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提交 2a2e22e3 编写于 作者: L Liu Yiqun
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Merge branch 'develop' into core_inference_fix_run

上级 5419da6e 899827f2
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Showing with 10387 addition and 2098 deletion
.pre-commit-config.yaml
浏览文件 @ 2a2e22e3
repos:
- repo: https://github.com/Lucas-C/pre-commit-hooks.git
sha: v1.0.1
hooks:
......@@ -25,6 +26,14 @@
entry: bash ./.clang_format.hook -i
language: system
files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto)$
- repo: local
hooks:
- id: cpplint-cpp-source
name: cpplint
description: Check C++ code style using cpplint.py.
entry: bash ./tools/codestyle/cpplint_pre_commit.hook
language: system
files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx)$
- repo: https://github.com/PaddlePaddle/pre-commit-golang
sha: 8337620115c25ff8333f1b1a493bd031049bd7c0
hooks:
......
.travis.yml
浏览文件 @ 2a2e22e3
......@@ -34,7 +34,7 @@ addons:
- automake
- libtool
- ccache
ssh_known_hosts: 52.76.173.135
ssh_known_hosts: 13.229.163.131
before_install:
- if [[ "$JOB" == "check_style" ]]; then sudo ln -s /usr/bin/clang-format-3.8 /usr/bin/clang-format; fi
# Paddle is using protobuf 3.1 currently. Protobuf 3.2 breaks the compatibility. So we specify the python
......
CMakeLists.txt
浏览文件 @ 2a2e22e3
......@@ -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)
......@@ -52,8 +53,7 @@ option(WITH_COVERAGE "Compile PaddlePaddle with code coverage" OFF)
option(COVERALLS_UPLOAD "Package code coverage data to coveralls" OFF)
option(ON_TRAVIS "Exclude special unit test on Travis CI" OFF)
option(WITH_C_API "Compile PaddlePaddle with C-API(Prediction)" OFF)
# TODO: Only compile PaddlePaddle fluid version by WITH_FLUID option.
option(WITH_FLUID "Compile PaddlePaddle fluid only(TODO)" OFF)
option(WITH_FLUID_ONLY "Compile PaddlePaddle fluid only" OFF)
option(WITH_GOLANG "Compile PaddlePaddle with GOLANG" OFF)
option(GLIDE_INSTALL "Download and install go dependencies " ON)
option(USE_NNPACK "Compile PaddlePaddle with NNPACK library" OFF)
......@@ -108,7 +108,7 @@ if (WITH_C_API AND WITH_PYTHON)
endif()
if (WITH_C_API)
set(WITH_FLUID OFF CACHE STRING "Disable install fluid when compile the C_API" FORCE)
set(WITH_FLUID_ONLY OFF CACHE STRING "Disable install fluid when compile the C_API" FORCE)
endif()
if(MOBILE_INFERENCE)
......@@ -146,6 +146,7 @@ include(external/cares)
include(external/grpc)
include(external/snappy) # download snappy
include(external/snappystream)
include(external/threadpool)
include(cudnn) # set cudnn libraries, must before configure
include(cupti)
......@@ -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()
......
benchmark/cluster/vgg16/vgg16_fluid.py
浏览文件 @ 2a2e22e3
......@@ -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
......
benchmark/cluster/vgg16/vgg16_tf.py
浏览文件 @ 2a2e22e3
......@@ -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
......
cmake/configure.cmake
浏览文件 @ 2a2e22e3
......@@ -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 ${MKLML_IOMP_LIB}")
......
cmake/external/boost.cmake
浏览文件 @ 2a2e22e3
......@@ -24,7 +24,7 @@ set(BOOST_PROJECT "extern_boost")
# So we use 1.41.0 here.
set(BOOST_VER "1.41.0")
set(BOOST_TAR "boost_1_41_0")
set(BOOST_URL "http://paddlepaddledeps.s3-website-us-west-1.amazonaws.com/${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)
......
cmake/external/eigen.cmake
浏览文件 @ 2a2e22e3
......@@ -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 ${CMAKE_CURRENT_BINARY_DIR}/eigen3_dummy.c)
......
cmake/external/mklml.cmake
浏览文件 @ 2a2e22e3
......@@ -34,7 +34,7 @@ SET(MKLML_DOWNLOAD_DIR "${MKLML_SOURCE_DIR}/src/${MKLML_PROJECT}")
SET(MKLML_DST_DIR "mklml")
SET(MKLML_INSTALL_ROOT "${THIRD_PARTY_PATH}/install")
SET(MKLML_INSTALL_DIR ${MKLML_INSTALL_ROOT}/${MKLML_DST_DIR})
SET(MKLML_ROOT ${MKLML_INSTALL_DIR}/${MKLML_VER})
SET(MKLML_ROOT ${MKLML_INSTALL_DIR})
SET(MKLML_INC_DIR ${MKLML_ROOT}/include)
SET(MKLML_LIB_DIR ${MKLML_ROOT}/lib)
SET(MKLML_LIB ${MKLML_LIB_DIR}/libmklml_intel.so)
......@@ -46,7 +46,7 @@ INCLUDE_DIRECTORIES(${MKLML_INC_DIR})
FILE(WRITE ${MKLML_DOWNLOAD_DIR}/CMakeLists.txt
"PROJECT(MKLML)\n"
"cmake_minimum_required(VERSION 3.0)\n"
"install(DIRECTORY ${MKLML_VER}\n"
"install(DIRECTORY ${MKLML_VER}/include ${MKLML_VER}/lib \n"
" DESTINATION ${MKLML_DST_DIR})\n")
ExternalProject_Add(
......
cmake/external/threadpool.cmake 0 → 100644
浏览文件 @ 2a2e22e3
INCLUDE(ExternalProject)
SET(THREADPOOL_SOURCE_DIR ${THIRD_PARTY_PATH}/threadpool)
SET(THREADPOOL_INCLUDE_DIR ${THREADPOOL_SOURCE_DIR}/src/extern_threadpool)
INCLUDE_DIRECTORIES(${THREADPOOL_INCLUDE_DIR})
ExternalProject_Add(
extern_threadpool
${EXTERNAL_PROJECT_LOG_ARGS}
GIT_REPOSITORY "https://github.com/progschj/ThreadPool.git"
GIT_TAG 9a42ec1329f259a5f4881a291db1dcb8f2ad9040
PREFIX ${THREADPOOL_SOURCE_DIR}
UPDATE_COMMAND ""
CONFIGURE_COMMAND ""
BUILD_COMMAND ""
INSTALL_COMMAND ""
TEST_COMMAND ""
)
if (${CMAKE_VERSION} VERSION_LESS "3.3.0")
set(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/threadpool_dummy.c)
file(WRITE ${dummyfile} "const char *dummy_threadpool = \"${dummyfile}\";")
add_library(simple_threadpool STATIC ${dummyfile})
else()
add_library(simple_threadpool INTERFACE)
endif()
add_dependencies(simple_threadpool extern_threadpool)
LIST(APPEND external_project_dependencies simple_threadpool)
cmake/generic.cmake
浏览文件 @ 2a2e22e3
......@@ -317,6 +317,82 @@ function(nv_test TARGET_NAME)
endif()
endfunction(nv_test)
function(hip_library TARGET_NAME)
if (WITH_AMD_GPU)
set(options STATIC static SHARED shared)
set(oneValueArgs "")
set(multiValueArgs SRCS DEPS)
cmake_parse_arguments(hip_library "${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 "")
......@@ -511,6 +587,9 @@ function(grpc_library TARGET_NAME)
get_filename_component(PROTO_WE ${grpc_library_PROTO} NAME_WE)
get_filename_component(PROTO_PATH ${ABS_PROTO} PATH)
#FIXME(putcn): the follwoing line is supposed to generate *.pb.h and cc, but
# somehow it didn't. line 602 to 604 is to patching this. Leaving this here
# for now to enable dist CI.
protobuf_generate_cpp(grpc_proto_srcs grpc_proto_hdrs "${ABS_PROTO}")
set(grpc_grpc_srcs "${CMAKE_CURRENT_BINARY_DIR}/${PROTO_WE}.grpc.pb.cc")
set(grpc_grpc_hdrs "${CMAKE_CURRENT_BINARY_DIR}/${PROTO_WE}.grpc.pb.h")
......@@ -521,6 +600,9 @@ function(grpc_library TARGET_NAME)
COMMAND ${PROTOBUF_PROTOC_EXECUTABLE}
ARGS --grpc_out "${CMAKE_CURRENT_BINARY_DIR}" -I "${PROTO_PATH}"
--plugin=protoc-gen-grpc="${GRPC_CPP_PLUGIN}" "${ABS_PROTO}"
COMMAND ${PROTOBUF_PROTOC_EXECUTABLE}
ARGS --cpp_out "${CMAKE_CURRENT_BINARY_DIR}" -I "${PROTO_PATH}"
"${ABS_PROTO}"
DEPENDS "${ABS_PROTO}" ${PROTOBUF_PROTOC_EXECUTABLE} extern_grpc)
# FIXME(typhoonzero): grpc generated code do not generate virtual-dtor, mark it
......
