提交 a5239ac7 编写于 作者: W wanghaoshuang

Merge branch 'develop' of https://github.com/paddlepaddle/paddle into voc_dataset

......@@ -19,3 +19,6 @@ third_party/
# clion workspace.
cmake-build-*
# generated while compiling
python/paddle/v2/framework/core.so
......@@ -97,6 +97,7 @@ include(external/swig) # download, build, install swig
include(external/warpctc) # download, build, install warpctc
include(external/any) # download libn::any
include(external/eigen) # download eigen3
include(external/pybind11) # download pybind11
include(cudnn) # set cudnn libraries, must before configure
include(configure) # add paddle env configuration
......
......@@ -2,8 +2,8 @@
[![Build Status](https://travis-ci.org/PaddlePaddle/Paddle.svg?branch=develop)](https://travis-ci.org/PaddlePaddle/Paddle)
[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://www.paddlepaddle.org/develop/doc/)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://www.paddlepaddle.org/doc_cn/)
[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://doc.paddlepaddle.org/develop/doc/)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://doc.paddlepaddle.org/develop/doc_cn/)
[![Coverage Status](https://coveralls.io/repos/github/PaddlePaddle/Paddle/badge.svg?branch=develop)](https://coveralls.io/github/PaddlePaddle/Paddle?branch=develop)
[![Release](https://img.shields.io/github/release/PaddlePaddle/Paddle.svg)](https://github.com/PaddlePaddle/Paddle/releases)
[![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE)
......@@ -61,35 +61,36 @@ Please refer to our [release announcement](https://github.com/PaddlePaddle/Paddl
## Installation
It is recommended to check out the
[Docker installation guide](http://www.paddlepaddle.org/develop/doc/getstarted/build_and_install/docker_install_en.html)
[Docker installation guide](http://doc.paddlepaddle.org/develop/doc/getstarted/build_and_install/docker_install_en.html)
before looking into the
[build from source guide](http://www.paddlepaddle.org/develop/doc/getstarted/build_and_install/build_from_source_en.html)
[build from source guide](http://doc.paddlepaddle.org/develop/doc/getstarted/build_and_install/build_from_source_en.html)
## Documentation
We provide [English](http://www.paddlepaddle.org/develop/doc/) and
[Chinese](http://www.paddlepaddle.org/doc_cn/) documentation.
We provide [English](http://doc.paddlepaddle.org/develop/doc/) and
[Chinese](http://doc.paddlepaddle.org/doc_cn/) documentation.
- [Deep Learning 101](http://book.paddlepaddle.org/index.html)
You might want to start from the this online interactive book that can run in Jupyter Notebook.
- [Distributed Training](http://www.paddlepaddle.org/develop/doc/howto/usage/cluster/cluster_train_en.html)
- [Distributed Training](http://doc.paddlepaddle.org/develop/doc/howto/usage/cluster/cluster_train_en.html)
You can run distributed training jobs on MPI clusters.
- [Distributed Training on Kubernetes](http://www.paddlepaddle.org/develop/doc/howto/usage/k8s/k8s_en.html)
- [Distributed Training on Kubernetes](http://doc.paddlepaddle.org/develop/doc/howto/usage/k8s/k8s_en.html)
You can also run distributed training jobs on Kubernetes clusters.
- [Python API](http://www.paddlepaddle.org/develop/doc/api/index_en.html)
- [Python API](http://doc.paddlepaddle.org/develop/doc/api/index_en.html)
Our new API enables much shorter programs.
- [How to Contribute](http://www.paddlepaddle.org/develop/doc/howto/dev/contribute_to_paddle_en.html)
- [How to Contribute](http://doc.paddlepaddle.org/develop/doc/howto/dev/contribute_to_paddle_en.html)
We appreciate your contributions!
## Ask Questions
You are welcome to submit questions and bug reports as [Github Issues](https://github.com/PaddlePaddle/Paddle/issues).
......
INCLUDE(ExternalProject)
SET(PYBIND_SOURCE_DIR ${THIRD_PARTY_PATH}/pybind)
INCLUDE_DIRECTORIES(${PYBIND_SOURCE_DIR}/src/extern_pybind/include)
ExternalProject_Add(
extern_pybind
${EXTERNAL_PROJECT_LOG_ARGS}
GIT_REPOSITORY "https://github.com/pybind/pybind11.git"
GIT_TAG "v2.1.1"
PREFIX ${PYBIND_SOURCE_DIR}
UPDATE_COMMAND ""
CONFIGURE_COMMAND ""
BUILD_COMMAND ""
INSTALL_COMMAND ""
TEST_COMMAND ""
)
if (${CMAKE_VERSION} VERSION_LESS "3.3.0")
set(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/pybind_dummy.c)
file(WRITE ${dummyfile} "const char * dummy_any = \"${dummyfile}\";")
add_library(pybind STATIC ${dummyfile})
else()
add_library(pybind INTERFACE)
endif()
add_dependencies(pybind extern_pybind)
LIST(APPEND external_project_dependencies pybind)
......@@ -18,6 +18,9 @@ INCLUDE(python_module)
FIND_PACKAGE(PythonInterp 2.7)
IF(WITH_PYTHON)
FIND_PACKAGE(PythonLibs 2.7)
# Fixme: Maybe find a static library. Get SHARED/STATIC by FIND_PACKAGE.
ADD_LIBRARY(python SHARED IMPORTED GLOBAL)
SET_PROPERTY(TARGET python PROPERTY IMPORTED_LOCATION ${PYTHON_LIBRARIES})
ENDIF(WITH_PYTHON)
SET(py_env "")
......
......@@ -109,7 +109,9 @@ set(COMMON_FLAGS
-Wno-unused-function
-Wno-error=literal-suffix
-Wno-error=sign-compare
-Wno-error=unused-local-typedefs)
-Wno-error=unused-local-typedefs
-Wno-error=parentheses-equality # Warnings in Pybind11
)
set(GPU_COMMON_FLAGS
-fPIC
......
......@@ -93,6 +93,7 @@ include_directories(${CMAKE_CURRENT_BINARY_DIR})
if(NOT APPLE)
find_package(Threads REQUIRED)
link_libraries(${CMAKE_THREAD_LIBS_INIT})
set(CMAKE_CXX_LINK_EXECUTABLE "${CMAKE_CXX_LINK_EXECUTABLE} -ldl")
endif(NOT APPLE)
function(merge_static_libs TARGET_NAME)
......
