Skip to content

P
Paddle

BaiXuePrincess / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
提交 ca89bfad 编写于 作者: L liaogang
浏览文件
操作

Fix paddle enforce special cases

上级 527c8597 635ac111
展开全部 隐藏空白更改
内联 并排
Showing with 4292 addition and 859 deletion
.pre-commit-config.yaml
浏览文件 @ ca89bfad
......@@ -21,10 +21,10 @@
sha: 28c0ea8a67a3e2dbbf4822ef44e85b63a0080a29
hooks:
- id: clang-formater
- repo: https://github.com/dnephin/pre-commit-golang
sha: e4693a4c282b4fc878eda172a929f7a6508e7d16
- repo: https://github.com/PaddlePaddle/pre-commit-golang
sha: 16398aeccf263adaf53b2495eed0406347d76281
hooks:
- id: go-fmt
files: (.*\.go)
- id: go-lint
files: (.*\.go)
types: [go]
- id: gometalinter
types: [go]
.travis.yml
浏览文件 @ ca89bfad
......@@ -41,6 +41,8 @@ before_install:
- pip install rarfile
- curl https://glide.sh/get | bash
- eval "$(GIMME_GO_VERSION=1.8.3 gimme)"
- go get -u github.com/alecthomas/gometalinter
- gometalinter --install
- |
function timeout() { perl -e 'alarm shift; exec @ARGV' "$@"; }
script:
......
CMakeLists.txt
浏览文件 @ ca89bfad
......@@ -13,7 +13,6 @@
# limitations under the License
cmake_minimum_required(VERSION 3.0)
set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/cmake")
set(PROJ_ROOT ${CMAKE_CURRENT_SOURCE_DIR})
set(PROJ_BINARY_ROOT ${CMAKE_CURRENT_BINARY_DIR})
......@@ -137,7 +136,8 @@ if(WITH_GPU)
endif(WITH_GPU)
if(USE_NNPACK)
list(APPEND EXTERNAL_LIBS ${NNPACK_LIB} ${PTHREADPOOL_LIB} "rt")
include(external/nnpack)
list(APPEND EXTERNAL_LIBS ${NNPACK_LIBS})
endif(USE_NNPACK)
add_subdirectory(proto)
......
Dockerfile
浏览文件 @ ca89bfad
......@@ -25,7 +25,7 @@ COPY ./paddle/scripts/docker/root/ /root/
RUN apt-get update && \
apt-get install -y \
git python-pip python-dev openssh-server bison \
wget unzip tar xz-utils bzip2 gzip coreutils ntp \
wget unzip unrar tar xz-utils bzip2 gzip coreutils ntp \
curl sed grep graphviz libjpeg-dev zlib1g-dev \
python-numpy python-matplotlib gcc g++ \
automake locales clang-format-3.8 swig doxygen cmake \
......
Dockerfile.android
浏览文件 @ ca89bfad
......@@ -14,6 +14,17 @@ RUN apt-get update && \
wget curl tar unzip gcc g++ locales clang-format-3.8 swig cmake && \
apt-get clean -y
# Install Go and glide
RUN wget -O go.tgz https://storage.googleapis.com/golang/go1.8.1.linux-amd64.tar.gz && \
tar -C /usr/local -xzf go.tgz && \
mkdir /root/gopath && \
mkdir /root/gopath/bin && \
mkdir /root/gopath/src && \
rm go.tgz
ENV GOROOT=/usr/local/go GOPATH=/root/gopath
# should not be in the same line with GOROOT definition, otherwise docker build could not find GOROOT.
ENV PATH=${PATH}:${GOROOT}/bin:${GOPATH}/bin
# git credential to skip password typing
RUN git config --global credential.helper store
......
cmake/configure.cmake
浏览文件 @ ca89bfad
......@@ -102,12 +102,19 @@ if(WITH_GOLANG)
message(FATAL_ERROR "no glide executeble found: $ENV{GOPATH}/bin/glide")
endif()
add_custom_target(go_vendor)
add_custom_command(TARGET go_vendor
# this command will only run when the file it depends is missing
# or has changed, or the output is missing.
add_custom_command(OUTPUT ${CMAKE_BINARY_DIR}/glide
COMMAND env GOPATH=${GOPATH} ${GLIDE} install
COMMAND touch ${CMAKE_BINARY_DIR}/glide
DEPENDS ${PROJ_ROOT}/go/glide.lock
WORKING_DIRECTORY "${PADDLE_IN_GOPATH}/go"
)
add_dependencies(go_vendor go_path)
)
# depends on the custom command which outputs
# ${CMAKE_BINARY_DIR}/glide, the custom command does not need to
# run every time this target is built.
add_custom_target(go_vendor DEPENDS ${CMAKE_BINARY_DIR}/glide go_path)
endif()
endif(WITH_GOLANG)
cmake/cpplint.cmake
浏览文件 @ ca89bfad
......@@ -27,7 +27,8 @@ set(IGNORE_PATTERN
.*cblas\\.h.*
.*\\.pb\\.txt
.*LtrDataProvider.*
.*MultiDataProvider.*)
.*MultiDataProvider.*
.*pb.*)
# add_style_check_target
#
......@@ -52,14 +53,13 @@ macro(add_style_check_target TARGET_NAME)
endif()
endforeach()
if(LINT MATCHES ON)
# cpplint code style
get_filename_component(base_filename ${filename} NAME)
set(CUR_GEN ${CMAKE_CURRENT_BINARY_DIR}/${base_filename}.cpplint)
add_custom_command(OUTPUT ${CUR_GEN}
PRE_BUILD
COMMAND env ${py_env} "${PYTHON_EXECUTABLE}" "${PROJ_ROOT}/paddle/scripts/cpplint.py"
"--filter=${STYLE_FILTER}"
"--write-success=${CUR_GEN}" ${filename}
DEPENDS ${filename}
add_custom_command(TARGET ${TARGET_NAME} PRE_BUILD
COMMAND "${PYTHON_EXECUTABLE}" "${PROJ_ROOT}/paddle/scripts/cpplint.py"
"--filter=${STYLE_FILTER}"
"--write-success=${CUR_GEN}" ${filename}
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
endif()
endforeach()
......
cmake/cross_compiling/android.cmake
浏览文件 @ ca89bfad
......@@ -108,6 +108,7 @@ IF("${CMAKE_VERSION}" VERSION_LESS "3.7.0")
ENDIF()
IF(ANDROID_ABI STREQUAL "arm64-v8a")
SET(ANDROID_TOOLCHAIN_NAME aarch64-linux-android)
SET(CMAKE_SYSTEM_PROCESSOR aarch64)
ENDIF()
SET(ANDROID_TOOLCHAIN_PREFIX "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_NAME}-")
ENDIF()
......@@ -166,7 +167,7 @@ IF("${CMAKE_VERSION}" VERSION_LESS "3.7.0")
ENDIF()
IF(ANDROID_ABI STREQUAL "arm64-v8a")
LIST(APPEND ANDROID_COMPILER_FLAGS -march=armv8-a)
LIST(APPEND ANDROID_COMPILER_FLAGS -march=armv8-a)
ENDIF()
STRING(REPLACE ";" " " ANDROID_COMPILER_FLAGS "${ANDROID_COMPILER_FLAGS}")
......@@ -193,6 +194,10 @@ ELSE()
SET(CMAKE_ANDROID_STANDALONE_TOOLCHAIN ${ANDROID_STANDALONE_TOOLCHAIN})
ENDIF()
SET(CMAKE_ANDROID_ARCH_ABI ${ANDROID_ABI})
SET(CMAKE_ANDROID_ARM_MODE ${ANDROID_ARM_MODE})
SET(CMAKE_ANDROID_ARM_NEON ${ANDROID_ARM_NEON})
IF(ANDROID_ABI MATCHES "^armeabi(-v7a)?$")
SET(CMAKE_ANDROID_ARM_MODE ${ANDROID_ARM_MODE})
IF(ANDROID_ABI STREQUAL "armeabi-v7a")
SET(CMAKE_ANDROID_ARM_NEON ${ANDROID_ARM_NEON})
ENDIF()
ENDIF()
ENDIF()
paddle/function/nnpack/nnpack.cmake → cmake/external/nnpack.cmake
浏览文件 @ ca89bfad
......@@ -7,10 +7,24 @@ set(NNPACK_ROOT $ENV{NNPACK_ROOT} CACHE PATH "Folder contains NNPACK")
find_path(NNPACK_INC_DIR nnpack.h PATHS ${NNPACK_ROOT}/include)
find_library(NNPACK_LIB NAMES nnpack PATHS ${NNPACK_ROOT}/lib)
find_library(PTHREADPOOL_LIB NAMES pthreadpool PATHS ${NNPACK_ROOT}/lib)
find_library(NNPACK_UKERNELS_LIB NAMES nnpack_ukernels PATHS ${NNPACK_ROOT}/lib)
find_library(NNPACK_CPUFEATURES_LIB NAMES cpufeatures PATHS ${NNPACK_ROOT}/lib)
if(NNPACK_INC_DIR AND NNPACK_LIB AND PTHREADPOOL_LIB)
set(NNPACK_FOUND ON)
INCLUDE_DIRECTORIES(${NNPACK_INC_DIR})
set(NNPACK_LIBS)
list(APPEND NNPACK_LIBS ${NNPACK_LIB} ${PTHREADPOOL_LIB})
if (NNPACK_UKERNELS_LIB)
list(APPEND NNPACK_LIBS ${NNPACK_UKERNELS_LIB})
endif()
if (NNPACK_CPUFEATURES_LIB)
list(APPEND NNPACK_LIBS ${NNPACK_CPUFEATURES_LIB})
endif()
if(NOT ANDROID)
list(APPEND NNPACK_LIBS "rt")
endif()
else()
message(FATAL_ERROR "Cannot find NNPACK in (${NNPACK_ROOT})")
endif()
cmake/generic.cmake
浏览文件 @ ca89bfad
......@@ -185,6 +185,10 @@ function(cc_library TARGET_NAME)
add_dependencies(${TARGET_NAME} ${cc_library_DEPS})
target_link_libraries(${TARGET_NAME} ${cc_library_DEPS})
endif()
# cpplint code style
add_style_check_target(${TARGET_NAME} ${cc_library_SRCS})
else(cc_library_SRCS)
if (cc_library_DEPS)
merge_static_libs(${TARGET_NAME} ${cc_library_DEPS})
......@@ -286,8 +290,22 @@ function(go_library TARGET_NAME)
set(${TARGET_NAME}_LIB_NAME "${CMAKE_STATIC_LIBRARY_PREFIX}${TARGET_NAME}${CMAKE_STATIC_LIBRARY_SUFFIX}" CACHE STRING "output library name for target ${TARGET_NAME}")
endif()
# Add dummy code to support `make target_name` under Terminal Command
set(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/${TARGET_NAME}_dummy.c)
# This custom command will always run since it depends on a not
# existing file.
add_custom_command(
OUTPUT dummy_rebulid_${TARGET_NAME}
COMMAND cmake -E touch ${dummyfile}
)
# Create a custom target that depends on the custom command output
# file, so the custom command can be referenced as a dependency by
# `add_dependencies`.
add_custom_target(rebuild_${TARGET_NAME}
DEPENDS dummy_rebulid_${TARGET_NAME}
)
# Add dummy code to support `make target_name` under Terminal Command
file(WRITE ${dummyfile} "const char * dummy = \"${dummyfile}\";")
if (go_library_SHARED OR go_library_shared)
add_library(${TARGET_NAME} SHARED ${dummyfile})
......@@ -298,6 +316,12 @@ function(go_library TARGET_NAME)
add_dependencies(${TARGET_NAME} ${go_library_DEPS})
endif(go_library_DEPS)
# The "source file" of the library is `${dummyfile}` which never
# change, so the target will never rebuild. Make the target depends
# on the custom command that touches the library "source file", so
# rebuild will always happen.
add_dependencies(${TARGET_NAME} rebuild_${TARGET_NAME})
set(${TARGET_NAME}_LIB_PATH "${CMAKE_CURRENT_BINARY_DIR}/${${TARGET_NAME}_LIB_NAME}" CACHE STRING "output library path for target ${TARGET_NAME}")
file(GLOB GO_SOURCE RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "*.go")
......@@ -338,7 +362,7 @@ function(go_test TARGET_NAME)
string(REPLACE "${PADDLE_GO_PATH}" "" CMAKE_CURRENT_SOURCE_REL_DIR ${CMAKE_CURRENT_SOURCE_DIR})
add_custom_target(${TARGET_NAME} ALL DEPENDS go_vendor ${go_test_DEPS})
add_custom_command(TARGET ${TARGET_NAME} POST_BUILD
COMMAND env GOPATH=${GOPATH} ${CMAKE_Go_COMPILER} test
COMMAND env GOPATH=${GOPATH} ${CMAKE_Go_COMPILER} test -race
-c -o "${CMAKE_CURRENT_BINARY_DIR}/${TARGET_NAME}"
".${CMAKE_CURRENT_SOURCE_REL_DIR}"
WORKING_DIRECTORY "${PADDLE_IN_GOPATH}/go")
......
go/cmd/master/master.go
浏览文件 @ ca89bfad
......@@ -11,6 +11,7 @@ import (
"github.com/namsral/flag"
log "github.com/sirupsen/logrus"
"github.com/topicai/candy"
"github.com/PaddlePaddle/Paddle/go/master"
"github.com/PaddlePaddle/Paddle/go/utils/networkhelper"
......@@ -20,11 +21,18 @@ func main() {
port := flag.Int("port", 8080, "port of the master server.")
ttlSec := flag.Int("ttl", 60, "etcd lease TTL in seconds.")
endpoints := flag.String("endpoints", "http://127.0.0.1:2379", "comma separated etcd endpoints. If empty, fault tolerance will not be enabled.")
taskTimeoutDur := flag.Duration("task_timout_dur", 20*time.Minute, "task timout duration.")
taskTimeoutMax := flag.Int("task_timeout_max", 3, "max timtout count for each task before it being declared failed task.")
chunkPerTask := flag.Int("chunk_per_task", 10, "chunk per task.")
taskTimeoutDur := flag.Duration("task-timout-dur", 20*time.Minute, "task timout duration.")
taskTimeoutMax := flag.Int("task-timeout-max", 3, "max timtout count for each task before it being declared failed task.")
chunkPerTask := flag.Int("chunk-per-task", 10, "chunk per task.")
logLevel := flag.String("log-level", "info",
"log level, possible values: debug, info, warning, error, fatal, panic")
flag.Parse()
level, e := log.ParseLevel(*logLevel)
candy.Must(e)
log.SetLevel(level)
if *endpoints == "" {
log.Warningln("-endpoints not set, fault tolerance not be enabled.")
}
......
go/cmd/pserver/pserver.go
浏览文件 @ ca89bfad
......@@ -40,7 +40,7 @@ func main() {
idx = *index
} else {
e = pserver.NewEtcdClient(*etcdEndpoint, *numPservers, *etcdTimeout)
idx, err = e.Register()
idx, err = e.Register(*port)
candy.Must(err)
cp, err = pserver.NewCheckpointFromFile(*checkpointPath, idx, e)
......
go/master/c/client.go
浏览文件 @ ca89bfad
......@@ -23,7 +23,6 @@ import (
log "github.com/sirupsen/logrus"
)
var nullPtr = unsafe.Pointer(uintptr(0))
var mu sync.Mutex
var handleMap = make(map[C.paddle_master_client]*master.Client)
var curHandle C.paddle_master_client
......@@ -114,13 +113,13 @@ func paddle_next_record(client C.paddle_master_client, record **C.uchar) C.int {
if err != nil {
// Error
// TODO: return the type of error?
*record = (*C.uchar)(nullPtr)
*record = (*C.uchar)(nil)
return -1
}
if len(r) == 0 {
// Empty record
*record = (*C.uchar)(nullPtr)
*record = (*C.uchar)(nil)
return 0
}
......
go/master/client.go
浏览文件 @ ca89bfad
......@@ -2,6 +2,7 @@ package master
import (
"os"
"time"
"github.com/PaddlePaddle/Paddle/go/connection"
"github.com/PaddlePaddle/recordio"
......@@ -36,9 +37,9 @@ func (c *Client) getRecords() {
for {
t, err := c.getTask()
if err != nil {
// TODO(helin): wait before move on with next
// getTask call.
log.Errorln(err)
log.Errorf("Get task failed, sleep 3 seconds and continue, %s", err)
time.Sleep(3 * time.Second)
continue
}
......@@ -68,7 +69,10 @@ func (c *Client) getRecords() {
// We treat a task as finished whenever the last data
// instance of the task is read. This is not exactly
// correct, but a reasonable approximation.
c.taskFinished(t.Meta.ID)
err = c.taskFinished(t.Meta.ID)
if err != nil {
log.Errorln(err)
}
}
}
......
go/master/client_internal_test.go
浏览文件 @ ca89bfad
......@@ -66,11 +66,21 @@ func TestGetFinishTask(t *testing.T) {
for i := 0; i < totalTask*chunkPerTask; i++ {
w := recordio.NewWriter(f, -1, -1)
w.Write(nil)
_, err = w.Write(nil)
if err != nil {
panic(err)
}
// call Close to force RecordIO writing a chunk.
w.Close()
err = w.Close()
if err != nil {
panic(err)
}
}
err = f.Close()
if err != nil {
panic(err)
}
f.Close()
// Manually intialize client to avoid calling c.getRecords()
c := &Client{}
......@@ -79,7 +89,11 @@ func TestGetFinishTask(t *testing.T) {
ch := make(chan string, 1)
ch <- addr
go c.monitorMaster(ch)
c.SetDataset([]string{path})
err = c.SetDataset([]string{path})
if err != nil {
panic(err)
}
checkOnePass := func(i int) {
var tasks []Task
for idx := 0; idx < totalTask; idx++ {
......
go/master/client_test.go
浏览文件 @ ca89bfad
......@@ -57,14 +57,30 @@ func TestNextRecord(t *testing.T) {
w := recordio.NewWriter(f, -1, -1)
for i := 0; i < total; i++ {
w.Write([]byte{byte(i)})
_, err = w.Write([]byte{byte(i)})
if err != nil {
panic(err)
}
}
err = w.Close()
if err != nil {
panic(err)
}
err = f.Close()
if err != nil {
panic(err)
}
w.Close()
f.Close()
curAddr := make(chan string, 1)
curAddr <- fmt.Sprintf(":%d", p)
c := master.NewClient(curAddr, 10)
c.SetDataset([]string{path})
err = c.SetDataset([]string{path})
if err != nil {
panic(err)
}
for pass := 0; pass < 50; pass++ {
received := make(map[byte]bool)
for i := 0; i < total; i++ {
......
go/master/etcd_client.go
浏览文件 @ ca89bfad
......@@ -30,7 +30,7 @@ type EtcdClient struct {
// NewEtcdClient creates a new EtcdClient.
func NewEtcdClient(endpoints []string, addr string, lockPath, addrPath, statePath string, ttlSec int) (*EtcdClient, error) {
log.Debugf("Connecting to etcd at %v", endpoints)
// TODO(helin): gracefully shutdown etcd store. Becuase etcd
// TODO(helin): gracefully shutdown etcd store. Because etcd
// store holds a etcd lock, even though the lock will expire
// when the lease timeout, we need to implement graceful
// shutdown to release the lock.
......@@ -60,7 +60,7 @@ func NewEtcdClient(endpoints []string, addr string, lockPath, addrPath, statePat
}
log.Debugf("Successfully acquired lock at %s.", lockPath)
put := clientv3.OpPut(addrPath, string(addr))
put := clientv3.OpPut(addrPath, addr)
resp, err := cli.Txn(context.Background()).If(lock.IsOwner()).Then(put).Commit()
if err != nil {
return nil, err
......
go/master/inmem_store.go
浏览文件 @ ca89bfad
......@@ -4,7 +4,7 @@ import "sync"
// InMemStore is an in memory implementation of Store interface.
//
// It does not tolerate the fault that casues the program to crash.