cmake/hip.cmake 0 → 100644
浏览文件 @ 2a2e22e3
if(NOT WITH_AMD_GPU)
return()
endif()
include_directories("/opt/rocm/include")
include_directories("/opt/rocm/hipblas/include")
include_directories("/opt/rocm/hiprand/include")
include_directories("/opt/rocm/rocrand/include")
include_directories("/opt/rocm/rccl/include")
include_directories("/opt/rocm/thrust")
list(APPEND EXTERNAL_LIBS "-L/opt/rocm/lib/ -lhip_hcc")
set(HIP_HCC_FLAGS "${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} ${HCC_HOME} <FLAGS> <CMAKE_CXX_LINK_FLAGS> <LINK_FLAGS> <OBJECTS> -o <TARGET> <LINK_LIBRARIES>")
set(CMAKE_HIP_CREATE_SHARED_LIBRARY "${HIP_HIPCC_CMAKE_LINKER_HELPER} ${HCC_HOME} <CMAKE_CXX_LINK_FLAGS> <LINK_FLAGS> <OBJECTS> -o <TARGET> <LINK_LIBRARIES> -shared")
set(CMAKE_HIP_CREATE_SHARED_MODULE "${HIP_HIPCC_CMAKE_LINKER_HELPER} ${HCC_HOME} <CMAKE_CXX_LINK_FLAGS> <LINK_FLAGS> <OBJECTS> -o <TARGET> <LINK_LIBRARIES> -shared")
cmake/inference_lib.cmake
浏览文件 @ 2a2e22e3
......@@ -69,6 +69,12 @@ if(NOT CBLAS_FOUND)
SRCS ${CBLAS_INSTALL_DIR}/lib ${CBLAS_INSTALL_DIR}/include
DSTS ${dst_dir} ${dst_dir}
)
elseif (WITH_MKLML)
set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/mklml")
copy(mklml_lib
SRCS ${MKLML_LIB} ${MKLML_IOMP_LIB} ${MKLML_INC_DIR}
DSTS ${dst_dir}/lib ${dst_dir}/lib ${dst_dir}
)
endif()
# paddle fluid module
......
doc/CMakeLists.txt
浏览文件 @ 2a2e22e3
add_custom_target(paddle_apis ALL
DEPENDS paddle_v2_apis paddle_fluid_apis)
add_custom_target(paddle_docs ALL
DEPENDS paddle_v2_docs paddle_v2_docs_cn
paddle_fluid_docs paddle_fluid_docs_cn)
add_subdirectory(v2)
add_subdirectory(fluid)
doc/design/images/parallel_executor_overview.dot 0 → 100644
浏览文件 @ 2a2e22e3
digraph G {
subgraph cluster_init {
label="Initialization"
startup_program [label="startup", shape=box]
node_w_g0 [label="W\nGPU0"]
startup_program -> node_w_g0 [label="Initialize"]
node_w_g1 [label="W\nGPU1"]
node_w_g0 -> node_w_g1 [label="broadcast"]
}
subgraph cluster_train {
label="forward_backward"
subgraph cluster_gpu0 {
label="GPU0"
fc_0 [label="fc\nGPU0", shape=box]
hidden_0 [label="hidden\nGPU0"]
node_w_g0 -> fc_0
fc_0 -> hidden_0
loss0 [label="loss\nGPU0"]
hidden_0 -> loss0 [label="many ops omitted"]
scale_loss_0 [label="scale_loss_gradient\nGPU0", shape=box]
loss_g0 [label="loss_grad\nGPU0"]
scale_loss_0->loss_g0
fc_g_0 [label="w_grad\nGPU0", shape=box]
loss0 -> fc_g_0
loss_g0 -> fc_g_0
hidden_0 -> fc_g_0
}
subgraph cluster_gpu1 {
label="GPU1"
fc_1 [label="fc\nGPU1", shape=box]
hidden_1 [label="hidden\nGPU1"]
node_w_g1 -> fc_1
fc_1 -> hidden_1
loss1 [label="loss\nGPU1"]
hidden_1 -> loss1 [label="many ops omitted"]
scale_loss_1 [label="scale_loss_gradient\nGPU1", shape=box]
loss_g1 [label="loss_grad\nGPU1"]
scale_loss_1->loss_g1
fc_g_1 [label="w_grad\nGPU1", shape=box]
loss1 -> fc_g_1
loss_g1 -> fc_g_1
hidden_1 -> fc_g_1
}
}
all_reduce_w [label="Merge Gradients(AllReduce)", shape=box]
fc_g_0 -> all_reduce_w
fc_g_1 -> all_reduce_w
fc_g_0_merged [label="w_grad\nMerged\nGPU0"]
fc_g_1_merged [label="w_grad\nMerged\nGPU1"]
all_reduce_w -> fc_g_0_merged
all_reduce_w -> fc_g_1_merged
subgraph cluster_optimization {
label="Optimization"
subgraph cluster_opt_gpu0 {
label="GPU0"
sgd_0 [label="SGD Op\nGPU0", shape=box]
fc_g_0_merged -> sgd_0
node_w_g0 -> sgd_0
optimized_w_0 [label="Optimized W\nGPU0"]
sgd_0 -> optimized_w_0
}
subgraph cluster_opt_gpu1 {
label="GPU1"
sgd_1 [label="SGD Op\nGPU1", shape=box]
fc_g_1_merged -> sgd_1
node_w_g1 -> sgd_1
optimized_w_1 [label="Optimized W\nGPU0"]
sgd_1 -> optimized_w_1
}
}
}
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# ParallelExecutor
## Background
Neural network models are defined as a `ProgramDesc` in Fluid. The `ProgramDesc` can be executed by an interpreter(i.e. the `executor` concept in Fluid). The instructions or operators in a `Program` will be executed, and the results will be fetched in Python side.
The executor is a very naive interpreter. It runs operators one by one. We can use `Parallel.Do` to support data parallelism, however, lacking device information in `ProgramDesc`; it is not possible to optimize the performance of `Parallel.Do`.
We want a `ProgramDesc` can be run on different nodes. It is better not to contain device information in `ProgramDesc`. However, we can write a high-performance interpreter, which can hold an alternative intermediate representation of `ProgramDesc`, to take full usage of Multi-GPUs.
ParallelExecutor is an interpreter of `ProgramDesc` which will [out-of-order execute](https://en.wikipedia.org/wiki/Out-of-order_execution) `Program` in data parallelism mode and maximise the utility of Multi-GPUs.
## Overview of MultiGPUs logic
The ParallelExecutor takes the startup program and main program as inputs. The parameters will be initialised on `GPU0` by startup program and will broadcast to multi-GPUs. The main program will be duplicated into multi-GPUs. The gradient will be merged during each iteration, and each device will optimize parameters independently. Since the gradients on each device will be merged before parameter optimization, the parameters will be the same on each device and it does not need to be broadcast the parameters.
![alt](images/parallel_executor_overview.png)
There are several optimizations for this logic.
1. We use an alternate representation in ParallelExecutor. It because the device information is critical for performance optimization.
2. The execution is out-of-order, i.e., an operator will be executed whenever the inputs of the operator are ready.
* GPU is a high-performance device; only one CPU thread cannot fulfil one GPU. So there is a thread pool to execute operators.
* Out-of-order also helps transpilers to generate `ProgramDesc`. It is no need to concern about the best order of performance when implementing a transpiler.
3. The streams of computation, merge gradients and fetch data are different.
The performance of `ResNeXt152` on `TitanX` which `batch_size=12` is shown below.
| Number of GPUs | 1 | 2 | 3 | 4|
| --- | --- | --- | --- | --- |
| Image/Sec | 17.9906 | 25.771 | 36.911 | 48.8428 |
| Speed Up | N/A | 1.43247029 | 2.05168255 | 2.71490667 |
## Static single assignment Graph
[Static single assignment form](https://en.wikipedia.org/wiki/Static_single_assignment_form)(`SSA` for short) is a common form for compiler optimization. To implement concurrent execution, we uses an `SSA` graph as an intermedia representation of `ProgramDesc`.
The `Program` is a directed acyclic graph, since a variable can be assigned multiple times. We enforce a variable will be assigned once, by adding version number to varaibles. We parsing the `Program` into a `SSA` graph. Also, ProgramExecutor duplicate `Program` into multi-devices. We also add a device number to varaibles and insert `NCCLAllReduce` into Graph.
The data structure of `SSA` graph is:
```c++
struct VarHandleBase {
OpHandleBase* generated_op_;
vector<OpHandleBase*> pending_ops_;
string name;
Place place;
size_t version;
};
struct OpHandleBase {
vector<OpHandleBase*> inputs_;
vector<OpHnadleBase*> outputs_;
};
struct SSAGraph {
// vars on each devices.