......@@ -37,7 +37,7 @@
\frac{\partial c(y)}{\partial x} = \frac{\partial c(y)}{\partial y} \frac{\partial y}{\partial x}
假设 :math:`z = f(W^T x + b)` ,那么
假设 :math:`z = W^T x + b` ,那么
.. math::
......
......@@ -29,7 +29,7 @@ Fully connected layer takes a dense input vector with dimension :math:`D_i`. It
where :math:`f(.)` is an nonlinear *activation* function, such as sigmoid, tanh, and Relu.
The transformation matrix :math:`W` and bias vector :math:`b` are the *parameters* of the layer. The *parameters* of a layer are learned during training in the *backward pass*. The backward pass computes the gradients of the output function with respect to all parameters and inputs. The optimizer can use chain rule to compute the gradients of the loss function with respect to each parameter.
The transformation matrix :math:`W` and bias vector :math:`b` are the *parameters* of the layer. The *parameters* of a layer are learned during training in the *backward pass*. The backward pass computes the gradients of the output function with respect to all parameters and inputs. The optimizer can use chain rule to compute the gradients of the loss function with respect to each parameter.
Suppose our loss function is :math:`c(y)`, then
......@@ -37,7 +37,7 @@ Suppose our loss function is :math:`c(y)`, then
\frac{\partial c(y)}{\partial x} = \frac{\partial c(y)}{\partial y} \frac{\partial y}{\partial x}
Suppose :math:`z = f(W^T x + b)`, then
Suppose :math:`z = W^T x + b`, then
.. math::
......@@ -48,7 +48,7 @@ This derivative can be automatically computed by our base layer class.
Then, for fully connected layer, we need to compute:
.. math::
\frac{\partial z}{\partial x} = W, \frac{\partial z_j}{\partial W_{ij}} = x_i, \frac{\partial z}{\partial b} = \mathbf 1
where :math:`\mathbf 1` is an all one vector, :math:`W_{ij}` is the number at the i-th row and j-th column of the matrix :math:`W`, :math:`z_j` is the j-th component of the vector :math:`z`, and :math:`x_i` is the i-th component of the vector :math:`x`.
......@@ -322,7 +322,7 @@ All the gradient check unit tests are located in :code:`paddle/gserver/tests/tes
/* weight */ true);
}
}
If you are creating a new file for the test, such as :code:`paddle/gserver/tests/testFCGrad.cpp`, you need to add the file to :code:`paddle/gserver/tests/CMakeLists.txt`. An example is given below. All the unit tests will run when you execute the command :code:`make tests`. Notice that some layers might need high accuracy for the gradient check unit tests to work well. You need to configure :code:`WITH_DOUBLE` to `ON` when configuring cmake.
.. code-block:: bash
......
......@@ -41,7 +41,7 @@ PaddlePaddle文档需要准备的环境相对较复杂,所以我们推荐使
python -c "import py_paddle"
如果提示错误,那么用户需要在本地编译安装PaddlePaddle,请参考 `源码编译文档 <http://www.paddlepaddle.org/develop/doc/getstarted/build_and_install/build_from_source_en.html>`_ 。
如果提示错误,那么用户需要在本地编译安装PaddlePaddle,请参考 `源码编译文档 <http://doc.paddlepaddle.org/develop/doc/getstarted/build_and_install/build_from_source_en.html>`_ 。
注意,用户在首次编译安装PaddlePaddle时,请将WITH_DOC选项关闭。在编译安装正确之后,请再次确认py_paddle包已经安装,即可进行下一步操作。
如果提示正确,可以执行以下命令编译生成文档,即
......@@ -68,9 +68,9 @@ PaddlePaddle文档使用 `sphinx`_ 自动生成,用户可以参考sphinx教程
如何更新www.paddlepaddle.org文档
================================
开发者给PaddlePaddle代码增加的注释以PR的形式提交到github中,提交方式可参见 `贡献文档 <http://paddlepaddle.org/develop/doc_cn/howto/dev/contribute_to_paddle_cn.html>`_ 。
目前PaddlePaddle的develop分支的文档是自动触发更新的,用户可以分别查看最新的 `中文文档 <http://www.paddlepaddle.org/develop/doc_cn/>`_ 和
`英文文档 <http://www.paddlepaddle.org/develop/doc/>`_ 。
开发者给PaddlePaddle代码增加的注释以PR的形式提交到github中,提交方式可参见 `贡献文档 <http://doc.paddlepaddle.org/develop/doc_cn/howto/dev/contribute_to_paddle_cn.html>`_ 。
目前PaddlePaddle的develop分支的文档是自动触发更新的,用户可以分别查看最新的 `中文文档 <http://doc.paddlepaddle.org/develop/doc_cn/>`_ 和
`英文文档 <http://doc.paddlepaddle.org/develop/doc/>`_ 。
......
......@@ -20,6 +20,8 @@ func main() {
"comma separated endpoint string for pserver to connect to etcd")
etcdTimeout := flag.Int("etcd-timeout", 5, "timeout for etcd calls")
numPservers := flag.Int("num-pservers", 1, "total pserver count in a training job")
checkpointPath := flag.String("checkpoint-path", "/checkpoints/", "save checkpoint path")
checkpointInterval := flag.Int("checkpoint-interval", 600, "save checkpoint per interval seconds")
logLevel := flag.String("log-level", "info",
"log level, possible values: debug, info, warning, error, fatal, panic")
flag.Parse()
......@@ -31,18 +33,20 @@ func main() {
log.SetLevel(level)
var idx int
var cp pserver.Checkpoint
var e *pserver.EtcdClient
if *index >= 0 {
idx = *index
} else {
timeout := time.Second * time.Duration((*etcdTimeout))
e := pserver.NewEtcdClient(*etcdEndpoint, *numPservers, timeout)
e = pserver.NewEtcdClient(*etcdEndpoint, *numPservers, timeout)
idx, err = e.Register()
if err != nil {
panic(err)
}
}
s, err := pserver.NewService(idx)
s, err := pserver.NewService(idx, *checkpointInterval, *checkpointPath, e, cp)
if err != nil {
panic(err)
}
......