// It does not tolerate the fault that causes the program to crash.
type InMemStore struct {
mu sync.Mutex
buf []byte
......
go/master/service.go
浏览文件 @ ca89bfad
......@@ -160,7 +160,7 @@ func (s *Service) recover() (bool, error) {
// snapshot *must* be called with s.mu being held.
func (s *Service) snapshot() error {
// TOOD(helin): etcd request has a size limit, so the snapshot
// TODO(helin): etcd request has a size limit, so the snapshot
// size is limited by the max request size. We should either
// divide the snapshot into smaller chunks and save under
// different keys, or configure the request size to be big
......@@ -215,6 +215,7 @@ func readChunks(globPaths []string) ([]Chunk, error) {
}
count := index.NumChunks()
log.Infof("readChunks: file %s has %d chunks", path, count)
for i := 0; i < count; i++ {
chunk := Chunk{
Path: path,
......@@ -288,7 +289,6 @@ func (s *Service) processFailedTask(t taskEntry, epoch int) {
log.Warningf("Task %v failed %d times, discard.", t.Task, t.NumFailure)
s.taskQueues.Todo = append(s.taskQueues.Todo, t)
return
}
func (s *Service) checkTimeoutFunc(taskID int, epoch int) func() {
......
go/pserver/client/c/cclient.go
浏览文件 @ ca89bfad
......@@ -34,7 +34,6 @@ import (
log "github.com/sirupsen/logrus"
)
var nullPtr = unsafe.Pointer(uintptr(0))
var mu sync.Mutex
var handleMap = make(map[C.paddle_pserver_client]*client.Client)
var curHandle C.paddle_pserver_client
......@@ -63,7 +62,7 @@ func remove(client C.paddle_pserver_client) *client.Client {
}
func cArrayToSlice(p unsafe.Pointer, len int) []byte {
if p == nullPtr {
if p == nil {
return nil
}
......@@ -101,11 +100,11 @@ func paddle_new_pserver_client(addrs *C.char, selected int) C.paddle_pserver_cli
}
//export paddle_new_etcd_pserver_client
func paddle_new_etcd_pserver_client(etcd_endpoints *C.char, selected int) C.paddle_pserver_client {
func paddle_new_etcd_pserver_client(etcdEndpoints *C.char, selected int) C.paddle_pserver_client {
// TODO(Longfei: use etcd lock to decide which trainer to initialize the parameters)
addr := C.GoString(etcd_endpoints)
etcd_client := client.NewEtcd(addr)
c := client.NewClient(etcd_client, etcd_client.Desired(), selector(selected != 0))
addr := C.GoString(etcdEndpoints)
etcdClient := client.NewEtcd(addr)
c := client.NewClient(etcdClient, etcdClient.Desired(), selector(selected != 0))
return add(c)
}
......@@ -124,20 +123,20 @@ func paddle_begin_init_params(client C.paddle_pserver_client) C.int {
}
//export paddle_init_param
func paddle_init_param(client C.paddle_pserver_client, param C.paddle_parameter, param_config unsafe.Pointer, config_len C.int) C.int {
func paddle_init_param(client C.paddle_pserver_client, param C.paddle_parameter, paramConfig unsafe.Pointer, configLen C.int) C.int {
et := pserver.ElementType(param.element_type)
name := C.GoString(param.name)
content := cArrayToSlice(unsafe.Pointer(param.content), int(param.content_len))
pc := pserver.ParameterWithConfig{
Param: pserver.Parameter{Name: name, ElementType: et, Content: content},
Config: cArrayToSlice(param_config, int(config_len)),
Config: cArrayToSlice(paramConfig, int(configLen)),
}
c := get(client)
err := c.InitParam(pc)
if err != nil {
if err.Error() == pserver.AlreadyInitialized {
log.Warningf("parameter %s already initialized, treat paddle_init_param as sucessful.", name)
log.Warningf("parameter %s already initialized, treat paddle_init_param as successful.", name)
return C.PSERVER_OK
}
log.Errorln(err)
......@@ -153,7 +152,7 @@ func paddle_finish_init_params(client C.paddle_pserver_client) C.int {
err := c.FinishInitParams()
if err != nil {
if err.Error() == pserver.AlreadyInitialized {
log.Warningln("parameters already initialized, treat paddle_finish_init_params as sucessful.")
log.Warningln("parameters already initialized, treat paddle_finish_init_params as successful.")
return C.PSERVER_OK
}
......@@ -223,12 +222,12 @@ func paddle_get_params(client C.paddle_pserver_client, dst **C.paddle_parameter,
p := ps[i]
param := *(**C.paddle_parameter)(unsafe.Pointer((uintptr(unsafe.Pointer(dst)) + uintptr(i)*unsafe.Sizeof(*dst))))
if unsafe.Pointer(param) == nullPtr {
if unsafe.Pointer(param) == nil {
log.Errorln("must pre-allocate parameter.")
return C.PSERVER_ERROR
}
if unsafe.Pointer(param.content) != nullPtr {
if unsafe.Pointer(param.content) != nil {
if int(param.content_len) != len(p.Content) {
log.Errorf("the pre-allocated content len does not match parameter content len. Pre-allocated len: %d, returned len: %d", param.content_len, len(p.Content))
return C.PSERVER_ERROR
......
go/pserver/client/c/test/test_train.py
浏览文件 @ ca89bfad
import paddle.v2 as paddle
import paddle.v2.dataset.uci_housing as uci_housing
import paddle.v2.master as master
import os
import cPickle as pickle
etcd_ip = os.getenv("MASTER_IP", "127.0.0.1")
etcd_endpoint = "http://" + etcd_ip + ":2379"
def cloud_reader():
print "connecting to master, etcd endpoints: ", etcd_endpoint
master_client = master.client(etcd_endpoint, 5, 64)
master_client.set_dataset(
["/pfs/dlnel/public/dataset/uci_housing/uci_housing-*-of-*"])
while 1:
r, e = master_client.next_record()
if not r:
break
yield pickle.loads(r)
def main():
......@@ -22,13 +40,13 @@ def main():
# create optimizer of new remote updater to pserver
optimizer = paddle.optimizer.Momentum(momentum=0)
#TODO(zhihong) : replace optimizer with new OptimizerConfig
print "etcd endoint: ", etcd_endpoint
trainer = paddle.trainer.SGD(cost=cost,
parameters=parameters,
update_equation=optimizer,
is_local=False,
pserver_spec="localhost:3000")
pserver_spec=etcd_endpoint,
use_etcd=True)
# event_handler to print training and testing info
def event_handler(event):
......@@ -47,11 +65,11 @@ def main():
print "Test %d, %.2f" % (event.pass_id, result.cost)
# training
# NOTE: use uci_housing.train() as reader for non-paddlecloud training
trainer.train(
reader=paddle.batch(
paddle.reader.shuffle(
uci_housing.train(), buf_size=500),
batch_size=2),
cloud_reader, buf_size=500), batch_size=2),
feeding={'x': 0,
'y': 1},
event_handler=event_handler,
......
go/pserver/client/client.go
浏览文件 @ ca89bfad
......@@ -233,7 +233,7 @@ func (c *Client) Save(path string) error {
func strHash(s string) uint32 {
h := fnv.New32a()
h.Write([]byte(s))
_, _ = h.Write([]byte(s))
return h.Sum32()
}
......
go/pserver/client/client_test.go
浏览文件 @ ca89bfad
......@@ -79,15 +79,33 @@ func initEtcdClient() {
log.Errorf("err %v", err)
}
ctx, cancel := context.WithTimeout(context.Background(), timeout)
client.Delete(ctx, pserver.PsDesired)
client.Delete(ctx, pserver.PsPath)
client.Put(ctx, pserver.PsDesired, strconv.Itoa(numPserver))
_, err = client.Delete(ctx, pserver.PsDesired)
if err != nil {
panic(err)
}
_, err = client.Delete(ctx, pserver.PsPath)
if err != nil {
panic(err)
}
_, err = client.Put(ctx, pserver.PsDesired, strconv.Itoa(numPserver))
if err != nil {
panic(err)
}
ports := initClient()
for i := 0; i < numPserver; i++ {
client.Put(ctx, pserver.PsPath+strconv.Itoa(i), ":"+strconv.Itoa(ports[i]))
_, err = client.Put(ctx, pserver.PsPath+strconv.Itoa(i), ":"+strconv.Itoa(ports[i]))
if err != nil {
panic(err)
}
}
cancel()
client.Close()
err = client.Close()
if err != nil {
panic(err)
}
}
type selector bool
......@@ -164,7 +182,7 @@ func testClient(t *testing.T, c *client.Client) {
wg.Add(1)
go func(gs []pserver.Gradient) {
err = c.SendGrads(gs)
err := c.SendGrads(gs)
if err != nil {
t.Fatal(err)
}
......
go/pserver/client/etcd_client.go
浏览文件 @ ca89bfad
......@@ -12,7 +12,7 @@ import (
)
const (
DefaultEtcdTimeout time.Duration = 5 * time.Second
defaultEtcdTimeout time.Duration = 5 * time.Second
)
// EtcdClient is used by pserver client that is a part of trainer process.
......@@ -47,7 +47,7 @@ func (p *EtcdClient) Desired() int {
psDesired, err = strconv.Atoi(string(resp.Kvs[0].Value))
if err != nil {
log.Errorf("psDesired %s invalid %v", psDesired, err)
log.Errorf("psDesired %d invalid %v", psDesired, err)
time.Sleep(p.timeout)
continue
}
......@@ -106,11 +106,11 @@ func NewEtcd(endpoints string) *EtcdClient {
for {
cli, err = clientv3.New(clientv3.Config{
Endpoints: ep,
DialTimeout: DefaultEtcdTimeout,
DialTimeout: defaultEtcdTimeout,
})
if err != nil {
log.Errorf("Init etcd connection failed: %v", err)
time.Sleep(DefaultEtcdTimeout)
time.Sleep(defaultEtcdTimeout)
continue
}
break
......@@ -118,7 +118,7 @@ func NewEtcd(endpoints string) *EtcdClient {
log.Infof("Connected to etcd: %s\n", endpoints)
client := &EtcdClient{
client: cli,
timeout: DefaultEtcdTimeout,
timeout: defaultEtcdTimeout,
endpoints: ep,
}
return client
......
go/pserver/etcd_client.go
浏览文件 @ ca89bfad
......@@ -49,7 +49,7 @@ func NewEtcdClient(endpoints string, numPservers int, timeout time.Duration) *Et
// Register registers the pserver on etcd
//
// Register returns the index of the current pserver.
func (e *EtcdClient) Register() (int, error) {
func (e *EtcdClient) Register(port int) (int, error) {
var err error
e.externalIP, err = networkhelper.GetExternalIP()
......@@ -116,7 +116,7 @@ func (e *EtcdClient) Register() (int, error) {
for {
ctx, cancel := context.WithTimeout(context.Background(), time.Second)
var err error
pserverIdx, err = e.registerPserverEtcd(ctx)
pserverIdx, err = e.registerPserverEtcd(ctx, port)
cancel()
if err != nil {
log.Warn(err)
......@@ -140,7 +140,7 @@ func (e *EtcdClient) initDesiredPservers(ctx context.Context, numPservers int) (
}
// registerPserverEtcd registers pserver node on etcd using transaction.
func (e *EtcdClient) registerPserverEtcd(ctx context.Context) (int, error) {
func (e *EtcdClient) registerPserverEtcd(ctx context.Context, port int) (int, error) {
var idx int
_, err := concurrency.NewSTM(e.etcdClient, func(c concurrency.STM) error {
registered := false
......@@ -156,8 +156,9 @@ func (e *EtcdClient) registerPserverEtcd(ctx context.Context) (int, error) {
log.Fatal(err)
}
// find the first id and write info
c.Put(psKey, e.externalIP, clientv3.WithLease(resp.ID))
log.Debugf("set pserver node %s with value %s", psKey, e.externalIP)
pserverAddr := e.externalIP + ":" + strconv.Itoa(port)
c.Put(psKey, pserverAddr, clientv3.WithLease(resp.ID))
log.Debugf("set pserver node %s with value %s", psKey, pserverAddr)
ch, kaerr := e.etcdClient.KeepAlive(context.TODO(), resp.ID)
if kaerr != nil {
log.Errorf("keepalive etcd node error: %v", kaerr)
......@@ -176,10 +177,10 @@ func (e *EtcdClient) registerPserverEtcd(ctx context.Context) (int, error) {
break
}
}
if registered == true {
if registered {
return nil
}
return errors.New("not registerd, may due to already have enough pservers")
return errors.New("not registered, may due to already have enough pservers")
}, concurrency.WithAbortContext(ctx), concurrency.WithIsolation(concurrency.RepeatableReads))
if err != nil {
......@@ -210,8 +211,5 @@ func (e *EtcdClient) PutKey(key string, value []byte, timeout time.Duration) err
ctx, cancel := context.WithTimeout(context.Background(), timeout)
_, err := e.etcdClient.Put(ctx, key, string(value))
cancel()
if err != nil {
return err
}
return nil
return err
}
go/pserver/optimizer.go
浏览文件 @ ca89bfad
......@@ -14,8 +14,6 @@ import (
log "github.com/sirupsen/logrus"
)
var nullPtr = unsafe.Pointer(uintptr(0))
type optimizer struct {
opt *C.struct_paddle_optimizer
elementType ElementType
......@@ -23,7 +21,7 @@ type optimizer struct {
}
func cArrayToSlice(p unsafe.Pointer, len int) []byte {
if p == nullPtr {
if p == nil {
return nil
}
......@@ -92,8 +90,8 @@ func (o *optimizer) UpdateParameter(g Gradient) error {
}
func (o *optimizer) Cleanup() {
if unsafe.Pointer(o.opt) != nullPtr {
if unsafe.Pointer(o.opt) != nil {
C.paddle_release_optimizer(o.opt)
o.opt = (*C.struct_paddle_optimizer)(nullPtr)
o.opt = (*C.struct_paddle_optimizer)(nil)
}
}
go/pserver/service.go
浏览文件 @ ca89bfad
......@@ -211,7 +211,7 @@ func (s *Service) GetParam(name string, parameter *Parameter) error {
// learning optimization methods are stochastic in
// nature. This race condition is allowed deliberately
// to save the program from making a copy of the
// paramter content.
// parameter content.
parameter.Name = name
parameter.ElementType = opt.elementType
parameter.Content = opt.GetWeights()
......@@ -219,7 +219,7 @@ func (s *Service) GetParam(name string, parameter *Parameter) error {
}
// pserver save checkpoint
func (s *Service) doCheckpoint() error {
func (s *Service) doCheckpoint() (err error) {
<-s.initialized
s.mu.Lock()
defer s.mu.Unlock()
......@@ -237,9 +237,9 @@ func (s *Service) doCheckpoint() error {
}
var buf bytes.Buffer
encoder := gob.NewEncoder(&buf)
err := encoder.Encode(cp)
err = encoder.Encode(cp)
if err != nil {
return err
return
}
cpMeta := checkpointMeta{}
......@@ -248,10 +248,14 @@ func (s *Service) doCheckpoint() error {
h := md5.New()
cpMeta.MD5 = hex.EncodeToString(h.Sum(buf.Bytes()))
cpMetajson, _ := json.Marshal(cpMeta)
cpMetajson, err := json.Marshal(cpMeta)
if err != nil {
return
}
err = s.client.PutKey(filepath.Join(PsCheckpoint, strconv.Itoa(s.idx)), cpMetajson, 3*time.Second)
if err != nil {
return err
return
}
if _, err = os.Stat(cpMeta.UUID); os.IsNotExist(err) {
log.Info("checkpoint does not exists.")
......@@ -264,15 +268,32 @@ func (s *Service) doCheckpoint() error {
}
}
f, err := os.Create(cpMeta.UUID)
defer f.Close()
if err != nil {
return err
return
}
defer func() {
closeErr := f.Close()
if closeErr != nil {
if err != nil {
log.Errorln(closeErr)
} else {
// Set closeErr as return value.
err = closeErr
}
}
}()
writer := bufio.NewWriter(f)
_, err = writer.Write(buf.Bytes())
writer.Flush()
if err != nil {
return err
return
}
return nil
err = writer.Flush()
if err != nil {
return
}
return
}
paddle/api/PaddleAPI.h
浏览文件 @ ca89bfad
......@@ -843,7 +843,8 @@ public:
bool useSparseUpdater);
static ParameterUpdater* createNewRemoteUpdater(
OptimizationConfig* config,
const std::string pserverSpec) throw(UnsupportError);
const std::string pserverSpec,
const bool useEtcd) throw(UnsupportError);
~ParameterUpdater();
/**
......
paddle/api/ParameterUpdater.cpp
浏览文件 @ ca89bfad
......@@ -33,11 +33,12 @@ ParameterUpdater *ParameterUpdater::createLocalUpdater(
ParameterUpdater *ParameterUpdater::createNewRemoteUpdater(
OptimizationConfig *config,
const std::string pserverSpec) throw(UnsupportError) {
const std::string pserverSpec,
const bool useEtcd) throw(UnsupportError) {
#ifndef PADDLE_WITHOUT_GOLANG
auto updater = new ParameterUpdater();
updater->m->updater.reset(new paddle::NewRemoteParameterUpdater(
config->m->getConfig(), pserverSpec));
config->m->getConfig(), pserverSpec, useEtcd));
return updater;
#else
throw UnsupportError();
......
paddle/framework/CMakeLists.txt
浏览文件 @ ca89bfad
# ddim lib
cc_library(ddim SRCS ddim.cc)
cc_library(ddim SRCS ddim.cc DEPS eigen3)
cc_test(ddim_test SRCS ddim_test.cc DEPS ddim)
nv_test(dim_test SRCS dim_test.cu DEPS ddim)
cc_test(tensor_test SRCS tensor_test.cc DEPS ddim)
cc_library(tensor SRCS tensor.cc DEPS ddim place paddle_memory)
cc_test(tensor_test SRCS tensor_test.cc DEPS tensor)
cc_test(eigen_test SRCS eigen_test.cc DEPS tensor)
cc_test(variable_test SRCS variable_test.cc)
cc_test(scope_test SRCS scope_test.cc)
cc_test(enforce_test SRCS enforce_test.cc)
proto_library(attr_type SRCS attr_type.proto)
proto_library(op_proto SRCS op_proto.proto DEPS attr_type)
cc_test(op_proto_test SRCS op_proto_test.cc DEPS op_proto protobuf)
proto_library(op_desc SRCS op_desc.proto DEPS attr_type)
cc_test(op_proto_test SRCS op_proto_test.cc DEPS op_proto protobuf)
cc_test(op_desc_test SRCS op_desc_test.cc DEPS op_desc protobuf)
cc_library(operator SRCS operator.cc DEPS op_desc device_context)
cc_library(operator SRCS operator.cc DEPS op_desc device_context tensor)
cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry)
cc_library(op_registry SRCS op_registry.cc DEPS op_proto op_desc)
cc_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry operator)
py_proto_compile(framework_py_proto SRCS attr_type.proto op_proto.proto op_desc.proto)
# Generate an empty __init__.py to make framework_py_proto as a valid python module.
add_custom_target(framework_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
add_dependencies(framework_py_proto framework_py_proto_init)
proto_library(net_proto SRCS net_proto.proto DEPS op_proto)
cc_library(net SRCS net.cc DEPS net_proto)
cc_library(net SRCS net.cc DEPS operator net_proto op_registry)
cc_test(net_op_test SRCS net_op_test.cc DEPS net)
paddle/framework/attr_checker.h
浏览文件 @ ca89bfad
......@@ -4,8 +4,9 @@
#include <functional>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <vector>
#include "paddle/framework/enforce.h"
#include "paddle/platform/enforce.h"
namespace paddle {
namespace framework {
......@@ -41,6 +42,35 @@ class DefaultValueSetter {
T default_value_;
};
template <typename T>
class EnumInContainer {
public:
explicit EnumInContainer(const std::unordered_set<T>& c) : container_(c) {}
void operator()(T& val) const {
PADDLE_ENFORCE(container_.find(val) != container_.end(),
"Value %s is not in enum container %s", val,
ContainerDebugString());
}
private:
std::string ContainerDebugString() const {
std::ostringstream sout;
sout << "[";
size_t cnt = 0;
for (auto& v : container_) {
sout << v;
++cnt;
if (cnt != container_.size()) {
sout << " ,";
}
}
sout << "]";
return sout.str();
}
std::unordered_set<T> container_;
};
// check whether a certain attribute fit its limits
// an attribute can have more than one limits
template <typename T>
......@@ -50,6 +80,11 @@ class TypedAttrChecker {
public:
TypedAttrChecker(const std::string& attr_name) : attr_name_(attr_name) {}
TypedAttrChecker& InEnum(const std::unordered_set<T>& range) {
value_checkers_.push_back(EnumInContainer<T>(range));
return *this;
}
TypedAttrChecker& LargerThan(const T& lower_bound) {
value_checkers_.push_back(LargerThanChecker<T>(lower_bound));
return *this;
......