// * the vars in each map in vector is on different device.
// * the map is mapping a variable name to variable handles
// with different versions
vector<std::unordered_map<string, vector<VarHandleBase>>> vars_;
// All ops
vector<OpHandleBase> ops_;
};
```
The variable handles are the wrapper of `Variables`. The operator handles are the wrapper of `OperatorBase`. Some `OpHandle` is not an `OperatorBase`, such as `NCCLAllReduceOpHandle`, because `AllReduceOpHandle` will use new device contexts.
When the `ProgramDesc` converted into an `SSA` Graph, the [data hazard](https://en.wikipedia.org/wiki/Hazard_(computer_architecture)) problem is also need to be taken care. The dummy variables, which represent the dependency between operators, will be manually inserted into SSA graph to resolve the [data hazard](https://en.wikipedia.org/wiki/Hazard_(computer_architecture)) problem.
## Execute SSA Graph
The SSA graph can be out-of-order executed by an approximate [topological sorting](https://en.wikipedia.org/wiki/Topological_sorting) algorithm. The algorithm is
1. Maintaining a map of an operator and its needed input number.
2. If a variable is not generated by an operator, i.e., `var.generated_op == nullptr`, decrease the needed input number of its pending operators.
3. If there is an operator which needed input number is decreased to zero, just run this operator.
4. After run this operator, just mark the variables are generated and repeat step 2 until all variables are generated.
Running an operator can be asynchronized. There is a thread pool to execute an `SSA` graph.
## Synchronize GPU Kernels
The GPU is a non-blocking device. The different streams need be synchronized when switing streams. In current implementation, the synchronization based on the following algorithm:
1. `OpHandle` will record `DeviceContext` that it is used.
2. In `OpHandle::Run`, if the `DeviceContext` of current operator is different from `DeviceContext` of any input variable, just wait the generate operator of this input variable.
The `wait` are implemented by two strategies:
1. Invoke `DeviceContext->Wait()`, It will wait all operators on this device contexts complete.
2. Uses `cudaStreamWaitEvent` to sending a event to the stream. It is a non-blocking call. The wait operators will be executed in GPU.
Generally, the `cudaStreamWaitEvent` will have a better perforamnce. However, `DeviceContext->Wait()` strategy is easier to debug. The strategy can be changed in runtime.
## What's next?
* Merging gradient of dense parameters has been done. However, the merging of sparse parameters has not been done.
* The CPU version of Parallel Executor has not been implemented. The out-of-order logic will make CPU compuatation faster, too.
* A better strategy to merge gradients can be introduced. We can shrink the gradients from `float32` to `int8` or `int4` while merging. It will significantly speed up multi-GPUs training without much loss of precision.
* Combine multi-Nodes implementation. By the benifit of out-of-order, sending and recving operator can be an blocking operator, and the transpiler does not need to concern about the best position of operator.
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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}
${SPHINX_HTML_DIR_EN})
add_dependencies(paddle_fluid_docs gen_proto_py)
# configured documentation tools and intermediate build results
set(BINARY_BUILD_DIR_CN "${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}
${SPHINX_HTML_DIR_CN})
add_dependencies(paddle_fluid_docs_cn gen_proto_py)
add_subdirectory(api)
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# configured documentation tools and intermediate build results
set(BINARY_BUILD_DIR_EN "${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_apis
html
${BINARY_BUILD_DIR_EN}
${SPHINX_CACHE_DIR_EN}
${CMAKE_CURRENT_SOURCE_DIR}
${SPHINX_HTML_DIR_EN})
add_dependencies(paddle_fluid_apis gen_proto_py framework_py_proto copy_paddle_pybind)
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../../v2/build_and_install/build_from_source_cn.rst
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../../v2/build_and_install/build_from_source_en.rst
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../../v2/build_and_install/index_cn.rst
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../../v2/build_and_install/index_en.rst
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../../v2/build_and_install/pip_install_en.rst
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梯度更新算法
------------
.. toctree::
:maxdepth: 1
parameter_average.md
doc/fluid/design/algorithm/index_en.rst 0 → 100644
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Gradient Update Algorithm
--------------------------------------
.. toctree::
:maxdepth: 1
parameter_average.md
doc/fluid/design/concepts/README.md
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......@@ -2,7 +2,7 @@ A few months ago when we were trying to replace CMake with Bazel, @emailweixu su
Here are some initial thoughts. Your comments are welcome!
### Required CMake Function
# Required CMake Function
I think we need only the following few CMake functions to make a project description mean and clean:
......@@ -25,7 +25,7 @@ Also,
- to describe external dependencies, we need `external_library`.
- to build shared libraries, we need `shared_library`.
### An Example Project
## An Example Project
Suppose that we have aforementioned functions defined in our `/cmake` directory. The following example `CMakeLists.txt` describes a project including the following source files:
......@@ -102,11 +102,11 @@ shared_library(api
```
### Implementation
## Implementation
As above example CMakeLists.txt executes, each function invocation adds "nodes" to a dependency graph. It also use this graph to generate CMake commands including `add_executable`, `add_dependencies`, `target_link_libraries`, and `add_test`.
### Using Package Manager For Go
## Using Package Manager For Go
Building Go binaries and libraries need to satisfy their dependencies, generally
we can do `go get ./...` to download and compile all external dependencies. The
......@@ -122,7 +122,7 @@ problems are:
at many cloud file hosting, so users what to compile paddle by themselves can
download this "vendor" package from a mirror site.
#### Choose A Suitable Tool
### Choose A Suitable Tool
As mentioned by @wangkuiyi, [Here](https://github.com/golang/go/wiki/PackageManagementTools)
list dozens of Go package managers. We choose the tool using following principles:
......@@ -140,7 +140,7 @@ management tool has been started at: https://github.com/golang/dep to resolve
such problems, but it's currently at Alpha stage. So the best choice now is
glide obviously.
#### Manage Go Packages
### Manage Go Packages
- Dependencies: `go/glide.yaml` will store the dependencies and their versions which
is directly imported by paddle. `go/glide.lock` will store all dependencies recursively
......
doc/fluid/design/concepts/cpp_data_feeding.md
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......@@ -113,7 +113,7 @@ To solve this problem, we introduce `ReaderHolder` as a wrapper. It acts as an e
To create and invoke readers, some new ops are introduced:
### CreateReaderOp
### Operators That Create Readers
Each reader has its creation op. File readers' creation ops have no input and yield the created file reader as its output. Decorated readers' creation ops take the underlying readers as inputs and then yield new decorated readers.
......@@ -153,19 +153,52 @@ double_buffer_reader = create_double_buffer_op(batch_reader)
The forwarding ops of the corresponding `main_program` would be like this:
```
while_op {
not_completed = true
pass_count = 0
while_op(not_completed) {
has_next = has_next_op(double_buffer_reader)
if_else_op(has_next) {
batch_data = read_op(double_buffer_reader)
... (subsequent training ops)
} else {
reset_op(double_buffer_reader)
increase_op(pass_count)
not_completed = less_than_op(pass_count, reqiured_pass_num)
}
}
```
Two important considerations for these programs are as follows:
A few important considerations for these programs are as follows:
1. The multiple\_reader is the batch\_reader's underlying reader, and the batch\_reader is the double\_buffer\_reader's underlying reader. `read_op`, `has_next_op` and other reader related ops will only invoke the top-most reader. In this case, it's the double\_buffer\_reader.
1. `not_completed`, `pass_count` and other variables shown above are all Fluid Variables.
2. All readers exist in both `startup_program` and `main_program`. And they are persistable.
2. The multiple\_reader is the batch\_reader's underlying reader, and the batch\_reader is the double\_buffer\_reader's underlying reader. `read_op`, `has_next_op` and other reader related ops will only invoke the top-most reader. In this case, it's the double\_buffer\_reader.
3. All readers exist in both `startup_program` and `main_program`. And they are persistable.
### Simplify Configuration by MultiPassReader
The Program configuration mentioned above is complicated. Users need to be very familiar to concepts of Program and Block to prevent making mistakes in their code. To make the usage of C++ readers more friendly to new users, we introduce `MultiPassReader`.
`MultiPassReader` is a decorated reader. A multi-pass reader is used to continuously yield data for several training passes. It takes the number of passes to run as one of its attributes('pass_num') and maintains a counter to record how many passes it has completed. Each time its underlying reader reaches the EOF, the multi-pass reader checks whether it has completed the training of given number of pass. If not, the underlying reader will be re-initialized and starts a new pass automatically. Before completing the whole training, the return of MultiPassReader's `HasNext()` will always be `true`.