......@@ -68,7 +68,7 @@ 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.ID)
c.taskFinished(t.Meta.ID)
}
}
......@@ -118,6 +118,11 @@ func (c *Client) taskFinished(taskID int) error {
return c.conn.Call("Service.TaskFinished", taskID, nil)
}
// TaskFailed tell the master server as task is failed.
func (c *Client) taskFailed(meta TaskMeta) error {
return c.conn.Call("Service.TaskFailed", meta, nil)
}
// NextRecord returns next record in the dataset.
//
// NextRecord will block until the next record is available. It is
......
......@@ -95,10 +95,16 @@ func TestGetFinishTask(t *testing.T) {
t.Fatalf("Should get error, pass: %d\n", i)
}
err = c.taskFinished(tasks[0].ID)
err = c.taskFinished(tasks[0].Meta.ID)
if err != nil {
t.Fatalf("Error: %v, pass: %d\n", err, i)
}
err = c.taskFailed(tasks[0].Meta)
if err != nil {
t.Fatalf("Error: %v, pass: %d\n", err, i)
}
tasks = tasks[1:]
task, err := c.getTask()
if err != nil {
......@@ -107,7 +113,7 @@ func TestGetFinishTask(t *testing.T) {
tasks = append(tasks, task)
for _, task := range tasks {
err = c.taskFinished(task.ID)
err = c.taskFinished(task.Meta.ID)
if err != nil {
t.Fatalf("Error: %v, pass: %d\n", err, i)
}
......
......@@ -31,30 +31,36 @@ type Chunk struct {
Index recordio.Index // chunk index
}
// TaskMeta is a struct which stores task's meta info.
type TaskMeta struct {
ID int
Epoch int
}
// Task is the basic unit of data instances assigned to trainers.
type Task struct {
ID int
Meta TaskMeta
Chunks []Chunk
}
type taskEntry struct {
Epoch int
NumTimeout int
Task Task
Task Task
// A task fails if it's timeout or trainer reports it exits unnormally.
NumFailure int
}
type taskQueues struct {
Todo []taskEntry
Pending map[int]taskEntry // map from task ID to task entry
Done []taskEntry
Failed []Task
Failed []taskEntry
}
// Service is the master server service.
type Service struct {
chunksPerTask int
timeoutDur time.Duration
timeoutMax int
failureMax int
ready chan struct{}
store Store
......@@ -73,7 +79,7 @@ func partition(chunks []Chunk, chunksPerTask int) []taskEntry {
var cur taskEntry
for i, c := range chunks {
if i%chunksPerTask == 0 && len(cur.Task.Chunks) > 0 {
cur.Task.ID = id
cur.Task.Meta.ID = id
id++
result = append(result, cur)
cur.Task.Chunks = nil
......@@ -83,7 +89,7 @@ func partition(chunks []Chunk, chunksPerTask int) []taskEntry {
}
if len(cur.Task.Chunks) > 0 {
cur.Task.ID = id
cur.Task.Meta.ID = id
result = append(result, cur)
}
......@@ -91,11 +97,11 @@ func partition(chunks []Chunk, chunksPerTask int) []taskEntry {
}
// NewService creates a new service.
func NewService(store Store, chunksPerTask int, timeoutDur time.Duration, timeoutMax int) (*Service, error) {
func NewService(store Store, chunksPerTask int, timeoutDur time.Duration, failureMax int) (*Service, error) {
s := &Service{}
s.chunksPerTask = chunksPerTask
s.timeoutDur = timeoutDur
s.timeoutMax = timeoutMax
s.failureMax = failureMax
s.taskQueues = taskQueues{}
s.taskQueues.Pending = make(map[int]taskEntry)
s.ready = make(chan struct{})
......@@ -257,6 +263,34 @@ func (s *Service) SetDataset(globPaths []string, dummy *int) error {
return nil
}
func (s *Service) processFailedTask(t taskEntry, epoch int) {
if t.Task.Meta.Epoch != epoch {
// new epoch, task launched after the
// schedule of this timeout check or failed status report.
return
}
defer func() {
err := s.snapshot()
if err != nil {
log.Errorln(err)
}
}()
delete(s.taskQueues.Pending, t.Task.Meta.ID)
t.NumFailure++
if t.NumFailure > s.failureMax {
log.Warningf("Task %v failed %d times, discard.", t.Task, t.NumFailure)
s.taskQueues.Failed = append(s.taskQueues.Failed, t)
return
}
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() {
return func() {
s.mu.Lock()
......@@ -267,30 +301,7 @@ func (s *Service) checkTimeoutFunc(taskID int, epoch int) func() {
return
}
if t.Epoch != epoch {
// new epoch, task launched after the
// schedule of this timeout check.
return
}
defer func() {
err := s.snapshot()
if err != nil {
log.Errorln(err)
}
}()
delete(s.taskQueues.Pending, t.Task.ID)
t.NumTimeout++
if t.NumTimeout > s.timeoutMax {
log.Warningf("Task %v timed out %d times, discard.", t.Task, t.NumTimeout)
s.taskQueues.Failed = append(s.taskQueues.Failed, t.Task)
return
}
log.Warningf("Task %v timed out %d times, retry.", t.Task, t.NumTimeout)
s.taskQueues.Todo = append(s.taskQueues.Todo, t)
s.processFailedTask(t, epoch)
}
}
......@@ -339,18 +350,18 @@ func (s *Service) GetTask(dummy int, task *Task) error {
}
t := s.taskQueues.Todo[0]
t.Epoch++
t.Task.Meta.Epoch++
s.taskQueues.Todo = s.taskQueues.Todo[1:]
s.taskQueues.Pending[t.Task.ID] = t
s.taskQueues.Pending[t.Task.Meta.ID] = t
err := s.snapshot()
if err != nil {
return err
}
*task = t.Task
log.WithFields(s.logFields()).Infof("Task #%d dispatched.", task.ID)
log.WithFields(s.logFields()).Infof("Task #%v dispatched.", t.Task.Meta)
time.AfterFunc(s.timeoutDur, s.checkTimeoutFunc(t.Task.ID, t.Epoch))
time.AfterFunc(s.timeoutDur, s.checkTimeoutFunc(t.Task.Meta.ID, t.Task.Meta.Epoch))
return nil
}
......@@ -365,13 +376,12 @@ func (s *Service) TaskFinished(taskID int, dummy *int) error {
t, ok := s.taskQueues.Pending[taskID]
if !ok {
err := errors.New("pending task not found")
log.WithFields(s.logFields()).Warningln("Pending task #%d not found.", taskID)
return err
return nil
}
// task finished, reset timeout
t.NumTimeout = 0
t.NumFailure = 0
s.taskQueues.Done = append(s.taskQueues.Done, t)
delete(s.taskQueues.Pending, taskID)
......@@ -389,3 +399,22 @@ func (s *Service) TaskFinished(taskID int, dummy *int) error {
}
return err
}
// TaskFailed tells the service that a task is failed.
