num_accumulates", + "(Tensor), The accumulating times of current window with " + "shape [1]."); + AddOutput( + "out_old_num_accumulates", + "(Tensor) The accumulating times of previous window with " + "shape [1]."); + AddOutput( + "out_num_updates", + "(Tensor), The total number of batches used by trainning " + "before this batch with shape [1]."); + + AddAttr("average_window", + "(float, default 0) " + "The rate of average window size relative to num_updates.") + .SetDefault(0); + AddAttr("max_average_window", + "(int64_t) " + "Maximum size of average window. It suggests that the " + "number of mini-batches " + "in one pass is appropriate value to set."); + AddAttr("min_average_window", + "(int64_t, default 10000L) " + "Minimu size of average window.") + .SetDefault(10000L); + + AddComment(R"DOC( +AverageAccumulates Operator. +Accumulate the sum of parameter whtin sliding window. The size of sliding window is +determined by 'average_window', 'max_average_window' and 'min_average_window'. +Memory was shared by Input(in_sum_1) and Output(out_sum_1) which acts as an accumulator 'sum_1'. +'sum_2', 'sum_3', 'num_accumulates', 'old_num_accumulates' and 'num_updates' were the same as 'sum_1'. + +All the accumulators were inited to zero before training. + +And for a mini-batch in training, accumulators were computed as below steps: + num_updates += 1 + num_accumulates += 1 + sum_1 += param + if num_updates % kMaxNumAccumulates == 0: + sum_2 += sum_1 + sum_1 = 0 + if num_accumulates >= min_average_window && num_accumulates >= min(max_average_window, num_updates * average_window): + sum_3 = sum_1 + sum_2 + sum_1 = 0 + sum_2 = 0 + old_num_accumulates = num_accumulates + num_accumulates = 0 + +)DOC"); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OPERATOR(average_accumulates, ops::AverageAccumulatesOp, + ops::AverageAccumulatesOpMaker, + paddle::framework::EmptyGradOpMaker); +REGISTER_OP_CPU_KERNEL( + average_accumulates, + ops::AverageAccumulatesKernel, + ops::AverageAccumulatesKernel); diff --git a/paddle/fluid/operators/average_accumulates_op.cu b/paddle/fluid/operators/average_accumulates_op.cu new file mode 100644 index 0000000000000000000000000000000000000000..270c46984465e5ca62eaa8da3955ce7a3eaa0c57 --- /dev/null +++ b/paddle/fluid/operators/average_accumulates_op.cu @@ -0,0 +1,63 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/fluid/operators/average_accumulates_op.h" +#include "paddle/fluid/platform/gpu_info.h" + +namespace paddle { +namespace operators { +template <> +void GetAccumulators( + const framework::ExecutionContext& ctx, int64_t& num_updates_, + int64_t& num_accumulates_, int64_t& old_num_accumulates_) { + auto* in_old_num_accumulates = ctx.Input("in_old_num_accumulates"); + auto* in_num_accumulates = ctx.Input("in_num_accumulates"); + auto* in_num_updates = ctx.Input("in_num_updates"); + auto stream = ctx.cuda_device_context().stream(); + memory::Copy(platform::CPUPlace(), &old_num_accumulates_, + platform::CUDAPlace(), in_old_num_accumulates->data(), + sizeof(int64_t), stream); + memory::Copy(platform::CPUPlace(), &num_accumulates_, platform::CUDAPlace(), + in_num_accumulates->data(), sizeof(int64_t), stream); + memory::Copy(platform::CPUPlace(), &num_updates_, platform::CUDAPlace(), + in_num_updates->data(), sizeof(int64_t), stream); +} + +template <> +void SetAccumulators( + const framework::ExecutionContext& ctx, int64_t num_updates_, + int64_t num_accumulates_, int64_t old_num_accumulates_) { + auto stream = ctx.cuda_device_context().stream(); + auto* out_old_num_accumulates = ctx.Output("out_old_num_accumulates"); + auto* out_num_accumulates = ctx.Output("out_num_accumulates"); + auto* out_num_updates = ctx.Output("out_num_updates"); + + memory::Copy(platform::CUDAPlace(), out_old_num_accumulates->data(), + platform::CPUPlace(), &old_num_accumulates_, sizeof(int64_t), + stream); + memory::Copy(platform::CUDAPlace(), out_num_accumulates->data(), + platform::CPUPlace(), &num_accumulates_, sizeof(int64_t), + stream); + memory::Copy(platform::CUDAPlace(), out_num_updates->data(), + platform::CPUPlace(), &num_updates_, sizeof(int64_t), stream); +} + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_CUDA_KERNEL( + average_accumulates, + ops::AverageAccumulatesKernel, + ops::AverageAccumulatesKernel); diff --git a/paddle/fluid/operators/average_accumulates_op.h b/paddle/fluid/operators/average_accumulates_op.h new file mode 100644 index 0000000000000000000000000000000000000000..f858109d1428dc67d94c253e5a39818eb2d4560d --- /dev/null +++ b/paddle/fluid/operators/average_accumulates_op.h @@ -0,0 +1,113 @@ +/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once +#include +#include "paddle/fluid/framework/eigen.h" +#include "paddle/fluid/framework/op_registry.h" +#include "paddle/fluid/operators/math/math_function.h" + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; + +template +using EigenVector = framework::EigenVector; + +template +void GetAccumulators(const framework::ExecutionContext& ctx, + int64_t& num_updates, int64_t& num_accumulates, + int64_t& old_num_accumulates); + +template +void SetAccumulators(const framework::ExecutionContext& ctx, + int64_t num_updates, int64_t num_accumulates, + int64_t old_num_accumulates); + +template +class AverageAccumulatesKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + // It is used to avoid loss of precision + static const int64_t kMaxNumAccumulates = 16384; + // Get accumulators from input + int64_t num_updates = 0; + int64_t num_accumulates = 0; + int64_t old_num_accumulates = 0; + GetAccumulators(ctx, num_updates, num_accumulates, + old_num_accumulates); + + // Get attrs + float average_window = ctx.Attr("average_window"); + int64_t max_average_window = ctx.Attr("max_average_window"); + int64_t min_average_window = ctx.Attr("min_average_window"); + min_average_window = + std::min(min_average_window, max_average_window); + + // Get inputs + auto* param = ctx.Input("param"); + auto* in_sum_1 = ctx.Input("in_sum_1"); + auto* in_sum_2 = ctx.Input("in_sum_2"); + auto* in_sum_3 = ctx.Input("in_sum_3"); + auto param_tensor = EigenVector::Flatten(*param); + auto in_sum_1_tensor = EigenVector::Flatten(*in_sum_1); + auto in_sum_2_tensor = EigenVector::Flatten(*in_sum_2); + auto in_sum_3_tensor = EigenVector::Flatten(*in_sum_3); + + // Get outputs + auto* out_sum_1 = ctx.Output("out_sum_1"); + auto* out_sum_2 = ctx.Output("out_sum_2"); + auto* out_sum_3 = ctx.Output("out_sum_3"); + auto out_sum_1_tensor = EigenVector::Flatten(*out_sum_1); + auto out_sum_2_tensor = EigenVector::Flatten(*out_sum_2); + auto out_sum_3_tensor = EigenVector::Flatten(*out_sum_3); + + // Compute + auto& place = *ctx.template device_context().eigen_device(); + math::SetConstant constant_functor; + ++num_updates; + ++num_accumulates; + out_sum_1_tensor.device(place) = in_sum_1_tensor + param_tensor; + out_sum_2_tensor.device(place) = in_sum_2_tensor; + out_sum_3_tensor.device(place) = in_sum_3_tensor; + if (num_updates % kMaxNumAccumulates == 0) { + // Move the sum to a different buffer to avoid loss of precision due to + // too many sums. + out_sum_2_tensor.device(place) = in_sum_2_tensor + in_sum_1_tensor; + constant_functor(ctx.template device_context(), out_sum_1, + 0.0); + } + if (num_accumulates >= min_average_window && + num_accumulates >= std::min(max_average_window, + num_updates * average_window)) { + // Now the average window is too long, discard the old sum. + out_sum_3_tensor.device(place) = in_sum_1_tensor + in_sum_2_tensor; + constant_functor(ctx.template device_context(), out_sum_1, + 0.0); + constant_functor(ctx.template device_context(), out_sum_2, + 0.0); + old_num_accumulates = num_accumulates; + num_accumulates = 0; + } + + // Set accumulators to output + SetAccumulators(ctx, num_updates, num_accumulates, + old_num_accumulates); + } +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/fluid/operators/batch_norm_op.cc b/paddle/fluid/operators/batch_norm_op.cc index 215ae229aff96d76fc948e19bdb42db319af65dc..36049ee6a4a0d2a251b6d10cf1ff05a9d9845089 100644 --- a/paddle/fluid/operators/batch_norm_op.cc +++ b/paddle/fluid/operators/batch_norm_op.cc @@ -80,6 +80,29 @@ class BatchNormOp : public framework::OperatorWithKernel { ctx->SetOutputDim("SavedVariance", {C}); ctx->ShareLoD("X", "Y"); } + + protected: + framework::OpKernelType GetExpectedKernelType( + const framework::ExecutionContext &ctx) const override { + auto input_data_type = + framework::ToDataType(ctx.Input("X")->type()); + // For float or float16 input tensor, the type of the scale, bias, mean, + // and var tensors should both be float. + auto bn_param_type = framework::proto::VarType::FP32; + PADDLE_ENFORCE_EQ(bn_param_type, + framework::ToDataType(ctx.Input("Scale")->type()), + "Scale input should be of float type"); + PADDLE_ENFORCE_EQ(bn_param_type, + framework::ToDataType(ctx.Input("Bias")->type()), + "Bias input should be of float type"); + PADDLE_ENFORCE_EQ(bn_param_type, + framework::ToDataType(ctx.Input("Mean")->type()), + "Mean input should be of float type"); + PADDLE_ENFORCE_EQ(bn_param_type, framework::ToDataType( + ctx.Input("Variance")->type()), + "Variance input should be of float type"); + return framework::OpKernelType(input_data_type, ctx.GetPlace()); + } }; class BatchNormOpMaker : public framework::OpProtoAndCheckerMaker { @@ -434,12 +457,39 @@ class BatchNormGradKernel } }; +class BatchNormGradMaker : public framework::SingleGradOpDescMaker { + public: + using framework::SingleGradOpDescMaker::SingleGradOpDescMaker; + + protected: + std::unique_ptr Apply() const override { + auto *op = new framework::OpDesc(); + op->SetType("batch_norm_grad"); + op->SetInput("X", Input("X")); + op->SetInput(framework::GradVarName("Y"), OutputGrad("Y")); + + op->SetInput("Scale", Input("Scale")); + op->SetInput("SavedMean", Output("SavedMean")); + op->SetInput("SavedVariance", Output("SavedVariance")); + + op->SetAttrMap(Attrs()); + + op->SetOutput(framework::GradVarName("X"), InputGrad("X")); + op->SetOutput(framework::GradVarName("Scale"), InputGrad("Scale")); + op->SetOutput(framework::GradVarName("Bias"), InputGrad("Bias")); + + return std::unique_ptr(op); + } +}; + } // namespace operators } // namespace paddle namespace ops = paddle::operators; -REGISTER_OP(batch_norm, ops::BatchNormOp, ops::BatchNormOpMaker, - batch_norm_grad, ops::BatchNormGradOp); +REGISTER_OPERATOR(batch_norm, ops::BatchNormOp, ops::BatchNormOpMaker, + ops::BatchNormGradMaker); +REGISTER_OPERATOR(batch_norm_grad, ops::BatchNormGradOp); + REGISTER_OP_CPU_KERNEL( batch_norm, ops::BatchNormKernel); diff --git a/paddle/fluid/operators/batch_norm_op.cu.cc b/paddle/fluid/operators/batch_norm_op.cu.cc index 2d1556efc66826ea9847de8311ccecdee0ea7871..6ceacc39924a7558e380aaf563aaf234f1bf30a5 100644 --- a/paddle/fluid/operators/batch_norm_op.cu.cc +++ b/paddle/fluid/operators/batch_norm_op.cu.cc @@ -18,6 +18,7 @@ limitations under the License. */ #include #include "paddle/fluid/operators/math/math_function.h" #include "paddle/fluid/platform/cudnn_helper.h" +#include "paddle/fluid/platform/float16.h" namespace paddle { namespace operators { @@ -26,6 +27,8 @@ using Tensor = framework::Tensor; using DataLayout = framework::DataLayout; template using CudnnDataType = platform::CudnnDataType; +template +using BatchNormParamType = typename CudnnDataType::BatchNormParamType; void ExtractNCWHD(const framework::DDim &dims, const DataLayout &data_layout, int *N, int *C, int *H, int *W, int *D) { @@ -104,8 +107,9 @@ class BatchNormKernel CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor( data_desc_, CudnnDataType::type, x_dims.size() > 3 ? x_dims.size() : 4, dims.data(), strides.data())); + // Note: PERSISTENT not implemented for inference CUDNN_ENFORCE(platform::dynload::cudnnDeriveBNTensorDescriptor( - bn_param_desc_, data_desc_, mode_)); + bn_param_desc_, data_desc_, is_test ? CUDNN_BATCHNORM_SPATIAL : mode_)); const auto *scale = ctx.Input("Scale"); const auto *bias = ctx.Input("Bias"); @@ -118,15 +122,16 @@ class BatchNormKernel // alloc memory y->mutable_data(ctx.GetPlace()); - mean_out->mutable_data(ctx.GetPlace()); - variance_out->mutable_data(ctx.GetPlace()); - saved_mean->mutable_data(ctx.GetPlace()); - saved_variance->mutable_data(ctx.GetPlace()); + mean_out->mutable_data>(ctx.GetPlace()); + variance_out->mutable_data>(ctx.GetPlace()); + saved_mean->mutable_data>(ctx.GetPlace()); + saved_variance->mutable_data>(ctx.GetPlace()); auto &dev_ctx = ctx.template device_context(); - math::SetConstant functor; - functor(dev_ctx, saved_mean, 0); - functor(dev_ctx, saved_variance, 0); + math::SetConstant> + functor; + functor(dev_ctx, saved_mean, static_cast>(0)); + functor(dev_ctx, saved_variance, static_cast>(0)); auto handle = dev_ctx.cudnn_handle(); @@ -147,8 +152,10 @@ class BatchNormKernel CUDNN_BATCHNORM_SPATIAL, CudnnDataType::kOne(), CudnnDataType::kZero(), data_desc_, x->template data(), data_desc_, y->template mutable_data(ctx.GetPlace()), - bn_param_desc_, scale->template data(), bias->template data(), - est_mean->template data(), est_var->template data(), epsilon)); + bn_param_desc_, scale->template data>(), + bias->template data>(), + est_mean->template data>(), + est_var->template data>(), epsilon)); } else { // Run training mode. // obtain running mean and running inv var, and see if we need to @@ -159,11 +166,16 @@ class BatchNormKernel handle, mode_, CudnnDataType::kOne(), CudnnDataType::kZero(), data_desc_, x->template data(), data_desc_, y->template mutable_data(ctx.GetPlace()), bn_param_desc_, - scale->template data(), bias->template data(), this_factor, - mean_out->template mutable_data(ctx.GetPlace()), - variance_out->template mutable_data(ctx.GetPlace()), epsilon, - saved_mean->template mutable_data(ctx.GetPlace()), - saved_variance->template mutable_data(ctx.GetPlace()))); + scale->template data>(), + bias->template data>(), this_factor, + mean_out->template mutable_data>( + ctx.GetPlace()), + variance_out->template mutable_data>( + ctx.GetPlace()), + epsilon, saved_mean->template mutable_data>( + ctx.GetPlace()), + saved_variance->template mutable_data>( + ctx.GetPlace()))); } // clean when exit. @@ -270,9 +282,9 @@ class BatchNormGradKernel } // namespace paddle namespace ops = paddle::operators; +namespace plat = paddle::platform; REGISTER_OP_CUDA_KERNEL( - batch_norm, - ops::BatchNormKernel); + batch_norm, ops::BatchNormKernel, + ops::BatchNormKernel); REGISTER_OP_CUDA_KERNEL( - batch_norm_grad, - ops::BatchNormGradKernel); + batch_norm_grad, ops::BatchNormGradKernel); diff --git a/paddle/fluid/operators/box_coder_op.cc b/paddle/fluid/operators/box_coder_op.cc index eccdd408a17a07a541480705242b137f8207c139..ec416f725e75fae57484751ee8a066c0b9da8a70 100644 --- a/paddle/fluid/operators/box_coder_op.cc +++ b/paddle/fluid/operators/box_coder_op.cc @@ -126,6 +126,7 @@ width and height. } // namespace paddle namespace ops = paddle::operators; -REGISTER_OP_WITHOUT_GRADIENT(box_coder, ops::BoxCoderOp, ops::BoxCoderOpMaker); +REGISTER_OPERATOR(box_coder, ops::BoxCoderOp, ops::BoxCoderOpMaker, + paddle::framework::EmptyGradOpMaker); REGISTER_OP_CPU_KERNEL(box_coder, ops::BoxCoderKernel, ops::BoxCoderKernel); diff --git a/paddle/fluid/operators/cross_entropy_op.h b/paddle/fluid/operators/cross_entropy_op.h index ec315695a68befc2e3de798fdb3fa146a903aaff..6da3a24dc89a85fe432b6350d3af7b0e84337c9d 100644 --- a/paddle/fluid/operators/cross_entropy_op.h +++ b/paddle/fluid/operators/cross_entropy_op.h @@ -78,7 +78,7 @@ class CrossEntropyGradientOpKernel : public framework::OpKernel { for (int64_t i = 0; i < batch_size; ++i) { PADDLE_ASSERT(label_data[i] >= 0 || label_data[i] < class_num); int64_t index = i * class_num + label_data[i]; - dx_data[index] = -dy_data[i] / x_data[index]; + dx_data[index] = math::TolerableValue()(-dy_data[i] / x_data[index]); } } } diff --git a/paddle/fluid/operators/detail/CMakeLists.txt b/paddle/fluid/operators/detail/CMakeLists.txt index 94395ccfbcbd74ee40552a5c70dc8b8063a5f851..2b19f0448955d2d7582f23ac133c14ffdf5c9e49 100644 --- a/paddle/fluid/operators/detail/CMakeLists.txt +++ b/paddle/fluid/operators/detail/CMakeLists.txt @@ -1,6 +1,8 @@ if(WITH_DISTRIBUTE) - grpc_library(sendrecvop_grpc SRCS bytebuffer_stream.cc sendrecvop_utils.cc grpc_client.cc grpc_server.cc PROTO send_recv.proto DEPS lod_tensor selected_rows) + grpc_library(sendrecvop_grpc SRCS bytebuffer_stream.cc sendrecvop_utils.cc grpc_client.cc + grpc_server.cc variable_response.cc PROTO send_recv.proto DEPS lod_tensor selected_rows) set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor") set_source_files_properties(test_serde.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS}) - cc_test(serde_test SRCS test_serde.cc DEPS grpc++_unsecure grpc_unsecure gpr cares zlib protobuf sendrecvop_grpc) + cc_test(serde_test SRCS test_serde.cc variable_response.cc DEPS grpc++_unsecure grpc_unsecure gpr + cares zlib protobuf sendrecvop_grpc) endif() diff --git a/paddle/fluid/operators/detail/bytebuffer_stream.h b/paddle/fluid/operators/detail/bytebuffer_stream.h index 099deb12d0e436427c147ab9b1eb553b712e14fb..1791a48aab1b66147f645c90757b35ef5f6e001b 100644 --- a/paddle/fluid/operators/detail/bytebuffer_stream.h +++ b/paddle/fluid/operators/detail/bytebuffer_stream.h @@ -23,9 +23,107 @@ limitations under the License. */ #include "google/protobuf/io/coded_stream.h" #include "google/protobuf/io/zero_copy_stream.h" +namespace grpc { +// A ZeroCopyInputStream that reads from grpc_byte_buffer +class GrpcBufferReader final + : public ::google::protobuf::io::ZeroCopyInputStream { + typedef void (CoreCodegenInterface::*OldReaderInitAPI)( + grpc_byte_buffer_reader* reader, grpc_byte_buffer* buffer); + typedef int (CoreCodegenInterface::*NewReaderInitAPI)( + grpc_byte_buffer_reader* reader, grpc_byte_buffer* buffer); + void ReaderInit(OldReaderInitAPI ptr, grpc_byte_buffer_reader* reader, + grpc_byte_buffer* buffer) { + (g_core_codegen_interface->*ptr)(reader, buffer); + } + void ReaderInit(NewReaderInitAPI ptr, grpc_byte_buffer_reader* reader, + grpc_byte_buffer* buffer) { + int result = (g_core_codegen_interface->*ptr)(reader, buffer); + (void)result; + } + + public: + explicit GrpcBufferReader(grpc_byte_buffer* buffer) + : byte_count_(0), backup_count_(0) { + ReaderInit(&CoreCodegenInterface::grpc_byte_buffer_reader_init, &reader_, + buffer); + } + ~GrpcBufferReader() override { + g_core_codegen_interface->grpc_byte_buffer_reader_destroy(&reader_); + } + + bool Next(const void** data, int* size) override { + if (backup_count_ > 0) { + *data = GRPC_SLICE_START_PTR(slice_) + GRPC_SLICE_LENGTH(slice_) - + backup_count_; + GPR_CODEGEN_ASSERT(backup_count_ <= INT_MAX); + *size = (int)backup_count_; + backup_count_ = 0; + return true; + } + if (!g_core_codegen_interface->grpc_byte_buffer_reader_next(&reader_, + &slice_)) { + return false; + } + g_core_codegen_interface->grpc_slice_unref(slice_); + *data = GRPC_SLICE_START_PTR(slice_); + // On win x64, int is only 32bit + GPR_CODEGEN_ASSERT(GRPC_SLICE_LENGTH(slice_) <= INT_MAX); + byte_count_ += * size = (int)GRPC_SLICE_LENGTH(slice_); + return true; + } + + void BackUp(int count) override { backup_count_ = count; } + + bool Skip(int count) override { + const void* data; + int size; + while (Next(&data, &size)) { + if (size >= count) { + BackUp(size - count); + return true; + } + // size < count; + count -= size; + } + // error or we have too large count; + return false; + } + + ::google::protobuf::int64 ByteCount() const override { + return byte_count_ - backup_count_; + } + + private: + int64_t byte_count_; + int64_t backup_count_; + grpc_byte_buffer_reader reader_; + grpc_slice slice_; +}; + +}; // namespace grpc + namespace paddle { namespace operators { namespace detail { +// Source provides a way for a particular RPC implementation to provide +// received data to ParseFrom. +class Source { + public: + virtual ~Source() {} + + // Return the stream that contains the data to be parsed. + // Note that this method might be invoked more than once if + // ParseFrom needs to fall back to a more expensive parsing method. + // Every call must return a stream pointing at the beginning of + // the serialized RecvTensorResponse. + // + // Note that a subsequent call to contents() invalidates previous + // results of contents(). + // + // Ownership of the returned stream is retained by the Source and + // should not be deleted by the caller. + virtual ::google::protobuf::io::ZeroCopyInputStream* contents() = 0; +}; // A ZeroCopyInputStream that reads from a grpc::ByteBuffer. class GrpcByteBufferSource @@ -46,6 +144,43 @@ class GrpcByteBufferSource ::google::protobuf::int64 byte_count_; }; +class GrpcByteBufferSourceWrapper : public Source { + public: + explicit GrpcByteBufferSourceWrapper(GrpcByteBufferSource* source) + : source_(source) {} + ::google::protobuf::io::ZeroCopyInputStream* contents() override { + return source_; + } + + private: + GrpcByteBufferSource* source_; +}; + +class GrpcByteSource : public Source { + public: + explicit GrpcByteSource(grpc_byte_buffer* buffer) : buffer_(buffer) {} + ~GrpcByteSource() override { DeleteStream(); } + + typedef ::grpc::GrpcBufferReader Reader; + + ::google::protobuf::io::ZeroCopyInputStream* contents() override { + DeleteStream(); + stream_ = new (&space_) Reader(buffer_); + return stream_; + } + + private: + void DeleteStream() { + if (stream_) { + stream_->~Reader(); + } + } + + grpc_byte_buffer* buffer_; // Not owned + Reader* stream_ = nullptr; // Points into space_ if non-nullptr + char space_[sizeof(Reader)]; +}; + } // namespace detail } // namespace operators } // namespace paddle diff --git a/paddle/fluid/operators/detail/grpc_client.cc b/paddle/fluid/operators/detail/grpc_client.cc index ddeeebec58e02f1686fd2e3d3e5ac1a4c4fd3c59..eb19685aa6cb73862b9e31afbf9c5138659b1b13 100644 --- a/paddle/fluid/operators/detail/grpc_client.cc +++ b/paddle/fluid/operators/detail/grpc_client.cc @@ -13,7 +13,9 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "grpc_client.h" +#include #include "paddle/fluid/framework/threadpool.h" + namespace paddle { namespace operators { namespace detail { @@ -31,8 +33,9 @@ bool RPCClient::AsyncSendVariable(const std::string& ep, framework::Async([var_name_val, p_ctx, ep_val, p_scope, time_out, ch, this] { auto* var = p_scope->FindVar(var_name_val); - sendrecv::VariableMessage req; - SerializeToMessage(var_name_val, var, *p_ctx, &req); + + ::grpc::ByteBuffer req; + SerializeToByteBuffer(var_name_val, var, *p_ctx, &req); // varhandle VarHandle var_h; @@ -46,8 +49,11 @@ bool RPCClient::AsyncSendVariable(const std::string& ep, s->Prepare(var_h, time_out); s->response_call_back_ = NULL; - auto rpc = s->stub_->AsyncSendVariable(s->context_.get(), req, &cq_); - rpc->Finish(&s->reply_, &s->status_, (void*)s); + auto call = std::move(s->stub_g_.PrepareUnaryCall( + s->context_.get(), "/sendrecv.SendRecvService/SendVariable", req, + &cq_)); + call->StartCall(); + call->Finish(&s->reply_, &s->status_, (void*)s); }); req_count_++; @@ -56,9 +62,19 @@ bool RPCClient::AsyncSendVariable(const std::string& ep, } void ProcGetResponse(const VarHandle& var_h, - const sendrecv::VariableMessage& ret_msg) { - auto* outvar = var_h.scope->FindVar(var_h.name); - DeserializeFromMessage(ret_msg, *var_h.ctx, outvar); + // const sendrecv::VariableMessage& ret_msg) { + const ::grpc::ByteBuffer& ret_msg) { + framework::Variable* outvar = NULL; + DeserializeFromByteBuffer(ret_msg, *var_h.ctx, var_h.scope, outvar); +} + +template +void RequestToByteBuffer(const T& proto, ::grpc::ByteBuffer* result) { + ::grpc::Slice slice(proto.ByteSizeLong()); + proto.SerializeWithCachedSizesToArray( + const_cast(reinterpret_cast(slice.begin()))); + ::grpc::ByteBuffer tmp(&slice, 1); + result->Swap(&tmp); } bool RPCClient::AsyncGetVariable(const std::string& ep, @@ -88,8 +104,13 @@ bool RPCClient::AsyncGetVariable(const std::string& ep, s->Prepare(var_h, time_out); s->response_call_back_ = ProcGetResponse; - auto rpc = s->stub_->AsyncGetVariable(s->context_.get(), req, &cq_); - rpc->Finish(&s->reply_, &s->status_, (void*)s); + ::grpc::ByteBuffer buf; + RequestToByteBuffer(req, &buf); + + auto call = std::move(s->stub_g_.PrepareUnaryCall( + s->context_.get(), "/sendrecv.SendRecvService/GetVariable", buf, &cq_)); + call->StartCall(); + call->Finish(&s->reply_, &s->status_, (void*)s); }); req_count_++; diff --git a/paddle/fluid/operators/detail/grpc_client.h b/paddle/fluid/operators/detail/grpc_client.h index f520367dd981288416631fdad15241fb5d811d07..8216ac52fbbb3dcd2f30957cde58a850a77b08d6 100644 --- a/paddle/fluid/operators/detail/grpc_client.h +++ b/paddle/fluid/operators/detail/grpc_client.h @@ -25,6 +25,11 @@ limitations under the License. */ #include #include +#include +#include +#include +#include + #include "paddle/fluid/framework/data_type.h" #include "paddle/fluid/framework/lod_tensor.h" #include "paddle/fluid/framework/scope.h" @@ -49,15 +54,11 @@ struct VarHandle { } }; -void ProcGetResponse(const VarHandle& var_h, - const sendrecv::VariableMessage& msg); +void ProcGetResponse(const VarHandle& var_h, const grpc::ByteBuffer& msg); class BaseProcessor { public: - explicit BaseProcessor(std::shared_ptr ch) { - stub_ = sendrecv::SendRecvService::NewStub(ch); - context_ = NULL; - } + explicit BaseProcessor(std::shared_ptr ch) { context_ = NULL; } virtual ~BaseProcessor() {} @@ -82,19 +83,18 @@ class BaseProcessor { virtual void Process() = 0; - std::unique_ptr stub_; std::unique_ptr context_; grpc::Status status_; VarHandle var_h_; }; -typedef std::function +typedef std::function RequestSendCallBack; class SendProcessor : public BaseProcessor { public: explicit SendProcessor(std::shared_ptr ch) - : BaseProcessor(ch) {} + : BaseProcessor(ch), stub_g_(ch) {} virtual ~SendProcessor() {} @@ -104,17 +104,18 @@ class SendProcessor : public BaseProcessor { } } - sendrecv::VoidMessage reply_; + ::grpc::GenericStub stub_g_; + ::grpc::ByteBuffer reply_; RequestSendCallBack response_call_back_ = NULL; }; -typedef std::function +typedef std::function RequestGetCallBack; class GetProcessor : public BaseProcessor { public: explicit GetProcessor(std::shared_ptr ch) - : BaseProcessor(ch) {} + : BaseProcessor(ch), stub_g_(ch) {} virtual ~GetProcessor() {} @@ -124,30 +125,37 @@ class GetProcessor : public BaseProcessor { } } - sendrecv::VariableMessage reply_; + ::grpc::ByteBuffer reply_; + ::grpc::GenericStub stub_g_; RequestGetCallBack response_call_back_ = ProcGetResponse; }; class BatchBarrierProcessor : public BaseProcessor { public: explicit BatchBarrierProcessor(std::shared_ptr ch) - : BaseProcessor(ch) {} + : BaseProcessor(ch) { + stub_ = sendrecv::SendRecvService::NewStub(ch); + } virtual ~BatchBarrierProcessor() {} virtual void Process() {} sendrecv::VoidMessage reply_; + std::unique_ptr stub_; }; class FetchBarrierProcessor : public BaseProcessor { public: explicit FetchBarrierProcessor(std::shared_ptr ch) - : BaseProcessor(ch) {} + : BaseProcessor(ch) { + stub_ = sendrecv::SendRecvService::NewStub(ch); + } virtual ~FetchBarrierProcessor() {} virtual void Process() {} sendrecv::VariableMessage reply_; + std::unique_ptr stub_; }; class RPCClient { diff --git a/paddle/fluid/operators/detail/grpc_server.cc b/paddle/fluid/operators/detail/grpc_server.cc index 8fff430cc4890925e4edba2fadb8eb7fc647d181..9691d1e86b111def5b82e022dd01795aaf5c7b0d 100644 --- a/paddle/fluid/operators/detail/grpc_server.cc +++ b/paddle/fluid/operators/detail/grpc_server.cc @@ -14,7 +14,7 @@ limitations under the License. */ #include "paddle/fluid/operators/detail/grpc_server.h" -using grpc::ServerAsyncResponseWriter; +using ::grpc::ServerAsyncResponseWriter; namespace paddle { namespace operators { @@ -26,9 +26,10 @@ enum CallStatus { PROCESS = 0, FINISH }; // https://stackoverflow.com/questions/41732884/grpc-multiple-services-in-cpp-async-server class RequestBase { public: - explicit RequestBase(sendrecv::SendRecvService::AsyncService* service, - grpc::ServerCompletionQueue* cq) - : service_(service), cq_(cq), status_(PROCESS) { + explicit RequestBase(GrpcService::AsyncService* service, + ::grpc::ServerCompletionQueue* cq, + const platform::DeviceContext* dev_ctx) + : service_(service), cq_(cq), status_(PROCESS), dev_ctx_(dev_ctx) { PADDLE_ENFORCE(cq_); } virtual ~RequestBase() {} @@ -42,55 +43,58 @@ class RequestBase { } protected: - grpc::ServerContext ctx_; - sendrecv::SendRecvService::AsyncService* service_; - grpc::ServerCompletionQueue* cq_; + ::grpc::ServerContext ctx_; + GrpcService::AsyncService* service_; + ::grpc::ServerCompletionQueue* cq_; CallStatus status_; + const platform::DeviceContext* dev_ctx_; }; -typedef std::pair MessageWithName; - class RequestSend final : public RequestBase { public: - explicit RequestSend(sendrecv::SendRecvService::AsyncService* service, - grpc::ServerCompletionQueue* cq, - SimpleBlockQueue* queue) - : RequestBase(service, cq), queue_(queue), responder_(&ctx_) { - service_->RequestSendVariable(&ctx_, &request_, &responder_, cq_, cq_, - this); + explicit RequestSend(GrpcService::AsyncService* service, + ::grpc::ServerCompletionQueue* cq, + framework::Scope* scope, ReceivedQueue* queue, + const platform::DeviceContext* dev_ctx) + : RequestBase(service, cq, dev_ctx), queue_(queue), responder_(&ctx_) { + request_.reset(new VariableResponse(scope, dev_ctx_)); + int method_id = static_cast(detail::GrpcMethod::kSendVariable); + service_->RequestAsyncUnary(method_id, &ctx_, request_.get(), &responder_, + cq_, cq_, this); } virtual ~RequestSend() {} - virtual std::string GetReqName() { return request_.varname(); } + virtual std::string GetReqName() { return request_->Varname(); } virtual void Process() { - MessageWithName msg_with_name = - std::make_pair(request_.varname(), std::move(request_)); - queue_->Push(std::move(msg_with_name)); - responder_.Finish(reply_, grpc::Status::OK, this); + queue_->Push(std::make_pair(request_->Varname(), request_)); + + sendrecv::VoidMessage reply; + responder_.Finish(reply, ::grpc::Status::OK, this); status_ = FINISH; } protected: - sendrecv::VariableMessage request_; - sendrecv::VoidMessage reply_; - SimpleBlockQueue* queue_; + std::shared_ptr request_; + ReceivedQueue* queue_; ServerAsyncResponseWriter responder_; }; class RequestGet final : public RequestBase { public: - explicit RequestGet(sendrecv::SendRecvService::AsyncService* service, - grpc::ServerCompletionQueue* cq, framework::Scope* scope, + explicit RequestGet(GrpcService::AsyncService* service, + ::grpc::ServerCompletionQueue* cq, + framework::Scope* scope, const platform::DeviceContext* dev_ctx, SimpleBlockQueue* queue) - : RequestBase(service, cq), + : RequestBase(service, cq, dev_ctx), responder_(&ctx_), scope_(scope), - dev_ctx_(dev_ctx), queue_(queue) { - service_->RequestGetVariable(&ctx_, &request_, &responder_, cq_, cq_, this); + int method_id = static_cast(detail::GrpcMethod::kGetVariable); + service_->RequestAsyncUnary(method_id, &ctx_, &request_, &responder_, cq_, + cq_, this); } virtual ~RequestGet() {} @@ -101,24 +105,26 @@ class RequestGet final : public RequestBase { // proc request. std::string var_name = request_.varname(); auto* var = scope_->FindVar(var_name); + + ::grpc::ByteBuffer reply; if (var_name != FETCH_BARRIER_MESSAGE) { - SerializeToMessage(var_name, var, *dev_ctx_, &reply_); + SerializeToByteBuffer(var_name, var, *dev_ctx_, &reply); } - // TODO(gongwb): check var's info. - responder_.Finish(reply_, grpc::Status::OK, this); + + responder_.Finish(reply, ::grpc::Status::OK, this); status_ = FINISH; - MessageWithName msg_with_name = - // request name reply - std::make_pair(var_name, std::move(reply_)); - queue_->Push(msg_with_name); + + if (var_name == FETCH_BARRIER_MESSAGE) { + sendrecv::VariableMessage msg; + MessageWithName msg_with_name = std::make_pair(var_name, msg); + queue_->Push(msg_with_name); + } } protected: sendrecv::VariableMessage request_; - sendrecv::VariableMessage reply_; - ServerAsyncResponseWriter responder_; + ServerAsyncResponseWriter<::grpc::ByteBuffer> responder_; framework::Scope* scope_; - const platform::DeviceContext* dev_ctx_; SimpleBlockQueue* queue_; }; @@ -133,8 +139,8 @@ void AsyncGRPCServer::WaitClientGet(int count) { } void AsyncGRPCServer::RunSyncUpdate() { - grpc::ServerBuilder builder; - builder.AddListeningPort(address_, grpc::InsecureServerCredentials()); + ::grpc::ServerBuilder builder; + builder.AddListeningPort(address_, ::grpc::InsecureServerCredentials()); builder.SetMaxSendMessageSize(std::numeric_limits::max()); builder.SetMaxReceiveMessageSize(std::numeric_limits::max()); builder.RegisterService(&service_); @@ -182,8 +188,8 @@ void AsyncGRPCServer::TryToRegisterNewSendOne() { if (is_shut_down_) { return; } - RequestSend* send = - new RequestSend(&service_, cq_send_.get(), &var_recv_queue_); + RequestSend* send = new RequestSend(&service_, cq_send_.get(), scope_, + &var_recv_queue_, dev_ctx_); VLOG(4) << "Create RequestSend status:" << send->Status(); } @@ -198,7 +204,7 @@ void AsyncGRPCServer::TryToRegisterNewGetOne() { } // FIXME(typhoonzero): change cq_name to enum. -void AsyncGRPCServer::HandleRequest(grpc::ServerCompletionQueue* cq, +void AsyncGRPCServer::HandleRequest(::grpc::ServerCompletionQueue* cq, std::string cq_name, std::function TryToRegisterNewOne) { TryToRegisterNewOne(); diff --git a/paddle/fluid/operators/detail/grpc_server.h b/paddle/fluid/operators/detail/grpc_server.h index b6666bcf96e484b0b17b935c0efb2930f19b19f2..10e6dd45a901d36de4a6577db4da05551645eb73 100644 --- a/paddle/fluid/operators/detail/grpc_server.h +++ b/paddle/fluid/operators/detail/grpc_server.h @@ -14,28 +14,31 @@ limitations under the License. */ #pragma once +#include +#include + #include "paddle/fluid/framework/lod_tensor.h" #include "paddle/fluid/framework/scope.h" #include "paddle/fluid/framework/selected_rows.h" #include "paddle/fluid/framework/var_type.h" -#include "paddle/fluid/operators/detail/simple_block_queue.h" - +#include "paddle/fluid/operators/detail/grpc_service.h" #include "paddle/fluid/operators/detail/send_recv.grpc.pb.h" #include "paddle/fluid/operators/detail/send_recv.pb.h" - -#include -#include -#include #include "paddle/fluid/operators/detail/sendrecvop_utils.h" +#include "paddle/fluid/operators/detail/simple_block_queue.h" namespace paddle { namespace operators { namespace detail { +typedef std::pair> + ReceivedMessage; +typedef SimpleBlockQueue ReceivedQueue; + typedef std::pair MessageWithName; class RequestBase; -class AsyncGRPCServer final : public sendrecv::SendRecvService::Service { +class AsyncGRPCServer final { public: explicit AsyncGRPCServer(const std::string &address) : address_(address) {} @@ -50,14 +53,16 @@ class AsyncGRPCServer final : public sendrecv::SendRecvService::Service { void SetDevCtx(const platform::DeviceContext *dev_ctx) { dev_ctx_ = dev_ctx; } - const MessageWithName Get() { return this->var_recv_queue_.Pop(); } + const ReceivedMessage Get() { return this->var_recv_queue_.Pop(); } - void Push(const MessageWithName &msg) { this->var_recv_queue_.Push(msg); } + void Push(const std::string &msg_name) { + this->var_recv_queue_.Push(std::make_pair(msg_name, nullptr)); + } void ShutDown(); protected: - void HandleRequest(grpc::ServerCompletionQueue *cq, std::string cq_name, + void HandleRequest(::grpc::ServerCompletionQueue *cq, std::string cq_name, std::function TryToRegisterNewOne); void TryToRegisterNewSendOne(); void TryToRegisterNewGetOne(); @@ -66,18 +71,19 @@ class AsyncGRPCServer final : public sendrecv::SendRecvService::Service { private: std::mutex cq_mutex_; volatile bool is_shut_down_ = false; - std::unique_ptr cq_send_; - std::unique_ptr cq_get_; + std::unique_ptr<::grpc::ServerCompletionQueue> cq_send_; + std::unique_ptr<::grpc::ServerCompletionQueue> cq_get_; - sendrecv::SendRecvService::AsyncService service_; - std::unique_ptr server_; + GrpcService::AsyncService service_; + std::unique_ptr<::grpc::Server> server_; std::string address_; framework::Scope *scope_; const platform::DeviceContext *dev_ctx_; + // received variable from RPC, operators fetch variable from this queue. - SimpleBlockQueue var_recv_queue_; SimpleBlockQueue var_get_queue_; + ReceivedQueue var_recv_queue_; // condition of the sub program std::mutex barrier_mutex_; diff --git a/paddle/fluid/operators/detail/grpc_service.h b/paddle/fluid/operators/detail/grpc_service.h new file mode 100644 index 0000000000000000000000000000000000000000..ae6f9db3bd31a4b4839b34e8e53dd87f1ecf4b1d --- /dev/null +++ b/paddle/fluid/operators/detail/grpc_service.h @@ -0,0 +1,118 @@ +// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include "paddle/fluid/operators/detail/variable_response.h" + +// NOTE: This method was originally created by tensorflow +// (https://github.com/tensorflow/tensorflow/) we borrow this +// method and did some modifications so that we can parse gRPC +// requests without too much copying of the tensor data. + +namespace grpc { +class CompletionQueue; +class Channel; +class RpcService; +class ServerCompletionQueue; +class ServerContext; + +// Support parsing/unparsing of tensorflow::VariableResponse. +// Wire-format is identical to RecvVariableResponse. +template <> +class SerializationTraits { + public: + static Status Serialize( + const paddle::operators::detail::VariableResponse& msg, + grpc_byte_buffer** bp, bool* own_buffer) { + PADDLE_ENFORCE(false, "SerializationTraits::Serialize not implemented!"); + return Status(); + } + static Status Deserialize(grpc_byte_buffer* buffer, + paddle::operators::detail::VariableResponse* msg, + int max_message_size = INT_MAX) { + if (buffer == nullptr) { + return Status(StatusCode::INTERNAL, "No payload"); + } + + Status result = g_core_codegen_interface->ok(); + if (result.ok()) { + paddle::operators::detail::GrpcByteSource source(buffer); + int ret = msg->Parse(&source); + if (ret != 0) { + result = Status(StatusCode::INTERNAL, "VariableResponse parse error"); + } + } + g_core_codegen_interface->grpc_byte_buffer_destroy(buffer); + return result; + } +}; +} // namespace grpc + +namespace paddle { +namespace operators { +namespace detail { + +enum class GrpcMethod { + kSendVariable, + kGetVariable, +}; + +static const int kGrpcNumMethods = + static_cast(GrpcMethod::kGetVariable) + 1; + +inline const char* GrpcMethodName(GrpcMethod id) { + switch (id) { + case GrpcMethod::kSendVariable: + return "/sendrecv.SendRecvService/SendVariable"; + case GrpcMethod::kGetVariable: + return "/sendrecv.SendRecvService/GetVariable"; + } + + // Shouldn't be reached. + PADDLE_ENFORCE(false, "Invalid id: not found valid method name"); + return nullptr; +} + +class GrpcService final { + public: + class AsyncService : public ::grpc::Service { + public: + AsyncService() { + for (int i = 0; i < kGrpcNumMethods; ++i) { + AddMethod(new ::grpc::internal::RpcServiceMethod( + GrpcMethodName(static_cast(i)), + ::grpc::internal::RpcMethod::NORMAL_RPC, nullptr)); + ::grpc::Service::MarkMethodAsync(i); + } + } + virtual ~AsyncService() {} + + // Make RequestAsyncUnary public for grpc_call.h + using ::grpc::Service::RequestAsyncUnary; + }; +}; + +} // namespace detail +} // namespace operator +} // namespace paddle diff --git a/paddle/fluid/operators/detail/send_recv.proto b/paddle/fluid/operators/detail/send_recv.proto index b0215d4a80c9440f09c35434903fd6166b03e8b0..598aaa4c51a6c5cd32eeffe08bbae849aee1a1df 100644 --- a/paddle/fluid/operators/detail/send_recv.proto +++ b/paddle/fluid/operators/detail/send_recv.proto @@ -32,6 +32,9 @@ enum VarType { SELECTED_ROWS = 1; } +// NOTICE(gongwb):don't modify this proto if you are not +// not familar with how we serialize in sendrecvop_utils.h +// and deserilize it in variable_response.h. message VariableMessage { enum Type { // Pod Types @@ -45,7 +48,6 @@ message VariableMessage { } message LodData { repeated int64 lod_data = 1; } - string varname = 1; // TODO(Yancey1989): reference framework::proto::VarDesc::VarType VarType type = 2; @@ -64,3 +66,5 @@ message VariableMessage { } message VoidMessage {} + +message TestMessage { int64 test_1 = 1; } diff --git a/paddle/fluid/operators/detail/sendrecvop_utils.cc b/paddle/fluid/operators/detail/sendrecvop_utils.cc index 39117eeeb611b025c426938c60ddf82c6af232ca..d7bbf79c50651943d91c38bbaab775f5ee8dc395 100644 --- a/paddle/fluid/operators/detail/sendrecvop_utils.cc +++ b/paddle/fluid/operators/detail/sendrecvop_utils.cc @@ -13,61 +13,19 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/fluid/operators/detail/sendrecvop_utils.h" +#include +#include #include "google/protobuf/io/coded_stream.h" #include "google/protobuf/io/zero_copy_stream.h" #include "paddle/fluid/framework/data_type.h" #include "paddle/fluid/operators/detail/bytebuffer_stream.h" #include "paddle/fluid/operators/detail/proto_encoder_helper.h" +#include "paddle/fluid/operators/detail/variable_response.h" namespace paddle { namespace operators { namespace detail { -void SerializeToMessage(const std::string& name, const framework::Variable* var, - const platform::DeviceContext& ctx, - sendrecv::VariableMessage* msg) { - msg->set_varname(name); - std::ostringstream oss; - switch (framework::ToVarType(var->Type())) { - case framework::proto::VarType_Type_LOD_TENSOR: - msg->set_type(sendrecv::VarType::LOD_TENSOR); - framework::SerializeToStream(oss, var->Get(), ctx); - break; - case framework::proto::VarType_Type_SELECTED_ROWS: - msg->set_type(sendrecv::VarType::SELECTED_ROWS); - framework::SerializeToStream(oss, var->Get(), - ctx); - break; - default: { - PADDLE_THROW("Serialize does not support type: %s", - typeid(var->Type()).name()); - break; - } - } - msg->set_serialized(oss.str()); -} - -void DeserializeFromMessage(const sendrecv::VariableMessage& msg, - const platform::DeviceContext& ctx, - framework::Variable* var) { - std::istringstream iss(msg.serialized()); - switch (msg.type()) { - case sendrecv::VarType::LOD_TENSOR: - DeserializeFromStream(iss, var->GetMutable(), ctx); - break; - case sendrecv::VarType::SELECTED_ROWS: { - DeserializeFromStream(iss, var->GetMutable(), - ctx); - break; - } - default: { - PADDLE_THROW("Deserialize does not support type: %s", - typeid(var->Type()).name()); - break; - } - } -} - void SerializeToByteBuffer(const std::string& name, framework::Variable* var, const platform::DeviceContext& ctx, ::grpc::ByteBuffer* msg) { @@ -123,6 +81,7 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var, static_cast(ctx); auto copy_size = tensor.memory_size(); payload = memory::Alloc(cpu, copy_size); + memory::Copy(cpu, payload, boost::get(tensor.place()), reinterpret_cast(tensor.data()), @@ -132,6 +91,7 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var, platform::CPUPlace cpu; memory::Free(cpu, backing); }; + #endif } else { payload = tensor.data(); @@ -219,80 +179,11 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var, void DeserializeFromByteBuffer(const ::grpc::ByteBuffer& msg, const platform::DeviceContext& ctx, - framework::Variable* var) { - sendrecv::VariableMessage meta; - GrpcByteBufferSource source; - source.Init(msg); - ::google::protobuf::io::CodedInputStream input(&source); - // do zerocopy parsing - PADDLE_ENFORCE(meta.ParseFromCodedStream(&input)); - PADDLE_ENFORCE(input.ConsumedEntireMessage()); - // dims is needed by both tensor and selectedrows - std::vector vecdims; - for (auto& d : meta.dims()) { - vecdims.push_back(d); - } - framework::DDim dims = framework::make_ddim(vecdims); - - if (meta.type() == sendrecv::LOD_TENSOR) { - auto* tensor = var->GetMutable(); - tensor->Resize(dims); - void* tensor_data = tensor->mutable_data( - ctx.GetPlace(), - paddle::operators::detail::ToTypeIndex(meta.data_type())); - framework::LoD lod; - for (int i = 0; i < meta.lod_level(); ++i) { - framework::Vector v; - for (int j = 0; j < meta.lod(i).lod_data_size(); ++j) { - v.push_back(meta.lod(i).lod_data(j)); - } - lod.push_back(v); - } - tensor->set_lod(lod); - // How to avoid copying and use the message buffer directly? - // Maybe need to find a way to release all memory except tensor content. - if (platform::is_gpu_place(ctx.GetPlace())) { -#ifdef PADDLE_WITH_CUDA - platform::CPUPlace cpu; - auto& gpu_dev_ctx = static_cast(ctx); - memory::Copy(boost::get(tensor->place()), - tensor_data, cpu, - reinterpret_cast(meta.serialized().data()), - meta.serialized().size(), gpu_dev_ctx.stream()); - ctx.Wait(); -#endif - } else { - memcpy(tensor_data, - reinterpret_cast(meta.serialized().data()), - meta.serialized().size()); - } - } else if (meta.type() == sendrecv::SELECTED_ROWS) { - auto* slr = var->GetMutable(); - auto* tensor = slr->mutable_value(); - int64_t* rows_data = slr->mutable_rows()->data(); - tensor->Resize(dims); - void* tensor_data = tensor->mutable_data( - ctx.GetPlace(), - paddle::operators::detail::ToTypeIndex(meta.data_type())); - if (platform::is_gpu_place(ctx.GetPlace())) { -#ifdef PADDLE_WITH_CUDA - platform::CPUPlace cpu; - auto& gpu_dev_ctx = static_cast(ctx); - memory::Copy(boost::get(tensor->place()), - tensor_data, cpu, - reinterpret_cast(meta.serialized().data()), - meta.serialized().size(), gpu_dev_ctx.stream()); - ctx.Wait(); -#endif - } else { - memcpy(tensor_data, - reinterpret_cast(meta.serialized().data()), - meta.serialized().size()); - } - // copy rows CPU data, GPU data will be copied lazly - memcpy(rows_data, reinterpret_cast(meta.rows().data()), - meta.rows().size()); - } + const framework::Scope* scope, + framework::Variable*& var) { + operators::detail::VariableResponse resp(scope, &ctx); + PADDLE_ENFORCE(resp.Parse(msg) == 0, "parse bytebuffer to tensor error!"); + var = resp.GetVar(); } } // namespace detail diff --git a/paddle/fluid/operators/detail/sendrecvop_utils.h b/paddle/fluid/operators/detail/sendrecvop_utils.h index 4fa6aefd3e0b1bd45ac52b1eff3b29126d79f03a..3b875627032a6b08cc70280b3cc825c2a703923f 100644 --- a/paddle/fluid/operators/detail/sendrecvop_utils.h +++ b/paddle/fluid/operators/detail/sendrecvop_utils.h @@ -21,6 +21,7 @@ limitations under the License. */ #include "paddle/fluid/framework/lod_tensor.h" #include "paddle/fluid/framework/scope.h" #include "paddle/fluid/framework/selected_rows.h" +#include "paddle/fluid/framework/tensor_util.h" #include "paddle/fluid/framework/var_type.h" #include "paddle/fluid/operators/detail/send_recv.grpc.pb.h" @@ -36,21 +37,14 @@ namespace detail { typedef void (*DestroyCallback)(void*); -void SerializeToMessage(const std::string& name, const framework::Variable* var, - const platform::DeviceContext& ctx, - sendrecv::VariableMessage* msg); - -void DeserializeFromMessage(const sendrecv::VariableMessage& msg, - const platform::DeviceContext& ctx, - framework::Variable* var); - void SerializeToByteBuffer(const std::string& name, framework::Variable* var, const platform::DeviceContext& ctx, ::grpc::ByteBuffer* msg); void DeserializeFromByteBuffer(const ::grpc::ByteBuffer& msg, const platform::DeviceContext& ctx, - framework::Variable* var); + const framework::Scope* scope, + framework::Variable*& var); inline std::type_index ToTypeIndex(sendrecv::VariableMessage::Type type) { switch (type) { diff --git a/paddle/fluid/operators/detail/test_serde.cc b/paddle/fluid/operators/detail/test_serde.cc index 2f06e5a686b996858d21930a1afa2861efca4a9b..e646c894d18d37f5343a10df2542a0e46ab13372 100644 --- a/paddle/fluid/operators/detail/test_serde.cc +++ b/paddle/fluid/operators/detail/test_serde.cc @@ -16,11 +16,13 @@ limitations under the License. */ #include #include +#include #include "gtest/gtest.h" #include "paddle/fluid/framework/lod_tensor.h" #include "paddle/fluid/framework/tensor_util.h" #include "paddle/fluid/framework/variable.h" #include "paddle/fluid/operators/detail/sendrecvop_utils.h" +#include "paddle/fluid/operators/detail/variable_response.h" #include "paddle/fluid/operators/math/math_function.h" #include "paddle/fluid/platform/place.h" #include "paddle/fluid/string/printf.h" @@ -31,19 +33,21 @@ namespace operators = paddle::operators; namespace math = paddle::operators::math; namespace memory = paddle::memory; -void RunSerdeTestTensor(platform::Place place) { - // serialize var to ByteBuffer - framework::Variable var; - auto* tensor = var.GetMutable(); - tensor->Resize(framework::make_ddim({4, 8, 4, 2})); - framework::LoD lod; - lod.push_back(framework::Vector({1, 3, 8})); - tensor->set_lod(lod); - int tensor_numel = 4 * 8 * 4 * 2; +void RunSerdeTestSelectedRows(platform::Place place) { platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance(); auto& ctx = *pool.Get(place); + + // serialize var to ByteBuffer + framework::Variable var; + auto* slr = var.GetMutable(); + auto* tensor = slr->mutable_value(); + auto* rows = slr->mutable_rows(); + tensor->Resize(framework::make_ddim({2, 10})); tensor->mutable_data(place); - math::set_constant(ctx, tensor, 31.9); + int tensor_numel = 2 * 10; + math::set_constant(ctx, tensor, 32.7); + rows->push_back(3); + rows->push_back(10); ::grpc::ByteBuffer msg; operators::detail::SerializeToByteBuffer("myvar", &var, ctx, &msg); @@ -56,62 +60,67 @@ void RunSerdeTestTensor(platform::Place place) { for (const auto& s : slices) { tmp.append(reinterpret_cast(s.begin()), s.size()); } + sendrecv::VariableMessage varmsg; EXPECT_TRUE(varmsg.ParseFromString(tmp)); + EXPECT_EQ(varmsg.varname(), "myvar"); - EXPECT_EQ(varmsg.type(), 0); - EXPECT_EQ(varmsg.dims()[0], 4); - EXPECT_EQ(varmsg.dims()[1], 8); - EXPECT_EQ(varmsg.dims()[2], 4); - EXPECT_EQ(varmsg.dims()[3], 2); - EXPECT_EQ(varmsg.lod_level(), 1); - EXPECT_EQ(varmsg.lod(0).lod_data(0), 1); - EXPECT_EQ(varmsg.lod(0).lod_data(1), 3); - EXPECT_EQ(varmsg.lod(0).lod_data(2), 8); + EXPECT_EQ(varmsg.type(), 1); const float* tensor_data = reinterpret_cast(varmsg.serialized().data()); + const int64_t* rows_data = + reinterpret_cast(varmsg.rows().data()); for (int i = 0; i < tensor_numel; ++i) { - EXPECT_FLOAT_EQ(tensor_data[i], 31.9); + EXPECT_FLOAT_EQ(tensor_data[i], 32.7); } - + EXPECT_EQ(rows_data[0], 3); + EXPECT_EQ(rows_data[1], 10); // deserialize zero-copy - framework::Variable var2; - operators::detail::DeserializeFromByteBuffer(msg, ctx, &var2); - auto tensor2 = var2.Get(); + // framework::Variable var2; + // operators::detail::DeserializeFromByteBuffer(msg, ctx, &var2); + framework::Scope scope; + scope.Var("myvar"); + operators::detail::VariableResponse resp(&scope, &ctx); + EXPECT_EQ(resp.Parse(msg), 0); + + framework::Variable* var2 = resp.GetVar(); + + auto* slr2 = var2->GetMutable(); + auto* tensor2 = slr2->mutable_value(); + auto* rows2 = slr2->mutable_rows(); float* tensor_data2 = nullptr; framework::Tensor tmp_tensor; if (platform::is_gpu_place(ctx.GetPlace())) { platform::CPUPlace cpu; - framework::TensorCopy(tensor2, cpu, &tmp_tensor); + framework::TensorCopy(*tensor2, cpu, &tmp_tensor); tensor_data2 = tmp_tensor.data(); } else { - tensor_data2 = const_cast(tensor2.data()); + tensor_data2 = const_cast(tensor2->data()); } + const int64_t* rows_data2 = rows2->data(); - EXPECT_EQ(varmsg.lod_level(), 1); - EXPECT_EQ(varmsg.lod(0).lod_data(0), 1); - EXPECT_EQ(varmsg.lod(0).lod_data(1), 3); - EXPECT_EQ(varmsg.lod(0).lod_data(2), 8); - for (int i = 0; i < tensor_numel; ++i) EXPECT_FLOAT_EQ(tensor_data2[i], 31.9); + for (int i = 0; i < tensor_numel; ++i) { + EXPECT_FLOAT_EQ(tensor_data2[i], 32.7); + } + EXPECT_EQ(rows_data2[0], 3); + EXPECT_EQ(rows_data2[1], 10); } -void RunSerdeTestSelectedRows(platform::Place place) { - platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance(); - auto& ctx = *pool.Get(place); - +void RunTestLodTensor(platform::Place place, int from_type = 0) { // serialize var to ByteBuffer framework::Variable var; - auto* slr = var.GetMutable(); - auto* tensor = slr->mutable_value(); - auto* rows = slr->mutable_rows(); - tensor->Resize(framework::make_ddim({2, 10})); + auto* tensor = var.GetMutable(); + tensor->Resize(framework::make_ddim({4, 8, 4, 2})); + framework::LoD lod; + lod.push_back(framework::Vector({1, 3, 8})); + tensor->set_lod(lod); + int tensor_numel = 4 * 8 * 4 * 2; + platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance(); + auto& ctx = *pool.Get(place); tensor->mutable_data(place); - int tensor_numel = 2 * 10; - math::set_constant(ctx, tensor, 32.7); - rows->push_back(3); - rows->push_back(10); + math::set_constant(ctx, tensor, 31.9); ::grpc::ByteBuffer msg; operators::detail::SerializeToByteBuffer("myvar", &var, ctx, &msg); @@ -126,61 +135,82 @@ void RunSerdeTestSelectedRows(platform::Place place) { } sendrecv::VariableMessage varmsg; EXPECT_TRUE(varmsg.ParseFromString(tmp)); - EXPECT_EQ(varmsg.varname(), "myvar"); - EXPECT_EQ(varmsg.type(), 1); + EXPECT_EQ(varmsg.type(), 0); + EXPECT_EQ(varmsg.dims()[0], 4); + EXPECT_EQ(varmsg.dims()[1], 8); + EXPECT_EQ(varmsg.dims()[2], 4); + EXPECT_EQ(varmsg.dims()[3], 2); + EXPECT_EQ(varmsg.lod_level(), 1); + EXPECT_EQ(varmsg.lod(0).lod_data(0), 1); + EXPECT_EQ(varmsg.lod(0).lod_data(1), 3); + EXPECT_EQ(varmsg.lod(0).lod_data(2), 8); const float* tensor_data = reinterpret_cast(varmsg.serialized().data()); - const int64_t* rows_data = - reinterpret_cast(varmsg.rows().data()); for (int i = 0; i < tensor_numel; ++i) { - EXPECT_FLOAT_EQ(tensor_data[i], 32.7); + EXPECT_FLOAT_EQ(tensor_data[i], 31.9); } - EXPECT_EQ(rows_data[0], 3); - EXPECT_EQ(rows_data[1], 10); + + // message binary + std::string str; + varmsg.SerializeToString(&str); + + // message bytebuffer + ::grpc::Slice slices_2[1]; + int num_slices = 1; + slices_2[0] = ::grpc::Slice(str.length()); + memcpy(const_cast(slices_2[0].begin()), str.c_str(), str.length()); + ::grpc::ByteBuffer bytebuffer2(&slices_2[0], num_slices); + // deserialize zero-copy - framework::Variable var2; - operators::detail::DeserializeFromByteBuffer(msg, ctx, &var2); + framework::Scope scope; + scope.Var("myvar"); + operators::detail::VariableResponse resp(&scope, &ctx); + if (from_type == 0) { + EXPECT_EQ(resp.Parse(msg), 0); + } else { + EXPECT_EQ(resp.Parse(bytebuffer2), 0); + } - auto* slr2 = var2.GetMutable(); - auto* tensor2 = slr2->mutable_value(); - auto* rows2 = slr2->mutable_rows(); + framework::Variable* var2 = resp.GetVar(); + + auto tensor2 = var2->Get(); float* tensor_data2 = nullptr; framework::Tensor tmp_tensor; if (platform::is_gpu_place(ctx.GetPlace())) { platform::CPUPlace cpu; - framework::TensorCopy(*tensor2, cpu, &tmp_tensor); + framework::TensorCopy(tensor2, cpu, &tmp_tensor); tensor_data2 = tmp_tensor.data(); } else { - tensor_data2 = const_cast(tensor2->data()); + tensor_data2 = const_cast(tensor2.data()); } - const int64_t* rows_data2 = rows2->data(); - for (int i = 0; i < tensor_numel; ++i) { - EXPECT_FLOAT_EQ(tensor_data2[i], 32.7); - } - EXPECT_EQ(rows_data2[0], 3); - EXPECT_EQ(rows_data2[1], 10); + EXPECT_EQ(varmsg.lod_level(), 1); + EXPECT_EQ(varmsg.lod(0).lod_data(0), 1); + EXPECT_EQ(varmsg.lod(0).lod_data(1), 3); + EXPECT_EQ(varmsg.lod(0).lod_data(2), 8); + for (int i = 0; i < tensor_numel; ++i) EXPECT_FLOAT_EQ(tensor_data2[i], 31.9); } -TEST(SelectedRows, CPU) { +TEST(LodTensor, Run) { platform::CPUPlace place; - RunSerdeTestSelectedRows(place); + RunTestLodTensor(place); + RunTestLodTensor(place, 1); +#ifdef PADDLE_WITH_CUDA + platform::CUDAPlace gpu(0); + RunTestLodTensor(gpu); + RunTestLodTensor(gpu, 1); +#endif } -TEST(SelectedRows, GPU) { - platform::CUDAPlace place; +TEST(SelectedRows, Run) { + platform::CPUPlace place; RunSerdeTestSelectedRows(place); -} -TEST(Tensor, CPU) { - platform::CPUPlace place; - RunSerdeTestTensor(place); +#ifdef PADDLE_WITH_CUDA + platform::CUDAPlace gpu; + RunSerdeTestSelectedRows(gpu); +#endif } - -TEST(Tensor, GPU) { - platform::CUDAPlace place; - RunSerdeTestTensor(place); -} \ No newline at end of file diff --git a/paddle/fluid/operators/detail/variable_response.cc b/paddle/fluid/operators/detail/variable_response.cc new file mode 100644 index 0000000000000000000000000000000000000000..12e8eb0b4da2252b104415aef4156bf100c3e565 --- /dev/null +++ b/paddle/fluid/operators/detail/variable_response.cc @@ -0,0 +1,400 @@ +// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paddle/fluid/operators/detail/variable_response.h" +#include +#include "paddle/fluid/operators/detail/send_recv.pb.h" +#include "paddle/fluid/operators/detail/sendrecvop_utils.h" + +namespace paddle { +namespace operators { +namespace detail { + +enum WireType { + WIRETYPE_VARINT = 0, + WIRETYPE_LENGTH_DELIMITED = 2, +}; + +inline int GetTagFieldNumber(uint32_t tag) { return tag >> 3; } + +inline WireType GetTagWireType(uint32_t tag) { + return static_cast(tag & 0x7); +} + +bool ReadVarintSizeAsInt(::google::protobuf::io::CodedInputStream* input, + int* result) { + uint64_t v; + if (input->ReadVarint64(&v) && v <= static_cast(INT_MAX)) { + *result = static_cast(v); + return true; + } else { + return false; + } +} + +bool ReadRaw(::google::protobuf::io::CodedInputStream* input, + const platform::DeviceContext& dev_ctx, platform::Place place, + void* dest, int size) { + const void* data = NULL; + int size_to_write = 0; + + if (platform::is_gpu_place(place)) { +#ifdef PADDLE_WITH_CUDA + auto& gpu_dev_ctx = + static_cast(dev_ctx); + platform::CPUPlace cpu; + + char* p = reinterpret_cast(dest); + while (size > 0) { + if (!input->GetDirectBufferPointer(&data, &size_to_write)) { + return false; + } + + memory::Copy(boost::get(place), + reinterpret_cast(p), cpu, data, size_to_write, + gpu_dev_ctx.stream()); + p += size_to_write; + size -= size_to_write; + + input->Skip(size_to_write); + } + gpu_dev_ctx.Wait(); +#else + PADDLE_THROW("Unexpected branch"); +#endif + return true; + } + + char* p = reinterpret_cast(dest); + while (size > 0) { + if (!input->GetDirectBufferPointer(&data, &size_to_write)) { + return false; + } + // TODO(gongwb): can we avoid copy? + platform::CPUPlace cpu; + memory::Copy(cpu, reinterpret_cast(p), cpu, data, size_to_write); + + p += size_to_write; + size -= size_to_write; + + input->Skip(size_to_write); + } + + return true; +} + +bool VariableResponse::CopyLodTensorData( + ::google::protobuf::io::CodedInputStream* input, + const platform::DeviceContext& ctx, framework::DDim& dims, int length) { + auto var = scope_->FindVar(meta_.varname()); + auto* tensor = var->GetMutable(); + tensor->Resize(dims); + + framework::LoD lod; + for (int i = 0; i < meta_.lod_level(); ++i) { + framework::Vector v; + for (int j = 0; j < meta_.lod(i).lod_data_size(); ++j) { + v.push_back(meta_.lod(i).lod_data(j)); + } + lod.push_back(v); + } + tensor->set_lod(lod); + + void* tensor_data = + tensor->mutable_data(ctx.GetPlace(), ToTypeIndex(meta_.data_type())); + + if (!ReadRaw(input, ctx, tensor->place(), tensor_data, length)) { + return false; + } + + return true; +} + +inline framework::DDim GetDims( + const ::google::protobuf::RepeatedField<::google::protobuf::int64>& dims) { + std::vector vecdims; + for (auto& d : dims) { + vecdims.push_back(d); + } + return framework::make_ddim(vecdims); +} + +bool VariableResponse::CopySelectRowsTensorData( + ::google::protobuf::io::CodedInputStream* input, + const platform::DeviceContext& ctx, framework::DDim& dims, int length) { + auto var = scope_->FindVar(meta_.varname()); + auto* slr = var->GetMutable(); + auto* tensor = slr->mutable_value(); + tensor->Resize(dims); + void* tensor_data = tensor->mutable_data( + ctx.GetPlace(), + paddle::operators::detail::ToTypeIndex(meta_.data_type())); + + if (!ReadRaw(input, ctx, tensor->place(), tensor_data, length)) { + return false; + } + + return true; +} + +bool VariableResponse::CopySelectRowsData( + ::google::protobuf::io::CodedInputStream* input, + const platform::DeviceContext& ctx, int length) { + auto var = scope_->FindVar(meta_.varname()); + auto* slr = var->GetMutable(); + int64_t* rows_data = slr->mutable_rows()->data(); + + // copy rows CPU data, GPU data will be copied lazily. + platform::CPUPlace cpu; + if (!ReadRaw(input, ctx, cpu, rows_data, length)) { + return false; + } + + return true; +} + +bool ParseLodData(::google::protobuf::io::CodedInputStream* input, + std::vector* lod) { + while (true) { + auto p = input->ReadTagWithCutoff(127); + int tag = GetTagFieldNumber(p.first); + WireType wt = GetTagWireType(p.first); + + if (!p.second) { + return (tag == 0); + } + + switch (tag) { + case sendrecv::VariableMessage_LodData::kLodDataFieldNumber: { + uint64_t v; + if (wt == WIRETYPE_VARINT) { + if (!input->ReadVarint64(&v)) { + return false; + } + lod->push_back(v); + break; + } + + if (wt == WIRETYPE_LENGTH_DELIMITED) { + int length = 0; + if (!input->ReadVarintSizeAsInt(&length)) { + return tag; + } + + for (int i = 0; i < length; i++) { + uint64_t v; + if (!input->ReadVarint64(&v)) { + return false; + } + lod->push_back(v); + } + break; + } + + return false; + } + default: { return false; } + } + } + + return true; +} + +int VariableResponse::Parse(const ::grpc::ByteBuffer& byte_buffer) { + GrpcByteBufferSource source; + source.Init(byte_buffer); + GrpcByteBufferSourceWrapper r(&source); + + return Parse(&r); +} + +int VariableResponse::Parse(Source* source) { + ::google::protobuf::io::ZeroCopyInputStream* input_stream = + source->contents(); + ::google::protobuf::io::CodedInputStream input(input_stream); + input.SetTotalBytesLimit(INT_MAX, INT_MAX); + + while (true) { + auto p = input.ReadTagWithCutoff(127); + int tag = GetTagFieldNumber(p.first); + WireType wt = GetTagWireType(p.first); + if (!p.second) { + if (tag != 0) { + return -1; + } + + return 0; + } + + switch (tag) { + case sendrecv::VariableMessage::kVarnameFieldNumber: { + uint32_t length; + if ((wt != WIRETYPE_LENGTH_DELIMITED) || !input.ReadVarint32(&length)) { + return tag; + } + + std::string temp; + if (!input.ReadString(&temp, length)) { + return tag; + } + + meta_.set_varname(temp); + break; + } + case sendrecv::VariableMessage::kTypeFieldNumber: { + uint64_t v; + if ((wt != WIRETYPE_VARINT) || !input.ReadVarint64(&v)) { + return