With `MultiPassReader`, the startup program would be like this:
```
multiple_reader = open_files_op(...)
batch_reader = create_batch_reader_op(multiple_reader)
multi_pass_reader = create_multi_pass_reader_op(batch_reader)
double_buffer_reader = create_double_buffer_op(multi_pass_reader)
... (other initializers)
```
The forwarding part of the corresponding `main_program` would be like this:
```
not_completed = true
while_op(not_completed) {
batch_data = read_op(double_buffer_reader)
... (subsequent training ops)
not_completed = has_next_op(double_buffer_reader)
}
```
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核心概念
-------------
.. toctree::
:maxdepth: 1
README.md
cpp_data_feeding.md
functions_operators_layers.md
program.md
variable.md
var_desc.md
tensor.md
tensor_array.md
lod_tensor.md
block.md
scope.md
executor.md
doc/fluid/design/concepts/index_en.rst 0 → 100644
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Core Concepts
--------------------------------------
.. toctree::
:maxdepth: 1
README.md
cpp_data_feeding.md
functions_operators_layers.md
program.md
variable.md
var_desc.md
tensor.md
tensor_array.md
lod_tensor.md
block.md
scope.md
executor.md
doc/fluid/design/concepts/scope.md
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......@@ -30,7 +30,7 @@ Scope is an association of a name to variable. All variables belong to `Scope`.
Variable can not belong to many scopes. If you want to use variables from parent scope, you can use `parent scope`.
1. Scope should destruct all Variables inside it when itself is destructed. User can never store `Variable` pointer somewhere else.
1. Scope should destruct all Variables inside it when itself is destructed. User can never store `Variable` pointer somewhere else.
Because Variable can only be got from Scope. When destroying Scope, we also need to destroy all the Variables in it. If user store `Variable` pointer to private data member or some global variable, the pointer will be an invalid pointer when associated `Scope` is destroyed.
......@@ -78,7 +78,7 @@ In `Scope` class, there is a private data member called `parent_`. `parent_` is
A local scope is very useful when we implement Recurrent Neural Network. Each timestep of an RNN should be a `Net`. Each `Net` of timestep (`StepNet` for short) should use an independent local scope. Just like variables in a while loop is inside a local scope in programming languages. By using a single `StepNet` and changing local scope, we can implement an RNN easily.
# Interface Design
## Interface Design
```cpp
class Variable {
......
doc/fluid/design/concepts/var_desc.md
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# Design Doc: Var_desc
## Background
PaddlePaddle divides the description of neural network computation into two stages: compile time and runtime. At compile time, the neural network computation is described as a `ProgramDesc` whereas at runtime an `Executor` interprets the `ProgramDesc` to compute the operations.
......
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# Channel Design
## Introduction
A Channel is a data structure that allows for synchronous interprocess
communication via message passing. It is a fundemental component of CSP
(communicating sequential processes), and allows for users to pass data
between threads without having to worry about synchronization.
## How to use it
Paddle offers python APIs to open and close channels, along with sending
and receiving data to/from a channel.
### Create a channel
Creates a new channel that takes in variables of a specific dtype.
- **fluid.make_channel(dtype, capacity=0)**
- **dtype**: The data type of variables being sent/received through channel
- **capacity**: The capacity of the channel. A capacity of 0 represents
an unbuffered channel. Capacity > 0 represents a buffered channel
```
ch = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR, 10)
```
### Close a channel
Closes a channel. Any pending senders and receivers will be awoken during
this time. Receivers can still receive from a closed channel, but senders
are not allowed to send any additional data to the channel (Paddle will
raise an exception if users try to send to a closed channel.)
- **fluid.channel_close(channel)**
```
fluid.channel_close(ch)
```
### Send data to a channel
Sends a variable to a channel. Currently, variables of dtype `LoDTensor`,
`LoDRankTable`, `LoDTensorArray`, `SelectedRows`, `ReaderHolder`, and
`ChannelHolder` are supported.
By default, the data of the Variable is moved from the sender to the receiver,
however the user can optionally copy the data before performing the send.
- **channel_send(channel, variable, is_copy=False)**
- **channel**: The channel to send the variable to
- **variable**: The variable to send to the channel
- **is_copy**: If set to True, channel_send will perform a variable assign
to copy the source variable to a new variable to be sent.
```
ch = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
var = fill_constant(shape=[1],dtype=core.VarDesc.VarType.INT32, value=100)
fluid.channel_send(ch, var, True)
```
### Receive data from a channel
Receives a variable from a channel. The data of the variable is moved to the
receiving variable.
- **channel_recv(channel, return_variable)**
- **channel**: The channel to receive the variable from
- **return_variable**: The destination variable used to store the data of the
variable received from the channel
```
ch = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
var = fill_constant(shape=[1],dtype=core.VarDesc.VarType.INT32, value=-1)
fluid.channel_recv(ch, var)
```
## How it Works
Channels provides a simple interface for different threads to share data.
To support the synchronization requirements, channels utilizes a series of
internal queues, locks, and conditional variables.
### QueueMessage
QueueMessage encapsulates the state of the channel send/receive operation to be
put in the **sendq/recvq**. It contains a condition variable used to lock the
thread (when there are no available sends/receives). In addition, it contains
a callback function to notify a thread when the QueueMessage is being
processed by the channel.
### Queues
- **buff_**: This queue holds the data buffer in a buffered channel. The
capacity is set to the capacity of the channel. This data buffer is not
used in an unbuffered channel.
- **sendq**: This queue holds the QueueMessage of any pending senders of a
channel. When a thread performs a channel_send operation on the channel, the
channel_send operation will put a new QueueMessage on the sendq and block the
current thread under two conditions:
1. The channel is buffered and is full
2. The channel is unbuffered and does not have a receiver
- **recvq**: This queue holds the QueueMessage of any pending receivers of a
channel. When a thread performs a channel_recv operation on the channel, the
channel_recv operation will put a new QueueMessage on the recvq and block the
current thread under two conditions:
1. The channel is buffered and there is no data on the buff_
2. The channel is unbuffered and does not have a sender
### State diagram
#### Channel Send
<p align="center">
<img src="./images/channel_send.png"/><br/>
</p>
#### Channel Receive
<p align="center">
<img src="./images/channel_recv.png"/><br/>
</p>
## Limitations and Considerations
### Variable Copy
In golang, variables in channels are copied from the sender to the receiver.
In Paddle, the data from our variables are **moved** from sender to receiver.
As a result, these variables should not be used after they are sent. We
provide a flag in channel_send method to allow users to copy the variable to
be sent before it is sent.
Please note that this is acheived by adding an **assign** operator and creating
a temporary variable that is sent in place of the original variable. Please
note that **assign** operator has limited support for only certain variables
datatypes.
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# go_op Design
## Introduction
The **go_op** allows user's of PaddlePaddle to run program blocks on a detached
thread. It works in conjuction with CSP operators (channel_send,
channel_receive, channel_open, channel_close, and select) to allow users to
concurrently process data and communicate easily between different threads.
## How to use it
```
channel = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
with fluid.Go():
# Send a tensor of value 99 to "channel" on a detached thread
tensor = fill_constant(shape=[1], dtype='int', value=99)
tensor.stop_gradient = True
fluid.channel_send(channel, tensor)
# Receive sent tensor from "channel" on the main thread
result = fill_constant(shape=[1], dtype='int', value=-1)
fluid.channel_recv(ch, result)
```
The go operator can be accessed by using the fluid.Go() control flow. This
will create a new sub block, where the user can add additional operators
to be ran on the thread.
**Note:** Since back propegation is currently not support in the go_op, users
should ensure that operators in the go block does not require gradient
calculations.
## How it Works
Similar to other control blocks, go_op will create a sub block and add it
as a child to the current block. Operators and variables defined in this
block will be added to the go sub_block.
In addition, the go operator will create a new child scope whose parent is
the global scope. Please refer to [block captures](#block-captures) for more
information.
When Paddle executor runs go_op, go_op will take the sub_block and pass it to
the executor.run method (along with a newly created local scope) on a detached
thread.
An example of the generated program description is shown below. Take note of
the **go_op** in particular. It is added as an operator in the current
block (in this example, block0). The **go_op** contains a `sub_block`
attribute, which points to the id of the block that will be executed in a
detached thread.
```
blocks {
idx: 0
parent_idx: -1
vars {
name: "return_value"
type {
type: LOD_TENSOR
lod_tensor {
tensor {
data_type: INT64
}
}
}
}
vars {
name: "status_recv"
type {
type: LOD_TENSOR
lod_tensor {
tensor {
data_type: BOOL
}
}
}
}
...
ops {
outputs {
parameter: "Out"
arguments: "channel"
}
type: "channel_create"
attrs {
name: "data_type"
type: INT
i: 7
}
attrs {
name: "capacity"
type: INT
i: 0
}
}
ops {
inputs {
parameter: "X"
arguments: "channel"
}
type: "go"
attrs {
name: "sub_block"
type: BLOCK
block_idx: 1
}
}
ops {
inputs {
parameter: "Channel"
arguments: "channel"
}
outputs {
parameter: "Out"
arguments: "return_value"
}
outputs {
parameter: "Status"
arguments: "status_recv"
}
type: "channel_recv"
}
...