func (s *Service) TaskFailed(meta TaskMeta, dummy *int) error {
select {
case <-s.ready:
}
s.mu.Lock()
defer s.mu.Unlock()
t, ok := s.taskQueues.Pending[meta.ID]
if !ok {
log.WithFields(s.logFields()).Warningln("TaskFailed:Pending task #%v not found.", t.Task.Meta)
return nil
}
s.processFailedTask(t, meta.Epoch)
return nil
}
......@@ -30,7 +30,7 @@ func TestPartionIndex(t *testing.T) {
cs := make([]Chunk, 100)
ts := partition(cs, 20)
for i := range ts {
if ts[i].Task.ID != i {
if ts[i].Task.Meta.ID != i {
t.Error(ts[i], i)
}
}
......
......@@ -19,7 +19,7 @@ def main():
# create parameters
parameters = paddle.parameters.create(cost)
# create optimizer
# create optimizer of new remote updater to pserver
optimizer = paddle.optimizer.Momentum(momentum=0)
#TODO(zhihong) : replace optimizer with new OptimizerConfig
......
......@@ -42,7 +42,8 @@ func initClient() [numPserver]int {
ports[i] = p
go func(l net.Listener) {
s, err := pserver.NewService(0)
var cp pserver.Checkpoint
s, err := pserver.NewService(0, 1, "", nil, cp)
if err != nil {
panic(err)
}
......@@ -174,7 +175,7 @@ func TestNativeClient(t *testing.T) {
// TODO: tmperary disable etcdClient test for dependency of etcd)
func EtcdClient(t *testing.T) {
initEtcdClient()
etcd_client := client.NewEtcd(etcdEndpoints)
c2 := client.NewClient(etcd_client, etcd_client.Desired(), selector(true))
etcdClient := client.NewEtcd(etcdEndpoints)
c2 := client.NewClient(etcdClient, etcdClient.Desired(), selector(true))
ClientTest(t, c2)
}
......@@ -18,6 +18,8 @@ const (
PsDesired = "/ps_desired"
// PsAddr is the base dir for pserver to store their addr
PsPath = "/ps/"
// PsCheckpoint is the etcd path for store checkpoints information
PsCheckpoint = "/checkpoints/"
)
// EtcdClient is the etcd client that the pserver uses for fault
......@@ -186,3 +188,14 @@ func (e *EtcdClient) registerPserverEtcd(ctx context.Context) (int, error) {
return idx, nil
}
// PutKey put into etcd with value by key specified
func (e *EtcdClient) PutKey(key string, value []byte, timeout int) error {
ctx, cancel := context.WithTimeout(context.Background(), time.Second*time.Duration(timeout))
_, err := e.etcdClient.Put(ctx, key, string(value))
cancel()
if err != nil {
return err
}
return nil
}
......@@ -35,22 +35,30 @@ func cArrayToSlice(p unsafe.Pointer, len int) []byte {
return (*[1 << 30]byte)(p)[:len:len]
}
func newOptimizer(paramWithConfigs ParameterWithConfig) *optimizer {
func newOptimizer(paramWithConfigs ParameterWithConfig, State []byte) *optimizer {
o := &optimizer{}
o.elementType = paramWithConfigs.Param.ElementType
p := paramWithConfigs.Param
c := paramWithConfigs.Config
s := State
paramBufferSize := C.size_t(len(p.Content) / C.sizeof_float)
log.WithFields(log.Fields{
"ElementType": p.ElementType,
"ParamSize": len(p.Content),
"ParamSize": paramBufferSize,
"ConfigSize": len(c),
"StateSize": len(s),
}).Info("New Optimizer Created with config:")
var cbuffer unsafe.Pointer
cbuffer = C.malloc(C.size_t(len(p.Content)))
C.memcpy(cbuffer, unsafe.Pointer(&p.Content[0]), C.size_t(len(p.Content)))
cbuffer = C.malloc(paramBufferSize)
C.memcpy(cbuffer, unsafe.Pointer(&p.Content[0]), paramBufferSize)
var cstate unsafe.Pointer
if len(s) != 0 {
cstate = unsafe.Pointer(&s[0])
}
o.opt = C.paddle_create_optimizer((*C.uchar)(&c[0]), C.int(len(c)),
C.paddle_element_type(p.ElementType), cbuffer, C.int(len(p.Content)/C.sizeof_float),
(*C.char)(nullPtr), 0)
C.paddle_element_type(p.ElementType), cbuffer, C.int(paramBufferSize), (*C.char)(cstate), C.int(len(s)))
return o
}
......@@ -60,6 +68,12 @@ func (o *optimizer) GetWeights() []byte {
return cArrayToSlice(buffer, int(bufferLen)*C.sizeof_float)
}
func (o *optimizer) GetStates() []byte {
var cbuffer *C.char
cbufferLen := C.paddle_optimizer_get_state(o.opt, &cbuffer)
return cArrayToSlice(unsafe.Pointer(cbuffer), int(cbufferLen))
}
func (o *optimizer) UpdateParameter(g Gradient) error {
if o.elementType != g.ElementType {
return fmt.Errorf("Name: %s, parameter and gradient element type not match, parameter: %v, gradient: %v", g.Name, o.elementType, g.ElementType)
......
......@@ -19,6 +19,6 @@ func TestOptimizerCreateRelease(t *testing.T) {
Param: p,
Config: config,
}
o := newOptimizer(param)
o := newOptimizer(param, nil)
o.Cleanup()
}
package pserver
import (
"bufio"
"bytes"
"crypto/md5"
"encoding/gob"
"encoding/hex"
"encoding/json"
"errors"
"fmt"
"os"
"path/filepath"
"strconv"
"sync"
"time"
log "github.com/sirupsen/logrus"
)
// ElementType is the type of elements of a Parameter.