tag; + } + + meta_.set_type(static_cast<::sendrecv::VarType>(v)); + break; + } + case sendrecv::VariableMessage::kDataTypeFieldNumber: { + uint64_t v = 0; + if ((wt != WIRETYPE_VARINT) || !input.ReadVarint64(&v)) { + return tag; + } + + meta_.set_data_type(static_cast<::sendrecv::VariableMessage_Type>(v)); + break; + } + case sendrecv::VariableMessage::kDimsFieldNumber: { + // not packed + if (wt == WIRETYPE_VARINT) { + uint64_t v; + if (!input.ReadVarint64(&v)) { + return tag; + } + meta_.add_dims(v); + break; + } + + // packed + if (wt == WIRETYPE_LENGTH_DELIMITED) { + int length = 0; + if (!input.ReadVarintSizeAsInt(&length)) { + return tag; + } + for (int i = 0; i < length; i++) { + uint64_t v; + if (!input.ReadVarint64(&v)) { + return tag; + } + meta_.add_dims(v); + } + break; + } + + return tag; + } + case sendrecv::VariableMessage::kLodLevelFieldNumber: { + uint64_t v = 0; + if ((wt != WIRETYPE_VARINT) || !input.ReadVarint64(&v)) { + return tag; + } + meta_.set_lod_level(static_cast(v)); + break; + } + case sendrecv::VariableMessage::kLodFieldNumber: { + int length = 0; + if (wt != WIRETYPE_LENGTH_DELIMITED || + !ReadVarintSizeAsInt(&input, &length)) { + return tag; + } + + std::pair<::google::protobuf::io::CodedInputStream::Limit, int> p = + input.IncrementRecursionDepthAndPushLimit(length); + + std::vector lod_data; + if (p.second < 0 || !ParseLodData(&input, &lod_data)) { + return tag; + } + + if (!input.DecrementRecursionDepthAndPopLimit(p.first)) { + return false; + } + + if (lod_data.size() == 0) { + break; + } + + auto lod = meta_.add_lod(); + for (uint32_t i = 0; i < lod_data.size(); i++) { + lod->add_lod_data(lod_data[i]); + } + break; + } + case sendrecv::VariableMessage::kSerializedFieldNumber: { + PADDLE_ENFORCE((meta_.type() == sendrecv::SELECTED_ROWS || + meta_.type() == sendrecv::LOD_TENSOR) && + meta_.varname() != "", + "meta info should be got first!"); + + int length = 0; + if (wt != WIRETYPE_LENGTH_DELIMITED || + !ReadVarintSizeAsInt(&input, &length)) { + return tag; + } + + framework::DDim dims = GetDims(meta_.dims()); + if (meta_.type() == sendrecv::LOD_TENSOR) { + PADDLE_ENFORCE(meta_.lod_size() >= 0, + "lod info should be got first!"); + if (!CopyLodTensorData(&input, *dev_ctx_, dims, length)) { + return tag; + } + break; + } + + if (meta_.type() == sendrecv::SELECTED_ROWS) { + if (!CopySelectRowsTensorData(&input, *dev_ctx_, dims, length)) { + return tag; + } + break; + } + + return tag; + } + case sendrecv::VariableMessage::kRowsFieldNumber: { + PADDLE_ENFORCE((meta_.type() == sendrecv::SELECTED_ROWS || + meta_.type() == sendrecv::LOD_TENSOR) && + meta_.varname() != "", + "meta info should be got first!"); + + int length = 0; + if (wt != WIRETYPE_LENGTH_DELIMITED || + !ReadVarintSizeAsInt(&input, &length)) { + return tag; + } + + if (!CopySelectRowsData(&input, *dev_ctx_, length)) { + return tag; + } + break; + } + + default: { + // Unknown tag, return unknown error. + return -1; + } + } + } + + return 0; +} + +}; // namespace detail +}; // namespace operators +}; // namespace paddle diff --git a/paddle/fluid/operators/detail/variable_response.h b/paddle/fluid/operators/detail/variable_response.h new file mode 100644 index 0000000000000000000000000000000000000000..e121ed7bce966d7dea94f71087f2187dcaa17cec --- /dev/null +++ b/paddle/fluid/operators/detail/variable_response.h @@ -0,0 +1,81 @@ +// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include "paddle/fluid/framework/data_type.h" +#include "paddle/fluid/framework/lod_tensor.h" +#include "paddle/fluid/framework/scope.h" +#include "paddle/fluid/framework/selected_rows.h" +#include "paddle/fluid/framework/var_type.h" + +#include "paddle/fluid/operators/detail/send_recv.grpc.pb.h" +#include "paddle/fluid/operators/detail/send_recv.pb.h" + +#include "google/protobuf/io/coded_stream.h" +#include "google/protobuf/io/zero_copy_stream.h" +#include "paddle/fluid/framework/tensor.h" +#include "paddle/fluid/operators/detail/bytebuffer_stream.h" + +namespace paddle { +namespace operators { +namespace detail { + +class VariableResponse { + public: + VariableResponse(const framework::Scope* scope, + const platform::DeviceContext* dev_ctx) + : scope_(scope), dev_ctx_(dev_ctx) {} + + virtual ~VariableResponse() {} + + // return: + // 0:ok. + // -1: unkown error. + // other: number of error field. + int Parse(Source* source); + + // return: + // 0:ok. + // -1: unkown error. + // other: number of error field. + int Parse(const ::grpc::ByteBuffer& byte_buffer); + + inline std::string Varname() { return meta_.varname(); } + + // should call parse first. + framework::Variable* GetVar() { return scope_->FindVar(meta_.varname()); } + + private: + bool CopySelectRowsTensorData(::google::protobuf::io::CodedInputStream* input, + const platform::DeviceContext& ctx, + framework::DDim& dims, int length); + + bool CopySelectRowsData(::google::protobuf::io::CodedInputStream* input, + const platform::DeviceContext& ctx, int length); + + bool CopyLodTensorData(::google::protobuf::io::CodedInputStream* input, + const platform::DeviceContext& ctx, + framework::DDim& dims, int length); + + private: + const framework::Scope* scope_; + const platform::DeviceContext* dev_ctx_; + // only Skeleton + sendrecv::VariableMessage meta_; +}; + +}; // namespace detail +}; // namespace operators +}; // namespace paddle diff --git a/paddle/fluid/operators/detection_map_op.cc b/paddle/fluid/operators/detection_map_op.cc index 73c84c2fe0155d21d7059938330e44fa3668c6df..93ef15b9332168a9c62abfd4d0827207173ece45 100644 --- a/paddle/fluid/operators/detection_map_op.cc +++ b/paddle/fluid/operators/detection_map_op.cc @@ -188,8 +188,8 @@ The general steps are as follows. First, calculate the true positive and } // namespace paddle namespace ops = paddle::operators; -REGISTER_OP_WITHOUT_GRADIENT(detection_map, ops::DetectionMAPOp, - ops::DetectionMAPOpMaker); +REGISTER_OPERATOR(detection_map, ops::DetectionMAPOp, ops::DetectionMAPOpMaker, + paddle::framework::EmptyGradOpMaker); REGISTER_OP_CPU_KERNEL( detection_map, ops::DetectionMAPOpKernel, ops::DetectionMAPOpKernel); diff --git a/paddle/fluid/operators/iou_similarity_op.cc b/paddle/fluid/operators/iou_similarity_op.cc index ffbd7c7814c3fdec9fef0580ccd1ea3661ac0012..4b78ec510d1fb73592ee8af9a641622f4d713f8d 100755 --- a/paddle/fluid/operators/iou_similarity_op.cc +++ b/paddle/fluid/operators/iou_similarity_op.cc @@ -87,8 +87,9 @@ $$ } // namespace paddle namespace ops = paddle::operators; -REGISTER_OP_WITHOUT_GRADIENT(iou_similarity, ops::IOUSimilarityOp, - ops::IOUSimilarityOpMaker); +REGISTER_OPERATOR(iou_similarity, ops::IOUSimilarityOp, + ops::IOUSimilarityOpMaker, + paddle::framework::EmptyGradOpMaker); REGISTER_OP_CPU_KERNEL( iou_similarity, diff --git a/paddle/fluid/operators/listen_and_serv_op.cc b/paddle/fluid/operators/listen_and_serv_op.cc index a594de67e05acd28ffedc5407beecfaea1281444..08b83375dd5462e67c3da2c6c7401dd5e54793f0 100644 --- a/paddle/fluid/operators/listen_and_serv_op.cc +++ b/paddle/fluid/operators/listen_and_serv_op.cc @@ -69,9 +69,7 @@ class ListenAndServOp : public framework::OperatorBase { } void Stop() override { - detail::MessageWithName term_msg; - term_msg.first = LISTEN_TERMINATE_MESSAGE; - rpc_service_->Push(term_msg); + rpc_service_->Push(LISTEN_TERMINATE_MESSAGE); rpc_service_->ShutDown(); server_thread_->join(); } @@ -108,7 +106,7 @@ class ListenAndServOp : public framework::OperatorBase { size_t recv_var_cnt = 0; int batch_barrier = 0; while (batch_barrier != fan_in) { - const detail::MessageWithName &v = rpc_service_->Get(); + const detail::ReceivedMessage v = rpc_service_->Get(); auto recv_var_name = v.first; if (recv_var_name == LISTEN_TERMINATE_MESSAGE) { LOG(INFO) << "received terminate message and exit"; @@ -121,12 +119,11 @@ class ListenAndServOp : public framework::OperatorBase { } else { VLOG(3) << "received grad: " << recv_var_name; recv_var_cnt++; - auto *var = recv_scope.FindVar(recv_var_name); + auto var = v.second->GetVar(); if (var == nullptr) { LOG(ERROR) << "Can not find server side var: " << recv_var_name; PADDLE_THROW("Can not find server side var"); } - detail::DeserializeFromMessage(v.second, dev_ctx, var); if (var->IsType()) { sparse_vars.push_back(var); } @@ -142,26 +139,25 @@ class ListenAndServOp : public framework::OperatorBase { // should be global ops. // NOTE: if is_gpu_place, CUDA kernels are laugched by multiple threads // and this will still work. + std::vector> fs; // block0 contains only listen_and_serv op, start run from block1. for (int blkid = 1; blkid < num_blocks - 1; ++blkid) { - fs.push_back(framework::Async([&executor, &program, &recv_scope, - blkid]() { - int run_block = blkid; // thread local - try { - executor.Run(*program, &recv_scope, run_block, - false /*create_local_scope*/, false /*create_vars*/); - } catch (std::exception &e) { - LOG(ERROR) << "run sub program error " << e.what(); - } - })); + fs.push_back( + framework::Async([&executor, &program, &recv_scope, blkid]() { + int run_block = blkid; // thread local + try { + executor.Run(*program, &recv_scope, run_block, false, false); + } catch (std::exception &e) { + LOG(ERROR) << "run sub program error " << e.what(); + } + })); } for (int i = 0; i < num_blocks - 2; ++i) fs[i].wait(); // Run global block at final step, or block1 if there are only 2 blocks if (num_blocks >= 2) { try { - executor.Run(*program, &recv_scope, num_blocks - 1, - false /*create_local_scope*/, false /*create_vars*/); + executor.Run(*program, &recv_scope, num_blocks - 1, false, false); } catch (std::exception &e) { LOG(ERROR) << "run sub program error " << e.what(); } @@ -180,6 +176,10 @@ class ListenAndServOp : public framework::OperatorBase { rpc_service_->WaitClientGet(fan_in); sparse_vars.clear(); } // while(true) + + // for (int i = 0; i < num_blocks; ++i) { + // delete blk_ctx_list[i]; + // } } protected: diff --git a/paddle/fluid/operators/lrn_mkldnn_op.cc b/paddle/fluid/operators/lrn_mkldnn_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..3bead16ce44c26b9d7a6f2a5c6b471612494d595 --- /dev/null +++ b/paddle/fluid/operators/lrn_mkldnn_op.cc @@ -0,0 +1,209 @@ +/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/fluid/framework/tensor.h" +#include "paddle/fluid/operators/lrn_op.h" +#include "paddle/fluid/platform/mkldnn_helper.h" + +namespace paddle { +namespace operators { + +using paddle::framework::Tensor; +using paddle::platform::MKLDNNDeviceContext; + +namespace { +template +std::shared_ptr insert_to_context(const std::string& key, + const MKLDNNDeviceContext& dev_ctx, + Args&&... args) { + auto p = std::static_pointer_cast(dev_ctx.GetBlob(key)); + + if (!p) { + p = std::make_shared(args...); + dev_ctx.SetBlob(key, std::static_pointer_cast(p)); + } + + return p; +} +} // namespace + +template +class LRNMKLDNNOpKernel : public paddle::framework::OpKernel { + public: + void Compute(const paddle::framework::ExecutionContext& ctx) const override { + PADDLE_ENFORCE(std::is_same::value, + "MKLDNN LRN must use float data."); + PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()), + "MKLDNN LRN must use CPUPlace."); + + auto& dev_ctx = ctx.template device_context(); + const auto& mkldnn_engine = dev_ctx.GetEngine(); + + auto x = ctx.Input("X"); + auto out = ctx.Output("Out"); + auto mid = ctx.Output("MidOut"); + + auto input_data = x->data(); + auto output_data = out->mutable_data(ctx.GetPlace()); + mid->mutable_data(ctx.GetPlace()); + + const int n = ctx.Attr("n"); + const float alpha = ctx.Attr("alpha"); + const float beta = ctx.Attr("beta"); + const float k = ctx.Attr("k"); + const bool is_test = ctx.Attr("is_test"); + + auto e_mid = framework::EigenTensor::From(*mid); + e_mid = e_mid.constant(k); + + auto dims = paddle::framework::vectorize2int(x->dims()); + + auto src_md = paddle::platform::MKLDNNMemDesc( + dims, mkldnn::memory::data_type::f32, mkldnn::memory::format::nchw); + + auto dst_md = paddle::platform::MKLDNNMemDesc( + dims, mkldnn::memory::data_type::f32, mkldnn::memory::format::nchw); + + auto forward_desc = mkldnn::lrn_forward::desc{mkldnn::prop_kind::forward, + mkldnn::lrn_across_channels, + src_md, + n, + alpha, + beta, + k}; + + auto src_memory_pd = mkldnn::memory::primitive_desc{src_md, mkldnn_engine}; + auto dst_memory = mkldnn::memory{{dst_md, mkldnn_engine}, + static_cast(output_data)}; + + std::unique_ptr forward_op = nullptr; + + if (!is_test) { + const std::string key = ctx.op().Output("Out"); + const std::string key_src_memory = key + "@lrn_src_memory"; + const std::string key_pd = key + "@lrn_pd"; + const std::string key_workspace_memory = key + "@lrn_workspace_memory"; + + auto forward_pd = insert_to_context( + key_pd, dev_ctx, forward_desc, mkldnn_engine); + + auto src_memory = insert_to_context( + key_src_memory, dev_ctx, src_memory_pd); + + src_memory->set_data_handle( + static_cast(const_cast(input_data))); + + auto workspace_memory = insert_to_context( + key_workspace_memory, dev_ctx, + forward_pd->workspace_primitive_desc()); + + forward_op.reset(new mkldnn::lrn_forward{*forward_pd, *src_memory, + *workspace_memory, dst_memory}); + + } else { + auto forward_pd = + mkldnn::lrn_forward::primitive_desc{forward_desc, mkldnn_engine}; + auto src_memory = mkldnn::memory{ + src_memory_pd, static_cast(const_cast(input_data))}; + auto workspace_memory = + mkldnn::memory{forward_pd.workspace_primitive_desc()}; + + forward_op.reset(new mkldnn::lrn_forward{forward_pd, src_memory, + workspace_memory, dst_memory}); + } + + std::vector pipeline = {*forward_op}; + mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait(); + } +}; + +template +class LRNMKLDNNGradOpKernel : public paddle::framework::OpKernel { + public: + void Compute(const paddle::framework::ExecutionContext& ctx) const override { + PADDLE_ENFORCE(std::is_same::value, + "MKLDNN LRN must use float data."); + PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()), + "MKLDNN LRN must use CPUPlace."); + + auto x = ctx.Input("X"); + + auto out_grad = ctx.Input(framework::GradVarName("Out")); + auto x_grad = ctx.Output(framework::GradVarName("X")); + + const std::string key = ctx.op().Input("Out"); + const std::string key_src_memory = key + "@lrn_src_memory"; + const std::string key_pd = key + "@lrn_pd"; + const std::string key_workspace_memory = key + "@lrn_workspace_memory"; + + const int n = ctx.Attr("n"); + const float alpha = ctx.Attr("alpha"); + const float beta = ctx.Attr("beta"); + const float k = ctx.Attr("k"); + + auto& dev_ctx = ctx.template device_context(); + const auto& mkldnn_engine = dev_ctx.GetEngine(); + + auto x_grad_data = x_grad->mutable_data(ctx.GetPlace()); + auto out_grad_data = out_grad->data(); + + auto dims = paddle::framework::vectorize2int(x->dims()); + + auto src_md = paddle::platform::MKLDNNMemDesc( + dims, mkldnn::memory::data_type::f32, mkldnn::memory::format::nchw); + + auto diff_src_md = paddle::platform::MKLDNNMemDesc( + dims, mkldnn::memory::data_type::f32, mkldnn::memory::format::nchw); + + auto diff_dst_md = paddle::platform::MKLDNNMemDesc( + dims, mkldnn::memory::data_type::f32, mkldnn::memory::format::nchw); + + auto diff_dst_memory = + mkldnn::memory{{diff_dst_md, mkldnn_engine}, + static_cast(const_cast(out_grad_data))}; + + auto diff_src_memory = mkldnn::memory{{diff_src_md, mkldnn_engine}, + static_cast(x_grad_data)}; + + auto backward_desc = mkldnn::lrn_backward::desc{ + mkldnn::lrn_across_channels, src_md, diff_src_md, n, alpha, beta, k}; + + auto forward_pd = dev_ctx.GetBlob(key_pd); + + auto backward_pd = mkldnn::lrn_backward::primitive_desc{ + backward_desc, mkldnn_engine, + *static_cast(forward_pd.get())}; + + std::shared_ptr workspace_memory = + dev_ctx.GetBlob(key_workspace_memory); + + auto src_memory = dev_ctx.GetBlob(key_src_memory); + auto backward_op = mkldnn::lrn_backward{ + backward_pd, *static_cast(src_memory.get()), + diff_dst_memory, *static_cast(workspace_memory.get()), + diff_src_memory}; + + std::vector pipeline = {backward_op}; + mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait(); + } +}; +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; + +REGISTER_OP_KERNEL(lrn, MKLDNN, paddle::platform::CPUPlace, + ops::LRNMKLDNNOpKernel); +REGISTER_OP_KERNEL(lrn_grad, MKLDNN, paddle::platform::CPUPlace, + ops::LRNMKLDNNGradOpKernel); diff --git a/paddle/fluid/operators/lrn_op.cc b/paddle/fluid/operators/lrn_op.cc index 692e85dcffa583abcb22a1629953badc67489efa..2b1947a187bbd17871107553127647032ac7d7f9 100644 --- a/paddle/fluid/operators/lrn_op.cc +++ b/paddle/fluid/operators/lrn_op.cc @@ -13,6 +13,9 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/fluid/operators/lrn_op.h" +#ifdef PADDLE_WITH_MKLDNN +#include "paddle/fluid/platform/mkldnn_helper.h" +#endif namespace paddle { namespace operators { @@ -116,6 +119,26 @@ struct LRNGradFunctor { template struct LRNGradFunctor; template struct LRNGradFunctor; +namespace { +framework::OpKernelType GetExpectedLRNKernel( + const framework::ExecutionContext& ctx) { + framework::LibraryType library_{framework::LibraryType::kPlain}; +#ifdef PADDLE_WITH_MKLDNN + if (library_ == framework::LibraryType::kPlain && + platform::CanMKLDNNBeUsed(ctx)) { + library_ = framework::LibraryType::kMKLDNN; + } +#endif + + std::string data_format = ctx.Attr("data_format"); + // TODO(pzelazko-intel): enable MKLDNN layout when it's ready + framework::DataLayout layout_ = framework::StringToDataLayout(data_format); + return framework::OpKernelType( + framework::ToDataType(ctx.Input("X")->type()), ctx.GetPlace(), + layout_, library_); +} +} // namespace + class LRNOp : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; @@ -135,6 +158,11 @@ class LRNOp : public framework::OperatorWithKernel { ctx->SetOutputDim("MidOut", x_dim); ctx->ShareLoD("X", /*->*/ "Out"); } + + framework::OpKernelType GetExpectedKernelType( + const framework::ExecutionContext& ctx) const override { + return GetExpectedLRNKernel(ctx); + } }; template @@ -176,6 +204,17 @@ class LRNOpMaker : public framework::OpProtoAndCheckerMaker { "beta is the power number.") .SetDefault(0.75) .GreaterThan(0.0); + AddAttr("use_mkldnn", + "(bool, default false) Only used in mkldnn kernel") + .SetDefault(false); + AddAttr( + "data_format", + "(string, default NCHW) Only used in " + "An optional string from: \"NHWC\", \"NCHW\". " + "Defaults to \"NHWC\". Specify the data format of the output data, " + "the input will be transformed automatically. ") + .SetDefault("AnyLayout"); + AddAttr("is_test", "").SetDefault(false); AddComment(R"DOC( Local Response Normalization Operator. @@ -223,8 +262,12 @@ class LRNOpGrad : public framework::OperatorWithKernel { auto x_dims = ctx->GetInputDim("X"); ctx->SetOutputDim(framework::GradVarName("X"), x_dims); } -}; + framework::OpKernelType GetExpectedKernelType( + const framework::ExecutionContext& ctx) const override { + return GetExpectedLRNKernel(ctx); + } +}; } // namespace operators } // namespace paddle diff --git a/paddle/fluid/operators/math/CMakeLists.txt b/paddle/fluid/operators/math/CMakeLists.txt index 547d081006f1c28ba73cb02d38e36bb612cea494..ee0e91132bce52998e9c45b37335618e4354e1cd 100644 --- a/paddle/fluid/operators/math/CMakeLists.txt +++ b/paddle/fluid/operators/math/CMakeLists.txt @@ -6,6 +6,7 @@ function(math_library TARGET) # But it handle split GPU/CPU code and link some common library. set(cc_srcs) set(cu_srcs) + set(hip_srcs) set(math_common_deps device_context framework_proto) set(multiValueArgs DEPS) cmake_parse_arguments(math_library "${options}" "${oneValueArgs}" @@ -17,10 +18,15 @@ function(math_library TARGET) if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${TARGET}.cu) list(APPEND cu_srcs ${TARGET}.cu) endif() + if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${TARGET}.hip.cu) + list(APPEND hip_srcs ${TARGET}.hip.cu) + endif() list(LENGTH cc_srcs cc_srcs_len) if (WITH_GPU) nv_library(${TARGET} SRCS ${cc_srcs} ${cu_srcs} DEPS ${math_library_DEPS} ${math_common_deps}) + elseif (WITH_AMD_GPU) + hip_library(${TARGET} SRCS ${cc_srcs} ${hip_srcs} DEPS ${math_library_DEPS} ${math_common_deps}) elseif(${cc_srcs_len} GREATER 0) cc_library(${TARGET} SRCS ${cc_srcs} DEPS ${math_library_DEPS} ${math_common_deps}) endif() diff --git a/paddle/fluid/operators/math/concat.hip.cu b/paddle/fluid/operators/math/concat.hip.cu new file mode 100644 index 0000000000000000000000000000000000000000..eacef0438883891671fec6e4001f862f619723cb --- /dev/null +++ b/paddle/fluid/operators/math/concat.hip.cu @@ -0,0 +1,15 @@ +/* Copyright (c) 2018 paddlepaddle Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include diff --git a/paddle/fluid/operators/math/math_function.cc