}
blocks {
idx: 1
parent_idx: 0
vars {
name: "status"
type {
type: LOD_TENSOR
lod_tensor {
tensor {
data_type: BOOL
}
}
}
}
...
ops {
outputs {
parameter: "Out"
arguments: "fill_constant_1.tmp_0"
}
type: "fill_constant"
attrs {
name: "force_cpu"
type: BOOLEAN
b: false
}
attrs {
name: "value"
type: FLOAT
f: 99.0
}
attrs {
name: "shape"
type: INTS
ints: 1
}
attrs {
name: "dtype"
type: INT
i: 3
}
}
ops {
inputs {
parameter: "Channel"
arguments: "channel"
}
inputs {
parameter: "X"
arguments: "fill_constant_1.tmp_0"
}
outputs {
parameter: "Status"
arguments: "status"
}
type: "channel_send"
attrs {
name: "copy"
type: BOOLEAN
b: false
}
}
```
## Current Limitations
#### <a name="block-captures"></a>Scopes and block captures:
Paddle utilizes [scopes](./../concepts/scope.md) to store variables used in a
block. When a block is executed, a new local scope is created from the parent
scope (ie: scope derived from the parent block) and associated with the new
child block. After the block finishes executing, then the local scope and
all associated variables in the scope is deleted.
This works well in a single threaded scenario, however with introduction of
go_op, a child block may continue to execute even after the parent block has
exited. If the go_op tries to access variables located in the parent block's
scope, it may receive a segmentation fault because the parent scope may have
been deleted.
We need to implement block closures in order to prevent access to parent
scope variables from causing a segmentation fault. As a temporary workaround,
please ensure that all variables accessed in the go block is not destructed
before it is being accessed. Currently, the go_op will explicitly enforce
this requirement and raise an exception if a variable could not be found in
the scope.
Please refer to [Closure issue](https://github.com/PaddlePaddle/Paddle/issues/8502)
for more details.
#### Green Threads
Golang utilizes `green threads`, which is a mechnism for the runtime library to
manage multiple threads (instead of natively by the OS). Green threads usually
allows for faster thread creation and switching, as there is less overhead
when spawning these threads. For the first version of CSP, we only support
OS threads.
#### Backward Propegation:
go_op currently does not support backwards propagation. Please use go_op with
non training operators.
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并发编程
------------
.. toctree::
:maxdepth: 1
concurrent_programming.md
parallel_do.md
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Concurrent Programming
-------------------------
.. toctree::
:maxdepth: 1
concurrent_programming.md
parallel_do.md
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数据类型
------------
.. toctree::
:maxdepth: 1
float16.md
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Data Type
------------
.. toctree::
:maxdepth: 1
float16.md
doc/fluid/design/dist_train/distributed_lookup_table_design.md
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## Design Doc: Distributed Lookup Table Operator
# Design Doc: Distributed Lookup Table Operator
A lookup table operator in PaddlePaddle where the table could be out
of the memory of a computer.
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分布式训练
------------
.. toctree::
:maxdepth: 1
distributed_architecture.md
distributed_lookup_table_design.md
parameter_server.md
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Distributed Training
---------------------
.. toctree::
:maxdepth: 1
distributed_architecture.md
distributed_lookup_table_design.md
parameter_server.md
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动态RNN
------------
.. toctree::
:maxdepth: 1
rnn.md
rnn_design.md
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Dynamic RNN
------------
.. toctree::
:maxdepth: 1
rnn.md
rnn_design.md
doc/fluid/design/dynamic_rnn/rnn_design.md
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......@@ -99,7 +99,7 @@ private:
- 由于传递过程是以复制`shared_ptr`的方式实现，因此框架只需要传递一次 `lod_start_pos`
2. 对于不感知 `lod_start_pos` 的Op足够透明
3. 需要修改 `lod_start_pos` 的producer Op可以在 `Run` 时更新自己的 `lod_start_pos` 数据
3. 需要修改 `lod_start_pos` 的producer Op可以在 `Run` 时更新自己的 `lod_start_pos` 数据
具体的设计分为以下3小节
......@@ -189,7 +189,7 @@ struct SortedSeqItem {
std::vector<SortedSeqItem> sorted_seqs;
```
来追踪序列排序后的位置，并添加一个新的接口
来追踪序列排序后的位置，并添加一个新的接口
```c++
std::vector<SortedSeqItem> SortBySeqLen(const LODTensor& tensor);
......@@ -233,7 +233,10 @@ x x
- 将每个序列concat 为规则的mini-batch表示
## 参考文献
1. [Tensorflow Bucketing](https://www.tensorflow.org/versions/r0.12/api_docs/python/contrib.training/bucketing)
2. [mxnet Bucketing](http://mxnet.io/how_to/bucketing.html)
3. [variable length input in RNN scenario](https://discuss.pytorch.org/t/about-the-variable-length-input-in-rnn-scenario/345/5)
4. [Level of details](https://en.wikipedia.org/wiki/Level_of_detail)
[Tensorflow Bucketing](https://www.tensorflow.org/versions/r0.12/api_docs/python/contrib.training/bucketing)
[mxnet Bucketing](http://mxnet.io/how_to/bucketing.html)
[variable length input in RNN scenario](https://discuss.pytorch.org/t/about-the-variable-length-input-in-rnn-scenario/345/5)
[Level of details](https://en.wikipedia.org/wiki/Level_of_detail)
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执行流程
-------------
.. toctree::
:maxdepth: 1
switch.md
if_else_op.md
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Execution Process
--------------------------------------
.. toctree::
:maxdepth: 1
switch.md
if_else_op.md
doc/fluid/design/execution/switch.md
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### Design Doc: Switch
# Design Doc: Switch
### Background
## Background
Many programming languages provide `switch` as a generalization of `if-elif-else`. We want to add it to Fluid.
......@@ -19,7 +19,7 @@ with switch() as switch:
fluid.print("Case 3")
```
### The Semantics
## The Semantics
1. A `switch` control-flow checks cases one-by-one.
1. The condition of each case is a boolean value, which is a scalar, and differs from the `fluid.if_else` control-flow, which condition could be a vector of boolean values.
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设计思想
------------
.. toctree::
:maxdepth: 1
motivation/index_cn.rst
execution/index_cn.rst
concepts/index_cn.rst
data_type/index_cn.rst
memory/index_cn.rst
muti_devices/index_cn.rst
dynamic_rnn/index_cn.rst
concurrent/index_cn.rst
algorithm/index_cn.rst
network/index_cn.rst
modules/index_cn.rst
interface/index_cn.rst
dist_train/index_cn.rst
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Design
------------
.. toctree::
:maxdepth: 1
motivation/index_en.rst
execution/index_en.rst
concepts/index_en.rst
data_type/index_en.rst
memory/index_en.rst
muti_devices/index_en.rst
dynamic_rnn/index_en.rst
concurrent/index_en.rst
algorithm/index_en.rst
network/index_en.rst
modules/index_en.rst
interface/index_en.rst
dist_train/index_en.rst
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多语言接口
------------
TBD
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Multi-Language Interface
-----------------------
TBD
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内存管理
------------
.. toctree::
:maxdepth: 1
memory_optimization.md
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Memory Management
-------------------
.. toctree::
:maxdepth: 1
memory_optimization.md
doc/fluid/design/modules/evaluator.md
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## Evaluator Design
# Evaluator Design
### Problem Statement
## Problem Statement
During training or inference, we provide an evaluation function to measure the model performance, for example, accuracy, precision, etc. In the operator based framework design, the data passes through the network pipeline batch by batch. As a result, inside the operator, we only calculate the metrics for one minibatch. Thus, we need to provide a mechanism to calculate the metrics for each N pass/batch the user wants.
### Evaluator Design
## Evaluator Design
Currently, every operation is expressed in the graph. We divide the evaluator process into three steps.
1. Initialize the metric state and add it into the block.
......@@ -14,11 +14,11 @@ Currently, every operation is expressed in the graph. We divide the evaluator pr
3. Merge the mini-batch statistics to form the evaluation result for multiple mini-batches. When it comes to distributed training/Multi-GPU training, aggregate the value from different devices.
### Implementation
This design is shown in the Python API.
Each metric operator needs to caculate the metric statistic and return the batch-aware states. Python side is responsible for accumulating the states for each pass.
## Implementation
This design is shown in the Python API.
Each metric operator needs to caculate the metric statistic and return the batch-aware states. Python side is responsible for accumulating the states for each pass.
```python
class Evaluator(object):
"""
......@@ -32,7 +32,7 @@ class Evaluator(object):
The initialization of Evaluator should be responsible for:
create metric states and append to the main_program
"""
"""
pass
def _update_ops(self, input, label, **kwargs)
......@@ -40,14 +40,14 @@ class Evaluator(object):
Add mini-batch evaluator caculate operators to the main_program.
Add increment operator to accumulate the metric states.