......@@ -39,26 +51,55 @@ type ParameterWithConfig struct {
Config []byte // parameter configuration in Proto Buffer format
}
// ParameterCheckpoint is Parameter and State checkpoint
type ParameterCheckpoint struct {
ParamConfig ParameterWithConfig
State []byte
}
// checkpoint signature
type checkpointMeta struct {
UUID string `json:"uuid"`
Md5sum string `json:"md5sum"`
Timestamp string `json:"timestamp"`
}
// Checkpoint is the pserver shard persist in file
type Checkpoint []ParameterCheckpoint
// Gradient is the gradient of the parameter.
type Gradient Parameter
// Service is the RPC service for pserver.
type Service struct {
initialized chan struct{}
idx int
mu sync.Mutex
optMap map[string]*optimizer
initialized chan struct{}
idx int
checkpointInterval time.Duration
checkpointPath string
client *EtcdClient
mu sync.Mutex
optMap map[string]*optimizer
}
// NewService creates a new service, will bypass etcd registration if no
// endpoints specified.
func NewService(idx int) (*Service, error) {
func NewService(idx int, seconds int, path string, client *EtcdClient, cp Checkpoint) (*Service, error) {
s := &Service{
idx: idx,
idx: idx,
checkpointInterval: time.Second * time.Duration(seconds),
checkpointPath: path,
client: client,
}
s.optMap = make(map[string]*optimizer)
s.initialized = make(chan struct{})
if cp != nil {
for _, item := range cp {
p := item.ParamConfig
st := item.State
s.optMap[p.Param.Name] = newOptimizer(p, st)
}
}
return s, nil
}
......@@ -78,7 +119,7 @@ func (s *Service) InitParam(paramWithConfigs ParameterWithConfig, dummy *int) er
// TODO(helin): check if paramWithConfigs.Param.Content is
// properly memory aligned, if not, make copy to a memory
// aligned region.
s.optMap[paramWithConfigs.Param.Name] = newOptimizer(paramWithConfigs)
s.optMap[paramWithConfigs.Param.Name] = newOptimizer(paramWithConfigs, nil)
return nil
}
......@@ -139,10 +180,57 @@ func (s *Service) GetParam(name string, parameter *Parameter) error {
return nil
}
// Save tells the parameter server to save parameters.
func (s *Service) Save(path string, dummy *int) error {
// pserver save checkpoint
func (s *Service) doCheckpoint() error {
<-s.initialized
s.mu.Lock()
defer s.mu.Unlock()
cp := make([]ParameterCheckpoint, 0, len(s.optMap))
index := 0
for name, opt := range s.optMap {
var pc ParameterCheckpoint
pc.ParamConfig.Param.Name = name
pc.ParamConfig.Param.ElementType = opt.elementType
pc.ParamConfig.Param.Content = opt.GetWeights()
pc.State = opt.GetStates()
cp[index] = pc
index++
}
var buf bytes.Buffer
encoder := gob.NewEncoder(&buf)
err := encoder.Encode(cp)
if err != nil {
return err
}
cpMeta := checkpointMeta{}
cpMeta.UUID = s.checkpointPath + strconv.Itoa(s.idx)
cpMeta.Timestamp = time.Now().String()
h := md5.New()
cpMeta.Md5sum = hex.EncodeToString(h.Sum(buf.Bytes()))
// TODO
cpMetajson, _ := json.Marshal(cpMeta)
err = s.client.PutKey(filepath.Join(PsCheckpoint, strconv.Itoa(s.idx)), cpMetajson, 3)
if err != nil {
return err
}
if _, err = os.Stat(cpMeta.UUID); os.IsNotExist(err) {
log.Info("checkpoint does not exists.")
} else {
err = os.Remove(cpMeta.UUID)
log.Infof("checkpoint %s already exsits, removing ", cpMeta.UUID)
}
f, err := os.Create(cpMeta.UUID)
defer f.Close()
if err != nil {
return err
}
writer := bufio.NewWriter(f)
_, err = writer.Write(buf.Bytes())
writer.Flush()
if err != nil {
return err
}
return nil
}
......@@ -15,7 +15,8 @@ const (
)
func TestServiceFull(t *testing.T) {
s, err := pserver.NewService(0)
var cp pserver.Checkpoint
s, err := pserver.NewService(0, 1, "", nil, cp)
if err != nil {
t.Error(err)
}
......@@ -86,7 +87,8 @@ func TestServiceFull(t *testing.T) {
}
func TestMultipleInit(t *testing.T) {
s, err := pserver.NewService(0)
var cp pserver.Checkpoint
s, err := pserver.NewService(0, 1, "", nil, cp)
if err != nil {
t.Error(err)
}
......@@ -102,7 +104,8 @@ func TestMultipleInit(t *testing.T) {
}
func TestUninitialized(t *testing.T) {
s, err := pserver.NewService(0)
var cp pserver.Checkpoint
s, err := pserver.NewService(0, 1, "", nil, cp)
err = s.SendGrad(pserver.Gradient{}, nil)
if err.Error() != pserver.Uninitialized {
t.FailNow()
......@@ -110,7 +113,8 @@ func TestUninitialized(t *testing.T) {
}
func TestBlockUntilInitialized(t *testing.T) {
s, err := pserver.NewService(0)
var cp pserver.Checkpoint
s, err := pserver.NewService(0, 1, "", nil, cp)
if err != nil {
t.Error(err)
}
......@@ -128,16 +132,6 @@ func TestBlockUntilInitialized(t *testing.T) {
ch <- struct{}{}
}()
wg.Add(1)
go func() {
err := s.Save("", nil)
if err != nil {
errCh <- err
}
wg.Done()
ch <- struct{}{}
}()
time.Sleep(50 * time.Millisecond)
select {
......@@ -170,3 +164,7 @@ func TestBlockUntilInitialized(t *testing.T) {
wg.Wait()
}
func TestCheckpointSpeed(t *testing.T) {
//TODO(zhihong): test speed
}
......@@ -15,6 +15,8 @@ if(Boost_FOUND)
add_subdirectory(memory)
add_subdirectory(platform)
add_subdirectory(framework)
add_subdirectory(operators)
add_subdirectory(pybind)
endif()
if(WITH_C_API)
......