b/paddle/fluid/operators/math/math_function.cc index 17e576a9d5c8f50fbe84b066a93460f03ae6bb08..299a0aed01dfe0448d896738d9fd33319b1b2887 100644 --- a/paddle/fluid/operators/math/math_function.cc +++ b/paddle/fluid/operators/math/math_function.cc @@ -278,6 +278,7 @@ void axpy( cblas_daxpy(n, alpha, x, 1, y, 1); } +template struct SetConstant; template struct SetConstant; template struct SetConstant; template struct SetConstant; diff --git a/paddle/fluid/operators/math/math_function.cu b/paddle/fluid/operators/math/math_function.cu index c6ca2693a053360ce5dc44765acf1520a11cce2c..1e909db5288afccb9dd0be08a45cf3c27048ae6f 100644 --- a/paddle/fluid/operators/math/math_function.cu +++ b/paddle/fluid/operators/math/math_function.cu @@ -348,6 +348,7 @@ void axpy( &alpha, x, 1, y, 1)); } +template struct SetConstant; template struct SetConstant; template struct SetConstant; template struct SetConstant; diff --git a/paddle/fluid/operators/math/softmax.cu b/paddle/fluid/operators/math/softmax.cu index 34ea6a91ce7743462d378cf471a5ec3a12ca51d1..5518ebed3f792a5acdfbb27976bc2c6dbd78069a 100644 --- a/paddle/fluid/operators/math/softmax.cu +++ b/paddle/fluid/operators/math/softmax.cu @@ -89,6 +89,7 @@ void SoftmaxGradCUDNNFunctor::operator()( XGrad->mutable_data(context.GetPlace()))); } +template class SoftmaxCUDNNFunctor; template class SoftmaxCUDNNFunctor; template class SoftmaxCUDNNFunctor; template class SoftmaxGradCUDNNFunctor; diff --git a/paddle/fluid/operators/mine_hard_examples_op.cc b/paddle/fluid/operators/mine_hard_examples_op.cc index 0e81d60878dce747b047abbe4641b71462373b2b..277901cff493445e1e85e92e22ea0ada0e1cba43 100644 --- a/paddle/fluid/operators/mine_hard_examples_op.cc +++ b/paddle/fluid/operators/mine_hard_examples_op.cc @@ -324,8 +324,9 @@ MatchIndices elements with value -1. } // namespace paddle namespace ops = paddle::operators; -REGISTER_OP_WITHOUT_GRADIENT(mine_hard_examples, ops::MineHardExamplesOp, - ops::MineHardExamplesOpMaker); +REGISTER_OPERATOR(mine_hard_examples, ops::MineHardExamplesOp, + ops::MineHardExamplesOpMaker, + paddle::framework::EmptyGradOpMaker); REGISTER_OP_CPU_KERNEL( mine_hard_examples, diff --git a/paddle/fluid/operators/prior_box_op.cc b/paddle/fluid/operators/prior_box_op.cc index 7ba55437cb20f802cc12ceea7777d7d78bba62a6..c22a55bce263423d5c17fffdb06b7ece02ae26da 100644 --- a/paddle/fluid/operators/prior_box_op.cc +++ b/paddle/fluid/operators/prior_box_op.cc @@ -168,7 +168,9 @@ https://arxiv.org/abs/1512.02325. } // namespace paddle namespace ops = paddle::operators; -REGISTER_OP_WITHOUT_GRADIENT(prior_box, ops::PriorBoxOp, ops::PriorBoxOpMaker); +REGISTER_OPERATOR(prior_box, ops::PriorBoxOp, ops::PriorBoxOpMaker, + paddle::framework::EmptyGradOpMaker); + REGISTER_OP_CPU_KERNEL( prior_box, ops::PriorBoxOpKernel, ops::PriorBoxOpKernel); diff --git a/paddle/fluid/operators/reader/CMakeLists.txt b/paddle/fluid/operators/reader/CMakeLists.txt index 744bd3b7ef71f83ad82979eb966369c2e9456a7d..6fa0195b9ae103418beb56cc4b0fa9ab59e93108 100644 --- a/paddle/fluid/operators/reader/CMakeLists.txt +++ b/paddle/fluid/operators/reader/CMakeLists.txt @@ -15,10 +15,12 @@ function(reader_library TARGET_NAME) PARENT_SCOPE) endfunction() +reader_library(open_files_op SRCS open_files_op.cc) reader_library(create_random_data_generator_op SRCS create_random_data_generator_op.cc) reader_library(create_shuffle_reader_op SRCS create_shuffle_reader_op.cc) reader_library(create_batch_reader_op SRCS create_batch_reader_op.cc) reader_library(create_recordio_file_reader_op SRCS create_recordio_file_reader_op.cc) reader_library(create_double_buffer_reader_op SRCS create_double_buffer_reader_op.cc) +reader_library(create_multi_pass_reader_op SRCS create_multi_pass_reader_op.cc) # Export local libraries to parent set(READER_LIBRARY ${LOCAL_READER_LIBS} PARENT_SCOPE) diff --git a/paddle/fluid/operators/reader/create_double_buffer_reader_op.cc b/paddle/fluid/operators/reader/create_double_buffer_reader_op.cc index bd0bb2ee3b0252f47318c59d9940d8dd478723de..76cdb794ccdb4a015ae8630940a5c26845e7a7b3 100644 --- a/paddle/fluid/operators/reader/create_double_buffer_reader_op.cc +++ b/paddle/fluid/operators/reader/create_double_buffer_reader_op.cc @@ -124,10 +124,13 @@ class CreateDoubleBufferReaderOpMaker : public DecoratedReaderMakerBase { }; void DoubleBufferReader::ReadNext(std::vector* out) { + if (!HasNext()) { + PADDLE_THROW("There is no next data!"); + } + if (local_buffer_.payloads_.empty()) { buffer_->Receive(&local_buffer_); } - *out = local_buffer_.payloads_; local_buffer_.payloads_.clear(); if (local_buffer_.ctx_) { diff --git a/paddle/fluid/operators/reader/create_multi_pass_reader_op.cc b/paddle/fluid/operators/reader/create_multi_pass_reader_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..4d4e9fb909eafea5328491a4097276577f28a5ba --- /dev/null +++ b/paddle/fluid/operators/reader/create_multi_pass_reader_op.cc @@ -0,0 +1,101 @@ +// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paddle/fluid/operators/detail/safe_ref.h" +#include "paddle/fluid/operators/reader/reader_op_registry.h" + +namespace paddle { +namespace operators { +namespace reader { + +class MultiPassReader : public framework::DecoratedReader { + public: + MultiPassReader(ReaderBase* reader, int pass_num) + : DecoratedReader(reader), pass_num_(pass_num), pass_count_(0) {} + + void ReadNext(std::vector* out) override { + if (!HasNext()) { + PADDLE_THROW("There is no next data!"); + } + reader_->ReadNext(out); + } + + bool HasNext() const override { + if (reader_->HasNext()) { + return true; + } else { + ++pass_count_; + if (pass_count_ >= pass_num_) { + return false; + } else { + reader_->ReInit(); + return true; + } + } + } + + void ReInit() override { + pass_count_ = 0; + reader_->ReInit(); + } + + private: + int pass_num_; + mutable int pass_count_; +}; + +class CreateMultiPassReaderOp : public framework::OperatorBase { + public: + using framework::OperatorBase::OperatorBase; + + private: + void RunImpl(const framework::Scope& scope, + const platform::Place& dev_place) const override { + const auto& underlying_reader = scope.FindVar(Input("UnderlyingReader")) + ->Get(); + auto& out = detail::Ref(scope.FindVar(Output("Out"))); + int pass_num = Attr("pass_num"); + out.GetMutable()->Reset( + new MultiPassReader(underlying_reader.Get(), pass_num)); + } +}; + +class CreateMultiPassReaderOpMaker : public DecoratedReaderMakerBase { + public: + CreateMultiPassReaderOpMaker(OpProto* op_proto, OpAttrChecker* op_checker) + : DecoratedReaderMakerBase(op_proto, op_checker) { + AddAttr("pass_num", "The number of pass to run.").GreaterThan(0); + AddComment(R"DOC( + CreateMultiPassReader Operator + + This operator creates a multi-pass reader. A multi-pass reader + is used to yield data for several pass training continuously. + It takes the the number of pass to run as one of its attributes + ('pass_num'), and maintains a pass counter to record how many + passes it has completed. When the underlying reader reach the EOF, + the multi-pass reader checks whether it has completed training + of the given number of pass. If not, the underlying reader will + be re-initialized and starts a new pass automatically. + )DOC"); + } +}; + +} // namespace reader +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators::reader; +REGISTER_DECORATED_READER_OPERATOR(create_multi_pass_reader, + ops::CreateMultiPassReaderOp, + ops::CreateMultiPassReaderOpMaker); diff --git a/paddle/fluid/operators/reader/open_files_op.cc b/paddle/fluid/operators/reader/open_files_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..414c76fea0bb916dfeafe38c0448a7a800889e03 --- /dev/null +++ b/paddle/fluid/operators/reader/open_files_op.cc @@ -0,0 +1,212 @@ +// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paddle/fluid/framework/channel.h" +#include "paddle/fluid/operators/reader/reader_op_registry.h" + +namespace paddle { +namespace operators { +namespace reader { + +class MultipleReader : public framework::ReaderBase { + public: + MultipleReader(const std::vector& file_names, + const std::vector& dims, size_t thread_num) + : file_names_(file_names), dims_(dims) { + prefetchers_.resize(thread_num); + StartNewScheduler(); + } + + void ReadNext(std::vector* out) override; + bool HasNext() const override; + void ReInit() override; + + ~MultipleReader() { EndScheduler(); } + + private: + void StartNewScheduler(); + void EndScheduler(); + void ScheduleThreadFunc(); + void PrefetchThreadFunc(std::string file_name, size_t thread_idx); + + std::vector file_names_; + std::vector dims_; + std::thread scheduler_; + std::vector prefetchers_; + framework::Channel* waiting_file_idx_; + framework::Channel* available_thread_idx_; + framework::Channel>* buffer_; + mutable std::vector local_buffer_; +}; + +void MultipleReader::ReadNext(std::vector* out) { + if (!HasNext()) { + PADDLE_THROW("There is no next data!"); + } + + if (local_buffer_.empty()) { + buffer_->Receive(&local_buffer_); + } + *out = local_buffer_; + local_buffer_.clear(); +} + +bool MultipleReader::HasNext() const { + return local_buffer_.empty() ? buffer_->Receive(&local_buffer_) : true; +} + +void MultipleReader::ReInit() { + EndScheduler(); + local_buffer_.clear(); + StartNewScheduler(); +} + +void MultipleReader::StartNewScheduler() { + size_t thread_num = prefetchers_.size(); + waiting_file_idx_ = framework::MakeChannel(file_names_.size()); + available_thread_idx_ = framework::MakeChannel(thread_num); + buffer_ = + framework::MakeChannel>(thread_num); + + for (size_t i = 0; i < file_names_.size(); ++i) { + waiting_file_idx_->Send(&i); + } + waiting_file_idx_->Close(); + for (size_t i = 0; i < thread_num; ++i) { + available_thread_idx_->Send(&i); + } + + scheduler_ = std::thread([this] { ScheduleThreadFunc(); }); +} + +void MultipleReader::EndScheduler() { + available_thread_idx_->Close(); + buffer_->Close(); + waiting_file_idx_->Close(); + if (scheduler_.joinable()) { + scheduler_.join(); + } + delete buffer_; + delete available_thread_idx_; + delete waiting_file_idx_; +} + +void MultipleReader::ScheduleThreadFunc() { + VLOG(5) << "MultipleReader schedule thread starts."; + size_t completed_thread_num = 0; + size_t thread_idx; + while (available_thread_idx_->Receive(&thread_idx)) { + std::thread& prefetcher = prefetchers_[thread_idx]; + if (prefetcher.joinable()) { + prefetcher.join(); + } + size_t file_idx; + if (waiting_file_idx_->Receive(&file_idx)) { + // Still have files to read. Start a new prefetch thread. + std::string file_name = file_names_[file_idx]; + prefetcher = std::thread([this, file_name, thread_idx] { + PrefetchThreadFunc(file_name, thread_idx); + }); + } else { + // No more file to read. + ++completed_thread_num; + if (completed_thread_num == prefetchers_.size()) { + buffer_->Close(); + break; + } + } + } + // If users invoke ReInit() when scheduler is running, it will close the + // 'avaiable_thread_idx_' and prefecther threads have no way to tell scheduler + // to release their resource. So a check is needed before scheduler ends. + for (auto& p : prefetchers_) { + if (p.joinable()) { + p.join(); + } + } + VLOG(5) << "MultipleReader schedule thread terminates."; +} + +void MultipleReader::PrefetchThreadFunc(std::string file_name, + size_t thread_idx) { + VLOG(5) << "The prefetch thread of file '" << file_name << "' starts."; + std::unique_ptr reader = + CreateReaderByFileName(file_name, dims_); + while (reader->HasNext()) { + std::vector ins; + reader->ReadNext(&ins); + if (!buffer_->Send(&ins)) { + VLOG(5) << "WARNING: The buffer channel has been closed. The prefetch " + "thread of file '" + << file_name << "' will terminate."; + break; + } + } + if (!available_thread_idx_->Send(&thread_idx)) { + VLOG(5) << "WARNING: The available_thread_idx_ channel has been closed. " + "Fail to send thread_idx."; + } + VLOG(5) << "The prefetch thread of file '" << file_name << "' terminates."; +} + +class OpenFilesOp : public framework::OperatorBase { + public: + using framework::OperatorBase::OperatorBase; + + private: + void RunImpl(const framework::Scope& scope, + const platform::Place& dev_place) const override { + const auto& shape_concat = Attr>("shape_concat"); + const auto& ranks = Attr>("ranks"); + PADDLE_ENFORCE(!shape_concat.empty() && !ranks.empty()); + PADDLE_ENFORCE_EQ(std::accumulate(ranks.begin(), ranks.end(), 0), + int(shape_concat.size()), + "The accumulate of all ranks should be equal to the " + "shape concat's length."); + const auto& file_names = Attr>("file_names"); + PADDLE_ENFORCE(!file_names.empty(), "No file to be read!"); + const size_t thread_num = Attr("thread_num"); + + auto* out = scope.FindVar(Output("Out")) + ->template GetMutable(); + out->Reset(new MultipleReader( + file_names, RestoreShapes(shape_concat, ranks), thread_num)); + } +}; + +class OpenFilesOpMaker : public FileReaderMakerBase { + public: + OpenFilesOpMaker(OpProto* op_proto, OpAttrChecker* op_checker) + : FileReaderMakerBase(op_proto, op_checker) { + AddAttr>("file_names", "Files to be read."); + AddAttr("thread_num", "The maximal concurrent prefetch thread number.") + .GreaterThan(0); + + AddComment(R"DOC( + OpenFiles Operator + + An OpenFilesOp creates a MultipleReader, which is able to + read data multi-threaded from multiple files. + )DOC"); + } +}; + +} // namespace reader +} // namespace operators +} // namespace paddle + +namespace reader = paddle::operators::reader; + +REGISTER_FILE_READER_OPERATOR(open_files, reader::OpenFilesOp, + reader::OpenFilesOpMaker); diff --git a/paddle/fluid/operators/reader/reader_op_registry.cc b/paddle/fluid/operators/reader/reader_op_registry.cc index 0ba4f3854431742eb354f8c90eb395f5d7b32b2e..fc8dc747ff0c2286f4516d8350f75d9887361924 100644 --- a/paddle/fluid/operators/reader/reader_op_registry.cc +++ b/paddle/fluid/operators/reader/reader_op_registry.cc @@ -36,6 +36,21 @@ std::unordered_map& FileReaderRegistry() { return regs; } +std::unique_ptr CreateReaderByFileName( + const std::string& file_name, const std::vector& dims) { + size_t separator_pos = file_name.find_last_of(kFileFormatSeparator); + PADDLE_ENFORCE_NE(separator_pos, std::string::npos, + "File name illegal! A legal file name should be like: " + "[file_name].[file_format] (e.g., 'data_file.recordio')."); + std::string filetype = file_name.substr(separator_pos + 1); + + auto itor = FileReaderRegistry().find(filetype); + PADDLE_ENFORCE(itor != FileReaderRegistry().end(), + "No file reader registered for '%s' format.", filetype); + framework::ReaderBase* reader = (itor->second)(file_name, dims); + return std::unique_ptr(reader); +} + FileReaderMakerBase::FileReaderMakerBase( framework::OpProtoAndCheckerMaker::OpProto* op_proto, framework::OpAttrChecker* op_checker) diff --git a/paddle/fluid/operators/reader/reader_op_registry.h b/paddle/fluid/operators/reader/reader_op_registry.h index 58f9b4ba35546571fd3b1d0c3ce128f18e248f01..929d32ad8b367865e33530f8517343c513ee9878 100644 --- a/paddle/fluid/operators/reader/reader_op_registry.h +++ b/paddle/fluid/operators/reader/reader_op_registry.h @@ -21,6 +21,8 @@ namespace paddle { namespace operators { namespace reader { +static constexpr char kFileFormatSeparator[] = "."; + using FileReaderCreator = std::function&)>; @@ -29,12 +31,15 @@ std::unordered_map& FileReaderRegistry(); template int RegisterFileReader(const std::string& filetype) { FileReaderRegistry()[filetype] = []( - const std::string& fn, const std::vector& dim) { - return new Reader(fn, dim); + const std::string& fn, const std::vector& dims) { + return new Reader(fn, dims); }; return 0; } +std::unique_ptr CreateReaderByFileName( + const std::string& file_name, const std::vector& dims); + extern std::vector RestoreShapes( const std::vector& shape_concat, const std::vector& ranks); diff --git a/paddle/fluid/operators/select_op.cc b/paddle/fluid/operators/select_op.cc index 8344a239df7b3fcbe91f91a17a3c5958013b55a6..c0bf0ff927481bc4da9cd6c4bb9b0c4a6841c891 100644 --- a/paddle/fluid/operators/select_op.cc +++ b/paddle/fluid/operators/select_op.cc @@ -27,6 +27,7 @@ namespace operators { static constexpr char kX[] = "X"; static constexpr char kCaseToExecute[] = "case_to_execute"; +static constexpr char kOutputs[] = "Out"; static constexpr char kCases[] = "cases"; static constexpr char kCasesBlock[] = "sub_block"; @@ -388,6 +389,10 @@ class SelectOpMaker : public framework::OpProtoAndCheckerMaker { "(Int) The variable the sets the index of the case to execute, " "after evaluating the channels being sent to and received from") .AsDuplicable(); + AddOutput(kOutputs, + "A set of variables, which will be assigned with values " + "generated by the operators inside the cases of Select Op.") + .AsDuplicable(); AddAttr>(kCases, "(String vector) Serialized list of" "all cases in the select op. Each" diff --git a/paddle/fluid/operators/send_op.cc b/paddle/fluid/operators/send_op.cc index 443f40e803ea31c3961ed77842bd0775e0f74f35..a77c38f633c776359648a7a3eb6fab0ada6de997 100644 --- a/paddle/fluid/operators/send_op.cc +++ b/paddle/fluid/operators/send_op.cc @@ -68,7 +68,7 @@ class SendOp : public framework::OperatorBase { for (size_t i = 0; i < ins.size(); i++) { if (NeedSend(scope, ins[i])) { - VLOG(3) << "sending " << ins[i] << " to " << epmap[i]; + VLOG(2) << "sending " << ins[i] << " to " << epmap[i]; rpc_client->AsyncSendVariable(epmap[i], ctx, scope, ins[i]); } else { VLOG(3) << "don't send no-initialied variable: " << ins[i]; @@ -77,20 +77,20 @@ class SendOp : public framework::OperatorBase { PADDLE_ENFORCE(rpc_client->Wait()); for (auto& ep : endpoints) { - VLOG(3) << "batch barrier, ep: " << ep; + VLOG(2) << "batch barrier, ep: " << ep; rpc_client->AsyncSendBatchBarrier(ep); } PADDLE_ENFORCE(rpc_client->Wait()); if (outs.size() > 0) { for (size_t i = 0; i < outs.size(); i++) { - VLOG(3) << "getting " << outs[i] << " from " << epmap[i]; + VLOG(2) << "getting " << outs[i] << " from " << epmap[i]; rpc_client->AsyncGetVariable(epmap[i], ctx, scope, outs[i]); } PADDLE_ENFORCE(rpc_client->Wait()); // tell pservers that current trainer have called fetch for (auto& ep : endpoints) { - VLOG(3) << "send fetch barrier, ep: " << ep; + VLOG(2) << "send fetch barrier, ep: " << ep; rpc_client->AsyncSendFetchBarrier(ep); } PADDLE_ENFORCE(rpc_client->Wait()); diff --git a/paddle/fluid/operators/softmax_cudnn_op.cu.cc b/paddle/fluid/operators/softmax_cudnn_op.cu.cc index 47cb336d87f8627d86ac33d6ac32c04d5d93f753..5596fa0648ccc151bc0d11de9c556599428a8d71 100644 --- a/paddle/fluid/operators/softmax_cudnn_op.cu.cc +++ b/paddle/fluid/operators/softmax_cudnn_op.cu.cc @@ -56,7 +56,9 @@ class SoftmaxGradCUDNNKernel : public framework::OpKernel { } // namespace paddle namespace ops = paddle::operators; -REGISTER_OP_KERNEL(softmax, CUDNN, ::paddle::platform::CUDAPlace, - ops::SoftmaxCUDNNKernel); -REGISTER_OP_KERNEL(softmax_grad, CUDNN, ::paddle::platform::CUDAPlace, +namespace plat = paddle::platform; +REGISTER_OP_KERNEL(softmax, CUDNN, plat::CUDAPlace, + ops::SoftmaxCUDNNKernel, + ops::SoftmaxCUDNNKernel); +REGISTER_OP_KERNEL(softmax_grad, CUDNN, plat::CUDAPlace, ops::SoftmaxGradCUDNNKernel); diff --git a/paddle/fluid/operators/softmax_mkldnn_op.cc b/paddle/fluid/operators/softmax_mkldnn_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..cf0244e8662e827a90d8472a097315680579ff6d --- /dev/null +++ b/paddle/fluid/operators/softmax_mkldnn_op.cc @@ -0,0 +1,84 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "mkldnn.hpp" +#include "paddle/fluid/operators/softmax_op.h" +#include "paddle/fluid/platform/mkldnn_helper.h" + +#include + +namespace paddle { +namespace operators { + +using paddle::framework::Tensor; +using paddle::platform::MKLDNNDeviceContext; +using paddle::platform::MKLDNNMemDesc; + +using mkldnn::memory; // Note: paddle has also "memory" namespace +using mkldnn::primitive; +using mkldnn::softmax_forward; +using mkldnn::prop_kind; +using mkldnn::stream; + +template +class SoftmaxMKLDNNKernel : public paddle::framework::OpKernel { + public: + void Compute(const paddle::framework::ExecutionContext& ctx) const override { + PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()), + "It must use CPUPlace."); + auto& dev_ctx = ctx.template device_context(); + auto mkldnn_engine = dev_ctx.GetEngine(); + const Tensor* input = ctx.Input("X"); + Tensor* output = ctx.Output("Out"); + PADDLE_ENFORCE(input->dims().size() == 2UL, + "The input of softmax op must be a 2D matrix."); + const T* input_data = input->data(); + // allocate memory for output + T* output_data = output->mutable_data(ctx.GetPlace()); + std::vector src_tz = paddle::framework::vectorize2int(input->dims()); + std::vector