"""
def reset(self, executor, reset_program=None):
"""
Reset metric states at the begin of each pass/user specified batch number.
Execute the reset_program to reset the states.
"""
def eval(self, executor, eval_program=None):
"""
......
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代码结构和重要模块
-----------------
.. toctree::
:maxdepth: 1
backward.md
python_api.md
regularization.md
infer_var_type.md
optimizer.md
prune.md
register_grad_op.md
net_op_design.md
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Code Structure and Important Modules
-------------------------------------
.. toctree::
:maxdepth: 1
backward.md
python_api.md
regularization.md
infer_var_type.md
optimizer.md
prune.md
register_grad_op.md
net_op_design.md
doc/fluid/design/modules/net_op_design.md
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# Network Design
`Network` is the container and controller of a set of operators,
user can build a real network from a `NetDesc` which is a protobuf message
user can build a real network from a `NetDesc` which is a protobuf message
and use `Network.Run()` to run all the operators in the network.
A network object knows all Operators belonging to this network. Variables,
which are inputs and outputs of these operators,
A network object knows all Operators belonging to this network. Variables,
which are inputs and outputs of these operators,
are created and managed by a hierarchy of Scope objects.
# API
## API
## Net
### Net
To make the `Network` extendable, a base class is defined like this
```c++
......@@ -43,8 +43,8 @@ class Net {
};
```
All network implementations should build networks from a protobuf message which
describes the structure of a real network; `Run` method should be implemented by
All network implementations should build networks from a protobuf message which
describes the structure of a real network; `Run` method should be implemented by
all implementations to offer a universal method to forward or backward compute a network.
`Net::Create` is a method of factory pattern and can be implemented like
......@@ -64,7 +64,7 @@ std::unique<Net> Net::Create(const NetDesc& def) {
```
Network is designed as the container of operators. to make it more extendable,
we decouple it from the related variable resources.
we decouple it from the related variable resources.
`Run(Scope* scope)` takes the scope as a argument so that it can run in different scopes.
......@@ -80,7 +80,7 @@ if (net) {
}
```
## `PlainNet` as a simple implementation of `BaseNet`
### `PlainNet` as a simple implementation of `BaseNet`
A very basic implementation is as follows. All it does is simply to run every operators in sequence.
......@@ -211,9 +211,9 @@ class NetBuilder final {
}
```
## Compatibility with RNN
### Compatibility with RNN
Benefitting from the decoupling of `PlainNet.Run` and `Scope`, `PlainNet` is compatible with future RNN design,
Benefitting from the decoupling of `PlainNet.Run` and `Scope`, `PlainNet` is compatible with future RNN design,
for example we can implement a simple recurrent neural network as follows
```c++
......
doc/fluid/design/modules/optimizer.md
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## Optimizer Design
# Optimizer Design
### The Problem
## The Problem
A PaddlePaddle program, or a block, is a sequence of operators operating variables. A training program needs to do three kinds of works:
......@@ -19,7 +19,7 @@ It's true that users should be able to create all these operators manually by ca
In this design, we propose a high-level API that automatically derives the optimisation pass and operators from the forward pass.
### High-level Python API to describe the training process
## High-level Python API to describe the training process
1. User write code to describe the network:
......@@ -54,7 +54,7 @@ In this design, we propose a high-level API that automatically derives the optim
sess.run(target= opt_op_list, ...)
```
#### Optimizer Python interface:
### Optimizer Python interface:
```python
class Optimizer(object):
......
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设计动机和目标
-------------
.. toctree::
:maxdepth: 1
api.md
refactorization.md
fluid.md
fluid_compiler.md
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Design Motivations and Goals
--------------------------------------
.. toctree::
:maxdepth: 1
api.md
refactorization.md
fluid.md
fluid_compiler.md
doc/fluid/design/motivation/refactorization.md
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......@@ -97,13 +97,13 @@ Compile Time -> IR -> Runtime
---
# Operator/OpWithKernel/OpKernel
## Operator/OpWithKernel/OpKernel
![class_diagram](http://api.paddlepaddle.org/graphviz?dot=https://gist.githubusercontent.com/reyoung/53df507f6749762675dff3e7ce53372f/raw/49caf1fb70820fb4a6c217634317c9306f361f36/op_op_with_kern_class_diagram.dot)
---
# Operator
## Operator
![class_diagram](http://api.paddlepaddle.org/graphviz?dot=https://gist.githubusercontent.com/reyoung/53df507f6749762675dff3e7ce53372f/raw/dd598e8f1976f5759f58af5e5ef94738a6b2e661/op.dot)
* `Operator` is the fundamental building block of the user interface.
......@@ -113,7 +113,7 @@ Compile Time -> IR -> Runtime
---
# OpWithKernel/Kernel
## OpWithKernel/Kernel
![class_diagram](http://api.paddlepaddle.org/graphviz?dot=https://gist.githubusercontent.com/reyoung/53df507f6749762675dff3e7ce53372f/raw/9d7f4eba185cf41c8e2fbfb40ae21890dbddcd39/op_with_kernel.dot)
......@@ -124,7 +124,7 @@ Compile Time -> IR -> Runtime
---
# Why separate Kernel and Operator
## Why separate Kernel and Operator
* Separate GPU and CPU code.
* Make Paddle capable of running without GPU.
......@@ -132,7 +132,7 @@ Compile Time -> IR -> Runtime
* For example, same multiplication op can have different implementations kernels such as FP16 kernel, FP32 kernel, MKL, eigen kernel.
---
# Libraries for Kernel development
## Libraries for Kernel development
* `Eigen::Tensor` contains basic math and element-wise functions.
* Note that `Eigen::Tensor` has broadcast implementation.
......@@ -143,16 +143,16 @@ Compile Time -> IR -> Runtime
* Hand-writing `GPUKernel` and `CPU` code
* Do not write in header (`.h`) files. CPU Kernel should be in cpp source (`.cc`) and GPU kernels should be in cuda (`.cu`) files. (GCC cannot compile GPU code.)
---
# Operator Registration
## Operator Registration
## Why is registration necessary?
### Why is registration necessary?
We need a method to build mappings between Op type names and Op classes.
## How is registration implemented?
### How is registration implemented?
Maintaining a map, whose key is the type name and the value is the corresponding Op constructor.
---
# The Registry Map
## The Registry Map
### `OpInfoMap`
......@@ -166,7 +166,7 @@ Maintaining a map, whose key is the type name and the value is the corresponding
- **`checker`**: Used to check attributes.
---
# Related Concepts
## Related Concepts
### Op_Maker
It's constructor takes `proto` and `checker`. They are completed during Op_Maker's construction. ([ScaleOpMaker](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37))
......@@ -178,7 +178,7 @@ REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class)
```
---
# Registration Process
## Registration Process
1. Write an Op class and its gradient Op class, if required.
2. Write an Op maker class. In the constructor of this class, describe the inputs, outputs and attributes of the operator.
3. Invoke the macro `REGISTER_OP`. This macro will
......@@ -186,13 +186,13 @@ REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class)
2. Using the completed `proto` and `checker`, it will add a new key-value pair to the `OpInfoMap`
---
# Backward Module (1/2)
## Backward Module (1/2)
### Create Backward Operator
- Mapping from forward Op to backward Op
![backward](https://gist.githubusercontent.com/dzhwinter/a6fbd4623ee76c459f7f94591fd1abf0/raw/61026ab6e518e66bde66a889bc42557a1fccff33/backward.png)
---
# Backward Module (2/2)
## Backward Module (2/2)
### Build Backward Network
- **Input**: a graph of forward operators
- **Output**: a graph of backward operators
......@@ -205,7 +205,7 @@ REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class)
---
# Scope, Variable, Tensor
## Scope, Variable, Tensor
* `Tensor` is an n-dimension array with type.
* Only dims and data pointers are stored in `Tensor`.
......@@ -218,8 +218,8 @@ REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class)
* `Scope` has a hierarchical structure. The local scope can get variables from its parent scope.
---
# Block (in design)
## the difference between original RNNOp and Block
## Block (in design)
### the difference between original RNNOp and Block
- As an operator is more intuitive than `RNNOp`,
- Offers a new interface `Eval(targets)` to deduce the minimal block to `Run`,
- Fits the compile-time/ runtime separation design paradigm.
......@@ -227,7 +227,7 @@ REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class)
- When graph executes, a Block with `BlockDesc` is passed. It then creates `Op` and `Var` instances and then invokes `Run`.
---
# Milestone
## Milestone
- Take Paddle/books as the main line, the requirement of the models motivates framework refactoring,
- Model migration
- Framework development gives **priority support** to model migration, for example,
......@@ -240,7 +240,7 @@ REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class)
- Accept imperfection, concentrate on solving the specific problem at the right price.
---
# Control the migration quality
## Control the migration quality
- Compare the performance of migrated models with old ones.
- Follow the google C++ style guide.