paddle/framework/ddim.cc
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/framework/ddim.h"
#include "paddle/platform/enforce.h"
namespace paddle {
namespace framework {
///@cond HIDDEN
/// @cond HIDDEN
template <int i>
Dim<i> make_dim(const int* d) {
......@@ -50,7 +65,7 @@ void make_ddim(DDim& ddim, const int* dims, int n) {
}
}
///@endcond
/// @endcond
DDim make_ddim(std::initializer_list<int> dims) {
DDim result(make_dim(0));
......@@ -64,11 +79,11 @@ DDim make_ddim(const std::vector<int>& dims) {
return result;
}
///@cond HIDDEN
/// @cond HIDDEN
// XXX For some reason, putting this in an anonymous namespace causes errors
class DynamicMutableIndexer : public boost::static_visitor<int&> {
public:
DynamicMutableIndexer(int idx) : idx_(idx) {}
explicit DynamicMutableIndexer(int idx) : idx_(idx) {}
template <int D>
int& operator()(Dim<D>& dim) const {
......@@ -81,7 +96,7 @@ class DynamicMutableIndexer : public boost::static_visitor<int&> {
class DynamicConstIndexer : public boost::static_visitor<int> {
public:
DynamicConstIndexer(int idx) : idx_(idx) {}
explicit DynamicConstIndexer(int idx) : idx_(idx) {}
template <int D>
int operator()(const Dim<D>& dim) const {
......@@ -92,7 +107,7 @@ class DynamicConstIndexer : public boost::static_visitor<int> {
int idx_;
};
///@endcond
/// @endcond
int& DDim::operator[](int idx) {
return boost::apply_visitor(DynamicMutableIndexer(idx), var);
......@@ -102,6 +117,8 @@ int DDim::operator[](int idx) const {
return boost::apply_visitor(DynamicConstIndexer(idx), var);
}
ssize_t DDim::size() const { return arity(*this); }
bool DDim::operator==(DDim d) const {
if (var.which() != d.getVar().which()) {
return false;
......@@ -155,11 +172,11 @@ int get(const DDim& ddim, int idx) { return ddim[idx]; }
void set(DDim& ddim, int idx, int value) { ddim[idx] = value; }
///@cond HIDDEN
/// @cond HIDDEN
struct VectorizeVisitor : public boost::static_visitor<> {
std::vector<int>& vector;
VectorizeVisitor(std::vector<int>& v) : vector(v) {}
explicit VectorizeVisitor(std::vector<int>& v) : vector(v) {}
template <typename T>
void operator()(const T& t) {
......@@ -169,7 +186,7 @@ struct VectorizeVisitor : public boost::static_visitor<> {
void operator()(const Dim<1>& t) { vector.push_back(t.head); }
};
///@endcond
/// @endcond
std::vector<int> vectorize(const DDim& ddim) {
std::vector<int> result;
......@@ -178,16 +195,59 @@ std::vector<int> vectorize(const DDim& ddim) {
return result;
}
struct ProductVisitor : public boost::static_visitor<ssize_t> {
template <int D>
ssize_t operator()(const Dim<D>& dim) {
return product(dim);
}
};
ssize_t product(const DDim& ddim) {
ssize_t result = 1;
std::vector<int> v = vectorize(ddim);
for (auto i : v) {
result *= i;
ProductVisitor visitor;
return boost::apply_visitor(visitor, ddim);
}
struct SliceVectorizeVisitor : public boost::static_visitor<> {
std::vector<int>& vector;
int begin;
int end;
SliceVectorizeVisitor(std::vector<int>& v, int b, int e)
: vector(v), begin(b), end(e) {
PADDLE_ENFORCE(begin < end,
"Begin index must be less than end index in ddim slice.");
PADDLE_ENFORCE(begin >= 0,
"Begin index can't be less than zero in ddim slice.");
}
return result;
template <int S>
void operator()(const Dim<S>& dim) {
if (begin == 0) {
vector.push_back(dim.head);
} else {
--begin;
}
--end;
if (end > 0) {
this->operator()(dim.tail);
}
}
void operator()(const Dim<1>& dim) {
PADDLE_ENFORCE(end == 1, "End index in ddim slice is out of bound.");
vector.push_back(dim.head);
}
};
DDim slice_ddim(const DDim& dim, int begin, int end) {
std::vector<int> vec;
vec.reserve(end - begin);
SliceVectorizeVisitor visitor(vec, begin, end);
boost::apply_visitor(visitor, dim);
return make_ddim(vec);
}
///\cond HIDDEN
/// \cond HIDDEN
struct ArityVisitor : boost::static_visitor<int> {
template <int D>
......@@ -196,15 +256,15 @@ struct ArityVisitor : boost::static_visitor<int> {
}
};
///\endcond
/// \endcond
int arity(const DDim& d) { return boost::apply_visitor(ArityVisitor(), d); }
///\cond HIDDEN
/// \cond HIDDEN
struct DDimPrinter : boost::static_visitor<void> {
std::ostream& os;
DDimPrinter(std::ostream& os_) : os(os_) {}
explicit DDimPrinter(std::ostream& os_) : os(os_) {}
template <typename T>
void operator()(const T& t) {
......@@ -212,7 +272,7 @@ struct DDimPrinter : boost::static_visitor<void> {
}
};
///\endcond
/// \endcond
std::ostream& operator<<(std::ostream& os, const DDim& ddim) {
DDimPrinter printer(os);
......@@ -220,5 +280,9 @@ std::ostream& operator<<(std::ostream& os, const DDim& ddim) {
return os;
}
DDim::DDim(std::initializer_list<int> init_list) {
*this = make_ddim(init_list);
}
} // namespace framework
} // namespace paddle
paddle/framework/ddim.h
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <boost/variant.hpp>
#include <initializer_list>
#include <stdexcept>
#include <vector>
#include "paddle/framework/dim.h"
#include "paddle/platform/enforce.h"
#include "unsupported/Eigen/CXX11/Tensor"
namespace paddle {
namespace framework {
......@@ -27,7 +42,9 @@ struct DDim {
DDim() : var(Dim<1>()) {}
template <int D>
DDim(const Dim<D>& in) : var(in) {}
explicit DDim(const Dim<D>& in) : var(in) {}
/*implicit*/ DDim(std::initializer_list<int> init_list);
template <int D>
DDim& operator=(const Dim<D>& in) {
......@@ -57,6 +74,8 @@ struct DDim {
DDim operator+(DDim d) const;
DDim operator*(DDim d) const;
ssize_t size() const;
};
/**
......@@ -81,6 +100,15 @@ std::vector<int> vectorize(const DDim& ddim);
ssize_t product(const DDim& ddim);
/**
* \brief Slice a ddim
*
* Slice dim with [begin, end).
* e.g. DDim d = make_ddim({1,2,3,4,5});
* slice_ddim(d, 1, 3); ====> {2,3}
*/
DDim slice_ddim(const DDim& dim, int begin, int end);
/**
* \brief What is the length of this dimension?
*
......
paddle/framework/ddim_test.cc
浏览文件 @ ca89bfad
......@@ -49,9 +49,30 @@ TEST(DDim, Equality) {
// arity of a DDim
EXPECT_EQ(paddle::framework::arity(ddim), 3);
EXPECT_EQ(ddim.size(), 3);
// product of a DDim
EXPECT_EQ(paddle::framework::product(vddim), 45);
EXPECT_EQ(
paddle::framework::product(paddle::framework::make_ddim({3, 2, 5, 3})),
90);
// slice a DDim
paddle::framework::DDim ddim2 =
paddle::framework::make_ddim({1, 2, 3, 4, 5, 6});
paddle::framework::DDim ss = paddle::framework::slice_ddim(ddim2, 2, 5);
EXPECT_EQ(arity(ss), 3);
EXPECT_EQ(ss[0], 3);
EXPECT_EQ(ss[1], 4);
EXPECT_EQ(ss[2], 5);
paddle::framework::DDim ss2 = paddle::framework::slice_ddim(ddim2, 0, 6);
EXPECT_EQ(arity(ss2), 6);
EXPECT_EQ(ss2[0], 1);
EXPECT_EQ(ss2[1], 2);
EXPECT_EQ(ss2[2], 3);
EXPECT_EQ(ss2[3], 4);
EXPECT_EQ(ss2[4], 5);
EXPECT_EQ(ss2[5], 6);
}
TEST(DDim, Print) {
......
paddle/framework/dim_test.cu
浏览文件 @ ca89bfad
#include <thrust/device_vector.h>
#include <sstream>
#include "paddle/framework/dim.h"
#include "gtest/gtest.h"
#include "paddle/framework/dim.h"
__global__ void test(paddle::framework::Dim<2>* o) {
o[0] = paddle::framework::make_dim(5, 6);
o[0] = paddle::framework::make_dim(5, 6);
}
__global__ void dyn_idx_gpu(int* o) {
auto d = paddle::framework::make_dim(5, 6);
o[0] = d[1];
auto d = paddle::framework::make_dim(5, 6);
o[0] = d[1];
}
TEST(Dim, Equality) {
// construct a Dim on the CPU
auto a = paddle::framework::make_dim(3, 4);
EXPECT_EQ(paddle::framework::get<0>(a), 3);
EXPECT_EQ(paddle::framework::get<1>(a), 4);
// construct a Dim on the GPU
thrust::device_vector<paddle::framework::Dim<2>> t(2);
test<<<1,1>>>(thrust::raw_pointer_cast(t.data()));
a = t[0];
EXPECT_EQ(paddle::framework::get<0>(a), 5);
EXPECT_EQ(paddle::framework::get<1>(a), 6);
// linearization
auto b = paddle::framework::make_dim(7, 8);
EXPECT_EQ(paddle::framework::linearize(a, b), 83);
// product
EXPECT_EQ(paddle::framework::product(a), 30);
// mutate a Dim
paddle::framework::get<1>(b) = 10;
EXPECT_EQ(paddle::framework::get<0>(b), 7);
EXPECT_EQ(paddle::framework::get<1>(b), 10);
// dynamic access
paddle::framework::get(b, 0) = 8;
b[1] = 11;
EXPECT_EQ(paddle::framework::get<0>(b), 8);
EXPECT_EQ(paddle::framework::get<1>(b), 11);
EXPECT_EQ(paddle::framework::get(b, 0), 8);
EXPECT_EQ(b[1], 11);
// dynamic access on GPU
thrust::device_vector<int> r(1);
dyn_idx_gpu<<<1,1>>>(thrust::raw_pointer_cast(r.data()));
int res = r[0];
EXPECT_EQ(res, 6);
// ex_prefix_mul
paddle::framework::Dim<3> c = paddle::framework::ex_prefix_mul(paddle::framework::Dim<3>(3, 4, 5));
EXPECT_EQ(paddle::framework::get<0>(c), 1);
EXPECT_EQ(paddle::framework::get<1>(c), 3);
EXPECT_EQ(paddle::framework::get<2>(c), 12);
// generate from an index
auto size = paddle::framework::make_dim(4, 5, 2);
c = paddle::framework::Dim<3>(14, size);
EXPECT_EQ(paddle::framework::get<0>(c), 2);
EXPECT_EQ(paddle::framework::get<1>(c), 3);
EXPECT_EQ(paddle::framework::get<2>(c), 0);
c = paddle::framework::Dim<3>(25, size);
EXPECT_EQ(paddle::framework::get<0>(c), 1);
EXPECT_EQ(paddle::framework::get<1>(c), 1);
EXPECT_EQ(paddle::framework::get<2>(c), 1);
// construct a Dim on the CPU
auto a = paddle::framework::make_dim(3, 4);
EXPECT_EQ(paddle::framework::get<0>(a), 3);
EXPECT_EQ(paddle::framework::get<1>(a), 4);
// construct a Dim on the GPU
thrust::device_vector<paddle::framework::Dim<2>> t(2);
test<<<1, 1>>>(thrust::raw_pointer_cast(t.data()));
a = t[0];
EXPECT_EQ(paddle::framework::get<0>(a), 5);
EXPECT_EQ(paddle::framework::get<1>(a), 6);
// linearization
auto b = paddle::framework::make_dim(7, 8);
EXPECT_EQ(paddle::framework::linearize(a, b), 83);
// product
EXPECT_EQ(paddle::framework::product(a), 30);
// mutate a Dim
paddle::framework::get<1>(b) = 10;
EXPECT_EQ(paddle::framework::get<0>(b), 7);
EXPECT_EQ(paddle::framework::get<1>(b), 10);
// dynamic access
paddle::framework::get(b, 0) = 8;
b[1] = 11;
EXPECT_EQ(paddle::framework::get<0>(b), 8);
EXPECT_EQ(paddle::framework::get<1>(b), 11);
EXPECT_EQ(paddle::framework::get(b, 0), 8);
EXPECT_EQ(b[1], 11);
// dynamic access on GPU
thrust::device_vector<int> r(1);
dyn_idx_gpu<<<1, 1>>>(thrust::raw_pointer_cast(r.data()));
int res = r[0];
EXPECT_EQ(res, 6);
// ex_prefix_mul
paddle::framework::Dim<3> c =
paddle::framework::ex_prefix_mul(paddle::framework::Dim<3>(3, 4, 5));
EXPECT_EQ(paddle::framework::get<0>(c), 1);
EXPECT_EQ(paddle::framework::get<1>(c), 3);
EXPECT_EQ(paddle::framework::get<2>(c), 12);
// generate from an index
auto size = paddle::framework::make_dim(4, 5, 2);
c = paddle::framework::Dim<3>(14, size);
EXPECT_EQ(paddle::framework::get<0>(c), 2);
EXPECT_EQ(paddle::framework::get<1>(c), 3);
EXPECT_EQ(paddle::framework::get<2>(c), 0);
c = paddle::framework::Dim<3>(25, size);
EXPECT_EQ(paddle::framework::get<0>(c), 1);
EXPECT_EQ(paddle::framework::get<1>(c), 1);
EXPECT_EQ(paddle::framework::get<2>(c), 1);
}
TEST(Dim, Bool) {
auto a = paddle::framework::make_dim(3, 4);
auto b = paddle::framework::make_dim(5, 6);
auto c = paddle::framework::make_dim(3, 4);
// in_bounds check
EXPECT_TRUE(paddle::framework::contained(a, b));
EXPECT_FALSE(paddle::framework::contained(b, a));
// comparison
EXPECT_TRUE(a == a);
EXPECT_FALSE(a == b);
EXPECT_TRUE(a == c);
auto a = paddle::framework::make_dim(3, 4);
auto b = paddle::framework::make_dim(5, 6);
auto c = paddle::framework::make_dim(3, 4);
// in_bounds check
EXPECT_TRUE(paddle::framework::contained(a, b));
EXPECT_FALSE(paddle::framework::contained(b, a));
// comparison
EXPECT_TRUE(a == a);
EXPECT_FALSE(a == b);
EXPECT_TRUE(a == c);
}
TEST(Dim, Print) {
{
std::stringstream ss;
auto a = paddle::framework::make_dim(2, 3);
ss << a;
EXPECT_EQ(ss.str(), "2, 3");
}
{
std::stringstream ss;
ss << paddle::framework::make_dim(8);
EXPECT_EQ(ss.str(), "8");
}
{
std::stringstream ss;
auto a = paddle::framework::make_dim(2, 3);
ss << a;
EXPECT_EQ(ss.str(), "2, 3");
}
{
std::stringstream ss;
ss << paddle::framework::make_dim(8);
EXPECT_EQ(ss.str(), "8");
}
}
paddle/framework/eigen.h 0 → 100644
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/tensor.h"
#include "unsupported/Eigen/CXX11/Tensor"
namespace paddle {
namespace framework {
// EigenDim converts paddle::platform::DDim into Eigen::DSizes.
template <int D>
struct EigenDim {
using Type = Eigen::DSizes<Eigen::DenseIndex, D>;
static Type From(const DDim& dims) {
PADDLE_ENFORCE(arity(dims) == D, "D must match arity(DDim)");
Type ret;
for (int d = 0; d < arity(dims); d++) {
ret[d] = dims[d];
}
return ret;
}
};
// Interpret paddle::platform::Tensor as EigenTensor and EigenConstTensor.
template <typename T, size_t D, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
struct EigenTensor {
// TODO(qijun) Now, default type in unaligned, and we will make a benchmark on
// the speed of aligned and unaligned version in future.
using Type = Eigen::TensorMap<Eigen::Tensor<T, D, MajorType, IndexType>>;
using ConstType =
Eigen::TensorMap<Eigen::Tensor<const T, D, MajorType, IndexType>>;
static Type From(Tensor& tensor, DDim dims) {
return Type(tensor.data<T>(), EigenDim<D>::From(dims));
}
static Type From(Tensor& tensor) { return From(tensor, tensor.dims_); }
static ConstType From(const Tensor& tensor, DDim dims) {
return ConstType(tensor.data<T>(), EigenDim<D>::From(dims));
}
static ConstType From(const Tensor& tensor) {
return From(tensor, tensor.dims_);
}
};
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
struct EigenVector : public EigenTensor<T, 1, MajorType, IndexType> {
// Flatten is to reshape a Tensor into a one dimension EigenVector
static typename EigenTensor<T, 1>::Type Flatten(Tensor& tensor) {
return EigenTensor<T, 1>::From(
tensor, make_ddim({static_cast<int>(product(tensor.dims_))}));
}
static typename EigenTensor<T, 1>::ConstType Flatten(const Tensor& tensor) {
return EigenTensor<T, 1>::From(
tensor, make_ddim({static_cast<int>(product(tensor.dims_))}));
}
};
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = EigenTensor<T, 2, MajorType, IndexType>;
} // namespace framework
} // namespace paddle
paddle/framework/eigen_test.cc 0 → 100644
浏览文件 @ ca89bfad
/*
Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#include "paddle/framework/eigen.h"
#include <gtest/gtest.h>
namespace paddle {
namespace framework {
TEST(EigenDim, From) {
EigenDim<3>::Type ed = EigenDim<3>::From(make_ddim({1, 2, 3}));
ASSERT_EQ(1, ed[0]);
ASSERT_EQ(2, ed[1]);
ASSERT_EQ(3, ed[2]);
}
TEST(Eigen, Tensor) {
Tensor t;
float* p = t.mutable_data<float>(make_ddim({1, 2, 3}), platform::CPUPlace());
for (int i = 0; i < 1 * 2 * 3; i++) {
p[i] = static_cast<float>(i);
}
EigenTensor<float, 3>::Type et = EigenTensor<float, 3>::From(t);
ASSERT_EQ(1, et.dimension(0));
ASSERT_EQ(2, et.dimension(1));
ASSERT_EQ(3, et.dimension(2));
for (int i = 0; i < 1; i++) {
for (int j = 0; j < 2; j++) {
for (int k = 0; k < 3; k++) {
ASSERT_NEAR((i * 2 + j) * 3 + k, et(i, j, k), 1e-6f);
}
}
}
}
TEST(Eigen, VectorFrom) {
Tensor t;
float* p = t.mutable_data<float>(make_ddim({6}), platform::CPUPlace());
for (int i = 0; i < 6; i++) {
p[i] = static_cast<float>(i);
}
EigenVector<float>::Type ev = EigenVector<float>::From(t);
ASSERT_EQ(6, ev.dimension(0));
for (int i = 0; i < 6; i++) {
ASSERT_NEAR(i, ev(i), 1e-6f);
}
}
TEST(Eigen, VectorFlatten) {
Tensor t;
float* p = t.mutable_data<float>(make_ddim({1, 2, 3}), platform::CPUPlace());
for (int i = 0; i < 1 * 2 * 3; i++) {
p[i] = static_cast<float>(i);
}
EigenVector<float>::Type ev = EigenVector<float>::Flatten(t);
ASSERT_EQ(1 * 2 * 3, ev.dimension(0));
for (int i = 0; i < 1 * 2 * 3; i++) {
ASSERT_NEAR(i, ev(i), 1e-6f);
}
}
TEST(Eigen, Matrix) {
Tensor t;
float* p = t.mutable_data<float>(make_ddim({2, 3}), platform::CPUPlace());
for (int i = 0; i < 2 * 3; i++) {
p[i] = static_cast<float>(i);
}
EigenMatrix<float>::Type em = EigenMatrix<float>::From(t);
ASSERT_EQ(2, em.dimension(0));
ASSERT_EQ(3, em.dimension(1));
for (int i = 0; i < 2; i++) {
for (int j = 0; j < 3; j++) {
ASSERT_NEAR(i * 3 + j, em(i, j), 1e-6f);
}
}
}
} // namespace framework
} // namespace paddle
paddle/framework/enforce.h 已删除 100644 → 0
浏览文件 @ 527c8597
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <paddle/string/printf.h>
#include <exception>
#include <sstream>
namespace paddle {
namespace framework {
/**
* @brief Enforce exception. Inherits std::exception
*
* All enforce condition not met, will throw an EnforceNotMet exception.
*/
class EnforceNotMet : public std::exception {
public:
EnforceNotMet(const std::string& msg, const char* file, int fileline) {
std::ostringstream sout;
sout << msg << " at [" << file << ":" << fileline << "];";
all_msg_ = sout.str();
}
const char* what() const noexcept override { return all_msg_.c_str(); }
private:
std::string all_msg_;
};
// From https://stackoverflow.com/questions/30130930/
// __buildin_expect is in C++ 11 standard. Since the condition which enforced
// should be true in most situation, it will make the compiler generate faster
// code by adding `UNLIKELY` macro.
#define UNLIKELY(condition) __builtin_expect(static_cast<bool>(condition), 0)
/**
* @brief Throw a EnforceNotMet exception, automatically filled __FILE__ &
* __LINE__
*
* This macro take __VA_ARGS__, user can pass any type if that type can
* serialize to std::ostream
*/
#define PADDLE_THROW(...) \
do { \
throw ::paddle::framework::EnforceNotMet( \
::paddle::string::Sprintf(__VA_ARGS__), __FILE__, __LINE__); \
} while (0)
/**
* @brief Enforce a condition, otherwise throw an EnforceNotMet
*/
#define PADDLE_ENFORCE(condition, ...) \
do { \
if (UNLIKELY(!(condition))) { \
PADDLE_THROW(__VA_ARGS__); \
} \
} while (0)
} // namespace framework
} // namespace paddle
paddle/framework/net.cc
浏览文件 @ ca89bfad
/*
Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#include "paddle/framework/net.h"
namespace paddle {
namespace framework {
PlainNet::PlainNet(const NetDesc& def) {}
void PlainNet::CompleteAddOp(bool calc) {
add_op_done_ = true;
if (!calc) return;
void PlainNet::InferShape(const ScopePtr& scope) const {
std::unordered_set<std::string> input_set;
std::unordered_set<std::string> output_set;
std::unordered_set<std::string> temp_output;
for (auto& op : ops_) {
op.InferShape();
for (auto& ipt : op->inputs_) {
if (!Contains(output_set, ipt)) { // Not other op's output
input_set.insert(ipt);
} else {
temp_output.insert(ipt);
}
}
for (auto& opt : op->outputs_) {
output_set.insert(opt);
}
}
inputs_.reserve(input_set.size());
std::copy(input_set.begin(), input_set.end(), std::back_inserter(inputs_));
outputs_.reserve(output_set.size());
std::vector<int> tmp_index;
tmp_index.reserve(temp_output.size());
int idx = 0;
for (auto& opt : output_set) {
if (Contains(temp_output, opt)) {
tmp_index.push_back(idx);
}
outputs_.push_back(opt);
++idx;
}
attrs_["temporary_index"] = tmp_index;
}
void PlainNet::Run(const ScopePtr& scope, const DeviceContext& ctx) const {
std::string PlainNet::DebugString() const {
std::ostringstream os;
os << this->type_ << ":" << std::endl;
for (auto& op : ops_) {
op.Run(ctx);
os << "\t" << op->DebugString() << std::endl;
}
return os.str();
}
} // namespace framework
} // namespace paddle
paddle/framework/net.h
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
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
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. */
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/net_proto.pb.h"
#include <paddle/framework/op_desc.pb.h>
#include <paddle/framework/operator.h>
#include "paddle/framework/op_proto.pb.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/scope.h"
#include "paddle/platform/device_context.h"
namespace paddle {
namespace framework {
using namespace paddle::platform;
// operator's index stored in a network.
typedef int OpIndex;
/**
* NOTE following codes are some definitions of unimplemented concepts.