dst_tz = paddle::framework::vectorize2int(output->dims()); + // MKL-DNN does support softmax over selected axis. Having 2D Tensor, + // we will make normalization after final eg. axis: 1 + PADDLE_ENFORCE(((src_tz[0] == dst_tz[0]) && (src_tz[1] == dst_tz[1])), + "Softmax input and output dimensions should match"); + // Same memory descriptor to be used for input and output + memory::dims softmax_tz = {src_tz[0], src_tz[1]}; + // Currently only supports NC data format + // TODO(jczaja-intel): support more formats + auto softmax_md = + MKLDNNMemDesc({softmax_tz}, memory::f32, memory::format::nc); + // Normalization is made after innermost dimension eg. C out of NC + auto softmax_desc = softmax_forward::desc(prop_kind::forward_scoring, + softmax_md, 1 /*dim: C*/); + // create memory primitives + auto softmax_src_memory = + memory({softmax_md, mkldnn_engine}, (void*)input_data); + auto softmax_dst_memory = + memory({softmax_md, mkldnn_engine}, (void*)output_data); + auto softmax_prim_desc = + softmax_forward::primitive_desc(softmax_desc, mkldnn_engine); + auto softmax = softmax_forward(softmax_prim_desc, softmax_src_memory, + softmax_dst_memory); + std::vector pipeline{softmax}; + stream(stream::kind::eager).submit(pipeline).wait(); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; + +REGISTER_OP_KERNEL(softmax, MKLDNN, ::paddle::platform::CPUPlace, + ops::SoftmaxMKLDNNKernel); diff --git a/paddle/fluid/operators/softmax_op.cc b/paddle/fluid/operators/softmax_op.cc index 1b63f8a499e5d20d2f10c3cd1024d1bcf78764d4..e2c0f915d96b7746191572fa27b725d90cb6e2e5 100644 --- a/paddle/fluid/operators/softmax_op.cc +++ b/paddle/fluid/operators/softmax_op.cc @@ -13,7 +13,13 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/fluid/operators/softmax_op.h" +#ifdef PADDLE_WITH_CUDA +#include "paddle/fluid/platform/cudnn_helper.h" +#endif +#ifdef PADDLE_WITH_MKLDNN +#include "paddle/fluid/platform/mkldnn_helper.h" +#endif namespace paddle { namespace operators { @@ -38,26 +44,32 @@ class SoftmaxOp : public framework::OperatorWithKernel { framework::OpKernelType GetExpectedKernelType( const framework::ExecutionContext& ctx) const override { // choose cudnn kernel if the runtime supported. - bool use_cudnn = ctx.Attr("use_cudnn"); - bool runtime_cudnn_support = false; + framework::LibraryType library_{framework::LibraryType::kPlain}; #ifdef PADDLE_WITH_CUDA - if (platform::is_gpu_place(ctx.GetPlace())) { - auto& dev_ctx = - ctx.template device_context(); - runtime_cudnn_support = dev_ctx.cudnn_handle() != nullptr ? true : false; + if (platform::CanCUDNNBeUsed(ctx)) { + library_ = framework::LibraryType::kCUDNN; } #endif - framework::LibraryType library_ = framework::LibraryType::kPlain; - if (use_cudnn && runtime_cudnn_support) { - library_ = framework::LibraryType::kCUDNN; +#ifdef PADDLE_WITH_MKLDNN + if (library_ == framework::LibraryType::kPlain && + platform::CanMKLDNNBeUsed(ctx)) { + library_ = framework::LibraryType::kMKLDNN; } +#endif + + auto input_data_type = + framework::ToDataType(ctx.Input("X")->type()); + if (input_data_type == framework::proto::VarType::FP16) { + PADDLE_ENFORCE_EQ(library_, framework::LibraryType::kCUDNN, + "float16 can only be used when CUDNN is used"); + } + std::string data_format = ctx.Attr("data_format"); - return framework::OpKernelType( - framework::ToDataType(ctx.Input("X")->type()), ctx.GetPlace(), - framework::StringToDataLayout(data_format), library_); + return framework::OpKernelType(input_data_type, ctx.GetPlace(), + framework::StringToDataLayout(data_format), + library_); } }; - class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker { public: SoftmaxOpMaker(OpProto* proto, OpAttrChecker* op_checker) @@ -77,6 +89,9 @@ class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker { "Defaults to \"NHWC\". Specify the data format of the output data, " "the input will be transformed automatically. ") .SetDefault("AnyLayout"); + AddAttr("use_mkldnn", + "(bool, default false) Only used in mkldnn kernel") + .SetDefault(false); AddComment(R"DOC( Softmax Operator. @@ -119,19 +134,12 @@ class SoftmaxOpGrad : public framework::OperatorWithKernel { framework::OpKernelType GetExpectedKernelType( const framework::ExecutionContext& ctx) const override { // choose cudnn kernel if the runtime supported. - bool use_cudnn = ctx.Attr("use_cudnn"); - bool runtime_cudnn_support = false; + framework::LibraryType library_{framework::LibraryType::kPlain}; #ifdef PADDLE_WITH_CUDA - if (platform::is_gpu_place(ctx.GetPlace())) { - auto& dev_ctx = - ctx.template device_context(); - runtime_cudnn_support = dev_ctx.cudnn_handle() != nullptr ? true : false; - } -#endif - framework::LibraryType library_ = framework::LibraryType::kPlain; - if (use_cudnn && runtime_cudnn_support) { + if (platform::CanCUDNNBeUsed(ctx)) { library_ = framework::LibraryType::kCUDNN; } +#endif std::string data_format = ctx.Attr("data_format"); return framework::OpKernelType( framework::ToDataType(ctx.Input("X")->type()), ctx.GetPlace(), diff --git a/paddle/fluid/operators/target_assign_op.cc b/paddle/fluid/operators/target_assign_op.cc index a894b12fa35a121eff0b8f9d2d0eecc5ae5185f3..33ff967e5e8f5afbaa62ba39ce596687ae0a71cd 100644 --- a/paddle/fluid/operators/target_assign_op.cc +++ b/paddle/fluid/operators/target_assign_op.cc @@ -153,8 +153,8 @@ template struct NegTargetAssignFunctor, diff --git a/paddle/fluid/platform/CMakeLists.txt b/paddle/fluid/platform/CMakeLists.txt index 7eec6ab657723c6390dfa14a78d6c49a76f2a279..686c0889140f0050b37192542ca98e2f3e5f23df 100644 --- a/paddle/fluid/platform/CMakeLists.txt +++ b/paddle/fluid/platform/CMakeLists.txt @@ -49,7 +49,7 @@ nv_test(device_context_test SRCS device_context_test.cu DEPS device_context gpu_ nv_test(cudnn_helper_test SRCS cudnn_helper_test.cc DEPS dynload_cuda) nv_test(transform_test SRCS transform_test.cu DEPS paddle_memory place device_context) -cc_library(device_tracer SRCS device_tracer.cc DEPS profiler_proto ${GPU_CTX_DEPS}) +cc_library(device_tracer SRCS device_tracer.cc DEPS boost profiler_proto ${GPU_CTX_DEPS}) cc_library(profiler SRCS profiler.cc DEPS device_context device_tracer) cc_test(profiler_test SRCS profiler_test.cc DEPS profiler) diff --git a/paddle/fluid/platform/cudnn_helper.h b/paddle/fluid/platform/cudnn_helper.h index 7e001ecc56173db76e8c576e7efd66f41192f292..7c604e14eb245232ed92f53a00b9bde45c2fbaec 100644 --- a/paddle/fluid/platform/cudnn_helper.h +++ b/paddle/fluid/platform/cudnn_helper.h @@ -86,7 +86,8 @@ class CudnnDataType { public: static const cudnnDataType_t type = CUDNN_DATA_HALF; // The scaling param type is float for HALF and FLOAT tensors - typedef const float ScalingParamType; + using ScalingParamType = const float; + using BatchNormParamType = float; static ScalingParamType* kOne() { static ScalingParamType v = 1.0; return &v; @@ -101,7 +102,8 @@ template <> class CudnnDataType { public: static const cudnnDataType_t type = CUDNN_DATA_FLOAT; - typedef const float ScalingParamType; + using ScalingParamType = const float; + using BatchNormParamType = float; static ScalingParamType* kOne() { static ScalingParamType v = 1.0; return &v; @@ -116,7 +118,8 @@ template <> class CudnnDataType { public: static const cudnnDataType_t type = CUDNN_DATA_DOUBLE; - typedef const double ScalingParamType; + using ScalingParamType = const double; + using BatchNormParamType = double; static ScalingParamType* kOne() { static ScalingParamType v = 1.0; return &v; diff --git a/paddle/fluid/platform/device_context.cc b/paddle/fluid/platform/device_context.cc index 98b4178177b0a8bafd6fe34a92be2a07a2fbc5a7..59b76a1edb5ec5900520fbccb6a6f8f6e7a70aa4 100644 --- a/paddle/fluid/platform/device_context.cc +++ b/paddle/fluid/platform/device_context.cc @@ -10,43 +10,45 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/fluid/platform/device_context.h" +#include #include "paddle/fluid/memory/memory.h" - namespace paddle { namespace platform { DeviceContextPool* DeviceContextPool::pool = nullptr; -const platform::DeviceContext* DeviceContextPool::Get( - const platform::Place& place) { +platform::DeviceContext* DeviceContextPool::Get(const platform::Place& place) { auto it = device_contexts_.find(place); if (it == device_contexts_.end()) { PADDLE_THROW( "'Place' is not supported, Please re-compile with WITH_GPU " "option"); } - return it->second; + return it->second.get(); } DeviceContextPool::DeviceContextPool( const std::vector& places) { PADDLE_ENFORCE_GT(places.size(), 0); - for (size_t i = 0; i < places.size(); i++) { - if (platform::is_cpu_place(places[i])) { + using PtrType = std::unique_ptr; + std::unordered_set set; + for (auto& p : places) { + set.insert(p); + } + + for (auto& p : set) { + if (platform::is_cpu_place(p)) { #ifdef PADDLE_WITH_MKLDNN - device_contexts_.emplace(places[i], - new platform::MKLDNNDeviceContext( - boost::get(places[i]))); + device_contexts_.emplace( + p, PtrType(new MKLDNNDeviceContext(boost::get(p)))); #else - device_contexts_.emplace(places[i], - new platform::CPUDeviceContext( - boost::get(places[i]))); + device_contexts_.emplace( + p, PtrType(new CPUDeviceContext(boost::get(p)))); #endif - } else if (platform::is_gpu_place(places[i])) { + } else if (platform::is_gpu_place(p)) { #ifdef PADDLE_WITH_CUDA - device_contexts_.emplace(places[i], - new platform::CUDADeviceContext( - boost::get(places[i]))); + device_contexts_.emplace( + p, PtrType(new CUDADeviceContext(boost::get(p)))); #else PADDLE_THROW( "'CUDAPlace' is not supported, Please re-compile with WITH_GPU " @@ -159,6 +161,7 @@ CUDADeviceContext::~CUDADeviceContext() { Place CUDADeviceContext::GetPlace() const { return place_; } void CUDADeviceContext::Wait() const { + std::lock_guard guard(mutex_); PADDLE_ENFORCE(cudaStreamSynchronize(stream_)); PADDLE_ENFORCE(cudaGetLastError()); } diff --git a/paddle/fluid/platform/device_context.h b/paddle/fluid/platform/device_context.h index 603b890af13b529c490c29112a73a09cc815d07a..202394c7be7e103a609dd0999fc883c794ef0edd 100644 --- a/paddle/fluid/platform/device_context.h +++ b/paddle/fluid/platform/device_context.h @@ -103,6 +103,7 @@ class CUDADeviceContext : public DeviceContext { std::unique_ptr eigen_device_; std::unique_ptr eigen_stream_; + mutable std::mutex mutex_; cudaStream_t stream_; cudnnHandle_t cudnn_handle_; cublasHandle_t cublas_handle_; @@ -159,7 +160,7 @@ class DeviceContextPool { } /*! \brief Return handle of single device context. */ - const platform::DeviceContext* Get(const platform::Place& place); + platform::DeviceContext* Get(const platform::Place& place); template const typename DefaultDeviceContextType::TYPE* GetByPlace( @@ -172,19 +173,8 @@ class DeviceContextPool { private: static DeviceContextPool* pool; - constexpr static int LEFT_SHIFT = 8; - struct Hash { - std::hash hash_; - size_t operator()(const platform::Place& place) const { - int pre_hash = place.which() << LEFT_SHIFT; - if (platform::is_gpu_place(place)) { - pre_hash += boost::get(place).GetDeviceId(); - } - return hash_(pre_hash); - } - }; - std::unordered_map + std::unordered_map, PlaceHash> device_contexts_; DISABLE_COPY_AND_ASSIGN(DeviceContextPool); }; diff --git a/paddle/fluid/platform/place.h b/paddle/fluid/platform/place.h index 501bddfc6ec8b5d0bf554b0911c32e47fd51ec15..4cc8b377b8b671eb5a446ecbae21ba9628fbd2c8 100644 --- a/paddle/fluid/platform/place.h +++ b/paddle/fluid/platform/place.h @@ -65,6 +65,18 @@ bool is_cpu_place(const Place &); bool places_are_same_class(const Place &, const Place &); bool is_same_place(const Place &, const Place &); +struct PlaceHash { + std::size_t operator()(const Place &p) const { + constexpr size_t num_dev_bits = 4; + std::hash ihash; + size_t dev_id = 0; + if (is_gpu_place(p)) { + dev_id = boost::get(p).device; + } + return ihash(dev_id << num_dev_bits | p.which()); + } +}; + std::ostream &operator<<(std::ostream &, const Place &); template diff --git a/paddle/fluid/pybind/CMakeLists.txt b/paddle/fluid/pybind/CMakeLists.txt index 8942b5c9430ffa4e499b0ad1d2b5acf6d18ec0ab..fe991033dfc2a6ccc66b0ca5588fe8f808d1eb43 100644 --- a/paddle/fluid/pybind/CMakeLists.txt +++ b/paddle/fluid/pybind/CMakeLists.txt @@ -1,9 +1,16 @@ if(WITH_PYTHON) - cc_library(paddle_pybind SHARED - SRCS pybind.cc exception.cc protobuf.cc const_value.cc recordio.cc - DEPS pybind python backward proto_desc paddle_memory executor prune init profiler feed_fetch_method - ${GLOB_OP_LIB}) - if(NOT APPLE AND NOT ANDROID) - target_link_libraries(paddle_pybind rt) - endif(NOT APPLE AND NOT ANDROID) + if(WITH_AMD_GPU) + hip_library(paddle_pybind SHARED + SRCS pybind.cc exception.cc protobuf.cc const_value.cc recordio.cc + DEPS pybind python backward proto_desc paddle_memory executor prune init profiler feed_fetch_method + ${GLOB_OP_LIB}) + else() + cc_library(paddle_pybind SHARED + SRCS pybind.cc exception.cc protobuf.cc const_value.cc recordio.cc + DEPS pybind python backward proto_desc paddle_memory executor prune init profiler feed_fetch_method + ${GLOB_OP_LIB}) + if(NOT APPLE AND NOT ANDROID) + target_link_libraries(paddle_pybind rt) + endif(NOT APPLE AND NOT ANDROID) + endif(WITH_AMD_GPU) endif(WITH_PYTHON) diff --git a/paddle/fluid/recordio/header.cc b/paddle/fluid/recordio/header.cc index e50de15b7c2b480357f5f6c7daa2b4a676749679..ed09d58f6a3e2dba50bf4407c0463480575b248e 100644 --- a/paddle/fluid/recordio/header.cc +++ b/paddle/fluid/recordio/header.cc @@ -29,8 +29,8 @@ Header::Header(uint32_t num, uint32_t sum, Compressor c, uint32_t cs) bool Header::Parse(std::istream& is) { uint32_t magic; - size_t read_size = - is.readsome(reinterpret_cast(&magic), sizeof(uint32_t)); + is.read(reinterpret_cast(&magic), sizeof(uint32_t)); + size_t read_size = is.gcount(); if (read_size < sizeof(uint32_t)) { return false; } diff --git a/paddle/fluid/recordio/scanner.cc b/paddle/fluid/recordio/scanner.cc index d842f8fe5a4c9d1a2b564c738d97fffb02f3ccb5..c22281dc97e05173ad76ce76959833b92f11c4ee 100644 --- a/paddle/fluid/recordio/scanner.cc +++ b/paddle/fluid/recordio/scanner.cc @@ -28,6 +28,7 @@ Scanner::Scanner(const std::string &filename) { } void Scanner::Reset() { + stream_->clear(); stream_->seekg(0, std::ios::beg); ParseNextChunk(); } diff --git a/paddle/scripts/docker/build.sh b/paddle/scripts/docker/build.sh old mode 100644 new mode 100755 index 6be2bd8fad9e33cf4e1dcafdd6b8f39111bdbe88..322f72e4a58c7e8f2c26d994477cbb55551c595a --- a/paddle/scripts/docker/build.sh +++ b/paddle/scripts/docker/build.sh @@ -35,8 +35,9 @@ function cmake_gen() { -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE:-Release} ${PYTHON_FLAGS} -DWITH_DSO=ON - -DWITH_DOC=OFF + -DWITH_DOC=${WITH_DOC:-OFF} -DWITH_GPU=${WITH_GPU:-OFF} + -DWITH_AMD_GPU=${WITH_AMD_GPU:-OFF} -DWITH_DISTRIBUTE=${WITH_DISTRIBUTE:-OFF} -DWITH_MKL=${WITH_MKL:-ON} -DWITH_AVX=${WITH_AVX:-OFF} @@ -50,6 +51,7 @@ function cmake_gen() { -DWITH_STYLE_CHECK=${WITH_STYLE_CHECK:-ON} -DWITH_TESTING=${WITH_TESTING:-ON} -DWITH_FAST_BUNDLE_TEST=ON + -DCMAKE_MODULE_PATH=/opt/rocm/hip/cmake -DCMAKE_EXPORT_COMPILE_COMMANDS=ON ======================================== EOF @@ -60,8 +62,9 @@ EOF -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE:-Release} \ ${PYTHON_FLAGS} \ -DWITH_DSO=ON \ - -DWITH_DOC=OFF \ + -DWITH_DOC=${WITH_DOC:-OFF} \ -DWITH_GPU=${WITH_GPU:-OFF} \ + -DWITH_AMD_GPU=${WITH_AMD_GPU:-OFF} \ -DWITH_DISTRIBUTE=${WITH_DISTRIBUTE:-OFF} \ -DWITH_MKL=${WITH_MKL:-ON} \ -DWITH_AVX=${WITH_AVX:-OFF} \ @@ -74,6 +77,7 @@ EOF -DWITH_STYLE_CHECK=${WITH_STYLE_CHECK:-ON} \ -DWITH_TESTING=${WITH_TESTING:-ON} \ -DWITH_FAST_BUNDLE_TEST=ON \ + -DCMAKE_MODULE_PATH=/opt/rocm/hip/cmake \ -DCMAKE_EXPORT_COMPILE_COMMANDS=ON } @@ -231,7 +235,7 @@ gen_capi_package gen_fluid_inference_lib if [[ ${WITH_C_API:-OFF} == "ON" ]]; then - printf "PaddlePaddle C-API libraries was generated on build/paddle.tgz\n" + printf "PaddlePaddle C-API libraries was generated on build/paddle.tgz\n" else printf "If you need to install PaddlePaddle in develop docker image," printf "please make install or pip install build/python/dist/*.whl.\n" diff --git a/paddle/scripts/tools/build_docs/.gitignore b/paddle/scripts/tools/build_docs/.gitignore deleted file mode 100644 index 6ec14c8f5bc3774a81dbe87c44f458594b38f12c..0000000000000000000000000000000000000000 --- a/paddle/scripts/tools/build_docs/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -doc -doc_cn diff --git a/paddle/scripts/tools/build_docs/build_docs.sh b/paddle/scripts/tools/build_docs/build_docs.sh deleted file mode 100755 index f9bc8bf63ae9afdfca1ff660bc83e62e71f03005..0000000000000000000000000000000000000000 --- a/paddle/scripts/tools/build_docs/build_docs.sh +++ /dev/null @@ -1,8 +0,0 @@ -#!/bin/bash -docker run --rm \ - -v $(git rev-parse --show-toplevel):/paddle \ - -e "WITH_GPU=OFF" \ - -e "WITH_AVX=ON" \ - -e "WITH_DOC=ON" \ - -e "WOBOQ=ON" \ - ${1:-"paddlepaddle/paddle:latest-dev"} diff --git a/python/paddle/fluid/concurrency.py b/python/paddle/fluid/concurrency.py index 0fc4981a8e9da09f15e6d0a5e5c6761e01328876..d65e1a6858373d8e172cb8112a10a77d2e9bd5bc 100644 --- a/python/paddle/fluid/concurrency.py +++ b/python/paddle/fluid/concurrency.py @@ -12,7 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. -from layers.control_flow import BlockGuard, Select +from layers.control_flow import BlockGuard, equal +from .framework import Operator from layer_helper import LayerHelper, unique_name from layers import fill_constant import core @@ -75,6 +76,185 @@ class Go(BlockGuard): attrs={'sub_block': go_block}) +class SelectCase(object): + DEFAULT = 0 + SEND = 1 + RECEIVE = 2 + + def __init__(self, + case_idx, + case_to_execute, + channel_action_fn=None, + channel=None, + value=None): + self.helper = LayerHelper('conditional_block') + self.main_program = self.helper.main_program + self.is_scalar_condition = True + + self.case_to_execute = case_to_execute + self.idx = case_idx + + # Since we aren't going to use the `channel_send` or `channel_recv` + # functions directly, we just need to capture the name. + self.action = (self.SEND + if channel_action_fn.__name__ == ('channel_send') else + self.RECEIVE) if channel_action_fn else self.DEFAULT + self.value = value + self.channel = channel + + def __enter__(self): + self.block = self.main_program.create_block() + + def construct_op(self): + main_program = self.helper.main_program + cases_block = main_program.current_block() + + inner_outputs = set() + input_set = set() + params = set() + + for op in self.block.ops: + # Iterate over all operators, get all the inputs + # and add as input to the SelectCase operator. + for iname in op.input_names: + for in_var_name in op.input(iname): + if in_var_name not in inner_outputs: + input_set.add(in_var_name) + + for oname in op.output_names: + for out_var_name in op.output(oname): + inner_outputs.add(out_var_name) + + param_list = [ + cases_block.var(each_name) for each_name in params + if each_name not in input_set + ] + + # Iterate over all operators, get all the outputs + # add to the output list of SelectCase operator only if + # they exist in the parent block. + out_vars = [] + for inner_out_name in inner_outputs: + if inner_out_name in cases_block.vars: + out_vars.append(cases_block.var(inner_out_name)) + + # First, create an op that will determine whether or not this is the + # conditional variable to execute. + should_execute_block = equal( + fill_constant( + shape=[1], dtype=core.VarDesc.VarType.INT32, value=self.idx), + self.case_to_execute) + + step_scope = cases_block.create_var( + type=core.VarDesc.VarType.STEP_SCOPES) + + cases_block.append_op( + type='conditional_block', + inputs={'X': [should_execute_block], + 'Params': param_list}, + outputs={'Out': out_vars, + 'Scope': [step_scope]}, + attrs={ + 'sub_block': self.block, + 'is_scalar_condition': self.is_scalar_condition + }) + + return '%s,%s,%s,%s' % (self.idx, self.action, self.channel.name + if self.channel else '', self.value.name + if self.value else '') + + def __exit__(self, exc_type, exc_val, exc_tb): + self.main_program.rollback() + if exc_type is not None: + return False # re-raise exception + return True + + +class Select(BlockGuard): + def __init__(self, name=None): + self.helper = LayerHelper('select', name=name) + self.cases = [] + + super(Select, self).__init__(self.helper.main_program) + self.case_to_execute = fill_constant( + shape=[1], dtype=core.VarDesc.VarType.INT32, value=-1) + + def __enter__(self): + super(Select, self).