- Build the automatic workflow of generating Python/C++ documentations.
......
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多设备支持
------------
.. toctree::
:maxdepth: 1
operator_kernel_type.md
kernel_selection.md
kernel_hint_design.md
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Multi-Device Support
----------------------
.. toctree::
:maxdepth: 1
operator_kernel_type.md
kernel_selection.md
kernel_hint_design.md
doc/fluid/design/muti_devices/kernel_hint_design.md
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## Problem
# Problem
In PaddlePaddle's [Design](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/switch_kernel.md), one Operator may have multiple kernels. Users may have some personal preference to choose a certain type of kernel for an operator, such as `force_cpu` to choose a CPU kernel, `use_cudnn` to choose a CUDNN kernel, we need to provide a way for users to do this.
In the current design, we use KernelType to describe one kernel.
......
doc/fluid/design/muti_devices/kernel_selection.md
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## Background
# Background
Every operator has many kernels because there are multiple data types, places, data layout, library type that Fluid supports. We use the `OpKernelType ` to describe kernel types that operators can hold.
The `OpKernelType ` is as follows:
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复杂网络设计
------------
.. toctree::
:maxdepth: 1
sequence_decoder.md
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Complex Network Design
------------------------
.. toctree::
:maxdepth: 1
sequence_decoder.md
doc/fluid/dev/api_doc_std_cn.md
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......@@ -45,11 +45,11 @@ API文档须使用reStructuredText格式撰写，该格式详情请参考[链接
- Python API Definition
- 格式：
[Python API Definition]
- 示例
```
fc(input,
size,
......@@ -63,19 +63,19 @@ API文档须使用reStructuredText格式撰写，该格式详情请参考[链接
```
- Function Description
- 格式
本模块应包含以下内容（排列顺序为文档撰写顺序）：
[Function Description]
[Formula]
[Symbols' Descriptions if necessary]
[References if necessary]
- 示例
[Function Description]
......@@ -119,18 +119,18 @@ API文档须使用reStructuredText格式撰写，该格式详情请参考[链接
[References if necessary]
因fc没有必要列出的参考文献，故该内容省略。其他情况下需明确给出对应的参考文献和对应连接，以 layer_norm 为例：
```
Refer to `Layer Normalization <https://arxiv.org/pdf/1607.06450v1.pdf>`_ for more details.
```
- Args Description
- 格式
\[Arg's Name\][(Data Type, Default Value)][Description]
- 示例
fc的部分参数注释如下：
......@@ -145,35 +145,35 @@ API文档须使用reStructuredText格式撰写，该格式详情请参考[链接
```
- Returns
- 格式
[Name][Shape]
- 示例
```
Returns:
A tensor variable storing the transformation result.
```
当返回值为包含多个参数的tuple时，应按顺序逐个介绍各参数，以dynamic_lstm为例：
```
Returns:
A tuple containing:
The hidden state of LSTM whose shape is (T X D).
The cell state of LSTM whose shape is (T X D).
```
- Raises
- 格式
[Exception Type][Condition]
- 示例
```
Raises:
ValueError: If the rank of the input is less than 2.
......@@ -182,7 +182,7 @@ API文档须使用reStructuredText格式撰写，该格式详情请参考[链接
- Note
- 格式
[Note]
- 示例
......@@ -198,15 +198,15 @@ API文档须使用reStructuredText格式撰写，该格式详情请参考[链接
2. When num_heads == 1, scaled_dot_product_attention has no learnable
parameters.
```
- Examples
- 格式
\[Python Code Snipper]
- 示例
```
Examples:
.. code-block:: python
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# API Doc Standard
- [API Doc Structure](#API Doc Structure)
- [Format and Examples](#Format and Examples)
- [Complete Example](#Complete Example)
## API Doc Structure
API Doc should contain the following parts(please write them in order):
- Python API Definition
The definition of API
- Function Description
Description of API's function.
The description includes: meaning, purpose and operation on input of API, reference and corresponding link(if any), formula(if necessary) and explanations of key variables in the formula.
- Args Description
Description of API parameters.
Introduce parameters one by one according to the order in API definition.
The introduction includes: data type, default value(if any), meaning, etc.
- Returns
Introduction of API returned value.
Introduce meaning of returned value, provide correspoding format if necessary.
If returned value is a tuple containing multiple parameters, then introduce parameters one by one in order.
- Raises（if any）
Abnormality, error that may occur, and possible reasons. If there are more than one possible abnormity or error, they should be listed in order.
- Note（if any）
Matters needing attention. If there are more than one matters, they should be listed in order.
- Examples
Examples of how to use API.
## Format and Examples
API documentation must obey reStructuredText format, please refer to [here](http://sphinx-doc-zh.readthedocs.io/en/latest/rest.html).
Format and examples of each part of API documantation are as follows: (take fc for example)
- Python API Definition
- Format
[Python API Definition]
- Example
```
fc(input,
size,
num_flatten_dims=1,
param_attr=None,
bias_attr=None,
act=None,
name=None,
main_program=None,
startup_program=None)
```
- Function Description
- Format
This part contains (please write them in order):
[Function Description]
[Formula]
[Symbols' Descriptions if necessary]
[References if necessary]
- Example
[Function Description]
```
**Fully Connected Layer**
The fully connected layer can take multiple tensors as its inputs. It
creates a variable called weights for each input tensor, which represents
a fully connected weight matrix from each input unit to each output unit.
The fully connected layer multiplies each input tensor with its coresponding
weight to produce an output Tensor. If multiple input tensors are given,
the results of multiple multiplications will be sumed up. If bias_attr is
not None, a bias variable will be created and added to the output. Finally,
if activation is not None, it will be applied to the output as well.
```
[Formula]
```
This process can be formulated as follows:
.. math::
Out = Act({\sum_{i=0}^{N-1}X_iW_i + b})
```
[Symbols' Descriptions if necessary]
```
In the above equation:
* :math:`N`: Number of the input.
* :math:`X_i`: The input tensor.
* :math:`W`: The weights created by this layer.
* :math:`b`: The bias parameter created by this layer (if needed).
* :math:`Act`: The activation function.
* :math:`Out`: The output tensor.
```
[References if necessary]
Since there is no need for reference of fc, we omit them here. Under other circumstances, please provide explicit reference and link, take layer_norm for example:
```
Refer to `Layer Normalization <https://arxiv.org/pdf/1607.06450v1.pdf>`_ for more details.
```
- Args Description
- Format
\[Arg's Name\][(Data Type, Default Value)][Description]
- Example
part of fc parameters are as follows:
```
Args:
input (Variable|list of Variable): The input tensor(s) of this layer, and the dimension of
the input tensor(s) is at least 2.
param_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for learnable
parameters/weights of this layer.
name (str, default None): The name of this layer.
```
- Returns
- Format
[Name][Shape]
- Example
```
Returns:
A tensor variable storing the transformation result.
```
when returned value contain more than one tuple, please introduce every parameter in order, take dynamic_lstm for example:
```
Returns:
A tuple containing:
The hidden state of LSTM whose shape is (T X D).
The cell state of LSTM whose shape is (T X D).
```
- Raises
- Format
[Exception Type][Condition]
- Example
```
Raises:
ValueError: If the rank of the input is less than 2.
```
- Note
- Format
[Note]
- Example
there is no Note in fc, so we omit this part. If there is any note, please write clearly. If there are more than one notes, please list them in order. Take scaled\_dot\_product\_attention for example:
```
Note:
1. When num_heads > 1, three linear projections are learned respectively
to map input queries, keys and values into queries', keys' and values'.
queries', keys' and values' have the same shapes with queries, keys
and values.
2. When num_heads == 1, scaled_dot_product_attention has no learnable
parameters.
```
- Examples
- Format
\[Python Code Snipper]
- Example
```
Examples:
.. code-block:: python
data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
fc = fluid.layers.fc(input=data, size=1000, act="tanh")
```
## Complete Example
Complete Example of fc please see [here](src/fc.py)。
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开发标准
------------
.. toctree::
:maxdepth: 1
new_op_en.md
new_op_kernel_en.md
use_eigen_en.md
name_convention.md
support_new_device.md
releasing_process.md
op_markdown_format.md
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Development
------------
.. toctree::
:maxdepth: 1
new_op_en.md
new_op_kernel_en.md
use_eigen_en.md
name_convention.md
support_new_device.md
releasing_process.md
op_markdown_format.md
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## Operator's Parameter Name Convention
# Operator's Parameter Name Convention
To make the operator document itself more clear, we recommend operator names obey the listing conventions.
### OpProtoMaker names
## OpProtoMaker names
When defining an operator in Paddle, a corresponding [OpProtoMaker](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/operator.h#L170) (TODO: OpProtoMaker Doc)need to be defined. All the Input/Output and Attributes will write into the [OpProto](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L61) , and will be used in client language to create operator.
......@@ -20,7 +20,7 @@ When defining an operator in Paddle, a corresponding [OpProtoMaker](https://gith
- Order.