* We write some basic implementation to make Net compilable. These APIs will
* keep updating if the concepts related are implemented.
*/
struct OpDesc;
struct OpAttrs {};
class Operator {
public:
Operator(const OpDesc &def) {}
void InferShape() const {}
void Run(const DeviceContext &ctx) const {}
};
/**
* @brief Network that manage the operators it has.
* @brief Network is also a type of Operator
*
* It will manage the operators it has.
*
* Network is the container and controller of a set of operators, user can build
* a real network from a NetDesc which is a protobuf message and use
* Network.Run() * to run all the operators in the network.
* Network is the container and controller of a set of operators.
* A network object knows all Operators belonging to this network. Variables,
* which are inputs and outputs of these operators, are created and managed by a
* hierarchy of Scope objects.
*
* This is the base class of network, all the networks should implement the apis
* This is the base class of network, all the networks should implement the APIs
* it defines.
*/
class Net {
class Net : public OperatorBase {
public:
/**
* @brief Infer shapes of all inputs and outputs of operators.
*/
virtual void InferShape(const ScopePtr &scope) const = 0;
/**
* @brief Run the network.
*
* Run all the operators and return success(true) or not, with all the
* variables are located in `scope`. `context` describes the detail execution
* environment for ops. `begin` and `end` specify the scope of `ops_` to run,
* If no positive indexes are provided, all operators in `ops_` will run.
*/
virtual void Run(const ScopePtr &scope, const DeviceContext &ctx) const = 0;
/**
* @brief Add an Operator according to `def`.
*/
virtual OpIndex AddOp(const OpProto &def) = 0;
/**
* @brief Add optimizer operators acctording to `attrs`.
*/
virtual void AddOptimizerOps(const OpAttrs &attrs) = 0;
/**
* @brief Add backward operators.
*/
virtual void AddBackwardOps() = 0;
/**
* @brief Create a network.
*/
static std::unique_ptr<Net> Create(const NetDesc &def = NetDesc());
virtual ~Net() {}
virtual void AddOp(const OperatorPtr& op) = 0;
virtual void CompleteAddOp(bool calc) = 0;
};
using NetPtr = std::shared_ptr<Net>;
/**
* @brief a basic implementation of Net.
*
......@@ -103,18 +54,14 @@ class Net {
class PlainNet : public Net {
public:
/**
* @brief Initialize a PlainNet.
*
* Initialize from a network describe by `def`. NetDesc is the definition of
* a network.
*/
PlainNet(const NetDesc &def);
/**
* Infer all the operators' input and output varialbes' shapes, will be called
* Infer all the operators' input and output variables' shapes, will be called
* before every mini-batch
*/
virtual void InferShape(const ScopePtr &scope) const override;
void InferShape(const ScopePtr& scope) const override {
for (auto& op : ops_) {
op->InferShape(scope);
}
}
/**
* @brief Run the network.
......@@ -123,49 +70,34 @@ class PlainNet : public Net {
* scope will be used instead. If no OpContext is provicded, default context
* will be used.
*/
virtual void Run(const ScopePtr &scope,
const DeviceContext &ctx) const override;
void Run(const ScopePtr& scope,
const platform::DeviceContext& dev_ctx) const override {
for (auto& op : ops_) {
op->Run(scope, dev_ctx);
}
}
/**
* @brief Add an operator to this network.
* @brief Add an operator by ptr
*/
virtual OpIndex AddOp(const OpProto &def) override;
void AddOp(const OperatorPtr& op) override {
PADDLE_ENFORCE(!add_op_done_, "Cannot AddOp when this network is sealed");
ops_.push_back(op);
}
/**
* @brief Add all optimizer operators related into the network.
*/
virtual void AddOptimizerOps(const OpAttrs &attrs) override;
void CompleteAddOp(bool calculate = true) override;
/**
* @brief Add all backward operators related into the network.
*/
virtual void AddBackwardOps() override;
virtual ~PlainNet() override {}
std::string DebugString() const override;
protected:
/**
* @brief Build the network.
*
* Create operators accordding to `def`, will be called by the constructor.
*/
void BuildNet(const NetDesc &def);
/**
* @brief Add an operator into this network.
*
* Add a operator which is identified as `type` and has attributes described
* in `attrs`, the `inputs` are the keys of readonly input variables,
* `outputs` are keys of mutable output variables. An `OpIndex` will be
* returned to indicate the offset of the new operator in `ops_`.
*/
OpIndex AddOp(const std::string &type, const std::vector<std::string> &inputs,
const std::vector<std::string> &outputs,
const OpAttrs &attrs = OpAttrs());
std::vector<OperatorPtr> ops_;
private:
// the operators owned by `Network`.
std::vector<Operator> ops_;
bool add_op_done_{false};
template <typename T, typename KeyType>
static bool Contains(T container, KeyType key) {
return container.find(key) != container.end();
}
};
} // namespace framework
......
paddle/framework/net_op_test.cc 0 → 100644
浏览文件 @ ca89bfad
#include <gtest/gtest.h>
#include <paddle/framework/net.h>
#include <paddle/framework/op_registry.h>
#include <paddle/framework/operator.h>
namespace pd = paddle::framework;
static int infer_shape_cnt = 0;
static int run_cnt = 0;
class TestOp : public pd::OperatorBase {
public:
void InferShape(const paddle::framework::ScopePtr& scope) const override {
++infer_shape_cnt;
}
void Run(const paddle::framework::ScopePtr& scope,
const paddle::platform::DeviceContext& dev_ctx) const override {
++run_cnt;
}
};
template <typename T>
void AssertSameVectorWithoutOrder(const std::vector<T>& expected,
const std::vector<T>& actual) {
ASSERT_EQ(expected.size(), actual.size());
std::unordered_set<T> expected_set;
for (auto& tmp : expected) {
expected_set.insert(tmp);
}
for (auto& act : actual) {
ASSERT_NE(expected_set.end(), expected_set.find(act));
}
}
TEST(OpKernel, all) {
auto net = std::make_shared<paddle::framework::PlainNet>();
ASSERT_NE(net, nullptr);
auto op1 = std::make_shared<TestOp>();
op1->inputs_ = {"x", "w1", "b1"};
op1->outputs_ = {"y"};
net->AddOp(op1);
auto op2 = std::make_shared<TestOp>();
op2->inputs_ = {"y", "w2", "b2"};
op2->outputs_ = {"z"};
net->AddOp(op2);
net->CompleteAddOp();
AssertSameVectorWithoutOrder({"x", "w1", "b1", "w2", "b2"}, net->inputs_);
AssertSameVectorWithoutOrder({"y", "z"}, net->outputs_);
auto tmp_idx_iter = net->attrs_.find("temporary_index");
ASSERT_NE(net->attrs_.end(), tmp_idx_iter);
auto& tmp_idx = boost::get<std::vector<int>>(tmp_idx_iter->second);
ASSERT_EQ(1UL, tmp_idx.size());
ASSERT_EQ("y", net->outputs_[tmp_idx[0]]);
auto scope = std::make_shared<pd::Scope>();
paddle::platform::CPUDeviceContext dev_ctx;
net->InferShape(scope);
net->Run(scope, dev_ctx);
ASSERT_EQ(2, infer_shape_cnt);
ASSERT_EQ(2, run_cnt);
ASSERT_THROW(net->AddOp(op2), std::runtime_error);
}
paddle/framework/op_registry.cc
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <paddle/framework/op_registry.h>
namespace paddle {
......@@ -33,4 +47,4 @@ void AttrTypeHelper::SetAttrType<std::vector<std::string>>(AttrProto* attr) {
attr->set_type(paddle::framework::AttrType::STRINGS);
}
} // namespace framework
} // namespace paddle
\ No newline at end of file
} // namespace paddle
paddle/framework/op_registry.h
浏览文件 @ ca89bfad
#pragma once
#include <algorithm>
#include <atomic>
#include <type_traits>
#include <unordered_map>
#include <unordered_set>
......@@ -61,7 +62,14 @@ class OpProtoAndCheckerMaker {
OpProtoAndCheckerMaker(OpProto* proto, OpAttrChecker* op_checker)
: proto_(proto), op_checker_(op_checker) {}
~OpProtoAndCheckerMaker() { CheckNoDuplicatedAttrs(); }
~OpProtoAndCheckerMaker() {
PADDLE_ENFORCE(validated_, "should call Validate after build");
}
void Validate() {
validated_ = true;
CheckNoDuplicatedInOutAttrs();
}
protected:
void AddInput(const std::string& name, const std::string& comment,
......@@ -163,19 +171,26 @@ Add a mark to which output is temporary is helpful for future optimization.
}
}
void CheckNoDuplicatedAttrs() {
void CheckNoDuplicatedInOutAttrs() {
std::unordered_set<std::string> names;
size_t cnt = 0;
auto checker = [&](const std::string& name) {
PADDLE_ENFORCE(!names.count(name), "[%s] is duplicated", name);
names.insert(name);
};
for (auto& attr : proto_->attrs()) {
names.insert(attr.name());
++cnt;
checker(attr.name());
}
for (auto& input : proto_->inputs()) {
checker(input.name());
}
for (auto& output : proto_->outputs()) {
checker(output.name());
}
PADDLE_ENFORCE(names.size() == cnt,
"Cannot register two attribute in same name!");
}
OpProto* proto_;
OpAttrChecker* op_checker_;
bool validated_{false};
bool has_multiple_input_{false};
bool has_multiple_output_{false};
bool has_temporary_output_{false};
......@@ -183,6 +198,8 @@ Add a mark to which output is temporary is helpful for future optimization.
class OpRegistry {
using OpCreator = std::function<OperatorBase*()>;
using VarIndexMap = std::unordered_map<std::string, int>;
using VarNameList = std::vector<std::string>;
public:
template <typename OpType, typename ProtoMakerType>
......@@ -190,36 +207,71 @@ class OpRegistry {
creators()[op_type] = [] { return new OpType; };
OpProto& op_proto = protos()[op_type];
OpAttrChecker& op_checker = op_checkers()[op_type];
ProtoMakerType(&op_proto, &op_checker);
auto maker = ProtoMakerType(&op_proto, &op_checker);
maker.Validate();
*op_proto.mutable_type() = op_type;
PADDLE_ENFORCE(
op_proto.IsInitialized(),
"Fail to initialize %s's OpProto, because %s is not initialized",
op_type, op_proto.InitializationErrorString());
VarIndexMaps()[op_type].reset(new VarIndexMap());
auto& varmap = *VarIndexMaps()[op_type];
int idx = 0;
for (auto& var : op_proto.inputs()) {
varmap[var.name()] = idx++;
}
idx = 0;
for (auto& var : op_proto.outputs()) {
varmap[var.name()] = idx++;
}
}
static OperatorPtr CreateOp(const std::string& type,
const VarNameList& inputs,
const VarNameList& outputs,
const AttributeMap& attrs) {
auto op_create_it = creators().find(type);
PADDLE_ENFORCE(op_create_it != creators().end(),
"Operator %s cannot be found", type);
auto op = op_create_it->second();
op->type_ = type;
op->inputs_ = inputs;
op->outputs_ = outputs;
op->attrs_ = attrs;
op_checkers().at(type).Check(op->attrs_);
GenerateTempVariableName(op);
{
auto var_index_it = VarIndexMaps().find(type);
if (var_index_it != VarIndexMaps().end()) {
op->in_out_idxs_ = var_index_it->second;
}
}
op->Init();
return OperatorPtr(op);
}
static OperatorPtr CreateOp(const OpDesc& op_desc) {
std::string op_type = op_desc.type();
OperatorPtr op(creators().at(op_type)());
op->desc_ = op_desc;
op->inputs_.reserve((size_t)op_desc.inputs_size());
std::vector<std::string> inputs;
inputs.reserve((size_t)op_desc.inputs_size());
std::copy(op_desc.inputs().begin(), op_desc.inputs().end(),
std::back_inserter(op->inputs_));
op->outputs_.reserve((size_t)op_desc.outputs_size());
std::back_inserter(inputs));
std::vector<std::string> outputs;
outputs.reserve((size_t)op_desc.outputs_size());
std::copy(op_desc.outputs().begin(), op_desc.outputs().end(),
std::back_inserter(op->outputs_));
std::back_inserter(outputs));
AttributeMap attrs;
for (auto& attr : op_desc.attrs()) {
op->attrs_[attr.name()] = AttrTypeHelper::GetAttrValue(attr);
attrs[attr.name()] = AttrTypeHelper::GetAttrValue(attr);
}
op_checkers().at(op_type).Check(op->attrs_);
op->Init();
return op;
}
private:
static std::unordered_map<std::string, OpCreator>& creators() {
static std::unordered_map<std::string, OpCreator> creators_;
return creators_;
return CreateOp(op_desc.type(), inputs, outputs, attrs);
}
static std::unordered_map<std::string, OpProto>& protos() {
......@@ -227,6 +279,29 @@ class OpRegistry {
return protos_;
};
private:
static std::unordered_map<std::string, std::shared_ptr<VarIndexMap>>&
VarIndexMaps() {
static std::unordered_map<std::string, std::shared_ptr<VarIndexMap>> maps_;
return maps_;
}
static void GenerateTempVariableName(OperatorBase* op) {
static std::atomic<size_t> gUniqId(0UL);
for (auto& outname : op->outputs_) {
if (outname == OperatorBase::TMP_VAR_NAME()) {
outname += op->type_;
outname += "@";
outname += std::to_string(gUniqId.fetch_add(1));
}
}
}
static std::unordered_map<std::string, OpCreator>& creators() {
static std::unordered_map<std::string, OpCreator> creators_;
return creators_;
}
static std::unordered_map<std::string, OpAttrChecker>& op_checkers() {
static std::unordered_map<std::string, OpAttrChecker> op_checkers_;
return op_checkers_;
......@@ -241,12 +316,18 @@ class OpRegisterHelper {
}
};
/**
* check if MACRO is used in GLOBAL NAMESPACE.
*/
#define STATIC_ASSERT_GLOBAL_NAMESPACE(uniq_name, msg) \
struct __test_global_namespace_##uniq_name##__ {}; \
static_assert(std::is_same<::__test_global_namespace_##uniq_name##__, \
__test_global_namespace_##uniq_name##__>::value, \
msg)
/**
* Macro to Register Operator.
*/
#define REGISTER_OP(__op_type, __op_class, __op_maker_class) \
STATIC_ASSERT_GLOBAL_NAMESPACE(__reg_op__##__op_type, \
"REGISTER_OP must be in global namespace"); \
......@@ -254,27 +335,36 @@ class OpRegisterHelper {
__op_register_##__op_type##__(#__op_type); \
int __op_register_##__op_type##_handle__() { return 0; }
#define REGISTER_OP_KERNEL(type, GPU_OR_CPU, PlaceType, KernelType) \
/**
* Macro to Register OperatorKernel.
*/
#define REGISTER_OP_KERNEL(type, DEVICE_TYPE, PlaceType, ...) \
STATIC_ASSERT_GLOBAL_NAMESPACE( \
__reg_op_kernel_##type##_##GPU_OR_CPU##__, \
__reg_op_kernel_##type##_##DEVICE_TYPE##__, \
"REGISTER_OP_KERNEL must be in global namespace"); \
struct __op_kernel_register__##type##__ { \
__op_kernel_register__##type##__() { \
::paddle::framework::OperatorWithKernel::OpKernelKey key; \
key.place_ = PlaceType(); \
::paddle::framework::OperatorWithKernel::AllOpKernels()[#type][key] \
.reset(new KernelType()); \
.reset(new __VA_ARGS__()); \
} \
}; \
static __op_kernel_register__##type##__ __reg_kernel_##type##__; \
int __op_kernel_register_##type##_handle_##GPU_OR_CPU##__() { return 0; }
int __op_kernel_register_##type##_handle_##DEVICE_TYPE##__() { return 0; }
#define REGISTER_OP_GPU_KERNEL(type, KernelType) \
REGISTER_OP_KERNEL(type, GPU, ::paddle::platform::GPUPlace, KernelType)
// (type, KernelType)
#define REGISTER_OP_GPU_KERNEL(type, ...) \
REGISTER_OP_KERNEL(type, GPU, ::paddle::platform::GPUPlace, __VA_ARGS__)
#define REGISTER_OP_CPU_KERNEL(type, KernelType) \
REGISTER_OP_KERNEL(type, CPU, ::paddle::platform::CPUPlace, KernelType)
// (type, KernelType)
#define REGISTER_OP_CPU_KERNEL(type, ...) \
REGISTER_OP_KERNEL(type, CPU, ::paddle::platform::CPUPlace, __VA_ARGS__)
/**
* Macro to mark what Operator and Kernel we will use and tell the compiler to
* link them into target.
*/
#define USE_OP_WITHOUT_KERNEL(op_type) \
STATIC_ASSERT_GLOBAL_NAMESPACE( \
__use_op_without_kernel_##op_type, \
......@@ -292,15 +382,16 @@ class OpRegisterHelper {
__attribute__((unused)) = \
__op_kernel_register_##op_type##_handle_##DEVICE_TYPE##__()
#ifdef PADDLE_ONLY_CPU
#define USE_OP(op_type) \
// use Operator with only cpu kernel.
#define USE_OP_CPU(op_type) \
USE_OP_WITHOUT_KERNEL(op_type); \
USE_OP_KERNEL(op_type, CPU);
USE_OP_KERNEL(op_type, CPU)
#ifdef PADDLE_ONLY_CPU
#define USE_OP(op_type) USE_OP_CPU(op_type)
#else
#define USE_OP(op_type) \
USE_OP_WITHOUT_KERNEL(op_type); \
USE_OP_KERNEL(op_type, CPU); \
#define USE_OP(op_type) \
USE_OP_CPU(op_type); \
USE_OP_KERNEL(op_type, GPU)
#endif
......