__enter__() + return self + + def case(self, channel_action_fn, channel, value): + """Create a new block for this condition. + """ + select_case = SelectCase( + len(self.cases), self.case_to_execute, channel_action_fn, channel, + value) + + self.cases.append(select_case) + + return select_case + + def default(self): + """Create a default case block for this condition. + """ + default_case = SelectCase(len(self.cases), self.case_to_execute) + + self.cases.append(default_case) + + return default_case + + def __exit__(self, exc_type, exc_val, exc_tb): + if exc_type is not None: + return False + + # Create a select op and another block to wrap its + # case blocks. + select_block = self.helper.main_program.current_block() + parent_block = self.helper.main_program.block(select_block.parent_idx) + + # Construct each case op, inside the newly created select block. + serialized_cases = [] + for case in self.cases: + serialized_cases.append(case.construct_op()) + + intermediate = set() + params = set() + + for case_block in select_block.ops: + if case_block.attrs and 'sub_block' in case_block.attrs: + for each_op in case_block.attrs['sub_block'].ops: + assert isinstance(each_op, Operator) + for iname in each_op.input_names: + for in_var_name in each_op.input(iname): + if in_var_name not in intermediate: + params.add(in_var_name) + + for oname in each_op.output_names: + for out_var_name in each_op.output(oname): + intermediate.add(out_var_name) + + out_list = [ + parent_block.var(var_name) for var_name in parent_block.vars + if var_name in intermediate + ] + + X = [select_block.var_recursive(x_name) for x_name in params] + + # Needs to be used by `equal` inside the cases block. + X.append(self.case_to_execute) + + # Construct the select op. + parent_block.append_op( + type='select', + inputs={'X': X, + 'case_to_execute': self.case_to_execute}, + attrs={'sub_block': select_block, + 'cases': serialized_cases}, + outputs={'Out': out_list}) + + return super(Select, self).__exit__(exc_type, exc_val, exc_tb) + + def make_channel(dtype, capacity=0): """ Helps implementation of a concurrent program by creating a "channel" of @@ -131,7 +311,7 @@ def make_channel(dtype, capacity=0): return channel -def channel_send(channel, value, copy=False): +def channel_send(channel, value, is_copy=False): """ Sends a value through a channel variable. Used by an unbuffered or buffered channel to pass data from within or to a concurrent Go block, where @@ -141,8 +321,8 @@ def channel_send(channel, value, copy=False): channel (Variable|Channel): Channel variable created using `make_channel`. value (Variable): Value to send to channel - copy (bool): Copy data while channel send. If False, then data - is moved. The input cannot be used after move. + is_copy (bool): Copy data while channel send. If False, then data + is moved. The input cannot be used after move. (default False) Returns: Variable: The boolean status on whether or not the channel successfully sent the passed value. @@ -166,7 +346,7 @@ def channel_send(channel, value, copy=False): X = value - if copy is True: + if is_copy is True: copied_X = helper.create_variable( name=unique_name.generate(value.name + '_copy'), type=value.type, diff --git a/python/paddle/fluid/debuger.py b/python/paddle/fluid/debuger.py index 97fa182c4007cc730c06e9f95259a2509e01ecdf..7b4afa9bf65e1369329cd4648c1f5c4bd8fa8357 100644 --- a/python/paddle/fluid/debuger.py +++ b/python/paddle/fluid/debuger.py @@ -16,7 +16,6 @@ import sys import re from graphviz import GraphPreviewGenerator import proto.framework_pb2 as framework_pb2 -import paddle.fluid.core as core _vartype2str_ = [ "UNK", @@ -126,7 +125,6 @@ def pprint_block_codes(block_desc, show_backward=False): def is_var_backward(var_desc): return "@GRAD" in var_desc.name - #print(type(block_desc)) if type(block_desc) is not framework_pb2.BlockDesc: block_desc = framework_pb2.BlockDesc.FromString( block_desc.serialize_to_string()) diff --git a/python/paddle/fluid/distribute_transpiler.py b/python/paddle/fluid/distribute_transpiler.py index ad655ee96cee0744e7bedb17163faf7d8d1d8877..62147d325b699a62bd39cfbaca44874b7fc19a0f 100644 --- a/python/paddle/fluid/distribute_transpiler.py +++ b/python/paddle/fluid/distribute_transpiler.py @@ -20,6 +20,7 @@ from layer_helper import LayerHelper from distributed_spliter import * import math from . import core +import debuger class VarBlock: @@ -289,6 +290,7 @@ class DistributeTranspiler: dtype=v.dtype, shape=v.shape) recv_inputs.append(var) + # step3 optimize_block = pserver_program.create_block(0) # step 4 @@ -563,6 +565,8 @@ class DistributeTranspiler: orig_var_name = "" if suff_idx >= 0: orig_var_name = varname[:suff_idx] + else: + orig_var_name = varname return orig_var_name def _append_pserver_ops(self, optimize_block, opt_op, endpoint, @@ -577,7 +581,8 @@ class DistributeTranspiler: grad_block = None for g in self.param_grad_ep_mapping[endpoint]["grads"]: if same_or_split_var( - self._orig_varname(g.name), opt_op.input(key)[0]): + self._orig_varname(g.name), + self._orig_varname(opt_op.input(key)[0])): grad_block = g break if not grad_block: @@ -748,7 +753,7 @@ class DistributeTranspiler: param_names = [ p.name for p in self.param_grad_ep_mapping[endpoint]["params"] ] - if op.input("Param") in param_names: + if op.input("Param")[0] in param_names: return True else: for n in param_names: diff --git a/python/paddle/fluid/framework.py b/python/paddle/fluid/framework.py index 70ecffd910a46570b5a8e576d88039fa5e22e726..3e78788f470556d2196b5104f69a0a3285543ec4 100644 --- a/python/paddle/fluid/framework.py +++ b/python/paddle/fluid/framework.py @@ -918,6 +918,24 @@ class Block(object): name=v.name) self.vars[new_p.name] = new_p + def clone_variable(self, var): + """ + Clone a variable into current block. + Args: + var: the variable to be cloned. + + Returns: + The new variable cloned from 'var' in current block. + """ + assert isinstance(var, Variable) + return self.create_var( + name=var.name, + shape=var.shape, + dtype=var.dtype, + type=var.type, + lod_level=var.lod_level, + persistable=True) + class Program(object): def __init__(self): @@ -960,14 +978,14 @@ class Program(object): """Clone the Program object Set for_test to False when we want to clone the program for training. - Set for_test to True when we want to clone the program for testing. + Set for_test to True when we want to clone the program for testing. Args: for_test(bool): Some operators, such as batch_norm and drop_out ops, behave differently in training and testing. If for_test is True, the is_test attributes in these operators will be set to True for - testing purposes, otherwise, they remain unchanged. - + testing purposes, otherwise, they remain unchanged. + Returns(Program): The cloned Program object. """ diff --git a/python/paddle/fluid/layer_helper.py b/python/paddle/fluid/layer_helper.py index da7e74c901e1f5be709c5f9d73f048bfda0c5549..58b668227168c5c5e080f3928035ad98303bbae9 100644 --- a/python/paddle/fluid/layer_helper.py +++ b/python/paddle/fluid/layer_helper.py @@ -399,6 +399,9 @@ class LayerHelper(object): if isinstance(act, basestring): act = {'type': act} tmp = self.create_tmp_variable(dtype=input_var.dtype) + + if 'use_mkldnn' in self.kwargs: + act['use_mkldnn'] = self.kwargs.get('use_mkldnn') act_type = act.pop('type') self.append_op( type=act_type, diff --git a/python/paddle/fluid/layers/control_flow.py b/python/paddle/fluid/layers/control_flow.py index 02cd0a05a11d8d1d52d42c2b62799f1093d0abc2..1bb1aa30ee1019c6f80eb64b6dc20459e7a3073b 100644 --- a/python/paddle/fluid/layers/control_flow.py +++ b/python/paddle/fluid/layers/control_flow.py @@ -16,7 +16,7 @@ import contextlib from layer_function_generator import autodoc from tensor import assign, fill_constant from .. import core -from ..framework import Program, Variable, Operator, Block +from ..framework import Program, Variable, Operator from ..layer_helper import LayerHelper, unique_name from ops import logical_and, logical_not, logical_or @@ -29,7 +29,6 @@ __all__ = [ 'WhileGuard', 'While', 'Switch', - 'Select', 'lod_rank_table', 'max_sequence_len', 'topk', @@ -1212,186 +1211,6 @@ class Switch(object): return True -class SelectCase(object): - DEFAULT = 0 - SEND = 1 - RECEIVE = 2 - - def __init__(self, - case_idx, - case_to_execute, - channel_action_fn=None, - channel=None, - value=None): - self.helper = LayerHelper('conditional_block') - self.main_program = self.helper.main_program - self.is_scalar_condition = True - - self.case_to_execute = case_to_execute - self.idx = case_idx - - # Since we aren't going to use the `channel_send` or `channel_recv` - # functions directly, we just need to capture the name. - self.action = (self.SEND - if channel_action_fn.__name__ == ('channel_send') else - self.RECEIVE) if channel_action_fn else (self.DEFAULT) - self.value = value - self.channel = channel - - def __enter__(self): - self.block = self.main_program.create_block() - - def construct_op(self): - main_program = self.helper.main_program - cases_block = main_program.current_block() - - inner_outputs = set() - input_set = set() - params = set() - - for op in self.block.ops: - # Iterate over all operators, get all the inputs - # and add as input to the SelectCase operator. - for iname in op.input_names: - for in_var_name in op.input(iname): - if in_var_name not in inner_outputs: - input_set.add(in_var_name) - - for oname in op.output_names: - for out_var_name in op.output(oname): - inner_outputs.add(out_var_name) - - param_list = [ - cases_block.var(each_name) for each_name in params - if each_name not in input_set - ] - - # Iterate over all operators, get all the outputs - # add to the output list of SelectCase operator only if - # they exist in the parent block. - out_vars = [] - for inner_out_name in inner_outputs: - if inner_out_name in cases_block.vars: - out_vars.append(cases_block.var(inner_out_name)) - - # First, create an op that will determine whether or not this is the - # conditional variable to execute. - should_execute_block = equal( - fill_constant( - shape=[1], dtype=core.VarDesc.VarType.INT32, value=self.idx), - self.case_to_execute) - - step_scope = cases_block.create_var( - type=core.VarDesc.VarType.STEP_SCOPES) - - cases_block.append_op( - type='conditional_block', - inputs={'X': [should_execute_block], - 'Params': param_list}, - outputs={'Out': out_vars, - 'Scope': [step_scope]}, - attrs={ - 'sub_block': self.block, - 'is_scalar_condition': self.is_scalar_condition - }) - - return '%s,%s,%s,%s' % (self.idx, self.action, self.channel.name - if self.channel else '', self.value.name - if self.value else '') - - def __exit__(self, exc_type, exc_val, exc_tb): - self.main_program.rollback() - if exc_type is not None: - return False # re-raise exception - return True - - -class Select(BlockGuard): - def __init__(self, name=None): - self.helper = LayerHelper('select', name=name) - self.cases = [] - - super(Select, self).__init__(self.helper.main_program) - self.case_to_execute = fill_constant( - shape=[1], dtype=core.VarDesc.VarType.INT32, value=-1) - - def __enter__(self): - super(Select, self).__enter__() - return self - - def case(self, channel_action_fn, channel, value): - """Create a new block for this condition. - """ - select_case = SelectCase( - len(self.cases), self.case_to_execute, channel_action_fn, channel, - value) - - self.cases.append(select_case) - - return select_case - - def default(self): - """Create a default case block for this condition. - """ - default_case = SelectCase(len(self.cases), self.case_to_execute) - - self.cases.append(default_case) - - return default_case - - def __exit__(self, exc_type, exc_val, exc_tb): - if exc_type is not None: - return False - - # Create a select op and another block to wrap its - # case blocks. - select_block = self.helper.main_program.current_block() - parent_block = self.helper.main_program.block(select_block.parent_idx) - - # Construct each case op, inside the newly created select block. - serialized_cases = [] - for case in self.cases: - serialized_cases.append(case.construct_op()) - - intermediate = set() - params = set() - - for case_block in select_block.ops: - if case_block.attrs and 'sub_block' in case_block.attrs: - for each_op in case_block.attrs['sub_block'].ops: - assert isinstance(each_op, Operator) - for iname in each_op.input_names: - for in_var_name in each_op.input(iname): - if in_var_name not in intermediate: - params.add(in_var_name) - - for oname in each_op.output_names: - for out_var_name in each_op.output(oname): - intermediate.add(out_var_name) - - # TODO(varunarora): Figure out if defining output is needed. - out_list = [ - parent_block.var(var_name) for var_name in parent_block.vars - if var_name in intermediate - ] - - X = [select_block.var_recursive(x_name) for x_name in params] - - # Needs to be used by `equal` inside the cases block. - X.append(self.case_to_execute) - - # Construct the select op. - parent_block.append_op( - type='select', - inputs={'X': X, - 'case_to_execute': self.case_to_execute}, - attrs={'sub_block': select_block, - 'cases': serialized_cases}, - outputs={}) - - return super(Select, self).__exit__(exc_type, exc_val, exc_tb) - - class IfElseBlockGuard(object): def __init__(self, is_true, ifelse): if not isinstance(ifelse, IfElse): diff --git a/python/paddle/fluid/layers/detection.py b/python/paddle/fluid/layers/detection.py index a889ab6bdc6ac9494ef992a97292b7a2536c41c4..cd519e1ee082d27ccadc6247c149701fac31e812 100644 --- a/python/paddle/fluid/layers/detection.py +++ b/python/paddle/fluid/layers/detection.py @@ -129,13 +129,11 @@ def detection_output(loc, prior_box_var=prior_box_var, target_box=loc, code_type='decode_center_size') - old_shape = scores.shape scores = ops.reshape(x=scores, shape=(-1, old_shape[-1])) scores = nn.softmax(input=scores) scores = ops.reshape(x=scores, shape=old_shape) scores = nn.transpose(scores, perm=[0, 2, 1]) - nmsed_outs = helper.create_tmp_variable(dtype=decoded_box.dtype) helper.append_op( type="multiclass_nms", @@ -475,6 +473,7 @@ def ssd_loss(location, # 2. Compute confidence for mining hard examples # 2.1. Get the target label based on matched indices gt_label = ops.reshape(x=gt_label, shape=gt_label.shape + (1, )) + gt_label.stop_gradient = True target_label, _ = target_assign( gt_label, matched_indices, mismatch_value=background_label) # 2.2. Compute confidence loss. @@ -482,10 +481,12 @@ def ssd_loss(location, confidence = __reshape_to_2d(confidence) target_label = tensor.cast(x=target_label, dtype='int64') target_label = __reshape_to_2d(target_label) + target_label.stop_gradient = True conf_loss = nn.softmax_with_cross_entropy(confidence, target_label) # 3. Mining hard examples conf_loss = ops.reshape(x=conf_loss, shape=(num, num_prior)) + conf_loss.stop_gradient = True neg_indices = helper.create_tmp_variable(dtype='int32') dtype = matched_indices.dtype updated_matched_indices = helper.create_tmp_variable(dtype=dtype) @@ -695,6 +696,8 @@ def multi_box_head(inputs, outputs={"Boxes": box, "Variances": var}, attrs=attrs, ) + box.stop_gradient = True + var.stop_gradient = True return box, var def _reshape_with_axis_(input, axis=1): diff --git a/python/paddle/fluid/layers/io.py b/python/paddle/fluid/layers/io.py index 9c91f395e7c9d7ca76c1a5cc310bc3bbc06daec9..bc5e291ad811315ddc9d101853d69c7f5ab5082d 100644 --- a/python/paddle/fluid/layers/io.py +++ b/python/paddle/fluid/layers/io.py @@ -21,7 +21,8 @@ from ..executor import global_scope __all__ = [ 'data', 'BlockGuardServ', 'ListenAndServ', 'Send', 'open_recordio_file', - 'read_file', 'create_shuffle_reader', 'create_double_buffer_reader' + 'open_files', 'read_file', 'create_shuffle_reader', + 'create_double_buffer_reader', 'create_multi_pass_reader' ] @@ -287,6 +288,36 @@ def open_recordio_file(filename, shapes, lod_levels, dtypes): startup_var) +def open_files(filenames, thread_num, shapes, lod_levels, dtypes): + dtypes = [convert_np_dtype_to_dtype_(dt) for dt in dtypes] + shape_concat = [] + ranks = [] + + for shape in shapes: + shape_concat.extend(shape) + ranks.append(len(shape)) + + var_name = unique_name('multiple_reader') + + startup_blk = default_startup_program().current_block() + startup_var = startup_blk.create_var(name=var_name) + startup_blk.append_op( + type='open_files', + outputs={'Out': [startup_var]}, + attrs={ + 'shape_concat': shape_concat, + 'lod_levels': lod_levels, + 'ranks': ranks, + 'file_names': filenames, + 'thread_num': thread_num + }) + + startup_var.desc.set_dtypes(dtypes) + startup_var.persistable = True + return _copy_reader_var_(default_main_program().current_block(), + startup_var) + + def __create_decorated_reader__(op_type, reader, attrs): var_name = unique_name(op_type) startup_blk = default_startup_program().current_block() @@ -314,6 +345,11 @@ def create_double_buffer_reader(reader, place=None): attrs) +def create_multi_pass_reader(reader, pass_num): + return __create_decorated_reader__('create_multi_pass_reader', reader, + {'pass_num': int(pass_num)}) + + def read_file(file_obj): helper = LayerHelper('read_file') out = [ diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index 2ce68f95057f7820d7ab59ba2b41171c7ecd3654..679de6ce2aa67abe1322702fcb371eded0130698 100644 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -82,6 +82,7 @@ def fc(input, num_flatten_dims=1, param_attr=None, bias_attr=None, + use_mkldnn=False, act=None, name=None): """ @@ -163,8 +164,11 @@ def fc(input, inputs={"X": input_var, "Y": w}, outputs={"Out": tmp}, - attrs={"x_num_col_dims": num_flatten_dims, - "y_num_col_dims": 1}) + attrs={ + "x_num_col_dims": num_flatten_dims, + "y_num_col_dims": 1, + 'use_mkldnn': use_mkldnn + }) mul_results.append(tmp) # sum diff --git a/python/paddle/fluid/layers/ops.py b/python/paddle/fluid/layers/ops.py index d7bad221c5fa7b18137bf317125195267437a644..f5c6b47d243dcf4ba985cfb41fc23b44d3ed809f 100644 --- a/python/paddle/fluid/layers/ops.py +++ b/python/paddle/fluid/layers/ops.py @@ -69,6 +69,7 @@ __all__ = [ 'gaussian_random_batch_size_like', 'cumsum', 'scatter', + 'sum', ] + __activations__ for _OP in set(__all__): diff --git a/python/paddle/fluid/optimizer.py b/python/paddle/fluid/optimizer.py index a33760a528f667b7afabafa19762eca7d1ef0635..180575c35dc6e115e11cccf9fff9fb2d3cd7e9a6 100644 --- a/python/paddle/fluid/optimizer.py +++ b/python/paddle/fluid/optimizer.py @@ -13,7 +13,7 @@ # limitations under the License. from collections import defaultdict - +from paddle.fluid.framework import Program import framework import layers from backward import append_backward @@ -23,9 +23,11 @@ from initializer import Constant from layer_helper import LayerHelper from regularizer import append_regularization_ops from clip import append_gradient_clip_ops, error_clip_callback +from contextlib import contextmanager __all__ = [ - 'SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad', 'Adadelta' + 'SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad', + 'Adadelta', 'ModelAverage' ] @@ -121,7 +123,12 @@ class Optimizer(object): """ pass - def _add_accumulator(self, name, param, dtype=None, fill_value=0.0): + def _add_accumulator(self, + name, + param, + dtype=None, + fill_value=0.0, + shape=None): """Utility function to add an accumulator for a parameter Args: @@ -135,17 +142,19 @@ class Optimizer(object): param.name in self._accumulators[name]): raise Exception("Accumulator {} already exists for parameter {}". format(name, param.name)) - + if shape == None: + shape = param.shape assert isinstance(self.helper, LayerHelper) var = self.helper.create_global_variable( name=unique_name.generate(name), persistable=True, dtype=dtype or param.dtype, type=param.type, - shape=param.shape) + shape=shape) self.helper.set_variable_initializer( var, initializer=Constant(value=float(fill_value))) self._accumulators[name][param.name] = var + return var def _get_accumulator(self, name, param): """Utility function to fetch an accumulator for a parameter @@ -797,3 +806,143 @@ Adamax = AdamaxOptimizer DecayedAdagrad = DecayedAdagradOptimizer Adadelta = AdadeltaOptimizer RMSProp = RMSPropOptimizer + + +class ModelAverage(Optimizer): + """Accumulate the average of parameters whtin sliding window. The average + result will be saved in temporary variables which can be applied to + parameter variables of current model by calling 'apply()' method. And the + 'restore()' method is used to restored the parameter values of current model. + + The size of average window is determined by average_window_rate, + min_average_window, max_average_window and current update times. + + Args: + params_grads: A list of parameter-grad variable pairs. + average_window_rate: The rate of average window. + min_average_window: The minimum size of average window. + max_average_window: The maximum size of average window. + + Examples: + ... + optimizer = fluid.optimizer.Momentum() + _, params_grads = optimizer.minimize(cost) + model_average = fluid.optimizer.ModelAverage(params_grads, 0.15, + min_average_window=10000, + max_average_window=20000) + for pass_id in range(args.pass_num): + for data in train_reader(): + exe.run(fluid.default_main_program()...) + + with model_average.apply(exe): + for data in test_reader(): + exe.run(inference_program...) + """ + + def __init__(self, + params_grads, + average_window_rate, + min_average_window=10000, + max_average_window=10000, + **kwargs): + super(ModelAverage, self).