......@@ -11,8 +11,14 @@ 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_desc_test SRCS op_desc_test.cc DEPS op_desc protobuf)
cc_test(op_registry_test SRCS op_registry_test.cc DEPS op_proto op_desc)
cc_library(operator SRCS operator.cc DEPS op_desc protobuf)
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)
#include "paddle/framework/net.h"
namespace paddle {
namespace framework {
PlainNet::PlainNet(const NetDesc& def) {}
void PlainNet::InferShape(Scope* scope) {
for (auto& op : ops_) {
op.InferShape();
}
}
void PlainNet::Run(std::shared_ptr<Scope> scope, DeviceContext* ctx) {
for (auto& op : ops_) {
op.Run(ctx);
}
}
} // namespace framework
} // namespace paddle
/* 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/net_proto.pb.h"
#include "paddle/framework/op_proto.pb.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() {}
void Run(DeviceContext *ctx) {}
};
/**
* @brief Network that 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.
* 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
* it defines.
*/
class Net {
public:
/**
* @brief Infer shapes of all inputs and outputs of operators.
*/
virtual void InferShape(Scope *scope) = 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(std::shared_ptr<Scope> scope, DeviceContext *ctx) = 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() {}
};
/**
* @brief a basic implementation of Net.
*
* PlainNet is a very simple Net, it create a list of operators, and run them
* sequentially following the order they added.
*/
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
* before every mini-batch
*/
virtual void InferShape(Scope *scope) override;
/**
* @brief Run the network.
*
* Run all the operators with the `scope`, if no scope is provided, default
* scope will be used instead. If no OpContext is provicded, default context
* will be used.
*/
virtual void Run(std::shared_ptr<Scope> scope, DeviceContext *ctx) override;
/**
* @brief Add an operator to this network.
*/
virtual OpIndex AddOp(const OpProto &def) override;
/**
* @brief Add all optimizer operators related into the network.
*/
virtual void AddOptimizerOps(const OpAttrs &attrs) override;
/**
* @brief Add all backward operators related into the network.
*/
virtual void AddBackwardOps() override;
virtual ~PlainNet() 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());
private:
// the operators owned by `Network`.
std::vector<Operator> ops_;
};
} // namespace framework
} // namespace paddle
syntax="proto2";
package paddle.framework;
import "op_proto.proto";
message NetDesc {
// network identification
optional string name = 1;
// operator contains in network
repeated OpProto operators = 2;
// network type to run with. e.g "plainNet", "DAG"
optional string net_type = 3;
// num worker always
optional int32 num_workers = 4;
}
/* 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 <gtest/gtest.h>
namespace paddle {
namespace framework {
class FakeFC : public Operator {}
} // namespace framework
} // namespace paddle
#include <paddle/framework/op_registry.h>
namespace paddle {
namespace framework {
template <>
void AttrTypeHelper::SetAttrType<int>(AttrProto* attr) {
attr->set_type(paddle::framework::AttrType::INT);
}
template <>
void AttrTypeHelper::SetAttrType<float>(AttrProto* attr) {
attr->set_type(paddle::framework::AttrType::FLOAT);
}
template <>
void AttrTypeHelper::SetAttrType<std::string>(AttrProto* attr) {
attr->set_type(paddle::framework::AttrType::STRING);
}
template <>
void AttrTypeHelper::SetAttrType<std::vector<int>>(AttrProto* attr) {
attr->set_type(paddle::framework::AttrType::INTS);
}
template <>
void AttrTypeHelper::SetAttrType<std::vector<float>>(AttrProto* attr) {
attr->set_type(paddle::framework::AttrType::FLOATS);
}
template <>
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
#pragma once
#include <algorithm>
#include "paddle/framework/attr_checker.h"
//#include "paddle/framework/op_base.h"
#include "paddle/framework/op_desc.pb.h"
#include "paddle/framework/op_proto.pb.h"
#include "paddle/framework/operator.h"
namespace paddle {
namespace framework {
//==================For test================//
class OpBase {
public:
std::vector<std::string> inputs_;
std::vector<std::string> outputs_;
AttributeMap attr_map_;
virtual std::string Run() const = 0;
virtual ~OpBase() {}
};
//=========================================//
// helper class to set attribute type
struct AttrTypeHelper {
template <typename T>
......@@ -64,36 +52,6 @@ struct AttrTypeHelper {
}
};
template <>
void AttrTypeHelper::SetAttrType<int>(AttrProto* attr) {
attr->set_type(paddle::framework::AttrType::INT);
}
template <>
void AttrTypeHelper::SetAttrType<float>(AttrProto* attr) {
attr->set_type(paddle::framework::AttrType::FLOAT);
}
template <>
void AttrTypeHelper::SetAttrType<std::string>(AttrProto* attr) {
attr->set_type(paddle::framework::AttrType::STRING);
}
template <>
void AttrTypeHelper::SetAttrType<std::vector<int>>(AttrProto* attr) {
attr->set_type(paddle::framework::AttrType::INTS);
}
template <>
void AttrTypeHelper::SetAttrType<std::vector<float>>(AttrProto* attr) {
attr->set_type(paddle::framework::AttrType::FLOATS);
}
template <>
void AttrTypeHelper::SetAttrType<std::vector<std::string>>(AttrProto* attr) {
attr->set_type(paddle::framework::AttrType::STRINGS);
}
// this class not only make proto but also init attribute checkers.