paddle/framework/op_registry_test.cc
浏览文件 @ ca89bfad
#include "paddle/framework/op_registry.h"
#include <gtest/gtest.h>
namespace pd = paddle::framework;
namespace paddle {
namespace framework {
class CosineOp : public OperatorBase {
......@@ -28,8 +30,6 @@ class MyTestOp : public OperatorBase {
void InferShape(const ScopePtr& scope) const override {}
void Run(const ScopePtr& scope,
const platform::DeviceContext& dev_ctx) const override {}
public:
};
class MyTestOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
......@@ -91,7 +91,7 @@ TEST(OpRegistry, IllegalAttr) {
try {
paddle::framework::OperatorPtr op __attribute__((unused)) =
paddle::framework::OpRegistry::CreateOp(op_desc);
} catch (paddle::framework::EnforceNotMet err) {
} catch (std::runtime_error& err) {
caught = true;
std::string msg = "larger_than check fail";
const char* err_msg = err.what();
......@@ -138,7 +138,7 @@ TEST(OpRegistry, CustomChecker) {
try {
paddle::framework::OperatorPtr op __attribute__((unused)) =
paddle::framework::OpRegistry::CreateOp(op_desc);
} catch (paddle::framework::EnforceNotMet err) {
} catch (std::runtime_error& err) {
caught = true;
std::string msg = "Attribute 'test_attr' is required!";
const char* err_msg = err.what();
......@@ -157,7 +157,7 @@ TEST(OpRegistry, CustomChecker) {
try {
paddle::framework::OperatorPtr op __attribute__((unused)) =
paddle::framework::OpRegistry::CreateOp(op_desc);
} catch (paddle::framework::EnforceNotMet err) {
} catch (std::runtime_error& err) {
caught = true;
std::string msg = "'test_attr' must be even!";
const char* err_msg = err.what();
......@@ -182,3 +182,35 @@ TEST(OpRegistry, CustomChecker) {
int test_attr = op->GetAttr<int>("test_attr");
ASSERT_EQ(test_attr, 4);
}
class TestAttrProtoMaker : public pd::OpProtoAndCheckerMaker {
public:
TestAttrProtoMaker(pd::OpProto* proto, pd::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddAttr<float>("scale", "scale of test op");
AddAttr<float>("scale", "scale of test op");
}
};
TEST(ProtoMaker, DuplicatedAttr) {
pd::OpProto op_proto;
pd::OpAttrChecker op_checker;
auto proto_maker = TestAttrProtoMaker(&op_proto, &op_checker);
ASSERT_THROW(proto_maker.Validate(), std::runtime_error);
}
class TestInOutProtoMaker : public pd::OpProtoAndCheckerMaker {
public:
TestInOutProtoMaker(pd::OpProto* proto, pd::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("input", "input of test op");
AddInput("input", "input of test op");
}
};
TEST(ProtoMaker, DuplicatedInOut) {
pd::OpProto op_proto;
pd::OpAttrChecker op_checker;
auto proto_maker = TestInOutProtoMaker(&op_proto, &op_checker);
ASSERT_THROW(proto_maker.Validate(), std::runtime_error);
}
paddle/framework/operator.cc
浏览文件 @ ca89bfad
......@@ -12,32 +12,92 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <algorithm>
#include "paddle/framework/operator.h"
namespace paddle {
namespace framework {
template <>
Eigen::DefaultDevice* KernelContext::GetEigenDevice<
platform::CPUPlace, Eigen::DefaultDevice>() const {
return device_context_.get_eigen_device<Eigen::DefaultDevice>();
}
#ifndef PADDLE_ONLY_CPU
template <>
Eigen::GpuDevice*
KernelContext::GetEigenDevice<platform::GPUPlace, Eigen::GpuDevice>() const {
return device_context_.get_eigen_device<Eigen::GpuDevice>();
}
#endif
const std::string& OperatorBase::Input(const std::string& name) const {
auto it = in_out_idxs_->find(name);
PADDLE_ENFORCE(it != in_out_idxs_->end(), "no key [%s] in in_out_idxs_",
name);
if (attrs_.count("input_format") == 0) {
return inputs_[it->second];
} else {
const auto& input_format = GetAttr<std::vector<int>>("input_format");
int idx = input_format[it->second];
return inputs_.at(idx);
}
}
std::vector<std::string> OperatorBase::Inputs(const std::string& name) const {
auto input_format = GetAttr<std::vector<int>>("input_format");
auto offset = in_out_idxs_->at(name);
return std::vector<std::string>{
inputs_.begin() + input_format.at(offset),
inputs_.begin() + input_format.at(offset + 1)};
}
const std::string& OperatorBase::Output(const std::string& name) const {
auto it = in_out_idxs_->find(name);
PADDLE_ENFORCE(it != in_out_idxs_->end(), "no key [%s] in in_out_idxs_",
name);
if (attrs_.count("output_format") == 0) {
return outputs_[it->second];
} else {
const auto& output_format = GetAttr<std::vector<int>>("output_format");
int idx = output_format[it->second];
return outputs_.at(idx);
}
}
std::vector<std::string> OperatorBase::Outputs(const std::string& name) const {
auto output_format = GetAttr<std::vector<int>>("output_format");
auto offset = in_out_idxs_->at(name);
return std::vector<std::string>{
outputs_.begin() + output_format.at(offset),
outputs_.begin() + output_format.at(offset + 1)};
}
std::string OperatorBase::DebugString() const {
std::stringstream ss;
ss << "=================\n";
ss << "type = " << desc_.type() << "\n";
ss << "inputs = [";
for (auto& ipt : inputs_) {
ss << ipt << ", ";
}
ss << "]\n";
ss << "outputs = [";
for (auto& opt : outputs_) {
ss << opt << ", ";
ss << "Op(" << type_ << "), inputs:(";
for (size_t i = 0; i < inputs_.size(); ++i) {
ss << inputs_[i];
if (i != inputs_.size() - 1) {
ss << ", ";
}
}
ss << "]\n";
ss << "attr_keys = [";
for (auto& attr : attrs_) {
ss << attr.first << ", ";
ss << "), outputs:(";
for (size_t i = 0; i < outputs_.size(); ++i) {
ss << outputs_[i];
if (i != outputs_.size() - 1) {
ss << ", ";
}
}
ss << "]\n";
ss << ").";
return ss.str();
}
} // namespace framework
} // namespace paddle
\ No newline at end of file
} // namespace paddle
paddle/framework/operator.h
浏览文件 @ ca89bfad
......@@ -14,21 +14,38 @@ limitations under the License. */
#pragma once
#include <paddle/framework/attr_checker.h>
#include <paddle/framework/op_desc.pb.h>
#include <paddle/framework/scope.h>
#include <paddle/framework/tensor.h>
#include <paddle/platform/device_context.h>
#include <paddle/platform/place.h>
#include <paddle/utils/Error.h>
#include <boost/variant.hpp>
#include <string>
#include <unordered_map>
#include <vector>
#include "paddle/framework/attr_checker.h"
#include "paddle/framework/op_desc.pb.h"
#include "paddle/framework/op_proto.pb.h"
#include "paddle/framework/scope.h"
#include "paddle/framework/tensor.h"
#include "paddle/platform/device_context.h"
#include "paddle/platform/place.h"
#include "paddle/utils/Error.h"
namespace paddle {
namespace framework {
template <typename T>
struct EigenDeviceConverter;
template <>
struct EigenDeviceConverter<platform::CPUPlace> {
using EigenDeviceType = Eigen::DefaultDevice;
};
#ifndef PADDLE_ONLY_CPU
template <>
struct EigenDeviceConverter<platform::GPUPlace> {
using EigenDeviceType = Eigen::GpuDevice;
};
#endif
class OperatorBase;
using OperatorPtr = std::shared_ptr<OperatorBase>;
/**
......@@ -39,6 +56,13 @@ using OperatorPtr = std::shared_ptr<OperatorBase>;
*/
class OperatorBase {
public:
/// If a variable is a empty variable, that name will be used.
static std::string EMPTY_VAR_NAME() { return "@EMPTY@"; }
/// If a variable is a temporary variable, that name will be set in Python,
/// but it will be convert to a unique name in scope after OpCreator.
static std::string TMP_VAR_NAME() { return "@TEMP@"; }
virtual ~OperatorBase() {}
template <typename T>
......@@ -48,7 +72,7 @@ class OperatorBase {
return boost::get<T>(attrs_.at(name));
}
std::string DebugString() const;
virtual std::string DebugString() const;
/// Init will be called after CreateOperator, you can put some initialization
/// logic here.
......@@ -62,14 +86,76 @@ class OperatorBase {
virtual void Run(const ScopePtr& scope,
const platform::DeviceContext& dev_ctx) const = 0;
protected:
std::string Type() const { return desc_.type(); }
// Get a input with argument's name described in `op_proto`
const std::string& Input(const std::string& name) const;
// Get a input which has multiple variables.
// TODO add a vector_view to prevent memory copy.
std::vector<std::string> Inputs(const std::string& name) const;
// Get a output with argument's name described in `op_proto`
const std::string& Output(const std::string& name) const;
// Get an output which has multiple variables.
// TODO add a vector_view to prevent memory copy.
std::vector<std::string> Outputs(const std::string& name) const;
public:
OpDesc desc_;
std::string type_;
std::vector<std::string> inputs_;
std::vector<std::string> outputs_;
AttributeMap attrs_;
// store the arguments' offset described in op_desc.
std::shared_ptr<std::unordered_map<std::string, int>> in_out_idxs_;
};
class KernelContext {
public:
KernelContext(const OperatorBase* op, const std::shared_ptr<Scope>& scope,
const platform::DeviceContext& device_context)
: op_(*op), scope_(scope), device_context_(device_context) {}
const Variable* Input(int index) const {
return scope_->GetVariable(op_.inputs_[index]);
}
Variable* Output(int index) const {
return scope_->GetVariable(op_.outputs_[index]);
}
const Variable* Input(const std::string& name) const {
return scope_->GetVariable(op_.Input(name));
}
const Variable* Output(const std::string& name) const {
return scope_->GetVariable(op_.Output(name));
}
const std::vector<const Variable*> Inputs(const std::string& name) const {
auto names = op_.Inputs(name);
std::vector<const Variable*> res;
std::transform(
names.begin(), names.end(), res.begin(),
[this](const std::string& name) { return scope_->GetVariable(name); });
return res;
}
const std::vector<const Variable*> Outputs(const std::string& name) const {
auto names = op_.Outputs(name);
std::vector<const Variable*> res;
std::transform(
names.begin(), names.end(), res.begin(),
[this](const std::string& name) { return scope_->GetVariable(name); });
return res;
}
template <typename PlaceType,
typename DeviceType =
typename EigenDeviceConverter<PlaceType>::EigenDeviceType>
DeviceType* GetEigenDevice() const;
platform::Place GetPlace() const { return device_context_.GetPlace(); }
const OperatorBase& op_;
const std::shared_ptr<Scope>& scope_;
const platform::DeviceContext& device_context_;
};
class OpKernel {
......@@ -80,24 +166,6 @@ class OpKernel {
* device resource such as CUDA stream, cublas handle, etc. from
* KernelContext. User should construct it before run the Operator.
*/
class KernelContext {
public:
KernelContext(const OperatorBase* op, const ScopePtr& scope,
const platform::DeviceContext& device_context)
: op_(*op), scope_(scope), device_context_(device_context) {}
const Variable* Input(int index) const {
return scope_->GetVariable(op_.inputs_[index]);
}
Variable* Output(int index) const {
return scope_->GetVariable(op_.outputs_[index]);
}
const OperatorBase& op_;
const ScopePtr& scope_;
const platform::DeviceContext& device_context_;
};
virtual void Compute(const KernelContext& context) const = 0;
......@@ -142,8 +210,8 @@ class OperatorWithKernel : public OperatorBase {
void Run(const ScopePtr& scope,
const platform::DeviceContext& dev_ctx) const final {
auto& opKernel = AllOpKernels().at(Type()).at(OpKernelKey(dev_ctx));
opKernel->Compute(OpKernel::KernelContext(this, scope, dev_ctx));
auto& opKernel = AllOpKernels().at(type_).at(OpKernelKey(dev_ctx));
opKernel->Compute(KernelContext(this, scope, dev_ctx));
}
static std::unordered_map<std::string /* op_type */, OpKernelMap>&
......@@ -151,6 +219,7 @@ class OperatorWithKernel : public OperatorBase {
static std::unordered_map<std::string, OpKernelMap> g_all_op_kernels;
return g_all_op_kernels;
}
void InferShape(const std::shared_ptr<Scope>& scope) const final {
std::vector<const Tensor*> ins;
VarNamesToTensors(scope, inputs_, &ins);
......
paddle/framework/operator_test.cc
浏览文件 @ ca89bfad
......@@ -19,14 +19,17 @@ limitations under the License. */
namespace paddle {
namespace framework {
class OperatorTest : public OperatorBase {
static int op_run_num = 0;
class OpWithoutKernelTest : public OperatorBase {
public:
void Init() override { x = 1; }
void InferShape(const ScopePtr& scope) const override {}
void Run(const ScopePtr& scope,
const platform::DeviceContext& dev_ctx) const override {
float scale = GetAttr<float>("scale");
ASSERT_NEAR(scale, 3.14, 1e-5);
op_run_num++;
ASSERT_EQ((int)inputs_.size(), 1);
ASSERT_EQ((int)outputs_.size(), 1);
ASSERT_EQ(scope->GetVariable(inputs_[0]), nullptr);
ASSERT_EQ(x, 1);
ASSERT_NE(scope->GetVariable(outputs_[0]), nullptr);
......@@ -36,15 +39,14 @@ class OperatorTest : public OperatorBase {
float x = 0;
};
class OperatorTestProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
class OpeWithoutKernelTestProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
public:
OperatorTestProtoAndCheckerMaker(OpProto* proto, OpAttrChecker* op_checker)
OpeWithoutKernelTestProtoAndCheckerMaker(OpProto* proto,
OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("input", "input of test op");
AddOutput("output", "output of test op");
AddAttr<float>("scale", "scale of cosine op")
.SetDefault(1.0)
.LargerThan(0.0);
AddAttr<float>("scale", "scale of cosine op");
AddComment("This is test op");
}
};
......@@ -52,8 +54,8 @@ class OperatorTestProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
} // namespace framework
} // namespace paddle
REGISTER_OP(test_operator, paddle::framework::OperatorTest,
paddle::framework::OperatorTestProtoAndCheckerMaker);
REGISTER_OP(test_operator, paddle::framework::OpWithoutKernelTest,
paddle::framework::OpeWithoutKernelTestProtoAndCheckerMaker);
TEST(OperatorBase, all) {
paddle::framework::OpDesc op_desc;
......@@ -63,18 +65,17 @@ TEST(OperatorBase, all) {
auto attr = op_desc.mutable_attrs()->Add();
attr->set_name("scale");
attr->set_type(paddle::framework::AttrType::FLOAT);
float scale = 3.14;
attr->set_f(scale);
attr->set_f(3.14);
paddle::platform::CPUDeviceContext device_context;
auto scope = std::make_shared<paddle::framework::Scope>();
paddle::framework::OperatorPtr op =
paddle::framework::OpRegistry::CreateOp(op_desc);
ASSERT_EQ(op->GetAttr<float>("scale"), scale);
scope->CreateVariable("OUT1");
ASSERT_EQ(paddle::framework::op_run_num, 0);
op->Run(scope, device_context);
std::cout << op->DebugString() << std::endl;
ASSERT_EQ(paddle::framework::op_run_num, 1);
}
namespace paddle {
......@@ -84,8 +85,8 @@ class OpKernelTestProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
public:
OpKernelTestProtoAndCheckerMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("input", "input of test op");
AddOutput("output", "output of test op");
AddInput("x", "input of test op");
AddOutput("y", "output of test op");
AddAttr<float>("scale", "scale of cosine op")
.SetDefault(1.0)
.LargerThan(0.0);
......@@ -93,19 +94,76 @@ class OpKernelTestProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
}
};
static int cpu_kernel_run_num = 0;
class OpWithKernelTest : public OperatorWithKernel {
protected:
void InferShape(const std::vector<const Tensor*>& inputs,
const std::vector<Tensor*>& outputs) const override {}
};
template <typename T1, typename T2>
class CPUKernelTest : public OpKernel {
public:
void Compute(const KernelContext& context) const {
float scale = context.op_.GetAttr<float>("scale");
ASSERT_NEAR(scale, 3.14, 1e-5);
void Compute(const KernelContext& ctx) const {
std::cout << "this is cpu kernel" << std::endl;
std::cout << context.op_.DebugString() << std::endl;
std::cout << ctx.op_.DebugString() << std::endl;
cpu_kernel_run_num++;
ASSERT_EQ(ctx.op_.Input("x"), "IN1");
ASSERT_EQ(ctx.op_.Output("y"), "OUT1");
}
};
// multiple inputs test
class OperatorMultiInputsTest : public OperatorBase {
public:
void Init() override { x = 1; }
void InferShape(const std::shared_ptr<Scope>& scope) const override {}
void Run(const std::shared_ptr<Scope>& scope,
const platform::DeviceContext& dev_ctx) const override {
ASSERT_EQ(scope->GetVariable(inputs_[0]), nullptr);
ASSERT_EQ(x, 1);
ASSERT_NE(scope->GetVariable(outputs_[0]), nullptr);
ASSERT_EQ(Input("x"), "IN1");
ASSERT_EQ(Input("y"), "OUT1");
}
public:
float x = 0;
};
class OpKernelTestMultiInputsProtoAndCheckerMaker
: public OpProtoAndCheckerMaker {
public:
OpKernelTestMultiInputsProtoAndCheckerMaker(OpProto* proto,
OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInputs("xs", "inputs of test op");
AddInput("k", "input of test op");
AddOutputs("ys", "outputs of test op");
AddAttr<float>("scale", "scale of cosine op")
.SetDefault(1.0)
.LargerThan(0.0);
AddComment("This is test op");
}
};
class CPUKernalMultiInputsTest : public OpKernel {
public:
void Compute(const KernelContext& ctx) const {
auto xs = ctx.op_.Inputs("xs");
ASSERT_EQ(xs.size(), 3UL);
ASSERT_EQ(xs[0], "x0");
ASSERT_EQ(xs[1], "x1");
ASSERT_EQ(xs[2], "x2");
auto k = ctx.op_.Input("k");
ASSERT_EQ(k, "k0");
auto ys = ctx.op_.Outputs("ys");
ASSERT_EQ(ys.size(), 2UL);
ASSERT_EQ(ys[0], "y0");
ASSERT_EQ(ys[1], "y1");
}
};
......@@ -114,8 +172,10 @@ class CPUKernelTest : public OpKernel {
REGISTER_OP(op_with_kernel, paddle::framework::OpWithKernelTest,
paddle::framework::OpKernelTestProtoAndCheckerMaker);
REGISTER_OP_CPU_KERNEL(op_with_kernel, paddle::framework::CPUKernelTest);
REGISTER_OP_CPU_KERNEL(op_with_kernel,
paddle::framework::CPUKernelTest<float, float>);
// test with single input
TEST(OpKernel, all) {
paddle::framework::OpDesc op_desc;
op_desc.set_type("op_with_kernel");
......@@ -131,5 +191,51 @@ TEST(OpKernel, all) {
paddle::framework::OperatorPtr op =
paddle::framework::OpRegistry::CreateOp(op_desc);
ASSERT_EQ(paddle::framework::cpu_kernel_run_num, 0);
op->Run(scope, cpu_device_context);
ASSERT_EQ(paddle::framework::cpu_kernel_run_num, 1);
}
REGISTER_OP(op_multi_inputs_with_kernel, paddle::framework::OpWithKernelTest,
paddle::framework::OpKernelTestMultiInputsProtoAndCheckerMaker);
REGISTER_OP_CPU_KERNEL(op_multi_inputs_with_kernel,
paddle::framework::CPUKernalMultiInputsTest);
// test with multi inputs
TEST(OpKernel, multi_inputs) {
using namespace paddle::framework;
OpDesc op_desc;
op_desc.set_type("op_multi_inputs_with_kernel");
*op_desc.mutable_inputs()->Add() = "x0";
*op_desc.mutable_inputs()->Add() = "x1";
*op_desc.mutable_inputs()->Add() = "x2";
*op_desc.mutable_inputs()->Add() = "k0";
*op_desc.mutable_outputs()->Add() = "y0";
*op_desc.mutable_outputs()->Add() = "y1";
auto attr = op_desc.mutable_attrs()->Add();
attr->set_name("scale");
attr->set_type(paddle::framework::AttrType::FLOAT);
attr->set_f(3.14);
auto attr0 = op_desc.mutable_attrs()->Add();
attr0->set_name("input_format");
attr0->set_type(paddle::framework::AttrType::INTS);
auto input_format = attr0->mutable_ints();
input_format->Add(0); // x0
input_format->Add(3); // k
input_format->Add(4); // end
auto attr1 = op_desc.mutable_attrs()->Add();
attr1->set_name("output_format");
attr1->set_type(paddle::framework::AttrType::INTS);
auto output_format = attr1->mutable_ints();
output_format->Add(0); // y0
output_format->Add(2); // y1
paddle::platform::CPUDeviceContext cpu_device_context;
auto scope = std::make_shared<Scope>();
OperatorPtr op(paddle::framework::OpRegistry::CreateOp(op_desc));
op->Run(scope, cpu_device_context);
}
paddle/framework/tensor.cc 0 → 100644
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <paddle/framework/tensor.h>
namespace paddle {
namespace framework {}
} // namespace paddle
paddle/framework/tensor.h
浏览文件 @ ca89bfad
......@@ -17,19 +17,34 @@ limitations under the License. */
#include <cstdint>
#include <cstring>
#include <memory>
#include <typeindex>
#include "paddle/framework/ddim.h"
#include "paddle/framework/enforce.h"
#include "paddle/memory/memory.h"
#include "paddle/platform/enforce.h"
#include "paddle/platform/place.h"
#include "unsupported/Eigen/CXX11/Tensor"
namespace paddle {
namespace pybind {
namespace details { // forward declare
template <bool less, size_t i, typename... args>
struct CastToPyBufferImpl;
} // namespace details
} // namespace pybind
namespace framework {
class Tensor {
public:
Tensor() : numel_(0), offset_(0) {}
template <bool less, size_t i, typename... args>
friend struct paddle::pybind::details::CastToPyBufferImpl;
template <typename T, size_t D, int MajorType, typename IndexType>
friend struct EigenTensor;
Tensor& operator=(const Tensor& src) = delete;
template <typename T, int MajorType, typename IndexType>
friend struct EigenVector;
public:
Tensor() : offset_(0) {}
template <typename T>
const T* data() const {
......@@ -39,21 +54,40 @@ class Tensor {
}
template <typename T>
T* mutable_data(DDim dims, paddle::platform::Place place) {
T* data() {
CheckDims<T>();
return reinterpret_cast<T*>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
offset_);
}
template <typename T>
T* mutable_data(DDim dims, platform::Place place) {
set_dims(dims);
return mutable_data<T>(place);
}
template <typename T>
T* mutable_data(paddle::platform::Place place) {
PADDLE_ENFORCE(numel_ > 0,
"Tensor::numel_ must be larger than zero to call "
T* mutable_data(platform::Place place) {
PADDLE_ENFORCE(product(dims_) > 0,
"Tensor's numel must be larger than zero to call "
"Tensor::mutable_data. Call Tensor::set_dim first.");
if (holder_ == nullptr ||
!(holder_->place() ==
place) /* some versions of boost::variant don't have operator!= */
|| holder_->size() < numel_ * sizeof(T) + offset_) {
holder_.reset(new PlaceholderImpl<T>(place, numel_ * sizeof(T)));
|| holder_->size() < product(dims_) * sizeof(T) + offset_) {
if (platform::is_cpu_place(place)) {
holder_.reset(new PlaceholderImpl<T, platform::CPUPlace>(
boost::get<platform::CPUPlace>(place), product(dims_) * sizeof(T)));
} else if (platform::is_gpu_place(place)) {
#ifdef PADDLE_ONLY_CPU
PADDLE_THROW("'GPUPlace' is not supported in CPU only device.");
#else
holder_.reset(new PlaceholderImpl<T, platform::GPUPlace>(
boost::get<platform::GPUPlace>(place), product(dims_) * sizeof(T)));
#endif
} else {
PADDLE_THROW("Unknown 'place'.");
}
offset_ = 0;
}
return reinterpret_cast<T*>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
......@@ -69,12 +103,12 @@ class Tensor {
}
template <typename T>
void CopyFrom(const Tensor& src, paddle::platform::Place dst_place) {
void CopyFrom(const Tensor& src, platform::Place dst_place) {
PADDLE_ENFORCE(platform::is_cpu_place(src.holder_->place()) &&
platform::is_cpu_place(dst_place),
"Tensor::CopyFrom only support CPU now.");
src.CheckDims<T>();
size_t size = src.numel_ * sizeof(T);
size_t size = product(src.dims_) * sizeof(T);
set_dims(src.dims());
const void* src_ptr = static_cast<const void*>(src.data<T>());
void* dst_ptr = static_cast<void*>(mutable_data<T>(dst_place));
......@@ -108,7 +142,6 @@ class Tensor {
return;
}
dims_ = dims;
numel_ = product(dims_);
}
DDim dims() const { return dims_; }
......@@ -119,52 +152,52 @@ class Tensor {
struct Placeholder {
virtual ~Placeholder() {}
virtual void* ptr() const = 0;
virtual paddle::platform::Place place() const = 0;
virtual platform::Place place() const = 0;
virtual size_t size() const = 0;
virtual std::type_index type() const = 0;
};
template <typename T>
template <typename T, typename PlaceType>
struct PlaceholderImpl : public Placeholder {
private:
template <typename PType>
class Deleter {
public:
Deleter(platform::Place place) : place_(place) {}
void operator()(T* ptr) {
paddle::memory::Free(place_, static_cast<void*>(ptr));
}
Deleter(PType place) : place_(place) {}
void operator()(T* ptr) { memory::Free(place_, static_cast<void*>(ptr)); }
private:
paddle::platform::Place place_;
PType place_;
};
public:
PlaceholderImpl(paddle::platform::Place place, size_t size)
: ptr_(static_cast<T*>(paddle::memory::Alloc(place, size)),
Deleter(place)),
PlaceholderImpl(PlaceType place, size_t size)
: ptr_(static_cast<T*>(memory::Alloc(place, size)),
Deleter<PlaceType>(place)),
place_(place),
size_(size) {}
virtual void* ptr() const { return static_cast<void*>(ptr_.get()); }
virtual size_t size() const { return size_; }
virtual paddle::platform::Place place() const { return place_; }
virtual std::type_index type() const { return std::type_index(typeid(T)); }
std::unique_ptr<T, Deleter> ptr_;
paddle::platform::Place place_; // record the place of ptr_.
size_t size_; // size of the memory block.
std::unique_ptr<T, Deleter<PlaceType>> ptr_;
platform::Place place_; // record the place of ptr_.
size_t size_; // size of the memory block.