__init__(0.0, **kwargs) + self.average_window = average_window_rate + self.min_average_window = min_average_window + self.max_average_window = max_average_window + self.params_grads = params_grads + for param, grad in self.params_grads: + if grad is not None: + self._append_average_accumulate_op(param) + + self.apply_program = Program() + block = self.apply_program.global_block() + with program_guard(main_program=self.apply_program): + for param_grad in self.params_grads: + if param_grad[1] is not None: + self._add_average_apply_op(block, param_grad) + + self.restore_program = Program() + block = self.restore_program.global_block() + with program_guard(main_program=self.restore_program): + for param_grad in self.params_grads: + if param_grad[1] is not None: + self._add_average_restore_op(block, param_grad) + + def _add_average_apply_op(self, block, param_grad): + param = block.clone_variable(param_grad[0]) + grad = block.clone_variable(param_grad[1]) + sum_1 = block.clone_variable(self._get_accumulator('sum_1', param)) + sum_2 = block.clone_variable(self._get_accumulator('sum_2', param)) + sum_3 = block.clone_variable(self._get_accumulator('sum_3', param)) + num_accumulates = block.clone_variable( + self._get_accumulator('num_accumulates', param)) + old_num_accumulates = block.clone_variable( + self._get_accumulator('old_num_accumulates', param)) + num_updates = block.clone_variable( + self._get_accumulator('num_updates', param)) + # backup param value to grad + layers.assign(input=param, output=grad) + # param = (sum_1 + sum_2 + sum_3) / (num_accumulates + old_num_accumulates) + tmp = layers.sum(x=[num_accumulates, old_num_accumulates]) + sum = layers.sum(x=[sum_1, sum_2, sum_3]) + tmp = layers.cast(x=tmp, dtype='float32') + sum = layers.cast(x=sum, dtype='float32') + layers.elementwise_div(x=sum, y=tmp, out=param) + + def _add_average_restore_op(self, block, param_grad): + param = block.clone_variable(param_grad[0]) + grad = block.clone_variable(param_grad[1]) + layers.assign(input=grad, output=param) + + def _append_average_accumulate_op(self, param): + self.helper = LayerHelper("average_accumulate") + sum_1 = self._add_accumulator('sum_1', param) + sum_2 = self._add_accumulator('sum_2', param) + sum_3 = self._add_accumulator('sum_3', param) + num_accumulates = self._add_accumulator( + 'num_accumulates', param, dtype='int64', shape=[1]) + old_num_accumulates = self._add_accumulator( + 'old_num_accumulates', param, dtype='int64', shape=[1]) + num_updates = self._add_accumulator( + 'num_updates', param, dtype='int64', shape=[1]) + + self.helper.append_op( + type='average_accumulates', + inputs={ + "param": param, + "in_sum_1": sum_1, + "in_sum_2": sum_2, + "in_sum_3": sum_3, + "in_num_accumulates": num_accumulates, + "in_old_num_accumulates": old_num_accumulates, + "in_num_updates": num_updates + }, + outputs={ + "out_sum_1": sum_1, + "out_sum_2": sum_2, + "out_sum_3": sum_3, + "out_num_accumulates": num_accumulates, + "out_old_num_accumulates": old_num_accumulates, + "out_num_updates": num_updates, + }, + attrs={ + "average_window": self.average_window, + "min_average_window": self.min_average_window, + "max_average_window": self.max_average_window, + }) + + @contextmanager + def apply(self, executor, need_restore=True): + """Apply average values to parameters of current model. + """ + executor.run(self.apply_program) + try: + yield + finally: + if need_restore: + self.restore(executor) + + def restore(self, executor): + """Restore parameter values of current model. + """ + executor.run(self.restore_program) diff --git a/python/paddle/fluid/tests/unittests/.gitignore b/python/paddle/fluid/tests/unittests/.gitignore index 6b3fc2a83c649c28d21c9a8a0b35c2f2fa04f269..ad02bdecf436bba925e2e3b7efb20c878df70dfd 100644 --- a/python/paddle/fluid/tests/unittests/.gitignore +++ b/python/paddle/fluid/tests/unittests/.gitignore @@ -1 +1,4 @@ mnist.recordio +mnist_0.recordio +mnist_1.recordio +mnist_2.recordio diff --git a/python/paddle/fluid/tests/unittests/test_activation_op.py b/python/paddle/fluid/tests/unittests/test_activation_op.py index eab41ebe711bd21bdc3b34ca83ab57388cc35ba2..1e3decfbaf0691e912b96b415b68353e626cf51e 100644 --- a/python/paddle/fluid/tests/unittests/test_activation_op.py +++ b/python/paddle/fluid/tests/unittests/test_activation_op.py @@ -14,6 +14,7 @@ import unittest import numpy as np +import paddle.fluid.core as core from op_test import OpTest from scipy.special import expit @@ -212,18 +213,39 @@ class TestRound(OpTest): class TestRelu(OpTest): def setUp(self): self.op_type = "relu" - x = np.random.uniform(-1, 1, [11, 17]).astype("float32") + self.dtype = np.float32 + self.init_dtype() + + x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype) # The same reason with TestAbs x[np.abs(x) < 0.005] = 0.02 - self.inputs = {'X': x} - self.outputs = {'Out': np.maximum(self.inputs['X'], 0)} + out = np.maximum(x, 0) + + self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)} + self.outputs = {'Out': out} def test_check_output(self): self.check_output() def test_check_grad(self): + if self.dtype == np.float16: + return self.check_grad(['X'], 'Out', max_relative_error=0.007) + def init_dtype(self): + pass + + +class TestFP16Relu(TestRelu): + def init_dtype(self): + self.dtype = np.float16 + + def test_check_output(self): + if core.is_compiled_with_cuda(): + place = core.CUDAPlace(0) + if core.is_float16_supported(place): + self.check_output_with_place(place, atol=1e-3) + class TestBRelu(OpTest): def setUp(self): diff --git a/python/paddle/fluid/tests/unittests/test_batch_norm_op.py b/python/paddle/fluid/tests/unittests/test_batch_norm_op.py index 80e6fa6df3c21aa19feb571916f11c41ccd6bb10..10aa63e18a6eeaa44e5b12f7532998dca2bc5e9f 100644 --- a/python/paddle/fluid/tests/unittests/test_batch_norm_op.py +++ b/python/paddle/fluid/tests/unittests/test_batch_norm_op.py @@ -31,6 +31,37 @@ def get_backward_op(scope, op, no_grad_set): return backward_op +def _reference_testing(x, scale, offset, mean, var, epsilon, data_format): + x_shape = x.shape + if len(x_shape) == 2: + if data_format == "NCHW": + x = np.reshape(x, (x.shape[0], x.shape[1], 1, 1)) + else: + x = np.reshape(x, (x.shape[0], 1, 1, x.shape[1])) + + if data_format == "NCHW": + n, c, h, w = x.shape + mean_tile = np.reshape(mean, (1, c, 1, 1)) + mean_tile = np.tile(mean_tile, (n, 1, h, w)) + var_tile = np.reshape(var, (1, c, 1, 1)) + var_tile = np.tile(var_tile, (n, 1, h, w)) + normalized = (x - mean_tile) / np.sqrt(var_tile + epsilon) + scale_tile = np.reshape(scale, (1, c, 1, 1)) + scale_tile = np.tile(scale_tile, (n, 1, h, w)) + offset_tile = np.reshape(offset, (1, c, 1, 1)) + offset_tile = np.reshape(offset_tile, (1, c, 1, 1)) + y = normalized * scale_tile + offset_tile + elif data_format == "NHWC": + normalized = (x - mean) / np.sqrt(var + epsilon) + y = normalized * scale + offset + else: + raise ValueError("Unknown data order.") + + if len(x_shape) == 2: + y = np.reshape(y, x_shape) + return y + + def _reference_training(x, scale, offset, epsilon, data_format): x_shape = x.shape if len(x_shape) == 2: @@ -155,11 +186,159 @@ def set_output_grad(scope, outputs, place, feed_dict=None): __set_tensor__(output, data) -class TestBatchNormOp(OpTest): +class TestBatchNormOpInference(OpTest): + def setUp(self): + self.dtype = np.float32 + def __assert_close(self, tensor, np_array, msg, atol=1e-4): self.assertTrue(np.allclose(np.array(tensor), np_array, atol=atol), msg) - def test_python(self): + def check_with_place(self, place, data_layout, dtype, shape): + epsilon = 0.00001 + if len(shape) == 2: + x_shape = shape + c = x_shape[1] + else: + n, h, w, c = shape[0], shape[1], shape[2], shape[3] + if data_layout == "NHWC": + x_shape = [n, h, w, c] + elif data_layout == "NCHW": + x_shape = [n, c, h, w] + else: + raise ValueError("Unknown data layout.") + scale_shape = [c] + + x_val = np.random.random_sample(x_shape).astype(dtype) + scale_val = np.random.random_sample(scale_shape).astype(np.float32) + bias_val = np.random.random_sample(scale_shape).astype(np.float32) + + mean = np.zeros(scale_shape).astype(np.float32) + variance = np.ones(scale_shape).astype(np.float32) + + y_out = _reference_testing(x_val, scale_val, bias_val, mean, variance, + epsilon, data_layout).astype(dtype) + + scope = core.Scope() + + # create input + x_tensor = create_or_get_tensor(scope, "x_val", + OpTest.np_dtype_to_fluid_dtype(x_val), + place) + scale_tensor = create_or_get_tensor( + scope, "scale_val", + OpTest.np_dtype_to_fluid_dtype(scale_val), place) + bias_tensor = create_or_get_tensor( + scope, "bias_val", OpTest.np_dtype_to_fluid_dtype(bias_val), place) + mean_tensor = create_or_get_tensor(scope, "mean", + OpTest.np_dtype_to_fluid_dtype(mean), + place) + variance_tensor = create_or_get_tensor( + scope, "variance", OpTest.np_dtype_to_fluid_dtype(variance), place) + + # create output + y_tensor = create_or_get_tensor(scope, "y_out", None, place) + saved_mean_tensor = create_or_get_tensor(scope, "saved_mean", None, + place) + saved_variance_tensor = create_or_get_tensor(scope, "saved_variance", + None, place) + mean_out_tensor = mean_tensor + variance_out_tensor = variance_tensor + + batch_norm_op = Operator( + "batch_norm", + # inputs + X="x_val", + Scale="scale_val", + Bias="bias_val", + Mean="mean", + Variance="variance", + # outputs + Y="y_out", + MeanOut="mean", + VarianceOut="variance", + SavedMean="saved_mean", + SavedVariance="saved_variance", + # attrs + is_test=True, + data_layout=data_layout, + epsilon=epsilon) + + batch_norm_op.run(scope, place) + + # check inference result + self.__assert_close( + y_tensor, + y_out, + "inference output are different at " + str(place) + ", " + + data_layout + ", " + str(np.dtype(dtype)) + + str(np.array(y_tensor)) + str(y_out), + atol=1e-3) + + def test_check_output(self): + places = [core.CPUPlace()] + if core.is_compiled_with_cuda() and core.op_support_gpu("batch_norm"): + places.append(core.CUDAPlace(0)) + + for place in places: + for data_format in ["NCHW", "NHWC"]: + self.check_with_place(place, data_format, self.dtype, + [2, 3, 4, 5]) + self.check_with_place(place, data_format, self.dtype, [2, 3]) + + +class TestFP16BatchNormOpInference(TestBatchNormOpInference): + def setUp(self): + self.dtype = np.float16 + + def test_check_output(self): + places = [] + if core.is_compiled_with_cuda() and core.op_support_gpu("batch_norm"): + place = core.CUDAPlace(0) + if core.is_float16_supported(place): + places.append(place) + + for place in places: + for data_format in ["NCHW", "NHWC"]: + self.check_with_place(place, data_format, self.dtype, + [2, 3, 4, 5]) + self.check_with_place(place, data_format, self.dtype, [2, 3]) + + +class TestBatchNormOpTraining(OpTest): + def __assert_close(self, tensor, np_array, msg, atol=1e-4): + self.assertTrue(np.allclose(np.array(tensor), np_array, atol=atol), msg) + + def test_python_testing(self): + data_format = "NHWC" + epsilon = 0.00001 + + n, h, w, c = 2, 3, 4, 5 + x_shape = [n, h, w, c] + scale_shape = [c] + + x_val = np.random.random_sample(x_shape).astype(np.float32) + scale_val = np.random.random_sample(scale_shape).astype(np.float32) + bias_val = np.random.random_sample(scale_shape).astype(np.float32) + + mean = np.zeros(scale_shape).astype(np.float32) + variance = np.ones(scale_shape).astype(np.float32) + + y_out = _reference_testing(x_val, scale_val, bias_val, mean, variance, + epsilon, "NHWC") + + # running N, C, H, W case + # should produce the same results + x_shape2 = [n, c, h, w] + x_val2 = np.transpose(x_val, (0, 3, 1, 2)) + y_out2 = _reference_testing(x_val2, scale_val, bias_val, mean, variance, + epsilon, "NCHW") + + # transfer (N, C, H, W) back to (N, H, W, C) + y_out2_trans = np.transpose(y_out2, (0, 2, 3, 1)) + self.__assert_close(y_out, y_out2_trans, "inference output") + print 'python: NHWC, NCHW, inference checking passed' + + def test_python_training(self): data_format = "NHWC" epsilon = 0.00001 momentum = 0.9 @@ -197,7 +376,7 @@ class TestBatchNormOp(OpTest): # transfer (N, C, H, W) back to (N, H, W, C) y_out2_trans = np.transpose(y_out2, (0, 2, 3, 1)) - self.__assert_close(y_out, y_out2_trans, "batch variance") + self.__assert_close(y_out, y_out2_trans, "batch output") print 'python: NHWC, NCHW, forward checking passed' # test backward now diff --git a/python/paddle/fluid/tests/unittests/test_lrn_op.py b/python/paddle/fluid/tests/unittests/test_lrn_op.py index eaff45cbb2a58798e9d55149510bec72eea370cd..2268eafdbd08cd0d6a175d19cedd79b7b984289b 100644 --- a/python/paddle/fluid/tests/unittests/test_lrn_op.py +++ b/python/paddle/fluid/tests/unittests/test_lrn_op.py @@ -87,5 +87,15 @@ class TestLRNOp(OpTest): self.check_grad(['X'], 'Out', max_relative_error=0.01) +class TestLRNMKLDNNOp(TestLRNOp): + def get_attrs(self): + attrs = TestLRNOp.get_attrs(self) + attrs['use_mkldnn'] = True + return attrs + + def test_check_output(self): + self.check_output(atol=0.002) + + if __name__ == "__main__": unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_multi_pass_reader.py b/python/paddle/fluid/tests/unittests/test_multi_pass_reader.py new file mode 100644 index 0000000000000000000000000000000000000000..8add353303e3626bbce68199a100306d4858766a --- /dev/null +++ b/python/paddle/fluid/tests/unittests/test_multi_pass_reader.py @@ -0,0 +1,65 @@ +# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import paddle.fluid as fluid +import paddle.v2 as paddle +import paddle.v2.dataset.mnist as mnist + + +class TestMultipleReader(unittest.TestCase): + def setUp(self): + self.batch_size = 64 + self.pass_num = 3 + # Convert mnist to recordio file + with fluid.program_guard(fluid.Program(), fluid.Program()): + data_file = paddle.batch(mnist.train(), batch_size=self.batch_size) + feeder = fluid.DataFeeder( + feed_list=[ + fluid.layers.data( + name='image', shape=[784]), + fluid.layers.data( + name='label', shape=[1], dtype='int64'), + ], + place=fluid.CPUPlace()) + self.num_batch = fluid.recordio_writer.convert_reader_to_recordio_file( + './mnist.recordio', data_file, feeder) + + def test_main(self): + with fluid.program_guard(fluid.Program(), fluid.Program()): + data_file = fluid.layers.open_recordio_file( + filename='./mnist.recordio', + shapes=[(-1, 784), (-1, 1)], + lod_levels=[0, 0], + dtypes=['float32', 'int64']) + data_file = fluid.layers.create_multi_pass_reader( + reader=data_file, pass_num=self.pass_num) + img, label = fluid.layers.read_file(data_file) + + if fluid.core.is_compiled_with_cuda(): + place = fluid.CUDAPlace(0) + else: + place = fluid.CPUPlace() + + exe = fluid.Executor(place) + exe.run(fluid.default_startup_program()) + + batch_count = 0 + while not data_file.eof(): + img_val, = exe.run(fetch_list=[img]) + batch_count += 1 + self.assertLessEqual(img_val.shape[0], self.batch_size) + data_file.reset() + self.assertEqual(batch_count, self.num_batch * self.pass_num) diff --git a/python/paddle/fluid/tests/unittests/test_multiple_reader.py b/python/paddle/fluid/tests/unittests/test_multiple_reader.py new file mode 100644 index 0000000000000000000000000000000000000000..69f8acf81efaba8fc0f3df4cfe3a42dc4e477df2 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/test_multiple_reader.py @@ -0,0 +1,74 @@ +# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import paddle.fluid as fluid +import paddle.v2 as paddle +import paddle.v2.dataset.mnist as mnist +from shutil import copyfile + + +class TestMultipleReader(unittest.TestCase): + def setUp(self): + self.batch_size = 64 + # Convert mnist to recordio file + with fluid.program_guard(fluid.Program(), fluid.Program()): + reader = paddle.batch(mnist.train(), batch_size=self.batch_size) + feeder = fluid.DataFeeder( + feed_list=[ # order is image and label + fluid.layers.data( + name='image', shape=[784]), + fluid.layers.data( + name='label', shape=[1], dtype='int64'), + ], + place=fluid.CPUPlace()) + self.num_batch = fluid.recordio_writer.convert_reader_to_recordio_file( + './mnist_0.recordio', reader, feeder) + copyfile('./mnist_0.recordio', './mnist_1.recordio') + copyfile('./mnist_0.recordio', './mnist_2.recordio') + + def main(self, thread_num): + file_list = [ + './mnist_0.recordio', './mnist_1.recordio', './mnist_2.recordio' + ] + with fluid.program_guard(fluid.Program(), fluid.Program()): + data_files = fluid.layers.open_files( + filenames=file_list, + thread_num=thread_num, + shapes=[(-1, 784), (-1, 1)], + lod_levels=[0, 0], + dtypes=['float32', 'int64']) + img, label = fluid.layers.read_file(data_files) + + if fluid.core.is_compiled_with_cuda(): + place = fluid.CUDAPlace(0) + else: + place = fluid.CPUPlace() + + exe = fluid.Executor(place) + exe.run(fluid.default_startup_program()) + + batch_count = 0 + while not data_files.eof(): + img_val, = exe.run(fetch_list=[img]) + batch_count += 1 + self.assertLessEqual(img_val.shape[0], self.batch_size) + data_files.reset() + self.assertEqual(batch_count, self.num_batch * 3) + + def test_main(self): + self.main(thread_num=3) # thread number equals to file number + self.main(thread_num=10) # thread number is larger than file number + self.main(thread_num=2) # thread number is less than file number diff --git a/python/paddle/fluid/tests/unittests/test_softmax_op.py b/python/paddle/fluid/tests/unittests/test_softmax_op.py index 4f20da2b926823db9e7ec92c95178b6d3d1feec9..33d60c7e31ce0817ad26ea1c1c974339936052d3 100644 --- a/python/paddle/fluid/tests/unittests/test_softmax_op.py +++ b/python/paddle/fluid/tests/unittests/test_softmax_op.py @@ -29,15 +29,20 @@ class TestSoftmaxOp(OpTest): def setUp(self): self.op_type = "softmax" self.use_cudnn = False - self.inputs = { - 'X': np.random.uniform(0.1, 1, [10, 10]).astype("float32") - } - self.outputs = { - 'Out': np.apply_along_axis(stable_softmax, 1, self.inputs['X']) + self.use_mkldnn = False + self.dtype = np.float32 + self.init_kernel_type() + + x = np.random.uniform(0.1, 1, [10, 10]).astype(self.dtype) + out = np.apply_along_axis(stable_softmax, 1, x) + self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)} + self.outputs = {'Out': out} + self.attrs = { + 'use_cudnn': self.use_cudnn, + 'use_mkldnn': self.use_mkldnn } - self.attrs = {'use_cudnn': self.use_cudnn, } - def init_op_type(self): + def init_kernel_type(self): pass def test_check_output(self): @@ -48,6 +53,8 @@ class TestSoftmaxOp(OpTest): self.check_output() def test_check_grad(self): + if self.dtype == np.float16: + return if self.use_cudnn: place = core.CUDAPlace(0) self.check_grad_with_place( @@ -57,8 +64,25 @@ class TestSoftmaxOp(OpTest): class TestSoftmaxCUDNNOp(TestSoftmaxOp): - def init_op_type(self): + def init_kernel_type(self): + self.use_cudnn = True + + +class TestSoftmaxFP16CUDNNOp(TestSoftmaxOp): + def init_kernel_type(self): self.use_cudnn = True + self.dtype = np.float16 + + def test_check_output(self): + if core.is_compiled_with_cuda(): + place = core.CUDAPlace(0) + if core.is_float16_supported(place): + self.check_output_with_place(place, atol=1e-3) + + +class TestSoftmaxMKLDNNOp(TestSoftmaxOp): + def init_kernel_type(self): + self.use_mkldnn = True if __name__ == "__main__": diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index eac2cb316835fda0a52ac9895eaa80914d0f1e5b..3684d1e8f73a21d9c6f2a5985f8b40ed6984057b 100644 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -2747,17 +2747,17 @@ def img_pool_layer(input, .. math:: - w & = 1 + \\frac{ceil(input\_width + 2 * padding - pool\_size)}{stride} + w & = 1 + ceil(\\frac{input\_width + 2 * padding - pool\_size}{stride}) - h & = 1 + \\frac{ceil(input\_height + 2 * padding\_y - pool\_size\_y)}{stride\_y} + h & = 1 + ceil(\\frac{input\_height + 2 * padding\_y - pool\_size\_y}{stride\_y}) - ceil_mode=False: .. math:: - w & = 1 + \\frac{floor(input\_width + 2 * padding - pool\_size)}{stride} + w & = 1 + floor(\\frac{input\_width + 2 * padding - pool\_size}{stride}) - h & = 1 + \\frac{floor(input\_height + 2 * padding\_y - pool\_size\_y)}{stride\_y} + h & = 1 + floor(\\frac{input\_height + 2 * padding\_y - pool\_size\_y}{stride\_y}) The example usage is: