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提交 db569f29 编写于 作者: Q qijun
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fix merge conflict

上级 5ece5c96 2e1d968f
展开全部 隐藏空白更改
内联 并排
Showing with 5783 addition and 537 deletion
.travis.yml
浏览文件 @ db569f29
......@@ -35,6 +35,8 @@ addons:
- libgoogle-glog-dev
- libgflags-dev
- libgtest-dev
- curl
- lcov
- graphviz
before_install:
- if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then sudo paddle/scripts/travis/before_install.linux.sh; fi
......
CMakeLists.txt
浏览文件 @ db569f29
......@@ -9,7 +9,7 @@ set(PADDLE_VERSION ${PADDLE_MAJOR_VERSION}.${PADDLE_MINOR_VERSION}.${PADDLE_PATC
set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_SOURCE_DIR}/cmake")
set(PROJ_ROOT ${CMAKE_SOURCE_DIR})
include(package)
include(swig)
find_package(SWIG 2.0)
find_package(CUDA QUIET)
find_package(Protobuf REQUIRED)
find_package(PythonLibs 2.7 REQUIRED)
......@@ -40,6 +40,9 @@ option(WITH_TESTING "Compile and run unittest for PaddlePaddle" ${GTEST_FOUND})
option(WITH_DOC "Compile PaddlePaddle with documentation" OFF)
option(WITH_SWIG_PY "Compile PaddlePaddle with py PaddlePaddle prediction api" ${SWIG_FOUND})
option(ON_TRAVIS "Running test on travis-ci or not." OFF)
option(ON_COVERALLS "Generating code coverage data on coveralls or not." OFF)
option(COVERALLS_UPLOAD "Uploading the generated coveralls json." ON)
if(NOT CMAKE_BUILD_TYPE)
set(CMAKE_BUILD_TYPE "RelWithDebInfo" CACHE STRING
"Choose the type of build, options are: Debug Release RelWithDebInfo MinSizeRel"
......@@ -49,11 +52,16 @@ endif()
include(enableCXX11)
include(cpplint)
include(ccache)
if(WITH_RDMA)
include(rdma)
endif()
include(util)
include(flags)
include(cudnn)
include(FindPythonModule)
include(check_packages)
include(swig)
include(coveralls)
# add PaddlePaddle version
if(DEFINED ENV{PADDLE_VERSION})
......@@ -129,9 +137,11 @@ else(WITH_PYTHON)
add_definitions(-DPADDLE_NO_PYTHON)
endif(WITH_PYTHON)
if(NOT WITH_RDMA)
add_definitions(-DPADDLE_DISABLE_RDMA)
endif()
if(WITH_RDMA)
include_directories("${RDMA_INC_DIR}")
else(WITH_RDMA)
add_definitions(-DPADDLE_DISABLE_RDMA)
endif(WITH_RDMA)
if(WITH_GLOG)
add_definitions(-DPADDLE_USE_GLOG)
......
ISSUE_TEMPLATE.md 0 → 100644
浏览文件 @ db569f29
Thank you for contributing to PaddlePaddle. Submitting an issue is a great help for us.
Both Chinese and English issues are welcome.
It's hard to solve a problem when important details are missing.
Before submitting the issue, look over the following criteria before handing your request in.
- [ ] Was there a similar issue submitted or resolved before ? You could search issue in the github.
- [ ] Did you retrieve your issue from widespread search engines ?
- [ ] Is my description of the issue clear enough to reproduce this problem?
* If some errors occurred, we need details about `how do you run your code?`, `what system do you use?`, `Are you using GPU or not?`, etc.
* If you use an recording [asciinema](https://asciinema.org/) to show what you are doing to make it happen, that's awesome! We could help you solve the problem more quickly.
- [ ] Is my description of the issue use the github markdown correctly?
* Please use the proper markdown syntaxes for styling all forms of writing, e.g, source code, error information, etc.
* Check out [this page](https://guides.github.com/features/mastering-markdown/) to find out much more about markdown.
README.md
浏览文件 @ db569f29
# PaddlePaddle
| **`Linux`** | **`License`** | **`Chat Room`** |
|----------------|---------------|-----------------|
|[![Build Status](https://travis-ci.org/baidu/Paddle.svg?branch=master)](https://travis-ci.org/baidu/Paddle)|[![License](https://img.shields.io/badge/license-Apache%202.0-green.svg)](LICENSE)|[![Join the chat at https://gitter.im/PaddlePaddle/Deep_Learning](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/PaddlePaddle/Deep_Learning?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)|
[![Build Status](https://travis-ci.org/baidu/Paddle.svg?branch=master)](https://travis-ci.org/baidu/Paddle)
[![Coverage Status](https://coveralls.io/repos/github/baidu/Paddle/badge.svg?branch=develop)](https://coveralls.io/github/baidu/Paddle?branch=develop)
[![Join the chat at https://gitter.im/PaddlePaddle/Deep_Learning](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/PaddlePaddle/Deep_Learning?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
[![License](https://img.shields.io/badge/license-Apache%202.0-green.svg)](LICENSE)
Welcome to the PaddlePaddle GitHub.
......
cmake/cblas.cmake
浏览文件 @ db569f29
# Find the CBlas libraries
# Find the CBlas and lapack libraries
#
# It will search MKL, atlas, OpenBlas, reference-cblas in order.
#
......@@ -19,6 +19,8 @@ set(MKL_ROOT $ENV{MKL_ROOT} CACHE PATH "Folder contains MKL")
find_path(MKL_INCLUDE_DIR mkl.h PATHS
${MKL_ROOT}/include)
find_path(MKL_INCLUDE_DIR mkl_lapacke.h PATHS
${MKL_ROOT}/include)
find_library(MKL_CORE_LIB NAMES mkl_core PATHS
${MKL_ROOT}/lib
${MKL_ROOT}/lib/intel64)
......@@ -37,6 +39,7 @@ if(MKL_INCLUDE_DIR AND MKL_CORE_LIB AND MKL_SEQUENTIAL_LIB AND MKL_INTEL_LP64)
${MKL_SEQUENTIAL_LIB}
${MKL_CORE_LIB})
add_definitions(-DPADDLE_USE_MKL)
message(STATUS "Found MKL (include: ${CBLAS_INC_DIR}, library: ${CBLAS_LIBS})")
return() # return file.
endif()
......@@ -55,15 +58,19 @@ set(ATLAS_LIB_SEARCH_PATHS
)
find_path(ATLAS_INC_DIR NAMES cblas.h
PATHS ${ATLAS_INCLUDE_SEARCH_PATHS})
find_path(ATLAS_CLAPACK_INC_DIR NAMES clapack.h
PATHS ${ATLAS_INCLUDE_SEARCH_PATHS})
find_library(ATLAS_CBLAS_LIB NAMES cblas libcblas.so.3
PATHS ${ATLAS_LIB_SEARCH_PATHS})
find_library(ATLAS_LIB NAMES atlas libatlas.so.3
find_library(ATLAS_LIB NAMES lapack_atlas liblapack_atlas.so.3
PATHS ${ATLAS_LIB_SEARCH_PATHS})
if(ATLAS_INC_DIR AND ATLAS_CBLAS_LIB AND ATLAS_LIB)
set(CBLAS_PROVIDER ATLAS)
set(CBLAS_INC_DIR ${ATLAS_INC_DIR})
set(CBLAS_INC_DIR ${ATLAS_INC_DIR} ${ATLAS_CLAPACK_INC_DIR})
set(CBLAS_LIBS ${ATLAS_LIB} ${ATLAS_CBLAS_LIB})
add_definitions(-DPADDLE_USE_ATLAS)
message(STATUS "Found Atlas (include: ${CBLAS_INC_DIR}, library: ${CBLAS_LIBS})")
return()
endif()
......@@ -83,6 +90,8 @@ set(OPENBLAS_LIB_SEARCH_PATHS
find_path(OPENBLAS_INC_DIR NAMES cblas.h
PATHS ${OPENBLAS_INCLUDE_SEARCH_PATHS})
find_path(OPENBLAS_LAPACKE_INC_DIR NAMES lapacke.h
PATHS ${OPENBLAS_INCLUDE_SEARCH_PATHS})
find_library(OPENBLAS_LIB NAMES openblas
PATHS ${OPENBLAS_LIB_SEARCH_PATHS})
......@@ -90,6 +99,7 @@ if(OPENBLAS_INC_DIR AND OPENBLAS_LIB)
set(CBLAS_PROVIDER OPENBLAS)
set(CBLAS_INC_DIR ${OPENBLAS_INC_DIR})
set(CBLAS_LIBS ${OPENBLAS_LIB})
message(STATUS "Found OpenBlas (include: ${CBLAS_INC_DIR}, library: ${CBLAS_LIBS})")
return()
endif()
......
cmake/coveralls.cmake 0 → 100644
浏览文件 @ db569f29
# CMake script for code coverage.
# If _COVERALLS_UPLOAD is ON, it will upload json files to overalls.io automatically.
# Param _COVERAGE_SRCS A list of coverage source files.
# Param _COVERALLS_UPLOAD Upload the result to coveralls.
# Param _CMAKE_SCRIPT_PATH CMake script path.
function(code_coverage _COVERAGE_SRCS _COVERALLS_UPLOAD _CMAKE_SCRIPT_PATH)
# clean previous gcov data.
file(REMOVE_RECURSE ${PROJECT_BINARY_DIR}/*.gcda)
# find curl for upload JSON soon.
if (_COVERALLS_UPLOAD)
find_program(CURL_EXECUTABLE curl)
if (NOT CURL_EXECUTABLE)
message(FATAL_ERROR "Coveralls: curl not found!")
endif()
endif()
# When passing a CMake list to an external process, the list
# will be converted from the format "1;2;3" to "1 2 3".
set(COVERAGE_SRCS "")
foreach (SINGLE_SRC ${_COVERAGE_SRCS})
set(COVERAGE_SRCS "${COVERAGE_SRCS}*${SINGLE_SRC}")
endforeach()
# query number of logical cores
cmake_host_system_information(RESULT core_size QUERY NUMBER_OF_LOGICAL_CORES)
# coveralls json file.
set(COVERALLS_FILE ${PROJECT_BINARY_DIR}/coveralls.json)
add_custom_target(coveralls_generate
# Run regress tests.
COMMAND ${CMAKE_CTEST_COMMAND}
-j ${core_size}
--output-on-failure
# Generate Gcov and translate it into coveralls JSON.
COMMAND ${CMAKE_COMMAND}
-DCOVERAGE_SRCS="${COVERAGE_SRCS}"
-DCOVERALLS_OUTPUT_FILE="${COVERALLS_FILE}"
-DCOV_PATH="${PROJECT_BINARY_DIR}"
-DPROJECT_ROOT="${PROJECT_SOURCE_DIR}"
-P "${_CMAKE_SCRIPT_PATH}/coverallsGcovJsons.cmake"
WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
COMMENT "Coveralls: generating coveralls output..."
)
if (_COVERALLS_UPLOAD)
message("COVERALLS UPLOAD: ON")
# Upload the JSON to coveralls.
add_custom_target(coveralls_upload
COMMAND ${CURL_EXECUTABLE}
-S -F json_file=@${COVERALLS_FILE}
https://coveralls.io/api/v1/jobs
DEPENDS coveralls_generate
WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
COMMENT "Coveralls: uploading coveralls output...")
add_custom_target(coveralls DEPENDS coveralls_upload)
else()
message("COVERALLS UPLOAD: OFF")
add_custom_target(coveralls DEPENDS coveralls_generate)
endif()
endfunction()
if(ON_COVERALLS)
set(CMAKE_BUILD_TYPE "Debug")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -O0 -fprofile-arcs -ftest-coverage")
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -g -O0 -fprofile-arcs -ftest-coverage")
set(EXCLUDE_DIRS
"demo/"
"build/"
"tests/"
".test_env/"
)
if(WITH_GPU)
file(GLOB_RECURSE PADDLE_SOURCES RELATIVE "${PROJECT_SOURCE_DIR}" "*.cpp" "*.cc" ".c" "*.cu")
else()
file(GLOB_RECURSE PADDLE_SOURCES RELATIVE "${PROJECT_SOURCE_DIR}" "*.cpp" "*.cc" "*.c")
endif()
# exclude trivial files in PADDLE_SOURCES
foreach(EXCLUDE_DIR ${EXCLUDE_DIRS})
foreach(TMP_PATH ${PADDLE_SOURCES})
string(FIND ${TMP_PATH} ${EXCLUDE_DIR} EXCLUDE_DIR_FOUND)
if(NOT ${EXCLUDE_DIR_FOUND} EQUAL -1)
list(REMOVE_ITEM PADDLE_SOURCES ${TMP_PATH})
endif()
endforeach(TMP_PATH)
endforeach()
# convert to absolute path
set(PADDLE_SRCS "")
foreach(PADDLE_SRC ${PADDLE_SOURCES})
set(PADDLE_SRCS "${PADDLE_SRCS};${PROJECT_SOURCE_DIR}/${PADDLE_SRC}")
endforeach()
code_coverage(
"${PADDLE_SRCS}"
${COVERALLS_UPLOAD}
"${PROJECT_SOURCE_DIR}/cmake"
)
endif()
cmake/coverallsGcovJsons.cmake 0 → 100644
浏览文件 @ db569f29
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
# Copyright (C) 2014 Joakim Söderberg <joakim.soderberg@gmail.com>
#
# This is intended to be run by a custom target in a CMake project like this.
# 0. Compile program with coverage support.
# 1. Clear coverage data. (Recursively delete *.gcda in build dir)
# 2. Run the unit tests.
# 3. Run this script specifying which source files the coverage should be performed on.
#
# This script will then use gcov to generate .gcov files in the directory specified
# via the COV_PATH var. This should probably be the same as your cmake build dir.
#
# It then parses the .gcov files to convert them into the Coveralls JSON format:
# https://coveralls.io/docs/api
#
CMAKE_MINIMUM_REQUIRED(VERSION 2.8)
# Since it's not possible to pass a CMake list properly in the
# "1;2;3" format to an external process, we have replaced the
# ";" with "*", so reverse that here so we get it back into the
# CMake list format.
string(REGEX REPLACE "\\*" ";" COVERAGE_SRCS ${COVERAGE_SRCS})
find_program(GCOV_EXECUTABLE gcov)
if (NOT GCOV_EXECUTABLE)
message(FATAL_ERROR "gcov not found! Aborting...")
endif()
find_package(Git)
# TODO: Add these git things to the coveralls json.
if (GIT_FOUND)
# Branch.
execute_process(
COMMAND ${GIT_EXECUTABLE} rev-parse --abbrev-ref HEAD
WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}
OUTPUT_VARIABLE GIT_BRANCH
OUTPUT_STRIP_TRAILING_WHITESPACE
)
macro (git_log_format FORMAT_CHARS VAR_NAME)
execute_process(
COMMAND ${GIT_EXECUTABLE} log -1 --pretty=format:%${FORMAT_CHARS}
WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}
OUTPUT_VARIABLE ${VAR_NAME}
OUTPUT_STRIP_TRAILING_WHITESPACE
)
endmacro()
git_log_format(an GIT_AUTHOR_EMAIL)
git_log_format(ae GIT_AUTHOR_EMAIL)
git_log_format(cn GIT_COMMITTER_NAME)
git_log_format(ce GIT_COMMITTER_EMAIL)
git_log_format(B GIT_COMMIT_MESSAGE)
message("Git exe: ${GIT_EXECUTABLE}")
message("Git branch: ${GIT_BRANCH}")
message("Git author: ${GIT_AUTHOR_NAME}")
message("Git e-mail: ${GIT_AUTHOR_EMAIL}")
message("Git commiter name: ${GIT_COMMITTER_NAME}")
message("Git commiter e-mail: ${GIT_COMMITTER_EMAIL}")
message("Git commit message: ${GIT_COMMIT_MESSAGE}")
endif()
############################# Macros #########################################
#
# This macro converts from the full path format gcov outputs:
#
# /path/to/project/root/build/#path#to#project#root#subdir#the_file.c.gcov
#
# to the original source file path the .gcov is for:
#
# /path/to/project/root/subdir/the_file.c
#
macro(get_source_path_from_gcov_filename _SRC_FILENAME _GCOV_FILENAME)
# /path/to/project/root/build/#path#to#project#root#subdir#the_file.c.gcov
# ->
# #path#to#project#root#subdir#the_file.c.gcov
get_filename_component(_GCOV_FILENAME_WEXT ${_GCOV_FILENAME} NAME)
# #path#to#project#root#subdir#the_file.c.gcov -> /path/to/project/root/subdir/the_file.c
string(REGEX REPLACE "\\.gcov$" "" SRC_FILENAME_TMP ${_GCOV_FILENAME_WEXT})
string(REGEX REPLACE "\#" "/" SRC_FILENAME_TMP ${SRC_FILENAME_TMP})
set(${_SRC_FILENAME} "${SRC_FILENAME_TMP}")
endmacro()
##############################################################################
# Get the coverage data.
file(GLOB_RECURSE GCDA_FILES "${COV_PATH}" "*.gcda")
message("GCDA files:")
# Get a list of all the object directories needed by gcov
# (The directories the .gcda files and .o files are found in)
# and run gcov on those.
foreach(GCDA ${GCDA_FILES})
message("Process: ${GCDA}")
message("------------------------------------------------------------------------------")
get_filename_component(GCDA_DIR ${GCDA} PATH)
#
# The -p below refers to "Preserve path components",
# This means that the generated gcov filename of a source file will
# keep the original files entire filepath, but / is replaced with #.
# Example:
#
# /path/to/project/root/build/CMakeFiles/the_file.dir/subdir/the_file.c.gcda
# ------------------------------------------------------------------------------
# File '/path/to/project/root/subdir/the_file.c'
# Lines executed:68.34% of 199
# /path/to/project/root/subdir/the_file.c:creating '#path#to#project#root#subdir#the_file.c.gcov'
#
# If -p is not specified then the file is named only "the_file.c.gcov"
#
execute_process(
COMMAND ${GCOV_EXECUTABLE} -p -o ${GCDA_DIR} ${GCDA}
WORKING_DIRECTORY ${GCDA_DIR}
)
endforeach()
# TODO: Make these be absolute path
file(GLOB_RECURSE ALL_GCOV_FILES "${COV_PATH}" "*.gcov")
# Get only the filenames to use for filtering.
#set(COVERAGE_SRCS_NAMES "")
#foreach (COVSRC ${COVERAGE_SRCS})
# get_filename_component(COVSRC_NAME ${COVSRC} NAME)
# message("${COVSRC} -> ${COVSRC_NAME}")
# list(APPEND COVERAGE_SRCS_NAMES "${COVSRC_NAME}")
#endforeach()
#
# Filter out all but the gcov files we want.
#
# We do this by comparing the list of COVERAGE_SRCS filepaths that the
# user wants the coverage data for with the paths of the generated .gcov files,
# so that we only keep the relevant gcov files.
#
# Example:
# COVERAGE_SRCS =
# /path/to/project/root/subdir/the_file.c
#
# ALL_GCOV_FILES =
# /path/to/project/root/build/#path#to#project#root#subdir#the_file.c.gcov
# /path/to/project/root/build/#path#to#project#root#subdir#other_file.c.gcov
#
# Result should be:
# GCOV_FILES =
# /path/to/project/root/build/#path#to#project#root#subdir#the_file.c.gcov
#
set(GCOV_FILES "")
#message("Look in coverage sources: ${COVERAGE_SRCS}")
message("\nFilter out unwanted GCOV files:")
message("===============================")
set(COVERAGE_SRCS_REMAINING ${COVERAGE_SRCS})
foreach (GCOV_FILE ${ALL_GCOV_FILES})
#
# /path/to/project/root/build/#path#to#project#root#subdir#the_file.c.gcov
# ->
# /path/to/project/root/subdir/the_file.c
get_source_path_from_gcov_filename(GCOV_SRC_PATH ${GCOV_FILE})
# Is this in the list of source files?
# TODO: We want to match against relative path filenames from the source file root...
list(FIND COVERAGE_SRCS ${GCOV_SRC_PATH} WAS_FOUND)
if (NOT WAS_FOUND EQUAL -1)
message("YES: ${GCOV_FILE}")
list(APPEND GCOV_FILES ${GCOV_FILE})
# We remove it from the list, so we don't bother searching for it again.
# Also files left in COVERAGE_SRCS_REMAINING after this loop ends should
# have coverage data generated from them (no lines are covered).
list(REMOVE_ITEM COVERAGE_SRCS_REMAINING ${GCOV_SRC_PATH})
else()
message("NO: ${GCOV_FILE}")
endif()
endforeach()
# TODO: Enable setting these
set(JSON_SERVICE_NAME "travis-ci")
set(JSON_SERVICE_JOB_ID $ENV{TRAVIS_JOB_ID})
set(JSON_TEMPLATE
"{
\"service_name\": \"\@JSON_SERVICE_NAME\@\",
\"service_job_id\": \"\@JSON_SERVICE_JOB_ID\@\",
\"source_files\": \@JSON_GCOV_FILES\@
}"
)
set(SRC_FILE_TEMPLATE
"{
\"name\": \"\@GCOV_SRC_REL_PATH\@\",
\"source_digest\": \"\@GCOV_CONTENTS_MD5\@\",
\"coverage\": \@GCOV_FILE_COVERAGE\@
}"
)
message("\nGenerate JSON for files:")
message("=========================")
set(JSON_GCOV_FILES "[")
# Read the GCOV files line by line and get the coverage data.
foreach (GCOV_FILE ${GCOV_FILES})
get_source_path_from_gcov_filename(GCOV_SRC_PATH ${GCOV_FILE})
file(RELATIVE_PATH GCOV_SRC_REL_PATH "${PROJECT_ROOT}" "${GCOV_SRC_PATH}")
# The new coveralls API doesn't need the entire source (Yay!)
# However, still keeping that part for now. Will cleanup in the future.
file(MD5 "${GCOV_SRC_PATH}" GCOV_CONTENTS_MD5)
message("MD5: ${GCOV_SRC_PATH} = ${GCOV_CONTENTS_MD5}")
# Loads the gcov file as a list of lines.
# (We first open the file and replace all occurences of [] with _
# because CMake will fail to parse a line containing unmatched brackets...
# also the \ to escaped \n in macros screws up things.)
# https://public.kitware.com/Bug/view.php?id=15369
file(READ ${GCOV_FILE} GCOV_CONTENTS)
string(REPLACE "[" "_" GCOV_CONTENTS "${GCOV_CONTENTS}")
string(REPLACE "]" "_" GCOV_CONTENTS "${GCOV_CONTENTS}")
string(REPLACE "\\" "_" GCOV_CONTENTS "${GCOV_CONTENTS}")
file(WRITE ${GCOV_FILE}_tmp "${GCOV_CONTENTS}")
file(STRINGS ${GCOV_FILE}_tmp GCOV_LINES)
list(LENGTH GCOV_LINES LINE_COUNT)
# Instead of trying to parse the source from the
# gcov file, simply read the file contents from the source file.
# (Parsing it from the gcov is hard because C-code uses ; in many places
# which also happens to be the same as the CMake list delimeter).
file(READ ${GCOV_SRC_PATH} GCOV_FILE_SOURCE)
string(REPLACE "\\" "\\\\" GCOV_FILE_SOURCE "${GCOV_FILE_SOURCE}")
string(REGEX REPLACE "\"" "\\\\\"" GCOV_FILE_SOURCE "${GCOV_FILE_SOURCE}")
string(REPLACE "\t" "\\\\t" GCOV_FILE_SOURCE "${GCOV_FILE_SOURCE}")
string(REPLACE "\r" "\\\\r" GCOV_FILE_SOURCE "${GCOV_FILE_SOURCE}")
string(REPLACE "\n" "\\\\n" GCOV_FILE_SOURCE "${GCOV_FILE_SOURCE}")
# According to http://json.org/ these should be escaped as well.
# Don't know how to do that in CMake however...
#string(REPLACE "\b" "\\\\b" GCOV_FILE_SOURCE "${GCOV_FILE_SOURCE}")
#string(REPLACE "\f" "\\\\f" GCOV_FILE_SOURCE "${GCOV_FILE_SOURCE}")
#string(REGEX REPLACE "\u([a-fA-F0-9]{4})" "\\\\u\\1" GCOV_FILE_SOURCE "${GCOV_FILE_SOURCE}")
# We want a json array of coverage data as a single string
# start building them from the contents of the .gcov
set(GCOV_FILE_COVERAGE "[")
set(GCOV_LINE_COUNT 1) # Line number for the .gcov.
set(DO_SKIP 0)
foreach (GCOV_LINE ${GCOV_LINES})
#message("${GCOV_LINE}")
# Example of what we're parsing:
# Hitcount |Line | Source
# " 8: 26: if (!allowed || (strlen(allowed) == 0))"
string(REGEX REPLACE
"^([^:]*):([^:]*):(.*)$"
"\\1;\\2;\\3"
RES
"${GCOV_LINE}")
# Check if we should exclude lines using the Lcov syntax.
string(REGEX MATCH "LCOV_EXCL_START" START_SKIP "${GCOV_LINE}")
string(REGEX MATCH "LCOV_EXCL_END" END_SKIP "${GCOV_LINE}")
string(REGEX MATCH "LCOV_EXCL_LINE" LINE_SKIP "${GCOV_LINE}")
set(RESET_SKIP 0)
if (LINE_SKIP AND NOT DO_SKIP)
set(DO_SKIP 1)
set(RESET_SKIP 1)
endif()
if (START_SKIP)
set(DO_SKIP 1)
message("${GCOV_LINE_COUNT}: Start skip")
endif()
if (END_SKIP)
set(DO_SKIP 0)
endif()
list(LENGTH RES RES_COUNT)
if (RES_COUNT GREATER 2)
list(GET RES 0 HITCOUNT)
list(GET RES 1 LINE)
list(GET RES 2 SOURCE)
string(STRIP ${HITCOUNT} HITCOUNT)
string(STRIP ${LINE} LINE)
# Lines with 0 line numbers are metadata and can be ignored.
if (NOT ${LINE} EQUAL 0)
if (DO_SKIP)
set(GCOV_FILE_COVERAGE "${GCOV_FILE_COVERAGE}null, ")
else()
# Translate the hitcount into valid JSON values.
if (${HITCOUNT} STREQUAL "#####")
set(GCOV_FILE_COVERAGE "${GCOV_FILE_COVERAGE}0, ")
elseif (${HITCOUNT} STREQUAL "-")
set(GCOV_FILE_COVERAGE "${GCOV_FILE_COVERAGE}null, ")
else()
set(GCOV_FILE_COVERAGE "${GCOV_FILE_COVERAGE}${HITCOUNT}, ")
endif()
endif()
endif()
else()
message(WARNING "Failed to properly parse line (RES_COUNT = ${RES_COUNT}) ${GCOV_FILE}:${GCOV_LINE_COUNT}\n-->${GCOV_LINE}")
endif()
if (RESET_SKIP)
set(DO_SKIP 0)
endif()
math(EXPR GCOV_LINE_COUNT "${GCOV_LINE_COUNT}+1")
endforeach()
message("${GCOV_LINE_COUNT} of ${LINE_COUNT} lines read!")
# Advanced way of removing the trailing comma in the JSON array.
# "[1, 2, 3, " -> "[1, 2, 3"
string(REGEX REPLACE ",[ ]*$" "" GCOV_FILE_COVERAGE ${GCOV_FILE_COVERAGE})
# Append the trailing ] to complete the JSON array.
set(GCOV_FILE_COVERAGE "${GCOV_FILE_COVERAGE}]")
# Generate the final JSON for this file.
message("Generate JSON for file: ${GCOV_SRC_REL_PATH}...")
string(CONFIGURE ${SRC_FILE_TEMPLATE} FILE_JSON)
set(JSON_GCOV_FILES "${JSON_GCOV_FILES}${FILE_JSON}, ")
endforeach()
# Loop through all files we couldn't find any coverage for
# as well, and generate JSON for those as well with 0% coverage.
foreach(NOT_COVERED_SRC ${COVERAGE_SRCS_REMAINING})
# Loads the source file as a list of lines.
file(STRINGS ${NOT_COVERED_SRC} SRC_LINES)
set(GCOV_FILE_COVERAGE "[")
set(GCOV_FILE_SOURCE "")
foreach (SOURCE ${SRC_LINES})
set(GCOV_FILE_COVERAGE "${GCOV_FILE_COVERAGE}0, ")
string(REPLACE "\\" "\\\\" SOURCE "${SOURCE}")
string(REGEX REPLACE "\"" "\\\\\"" SOURCE "${SOURCE}")
string(REPLACE "\t" "\\\\t" SOURCE "${SOURCE}")
string(REPLACE "\r" "\\\\r" SOURCE "${SOURCE}")
set(GCOV_FILE_SOURCE "${GCOV_FILE_SOURCE}${SOURCE}\\n")
endforeach()
# Remove trailing comma, and complete JSON array with ]
string(REGEX REPLACE ",[ ]*$" "" GCOV_FILE_COVERAGE ${GCOV_FILE_COVERAGE})
set(GCOV_FILE_COVERAGE "${GCOV_FILE_COVERAGE}]")
# Generate the final JSON for this file.
message("Generate JSON for non-gcov file: ${NOT_COVERED_SRC}...")
string(CONFIGURE ${SRC_FILE_TEMPLATE} FILE_JSON)
set(JSON_GCOV_FILES "${JSON_GCOV_FILES}${FILE_JSON}, ")
endforeach()
# Get rid of trailing comma.
string(REGEX REPLACE ",[ ]*$" "" JSON_GCOV_FILES ${JSON_GCOV_FILES})
set(JSON_GCOV_FILES "${JSON_GCOV_FILES}]")
# Generate the final complete JSON!
message("Generate final JSON...")
string(CONFIGURE ${JSON_TEMPLATE} JSON)
file(WRITE "${COVERALLS_OUTPUT_FILE}" "${JSON}")
message("###########################################################################")
message("Generated coveralls JSON containing coverage data:")
message("${COVERALLS_OUTPUT_FILE}")
message("###########################################################################")
cmake/rdma.cmake 0 → 100644
浏览文件 @ db569f29
# user should download rdma first from subversion repository
# execute following instruction to download svn mannally
# svn co https://svn.baidu.com/sys/ip/trunk/rdma/sockrdmav1 rdma/
# svn co https://svn.baidu.com/sys/ip/trunk/rdma/thirdparty rdma/
# we use static output in svn repositories to avoid implict bugs from not standard runtime env.
set(RDMA_ROOT $ENV{RDMA_ROOT} CACHE PATH "Folder contains RDMA sock library and thirdparty library")
function(generate_rdma_links)
#redirect to current DIR to isolate the pollution from system runtime environment
#it can benifits unified control for different gcc environment.
#e.g, by default gcc48 did not refer /usr/lib64 which could contain low version
#runtime libraries that will crash process while loading it. That redirect trick
#can fix it.
execute_process(
COMMAND mkdir -p librdma
COMMAND ln -s -f /usr/lib64/libibverbs.so.1.0.0 librdma/libibverbs.so.1
COMMAND ln -s -f /usr/lib64/libibverbs.so.1.0.0 librdma/libibverbs.so
COMMAND ln -s -f /usr/lib64/librdmacm.so.1.0.0 librdma/librdmacm.so.1
COMMAND ln -s -f /usr/lib64/librdmacm.so.1.0.0 librdma/librdmacm.so
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
)
endfunction(generate_rdma_links)
#check and set headers
find_path(RDMA_INC_SXISOCK sxi_sock.h PATHS ${RDMA_ROOT}/sockrdmav1/output/include)
find_path(RDMA_INC_XIO libxio.h PATHS ${RDMA_ROOT}/thirdparty/output/accelio)
find_path(RDMA_INC_EVENT event2 PATHS ${RDMA_ROOT}/thirdparty/output/libevent)
find_path(RDMA_INC_NUMA numa.h PATHS ${RDMA_ROOT}/thirdparty/output/libnuma)
#check and set libs
find_library(RDMA_LIB_SXISOCK NAMES sxisock PATHS ${RDMA_ROOT}/sockrdmav1/output)
find_library(RDMA_LIB_XIO NAMES xio PATHS ${RDMA_ROOT}/thirdparty/output/accelio)
find_library(RDMA_LIB_EVENT NAMES event PATHS ${RDMA_ROOT}/thirdparty/output/libevent)
find_library(RDMA_LIB_EVENT_CORE NAMES event_core PATHS ${RDMA_ROOT}/thirdparty/output/libevent)
find_library(RDMA_LIB_EVENT_EXTRA NAMES event_extra PATHS ${RDMA_ROOT}/thirdparty/output/libevent)
find_library(RDMA_LIB_EVENT_PTHREADS NAMES event_pthreads PATHS ${RDMA_ROOT}/thirdparty/output/libevent)
find_library(RDMA_LIB_NUMA NAMES numa PATHS ${RDMA_ROOT}/thirdparty/output/libnuma)
if(
RDMA_INC_SXISOCK AND
RDMA_INC_XIO AND
RDMA_INC_EVENT AND
RDMA_INC_NUMA AND
RDMA_LIB_SXISOCK AND
RDMA_LIB_XIO AND
RDMA_LIB_EVENT AND
RDMA_LIB_EVENT_CORE AND
RDMA_LIB_EVENT_EXTRA AND
RDMA_LIB_EVENT_PTHREADS AND
RDMA_LIB_NUMA
)
set(RDMA_INC_DIR
${RDMA_INC_SXISOCK}
${RDMA_INC_XIO}
${RDMA_INC_EVENT}
${RDMA_INC_NUMA})
set(RDMA_LIBS
${RDMA_LIB_SXISOCK}
${RDMA_LIB_XIO}
${RDMA_LIB_EVENT}
${RDMA_LIB_EVENT_CORE}
${RDMA_LIB_EVENT_EXTRA}
${RDMA_LIB_EVENT_PTHREADS}
${RDMA_LIB_NUMA}
)
set(RDMA_LD_FLAGS "-L./librdma -libverbs -lrdmacm -Xlinker -rpath ./librdma")
return()
endif()
#if this module is not called, RDMA_INC_DIR RDMA_LIBS will be null, so top module always refer this variable
message(FATAL_ERROR, "RDMA libraries are not found, try to set RDMA_ROOT or check all related libraries.")
cmake/swig.cmake
浏览文件 @ db569f29
find_program(
SWIG_BINARY_PATH
swig)
if(${SWIG_BINARY_PATH} STREQUAL "SWIG_BINARY_PATH-NOTFOUND")
set(SWIG_FOUND OFF)
else()
set(SWIG_FOUND ON)
endif()
set(MIN_SWIG_VERSION 2)
if(SWIG_FOUND)
execute_process(COMMAND sh -c "${SWIG_BINARY_PATH} -version | grep Version | cut -f3 -d' '"
OUTPUT_VARIABLE _SWIG_VERSION
OUTPUT_STRIP_TRAILING_WHITESPACE)
if(${_SWIG_VERSION} VERSION_LESS ${MIN_SWIG_VERSION})
message("swig version ${MIN_SWIG_VERSION} or greater is needed for generating python api. "
"Only version ${_SWIG_VERSION} is found. Set SWIG_FOUND to FALSE")
set(SWIG_FOUND FALSE)
endif(${_SWIG_VERSION} VERSION_LESS ${MIN_SWIG_VERSION})
endif(SWIG_FOUND)
function(generate_python_api target_name)
add_custom_command(OUTPUT ${PROJ_ROOT}/paddle/py_paddle/swig_paddle.py
${PROJ_ROOT}/paddle/Paddle_wrap.cxx
......
cmake/util.cmake
浏览文件 @ db569f29
......@@ -67,6 +67,10 @@ endmacro()
#
# It will handle WITH_PYTHON/WITH_GLOG etc.
function(link_paddle_exe TARGET_NAME)
if(WITH_RDMA)
generate_rdma_links()
endif()
if(WITH_METRIC)
if(WITH_GPU)
set(METRIC_LIBS paddle_metric_learning paddle_dserver_lib metric metric_cpu)
......@@ -109,6 +113,12 @@ function(link_paddle_exe TARGET_NAME)
${ZLIB_LIBRARIES}
${INTERAL_LIBS}
${CMAKE_DL_LIBS})
if(WITH_RDMA)
target_link_libraries(${TARGET_NAME}
${RDMA_LD_FLAGS}
${RDMA_LIBS})
endif()
if(WITH_PYTHON)
target_link_libraries(${TARGET_NAME}
......
demo/semantic_role_labeling/.gitignore 0 → 100644
浏览文件 @ db569f29
*.pyc
train.log
data/feature
data/conll05st-release/
data/src.dict
data/test.wsj.props
data/test.wsj.seq_pair
data/test.wsj.words
data/tgt.dict
output
doc/build/build_from_source.md
浏览文件 @ db569f29
......@@ -4,7 +4,6 @@ Installing from Sources
* [1. Download and Setup](#download)
* [2. Requirements](#requirements)
* [3. Build on Ubuntu](#ubuntu)
* [4. Build on Mac OS X](#mac)
## <span id="download">Download and Setup</span>
You can download PaddlePaddle from the [github source](https://github.com/gangliao/Paddle).
......@@ -191,122 +190,3 @@ sudo pip install <path to install>/opt/paddle/share/wheels/*.whl
# or just run
sudo paddle version
```
## <span id="mac">Building on Mac OS X</span>
### Prerequisites
This guide is based on Mac OS X 10.11 (El Capitan). Note that if you are running an up to date version of OS X,
you will already have Python 2.7.10 and Numpy 1.8 installed.
The best option is to use the package manager homebrew to handle installations and upgrades for you.
To install [homebrew](http://brew.sh/), first open a terminal window (you can find Terminal in the Utilities folder in Applications), and issue the command:
```bash
# install brew
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
# install pip
easy_install pip
```
### Install Dependencies
- **CPU Dependencies**
```bash
# Install fundamental dependents
brew install glog gflags cmake protobuf openblas
# Install google test on Mac OS X
# Download gtest 1.7.0
wget https://github.com/google/googletest/archive/release-1.7.0.tar.gz
tar -xvf googletest-release-1.7.0.tar.gz && cd googletest-release-1.7.0
# Build gtest
mkdir build && cmake ..
make
# Install gtest library
sudo cp -r ../include/gtest /usr/local/include/
sudo cp lib*.a /usr/local/lib
```
- **GPU Dependencies(optional)**
To build GPU version, you will need the following installed:
1. a CUDA-capable GPU
2. Mac OS X 10.11 or later
2. the Clang compiler and toolchain installed using Xcode
3. NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads)
4. NVIDIA cuDNN Library (availabel at https://developer.nvidia.com/cudnn)
The CUDA development environment relies on tight integration with the host development environment,
including the host compiler and C runtime libraries, and is therefore only supported on
distribution versions that have been qualified for this CUDA Toolkit release.
1. After downloading cuDNN library, issue the following commands:
```bash
sudo tar -xzf cudnn-7.5-osx-x64-v5.0-ga.tgz -C /usr/local
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib/libcudnn*
```
2. Then you need to set DYLD\_LIBRARY\_PATH, PATH environment variables in ~/.bashrc.
```bash
export DYLD_LIBRARY_PATH=/usr/local/cuda/lib:$DYLD_LIBRARY_PATH
export PATH=/usr/local/cuda/bin:$PATH
```
### Build and Install
As usual, the best option is to create build folder under paddle project directory.
```bash
mkdir build && cd build
cmake ..
```
CMake first check PaddlePaddle's dependencies in system default path. After installing some optional
libraries, corresponding build option will be set automatically (for instance, glog, gtest and gflags).
If still not found, you can manually set it based on CMake error information from your screen.
As a simple example, consider the following:
- **Only CPU**
```bash
cmake .. -DWITH_GPU=OFF
```
- **GPU**
```bash
cmake .. -DWITH_GPU=ON
```
- **GPU with doc and swig**
```bash
cmake .. -DWITH_GPU=ON -DWITH_DOC=ON -DWITH_SWIG_PY=ON
```
Finally, you can build PaddlePaddle:
```bash
# you can add build option here, such as:
cmake .. -DWITH_GPU=ON -DCMAKE_INSTALL_PREFIX=<installation path>
# please use sudo make install, if you want to install PaddlePaddle into the system
make -j `sysctl -n hw.ncpu` && make install
# set PaddlePaddle installation path in ~/.bashrc
export PATH=<installation path>/bin:$PATH
```
**Note:**
If you set `WITH_SWIG_PY=ON`, related python dependencies also need to be installed.
Otherwise, PaddlePaddle will automatically install python dependencies
at first time when user run paddle commands, such as `paddle version`, `paddle train`.
It may require sudo privileges:
```bash
# you can run
sudo pip install <path to install>/opt/paddle/share/wheels/*.whl
# or just run
sudo paddle version
```
doc/ui/api/trainer_config_helpers/activations.rst
浏览文件 @ db569f29
......@@ -32,6 +32,13 @@ LinearActivation
.. automodule:: paddle.trainer_config_helpers.activations
:members: LinearActivation
:noindex:
LogActivation
==================
.. automodule:: paddle.trainer_config_helpers.activations
:members: LogActivation
:noindex:
SquareActivation
================
......
paddle/cuda/include/hl_cuda_cublas.h
浏览文件 @ db569f29
......@@ -21,8 +21,8 @@ limitations under the License. */
/**
* @brief Matrix transpose: C_d = T(A_d)
*
* @param[in] A_d input matrix (M x N).
* @param[out] C_d output matrix (N x M).
* @param[in] A_d input matrix (dimM x dimN).
* @param[out] C_d output matrix (dimN x dimM).
* @param[in] dimM matrix height.
* @param[in] dimN matrix width.
* @param[in] lda the first dimension of A_d.
......@@ -39,8 +39,8 @@ extern void hl_matrix_transpose(real *A_d,
/*
* @brief Matrix transpose, while lda = dimN, ldc = dimM.
*
* @param[in] A_d input matrix (M x N).
* @param[out] C_d output matrix (N x M).
* @param[in] A_d input matrix (dimM x dimN).
* @param[out] C_d output matrix (dimN x dimM).
* @param[in] dimM matrix height.
* @param[in] dimN matrix width.
*
......@@ -50,6 +50,22 @@ extern void hl_matrix_transpose(real *A_d,
int dimM,
int dimN);
/*
* @brief Matrix inverse
*
* @param[in] A_d input matrix (dimN x dimN).
* @param[out] C_d output matrix (dimN x dimN).
* @param[in] dimN matrix height = matrix width
* @param[in] lda the first dimension of A_d
* @param[in] ldc the first dimension of C_d
*
*/
extern void hl_matrix_inverse(real *A_d,
real *C_d,
int dimN,
int lda,
int ldc);
/**
* @brief C_d = alpha*(op(A_d) * op(B_d)) + beta*C_d
*
......
paddle/cuda/include/stub/hl_cuda_cublas_stub.h
浏览文件 @ db569f29
......@@ -30,6 +30,12 @@ inline void hl_matrix_transpose(real *A_d,
int dimM,
int dimN) {}
inline void hl_matrix_inverse(real *A_d,
real *C_d,
int dimN,
int lda,
int ldc) {}
inline void hl_matrix_mul(real *A_d, hl_trans_op_t transa,
real *B_d, hl_trans_op_t transb,
real *C_d,
......
paddle/cuda/src/hl_cuda_cublas.cc
浏览文件 @ db569f29
......@@ -15,6 +15,7 @@ limitations under the License. */
#include <sys/time.h>
#include <mutex>
#include "hl_cuda.h"
#include "hl_cuda_cublas.h"
#include "hl_thread.ph"
#include "hl_dso_loader.h"
......@@ -75,6 +76,10 @@ DYNAMIC_LOAD_CUBLAS_WRAP(cublasSgemmBatched)
DYNAMIC_LOAD_CUBLAS_WRAP(cublasDgemmBatched)
DYNAMIC_LOAD_CUBLAS_WRAP(cublasCgemmBatched)
DYNAMIC_LOAD_CUBLAS_WRAP(cublasZgemmBatched)
DYNAMIC_LOAD_CUBLAS_WRAP(cublasSgetrfBatched)
DYNAMIC_LOAD_CUBLAS_WRAP(cublasSgetriBatched)
DYNAMIC_LOAD_CUBLAS_WRAP(cublasDgetrfBatched)
DYNAMIC_LOAD_CUBLAS_WRAP(cublasDgetriBatched)
CUBLAS_BLAS_ROUTINE_EACH(DYNAMIC_LOAD_CUBLAS_V2_WRAP)
#undef DYNAMIC_LOAD_CUBLAS_WRAP
......@@ -88,10 +93,14 @@ CUBLAS_BLAS_ROUTINE_EACH(DYNAMIC_LOAD_CUBLAS_V2_WRAP)
#define CUBLAS_GEAM dynload::cublasSgeam
#define CUBLAS_GEMV dynload::cublasSgemv
#define CUBLAS_GEMM dynload::cublasSgemm
#define CUBLAS_GETRF dynload::cublasSgetrfBatched
#define CUBLAS_GETRI dynload::cublasSgetriBatched
#else
#define CUBLAS_GEAM dynload::cublasDgeam
#define CUBLAS_GEMV dynload::cublasDgemv
#define CUBLAS_GEMM dynload::cublasDgemm
#define CUBLAS_GETRF dynload::cublasDgetrfBatched
#define CUBLAS_GETRI dynload::cublasDgetriBatched
#endif
const char* hl_cublas_get_error_string(cublasStatus_t status) {
......@@ -162,6 +171,54 @@ void hl_matrix_transpose(real *A_d, real *C_d, int dimM, int dimN) {
hl_matrix_transpose(A_d, C_d, dimM, dimN, dimN, dimM);
}
void hl_matrix_inverse(real *A_d, real *C_d, int dimN, int lda, int ldc) {
/* Solve Ax = I */
CHECK_NOTNULL(A_d);
CHECK_NOTNULL(C_d);
/* Step 1: Compute the LU decomposition of matrix A */
real **inout_h = &A_d;
real **inout_d = (real **)hl_malloc_device(sizeof(real *));
hl_memcpy(inout_d, inout_h, sizeof(real *));
int *pivot_d = (int *)hl_malloc_device(dimN*sizeof(int));
int *info_d = (int *)t_resource.gpu_mem;
/* Note: cublasSgetrfBatched is used to calculate a number of
small-sized matrices. There may be a better way to reconstruct
the API for better performance.
*/
CHECK_CUBLAS(CUBLAS_GETRF(t_resource.handle,
dimN, inout_d, lda, pivot_d,
info_d, 1));
int info_h;
hl_memcpy(&info_h, info_d, sizeof(int));
if (info_h != 0) {
LOG(FATAL) << "Factorization of matrix failed: matrix may be singular.\n";
}
/* Step 2: Compute the inverse of the matrix given its LU decomposition */
real **out_h = &C_d;
real **out_d = (real **)hl_malloc_device(sizeof(real *));
hl_memcpy(out_d, out_h, sizeof(real *));
CHECK_CUBLAS(CUBLAS_GETRI(t_resource.handle,
dimN, (const real **)inout_d, lda, pivot_d,
out_d, ldc, info_d, 1));
hl_memcpy(&info_h, info_d, sizeof(int));
if (info_h != 0) {
LOG(FATAL) << "Inversion of matrix failed: matrix may be singular.\n";
}
hl_free_mem_device(inout_d);
hl_free_mem_device(pivot_d);
hl_free_mem_device(out_d);
CHECK_SYNC("hl_matrix_inverse failed");
}
void hl_matrix_mul(real *A_d, hl_trans_op_t transa,
real *B_d, hl_trans_op_t transb,
real *C_d,
......
paddle/cuda/src/hl_cuda_cudnn.cc
浏览文件 @ db569f29
......@@ -41,65 +41,28 @@ void* cudnn_dso_handle = nullptr;
#ifdef PADDLE_USE_DSO
#define DYNAMIC_LOAD_CUDNN_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
cudnnStatus_t operator()(Args... args) { \
typedef cudnnStatus_t (*cudnnFunc)(Args...); \
std::call_once(cudnn_dso_flag, GetCudnnDsoHandle, \
&cudnn_dso_handle); \
void* p_##__name = dlsym(cudnn_dso_handle, #__name); \
return reinterpret_cast<cudnnFunc>(p_##__name)(args...); \
} \
#define DYNAMIC_LOAD_CUDNN_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
auto operator()(Args... args) -> decltype(__name(args...)) { \
using cudnn_func = decltype(__name(args...))(*)(Args...); \
std::call_once(cudnn_dso_flag, GetCudnnDsoHandle, \
&cudnn_dso_handle); \
void* p_##__name = dlsym(cudnn_dso_handle, #__name); \
return reinterpret_cast<cudnn_func>(p_##__name)(args...); \
} \
} __name; /* struct DynLoad__##__name */
struct DynLoad__cudnnGetVersion {
template <typename... Args>
size_t operator()(Args... args) {
typedef size_t (*cudnnFunc)(Args...);
std::call_once(cudnn_dso_flag, GetCudnnDsoHandle,
&cudnn_dso_handle);
void* p_name = dlsym(cudnn_dso_handle, "cudnnGetVersion");
return reinterpret_cast<cudnnFunc>(p_name)(args...);
}
} cudnnGetVersion; /* struct DynLoad__##__name */
struct DynLoad__cudnnGetErrorString {
template <typename... Args>
const char* operator()(Args... args) {
typedef const char* (*cudnnFunc)(Args...);
std::call_once(cudnn_dso_flag, GetCudnnDsoHandle,
&cudnn_dso_handle);
void* p_name = dlsym(cudnn_dso_handle, "cudnnGetErrorString");
return reinterpret_cast<cudnnFunc>(p_name)(args...);
}
} cudnnGetErrorString; /* struct DynLoad__##__name */
#else
#define DYNAMIC_LOAD_CUDNN_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
cudnnStatus_t operator()(Args... args) { \
return __name(args...); \
} \
#define DYNAMIC_LOAD_CUDNN_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
auto operator()(Args... args) -> decltype(__name(args...)) { \
return __name(args...); \
} \
} __name; /* struct DynLoad__##__name */
struct DynLoad__cudnnGetVersion {
template <typename... Args>
size_t operator()(Args... args) {
return cudnnGetVersion(args...);
}
} cudnnGetVersion; /* struct DynLoad__##__name */
struct DynLoad__cudnnGetErrorString {
template <typename... Args>
const char* operator()(Args... args) {
return cudnnGetErrorString(args...);
}
} cudnnGetErrorString; /* struct DynLoad__##__name */
#endif
/**
......@@ -133,7 +96,9 @@ struct DynLoad__cudnnGetErrorString {
__macro(cudnnPoolingForward) \
__macro(cudnnPoolingBackward) \
__macro(cudnnSoftmaxBackward) \
__macro(cudnnSoftmaxForward)
__macro(cudnnSoftmaxForward) \
__macro(cudnnGetVersion) \
__macro(cudnnGetErrorString)
CUDNN_DNN_ROUTINE_EACH(DYNAMIC_LOAD_CUDNN_WRAP)
#define CUDNN_DNN_ROUTINE_EACH_R2(__macro) \
......
paddle/cuda/src/hl_cuda_device.cc
浏览文件 @ db569f29
......@@ -85,44 +85,24 @@ void* cudart_dso_handle = nullptr;
#define DYNAMIC_LOAD_CUDART_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
cudaError_t operator()(Args... args) { \
typedef cudaError_t (*cudartFunc)(Args...); \
auto operator()(Args... args) -> decltype(__name(args...)) { \
using cudart_func = decltype(__name(args...))(*)(Args...); \
std::call_once(cudart_dso_flag, GetCudartDsoHandle, \
&cudart_dso_handle); \
void* p_##__name = dlsym(cudart_dso_handle, #__name); \
return reinterpret_cast<cudartFunc>(p_##__name)(args...); \
return reinterpret_cast<cudart_func>(p_##__name)(args...); \
} \
} __name; /* struct DynLoad__##__name */
#else
#define DYNAMIC_LOAD_CUDART_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
cudaError_t operator()(Args... args) { \
auto operator()(Args... args) -> decltype(__name(args...)) { \
return __name(args...); \
} \
} __name; /* struct DynLoad__##__name */
#endif
#ifdef PADDLE_USE_DSO
struct DynLoad__cudaGetErrorString {
template <typename... Args>
const char* operator()(Args... args) {
typedef const char* (*cudaFunc)(Args...);
std::call_once(cudart_dso_flag, GetCudartDsoHandle,
&cudart_dso_handle);
void* p_func = dlsym(cudart_dso_handle, "cudaGetErrorString");
return reinterpret_cast<cudaFunc>(p_func)(args...);
}
} cudaGetErrorString; /* struct DynLoad__cudaGetErrorString */
#else
struct DynLoad__cudaGetErrorString {
template <typename... Args>
const char* operator()(Args... args) {
return cudaGetErrorString(args...);
}
} cudaGetErrorString; /* struct DynLoad__cudaGetErrorString */
#endif
/* include all needed cuda functions in HPPL */
#define CUDA_ROUTINE_EACH(__macro) \
__macro(cudaMalloc) \
......@@ -152,7 +132,8 @@ struct DynLoad__cudaGetErrorString {
__macro(cudaSetDeviceFlags) \
__macro(cudaGetLastError) \
__macro(cudaFuncSetCacheConfig) \
__macro(cudaRuntimeGetVersion)
__macro(cudaRuntimeGetVersion) \
__macro(cudaGetErrorString)
CUDA_ROUTINE_EACH(DYNAMIC_LOAD_CUDART_WRAP)
......
paddle/cuda/src/hl_dso_loader.cc
浏览文件 @ db569f29
......@@ -49,14 +49,14 @@ static inline std::string join(const std::string& part1, const std::string& part
static inline void GetDsoHandleFromDefaultPath(
std::string& dso_path, void** dso_handle, int dynload_flags) {
LOG(INFO) << "Try to find cuda library: " << dso_path
<< "from default system path.";
<< " from default system path.";
// default search from LD_LIBRARY_PATH/DYLD_LIBRARY_PATH
*dso_handle = dlopen(dso_path.c_str(), dynload_flags);
// DYLD_LIBRARY_PATH is disabled after Mac OS 10.11 to
// bring System Integrity Projection (SIP), if dso_handle
// is null, search from default package path in Mac OS.
#if defined(__APPLE__) or defined(__OSX__)
#if defined(__APPLE__) || defined(__OSX__)
if (nullptr == *dso_handle) {
dso_path = join("/usr/local/cuda/lib/", dso_path);
*dso_handle = dlopen(dso_path.c_str(), dynload_flags);
......
paddle/gserver/activations/ActivationFunction.cpp
浏览文件 @ db569f29
......@@ -295,6 +295,7 @@ void forward(Argument& act) {
void backward(Argument& act) { act.grad->squareDerivative(*act.in); }
END_DEFINE_ACTIVATION(square)
/**
* @brief Exponential Activation.
* \f[
......@@ -307,8 +308,36 @@ void forward(Argument& act) { act.value->exp(*act.value); }
void backward(Argument& act) { act.grad->expDerivative(*act.value); }
END_DEFINE_ACTIVATION(exponential)
/**
* @brief Logarithm Activation.
* \f[
* f(z) = log(z)
* \f]
*/
BEGIN_DEFINE_ACTIVATION(log)
void forward(Argument& act) {
SetDevice device(act.deviceId);
Matrix::resizeOrCreate(act.in, act.value->getHeight(), act.value->getWidth(),
/* trans */ false, useGpu(act.deviceId));
act.in->copyFrom(*act.value);
act.value->log(*act.value);
}
void backward(Argument& act) { act.grad->dotDiv(*act.grad, *act.in); }
END_DEFINE_ACTIVATION(log)
ActivationFunction* ActivationFunction::create(const std::string& type) {
return gActivationRegistrar.createByType(type);
}
std::vector<std::string> ActivationFunction::getAllRegisteredTypes() {
std::vector<std::string> types;
gActivationRegistrar.forEachType([&](const std::string& type) {
types.push_back(type);
});
return types;
}
} // namespace paddle
paddle/gserver/activations/ActivationFunction.h
浏览文件 @ db569f29
......@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include <string>
#include <vector>
namespace paddle {
......@@ -32,6 +33,7 @@ struct Argument;
class ActivationFunction {
public:
static ActivationFunction* create(const std::string& type);
static std::vector<std::string> getAllRegisteredTypes();
ActivationFunction() {}
......
paddle/gserver/dataproviders/DataProvider.cpp
浏览文件 @ db569f29
......@@ -131,9 +131,10 @@ void DoubleBuffer::asyncLoadBatch() {
taskReadySem_.wait();
if (stopping_) break;
while (batchSize_ == 0) {
while (batchSize_ == 0 && !stopping_) {
usleep(5);
}
if (stopping_) break;
do {
DataBatch newBatch;
......
paddle/gserver/dataproviders/PyDataProvider2.cpp
浏览文件 @ db569f29
......@@ -433,26 +433,34 @@ private:
inline void resetImpl(bool startNewThread) {
DBG << "Reseting " << startNewThread;
exit_.store(true);
if (loadThread_) { // is loading.
exit_.store(true);
loadThread_->join();
loadThread_.reset();
}
{
PyGuard g;
callingContexts_.clear();
this->pullCV_.notify_one();
}
std::lock_guard<std::mutex> guard(mutexForReset_);
{
PyGuard g;
dataPool_.clear();
}
poolActualSize_ = 0;
exit_ = false;
if (startNewThread && cache_->reset()) {
DBG << "Start new thread.";
loadThread_.reset(new std::thread([this] {
exit_ = false;
loadThread();
}));
callingContextCreated_.wait();
}
DBG << "Reset done";
exit_ = false;
}
private:
......@@ -465,6 +473,8 @@ private:
std::condition_variable pullCV_;
std::mutex mtx_;
std::mutex mutexForReset_;
ThreadBarrier callingContextCreated_;
std::unique_ptr<IPyDataProviderCache> cache_;
......@@ -529,6 +539,7 @@ public:
* Loading a batch of data.
*/
int64_t getNextBatchInternal(int64_t size_, DataBatch *batch) {
std::lock_guard<std::mutex> guard(mutexForReset_);
REGISTER_TIMER("PyDP2.getNextBatchInternal")
CHECK_GE(size_, 0);
size_t size = (size_t) size_;
......@@ -554,6 +565,10 @@ public:
} else { // loading from cache.
poolPtr = this->cache_->load();
}
if (exit_) {
// PyDataProvider is destructing.
return 0;
}
CHECK(poolPtr != nullptr);
std::deque<PyObjectPtr>& pool = *poolPtr;
......
paddle/gserver/gradientmachines/ParallelNeuralNetwork.cpp
浏览文件 @ db569f29
......@@ -28,6 +28,12 @@ void ParallelNeuralNetwork::init(
const std::vector<ParameterType>& parameterTypes, bool useGpu) {
NeuralNetwork::init(config, callback, parameterTypes, useGpu);
if (config.type() == "recurrent_nn") {
LOG(FATAL)
<< "You can not add `--parallel_nn=true` on the command line, "
<< "parallel_nn training mode does not support the recurrent_nn model.";
}
useGpu_ = useGpu;
numDevices_ = 0;
if (useGpu_) {
......
paddle/gserver/layers/ConvBaseLayer.cpp
浏览文件 @ db569f29
......@@ -12,7 +12,6 @@ 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/utils/Logging.h"
#include "ConvBaseLayer.h"
namespace paddle {
......@@ -78,10 +77,10 @@ size_t ConvBaseLayer::calOutputSize() {
imgSizeH_[i] = config_.inputs(i).conv_conf().img_size();
if (imgSizeW_[i] == 0)
imgSizeW_[i] = config_.inputs(i).conv_conf().img_size();
outputH_.push_back(
outputSize(imgSizeH_[i], filterSizeY_[i], paddingY_[i], strideY_[i]));
outputW_.push_back(
outputSize(imgSizeW_[i], filterSize_[i], padding_[i], stride_[i]));
outputH_.push_back(outputSize(imgSizeH_[i], filterSizeY_[i], paddingY_[i],
strideY_[i], caffeMode_));
outputW_.push_back(outputSize(imgSizeW_[i], filterSize_[i], padding_[i],
stride_[i], caffeMode_));
CHECK_EQ(outputH_[i], outputH_[0]);
CHECK_EQ(outputW_[i], outputW_[0]);
}
......
paddle/gserver/layers/ConvBaseLayer.h
浏览文件 @ db569f29
......@@ -16,6 +16,7 @@ limitations under the License. */
#pragma once
#include "Layer.h"
#include "paddle/math/MathUtils.h"
namespace paddle {
/**
......@@ -87,31 +88,6 @@ public:
virtual size_t calOutputSize();
Weight& getWeight(int idx) { return *weights_[idx]; }
/**
* Calculate output size based on caffeMode_.
* - input(+padding): 0123456789
* - imageSize(+padding) = 10;
* - filterSize = 3;
* - stride = 2;
* - caffeMode_ is true:
- output: (012), (234), (456), (678)
- outputSize = 4;
* - caffeMode_ is false:
* - output: (012), (234), (456), (678), (9)
* - outputSize = 5;
*/
int outputSize(int imageSize, int filterSize, int padding, int stride) {
int outputSize;
if (!caffeMode_) {
outputSize =
(imageSize - filterSize + 2 * padding + stride - 1) / stride + 1;
} else {
outputSize = (imageSize - filterSize + 2 * padding) / stride + 1;
}
CHECK_GE(outputSize, 1);
return outputSize;
}
};
} // namespace paddle
paddle/gserver/layers/ConvOperator.cpp
浏览文件 @ db569f29
......@@ -12,8 +12,8 @@ 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/math/Matrix.h"
#include "paddle/math/MathUtils.h"
#include "Operator.h"
namespace paddle {
......@@ -35,8 +35,8 @@ public:
*/
virtual ~ConvOperator() {
if (workSpaceInBytes_ != 0) {
hl_free_mem_device(workSpace_);
workSpaceInBytes_ = 0;
hl_free_mem_device(workSpace_);
workSpaceInBytes_ = 0;
}
hl_destroy_tensor_descriptor(inputDesc_);
......@@ -83,33 +83,6 @@ private:
filterSize_ * filterSizeY_ * channels_ * numFilters_);
}
/**
* Calculate output size.
*/
int outputSize(int imageSize, int filterSize, int padding, int stride) {
int outputSize;
if (!caffeMode_) {
/* input(+padding): 0123456789
* imageSize(+padding) = 10;
* filterSize = 3;
* stride = 2;
* output: (012), (234), (456), (678), (9)
* outputSize = 5;
*/
outputSize =
(imageSize - filterSize + 2 * padding + stride - 1) / stride + 1;
} else {
/* input(+padding): 0123456789
* imageSize(+padding) = 10;
* filterSize = 3;
* stride = 2;
* output: (012), (234), (456), (678)
* outputSize = 4;
*/
outputSize = (imageSize - filterSize + 2 * padding) / stride + 1;
}
return outputSize;
}
/// Most of member variables are same with CudnnConvLayer.
/// There is no explanation here.
int imageH_, imageW_, outputH_, outputW_;
......@@ -129,7 +102,7 @@ private:
int fwdAlgo_, bwdFilterAlgo_, bwdDataAlgo_;
size_t fwdLimitBytes_, bwdDataLimitBytes_, bwdFilterLimitBytes_;
size_t workSpaceInBytes_;
void* workSpace_;
void *workSpace_;
bool isSelectAlgo_;
};
......@@ -160,7 +133,7 @@ ConvOperator::ConvOperator(const OperatorConfig &config, bool useGpu)
void ConvOperator::allocConvWorkSpace(size_t maxWorkSpace) {
if (maxWorkSpace > workSpaceInBytes_) {
if (workSpaceInBytes_ != 0) {
hl_free_mem_device(workSpace_);
hl_free_mem_device(workSpace_);
}
// total amount of storage needed
workSpace_ = hl_malloc_device(maxWorkSpace);
......@@ -168,14 +141,13 @@ void ConvOperator::allocConvWorkSpace(size_t maxWorkSpace) {
}
}
void ConvOperator::reshape(int batchSize) {
imageH_ = ins_[0]->getFrameHeight();
imageW_ = ins_[0]->getFrameWidth();
if (imageH_ == 0) imageH_ = imgSize_;
if (imageW_ == 0) imageW_ = imgSize_;
outputH_ = outputSize(imageH_, filterSizeY_, paddingY_, strideY_);
outputW_ = outputSize(imageW_, filterSize_, padding_, stride_);
outputH_ = outputSize(imageH_, filterSizeY_, paddingY_, strideY_, caffeMode_);
outputW_ = outputSize(imageW_, filterSize_, padding_, stride_, caffeMode_);
out_->setFrameHeight(outputH_);
out_->setFrameWidth(outputW_);
......@@ -183,10 +155,10 @@ void ConvOperator::reshape(int batchSize) {
reshapeImageDescriptors();
if (!isSelectAlgo_) {
hl_conv_workspace(inputDesc_, outputDesc_, filterDesc_,
convDesc_, &fwdAlgo_, &fwdLimitBytes_,
&bwdDataAlgo_, &bwdDataLimitBytes_,
&bwdFilterAlgo_, &bwdFilterLimitBytes_);
hl_conv_workspace(inputDesc_, outputDesc_, filterDesc_, convDesc_,
&fwdAlgo_, &fwdLimitBytes_, &bwdDataAlgo_,
&bwdDataLimitBytes_, &bwdFilterAlgo_,
&bwdFilterLimitBytes_);
size_t maxWorkSpace = 0;
maxWorkSpace = std::max(fwdLimitBytes_, bwdDataLimitBytes_);
......@@ -202,7 +174,8 @@ void ConvOperator::computeConvSizes() {
hl_create_filter_descriptor(&filterDesc_, channels_, numFilters_,
filterSizeY_, filterSize_);
hl_create_tensor_descriptor(&inputDesc_);
int outputX = outputSize(imgSize_, filterSize_, padding_, stride_);
int outputX =
outputSize(imgSize_, filterSize_, padding_, stride_, caffeMode_);
CHECK_EQ(outputX, outputX_);
hl_create_tensor_descriptor(&outputDesc_);
hl_create_convolution_descriptor(&convDesc_, inputDesc_, filterDesc_,
......@@ -211,13 +184,13 @@ void ConvOperator::computeConvSizes() {
void ConvOperator::reshapeImageDescriptors() {
hl_tensor_reshape(inputDesc_, 1, channels_, imageH_, imageW_,
channels_ * imageH_ * imageW_, imageH_ * imageW_,
imageW_, 1);
channels_ * imageH_ * imageW_, imageH_ * imageW_, imageW_,
1);
hl_tensor_reshape(outputDesc_, 1, numFilters_, outputH_, outputW_,
numFilters_ * outputH_ * outputW_, outputH_ * outputW_,
outputW_, 1);
hl_reset_convolution_descriptor(convDesc_, inputDesc_, filterDesc_,
paddingY_, padding_, strideY_, stride_);
hl_reset_convolution_descriptor(convDesc_, inputDesc_, filterDesc_, paddingY_,
padding_, strideY_, stride_);
inputOffset_ = channels_ * imageH_ * imageW_;
outputOffset_ = numFilters_ * outputH_ * outputW_;
weightOffset_ = numFilters_ * channels_ * filterSize_ * filterSize_;
......@@ -273,18 +246,17 @@ void ConvOperator::backward() {
real *weightGrad = ins_[1]->grad->getData() + weightOffset_ * batchId;
hl_convolution_backward_filter(inputDesc_, inputData, outputDesc_,
outGrad, filterDesc_, weightGrad,
convDesc_, workSpace_,
workSpaceInBytes_, bwdFilterAlgo_);
convDesc_, workSpace_, workSpaceInBytes_,
bwdFilterAlgo_);
}
MatrixPtr preGrad = ins_[0]->grad;
if (NULL != preGrad) {
real *inputGrad = preGrad->getData() + inputOffset_ * batchId;
real *wgtData = ins_[1]->value->getData() + weightOffset_ * batchId;
hl_convolution_backward_data(inputDesc_, inputGrad, outputDesc_,
outGrad, filterDesc_, wgtData,
convDesc_, workSpace_,
workSpaceInBytes_, bwdDataAlgo_);
hl_convolution_backward_data(
inputDesc_, inputGrad, outputDesc_, outGrad, filterDesc_, wgtData,
convDesc_, workSpace_, workSpaceInBytes_, bwdDataAlgo_);
}
}
}
......
paddle/gserver/layers/ConvProjection.h
浏览文件 @ db569f29
......@@ -12,10 +12,10 @@ 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 "Projection.h"
#include "paddle/math/MathUtils.h"
namespace paddle {
......@@ -42,17 +42,15 @@ protected:
void reshapeTensorDesc(int batchSize);
void reshape(int batchSize);
int outputSize(int imageSize, int filterSize, int padding, int stride) {
return (imageSize - filterSize + 2 * padding) / stride + 1;
}
size_t calOutputSize() {
imageH_ = in_->getFrameHeight();
imageW_ = in_->getFrameWidth();
if (imageH_ == 0) imageH_ = configImgH_;
if (imageW_ == 0) imageW_ = configImgW_;
outputH_ = outputSize(imageH_, filterH_, paddingH_, strideH_);
outputW_ = outputSize(imageW_, filterW_, paddingW_, strideW_);
outputH_ = outputSize(imageH_, filterH_, paddingH_, strideH_,
/* caffeMode */ true);
outputW_ = outputSize(imageW_, filterW_, paddingW_, strideW_,
/* caffeMode */ true);
const_cast<Argument*>(out_)->setFrameHeight(outputH_);
const_cast<Argument*>(out_)->setFrameWidth(outputW_);
......
paddle/gserver/layers/CudnnPoolLayer.cpp
浏览文件 @ db569f29
......@@ -12,7 +12,6 @@ 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/utils/Logging.h"
#include "paddle/utils/Stat.h"
#include "paddle/math/Matrix.h"
......@@ -62,9 +61,9 @@ bool CudnnPoolLayer::init(const LayerMap &layerMap,
strideHeight = strideY_;
strideWidth = stride_;
hl_create_pooling_descriptor(&poolingDesc_, mode_, windowHeight,
windowWidth, heightPadding, widthPadding,
strideHeight, strideWidth);
hl_create_pooling_descriptor(&poolingDesc_, mode_, windowHeight, windowWidth,
heightPadding, widthPadding, strideHeight,
strideWidth);
return true;
}
......@@ -80,8 +79,10 @@ void CudnnPoolLayer::reshape(int batchSize) {
}
CHECK_EQ(inputLayers_[0]->getOutput().value->getWidth(),
channels_ * imageH_ * imageW_);
outputH_ = outputSize(imageH_, sizeY_, confPaddingY_, strideY_);
outputW_ = outputSize(imageW_, sizeX_, confPadding_, stride_);
outputH_ = outputSize(imageH_, sizeY_, confPaddingY_, strideY_,
/* caffeMode */ false);
outputW_ =
outputSize(imageW_, sizeX_, confPadding_, stride_, /* caffeMode */ false);
getOutput().setFrameHeight(outputH_);
getOutput().setFrameWidth(outputW_);
......@@ -99,8 +100,7 @@ void CudnnPoolLayer::forward(PassType passType) {
real *inputData = getInputValue(0)->getData();
real *outData = getOutputValue()->getData();
hl_pooling_forward(inputDesc_, inputData, outputDesc_, outData,
poolingDesc_);
hl_pooling_forward(inputDesc_, inputData, outputDesc_, outData, poolingDesc_);
}
void CudnnPoolLayer::backward(const UpdateCallback &callback) {
......@@ -113,8 +113,8 @@ void CudnnPoolLayer::backward(const UpdateCallback &callback) {
real *inputGrad = getInputGrad(0)->getData();
real *outData = getOutputValue()->getData();
real *outGrad = getOutputGrad()->getData();
hl_pooling_backward(inputDesc_, inputData, inputGrad, outputDesc_,
outData, outGrad, poolingDesc_);
hl_pooling_backward(inputDesc_, inputData, inputGrad, outputDesc_, outData,
outGrad, poolingDesc_);
}
CudnnPoolLayer::~CudnnPoolLayer() {
......
paddle/gserver/layers/PoolLayer.h
浏览文件 @ db569f29
......@@ -17,6 +17,7 @@ limitations under the License. */
#include "Layer.h"
#include "paddle/math/Matrix.h"
#include "paddle/math/MathUtils.h"
#include <vector>
namespace paddle {
......@@ -47,16 +48,6 @@ public:
static Layer* create(const LayerConfig& config);
virtual bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
/**
* Calculate output size according window size and padding size.
*/
int outputSize(int imageSize, int windowSize, int padding, int stride) {
int outputSize;
outputSize =
(imageSize - windowSize + 2 * padding + stride - 1) / stride + 1;
return outputSize;
}
};
} // namespace paddle
paddle/gserver/layers/PoolProjectionLayer.cpp
浏览文件 @ db569f29
......@@ -12,7 +12,6 @@ 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/utils/Logging.h"
#include "paddle/utils/Stat.h"
#include "PoolProjectionLayer.h"
......@@ -32,8 +31,10 @@ size_t PoolProjectionLayer::getSize() {
imgSizeW_ = imgSize_;
}
outputH_ = outputSize(imgSizeH_, sizeY_, confPaddingY_, strideY_);
outputW_ = outputSize(imgSizeW_, sizeX_, confPadding_, stride_);
outputH_ = outputSize(imgSizeH_, sizeY_, confPaddingY_, strideY_,
/* caffeMode */ false);
outputW_ = outputSize(imgSizeW_, sizeX_, confPadding_, stride_,
/* caffeMode */ false);
layerSize = outputH_ * outputW_ * channels_;
......
paddle/gserver/layers/SpatialPyramidPoolLayer.cpp
浏览文件 @ db569f29
......@@ -34,14 +34,12 @@ ProjectionConfig SpatialPyramidPoolLayer::getConfig(size_t imgSizeW,
int numBins = std::pow(2, pyramidLevel);
int sizeH = std::ceil(imgSizeH / static_cast<double>(numBins));
int remainderH = sizeH * numBins - imgSizeH;
int paddingH = (remainderH + 1) / 2;
int outSizeH = outputSize(imgSizeH, sizeH, paddingH, sizeH);
int paddingH = (sizeH * numBins - imgSizeH + 1) / 2;
int outSizeH = outputSize(imgSizeH, sizeH, paddingH, sizeH, true);
int sizeW = std::ceil(imgSizeW / static_cast<double>(numBins));
int remainderW = sizeW * numBins - imgSizeW;
int paddingW = (remainderW + 1) / 2;
int outSizeW = outputSize(imgSizeW, sizeW, paddingW, sizeW);
int paddingW = (sizeW * numBins - imgSizeW + 1) / 2;
int outSizeW = outputSize(imgSizeW, sizeW, paddingW, sizeW, true);
conf->set_stride(sizeW);
conf->set_stride_y(sizeH);
......@@ -55,16 +53,23 @@ ProjectionConfig SpatialPyramidPoolLayer::getConfig(size_t imgSizeW,
return config;
}
void SpatialPyramidPoolLayer::splitInput(Argument& input, size_t height,
size_t width, bool useGpu) {
input.value = getInput(0).value;
if (passType_ != PASS_TEST && needGradient()) {
Matrix::resizeOrCreate(input.grad, height, width, /* trans */ false,
useGpu);
input.grad->zeroMem();
}
size_t SpatialPyramidPoolLayer::getSize() {
CHECK_EQ(inputLayers_.size(), 1UL);
size_t layerSize = 0;
imgSizeH_ = inputLayers_[0]->getOutput().getFrameHeight();
imgSizeW_ = inputLayers_[0]->getOutput().getFrameWidth();
size_t outputH = 1;
size_t outputW = (std::pow(4, pyramidHeight_) - 1) / (4 - 1);
layerSize = outputH * outputW * channels_;
getOutput().setFrameHeight(outputH);
getOutput().setFrameWidth(outputW);
return layerSize;
}
bool SpatialPyramidPoolLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
Layer::init(layerMap, parameterMap);
......@@ -79,7 +84,6 @@ bool SpatialPyramidPoolLayer::init(const LayerMap& layerMap,
imgSizeH_ = sppConf.has_img_size_y() ? sppConf.img_size_y() : imgSizeW_;
poolProjections_.reserve(pyramidHeight_);
projCol_.reserve(pyramidHeight_);
projInput_.reserve(pyramidHeight_);
projOutput_.resize(pyramidHeight_);
size_t startCol = 0;
......@@ -91,7 +95,6 @@ bool SpatialPyramidPoolLayer::init(const LayerMap& layerMap,
endCol += poolProjections_[i]->getOutputSize();
projCol_.push_back(std::make_pair(startCol, endCol));
startCol = endCol;
projInput_.emplace_back(Argument());
}
CHECK_EQ(endCol, getSize());
return true;
......@@ -107,11 +110,9 @@ void SpatialPyramidPoolLayer::forward(PassType passType) {
size_t endCol = projCol_[i].second;
projOutput_[i].value = output_.value->subColMatrix(startCol, endCol);
projOutput_[i].grad = output_.grad->subColMatrix(startCol, endCol);
splitInput(projInput_[i], getInput(0).value->getHeight(),
getInput(0).value->getWidth(), useGpu_);
}
for (size_t i = 0; i < pyramidHeight_; i++) {
poolProjections_[i]->forward(&projInput_[i], &projOutput_[i], passType);
poolProjections_[i]->forward(&getInput(0), &projOutput_[i], passType);
}
}
......@@ -119,7 +120,6 @@ void SpatialPyramidPoolLayer::backward(const UpdateCallback& callback) {
for (size_t i = 0; i < pyramidHeight_; i++) {
if (poolProjections_[i]) {
poolProjections_[i]->backward(callback);
getInput(0).grad->add(*projInput_[i].grad);
}
}
}
......
paddle/gserver/layers/SpatialPyramidPoolLayer.h
浏览文件 @ db569f29
......@@ -18,6 +18,7 @@ limitations under the License. */
#include "Layer.h"
#include "PoolProjection.h"
#include "paddle/utils/Logging.h"
#include "paddle/math/MathUtils.h"
namespace paddle {
......@@ -30,7 +31,6 @@ protected:
std::string poolType_;
std::vector<std::unique_ptr<PoolProjection>> poolProjections_;
std::vector<Argument> projInput_;
std::vector<Argument> projOutput_;
std::vector<std::pair<size_t, size_t>> projCol_;
......@@ -41,13 +41,8 @@ public:
virtual bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
ProjectionConfig getConfig(size_t sizeX_, size_t sizeY_, size_t channels,
size_t pyamidLevel_, std::string& poolType_);
int outputSize(int imageSize, int windowSize, int padding, int stride) {
return (imageSize - windowSize + 2 * padding) / stride + 1;
}
size_t getSize();
virtual void forward(PassType passType);
virtual void backward(const UpdateCallback& callback = nullptr);
void splitInput(Argument& input, size_t height, size_t width, bool useGpu);
};
} // namespace paddle
paddle/gserver/tests/CMakeLists.txt
浏览文件 @ db569f29
......@@ -20,6 +20,13 @@ add_unittest_without_exec(test_LayerGrad
add_test(NAME test_LayerGrad
COMMAND test_LayerGrad)
add_unittest_without_exec(test_ActivationGrad
test_ActivationGrad.cpp
LayerGradUtil.cpp
TestUtil.cpp)
add_test(NAME test_ActivationGrad
COMMAND test_ActivationGrad)
################## test_Evaluator #######################
add_unittest(test_Evaluator
test_Evaluator.cpp
......
paddle/gserver/tests/test_ActivationGrad.cpp 0 → 100644
浏览文件 @ db569f29
/* Copyright (c) 2016 Baidu, Inc. 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 <gtest/gtest.h>
#include <vector>
#include <string>
#include "paddle/gserver/layers/DataLayer.h"
#include "ModelConfig.pb.h"
#include "paddle/trainer/Trainer.h"
#include "TestUtil.h"
#include "LayerGradUtil.h"
using namespace paddle; // NOLINT
using namespace std; // NOLINT
P_DECLARE_bool(use_gpu);
P_DECLARE_bool(thread_local_rand_use_global_seed);
void testActivation(const string& act) {
LOG(INFO) << "test activation: " << act;
size_t size = 10;
TestConfig config;
config.biasSize = 0;
config.layerConfig.set_type("addto");
config.layerConfig.set_size(size);
config.layerConfig.set_active_type(act);
config.inputDefs.push_back({INPUT_DATA, "layer_0", size, 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config,
act + "_activation",
100,
/* trans= */false,
useGpu,
/* useWeight */true);
}
}
TEST(Activation, activation) {
auto types = ActivationFunction::getAllRegisteredTypes();
std::set<string> excluded{"sequence_softmax"};
for (auto type : types) {
if (excluded.count(type)) continue;
testActivation(type);
}
}
int main(int argc, char** argv) {
testing::InitGoogleTest(&argc, argv);
initMain(argc, argv);
FLAGS_thread_local_rand_use_global_seed = true;
srand(1);
return RUN_ALL_TESTS();
}
paddle/gserver/tests/test_LayerGrad.cpp
浏览文件 @ db569f29
......@@ -18,6 +18,7 @@ limitations under the License. */
#include "ModelConfig.pb.h"
#include "paddle/gserver/layers/DataLayer.h"
#include "paddle/trainer/Trainer.h"
#include "paddle/math/MathUtils.h"
#include "LayerGradUtil.h"
#include "TestUtil.h"
......@@ -134,7 +135,6 @@ TEST(Projection, identity) {
}
}
#ifndef PADDLE_ONLY_CPU
TEST(Projection, conv) {
const int NUM_FILTERS = 16;
......@@ -158,21 +158,23 @@ TEST(Projection, conv) {
conv->set_groups(1);
conv->set_filter_channels(conv->channels() / conv->groups());
conv->set_img_size(IMAGE_SIZE);
int outputSize = (2 * conv->padding() + conv->img_size() -
conv->filter_size()) / conv->stride() + 1;
int outputSizeY = (2 * conv->padding_y() + conv->img_size() -
conv->filter_size_y()) / conv->stride_y() + 1;
conv->set_output_x(outputSize);
int output_x =
outputSize(conv->img_size(), conv->filter_size(), conv->padding(),
conv->stride(), /* caffeMode */ true);
int output_y =
outputSize(conv->img_size(), conv->filter_size_y(), conv->padding_y(),
conv->stride_y(), /* caffeMode */ true);
conv->set_output_x(output_x);
conf.set_input_size(IMAGE_SIZE * IMAGE_SIZE * CHANNELS);
conf.set_output_size(outputSize * outputSizeY * NUM_FILTERS);
conf.set_output_size(output_x * output_y * NUM_FILTERS);
testProjectionGrad(conf, INPUT_DATA,
testProjectionGrad(
conf, INPUT_DATA,
/* parameterSize */ NUM_FILTERS * CHANNELS * FILTER_SIZE * FILTER_SIZE_Y,
/* batchSize */ 100, true, false, NUM_FILTERS, true);
}
#endif
TEST(Layer, concat) {
TestConfig config;
config.biasSize = 0;
......@@ -293,10 +295,9 @@ void testConvLayer(const string& type, bool trans, bool useGpu) {
conv->set_groups(1);
conv->set_filter_channels(conv->channels() / conv->groups());
conv->set_img_size(16);
conv->set_output_x(
(2 * conv->padding() + conv->img_size() - conv->filter_size()) /
((float)conv->stride()) +
1.5);
conv->set_output_x(outputSize(conv->img_size(), conv->filter_size(),
conv->padding(), conv->stride(),
/* caffeMode */ true));
config.layerConfig.set_size(conv->output_x() * conv->output_x() *
config.layerConfig.num_filters());
......@@ -329,15 +330,13 @@ TEST(Layer, blockExpandLayer) {
blockExpand->set_stride_x(2);
blockExpand->set_stride_y(2);
blockExpand->set_output_x(
1 +
(2 * blockExpand->padding_x() + blockExpand->img_size_x() -
blockExpand->block_x() + blockExpand->stride_x() - 1) /
blockExpand->stride_x());
outputSize(blockExpand->img_size_x(), blockExpand->block_x(),
blockExpand->padding_x(), blockExpand->stride_x(),
/* caffeMode */ false));
blockExpand->set_output_y(
1 +
(2 * blockExpand->padding_y() + blockExpand->img_size_y() -
blockExpand->block_y() + blockExpand->stride_y() - 1) /
blockExpand->stride_y());
outputSize(blockExpand->img_size_y(), blockExpand->block_y(),
blockExpand->padding_y(), blockExpand->stride_y(),
/* caffeMode */ false));
config.layerConfig.set_size(blockExpand->block_x() * blockExpand->block_y() *
blockExpand->channels());
......@@ -862,8 +861,8 @@ void setPoolConfig(TestConfig* config, PoolConfig* pool,
pool->set_stride(sw);
pool->set_stride_y(sh);
int ow = (pool->img_size() - kw + 2 * pw + sw - 1) / sw + 1;
int oh = (pool->img_size_y() - kh + 2 * ph + sh - 1) / sh + 1;
int ow = outputSize(pool->img_size(), kw, pw, sw, /* caffeMode */ false);
int oh = outputSize(pool->img_size_y(), kh, ph, sh, /* caffeMode */ false);
pool->set_output_x(ow);
pool->set_output_y(oh);
}
......@@ -1281,12 +1280,11 @@ TEST(Operator, conv) {
conv->set_groups(1);
conv->set_filter_channels(conv->channels() / conv->groups());
conv->set_img_size(IMAGE_SIZE);
int outputSize =
int(1.0 * (2 * conv->padding() + conv->img_size() - conv->filter_size()) /
conv->stride()) +
1;
conv->set_output_x(outputSize);
config.layerConfig.set_size(outputSize * outputSize *
int output_x =
outputSize(conv->img_size(), conv->filter_size(), conv->padding(),
conv->stride(), /* caffeMode */ true);
conv->set_output_x(output_x);
config.layerConfig.set_size(output_x * output_x *
config.layerConfig.num_filters());
config.layerConfig.set_size(conv->output_x() * conv->output_x() *
NUM_FILTERS);
......
paddle/gserver/tests/test_PyDataProvider2.cpp
浏览文件 @ db569f29
......@@ -353,6 +353,23 @@ TEST(PyDataProvider2, test_check) {
}
}
TEST(PyDataProvider2, multiThread) {
paddle::DataConfig config;
config.set_type("py2");
config.set_files(FLAGS_train_list.c_str());
config.set_load_data_module("test_PyDataProvider2");
config.set_load_data_object("test_dense_no_seq");
config.set_async_load_data(true);
std::unique_ptr<paddle::DataProvider> provider(
paddle::DataProvider::create(config, false));
provider->reset();
paddle::DataBatch batch;
provider->getNextBatch(100, &batch);
provider->reset();
provider.reset();
}
int main(int argc, char** argv) {
testing::InitGoogleTest(&argc, argv);
paddle::initMain(argc, argv);
......
paddle/math/MathFunctions.cpp
浏览文件 @ db569f29
......@@ -39,6 +39,46 @@ void gemm<double>(const CBLAS_TRANSPOSE transA, const CBLAS_TRANSPOSE transB,
beta, C, ldc);
}
template<>
int getrf<float>(const CBLAS_ORDER order, const int M, const int N,
float *A, const int lda, int *ipiv) {
#ifdef PADDLE_USE_ATLAS
return clapack_sgetrf(order, M, N, A, lda, ipiv);
#else
return LAPACKE_sgetrf(order, M, N, A, lda, ipiv);
#endif
}
template<>
int getrf<double>(const CBLAS_ORDER order, const int M, const int N,
double *A, const int lda, int *ipiv) {
#ifdef PADDLE_USE_ATLAS
return clapack_dgetrf(order, M, N, A, lda, ipiv);
#else
return LAPACKE_dgetrf(order, M, N, A, lda, ipiv);
#endif
}
template<>
int getri<float>(const CBLAS_ORDER order, const int N, float *A,
const int lda, const int *ipiv) {
#ifdef PADDLE_USE_ATLAS
return clapack_sgetri(order, N, A, lda, ipiv);
#else
return LAPACKE_sgetri(order, N, A, lda, ipiv);
#endif
}
template<>
int getri<double>(const CBLAS_ORDER order, const int N, double *A,
const int lda, const int *ipiv) {
#ifdef PADDLE_USE_ATLAS
return clapack_dgetri(order, N, A, lda, ipiv);
#else
return LAPACKE_dgetri(order, N, A, lda, ipiv);
#endif
}
template<>
void axpy<float>(const int n, const float alpha, const float* x, float* y) {
cblas_saxpy(n, alpha, x, 1, y, 1);
......
paddle/math/MathFunctions.h
浏览文件 @ db569f29
......@@ -17,10 +17,18 @@ limitations under the License. */
#ifdef PADDLE_USE_MKL
#include <mkl.h>
#include <mkl_lapacke.h>
#else
extern "C" {
#include <cblas.h>
}
#ifdef PADDLE_USE_ATLAS
extern "C" {
#include <clapack.h>
}
#else
#include <lapacke.h>
#endif
#endif
#include <cmath>
......@@ -34,6 +42,14 @@ void gemm(const CBLAS_TRANSPOSE transA, const CBLAS_TRANSPOSE transB,
const T* B, const int ldb,
const T beta, T* C, const int ldc);
template<class T>
int getrf(const CBLAS_ORDER Order, const int M, const int N,
T *A, const int lda, int *ipiv);
template<class T>
int getri(const CBLAS_ORDER Order, const int N, T *A,
const int lda, const int *ipiv);
template<class T>
void axpy(const int n, const T alpha, const T* x, T* y);
......
paddle/math/MathUtils.cpp
浏览文件 @ db569f29
......@@ -12,7 +12,6 @@ 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 "MathUtils.h"
#include <algorithm>
#include "paddle/utils/Logging.h"
......@@ -24,11 +23,7 @@ namespace paddle {
* major is rows and minor is cols, according to
* major value to initialize minor value"
*/
void sparseRand(int* major,
int* minor,
int nnz,
int majorLen,
int minorMax,
void sparseRand(int* major, int* minor, int nnz, int majorLen, int minorMax,
bool useGpu) {
CHECK(size_t(nnz) > size_t(1));
int* cpuMajor;
......@@ -72,5 +67,17 @@ void sparseRand(int* major,
}
}
int outputSize(int imageSize, int filterSize, int padding, int stride,
bool caffeMode) {
int outputSize;
if (!caffeMode) {
outputSize =
(imageSize - filterSize + 2 * padding + stride - 1) / stride + 1;
} else {
outputSize = (imageSize - filterSize + 2 * padding) / stride + 1;
}
CHECK_GE(outputSize, 1);
return outputSize;
}
} // namespace paddle
paddle/math/MathUtils.h
浏览文件 @ db569f29
......@@ -44,4 +44,20 @@ namespace paddle {
void sparseRand(int* major, int* minor, int nnz, int majorLen, int minorMax,
bool useGpu);
/**
* Calculate output size based on caffeMode_.
* - input(+padding): 0123456789
* - imageSize(+padding) = 10;
* - filterSize = 3;
* - stride = 2;
* - caffeMode is true:
- output: (012), (234), (456), (678)
- outputSize = 4;
* - caffeMode is false:
* - output: (012), (234), (456), (678), (9)
* - outputSize = 5;
*/
int outputSize(int imageSize, int filterSize, int padding, int stride,
bool caffeMode);
} // namespace paddle
paddle/math/Matrix.cpp
浏览文件 @ db569f29
......@@ -335,6 +335,30 @@ void GpuMatrix::transpose(MatrixPtr matTrans, bool memAlloc) {
hl_matrix_transpose(data, dataTrans, height_, width_, lda, ldc);
}
MatrixPtr GpuMatrix::getInverse() {
MatrixPtr matInv;
inverse(matInv, true);
return matInv;
}
void GpuMatrix::inverse(MatrixPtr matInv, bool memAlloc) {
CHECK_EQ(height_, width_);
if (memAlloc) {
matInv = std::make_shared<GpuMatrix>(height_, width_);
} else {
CHECK(matInv != NULL);
}
real* data = getData();
real* dataInv = matInv->getData();
int lda = getStride();
int ldc = matInv->getStride();
hl_matrix_inverse(data, dataInv, height_, lda, ldc);
}
void GpuMatrix::addBias(Matrix& b, real scale) {
CHECK(b.getHeight() == 1) << "the Bias should be a vector";
BaseMatrix::addBias(b, scale);
......@@ -1417,6 +1441,47 @@ void CpuMatrix::transpose(MatrixPtr matTrans, bool memAlloc) {
}
}
MatrixPtr CpuMatrix::getInverse() {
MatrixPtr matInv;
inverse(matInv, true);
return matInv;
}
void CpuMatrix::inverse(MatrixPtr matInv, bool memAlloc) {
CHECK_EQ(height_, width_);
if (memAlloc) {
matInv = std::make_shared<CpuMatrix>(height_, width_);
} else {
CHECK(matInv != NULL);
}
CHECK_EQ(height_, matInv->getHeight());
CHECK_EQ(width_, matInv->getWidth());
matInv->copyFrom(*this);
real* data = getData();
real* dataInv = matInv->getData();
int ldc = matInv->getStride();
if (height_ == 1) {
CHECK_NE(*data, 0);
*dataInv = 1.0 / (*data);
return;
}
/* Compute the LU decomposition of the matrix */
std::vector<int> ipiv(height_);
CBLAS_ORDER order = (matInv->isTransposed() ? CblasColMajor : CblasRowMajor);
int info = getrf<real>(order, height_, height_, dataInv, ldc, ipiv.data());
CHECK_EQ(info, 0);
/* Compute the inverse of the matrix given its LU decompsotion */
info = getri<real>(order, height_, dataInv, ldc, ipiv.data());
CHECK_EQ(info, 0);
}
void CpuMatrix::convExpand(Matrix& feature, int feaImgHeight, int feaImgWidth,
int channels, int blockH, int blockW, int strideH,
int strideW, int paddingH, int paddingW, int outputH,
......
paddle/math/Matrix.h
浏览文件 @ db569f29
......@@ -328,6 +328,20 @@ public:
LOG(FATAL) << "Not implemented";
}
virtual MatrixPtr getInverse() {
LOG(FATAL) << "Not implemented";
}
/**
* @brief inverse.
*
* if allocate matInv's memory outside, then set memAlloc as false;
* else set as true.
*/
virtual void inverse(MatrixPtr matInv, bool memAlloc) {
LOG(FATAL) << "Not implemented";
}
public:
/// Only set all variables to 0 or NULL but not free them.
virtual void clear() {
......@@ -1043,6 +1057,9 @@ public:
MatrixPtr getTranspose();
void transpose(MatrixPtr matTrans, bool memAlloc);
MatrixPtr getInverse();
void inverse(MatrixPtr matInv, bool memAlloc);
/// add b to each sample of this.
void addBias(Matrix& b, real scale);
void addSharedBias(Matrix& b, real scale);
......@@ -1282,6 +1299,9 @@ public:
MatrixPtr getTranspose();
void transpose(MatrixPtr matTrans, bool memAlloc);
MatrixPtr getInverse();
void inverse(MatrixPtr matInv, bool memAlloc);
void copyFrom(const Matrix& src);
void copyFrom(const Matrix& src, hl_stream_t stream);
......
paddle/math/tests/test_matrixCompare.cpp
浏览文件 @ db569f29
......@@ -641,9 +641,32 @@ void testMatrixTranspose(int height, int width) {
MatrixCheckEqual(*cpuT, *outputCheck);
}
void testMatrixInverse(int height) {
MatrixPtr cpu = std::make_shared<CpuMatrix>(height, height);
MatrixPtr gpu = std::make_shared<GpuMatrix>(height, height);
MatrixPtr cpuI = std::make_shared<CpuMatrix>(height, height);
MatrixPtr gpuI = std::make_shared<GpuMatrix>(height, height);
cpu->randomizeUniform();
gpu->copyFrom(*cpu);
cpu->inverse(cpuI, false);
gpu->inverse(gpuI, false);
MatrixPtr outputCheck = std::make_shared<CpuMatrix>(height, height);
outputCheck->copyFrom(*gpuI);
MatrixCheckErr(*cpuI, *outputCheck);
outputCheck->mul(cpu, cpuI);
cpu->zeroMem();
for (int i = 0; i < height; i++) {
cpu->getRowBuf(i)[i] = 1.0;
}
MatrixCheckErr(*cpu, *outputCheck);
}
TEST(Matrix, unary) {
for (auto height : {1, 11, 73, 128, 200, 330}) {
for (auto width : {1, 32, 100, 512, 1000, 3210}) {
for (auto height : {1, 3, 11, 73, 128, 200, 330}) {
for (auto width : {1, 3, 32, 100, 512, 1000, 3210}) {
VLOG(3) << " height=" << height << " width=" << width;
// applyUnary
......@@ -675,6 +698,8 @@ TEST(Matrix, unary) {
// transpose
testMatrixTranspose(height, width);
}
// inverse
testMatrixInverse(height);
}
}
......
paddle/scripts/submit_local.sh.in
浏览文件 @ db569f29
......@@ -68,7 +68,7 @@ EOF
if [ $? -eq 1 ]; then # Older version installed, or not installed at all
echo "First time run paddle, need to install some python dependencies."
BASEDIR=$(dirname "$0")
pip install ${BASEDIR}/../opt/paddle/share/wheels/*.whl
pip install ${BASEDIR}/../opt/paddle/share/wheels/*-@PADDLE_VERSION@-*.whl
if [ $? -ne 0 ]; then
echo "pip install wheels failed. "
echo "Please use 'sudo paddle' at the first time you use PaddlePaddle"
......
paddle/scripts/travis/build_and_test.sh
浏览文件 @ db569f29
......@@ -6,17 +6,19 @@ if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then
fi
cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_DOC=OFF -DWITH_TESTING=ON -DON_TRAVIS=ON ${CMAKE_EXTRA}
cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_DOC=OFF -DWITH_TESTING=ON -DON_TRAVIS=ON -DON_COVERALLS=ON ${CMAKE_EXTRA}
NPROC=1
if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then
NRPOC=`nproc`
make -j $NPROC
make coveralls
elif [[ "$TRAVIS_OS_NAME" == "osx" ]]; then
NPROC=`sysctl -n hw.ncpu`
make -j $NPROC
env CTEST_OUTPUT_ON_FAILURE=1 make test ARGS="-j $NPROC"
fi
make -j $NPROC
env CTEST_OUTPUT_ON_FAILURE=1 make test ARGS="-j $NPROC"
sudo make install
sudo paddle version
paddle/utils/Util.cpp
浏览文件 @ db569f29
......@@ -106,7 +106,7 @@ pid_t getTID() {
#endif
pid_t tid = syscall(__NR_gettid);
#endif
CHECK_NE(tid, -1);
CHECK_NE((int)tid, -1);
return tid;
}
......
python/paddle/trainer/config_parser.py
浏览文件 @ db569f29
......@@ -782,7 +782,9 @@ class Pool(Cfg):
padding = None,
padding_y = None):
self.add_keys(locals())
# please refer to the comments in proto/ModelConfig.proto
@config_class
class SpatialPyramidPool(Cfg):
def __init__(
self,
......@@ -1015,6 +1017,17 @@ def TestData(data_config, async_load_data=None):
" Data definition")
g_config.test_data_config.async_load_data = async_load_data
'''
caffe_mode: compute the output size using floor instead of ceil,
which is consistent of caffe and CuDNN's convention.
'''
def cnn_output_size(img_size, filter_size, padding, stride, caffe_mode):
output = (2 * padding + img_size - filter_size) / float(stride)
if caffe_mode:
return 1 + int(math.floor(output))
else:
return 1 + int(math.ceil(output))
def parse_pool(pool, input_layer_name, pool_conf):
pool_conf.pool_type = pool.pool_type
config_assert(pool.pool_type in ['max-projection', 'avg-projection',
......@@ -1045,12 +1058,10 @@ def parse_pool(pool, input_layer_name, pool_conf):
if pool.padding is not None:
pool_conf.padding = pool.padding
pool_conf.padding_y = default(pool.padding_y, pool_conf.padding)
pool_conf.output_x = int(math.ceil((pool_conf.img_size + \
2*pool_conf.padding - pool_conf.size_x) / \
float(pool_conf.stride))) + 1
pool_conf.output_y = int(math.ceil((pool_conf.img_size_y + \
2*pool_conf.padding_y - pool_conf.size_y) / \
float(pool_conf.stride_y))) + 1
pool_conf.output_x = cnn_output_size(pool_conf.img_size, pool_conf.size_x,
pool_conf.padding, pool_conf.stride, False)
pool_conf.output_y = cnn_output_size(pool_conf.img_size_y, pool_conf.size_y,
pool_conf.padding_y, pool_conf.stride_y, False)
def parse_spp(spp, input_layer_name, spp_conf):
spp_conf.pool_type = spp.pool_type
......@@ -1097,10 +1108,7 @@ def parse_norm(norm, input_layer_name, norm_conf):
norm_conf.scale /= norm.size
else:
norm_conf.scale /= norm.size ** 2
'''
caffe_mode: compute the output size using floor instead of ceil,
which is consistent of caffe and CuDNN's convention.
'''
def parse_conv(conv, input_layer_name, conv_conf):
conv_conf.filter_size = conv.filter_size
conv_conf.filter_size_y = conv.filter_size_y
......@@ -1121,14 +1129,9 @@ def parse_conv(conv, input_layer_name, conv_conf):
("Input layer %s: Incorrect input image size %d for input "
+ "image pixels %d")
% (input_layer_name, conv_conf.img_size, img_pixels))
if conv.caffe_mode:
conv_conf.output_x = \
1 + int(math.floor((2 * conv.padding + conv_conf.img_size \
- conv.filter_size) / float(conv.stride)))
else:
conv_conf.output_x = \
1 + int(math.ceil((2 * conv.padding + conv_conf.img_size \
- conv.filter_size) / float(conv.stride)))
conv_conf.output_x = cnn_output_size(conv_conf.img_size, conv_conf.filter_size,
conv_conf.padding, conv_conf.stride,
conv_conf.caffe_mode)
def parse_block_expand(block_expand, input_layer_name, block_expand_conf):
block_expand_conf.channels = block_expand.channels
......@@ -1143,18 +1146,16 @@ def parse_block_expand(block_expand, input_layer_name, block_expand_conf):
if block_expand_conf.img_size_x == 0:
block_expand_conf.output_x = 0
else:
block_expand_conf.output_x = \
1 + \
int(math.ceil((2 * block_expand.padding_x + block_expand.img_size_x \
- block_expand.block_x) / float(block_expand.stride_x)))
block_expand_conf.output_x = cnn_output_size(
block_expand.img_size_x, block_expand.block_x,
block_expand.padding_x, block_expand.stride_x, False)
if block_expand_conf.img_size_y == 0:
block_expand_conf.output_y = 0
block_expand_conf.output_y = 0
else:
block_expand_conf.output_y = \
1 + \
int(math.ceil((2 * block_expand.padding_y + block_expand.img_size_y \
- block_expand.block_y) / float(block_expand.stride_y)))
block_expand_conf.output_y = cnn_output_size(
block_expand.img_size_y, block_expand.block_y,
block_expand.padding_y, block_expand.stride_y, False)
def parse_maxout(maxout, input_layer_name, maxout_conf):
maxout_conf.channels = maxout.channels
......@@ -2617,8 +2618,9 @@ class MixedLayer(LayerBase):
for input in self.inputs:
psize += input.calc_bias_size()
self.config.bias_size = psize
self.create_bias_parameter(bias, psize)
if bias:
self.config.bias_size = psize
self.create_bias_parameter(bias, psize)
if error_clipping_threshold is not None:
self.config.error_clipping_threshold = error_clipping_threshold
......@@ -2703,8 +2705,9 @@ class ConcatenateLayer2(LayerBase):
for input in self.inputs:
psize += input.calc_bias_size()
self.config.bias_size = psize
self.create_bias_parameter(bias, psize)
if bias:
self.config.bias_size = psize
self.create_bias_parameter(bias, psize)
@config_layer('recurrent')
class RecurrentLayer(LayerBase):
......
python/paddle/trainer_config_helpers/activations.py
浏览文件 @ db569f29
......@@ -199,3 +199,12 @@ class ExpActivation(BaseActivation):
f(z) = e^z.
"""
def __init__(self): BaseActivation.__init__(self, 'exponential', False)
class LogActivation(BaseActivation):
"""
Logarithm Activation.
.. math::
f(z) = log(z)
"""
def __init__(self): BaseActivation.__init__(self, 'log', False)
python/paddle/trainer_config_helpers/default_decorators.py
浏览文件 @ db569f29
......@@ -13,6 +13,7 @@
# limitations under the License.
import functools
import inspect
from .attrs import ParamAttr
from .activations import TanhActivation
from paddle.trainer.config_parser import *
......@@ -37,8 +38,12 @@ def wrap_param_default(param_names=None, default_factory=None,
@functools.wraps(func)
def __wrapper__(*args, **kwargs):
if len(args) != 0:
logger.warning("please use keyword arguments in paddle config.")
argspec = inspect.getargspec(func)
num_positional = len(argspec.args)
if argspec.defaults:
num_positional -= len(argspec.defaults)
if not argspec.varargs and len(args) > num_positional:
logger.fatal("Must use keyword arguments for non-positional args")
for name in param_names:
if not_set_callback(kwargs, name): # Not set
kwargs[name] = default_factory(func)
......
python/paddle/trainer_config_helpers/layers.py
浏览文件 @ db569f29
......@@ -56,7 +56,7 @@ __all__ = ["full_matrix_projection", "AggregateLevel", "ExpandLevel",
'rank_cost', 'lambda_cost', 'huber_cost',
'block_expand_layer',
'maxout_layer', 'out_prod_layer', 'print_layer',
'spp_layer',
# 'spp_layer',
]
......@@ -112,7 +112,7 @@ class LayerType(object):
LINEAR_COMBINATION_LAYER = "convex_comb"
BLOCK_EXPAND = "blockexpand"
MAXOUT = "maxout"
SPP_LAYER = "spp"
# SPP_LAYER = "spp"
PRINT_LAYER = "print"
......@@ -566,7 +566,7 @@ class MixedLayerType(LayerOutput):
self.inputs = []
self.finalized = False
def __add__(self, other):
def __iadd__(self, other):
"""
+ += operator
:param other: Other projection.
......@@ -1711,60 +1711,60 @@ def img_pool_layer(input, pool_size, name=None,
num_filters=num_channels)
@wrap_name_default("spp")
@layer_support()
def spp_layer(input, name=None, num_channels=None, pool_type=None,
pyramid_height=None, img_width=None, layer_attr=None):
pass
"""
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.
The details please refer to
`Kaiming He's paper <https://arxiv.org/abs/1406.4729>`_.
:param name: layer name.
:type name: basestring
:param input: layer's input.
:type input: LayerOutput
:param num_channels: number of input channel.
:type num_channels: int
:param pool_type: Pooling type. MaxPooling or AveragePooling. Default is MaxPooling.
:type scale: BasePoolingType
:param pyramid_height: pyramid height.
:type pyramid_height: int
:param img_width: the width of input feature map. If it is None, the input feature
map should be square.
:type img_width: int|None
:param layer_attr: Extra Layer Attribute.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
if num_channels is None:
assert input.num_filters is not None
num_channels = input.num_filters
if pool_type is None:
pool_type = MaxPooling()
elif isinstance(pool_type, AvgPooling):
pool_type.name = 'avg'
type_name = pool_type.name
if (isinstance(pool_type, AvgPooling) or isinstance(pool_type, MaxPooling)):
type_name += '-projection'
Layer(
name=name,
type=LayerType.SPP_LAYER,
inputs=Input(input.name,
spp=SpatialPyramidPool(pool_type=type_name,
channels=num_channels,
pyramid_height=pyramid_height,
img_width=img_width)
),
**ExtraLayerAttribute.to_kwargs(layer_attr)
)
return LayerOutput(name, LayerType.SPP_LAYER, parents=[input],
num_filters=num_channels)
# @wrap_name_default("spp")
# @layer_support()
# def spp_layer(input, name=None, num_channels=None, pool_type=None,
# pyramid_height=None, img_width=None, layer_attr=None):
# pass
# """
# Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.
# The details please refer to
# `Kaiming He's paper <https://arxiv.org/abs/1406.4729>`_.
# :param name: layer name.
# :type name: basestring
# :param input: layer's input.
# :type input: LayerOutput
# :param num_channels: number of input channel.
# :type num_channels: int
# :param pool_type: Pooling type. MaxPooling or AveragePooling. Default is MaxPooling.
# :type scale: BasePoolingType
# :param pyramid_height: pyramid height.
# :type pyramid_height: int
# :param img_width: the width of input feature map. If it is None, the input feature
# map should be square.
# :type img_width: int|None
# :param layer_attr: Extra Layer Attribute.
# :type layer_attr: ExtraLayerAttribute
# :return: LayerOutput object.
# :rtype: LayerOutput
# """
# if num_channels is None:
# assert input.num_filters is not None
# num_channels = input.num_filters
# if pool_type is None:
# pool_type = MaxPooling()
# elif isinstance(pool_type, AvgPooling):
# pool_type.name = 'avg'
# type_name = pool_type.name
# if (isinstance(pool_type, AvgPooling) or isinstance(pool_type, MaxPooling)):
# type_name += '-projection'
# Layer(
# name=name,
# type=LayerType.SPP_LAYER,
# inputs=Input(input.name,
# spp=SpatialPyramidPool(pool_type=type_name,
# channels=num_channels,
# pyramid_height=pyramid_height,
# img_width=img_width)
# ),
# **ExtraLayerAttribute.to_kwargs(layer_attr)
# )
# return LayerOutput(name, LayerType.SPP_LAYER, parents=[input],
# num_filters=num_channels)
def __img_norm_layer__(name, input, size, norm_type, scale, power,
......
python/paddle/trainer_config_helpers/math.py 0 → 100644
浏览文件 @ db569f29
# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .layers import LayerOutput, mixed_layer, identity_projection, \
slope_intercept_layer
from .attrs import is_compatible_with
from .default_decorators import *
import activations as act
__all__ = []
def register_unary_math_op(op_name, act):
def op(input, name=None):
return mixed_layer(input=[identity_projection(input=input)],
name=name,
act=act)
op = wrap_name_default(op_name)(op)
op.__doc__ = type(act).__doc__
globals()[op_name] = op
__all__.append(op_name)
register_unary_math_op('exp', act.ExpActivation())
register_unary_math_op('log', act.LogActivation())
register_unary_math_op('abs', act.AbsActivation())
register_unary_math_op('sigmoid', act.SigmoidActivation())
register_unary_math_op('tanh', act.TanhActivation())
register_unary_math_op('square', act.SquareActivation())
def add(layeroutput, other):
if is_compatible_with(other, float):
return slope_intercept_layer(input=layeroutput, intercept=other)
assert isinstance(other, LayerOutput)
return mixed_layer(input=[identity_projection(input=layeroutput),
identity_projection(input=other)])
LayerOutput.__radd__ = add
LayerOutput.__add__ = add
def sub(layeroutput, other):
if is_compatible_with(other, float):
return slope_intercept_layer(input=layeroutput, intercept=other)
assert isinstance(other, LayerOutput)
neg = slope_intercept_layer(input=other, slope=-1.0)
return mixed_layer(input=[identity_projection(input=layeroutput),
identity_projection(input=neg)])
LayerOutput.__sub__ = sub
def rsub(layeroutput, other):
neg = slope_intercept_layer(input=layeroutput, slope=-1.0)
return add(neg, other)
LayerOutput.__rsub__ = rsub
python/paddle/trainer_config_helpers/tests/configs/check.md5 已删除 100644 → 0
浏览文件 @ 5ece5c96
86c0815275a9d5eb902e23c6a592f58a img_layers.protostr
a5d9259ff1fd7ca23d0ef090052cb1f2 last_first_seq.protostr
9c038249ec8ff719753a746cdb04c026 layer_activations.protostr
34e04043cbb12931c47fa44ec50eeffc projections.protostr
7334ba0a4544f0623231330fc51d390d shared_fc.protostr
bb8e233b05b8e07f9ed386b7aee4f2c6 shared_lstm.protostr
6b39e34beea8dfb782bee9bd3dea9eb5 simple_rnn_layers.protostr
f98e79e1630d5eb827c300e64836d269 test_bi_grumemory.protostr
0fc1409600f1a3301da994ab9d28b0bf test_cost_layers.protostr
6cd5f28a3416344f20120698470e0a4c test_cost_layers_with_weight.protostr
144bc6d3a509de74115fa623741797ed test_expand_layer.protostr
2378518bdb71e8c6e888b1842923df58 test_fc.protostr
8bb44e1e5072d0c261572307e7672bda test_grumemory_layer.protostr
1f3510672dce7a9ed25317fc58579ac7 test_hsigmoid.protostr
d350bd91a0dc13e854b1364c3d9339c6 test_lstmemory_layer.protostr
5433ed33d4e7414eaf658f2a55946186 test_maxout.protostr
251a948ba41c1071afcd3d9cf9c233f7 test_ntm_layers.protostr
e6ff04e70aea27c7b06d808cc49c9497 test_print_layer.protostr
fded24727338fb8ce44d9951ed8aea08 test_rnn_group.protostr
67d6fde3afb54f389d0ce4ff14726fe1 test_sequence_pooling.protostr
f586a548ef4350ba1ed47a81859a64cb unused_layers.protostr
f937a5a6e7e8864b4d8cf56b0f7c7f44 util_layers.protostr
60c9a71e19bd4b2a1253712799d0ae70 test_spp_layer.protostr
python/paddle/trainer_config_helpers/tests/configs/generate_protostr.sh
浏览文件 @ db569f29
......@@ -4,16 +4,20 @@ set -e
cd `dirname $0`
export PYTHONPATH=$PWD/../../../../
protostr=$PWD/protostr
configs=(test_fc layer_activations projections test_print_layer
test_sequence_pooling test_lstmemory_layer test_grumemory_layer
last_first_seq test_expand_layer test_ntm_layers test_hsigmoid
img_layers util_layers simple_rnn_layers unused_layers test_cost_layers
test_rnn_group shared_fc shared_lstm test_cost_layers_with_weight
test_maxout test_bi_grumemory test_spp_layer)
# test_maxout test_bi_grumemory math_ops test_spp_layer)
test_maxout test_bi_grumemory math_ops test_spp_layer)
for conf in ${configs[*]}
do
echo "Generating " $conf
python -m paddle.utils.dump_config $conf.py > $conf.protostr
python -m paddle.utils.dump_config $conf.py > $protostr/$conf.protostr.unitest
done
python/paddle/trainer_config_helpers/tests/configs/math_ops.py 0 → 100644
浏览文件 @ db569f29
from paddle.trainer_config_helpers import *
from paddle.trainer_config_helpers import math
settings(
batch_size=1000,
learning_rate=1e-5
)
x = data_layer(name='data', size=100)
x = math.exp(x)
x = math.log(x)
x = math.abs(x)
x = math.sigmoid(x)
x = math.square(x)
x = math.square(x)
y = 1 + x
y = y + 1
y = x + y
y = y - x
y = y - 2
y = 2 - y
outputs(y)
python/paddle/trainer_config_helpers/tests/configs/protostr/img_layers.protostr 0 → 100644
浏览文件 @ db569f29
type: "nn"
layers {
name: "image"
type: "data"
size: 65536
active_type: ""
}
layers {
name: "__conv_0__"
type: "exconv"
size: 3297856
active_type: ""
inputs {
input_layer_name: "image"
input_parameter_name: "___conv_0__.w0"
conv_conf {
filter_size: 32
channels: 1
stride: 1
padding: 1
groups: 1
filter_channels: 1
output_x: 227
img_size: 256
caffe_mode: true
filter_size_y: 32
padding_y: 1
stride_y: 1
}
}
bias_parameter_name: "___conv_0__.wbias"
num_filters: 64
shared_biases: true
}
layers {
name: "__batch_norm_0__"
type: "batch_norm"
size: 3297856
active_type: "relu"
inputs {
input_layer_name: "__conv_0__"
input_parameter_name: "___batch_norm_0__.w0"
image_conf {
channels: 64
img_size: 227
}
}
inputs {
input_layer_name: "__conv_0__"
input_parameter_name: "___batch_norm_0__.w1"
}
inputs {
input_layer_name: "__conv_0__"
input_parameter_name: "___batch_norm_0__.w2"
}
bias_parameter_name: "___batch_norm_0__.wbias"
moving_average_fraction: 0.9
}
layers {
name: "__crmnorm_0__"
type: "norm"
size: 3297856
active_type: ""
inputs {
input_layer_name: "__batch_norm_0__"
norm_conf {
norm_type: "cmrnorm-projection"
channels: 64
size: 32
scale: 0.0004
pow: 0.75
output_x: 227
img_size: 227
blocked: false
}
}
}
layers {
name: "__pool_0__"
type: "pool"
size: 2458624
active_type: ""
inputs {
input_layer_name: "__conv_0__"
pool_conf {
pool_type: "max-projection"
channels: 64
size_x: 32
stride: 1
output_x: 196
img_size: 227
padding: 0
size_y: 32
stride_y: 1
output_y: 196
img_size_y: 227
padding_y: 0
}
}
}
parameters {
name: "___conv_0__.w0"
size: 65536
initial_mean: 0.0
initial_std: 0.0441941738242
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___conv_0__.wbias"
size: 64
initial_mean: 0.0
initial_std: 0.0
dims: 64
dims: 1
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___batch_norm_0__.w0"
size: 64
initial_mean: 1.0
initial_std: 0.0
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___batch_norm_0__.w1"
size: 64
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 64
initial_strategy: 0
initial_smart: false
is_static: true
is_shared: true
}
parameters {
name: "___batch_norm_0__.w2"
size: 64
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 64
initial_strategy: 0
initial_smart: false
is_static: true
is_shared: true
}
parameters {
name: "___batch_norm_0__.wbias"
size: 64
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 64
initial_strategy: 0
initial_smart: false
}
input_layer_names: "image"
output_layer_names: "__pool_0__"
output_layer_names: "__crmnorm_0__"
sub_models {
name: "root"
layer_names: "image"
layer_names: "__conv_0__"
layer_names: "__batch_norm_0__"
layer_names: "__crmnorm_0__"
layer_names: "__pool_0__"
input_layer_names: "image"
output_layer_names: "__pool_0__"
output_layer_names: "__crmnorm_0__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/last_first_seq.protostr 0 → 100644
浏览文件 @ db569f29
type: "nn"
layers {
name: "data"
type: "data"
size: 30
active_type: ""
}
layers {
name: "__first_seq_0__"
type: "seqlastins"
size: 30
active_type: "linear"
inputs {
input_layer_name: "data"
}
select_first: true
trans_type: "seq"
}
layers {
name: "__first_seq_1__"
type: "seqlastins"
size: 30
active_type: "linear"
inputs {
input_layer_name: "data"
}
select_first: true
trans_type: "non-seq"
}
layers {
name: "__last_seq_0__"
type: "seqlastins"
size: 30
active_type: "linear"
inputs {
input_layer_name: "data"
}
trans_type: "seq"
}
layers {
name: "__last_seq_1__"
type: "seqlastins"
size: 30
active_type: "linear"
inputs {
input_layer_name: "data"
}
trans_type: "non-seq"
}
input_layer_names: "data"
output_layer_names: "__first_seq_0__"
output_layer_names: "__first_seq_1__"
output_layer_names: "__last_seq_0__"
output_layer_names: "__last_seq_1__"
sub_models {
name: "root"
layer_names: "data"
layer_names: "__first_seq_0__"
layer_names: "__first_seq_1__"
layer_names: "__last_seq_0__"
layer_names: "__last_seq_1__"
input_layer_names: "data"
output_layer_names: "__first_seq_0__"
output_layer_names: "__first_seq_1__"
output_layer_names: "__last_seq_0__"
output_layer_names: "__last_seq_1__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/layer_activations.protostr 0 → 100644
浏览文件 @ db569f29
type: "nn"
layers {
name: "input"
type: "data"
size: 100
active_type: ""
}
layers {
name: "layer_0"
type: "fc"
size: 100
active_type: "tanh"
inputs {
input_layer_name: "input"
input_parameter_name: "_layer_0.w0"
}
bias_parameter_name: "_layer_0.wbias"
}
layers {
name: "layer_1"
type: "fc"
size: 100
active_type: "sigmoid"
inputs {
input_layer_name: "input"
input_parameter_name: "_layer_1.w0"
}
bias_parameter_name: "_layer_1.wbias"
}
layers {
name: "layer_2"
type: "fc"
size: 100
active_type: "softmax"
inputs {
input_layer_name: "input"
input_parameter_name: "_layer_2.w0"
}
bias_parameter_name: "_layer_2.wbias"
}
layers {
name: "layer_3"
type: "fc"
size: 100
active_type: ""
inputs {
input_layer_name: "input"
input_parameter_name: "_layer_3.w0"
}
bias_parameter_name: "_layer_3.wbias"
}
layers {
name: "layer_4"
type: "fc"
size: 100
active_type: ""
inputs {
input_layer_name: "input"
input_parameter_name: "_layer_4.w0"
}
bias_parameter_name: "_layer_4.wbias"
}
layers {
name: "layer_5"
type: "fc"
size: 100
active_type: "exponential"
inputs {
input_layer_name: "input"
input_parameter_name: "_layer_5.w0"
}
bias_parameter_name: "_layer_5.wbias"
}
layers {
name: "layer_6"
type: "fc"
size: 100
active_type: "relu"
inputs {
input_layer_name: "input"
input_parameter_name: "_layer_6.w0"
}
bias_parameter_name: "_layer_6.wbias"
}
layers {
name: "layer_7"
type: "fc"
size: 100
active_type: "brelu"
inputs {
input_layer_name: "input"
input_parameter_name: "_layer_7.w0"
}
bias_parameter_name: "_layer_7.wbias"
}
layers {
name: "layer_8"
type: "fc"
size: 100
active_type: "softrelu"
inputs {
input_layer_name: "input"
input_parameter_name: "_layer_8.w0"
}
bias_parameter_name: "_layer_8.wbias"
}
layers {
name: "layer_9"
type: "fc"
size: 100
active_type: "stanh"
inputs {
input_layer_name: "input"
input_parameter_name: "_layer_9.w0"
}
bias_parameter_name: "_layer_9.wbias"
}
layers {
name: "layer_10"
type: "fc"
size: 100
active_type: "abs"
inputs {
input_layer_name: "input"
input_parameter_name: "_layer_10.w0"
}
bias_parameter_name: "_layer_10.wbias"
}
layers {
name: "layer_11"
type: "fc"
size: 100
active_type: "square"
inputs {
input_layer_name: "input"
input_parameter_name: "_layer_11.w0"
}
bias_parameter_name: "_layer_11.wbias"
}
parameters {
name: "_layer_0.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_layer_0.wbias"
size: 100
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 100
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_layer_1.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_layer_1.wbias"
size: 100
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 100
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_layer_2.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_layer_2.wbias"
size: 100
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 100
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_layer_3.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_layer_3.wbias"
size: 100
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 100
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_layer_4.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_layer_4.wbias"
size: 100
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 100
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_layer_5.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_layer_5.wbias"
size: 100
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 100
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_layer_6.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_layer_6.wbias"
size: 100
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 100
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_layer_7.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_layer_7.wbias"
size: 100
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 100
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_layer_8.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_layer_8.wbias"
size: 100
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 100
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_layer_9.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_layer_9.wbias"
size: 100
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 100
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_layer_10.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_layer_10.wbias"
size: 100
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 100
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_layer_11.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_layer_11.wbias"
size: 100
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 100
initial_strategy: 0
initial_smart: false
}
input_layer_names: "input"
output_layer_names: "layer_0"
output_layer_names: "layer_1"
output_layer_names: "layer_2"
output_layer_names: "layer_3"
output_layer_names: "layer_4"
output_layer_names: "layer_5"
output_layer_names: "layer_6"
output_layer_names: "layer_7"
output_layer_names: "layer_8"
output_layer_names: "layer_9"
output_layer_names: "layer_10"
output_layer_names: "layer_11"
sub_models {
name: "root"
layer_names: "input"
layer_names: "layer_0"
layer_names: "layer_1"
layer_names: "layer_2"
layer_names: "layer_3"
layer_names: "layer_4"
layer_names: "layer_5"
layer_names: "layer_6"
layer_names: "layer_7"
layer_names: "layer_8"
layer_names: "layer_9"
layer_names: "layer_10"
layer_names: "layer_11"
input_layer_names: "input"
output_layer_names: "layer_0"
output_layer_names: "layer_1"
output_layer_names: "layer_2"
output_layer_names: "layer_3"
output_layer_names: "layer_4"
output_layer_names: "layer_5"
output_layer_names: "layer_6"
output_layer_names: "layer_7"
output_layer_names: "layer_8"
output_layer_names: "layer_9"
output_layer_names: "layer_10"
output_layer_names: "layer_11"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/math_ops.protostr 0 → 100644
浏览文件 @ db569f29
type: "nn"
layers {
name: "data"
type: "data"
size: 100
active_type: ""
}
layers {
name: "__exp_0__"
type: "mixed"
size: 100
active_type: "exponential"
inputs {
input_layer_name: "data"
proj_conf {
type: "identity"
name: "___exp_0__.w0"
input_size: 100
output_size: 100
}
}
}
layers {
name: "__log_0__"
type: "mixed"
size: 100
active_type: "log"
inputs {
input_layer_name: "__exp_0__"
proj_conf {
type: "identity"
name: "___log_0__.w0"
input_size: 100
output_size: 100
}
}
}
layers {
name: "__abs_0__"
type: "mixed"
size: 100
active_type: "abs"
inputs {
input_layer_name: "__log_0__"
proj_conf {
type: "identity"
name: "___abs_0__.w0"
input_size: 100
output_size: 100
}
}
}
layers {
name: "__sigmoid_0__"
type: "mixed"
size: 100
active_type: "sigmoid"
inputs {
input_layer_name: "__abs_0__"
proj_conf {
type: "identity"
name: "___sigmoid_0__.w0"
input_size: 100
output_size: 100
}
}
}
layers {
name: "__square_0__"
type: "mixed"
size: 100
active_type: "square"
inputs {
input_layer_name: "__sigmoid_0__"
proj_conf {
type: "identity"
name: "___square_0__.w0"
input_size: 100
output_size: 100
}
}
}
layers {
name: "__square_1__"
type: "mixed"
size: 100
active_type: "square"
inputs {
input_layer_name: "__square_0__"
proj_conf {
type: "identity"
name: "___square_1__.w0"
input_size: 100
output_size: 100
}
}
}
layers {
name: "__slope_intercept_layer_0__"
type: "slope_intercept"
size: 100
active_type: ""
inputs {
input_layer_name: "__square_1__"
}
slope: 1.0
intercept: 1
}
layers {
name: "__slope_intercept_layer_1__"
type: "slope_intercept"
size: 100
active_type: ""
inputs {
input_layer_name: "__slope_intercept_layer_0__"
}
slope: 1.0
intercept: 1
}
layers {
name: "__mixed_0__"
type: "mixed"
size: 100
active_type: ""
inputs {
input_layer_name: "__square_1__"
proj_conf {
type: "identity"
name: "___mixed_0__.w0"
input_size: 100
output_size: 100
}
}
inputs {
input_layer_name: "__slope_intercept_layer_1__"
proj_conf {
type: "identity"
name: "___mixed_0__.w1"
input_size: 100
output_size: 100
}
}
}
layers {
name: "__slope_intercept_layer_2__"
type: "slope_intercept"
size: 100
active_type: ""
inputs {
input_layer_name: "__square_1__"
}
slope: -1.0
intercept: 0.0
}
layers {
name: "__mixed_1__"
type: "mixed"
size: 100
active_type: ""
inputs {
input_layer_name: "__mixed_0__"
proj_conf {
type: "identity"
name: "___mixed_1__.w0"
input_size: 100
output_size: 100
}
}
inputs {
input_layer_name: "__slope_intercept_layer_2__"
proj_conf {
type: "identity"
name: "___mixed_1__.w1"
input_size: 100
output_size: 100
}
}
}
layers {
name: "__slope_intercept_layer_3__"
type: "slope_intercept"
size: 100
active_type: ""
inputs {
input_layer_name: "__mixed_1__"
}
slope: 1.0
intercept: 2
}
layers {
name: "__slope_intercept_layer_4__"
type: "slope_intercept"
size: 100
active_type: ""
inputs {
input_layer_name: "__slope_intercept_layer_3__"
}
slope: -1.0
intercept: 0.0
}
layers {
name: "__slope_intercept_layer_5__"
type: "slope_intercept"
size: 100
active_type: ""
inputs {
input_layer_name: "__slope_intercept_layer_4__"
}
slope: 1.0
intercept: 2
}
input_layer_names: "data"
output_layer_names: "__slope_intercept_layer_5__"
sub_models {
name: "root"
layer_names: "data"
layer_names: "__exp_0__"
layer_names: "__log_0__"
layer_names: "__abs_0__"
layer_names: "__sigmoid_0__"
layer_names: "__square_0__"
layer_names: "__square_1__"
layer_names: "__slope_intercept_layer_0__"
layer_names: "__slope_intercept_layer_1__"
layer_names: "__mixed_0__"
layer_names: "__slope_intercept_layer_2__"
layer_names: "__mixed_1__"
layer_names: "__slope_intercept_layer_3__"
layer_names: "__slope_intercept_layer_4__"
layer_names: "__slope_intercept_layer_5__"
input_layer_names: "data"
output_layer_names: "__slope_intercept_layer_5__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/projections.protostr 0 → 100644
浏览文件 @ db569f29
type: "nn"
layers {
name: "test"
type: "data"
size: 100
active_type: ""
}
layers {
name: "__embedding_0__"
type: "mixed"
size: 256
active_type: ""
inputs {
input_layer_name: "test"
input_parameter_name: "___embedding_0__.w0"
proj_conf {
type: "table"
name: "___embedding_0__.w0"
input_size: 100
output_size: 256
}
}
}
layers {
name: "__mixed_0__"
type: "mixed"
size: 100
active_type: ""
inputs {
input_layer_name: "__embedding_0__"
input_parameter_name: "___mixed_0__.w0"
proj_conf {
type: "fc"
name: "___mixed_0__.w0"
input_size: 256
output_size: 100
}
}
}
layers {
name: "__mixed_1__"
type: "mixed"
size: 100
active_type: ""
inputs {
input_layer_name: "__mixed_0__"
input_parameter_name: "___mixed_1__.w0"
proj_conf {
type: "table"
name: "___mixed_1__.w0"
input_size: 100
output_size: 100
}
}
}
layers {
name: "__mixed_2__"
type: "mixed"
size: 100
active_type: ""
inputs {
input_layer_name: "__mixed_1__"
proj_conf {
type: "identity"
name: "___mixed_2__.w0"
input_size: 100
output_size: 100
}
}
}
layers {
name: "__mixed_3__"
type: "mixed"
size: 100
active_type: ""
inputs {
input_layer_name: "__mixed_2__"
input_parameter_name: "___mixed_3__.w0"
proj_conf {
type: "dot_mul"
name: "___mixed_3__.w0"
input_size: 100
output_size: 100
}
}
}
layers {
name: "__mixed_4__"
type: "mixed"
size: 300
active_type: ""
inputs {
input_layer_name: "__mixed_3__"
input_parameter_name: "___mixed_4__.w0"
proj_conf {
type: "context"
name: "___mixed_4__.w0"
input_size: 100
output_size: 300
context_start: -1
context_length: 3
trainable_padding: true
}
}
}
layers {
name: "__mixed_5__"
type: "mixed"
size: 100
active_type: ""
inputs {
input_layer_name: "__mixed_2__"
}
inputs {
input_layer_name: "__mixed_3__"
}
operator_confs {
type: "dot_mul"
input_indices: 0
input_indices: 1
input_sizes: 100
input_sizes: 100
output_size: 100
dotmul_scale: 1
}
}
layers {
name: "img"
type: "data"
size: 1024
active_type: ""
}
layers {
name: "filter"
type: "data"
size: 576
active_type: ""
}
layers {
name: "__mixed_6__"
type: "mixed"
size: 57600
active_type: ""
inputs {
input_layer_name: "img"
}
inputs {
input_layer_name: "filter"
}
operator_confs {
type: "conv"
input_indices: 0
input_indices: 1
input_sizes: 1024
input_sizes: 576
output_size: 57600
conv_conf {
filter_size: 3
channels: 1
stride: 1
padding: 0
groups: 1
filter_channels: 1
output_x: 30
img_size: 32
caffe_mode: true
filter_size_y: 3
padding_y: 0
stride_y: 1
}
num_filters: 64
}
}
layers {
name: "__mixed_7__"
type: "mixed"
size: 100
active_type: ""
inputs {
input_layer_name: "__mixed_4__"
input_parameter_name: "___mixed_7__.w0"
proj_conf {
type: "fc"
name: "___mixed_7__.w0"
input_size: 300
output_size: 100
}
}
inputs {
input_layer_name: "__mixed_5__"
input_parameter_name: "___mixed_7__.w1"
proj_conf {
type: "trans_fc"
name: "___mixed_7__.w1"
input_size: 100
output_size: 100
}
}
inputs {
input_layer_name: "__mixed_6__"
input_parameter_name: "___mixed_7__.w2"
proj_conf {
type: "fc"
name: "___mixed_7__.w2"
input_size: 57600
output_size: 100
}
}
drop_rate: 0.5
}
parameters {
name: "___embedding_0__.w0"
size: 25600
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 256
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___mixed_0__.w0"
size: 25600
initial_mean: 0.0
initial_std: 0.0625
dims: 256
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___mixed_1__.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___mixed_3__.w0"
size: 100
initial_mean: 0.0
initial_std: 1.0
dims: 1
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___mixed_4__.w0"
size: 200
initial_mean: 0.0
initial_std: 0.0
dims: 2
dims: 100
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___mixed_7__.w0"
size: 30000
initial_mean: 0.0
initial_std: 0.057735026919
dims: 300
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___mixed_7__.w1"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___mixed_7__.w2"
size: 5760000
initial_mean: 0.0
initial_std: 0.00416666666667
dims: 57600
dims: 100
initial_strategy: 0
initial_smart: true
}
input_layer_names: "test"
input_layer_names: "img"
input_layer_names: "filter"
output_layer_names: "__mixed_7__"
sub_models {
name: "root"
layer_names: "test"
layer_names: "__embedding_0__"
layer_names: "__mixed_0__"
layer_names: "__mixed_1__"
layer_names: "__mixed_2__"
layer_names: "__mixed_3__"
layer_names: "__mixed_4__"
layer_names: "__mixed_5__"
layer_names: "img"
layer_names: "filter"
layer_names: "__mixed_6__"
layer_names: "__mixed_7__"
input_layer_names: "test"
input_layer_names: "img"
input_layer_names: "filter"
output_layer_names: "__mixed_7__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/shared_fc.protostr 0 → 100644
浏览文件 @ db569f29
type: "nn"
layers {
name: "feature_a"
type: "data"
size: 200
active_type: ""
}
layers {
name: "feature_b"
type: "data"
size: 200
active_type: ""
}
layers {
name: "__fc_layer_0__"
type: "fc"
size: 200
active_type: "tanh"
inputs {
input_layer_name: "feature_a"
input_parameter_name: "fc_param"
}
bias_parameter_name: "bias_param"
}
layers {
name: "__fc_layer_1__"
type: "fc"
size: 200
active_type: "tanh"
inputs {
input_layer_name: "feature_b"
input_parameter_name: "fc_param"
}
bias_parameter_name: "bias_param"
}
layers {
name: "__fc_layer_2__"
type: "fc"
size: 10
active_type: "softmax"
inputs {
input_layer_name: "__fc_layer_0__"
input_parameter_name: "softmax_param"
}
inputs {
input_layer_name: "__fc_layer_1__"
input_parameter_name: "softmax_param"
}
}
layers {
name: "label"
type: "data"
size: 10
active_type: ""
}
layers {
name: "__cost_0__"
type: "multi-class-cross-entropy"
size: 1
active_type: ""
inputs {
input_layer_name: "__fc_layer_2__"
}
inputs {
input_layer_name: "label"
}
coeff: 1.0
}
parameters {
name: "fc_param"
size: 40000
initial_mean: 0.0
initial_std: 1.0
dims: 200
dims: 200
initial_strategy: 1
initial_smart: false
}
parameters {
name: "bias_param"
size: 200
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 200
initial_strategy: 0
initial_smart: false
}
parameters {
name: "softmax_param"
size: 2000
initial_mean: 0.0
initial_std: 1.0
dims: 200
dims: 10
initial_strategy: 1
initial_smart: false
}
input_layer_names: "feature_a"
input_layer_names: "feature_b"
input_layer_names: "label"
output_layer_names: "__cost_0__"
evaluators {
name: "classification_error_evaluator"
type: "classification_error"
input_layers: "__fc_layer_2__"
input_layers: "label"
}
sub_models {
name: "root"
layer_names: "feature_a"
layer_names: "feature_b"
layer_names: "__fc_layer_0__"
layer_names: "__fc_layer_1__"
layer_names: "__fc_layer_2__"
layer_names: "label"
layer_names: "__cost_0__"
input_layer_names: "feature_a"
input_layer_names: "feature_b"
input_layer_names: "label"
output_layer_names: "__cost_0__"
evaluator_names: "classification_error_evaluator"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/shared_lstm.protostr 0 → 100644
浏览文件 @ db569f29
type: "recurrent_nn"
layers {
name: "data_a"
type: "data"
size: 100
active_type: ""
}
layers {
name: "data_b"
type: "data"
size: 100
active_type: ""
}
layers {
name: "__mixed_0__"
type: "mixed"
size: 400
active_type: ""
inputs {
input_layer_name: "data_a"
input_parameter_name: "mixed_param"
proj_conf {
type: "fc"
name: "___mixed_0__.w0"
input_size: 100
output_size: 400
}
}
}
layers {
name: "__mixed_1__"
type: "mixed"
size: 400
active_type: ""
inputs {
input_layer_name: "data_b"
input_parameter_name: "mixed_param"
proj_conf {
type: "fc"
name: "___mixed_1__.w0"
input_size: 100
output_size: 400
}
}
}
layers {
name: "__lstm_group_0___recurrent_group"
type: "recurrent_layer_group"
active_type: ""
}
layers {
name: "__mixed_0__@__lstm_group_0___recurrent_group"
type: "scatter_agent"
size: 400
active_type: ""
}
layers {
name: "__lstm_group_0__+delay1@__lstm_group_0___recurrent_group"
type: "agent"
size: 100
active_type: ""
}
layers {
name: "__lstm_group_0___state+delay1@__lstm_group_0___recurrent_group"
type: "agent"
size: 100
active_type: ""
}
layers {
name: "__lstm_group_0___input_recurrent@__lstm_group_0___recurrent_group"
type: "mixed"
size: 400
active_type: ""
inputs {
input_layer_name: "__mixed_0__@__lstm_group_0___recurrent_group"
proj_conf {
type: "identity"
name: "___lstm_group_0___input_recurrent.w0"
input_size: 400
output_size: 400
}
}
inputs {
input_layer_name: "__lstm_group_0__+delay1@__lstm_group_0___recurrent_group"
input_parameter_name: "lstm_param"
proj_conf {
type: "fc"
name: "___lstm_group_0___input_recurrent.w1"
input_size: 100
output_size: 400
}
}
}
layers {
name: "__lstm_group_0__@__lstm_group_0___recurrent_group"
type: "lstm_step"
size: 100
active_type: "tanh"
inputs {
input_layer_name: "__lstm_group_0___input_recurrent@__lstm_group_0___recurrent_group"
}
inputs {
input_layer_name: "__lstm_group_0___state+delay1@__lstm_group_0___recurrent_group"
}
bias_parameter_name: "lstm_bias"
active_gate_type: "sigmoid"
active_state_type: "sigmoid"
}
layers {
name: "__lstm_group_0___state@__lstm_group_0___recurrent_group"
type: "get_output"
size: 100
active_type: ""
inputs {
input_layer_name: "__lstm_group_0__@__lstm_group_0___recurrent_group"
input_layer_argument: "state"
}
}
layers {
name: "__lstm_group_0__"
type: "gather_agent"
size: 100
active_type: ""
}
layers {
name: "__lstm_group_1___recurrent_group"
type: "recurrent_layer_group"
active_type: ""
}
layers {
name: "__mixed_1__@__lstm_group_1___recurrent_group"
type: "scatter_agent"
size: 400
active_type: ""
}
layers {
name: "__lstm_group_1__+delay1@__lstm_group_1___recurrent_group"
type: "agent"
size: 100
active_type: ""
}
layers {
name: "__lstm_group_1___state+delay1@__lstm_group_1___recurrent_group"
type: "agent"
size: 100
active_type: ""
}
layers {
name: "__lstm_group_1___input_recurrent@__lstm_group_1___recurrent_group"
type: "mixed"
size: 400
active_type: ""
inputs {
input_layer_name: "__mixed_1__@__lstm_group_1___recurrent_group"
proj_conf {
type: "identity"
name: "___lstm_group_1___input_recurrent.w0"
input_size: 400
output_size: 400
}
}
inputs {
input_layer_name: "__lstm_group_1__+delay1@__lstm_group_1___recurrent_group"
input_parameter_name: "lstm_param"
proj_conf {
type: "fc"
name: "___lstm_group_1___input_recurrent.w1"
input_size: 100
output_size: 400
}
}
}
layers {
name: "__lstm_group_1__@__lstm_group_1___recurrent_group"
type: "lstm_step"
size: 100
active_type: "tanh"
inputs {
input_layer_name: "__lstm_group_1___input_recurrent@__lstm_group_1___recurrent_group"
}
inputs {
input_layer_name: "__lstm_group_1___state+delay1@__lstm_group_1___recurrent_group"
}
bias_parameter_name: "lstm_bias"
active_gate_type: "sigmoid"
active_state_type: "sigmoid"
}
layers {
name: "__lstm_group_1___state@__lstm_group_1___recurrent_group"
type: "get_output"
size: 100
active_type: ""
inputs {
input_layer_name: "__lstm_group_1__@__lstm_group_1___recurrent_group"
input_layer_argument: "state"
}
}
layers {
name: "__lstm_group_1__"
type: "gather_agent"
size: 100
active_type: ""
}
layers {
name: "__last_seq_0__"
type: "seqlastins"
size: 100
active_type: "linear"
inputs {
input_layer_name: "__lstm_group_0__"
}
trans_type: "non-seq"
}
layers {
name: "__last_seq_1__"
type: "seqlastins"
size: 100
active_type: "linear"
inputs {
input_layer_name: "__lstm_group_1__"
}
trans_type: "non-seq"
}
layers {
name: "__fc_layer_0__"
type: "fc"
size: 10
active_type: "softmax"
inputs {
input_layer_name: "__last_seq_0__"
input_parameter_name: "softmax_param"
}
inputs {
input_layer_name: "__last_seq_1__"
input_parameter_name: "softmax_param"
}
}
layers {
name: "label"
type: "data"
size: 10
active_type: ""
}
layers {
name: "__cost_0__"
type: "multi-class-cross-entropy"
size: 1
active_type: ""
inputs {
input_layer_name: "__fc_layer_0__"
}
inputs {
input_layer_name: "label"
}
coeff: 1.0
}
parameters {
name: "mixed_param"
size: 40000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 400
initial_strategy: 0
initial_smart: true
}
parameters {
name: "lstm_param"
size: 40000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 400
initial_strategy: 0
initial_smart: true
}
parameters {
name: "lstm_bias"
size: 300
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 300
initial_strategy: 0
initial_smart: false
}
parameters {
name: "softmax_param"
size: 1000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 10
initial_strategy: 0
initial_smart: true
}
input_layer_names: "data_a"
input_layer_names: "data_b"
input_layer_names: "label"
output_layer_names: "__cost_0__"
evaluators {
name: "classification_error_evaluator"
type: "classification_error"
input_layers: "__fc_layer_0__"
input_layers: "label"
}
sub_models {
name: "root"
layer_names: "data_a"
layer_names: "data_b"
layer_names: "__mixed_0__"
layer_names: "__mixed_1__"
layer_names: "__lstm_group_0___recurrent_group"
layer_names: "__lstm_group_0__"
layer_names: "__lstm_group_1___recurrent_group"
layer_names: "__lstm_group_1__"
layer_names: "__last_seq_0__"
layer_names: "__last_seq_1__"
layer_names: "__fc_layer_0__"
layer_names: "label"
layer_names: "__cost_0__"
input_layer_names: "data_a"
input_layer_names: "data_b"
input_layer_names: "label"
output_layer_names: "__cost_0__"
evaluator_names: "classification_error_evaluator"
is_recurrent_layer_group: false
}
sub_models {
name: "__lstm_group_0___recurrent_group"
layer_names: "__mixed_0__@__lstm_group_0___recurrent_group"
layer_names: "__lstm_group_0__+delay1@__lstm_group_0___recurrent_group"
layer_names: "__lstm_group_0___state+delay1@__lstm_group_0___recurrent_group"
layer_names: "__lstm_group_0___input_recurrent@__lstm_group_0___recurrent_group"
layer_names: "__lstm_group_0__@__lstm_group_0___recurrent_group"
layer_names: "__lstm_group_0___state@__lstm_group_0___recurrent_group"
is_recurrent_layer_group: true
reversed: false
memories {
layer_name: "__lstm_group_0__@__lstm_group_0___recurrent_group"
link_name: "__lstm_group_0__+delay1@__lstm_group_0___recurrent_group"
is_sequence: false
}
memories {
layer_name: "__lstm_group_0___state@__lstm_group_0___recurrent_group"
link_name: "__lstm_group_0___state+delay1@__lstm_group_0___recurrent_group"
is_sequence: false
}
in_links {
layer_name: "__mixed_0__"
link_name: "__mixed_0__@__lstm_group_0___recurrent_group"
has_subseq: false
}
out_links {
layer_name: "__lstm_group_0__@__lstm_group_0___recurrent_group"
link_name: "__lstm_group_0__"
has_subseq: false
}
target_inlinkid: -1
}
sub_models {
name: "__lstm_group_1___recurrent_group"
layer_names: "__mixed_1__@__lstm_group_1___recurrent_group"
layer_names: "__lstm_group_1__+delay1@__lstm_group_1___recurrent_group"
layer_names: "__lstm_group_1___state+delay1@__lstm_group_1___recurrent_group"
layer_names: "__lstm_group_1___input_recurrent@__lstm_group_1___recurrent_group"
layer_names: "__lstm_group_1__@__lstm_group_1___recurrent_group"
layer_names: "__lstm_group_1___state@__lstm_group_1___recurrent_group"
is_recurrent_layer_group: true
reversed: false
memories {
layer_name: "__lstm_group_1__@__lstm_group_1___recurrent_group"
link_name: "__lstm_group_1__+delay1@__lstm_group_1___recurrent_group"
is_sequence: false
}
memories {
layer_name: "__lstm_group_1___state@__lstm_group_1___recurrent_group"
link_name: "__lstm_group_1___state+delay1@__lstm_group_1___recurrent_group"
is_sequence: false
}
in_links {
layer_name: "__mixed_1__"
link_name: "__mixed_1__@__lstm_group_1___recurrent_group"
has_subseq: false
}
out_links {
layer_name: "__lstm_group_1__@__lstm_group_1___recurrent_group"
link_name: "__lstm_group_1__"
has_subseq: false
}
target_inlinkid: -1
}
python/paddle/trainer_config_helpers/tests/configs/protostr/simple_rnn_layers.protostr 0 → 100644
浏览文件 @ db569f29
type: "nn"
layers {
name: "data"
type: "data"
size: 200
active_type: ""
}
layers {
name: "__fc_layer_0__"
type: "fc"
size: 200
active_type: "sigmoid"
inputs {
input_layer_name: "data"
input_parameter_name: "___fc_layer_0__.w0"
}
bias_parameter_name: "___fc_layer_0__.wbias"
}
layers {
name: "__recurrent_layer_0__"
type: "recurrent"
size: 200
active_type: "sigmoid"
inputs {
input_layer_name: "__fc_layer_0__"
input_parameter_name: "___recurrent_layer_0__.w0"
}
bias_parameter_name: "___recurrent_layer_0__.wbias"
reversed: false
}
layers {
name: "__recurrent_layer_1__"
type: "recurrent"
size: 200
active_type: "sigmoid"
inputs {
input_layer_name: "__fc_layer_0__"
input_parameter_name: "___recurrent_layer_1__.w0"
}
bias_parameter_name: "___recurrent_layer_1__.wbias"
reversed: true
}
layers {
name: "__fc_layer_1__"
type: "fc"
size: 800
active_type: ""
inputs {
input_layer_name: "__fc_layer_0__"
input_parameter_name: "___fc_layer_1__.w0"
}
}
layers {
name: "__lstmemory_0__"
type: "lstmemory"
size: 200
active_type: "sigmoid"
inputs {
input_layer_name: "__fc_layer_1__"
input_parameter_name: "___lstmemory_0__.w0"
}
bias_parameter_name: "___lstmemory_0__.wbias"
reversed: false
active_gate_type: "sigmoid"
active_state_type: "tanh"
}
layers {
name: "__fc_layer_2__"
type: "fc"
size: 800
active_type: ""
inputs {
input_layer_name: "__fc_layer_0__"
input_parameter_name: "___fc_layer_2__.w0"
}
}
layers {
name: "__lstmemory_1__"
type: "lstmemory"
size: 200
active_type: "sigmoid"
inputs {
input_layer_name: "__fc_layer_2__"
input_parameter_name: "___lstmemory_1__.w0"
}
bias_parameter_name: "___lstmemory_1__.wbias"
reversed: true
active_gate_type: "sigmoid"
active_state_type: "tanh"
}
layers {
name: "__fc_layer_3__"
type: "fc"
size: 600
active_type: ""
inputs {
input_layer_name: "__fc_layer_0__"
input_parameter_name: "___fc_layer_3__.w0"
}
}
layers {
name: "__gru_0__"
type: "gated_recurrent"
size: 200
active_type: "sigmoid"
inputs {
input_layer_name: "__fc_layer_3__"
input_parameter_name: "___gru_0__.w0"
}
bias_parameter_name: "___gru_0__.wbias"
reversed: false
active_gate_type: "sigmoid"
}
layers {
name: "__fc_layer_4__"
type: "fc"
size: 600
active_type: ""
inputs {
input_layer_name: "__fc_layer_0__"
input_parameter_name: "___fc_layer_4__.w0"
}
}
layers {
name: "__gru_1__"
type: "gated_recurrent"
size: 200
active_type: "sigmoid"
inputs {
input_layer_name: "__fc_layer_4__"
input_parameter_name: "___gru_1__.w0"
}
bias_parameter_name: "___gru_1__.wbias"
reversed: true
active_gate_type: "sigmoid"
}
layers {
name: "__last_seq_0__"
type: "seqlastins"
size: 200
active_type: "linear"
inputs {
input_layer_name: "__recurrent_layer_0__"
}
trans_type: "non-seq"
}
layers {
name: "__first_seq_0__"
type: "seqlastins"
size: 200
active_type: "linear"
inputs {
input_layer_name: "__recurrent_layer_1__"
}
select_first: true
trans_type: "non-seq"
}
layers {
name: "__last_seq_1__"
type: "seqlastins"
size: 200
active_type: "linear"
inputs {
input_layer_name: "__lstmemory_0__"
}
trans_type: "non-seq"
}
layers {
name: "__first_seq_1__"
type: "seqlastins"
size: 200
active_type: "linear"
inputs {
input_layer_name: "__lstmemory_1__"
}
select_first: true
trans_type: "non-seq"
}
layers {
name: "__last_seq_2__"
type: "seqlastins"
size: 200
active_type: "linear"
inputs {
input_layer_name: "__gru_0__"
}
trans_type: "non-seq"
}
layers {
name: "__first_seq_2__"
type: "seqlastins"
size: 200
active_type: "linear"
inputs {
input_layer_name: "__gru_1__"
}
select_first: true
trans_type: "non-seq"
}
parameters {
name: "___fc_layer_0__.w0"
size: 40000
initial_mean: 0.0
initial_std: 0.0707106781187
dims: 200
dims: 200
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___fc_layer_0__.wbias"
size: 200
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 200
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___recurrent_layer_0__.w0"
size: 40000
initial_mean: 0.0
initial_std: 0.0707106781187
dims: 200
dims: 200
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___recurrent_layer_0__.wbias"
size: 200
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 200
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___recurrent_layer_1__.w0"
size: 40000
initial_mean: 0.0
initial_std: 0.0707106781187
dims: 200
dims: 200
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___recurrent_layer_1__.wbias"
size: 200
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 200
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___fc_layer_1__.w0"
size: 160000
initial_mean: 0.0
initial_std: 0.0707106781187
dims: 200
dims: 800
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___lstmemory_0__.w0"
size: 160000
initial_mean: 0.0
initial_std: 0.0707106781187
dims: 200
dims: 200
dims: 4
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___lstmemory_0__.wbias"
size: 1400
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 1400
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___fc_layer_2__.w0"
size: 160000
initial_mean: 0.0
initial_std: 0.0707106781187
dims: 200
dims: 800
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___lstmemory_1__.w0"
size: 160000
initial_mean: 0.0
initial_std: 0.0707106781187
dims: 200
dims: 200
dims: 4
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___lstmemory_1__.wbias"
size: 1400
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 1400
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___fc_layer_3__.w0"
size: 120000
initial_mean: 0.0
initial_std: 0.0707106781187
dims: 200
dims: 600
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___gru_0__.w0"
size: 120000
initial_mean: 0.0
initial_std: 0.0707106781187
dims: 200
dims: 600
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___gru_0__.wbias"
size: 600
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 600
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___fc_layer_4__.w0"
size: 120000
initial_mean: 0.0
initial_std: 0.0707106781187
dims: 200
dims: 600
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___gru_1__.w0"
size: 120000
initial_mean: 0.0
initial_std: 0.0707106781187
dims: 200
dims: 600
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___gru_1__.wbias"
size: 600
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 600
initial_strategy: 0
initial_smart: false
}
input_layer_names: "data"
output_layer_names: "__last_seq_0__"
output_layer_names: "__first_seq_0__"
output_layer_names: "__last_seq_1__"
output_layer_names: "__first_seq_1__"
output_layer_names: "__last_seq_2__"
output_layer_names: "__first_seq_2__"
sub_models {
name: "root"
layer_names: "data"
layer_names: "__fc_layer_0__"
layer_names: "__recurrent_layer_0__"
layer_names: "__recurrent_layer_1__"
layer_names: "__fc_layer_1__"
layer_names: "__lstmemory_0__"
layer_names: "__fc_layer_2__"
layer_names: "__lstmemory_1__"
layer_names: "__fc_layer_3__"
layer_names: "__gru_0__"
layer_names: "__fc_layer_4__"
layer_names: "__gru_1__"
layer_names: "__last_seq_0__"
layer_names: "__first_seq_0__"
layer_names: "__last_seq_1__"
layer_names: "__first_seq_1__"
layer_names: "__last_seq_2__"
layer_names: "__first_seq_2__"
input_layer_names: "data"
output_layer_names: "__last_seq_0__"
output_layer_names: "__first_seq_0__"
output_layer_names: "__last_seq_1__"
output_layer_names: "__first_seq_1__"
output_layer_names: "__last_seq_2__"
output_layer_names: "__first_seq_2__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/test_expand_layer.protostr 0 → 100644
浏览文件 @ db569f29
type: "nn"
layers {
name: "data"
type: "data"
size: 30
active_type: ""
}
layers {
name: "data_seq"
type: "data"
size: 30
active_type: ""
}
layers {
name: "__expand_layer_0__"
type: "expand"
size: 30
active_type: ""
inputs {
input_layer_name: "data"
}
inputs {
input_layer_name: "data_seq"
}
trans_type: "seq"
}
layers {
name: "__expand_layer_1__"
type: "expand"
size: 30
active_type: ""
inputs {
input_layer_name: "data"
}
inputs {
input_layer_name: "data_seq"
}
trans_type: "non-seq"
}
input_layer_names: "data"
input_layer_names: "data_seq"
output_layer_names: "__expand_layer_0__"
output_layer_names: "__expand_layer_1__"
sub_models {
name: "root"
layer_names: "data"
layer_names: "data_seq"
layer_names: "__expand_layer_0__"
layer_names: "__expand_layer_1__"
input_layer_names: "data"
input_layer_names: "data_seq"
output_layer_names: "__expand_layer_0__"
output_layer_names: "__expand_layer_1__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/test_print_layer.protostr 0 → 100644
浏览文件 @ db569f29
type: "nn"
layers {
name: "input"
type: "data"
size: 100
active_type: ""
}
layers {
name: "__print_0__"
type: "print"
active_type: ""
inputs {
input_layer_name: "input"
}
}
input_layer_names: "input"
output_layer_names: "input"
sub_models {
name: "root"
layer_names: "input"
layer_names: "__print_0__"
input_layer_names: "input"
output_layer_names: "input"
is_recurrent_layer_group: false
}
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