};
template <typename T>
inline void CheckDims() const {
PADDLE_ENFORCE(holder_ != nullptr,
"Tenosr holds no memory. Call Tensor::mutable_data first.");
PADDLE_ENFORCE(holder_->size() >= numel_ * sizeof(T) + offset_,
PADDLE_ENFORCE(holder_->size() >= product(dims_) * sizeof(T) + offset_,
"Tensor's dims_ is out of bound. Call Tensor::mutable_data "
"first to re-allocate memory.");
}
std::shared_ptr<Placeholder> holder_; // holds the memory block if allocated.
DDim dims_;
size_t numel_; // cache of `product(dims_)`
size_t offset_; // marks the begin of tensor data area.
};
......
paddle/framework/tensor_test.cc
浏览文件 @ ca89bfad
......@@ -33,7 +33,7 @@ TEST(Tensor, DataAssert) {
bool caught = false;
try {
src_tensor.data<double>();
} catch (paddle::framework::EnforceNotMet err) {
} catch (std::runtime_error& err) {
caught = true;
std::string msg =
"Tenosr holds no memory. Call Tensor::mutable_data first.";
......@@ -47,7 +47,7 @@ TEST(Tensor, DataAssert) {
/* following tests are not available at present
because Memory::Alloc() and Memory::Free() have not been ready.
*/
TEST(Tensor, MutableData) {
using namespace paddle::framework;
using namespace paddle::platform;
......@@ -72,7 +72,7 @@ TEST(Tensor, MutableData) {
p2 = src_tensor.mutable_data<float>(make_ddim({2, 2}), CPUPlace());
EXPECT_EQ(p1, p2);
}
#ifdef __CUDACC__
{
Tensor src_tensor;
float* p1 = nullptr;
......@@ -94,6 +94,7 @@ TEST(Tensor, MutableData) {
p2 = src_tensor.mutable_data<float>(make_ddim({2, 2}), GPUPlace());
EXPECT_EQ(p1, p2);
}
#endif
}
TEST(Tensor, ShareDataFrom) {
......@@ -106,11 +107,13 @@ TEST(Tensor, ShareDataFrom) {
bool caught = false;
try {
dst_tensor.ShareDataFrom<float>(src_tensor);
} catch (EnforceNotMet err) {
} catch (std::runtime_error& err) {
caught = true;
std::string msg = "Tenosr holds no memory. Call Tensor::mutable_data
first."; const char* what = err.what(); for (size_t i = 0; i < msg.length();
++i) { ASSERT_EQ(what[i], msg[i]);
std::string msg =
"Tenosr holds no memory. Call Tensor::mutable_data first.";
const char* what = err.what();
for (size_t i = 0; i < msg.length(); ++i) {
ASSERT_EQ(what[i], msg[i]);
}
}
ASSERT_TRUE(caught);
......@@ -120,6 +123,7 @@ first."; const char* what = err.what(); for (size_t i = 0; i < msg.length();
ASSERT_EQ(src_tensor.data<int>(), dst_tensor.data<int>());
}
#ifdef __CUDACC__
{
Tensor src_tensor;
Tensor dst_tensor;
......@@ -127,6 +131,7 @@ first."; const char* what = err.what(); for (size_t i = 0; i < msg.length();
dst_tensor.ShareDataFrom<int>(src_tensor);
ASSERT_EQ(src_tensor.data<int>(), dst_tensor.data<int>());
}
#endif
}
TEST(Tensor, Slice) {
......@@ -155,6 +160,7 @@ TEST(Tensor, Slice) {
EXPECT_EQ(src_data_address + 3 * 4 * 1 * sizeof(int), slice_data_address);
}
#ifdef __CUDACC__
{
Tensor src_tensor;
src_tensor.mutable_data<double>(make_ddim({6, 9}), GPUPlace());
......@@ -176,6 +182,7 @@ TEST(Tensor, Slice) {
EXPECT_EQ(slice_data_address, slice_mutable_data_address);
EXPECT_EQ(src_data_address + 9 * 2 * sizeof(double), slice_data_address);
}
#endif
}
TEST(Tensor, CopyFrom) {
......@@ -203,4 +210,3 @@ TEST(Tensor, CopyFrom) {
EXPECT_EQ(dst_ptr[i], slice_ptr[i]);
}
}
*/
\ No newline at end of file
paddle/function/CMakeLists.txt
浏览文件 @ ca89bfad
......@@ -11,7 +11,6 @@ if(WITH_GPU)
endif()
if(USE_NNPACK)
include(nnpack/nnpack.cmake)
list(APPEND cpp_files nnpack/NNPACKConvOp.cpp)
if(WITH_TESTING)
add_unittest(NNPACKConvOpTest nnpack/NNPACKConvOpTest.cpp)
......@@ -37,6 +36,7 @@ if(WITH_GPU)
add_simple_unittest(MulOpTest)
add_simple_unittest(CosSimOpTest)
add_simple_unittest(RowConvOpTest)
add_simple_unittest(CropOpTest)
endif()
add_simple_unittest(ConvOpTest)
......
paddle/function/CropOp.cpp 0 → 100644
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "CropOp.h"
#include "paddle/function/TensorShape.h"
#include "paddle/math/Vector.h"
namespace paddle {
template <>
void Crop<DEVICE_TYPE_CPU>(real* outputs,
const real* inputs,
const TensorShape inShape,
const TensorShape outShape,
const FuncConfig& conf) {
std::vector<uint32_t> crop_corner =
conf.get<std::vector<uint32_t>>("crop_corner");
int cCrop = crop_corner[1];
int hCrop = crop_corner[2];
int wCrop = crop_corner[3];
int num = inShape[0];
int inC = inShape[1];
int inH = inShape[2];
int inW = inShape[3];
int outC = outShape[1];
int outH = outShape[2];
int outW = outShape[3];
for (int n = 0; n < num; n++) {
for (int c = 0; c < outC; c++) {
for (int h = 0; h < outH; h++) {
int outoff = ((n * outC + c) * outH + h) * outW;
int inoff = ((n * inC + c + cCrop) * inH + h + hCrop) * inW + wCrop;
memcpy(outputs + outoff, inputs + inoff, outW * sizeof(real));
}
}
}
}
template <>
void CropGrad<DEVICE_TYPE_CPU>(const real* inGrad,
real* outGrad,
const TensorShape inShape,
const TensorShape outShape,
const FuncConfig& conf) {
std::vector<uint32_t> crop_corner =
conf.get<std::vector<uint32_t>>("crop_corner");
int cCrop = crop_corner[1];
int hCrop = crop_corner[2];
int wCrop = crop_corner[3];
int num = outShape[0];
int outC = outShape[1];
int outH = outShape[2];
int outW = outShape[3];
int inC = inShape[1];
int inH = inShape[2];
int inW = inShape[3];
for (int n = 0; n < num; n++) {
for (int c = 0; c < inC; c++) {
for (int h = 0; h < inH; h++) {
int outoff = ((n * outC + c + cCrop) * outH + h + hCrop) * outW + wCrop;
int inoff = ((n * inC + c) * inH + h) * inW;
CpuVector inG = CpuVector(inW, const_cast<real*>(inGrad + inoff));
CpuVector outG = CpuVector(inW, outGrad + outoff);
outG += inG;
}
}
}
}
/**
* \brief Crop input according to the specify corner and shape.
* The input and output is a 4D tensor. In CropFunc, we only
* crop the 2nd to 4th dimension.
*
* Argument in this Function:
* \param pad_ A struct object contains the cropping corner and shape.
* \param inputs A 4D tensor, only one input.
* \param outputs A 4D tensor, the output value after cropping.
*
* For example,
* Input(2,2,2,3) = [
* [ [[1,2,3], [3,4,5]],
* [[2,3,5], [1,6,7]] ],
* [ [[4,3,1], [1,8,7]],
* [[3,8,9], [2,3,5]] ]
* ] # the input shape is (2,2,2,3)
*
* pad_: if corner = (0,1,1) and crop_shape = (2,1,2)
* Output(2,2,1,2) = [
* [ [[4,5]],
* [[6,7]] ],
* [ [[8,7]],
* [[3,5]] ]
* ] # the input shape is (2,2,2,3)
*/
template <DeviceType Device>
class CropFunc : public FunctionBase {
public:
void init(const FuncConfig& config) override { conf_ = config; }
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ(1UL, inputs.size());
CHECK_EQ(1UL, outputs.size());
CHECK_EQ(outputs[0].getArgType(), ASSIGN_TO);
TensorShape inShape = inputs[0].shape();
TensorShape outShape = outputs[0].shape();
Crop<Device>(outputs[0].data<real>(),
inputs[0].data<real>(),
inShape,
outShape,
conf_);
}
private:
FuncConfig conf_;
};
/**
* \brief The backward propagation of cropping Function.
*
* Argument in this Function:
* \param crop_ The same meaning as it in CropFunc.
* \param inputs The gradient with respect to the output value of CropFunc.
* \param outputs The gradient with respect to the input value of CropFunc.
*/
template <DeviceType Device>
class CropGradFunc : public FunctionBase {
public:
void init(const FuncConfig& config) override { conf_ = config; }
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ(1UL, inputs.size());
CHECK_EQ(1UL, outputs.size());
CHECK_EQ(outputs[0].getArgType(), ADD_TO);
TensorShape outShape = outputs[0].shape();
TensorShape inShape = inputs[0].shape();
CropGrad<Device>(inputs[0].data<real>(),
outputs[0].data<real>(),
inShape,
outShape,
conf_);
}
private:
FuncConfig conf_;
};
REGISTER_TYPED_FUNC(Crop, CPU, CropFunc);
REGISTER_TYPED_FUNC(CropGrad, CPU, CropGradFunc);
#ifndef PADDLE_ONLY_CPU
REGISTER_TYPED_FUNC(Crop, GPU, CropFunc);
REGISTER_TYPED_FUNC(CropGrad, GPU, CropGradFunc);
#endif
} // namespace paddle
paddle/function/CropOp.h 0 → 100644
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "Function.h"
namespace paddle {
/**
* \brief This funtion crops inputs according to the specify start point and
*shape.
*
* \param[out] outputs save results.
* \param[in] inputs input data.
* \param[in] inShape the shape of input tensor.
* \param[in] conf the cropping config
*/
template <DeviceType Device>
void Crop(real* outputs,
const real* inputs,
const TensorShape inShape,
const TensorShape outShape,
const FuncConfig& conf);
/**
* \brief Cropping operation backward.
*
* \param[out] inGrad gradients of previous layer
* \param[in] outGrad output gradient
* \param[in] inShape the shape of input tensor.
* \param[in] conf the cropping config
*/
template <DeviceType Device>
void CropGrad(const real* inGrad,
real* outGrad,
const TensorShape inShape,
const TensorShape outShape,
const FuncConfig& conf);
} // namespace paddle
paddle/function/CropOpGpu.cu 0 → 100644
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "hl_base.h"
#include "CropOp.h"
namespace paddle {
__global__ void KeCrop(real* outputs, const real* inputs,
int inC, int inH, int inW,
int cropC, int cropH, int cropW,
int outC, int outH, int outW, int nthreads) {
const int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < nthreads) {
const int w = idx % outW;
const int h = (idx / outW) % outH;
const int c = (idx / outW / outH) % outC;
const int n = idx / outW / outH / outC;
const int off = ((n * inC + c + cropC) * inH + h + cropH) * inW + cropW + w;
outputs[idx] = inputs[off];
}
}
template <>
void Crop<DEVICE_TYPE_GPU>(real* outputs,
const real* inputs,
const TensorShape inShape,
const TensorShape outShape,
const FuncConfig& conf) {
std::vector<uint32_t> crop_corner = conf.get<std::vector<uint32_t>>("crop_corner");
int cropC = crop_corner[1];
int cropH = crop_corner[2];
int cropW = crop_corner[3];
int num = inShape[0];
int inC = inShape[1];
int inH = inShape[2];
int inW = inShape[3];
int outC = outShape[1];
int outH = outShape[2];
int outW = outShape[3];
size_t nth = num * outC * outH * outW;
int blockSize = 1024;
int gridSize = (nth + blockSize - 1) / blockSize;
KeCrop<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>
(outputs, inputs, inC, inH, inW, cropC, cropH, cropW,
outC, outH, outW, nth);
CHECK_SYNC("Crop");
}
__global__ void KeCropDiff(const real* inGrad, real* outGrad,
int inC, int inH, int inW,
int cropC, int cropH, int cropW,
int outC, int outH, int outW, int nthreads) {
const int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < nthreads) {
const int w = idx % inW;
const int h = (idx / inW) % inH;
const int c = (idx / inW / inH) % inC;
const int n = idx / inW / inH / inC;
const int off = ((n * outC + c + cropC) * outH + h + cropH) * outW + cropW + w;
outGrad[off] += inGrad[idx];
}
}
template <>
void CropGrad<DEVICE_TYPE_GPU>(const real* inGrad,
real* outGrad,
const TensorShape inShape,
const TensorShape outShape,
const FuncConfig& conf) {
std::vector<uint32_t> crop_corner = conf.get<std::vector<uint32_t>>("crop_corner");
int cropC = crop_corner[1];
int cropH = crop_corner[2];
int cropW = crop_corner[3];
int num = outShape[0];
int outC = outShape[1];
int outH = outShape[2];
int outW = outShape[3];
int inC = inShape[1];
int inH = inShape[2];
int inW = inShape[3];
size_t nth = num * inC * inH * inW;
int blockSize = 1024;
int gridSize = (nth + blockSize - 1) / blockSize;
KeCropDiff <<<gridSize, blockSize, 0, STREAM_DEFAULT>>>
(inGrad, outGrad, inC, inH, inW, cropC, cropH, cropW,
outC, outH, outW, nth);
CHECK_SYNC("CropGrad");
}
} // namespace paddle
paddle/function/CropOpTest.cpp 0 → 100644
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include "FunctionTest.h"
namespace paddle {
TEST(Crop, real) {
for (size_t numSamples : {5, 32}) {
for (size_t channels : {5, 5, 32}) {
for (size_t imgSizeH : {5, 33, 100}) {
for (size_t imgSizeW : {5, 32, 96}) {
VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
<< " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW;
for (bool test_grad : {false, true}) {
CpuGpuFuncCompare compare(
test_grad ? "CropGrad" : "Crop",
FuncConfig()
.set<std::vector<uint32_t>>("crop_corner", {0, 1, 1, 1})
.set<std::vector<uint32_t>>("crop_shape", {0, 2, 3, 3}));
TensorShape inDims{numSamples, channels, imgSizeH, imgSizeW};
TensorShape outDims{numSamples, 2, 3, 3};
compare.addInputs(
BufferArg(VALUE_TYPE_FLOAT, test_grad ? outDims : inDims));
compare.addOutputs(BufferArg(VALUE_TYPE_FLOAT,
test_grad ? inDims : outDims,
test_grad ? ADD_TO : ASSIGN_TO),
test_grad ? ADD_TO : ASSIGN_TO);
compare.run();
}
}
}
}
}
}
} // namespace paddle
paddle/function/GemmConvOp.cpp
浏览文件 @ ca89bfad
......@@ -117,8 +117,7 @@ public:
ConvFunctionBase::init(config);
}
virtual void check(const BufferArgs& inputs,
const BufferArgs& outputs) override {
void check(const BufferArgs& inputs, const BufferArgs& outputs) override {
const TensorShape& input = inputs[0].shape();
const TensorShape& filter = inputs[1].shape();
const TensorShape& output = outputs[0].shape();
......@@ -217,8 +216,7 @@ public:
ConvFunctionBase::init(config);
}
virtual void check(const BufferArgs& inputs,
const BufferArgs& outputs) override {
void check(const BufferArgs& inputs, const BufferArgs& outputs) override {
const TensorShape& output = inputs[0].shape();
const TensorShape& filter = inputs[1].shape();
const TensorShape& input = outputs[0].shape();
......@@ -311,8 +309,7 @@ public:
ConvFunctionBase::init(config);
}
virtual void check(const BufferArgs& inputs,
const BufferArgs& outputs) override {
void check(const BufferArgs& inputs, const BufferArgs& outputs) override {
const TensorShape& output = inputs[0].shape();
const TensorShape& input = inputs[1].shape();
const TensorShape& filter = outputs[0].shape();
......
paddle/function/NaiveConvOp.cpp
浏览文件 @ ca89bfad
......@@ -90,8 +90,7 @@ public:
ConvFunctionBase::init(config);
}
virtual void check(const BufferArgs& inputs,
const BufferArgs& outputs) override {
void check(const BufferArgs& inputs, const BufferArgs& outputs) override {
const TensorShape& input = inputs[0].shape();
const TensorShape& filter = inputs[1].shape();
const TensorShape& output = outputs[0].shape();
......
paddle/function/RowConvOpGpu.cu
浏览文件 @ ca89bfad
......@@ -32,7 +32,7 @@ __global__ void KeRowConv(real* y, const real* x, const real* w,
for (int i = tidy; i < context; i += blky) {
sw[i][tidx] = gidx + tidx < width ? w[i*width + gidx + tidx] : 0.0;
}
__syncthreads();
for (int i = 0; i < numSeq; ++i) {
......@@ -144,12 +144,15 @@ __global__ void KeRowConvBwWeight(real* dw, const real* x, const real* dy,
int yoff = start + j;
// transpose
sh_x[tidx][tidy] = (xoff < width && yoff < end) ? x[yoff * width + xoff] : 0.0;
sh_dy[tidx][tidy + context - 1] = (xoff < width && yoff < end) ? dy[yoff * width + xoff] : 0.0;
sh_x[tidx][tidy] = (xoff < width && yoff < end) ?
x[yoff * width + xoff] : 0.0;
sh_dy[tidx][tidy + context - 1] = (xoff < width && yoff < end) ?
dy[yoff * width + xoff] : 0.0;
__syncthreads();
if (tidy < (context - 1)) {
yoff = yoff - context + 1;
sh_dy[tidx][tidy] = (xoff < width && yoff >= start) ? dy[yoff * width + xoff] : 0.0;
sh_dy[tidx][tidy] = (xoff < width && yoff >= start) ?
dy[yoff * width + xoff] : 0.0;
}
__syncthreads();
......@@ -199,11 +202,13 @@ __global__ void KeRowConvBwWeight2(real* dw, const real* x, const real* dy,
int yoff = start + j;
// transpose
sh_x[tidx][tidy] = (xoff < width && yoff < end) ? x[yoff * width + xoff] : 0.0;
sh_x[tidx][tidy] = (xoff < width && yoff < end) ?
x[yoff * width + xoff] : 0.0;
__syncthreads();
for (int t = 0; t < context; t++) {
sh_dy[tidx][tidy] = (xoff < width && (yoff - t) >= start && yoff - t < end) ? dy[(yoff - t) * width + xoff] : 0.0;
sh_dy[tidx][tidy] = (xoff < width && (yoff - t) >= start &&
yoff - t < end) ? dy[(yoff - t) * width + xoff] : 0.0;
__syncthreads();
real val = sh_x[tidy][tidx] * sh_dy[tidy][tidx];
......@@ -239,7 +244,7 @@ __global__ void KeRowConvBwData(real* dx, const real* w, const real* dy,
for (int i = tidy; i < context; i += blky) {
sw[i][tidx] = gidx + tidx < width ? w[i*width + gidx + tidx] : 0.0;
}
__syncthreads();
for (int i = 0; i < numSeq; ++i) {
......@@ -312,7 +317,7 @@ void RowConvGrad<DEVICE_TYPE_GPU>(const GpuMatrix& outG,
dim3 dimBlock(32, 32);
dim3 dimGrid(DIVUP(width, dimBlock.x), 1);
real* dw = filterG.getData();
if (contextLength <= 32) {
if (contextLength <= 32) {
KeRowConvBwWeight<32, 32, 32>
<<<dimGrid, dimBlock, 0, STREAM_DEFAULT>>>
(dw, x, dy, starts, height, width, numSeq, contextLength);
......
paddle/function/nnpack/NNPACKConvOp.cpp
浏览文件 @ ca89bfad
......@@ -16,7 +16,7 @@ limitations under the License. */
#include "paddle/function/ConvOp.h"
DEFINE_bool(nnpack_allocate_outside,
false,
true,
"Allocate and free workspace memory outside the NNPACK interface.");
DEFINE_int32(nnpack_num_threads,
0,
......@@ -58,18 +58,10 @@ public:
workspaceBuffer_ = nullptr;
workspaceSize_ = 0;
threadpool_ = nullptr;
if (FLAGS_nnpack_num_threads) {
threadpool_ = pthreadpool_create(FLAGS_nnpack_num_threads);
VLOG(3) << "Number of threads "
<< pthreadpool_get_threads_count(threadpool_);
}
create_nnpack_threadpool();
}
~NNPACKConvFunction() {
if (threadpool_) {
pthreadpool_destroy(threadpool_);
}
if (workspaceBuffer_) {
free(workspaceBuffer_);
}
......@@ -225,14 +217,25 @@ public:
}
}
static void create_nnpack_threadpool() {
if (FLAGS_nnpack_num_threads && threadpool_ == nullptr) {
threadpool_ = pthreadpool_create(FLAGS_nnpack_num_threads);
VLOG(3) << "Number of threads "
<< pthreadpool_get_threads_count(threadpool_);
}
}
private:
nnp_convolution_algorithm algorithm_;
nnp_convolution_transform_strategy transform_strategy_;
void* workspaceBuffer_;
size_t workspaceSize_;
pthreadpool_t threadpool_;
static pthreadpool_t threadpool_;
};
template <DeviceType Device>
pthreadpool_t NNPACKConvFunction<Device>::threadpool_ = nullptr;
REGISTER_TYPED_FUNC(NNPACKConv, CPU, NNPACKConvFunction);
} // namespace paddle
paddle/gserver/gradientmachines/NeuralNetwork.cpp
浏览文件 @ ca89bfad
......@@ -403,7 +403,7 @@ public:
: layerName_(layerName) {
addEvaluator(std::move(evaluator));
}
virtual void eval(const NeuralNetwork& nn) override {
void eval(const NeuralNetwork& nn) override {
const LayerPtr& layer = nn.getLayer(layerName_);
CHECK(layer) << "Nonexisted layer: " << layerName_ << " in submodel "
<< nn.getName();
......
paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp
浏览文件 @ ca89bfad
......@@ -636,7 +636,7 @@ void lenToStarts(std::vector<int>& starts) {
}
starts.back() = pos;
}
}
} // namespace
void RecurrentGradientMachine::calcSequenceStartPositions() {
std::vector<int> starts(commonSeqInfo_.size() + 1);
......
paddle/gserver/layers/AgentLayer.cpp
浏览文件 @ ca89bfad
......@@ -124,7 +124,7 @@ void copyElements(const IVector& srcVec,
dest[index[i]] = src[i];
}
}
}
} // namespace
void GatherAgentLayer::forwardIds(PassType passType) {
IVectorPtr realId = realLayers_[0]->getOutputLabel();
......
paddle/gserver/layers/CropLayer.cpp 0 → 100644
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "CropLayer.h"
#include "paddle/utils/Stat.h"
namespace paddle {
REGISTER_LAYER(crop, CropLayer);
bool CropLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
/* Initialize the basic parent class */
Layer::init(layerMap, parameterMap);
CHECK_LE(static_cast<int>(inputLayers_.size()), 2);
CHECK_GE(static_cast<int>(inputLayers_.size()), 1);
crop_axis_ = config_.axis();
for (int i = 0; i < config_.offset_size(); i++) {
crop_offsets_.push_back(config_.offset(i));
}
// 1. get input_0 shape
auto& input0_img_conf = config_.inputs(0).image_conf();
inDims_ = TensorShape({0,
input0_img_conf.channels(),
input0_img_conf.has_img_size_y()
? input0_img_conf.img_size_y()
: input0_img_conf.img_size(),
input0_img_conf.img_size()});
// 2. get target dims from config
if (config_.inputs_size() == 1) {
targetDims_ = TensorShape({config_.shape(0),
config_.shape(1),
config_.shape(2),
config_.shape(3)});
} else {
// 2. get input_1 shape
auto& input1_img_conf = config_.inputs(1).image_conf();
targetDims_ = TensorShape({0,
input1_img_conf.channels(),
input1_img_conf.has_img_size_y()
? input1_img_conf.img_size_y()
: input1_img_conf.img_size(),
input1_img_conf.img_size()});
}
// 3. get final crop corner
int dimSize = 4;
crop_corner_ = {0, 0, 0, 0};
for (int i = 0; i < dimSize; i++) {
if (i >= crop_axis_) {
if (crop_offsets_.size() > 1) {
crop_corner_[i] = crop_offsets_[i - crop_axis_];
} else {
crop_corner_[i] = crop_offsets_[0];
}
}
}
outDims_ = TensorShape(4);
createFunction(
forward_, "Crop", FuncConfig().set("crop_corner", crop_corner_));
createFunction(
backward_, "CropGrad", FuncConfig().set("crop_corner", crop_corner_));
return true;
}
void CropLayer::setOutDims() {
MatrixPtr input = inputLayers_[1]->getOutputValue();
size_t batchSize = input->getHeight();
// get target dims from input_1
if (config_.inputs_size() == 2) {
targetDims_.setDim(0, batchSize);
int ch = config_.inputs(0).image_conf().channels();
if (ch != 0) targetDims_.setDim(1, ch);
int h = inputLayers_[1]->getOutput().getFrameHeight();
if (h != 0) targetDims_.setDim(2, h);
int w = inputLayers_[1]->getOutput().getFrameWidth();
if (w != 0) targetDims_.setDim(3, w);
}
// get final crop shape from target dims and crop axis
std::vector<uint32_t> crop_shape;
int dimSize = 4;
for (int i = 0; i < dimSize; i++) {
if (i >= crop_axis_) {
crop_shape.push_back(targetDims_[i]);
} else {
crop_shape.push_back(inDims_[i]);
}
}
outDims_.reshape(
{crop_shape[0], crop_shape[1], crop_shape[2], crop_shape[3]});
output_.setFrameHeight(crop_shape[2]);
output_.setFrameWidth(crop_shape[3]);
}
void CropLayer::setInDims() {
MatrixPtr input = inputLayers_[0]->getOutputValue();
size_t batchSize = input->getHeight();
inDims_.setDim(0, batchSize);
int h = inputLayers_[0]->getOutput().getFrameHeight();
if (h != 0) inDims_.setDim(2, h);
int w = inputLayers_[0]->getOutput().getFrameWidth();
if (w != 0) inDims_.setDim(3, w);
}
void CropLayer::forward(PassType passType) {
Layer::forward(passType);
setInDims();
setOutDims();
int size = outDims_[1] * outDims_[2] * outDims_[3];
resetOutput(outDims_[0], size);
MatrixPtr outV = getOutputValue();
REGISTER_TIMER_INFO("CropForward", getName().c_str());
BufferArgs inputs;
BufferArgs outputs;
inputs.addArg(*getInputValue(0), inDims_);
outputs.addArg(*getOutputValue(), outDims_, ASSIGN_TO);
forward_[0]->calc(inputs, outputs);
}
void CropLayer::backward(const UpdateCallback& callback) {
(void)callback;
REGISTER_TIMER_INFO("CropBackward", getName().c_str());
BufferArgs inputs;
BufferArgs outputs;
inputs.addArg(*getOutputGrad(), outDims_);
outputs.addArg(*getInputGrad(0), inDims_, ADD_TO);
backward_[0]->calc(inputs, outputs);
}
} // namespace paddle
paddle/gserver/layers/CropLayer.h 0 → 100644
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "Layer.h"
namespace paddle {
/**
* \brief This layer crop input according to the specify conf.
* input_0: input to be cropped
* input_1: optional reference input
* axis: start dimension to be croped
* offset: offset of cropping in each dimension
* shape: if reference input layer was not setted,
* crop input as this shape conf
*/
class CropLayer : public Layer {
public:
explicit CropLayer(const LayerConfig& config) : Layer(config) {}
~CropLayer() {}
bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) override;
void forward(PassType passType) override;
void backward(const UpdateCallback& callback = nullptr) override;
protected:
void setOutDims();
void setInDims();
int32_t crop_axis_;
std::vector<uint32_t> crop_offsets_;
std::vector<uint32_t> crop_corner_;
TensorShape inDims_;
TensorShape targetDims_;
TensorShape outDims_;
};
} // namespace paddle
paddle/gserver/layers/Layer.cpp
浏览文件 @ ca89bfad
......@@ -359,12 +359,11 @@ void Layer::backwardActivation() {
/* Do error clipping */
if (config_.error_clipping_threshold() > 0.0f) {
if (FLAGS_log_error_clipping) {
CpuVector outGradVec(0, nullptr);
outGradVec.subVecFrom(
output_.grad->getData(), 0, output_.grad->getElementCnt());
real maxAbsGrad = outGradVec.getAbsMax();
VectorPtr outGradVec = Vector::create(
output_.grad->getData(), output_.grad->getElementCnt(), useGpu_);
real maxAbsGrad = outGradVec->getAbsMax();
if (maxAbsGrad > config_.error_clipping_threshold()) {
real avgAbsGrad = outGradVec.getAbsSum() / outGradVec.getSize();
real avgAbsGrad = outGradVec->getAbsSum() / outGradVec->getSize();
LOG(INFO) << " layer=" << config_.name() << " need clipping,"
<< " max error=" << maxAbsGrad << " avg error=" << avgAbsGrad;
}
......
paddle/gserver/tests/CMakeLists.txt
浏览文件 @ ca89bfad
......@@ -56,7 +56,7 @@ add_test(NAME test_DetectionOutput
add_unittest_without_exec(test_ConvUnify
test_ConvUnify.cpp
LayerGradUtil.cpp)
add_test(NAME test_ConvUnify
COMMAND test_ConvUnify)
################# test_BatchNorm #######################
......
paddle/gserver/tests/test_LayerGrad.cpp
浏览文件 @ ca89bfad
......@@ -1802,6 +1802,34 @@ TEST(Layer, RowConvLayer) {
}
}
TEST(Layer, CropLayer) {
TestConfig config;
// config input_0
config.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 0});
LayerInputConfig* input = config.layerConfig.add_inputs();
ImageConfig* img = input->mutable_image_conf();
img->set_channels(4);
img->set_img_size(16);
config.layerConfig.set_axis(2);
config.layerConfig.add_offset(0);
config.layerConfig.add_offset(0);
// config input_1
config.inputDefs.push_back({INPUT_DATA, "layer_1", 128, 0});
input = config.layerConfig.add_inputs();
img = input->mutable_image_conf();
img->set_channels(2);
img->set_img_size(8);
// config crop layer
config.layerConfig.set_type("crop");
config.layerConfig.set_name("cropLayer");
for (auto useGpu : {false, true}) {
testLayerGrad(config, "crop", 100, false, useGpu, false);
}
}
int main(int argc, char** argv) {
testing::InitGoogleTest(&argc, argv);
initMain(argc, argv);
......
paddle/memory/detail/memory_block.cc
浏览文件 @ ca89bfad
......@@ -152,6 +152,6 @@ MemoryBlock* MemoryBlock::metadata() const {
reinterpret_cast<const Metadata*>(this) - 1));
}
} // detail
} // memory
} // paddle
} // namespace detail
} // namespace memory
} // namespace paddle
paddle/memory/detail/system_allocator.cc
浏览文件 @ ca89bfad
......@@ -14,7 +14,7 @@ limitations under the License. */
#include "paddle/memory/detail/system_allocator.h"
#include "paddle/platform/assert.h"
#include "paddle/platform/error.h"
#include "paddle/platform/enforce.h"
#include "paddle/platform/gpu_info.h"
#include <stdlib.h> // for malloc and free
......@@ -128,8 +128,7 @@ void GPUAllocator::Free(void* p, size_t size, size_t index) {
// process is terminating, in which case we don't care if
// cudaFree succeeds.
if (err != cudaErrorCudartUnloading) {
platform::throw_on_error(err,
"cudaFree{Host} failed in GPUAllocator::Free.");
PADDLE_ENFORCE(err, "cudaFree{Host} failed in GPUAllocator::Free.");
}
}
......
paddle/operators/CMakeLists.txt
浏览文件 @ ca89bfad
if(WITH_GPU)
nv_library(add_op SRCS add_op.cc add_op.cu DEPS operator op_registry glog ddim)
else()
cc_library(add_op SRCS add_op.cc DEPS operator op_registry glog ddim)
endif()
function(op_library TARGET)
# op_library is a function to create op library. The interface is same as
# cc_library. But it handle split GPU/CPU code and link some common library
# for ops.
set(cc_srcs)
set(cu_srcs)
set(op_common_deps operator op_registry)
set(options "")
set(oneValueArgs "")
set(multiValueArgs SRCS DEPS)
cmake_parse_arguments(op_library "${options}" "${oneValueArgs}"
"${multiValueArgs}" ${ARGN})
foreach(src ${op_library_SRCS})
if (${src} MATCHES ".*\\.cu$")
list(APPEND cu_srcs ${src})
elseif(${src} MATCHES ".*\\.cc$")
list(APPEND cc_srcs ${src})
else()
message(FATAL_ERROR "${TARGET} Source file ${src} should only be .cc or .cu")
endif()
endforeach()
list(LENGTH cc_srcs cc_srcs_len)
if (${cc_srcs_len} EQUAL 0)
message(FATAL_ERROR "The op library ${TARGET} should contains at least one .cc file")
endif()
list(LENGTH cu_srcs cu_srcs_len)
list(LENGTH op_library_DEPS dep_len)
if (${cu_srcs_len} EQUAL 0 AND ${dep_len} EQUAL 0)
message(WARNING "The op library ${TARGET} not support GPU!")
endif()
if (WITH_GPU)
nv_library(${TARGET} SRCS ${cc_srcs} ${cu_srcs} DEPS ${op_library_DEPS}
${op_common_deps})
else()
cc_library(${TARGET} SRCS ${cc_srcs} DEPS ${op_library_DEPS}
${op_common_deps})
endif()
endfunction()
op_library(add_op SRCS add_op.cc add_op.cu)
cc_test(add_op_test SRCS add_op_test.cc DEPS add_op)
op_library(mul_op SRCS mul_op.cc mul_op.cu)
op_library(rowwise_add_op SRCS rowwise_add_op.cu rowwise_add_op.cc)
op_library(sigmoid_op SRCS sigmoid_op.cu sigmoid_op.cc)
op_library(softmax_op SRCS softmax_op.cc softmax_op.cu)
op_library(fc_op SRCS fc_op.cc DEPS mul_op rowwise_add_op sigmoid_op
softmax_op net)
op_library(sgd_op SRCS sgd_op.cc sgd_op.cu)
paddle/operators/add_op.cc
浏览文件 @ ca89bfad
#include <paddle/framework/op_registry.h>
#include <paddle/framework/tensor.h>
#include <paddle/operators/add_op.h>
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/add_op.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/tensor.h"
namespace paddle {
namespace operators {
......@@ -17,8 +31,7 @@ protected:
"Inputs/Outputs of AddOp must all be set");
PADDLE_ENFORCE(inputs[0]->dims() == inputs[1]->dims(),
"Two input of Add Op's dimension must be same.");
// Need set dims in Tensor
// outputs[0]->set_dims(inputs[0]->dims())
outputs[0]->set_dims(inputs[0]->dims());
}
};
......@@ -36,9 +49,10 @@ The equation is: Out = X + Y
)DOC");
}
};
} // namespace op
} // namespace operators
} // namespace paddle
REGISTER_OP(add_two, paddle::operators::AddOp, paddle::operators::AddOpMaker);
REGISTER_OP_CPU_KERNEL(
add_two, ::paddle::operators::AddKernel<::paddle::platform::CPUPlace>);
\ No newline at end of file
typedef paddle::operators::AddKernel<::paddle::platform::CPUPlace, float>
AddKernel_CPU_float;
REGISTER_OP_CPU_KERNEL(add_two, AddKernel_CPU_float);
paddle/operators/add_op.cu
浏览文件 @ ca89bfad
#include <paddle/operators/add_op.h>
#include <paddle/framework/op_registry.h>
#include "paddle/operators/add_op.h"
#include "paddle/framework/op_registry.h"
typedef paddle::operators::AddKernel<::paddle::platform::GPUPlace, float> AddKernel_GPU_float;
REGISTER_OP_GPU_KERNEL(add_two,
paddle::operators::AddKernel<paddle::platform::GPUPlace>);
\ No newline at end of file
AddKernel_GPU_float);
\ No newline at end of file
paddle/operators/add_op.h
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <glog/logging.h>
#include <paddle/framework/operator.h>
#include "glog/logging.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/operator.h"
namespace paddle {
namespace operators {
template <typename Place>
template <typename Place, typename T>
class AddKernel : public framework::OpKernel {
public:
void Compute(const KernelContext &context) const override {
LOG(INFO) << "Add kernel in " << typeid(Place).name();
void Compute(const framework::KernelContext& context) const override {
auto input0 = context.Input(0)->Get<framework::Tensor>();
auto input1 = context.Input(1)->Get<framework::Tensor>();
auto* output = context.Output(0)->GetMutable<framework::Tensor>();
output->mutable_data<T>(context.GetPlace());
framework::EigenVector<T>::Flatten(*output).device(
*(context.GetEigenDevice<Place>())) =
framework::EigenVector<T>::Flatten(input0) +
framework::EigenVector<T>::Flatten(input1);
}
};
} // namespace op
} // namespace operators
} // namespace paddle
paddle/operators/add_op_test.cc
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#define private public
#include <paddle/framework/op_registry.h>
......
paddle/operators/fc_op.cc 0 → 100644
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/framework/net.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
namespace paddle {
namespace operators {
class FullyConnectedOp : public framework::PlainNet {
public:
void Init() override {
AddOp(framework::OpRegistry::CreateOp("mul",
{
Input("X"), Input("W"),
},
{Output("before_act")},
{}));
auto b = Input("b");
if (b != framework::OperatorBase::EMPTY_VAR_NAME()) {
AddOp(framework::OpRegistry::CreateOp("rowwise_add",
{Output("before_act"), Input("b")},
{Output("before_act")},
{}));
}
auto activation = GetAttr<std::string>("activation");
AddOp(framework::OpRegistry::CreateOp(
activation, {Output("before_act")}, {Output("Y")}, {}));
CompleteAddOp(false);
}
};
class FullyConnectedOpMaker : public framework::OpProtoAndCheckerMaker {
public:
FullyConnectedOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "the input of fc operator");
AddInput("W", "the weight of fc operator");
AddInput("b", "the bias of fc operator");
AddOutput("Y", "the output of fc operator");
AddOutput(
"before_act", "the before activation output of fc operator", true);
AddAttr<std::string>("activation", "The activation key for fc layer")
.SetDefault("sigmoid")
.InEnum({"sigmoid", "softmax"});
//! TODO(yuyang18): Complete comment;
AddComment("FullyConnected Operator");
}
};
} // namespace operators
} // namespace paddle
USE_OP(mul);
USE_OP(rowwise_add);
USE_OP(sigmoid);
USE_OP(softmax);
REGISTER_OP(fc,
paddle::operators::FullyConnectedOp,
paddle::operators::FullyConnectedOpMaker);
paddle/operators/mul_op.cc 0 → 100644
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <paddle/framework/op_registry.h>
#include <paddle/framework/tensor.h>
#include <paddle/operators/mul_op.h>
namespace paddle {
namespace operators {
class MulOp : public framework::OperatorWithKernel {
protected:
void InferShape(
const std::vector<const framework::Tensor *> &inputs,
const std::vector<framework::Tensor *> &outputs) const override {
PADDLE_ENFORCE(inputs.size() == 2, "The mul op must take two inputs");
auto dim0 = inputs[0]->dims();
auto dim1 = inputs[1]->dims();
PADDLE_ENFORCE(dim0.size() == 2 && dim1.size() == 2,
"The input of mul op must be matrix");
PADDLE_ENFORCE(
dim0[1] == dim1[0],
"First matrix's width must be equal with second matrix's height.");
PADDLE_ENFORCE(outputs.size() == 1, "The mul op must take one output");
outputs[0]->set_dims({dim0[0], dim1[1]});
}
};
class MulOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MulOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The first input of mul op");
AddInput("Y", "The second input of mul op");
AddOutput("Out", "The output of mul op");
AddComment(R"DOC(
Two Element Mul Operator.