- Follow the order of Input/Output, then Attribute, then Comments. See the example in best practice.
### Best Practice
## Best Practice
Here we give some examples to show how these rules will be used.
......
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## Add Kernels for a New Device
# Add Kernels for a New Device
### Background
## Background
PaddlePaddle Fluid have hundreds of operators. Each operator could have one or more kernels. A kernel is an implementation of the operator for a certain device, which could be a hardware device, e.g., the CUDA GPU, or a library that utilizes a device, e.g., Intel MKL that makes full use of the Xeon CPU.
[This document](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/new_op_en.md) explains how to add an operator, and its kernels. The kernels of an operator are indexed by a C++ type [`OpKernelType`](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/operator_kernel_type.md). An operator chooses the right kernel at runtime. This choosing mechanism is described [here](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/switch_kernel.md).
### Write Kernels for A New Device
## Write Kernels for A New Device
#### Add A New Device
### Add A New Device
For some historical reaons, we misuse the word *library* for *device*. For example, we call the deivce type by *library type*. An example is the header file [`library_type.h`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/library_type.h#L24). We will correct this ASAP.
......@@ -23,7 +23,7 @@ enum class LibraryType {
```
#### Add A New [Place](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/platform/place.h#L53)
### Add A New [Place](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/platform/place.h#L53)
If you have a new kind of Device, firstly you need to add a new kind of [`Place`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/platform/place.h#L53). For example `CUDAPlace`:
......@@ -45,7 +45,7 @@ struct CUDAPlace {
typedef boost::variant<CUDAPlace, CPUPlace> Place;
```
#### Add [device context]((https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/platform/device_context.h#L37))
### Add [device context]((https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/platform/device_context.h#L37))
After a new kind of Device is added, you should add a corresponding [DeviceContext](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/platform/device_context.h#L37) for it.
```cpp
......@@ -58,7 +58,7 @@ class DeviceContext {
};
```
#### Implement new [OpKernel](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/operator.h#L351) for your Device.
### Implement new [OpKernel](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/operator.h#L351) for your Device.
A detailed documentation can be found in [`new_op_and_kernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/new_op_en.md)
......@@ -85,7 +85,7 @@ class OpKernel : public OpKernelBase {
```
#### Register the OpKernel to framework
### Register the OpKernel to framework
After writing the components described above, we should register the kernel to the framework.
......@@ -107,7 +107,7 @@ take [`conv2d`]((https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/oper
REGISTER_OP_KERNEL(conv2d, CPU, paddle::platform::CPUPlace,
paddle::operators::GemmConvKernel<paddle::platform::CPUDeviceContext, float>,
paddle::operators::GemmConvKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_KERNEL(conv2d, CUDNN, ::paddle::platform::CUDAPlace,
paddle::operators::CUDNNConvOpKernel<float>,
paddle::operators::CUDNNConvOpKernel<double>);
......
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......@@ -15,26 +15,26 @@ The signature of the operator.
Each section mentioned above has been covered in further detail in the rest of the document.
# PaddlePaddle Operator Name
## PaddlePaddle Operator Name
This should be in all small letters, in case of multiple words, we separate them with an underscore. For example:
`array to lod tensor` should be written as `array_to_lod_tensor`.
This naming convention should be standard across all PaddlePaddle operators.
# Standard Operator Name
## Standard Operator Name
This is the standard name of the operator as used in the community. The general standard is usually:
- Standard abbreviations like `SGD` are written in all capital letters.
- Operator names that have multiple words inside a single word use `camelCase` (capitalize word boundaries inside of a word).
- Keep numbers inside a word as is, with no boundary delimiters.
- Follow the name of the operator with the keyword: `Activation Operator.`
# Operator description
## Operator description
This section should contain the description of what the operator does, including the operation performed, the literature from where it comes and was introduced first, and other important details. The relevant paper/article including the hyperlink should be cited in this section.
# LaTeX equation
## LaTeX equation
This section should contain an overall equation of the update or operation that the operator performs. The variables used in the equation should follow the naming convention of operators as described [here](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/name_convention.md). Two words in the same word should be separated by an underscore (`_`).
# The signature
## The signature
This section describes the signature of the operator. A list of Inputs and Outputs, each of which have a small description of what the variable represents and the type of variable. The variable names follow the `CamelCase` naming convention. The proposed format for this is:
`Section :
VariableName : (VariableType) VariableDescription
......
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## 在Paddle中如何使用Eigen
# 在Paddle中如何使用Eigen
神经网络本质上是一个计算图，计算需要的数据存放在`Tensor`中，而计算过程是由`Operartor`来描述的。在执行时，`Operator`调用对应`OpKernel`中的`Compute`接口，实现对`Tensor`的操作。
### Eigen Tensor模块
## Eigen Tensor模块
Eigen Tensor模块对element-wise计算提供了强大的支持，并且书写一份代码，可以同时在CPU、GPU执行。但Eigen Tensor是一个正在开发中的模块，因此可能测试不够完备，文档较少。
关于Eigen Tensor模块的详细介绍请参考[文档1](https://github.com/RLovelett/eigen/blob/master/unsupported/Eigen/CXX11/src/Tensor/README.md) 和[文档2](https://bitbucket.org/eigen/eigen/src/default/unsupported/Eigen/CXX11/src/Tensor/README.md)
### paddle::framework::Tensor
## paddle::framework::Tensor
Paddle Tensor定义在framework目录下，其主要接口如下：
......@@ -20,14 +20,14 @@ class Tensor {
/*! Return a pointer to mutable memory block. */
template <typename T>
inline T* data();
/**
* @brief Return a pointer to mutable memory block.
* @note If not exist, then allocation.
*/
template <typename T>
inline T* mutable_data(platform::Place place);
/**
* @brief Return a pointer to mutable memory block.
*
......@@ -38,17 +38,17 @@ class Tensor {
*/
template <typename T>
inline T* mutable_data(DDim dims, platform::Place place);
/*! Resize the dimensions of the memory block. */
inline Tensor& Resize(const DDim& dims);
/*! Return the dimensions of the memory block. */
inline const DDim& dims() const;
private:
/*! holds the memory block if allocated. */
std::shared_ptr<Placeholder> holder_;
/*! points to dimensions of memory block. */
DDim dim_;
};
......@@ -129,7 +129,7 @@ From是EigenTensor模板提供的一个接口，可以实现从paddle::framework
### 实现计算
## 实现计算
当需要完成计算时，我们需要等式左边的EigenTensor调用device接口。在这里需要注意的是，这里的EigenTensor之间的运算只是改变了原有Tensor中的数据，而不会改变原有Tensor的shape信息。
......
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## How to use Eigen in Paddle
# How to use Eigen in Paddle
Essentially, a neural network is a compute graph. T data needed for the computation is stored in `Tensor`s and its computation procedure is described by `Operator`s. An `Operator` calls the `Compute` interface in its corresponding `OpKernel` and operates on the `Tensor`.
### Eigen Tensor Module
## Eigen Tensor Module
The Eigen Tensor module supports powerful element-wise computation. In addition, a piece of code written using it can be run on both the CPU and the GPU.
......@@ -12,7 +12,7 @@ Note that Eigen Tensor is still being actively developed, so its tests are not c
For details on Eigen Tensor module, please see [doc 1](https://github.com/RLovelett/eigen/blob/master/unsupported/Eigen/CXX11/src/Tensor/README.md) and [doc 2](https://bitbucket.org/eigen/eigen/src/default/unsupported/Eigen/CXX11/src/Tensor/README.md).
### paddle::framework::Tensor
## paddle::framework::Tensor
Paddle Tensor's is defined in the framework directory with the following interface:
......@@ -105,7 +105,7 @@ void Compute(const framework::ExecutionContext& context) const override {
```
### paddle::framework::Tensor到EigenTensor的转换
## paddle::framework::Tensor到EigenTensor的转换
As shown above, in actual computation, we need to transform the input and output `Tensor`s into formats Eigen supports. We show some functions in [eigen.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/framework/eigen.h) to implement the transformation from `paddle::framework::Tensor`to `EigenTensor/EigenMatrix/EigenVector/EigenScalar`.
......@@ -129,7 +129,7 @@ For more transformations, see the [unit tests](https://github.com/PaddlePaddle/P
### Implementing Computation
## Implementing Computation
While computing, the device interface is needed from the EigenTensors on the left hand side of the assignments. Note that the computation between EigenTensors only changes the data originally inthe Tensor and does not change all the shape information associated with the Tensor.
......
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基本使用概念
============
TBD
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新手入门
============
如果需要快速了解PaddlePaddle的使用，可以参考以下指南。
.. toctree::
:maxdepth: 1
quickstart_cn.rst
在使用PaddlePaddle构建应用时，需要了解一些基本概念。
这里以一个线性回归为例子，详细介绍了PaddlePaddle的使用流程，包括数据格式，模型配置与训练等。
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