The equation is: Out = X * Y
)DOC");
}
};
} // namespace operators
} // namespace paddle
REGISTER_OP(mul, paddle::operators::MulOp, paddle::operators::MulOpMaker);
REGISTER_OP_CPU_KERNEL(
mul, paddle::operators::MulKernel<paddle::platform::CPUPlace>);
paddle/operators/mul_op.cu 0 → 100644
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <paddle/operators/mul_op.h>
#include <paddle/framework/op_registry.h>
REGISTER_OP_GPU_KERNEL(mul,
paddle::operators::MulKernel<paddle::platform
::GPUPlace>);
\ No newline at end of file
paddle/operators/mul_op.h 0 → 100644
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <glog/logging.h>
#include <paddle/framework/operator.h>
namespace paddle {
namespace operators {
template <typename Place>
class MulKernel : public framework::OpKernel {
public:
void Compute(const framework::KernelContext &context) const override {
LOG(INFO) << "Mul kernel in " << typeid(Place).name();
}
};
} // namespace operators
} // namespace paddle
paddle/operators/rowwise_add_op.cc 0 → 100644
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <paddle/framework/op_registry.h>
#include <paddle/operators/rowwise_add_op.h>
namespace paddle {
namespace operators {
class RowWiseAddOp : public framework::OperatorWithKernel {
protected:
void InferShape(
const std::vector<const framework::Tensor *> &inputs,
const std::vector<framework::Tensor *> &outputs) const override {
PADDLE_ENFORCE(inputs.size() == 2UL, "Two inputs is needed by rowwise add");
auto dim0 = inputs[0]->dims();
auto dim1 = inputs[1]->dims();
PADDLE_ENFORCE(dim0.size() == 2, "Input 0 must be matrix");
PADDLE_ENFORCE(dim1.size() == 1, "The second input must be vector");
PADDLE_ENFORCE(dim0[1] == dim1[0], "The width of two input must be same");
PADDLE_ENFORCE(outputs.size() == 1, "The output size must be 1");
outputs[0]->set_dims(inputs[0]->dims());
}
};
class RowWiseAddOpMaker : public framework::OpProtoAndCheckerMaker {
public:
RowWiseAddOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The left input of row-wise add op, must be matrix");
AddInput("b", "The right input of row-wise add op, must be vector");
AddOutput("Out", "The output of row-wise add op");
AddComment(R"DOC(Row-wise Add operator
for i in xrange(X.shape[0]):
Out = X[i] + b
)DOC");
}
};
} // namespace operators
} // namespace paddle
REGISTER_OP(rowwise_add,
paddle::operators::RowWiseAddOp,
paddle::operators::RowWiseAddOpMaker);
REGISTER_OP_CPU_KERNEL(
rowwise_add,
paddle::operators::RowWiseAddKernel<paddle::platform::CPUPlace>);
paddle/operators/rowwise_add_op.cu 0 → 100644
浏览文件 @ ca89bfad
#include <paddle/framework/op_registry.h>
#include <paddle/operators/rowwise_add_op.h>
REGISTER_OP_GPU_KERNEL(
rowwise_add,
paddle::operators::RowWiseAddKernel<paddle::platform ::GPUPlace>);
paddle/operators/rowwise_add_op.h 0 → 100644
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <glog/logging.h>
#include <paddle/framework/operator.h>
namespace paddle {
namespace operators {
template <typename Place>
class RowWiseAddKernel : public framework::OpKernel {
public:
void Compute(const framework::KernelContext &context) const override {
LOG(INFO) << "RowWiseAdd kernel in " << typeid(Place).name();
}
};
} // namespace operators
} // namespace paddle
paddle/operators/sgd_op.cc 0 → 100644
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/sgd_op.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/tensor.h"
namespace paddle {
namespace operators {
class SGDOp : public framework::OperatorWithKernel {
protected:
void InferShape(
const std::vector<const framework::Tensor *> &inputs,
const std::vector<framework::Tensor *> &outputs) const override {
PADDLE_ENFORCE(inputs.size() == 2, "Input size of SGDOp must be two");
PADDLE_ENFORCE(outputs.size() == 1, "Output size of SGDOp must be one");
PADDLE_ENFORCE(inputs[0] != nullptr, "inputs[0] mast be set");
PADDLE_ENFORCE(inputs[1] != nullptr, "inputs[1] mast be set");
PADDLE_ENFORCE(outputs[0] != nullptr, "outputs[0] mast be set");
PADDLE_ENFORCE(inputs[0]->dims() == inputs[1]->dims(),
"Two input of SGD Op's dimension must be same.");
outputs[0]->set_dims(inputs[0]->dims());
}
};
class SGDOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SGDOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("param", "input parameter");
AddInput("grad", "input gradient");
AddOutput("param_out", "output parameter");
AddAttr<float>("learning_rate", "learning rate of sgd");
AddComment(R"DOC(
Simplest sgd algorithm.
param_out = param - learning_rate * grad;
)DOC");
}
};
} // namespace operators
} // namespace paddle
REGISTER_OP(sgd, paddle::operators::SGDOp, paddle::operators::SGDOpMaker);
typedef paddle::operators::SGDOpKernel<::paddle::platform::CPUPlace, float>
SGDOpKernel_CPU_float;
REGISTER_OP_CPU_KERNEL(sgd, SGDOpKernel_CPU_float);
paddle/operators/sgd_op.cu 0 → 100644
浏览文件 @ ca89bfad
#include "paddle/operators/sgd_op.h"
#include "paddle/framework/op_registry.h"
typedef paddle::operators::SGDOpKernel<::paddle::platform::GPUPlace, float> SGDOpKernel_GPU_float;
REGISTER_OP_GPU_KERNEL(sgd, SGDOpKernel_GPU_float);
\ No newline at end of file
paddle/operators/sgd_op.h 0 → 100644
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "glog/logging.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/operator.h"
namespace paddle {
namespace operators {
template <typename Place, typename T>
class SGDOpKernel : public framework::OpKernel {
public:
void Compute(const framework::KernelContext& ctx) const override {
auto param = ctx.Input("param")->Get<framework::Tensor>();
auto grad = ctx.Input("grad")->Get<framework::Tensor>();
auto* param_out = ctx.Output(0)->GetMutable<framework::Tensor>();
float lr = ctx.op_.GetAttr<float>("learning_rate");
param_out->mutable_data<T>(ctx.GetPlace());
framework::EigenVector<T>::Flatten(*param_out)
.device(*(ctx.GetEigenDevice<Place>())) =
framework::EigenVector<T>::Flatten(param) -
lr * framework::EigenVector<T>::Flatten(grad);
}
};
} // namespace operators
} // namespace paddle
paddle/operators/sgd_op_test.cc 0 → 100644
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include <paddle/framework/op_registry.h>
USE_OP(sgd);
TEST(SGDOp, GetOpProto) {
auto& protos = paddle::framework::OpRegistry::protos();
auto it = protos.find("sgd");
ASSERT_NE(it, protos.end());
}
paddle/operators/sigmoid_op.cc 0 → 100644
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <paddle/framework/op_registry.h>
#include <paddle/operators/sigmoid_op.h>
namespace paddle {
namespace operators {
class SigmoidOp : public framework::OperatorWithKernel {
protected:
void InferShape(
const std::vector<const framework::Tensor *> &inputs,
const std::vector<framework::Tensor *> &outputs) const override {
PADDLE_ENFORCE(inputs.size() == 1, "Sigmoid Op only have one input");
PADDLE_ENFORCE(outputs.size() == 1, "Sigmoid Op only have one output");
outputs[0]->set_dims(inputs[0]->dims());
}
};
class SigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SigmoidOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "sigmoid input");
AddInput("Y", "sigmoid output");
AddComment("Sigmoid function");
}
};
} // namespace operators
} // namespace paddle
REGISTER_OP(sigmoid,
paddle::operators::SigmoidOp,
paddle::operators::SigmoidOpMaker);
REGISTER_OP_CPU_KERNEL(
sigmoid, paddle::operators::SigmoidKernel<paddle::platform::CPUPlace>);
paddle/operators/sigmoid_op.cu 0 → 100644
浏览文件 @ ca89bfad
#include <paddle/operators/sigmoid_op.h>
#include <paddle/framework/op_registry.h>
REGISTER_OP_GPU_KERNEL(
sigmoid, paddle::operators::SigmoidKernel<paddle::platform::GPUPlace>);
paddle/operators/sigmoid_op.h 0 → 100644
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <glog/logging.h>
#include <paddle/framework/operator.h>
namespace paddle {
namespace operators {
template <typename Place>
class SigmoidKernel : public framework::OpKernel {
public:
void Compute(const framework::KernelContext &context) const override {
LOG(INFO) << "Sigmoid kernel in " << typeid(Place).name();
}
};
} // namespace operators
} // namespace paddle
paddle/operators/softmax_op.cc 0 → 100644
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <paddle/framework/op_registry.h>
#include <paddle/operators/softmax_op.h>
namespace paddle {
namespace operators {
class SoftmaxOp : public framework::OperatorWithKernel {
protected:
void InferShape(
const std::vector<const framework::Tensor *> &inputs,
const std::vector<framework::Tensor *> &outputs) const override {
PADDLE_ENFORCE(inputs.size() == 1, "Only one input is need for softmax");
PADDLE_ENFORCE(outputs.size() == 1, "Only one output is need for softmax");
outputs[0]->set_dims(inputs[0]->dims());
}
};
class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SoftmaxOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "input of softmax");
AddOutput("Y", "output of softmax");
AddComment("Softmax Op");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(softmax, ops::SoftmaxOp, ops::SoftmaxOpMaker);
REGISTER_OP_CPU_KERNEL(softmax, ops::SoftmaxKernel<paddle::platform::CPUPlace>);
paddle/operators/softmax_op.cu 0 → 100644
浏览文件 @ ca89bfad
#include <paddle/framework/op_registry.h>
#include <paddle/operators/softmax_op.h>
REGISTER_OP_GPU_KERNEL(
softmax, paddle::operators::SoftmaxKernel<paddle::platform::GPUPlace>);
paddle/operators/softmax_op.h 0 → 100644
浏览文件 @ ca89bfad
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <glog/logging.h>
#include <paddle/framework/operator.h>
namespace paddle {
namespace operators {
template <typename Place>
class SoftmaxKernel : public framework::OpKernel {
public:
void Compute(const framework::KernelContext &context) const override {
LOG(INFO) << "Softmax kernel in " << typeid(Place).name();
}
};
} // namespace operators
} // namespace paddle
paddle/optimizer/parameter_optimizer_test.cpp
浏览文件 @ ca89bfad
/*
Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#include "parameter_optimizer.h"
#include <cmath>
#include <map>
......@@ -5,21 +21,18 @@
#include "gtest/gtest.h"
#include "lr_policy.h"
using namespace paddle;
using namespace paddle::optimizer;
Tensor* FillTensor(size_t size) {
Tensor* param = new Tensor(size);
Tensor& p = *param;
paddle::optimizer::Tensor* FillTensor(size_t size) {
paddle::optimizer::Tensor* param = new paddle::optimizer::Tensor(size);
paddle::optimizer::Tensor& p = *param;
for (size_t i = 0; i < p.size(); ++i) {
p[i] = (float)rand() / (float)RAND_MAX;
}
return param;
}
Tensor* FixedTensor(size_t size) {
Tensor* param = new Tensor(size);
Tensor& p = *param;
paddle::optimizer::Tensor* FixedTensor(size_t size) {
paddle::optimizer::Tensor* param = new paddle::optimizer::Tensor(size);
paddle::optimizer::Tensor& p = *param;
for (size_t i = 0; i < p.size(); ++i) {
p[i] = i;
}
......@@ -28,7 +41,8 @@ Tensor* FixedTensor(size_t size) {
class OptimizerTest : public testing::Test {
public:
// init tensor shape
virtual ~OptimizerTest() {}
// init paddle::optimizer::Tensor shape
const size_t kSize = 5;
virtual void SetUp() {
......@@ -38,34 +52,36 @@ public:
virtual void TearDown() {}
void CreateSGD() {
Tensor* parameter = FixedTensor(kSize);
config_.set_optimizer(OptimizerConfig::SGD);
paddle::optimizer::Tensor* parameter = FixedTensor(kSize);
config_.set_optimizer(paddle::OptimizerConfig::SGD);
config_.mutable_sgd()->set_momentum(0.0);
config_.mutable_sgd()->set_decay(0.0);
config_.mutable_sgd()->set_nesterov(false);
config_.set_lr_policy(OptimizerConfig::Const);
config_.set_lr_policy(paddle::OptimizerConfig::Const);
config_.mutable_const_lr()->set_learning_rate(0.1);
std::string str = config_.SerializeAsString();
ParameterOptimizer* opt = ParameterOptimizer::Create(str, parameter);
paddle::optimizer::ParameterOptimizer* opt =
paddle::optimizer::ParameterOptimizer::Create(str, parameter);
opts_.push_back(opt);
}
void CreateAdam() {
Tensor* parameter = FixedTensor(kSize);
config_.set_optimizer(OptimizerConfig::Adam);
paddle::optimizer::Tensor* parameter = FixedTensor(kSize);
config_.set_optimizer(paddle::OptimizerConfig::Adam);
config_.mutable_adam()->set_beta_1(0.9);
config_.mutable_adam()->set_beta_2(0.1);
config_.mutable_adam()->set_epsilon(1e-3);
config_.mutable_adam()->set_decay(0.0);
config_.set_lr_policy(OptimizerConfig::Const);
config_.set_lr_policy(paddle::OptimizerConfig::Const);
config_.mutable_const_lr()->set_learning_rate(0.1);
std::string str = config_.SerializeAsString();
ParameterOptimizer* opt = ParameterOptimizer::Create(str, parameter);
paddle::optimizer::ParameterOptimizer* opt =
paddle::optimizer::ParameterOptimizer::Create(str, parameter);
opts_.push_back(opt);
}
void TestGetWeight() {
Tensor* p = FixedTensor(kSize);
paddle::optimizer::Tensor* p = FixedTensor(kSize);
for (size_t i = 0; i < opts_.size(); ++i) {
int s = 0;
float* newp = (float*)opts_[i]->get_weight(&s);
......@@ -76,7 +92,7 @@ public:
}
void TestUpdate() {
Tensor* g = FixedTensor(kSize);
paddle::optimizer::Tensor* g = FixedTensor(kSize);
for (size_t i = 0; i < opts_.size(); ++i) {
opts_[i]->Update(g);
}
......@@ -91,8 +107,8 @@ public:
}
private:
std::vector<ParameterOptimizer*> opts_;
OptimizerConfig config_;
std::vector<paddle::optimizer::ParameterOptimizer*> opts_;
paddle::OptimizerConfig config_;
};
TEST_F(OptimizerTest, TestGetWeight) { TestGetWeight(); }
......
paddle/optimizer/serialization_test.cpp
浏览文件 @ ca89bfad
/*
Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#include "serialization.h"
#include "gtest/gtest.h"
using namespace paddle;
using namespace paddle::optimizer;
TEST(TensorToProto, Case1) {
Tensor t(3), t1(3);
paddle::optimizer::Tensor t(3), t1(3);
for (size_t i = 0; i < t.size(); ++i) {
t[i] = i;
t1[i] = 0;
}
TensorProto proto;
TensorToProto(t, &proto);
ProtoToTensor(proto, &t1);
paddle::TensorProto proto;
paddle::optimizer::TensorToProto(t, &proto);
paddle::optimizer::ProtoToTensor(proto, &t1);
for (size_t i = 0; i < t1.size(); ++i) {
EXPECT_EQ(t1[i], t[i]);
}
......
paddle/platform/CMakeLists.txt
浏览文件 @ ca89bfad
......@@ -8,6 +8,8 @@ cc_test(place_test SRCS place_test.cc DEPS place glog gflags)
add_subdirectory(dynload)
cc_test(enforce_test SRCS enforce_test.cc)
IF(WITH_GPU)
set(GPU_CTX_DEPS dynload_cuda dynamic_loader)
ELSE()
......
paddle/platform/cpu_info.cc
浏览文件 @ ca89bfad
......@@ -22,7 +22,6 @@ limitations under the License. */
#endif
#include "gflags/gflags.h"
#include "paddle/platform/error.h"
DEFINE_double(fraction_of_cpu_memory_to_use, 1,
"Default use 100% of CPU memory for PaddlePaddle,"
......@@ -41,8 +40,8 @@ inline size_t CpuTotalPhysicalMemory() {
if (sysctl(mib, 2, &size, &len, NULL, 0) == 0) return (size_t)size;
return 0L;
#else
long pages = sysconf(_SC_PHYS_PAGES);
long page_size = sysconf(_SC_PAGE_SIZE);
int64_t pages = sysconf(_SC_PHYS_PAGES);
int64_t page_size = sysconf(_SC_PAGE_SIZE);
return pages * page_size;
#endif
}
......
paddle/platform/device_context.cc
浏览文件 @ ca89bfad
......@@ -15,14 +15,15 @@ namespace paddle {
namespace platform {
template <>
Eigen::DefaultDevice* DeviceContext::get_eigen_device<Eigen::DefaultDevice>() {
return reinterpret_cast<CPUDeviceContext*>(this)->eigen_device();
Eigen::DefaultDevice* DeviceContext::get_eigen_device<Eigen::DefaultDevice>()
const {
return reinterpret_cast<const CPUDeviceContext*>(this)->eigen_device();
}
#ifndef PADDLE_ONLY_CPU
template <>
Eigen::GpuDevice* DeviceContext::get_eigen_device<Eigen::GpuDevice>() {
return reinterpret_cast<CUDADeviceContext*>(this)->eigen_device();
Eigen::GpuDevice* DeviceContext::get_eigen_device<Eigen::GpuDevice>() const {
return reinterpret_cast<const CUDADeviceContext*>(this)->eigen_device();
}
#endif
......
paddle/platform/device_context.h
浏览文件 @ ca89bfad
此差异已折叠。
paddle/platform/dynload/dynamic_loader.cc
浏览文件 @ ca89bfad
......@@ -19,7 +19,7 @@ limitations under the License. */
#include <string>
#include "gflags/gflags.h"
#include "glog/logging.h"
#include "paddle/framework/enforce.h"
#include "paddle/platform/enforce.h"
DEFINE_string(cudnn_dir, "",
"Specify path for loading libcudnn.so. For instance, "
......
paddle/platform/enforce.h 0 → 100644
浏览文件 @ ca89bfad
此差异已折叠。
paddle/platform/error.h 已删除 100644 → 0
浏览文件 @ 527c8597
此差异已折叠。
paddle/platform/gpu_info.cc
浏览文件 @ ca89bfad
此差异已折叠。
paddle/platform/place.cc
浏览文件 @ ca89bfad
此差异已折叠。
paddle/pybind/CMakeLists.txt
浏览文件 @ ca89bfad
此差异已折叠。
paddle/pybind/pybind.cc
浏览文件 @ ca89bfad
此差异已折叠。
paddle/pybind/tensor_bind.h 0 → 100644
浏览文件 @ ca89bfad
此差异已折叠。
paddle/scripts/docker/build.sh
浏览文件 @ ca89bfad
此差异已折叠。
paddle/utils/DynamicLoader.h
浏览文件 @ ca89bfad
此差异已折叠。
paddle/utils/ThreadLocal.h
浏览文件 @ ca89bfad
此差异已折叠。
proto/ModelConfig.proto
浏览文件 @ ca89bfad
此差异已折叠。
python/paddle/v2/__init__.py
浏览文件 @ ca89bfad
此差异已折叠。
python/paddle/v2/data_feeder.py
浏览文件 @ ca89bfad
此差异已折叠。
python/paddle/v2/dataset/common.py
浏览文件 @ ca89bfad
此差异已折叠。
python/paddle/v2/dataset/mq2007.py
浏览文件 @ ca89bfad
此差异已折叠。
python/paddle/v2/dataset/tests/voc2012_test.py 0 → 100644
浏览文件 @ ca89bfad
此差异已折叠。
python/paddle/v2/dataset/voc2012.py 0 → 100644
浏览文件 @ ca89bfad
此差异已折叠。
python/paddle/v2/event.py
浏览文件 @ ca89bfad
此差异已折叠。
python/paddle/v2/framework/tests/op_test_util.py 0 → 100644
浏览文件 @ ca89bfad
此差异已折叠。
python/paddle/v2/framework/tests/test_add_two_op.py 0 → 100644
浏览文件 @ ca89bfad
此差异已折叠。
python/paddle/v2/framework/tests/test_fc_op.py 0 → 100644
浏览文件 @ ca89bfad
此差异已折叠。
python/paddle/v2/framework/tests/test_sgd_op.py 0 → 100644
浏览文件 @ ca89bfad
此差异已折叠。
python/paddle/v2/framework/tests/test_tensor.py 0 → 100644
浏览文件 @ ca89bfad
此差异已折叠。
python/paddle/v2/inference.py
浏览文件 @ ca89bfad
此差异已折叠。
python/paddle/v2/master/client.py
浏览文件 @ ca89bfad
此差异已折叠。
python/paddle/v2/optimizer.py
浏览文件 @ ca89bfad
此差异已折叠。
python/paddle/v2/parameters.py
浏览文件 @ ca89bfad
此差异已折叠。
python/paddle/v2/trainer.py
浏览文件 @ ca89bfad
此差异已折叠。
python/setup.py.in
浏览文件 @ ca89bfad
此差异已折叠。
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或