class OpProtoAndCheckerMaker {
public:
......@@ -103,22 +61,22 @@ class OpProtoAndCheckerMaker {
protected:
void AddInput(const std::string& name, const std::string& comment) {
auto input = proto_->mutable_inputs()->Add();
*(input->mutable_name()) = name;
*(input->mutable_comment()) = comment;
*input->mutable_name() = name;
*input->mutable_comment() = comment;
}
void AddOutput(const std::string& name, const std::string& comment) {
auto output = proto_->mutable_outputs()->Add();
*(output->mutable_name()) = name;
*(output->mutable_comment()) = comment;
*output->mutable_name() = name;
*output->mutable_comment() = comment;
}
template <typename T>
TypedAttrChecker<T>& AddAttr(const std::string& name,
const std::string& comment) {
auto attr = proto_->mutable_attrs()->Add();
*(attr->mutable_name()) = name;
*(attr->mutable_comment()) = comment;
*attr->mutable_name() = name;
*attr->mutable_comment() = comment;
AttrTypeHelper::SetAttrType<T>(attr);
return op_checker_->AddAttrChecker<T>(name);
}
......@@ -134,49 +92,52 @@ class OpProtoAndCheckerMaker {
};
class OpRegistry {
typedef std::function<OpBase*()> OpCreator;
using OpCreator = std::function<OperatorBase*()>;
public:
template <typename OpType, typename ProtoMakerType>
static void RegisterOp(const std::string& op_type) {
creators_[op_type] = []() { return new OpType; };
OpProto& op_proto = protos_[op_type];
OpAttrChecker& op_checker = op_checkers_[op_type];
creators()[op_type] = [] { return new OpType; };
OpProto& op_proto = protos()[op_type];
OpAttrChecker& op_checker = op_checkers()[op_type];
ProtoMakerType(&op_proto, &op_checker);
PADDLE_ENFORCE(op_proto.IsInitialized() == true,
PADDLE_ENFORCE(op_proto.IsInitialized(),
"Fail to initialize %s's OpProto !", op_type);
}
static OpBase* CreateOp(const OpDesc& op_desc) {
static OperatorBase* CreateOp(const OpDesc& op_desc) {
std::string op_type = op_desc.type();
OpBase* op = (creators_.at(op_type))();
(op->inputs_).resize(op_desc.inputs_size());
for (int i = 0; i < op_desc.inputs_size(); ++i) {
(op->inputs_)[i] = op_desc.inputs(i);
}
(op->outputs_).resize(op_desc.outputs_size());
for (int i = 0; i < op_desc.outputs_size(); ++i) {
(op->outputs_)[i] = op_desc.outputs(i);
OperatorBase* op = creators().at(op_type)();
op->desc_ = op_desc;
op->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::copy(op_desc.outputs().begin(), op_desc.outputs().end(),
std::back_inserter(op->outputs_));
for (auto& attr : op_desc.attrs()) {
op->attrs_[attr.name()] = AttrTypeHelper::GetAttrValue(attr);
}
for (int i = 0; i < op_desc.attrs_size(); ++i) {
const AttrDesc& ith_attr = op_desc.attrs(i);
std::string name = ith_attr.name();
(op->attr_map_)[name] = AttrTypeHelper::GetAttrValue(ith_attr);
}
const OpAttrChecker& op_checker = op_checkers_.at(op_type);
op_checker.Check(op->attr_map_);
op_checkers().at(op_type).Check(op->attrs_);
return op;
}
private:
static std::unordered_map<std::string, OpCreator> creators_;
static std::unordered_map<std::string, OpProto> protos_;
static std::unordered_map<std::string, OpAttrChecker> op_checkers_;
};
static std::unordered_map<std::string, OpCreator>& creators() {
static std::unordered_map<std::string, OpCreator> creators_;
return creators_;
}
static std::unordered_map<std::string, OpProto>& protos() {
static std::unordered_map<std::string, OpProto> protos_;
return protos_;
};
std::unordered_map<std::string, std::function<OpBase*()>> OpRegistry::creators_;
std::unordered_map<std::string, OpProto> OpRegistry::protos_;
std::unordered_map<std::string, OpAttrChecker> OpRegistry::op_checkers_;
static std::unordered_map<std::string, OpAttrChecker>& op_checkers() {
static std::unordered_map<std::string, OpAttrChecker> op_checkers_;
return op_checkers_;
};
};
template <typename OpType, typename ProtoMakerType>
class OpRegisterHelper {
......@@ -194,60 +155,5 @@ class OpRegisterHelper {
const OpRegisterHelper<__op_class, __op_maker_class> \
__op_class##Register::reg(#__op_type);
// Demos
class CosineOp : public OpBase {
public:
virtual std::string Run() const {
std::string msg = "CosineOp runs! scale = " +
std::to_string(boost::get<float>(attr_map_.at("scale")));
return msg;
}
};
class CosineOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
public:
CosineOpProtoAndCheckerMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("input", "input of cosine op");
AddOutput("output", "output of cosine op");
AddAttr<float>("scale", "scale of cosine op")
.SetDefault(1.0)
.LargerThan(0.0);
AddType("cos");
AddComment("This is cos op");
}
};
REGISTER_OP(CosineOp, CosineOpProtoAndCheckerMaker, cos_sim)
class MyTestOp : public OpBase {
public:
virtual std::string Run() const {
std::string msg =
"MyTestOp runs! test_attr = " +
std::to_string(boost::get<int>(attr_map_.at("test_attr")));
return msg;
}
};
class MyTestOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
public:
MyTestOpProtoAndCheckerMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("input", "input of cosine op");
AddOutput("output", "output of cosine op");
auto my_checker = [](int i) {
PADDLE_ENFORCE(i % 2 == 0, "'test_attr' must be even!");
};
AddAttr<int>("test_attr", "a simple test attribute")
.AddCustomChecker(my_checker);
AddType("my_test_op");
AddComment("This is my_test op");
}
};
REGISTER_OP(MyTestOp, MyTestOpProtoAndCheckerMaker, my_test_op)
} // namespace framework
} // namespace paddle
#include "paddle/framework/op_registry.h"
#include <gtest/gtest.h>
#include "paddle/framework/operator.h"
#include "paddle/operators/demo_op.h"
using namespace paddle::framework;
namespace paddle {
namespace framework {
class CosineOp : public OperatorWithKernel {
public:
void Run(const OpRunContext* context) const override {
printf("%s\n", DebugString().c_str());
}
};
class CosineOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
public:
CosineOpProtoAndCheckerMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("input", "input of cosine op");
AddOutput("output", "output of cosine op");
AddAttr<float>("scale", "scale of cosine op")
.SetDefault(1.0)
.LargerThan(0.0);
AddType("cos");
AddComment("This is cos op");
}
};
REGISTER_OP(CosineOp, CosineOpProtoAndCheckerMaker, cos_sim)
class MyTestOp : public OperatorWithKernel {
public:
void Run(const OpRunContext* ctx) const override {
printf("%s\n", DebugString().c_str());
printf("test_attr = %d\n", ctx->op_->GetAttr<int>("test_attr"));
}
};
class MyTestOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
public:
MyTestOpProtoAndCheckerMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("input", "input of cosine op");
AddOutput("output", "output of cosine op");
auto my_checker = [](int i) {
PADDLE_ENFORCE(i % 2 == 0, "'test_attr' must be even!");
};
AddAttr<int>("test_attr", "a simple test attribute")
.AddCustomChecker(my_checker);
AddType("my_test_op");
AddComment("This is my_test op");
}
};
REGISTER_OP(MyTestOp, MyTestOpProtoAndCheckerMaker, my_test_op)
} // namespace framework
} // namespace paddle
TEST(OpRegistry, CreateOp) {
paddle::framework::OpDesc op_desc;
......@@ -7,19 +64,19 @@ TEST(OpRegistry, CreateOp) {
op_desc.add_inputs("aa");
op_desc.add_outputs("bb");
float scale = 3.3;
auto attr = op_desc.mutable_attrs()->Add();
attr->set_name("scale");
attr->set_type(paddle::framework::AttrType::FLOAT);
attr->set_f(3.3);
attr->set_f(scale);
paddle::framework::OpBase* op =
paddle::framework::OperatorBase* op =
paddle::framework::OpRegistry::CreateOp(op_desc);
std::string debug_str = op->Run();
std::string str = "CosineOp runs! scale = " + std::to_string(3.3);
ASSERT_EQ(str.size(), debug_str.size());
for (size_t i = 0; i < debug_str.length(); ++i) {
ASSERT_EQ(debug_str[i], str[i]);
}
auto scope = std::make_shared<Scope>();
auto dev_ctx = DeviceContext();
op->Run(scope, &dev_ctx);
float scale_get = op->GetAttr<float>("scale");
ASSERT_EQ(scale_get, scale);
}
TEST(OpRegistry, IllegalAttr) {
......@@ -35,7 +92,7 @@ TEST(OpRegistry, IllegalAttr) {
bool caught = false;
try {
paddle::framework::OpBase* op __attribute__((unused)) =
paddle::framework::OperatorBase* op __attribute__((unused)) =
paddle::framework::OpRegistry::CreateOp(op_desc);
} catch (paddle::framework::EnforceNotMet err) {
caught = true;
......@@ -54,15 +111,14 @@ TEST(OpRegistry, DefaultValue) {
op_desc.add_inputs("aa");
op_desc.add_outputs("bb");
paddle::framework::OpBase* op =
ASSERT_TRUE(op_desc.IsInitialized());
paddle::framework::OperatorBase* op =
paddle::framework::OpRegistry::CreateOp(op_desc);
std::string debug_str = op->Run();
float default_value = 1.0;
std::string str = "CosineOp runs! scale = " + std::to_string(default_value);
ASSERT_EQ(str.size(), debug_str.size());
for (size_t i = 0; i < debug_str.length(); ++i) {
ASSERT_EQ(debug_str[i], str[i]);
}
auto scope = std::make_shared<Scope>();
auto dev_ctx = DeviceContext();
op->Run(scope, &dev_ctx);
ASSERT_EQ(op->GetAttr<float>("scale"), 1.0);
}
TEST(OpRegistry, CustomChecker) {
......@@ -74,7 +130,7 @@ TEST(OpRegistry, CustomChecker) {
// attr 'test_attr' is not set
bool caught = false;
try {
paddle::framework::OpBase* op __attribute__((unused)) =
paddle::framework::OperatorBase* op __attribute__((unused)) =
paddle::framework::OpRegistry::CreateOp(op_desc);
} catch (paddle::framework::EnforceNotMet err) {
caught = true;
......@@ -93,7 +149,7 @@ TEST(OpRegistry, CustomChecker) {
attr->set_i(3);
caught = false;
try {
paddle::framework::OpBase* op __attribute__((unused)) =
paddle::framework::OperatorBase* op __attribute__((unused)) =
paddle::framework::OpRegistry::CreateOp(op_desc);
} catch (paddle::framework::EnforceNotMet err) {
caught = true;
......@@ -111,12 +167,16 @@ TEST(OpRegistry, CustomChecker) {
attr->set_name("test_attr");
attr->set_type(paddle::framework::AttrType::INT);
attr->set_i(4);
paddle::framework::OpBase* op =
paddle::framework::OperatorBase* op =
paddle::framework::OpRegistry::CreateOp(op_desc);
std::string debug_str = op->Run();
std::string str = "MyTestOp runs! test_attr = " + std::to_string(4);
ASSERT_EQ(str.size(), debug_str.size());
for (size_t i = 0; i < debug_str.length(); ++i) {
ASSERT_EQ(debug_str[i], str[i]);
}
auto dev_ctx = DeviceContext();
auto scope = std::make_shared<Scope>();
op->Run(scope, &dev_ctx);
int test_attr = op->GetAttr<int>("test_attr");
ASSERT_EQ(test_attr, 4);
}
int main(int argc, char** argv) {
testing::InitGoogleTest(&argc, argv);
return RUN_ALL_TESTS();
}
\ No newline at end of file
/* 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/operator.h"
namespace paddle {
namespace framework {
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 << "]\n";
ss << "attr_keys = [";
for (auto& attr : attrs_) {
ss << attr.first << ", ";
}
ss << "]\n";
return ss.str();
}
const Variable* OpRunContext::Input(int index) const {
return scope_->GetVariable(op_->inputs_[index]);
}
Variable* OpRunContext::Output(int index) const {
return scope_->GetVariable(op_->outputs_[index]);
}
} // namespace framework
} // namespace paddle
\ No newline at end of file
/* 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 <string>
#include <unordered_map>
#include <vector>
#include "paddle/framework/attr_checker.h"
#include "paddle/framework/op_desc.pb.h"
#include "paddle/framework/scope.h"
#include "paddle/utils/Error.h"
namespace paddle {
namespace framework {
class OperatorBase;
class DeviceContext {};
/**
* OpRunContext is the only parameter of Operator's Run function.
* Run will get input/output variables, state such as momentum and
* device resource such as CUDA stream, cublas handle, etc. from
* OpRunContext. User should construct it before run the Operator.
*/
class OpRunContext {
public:
OpRunContext(const OperatorBase* op, const std::shared_ptr<Scope> scope,
const DeviceContext* device_context)
: op_(op), scope_(scope), device_context_(device_context) {}
const Variable* Input(int index) const;
Variable* Output(int index) const;
public:
const OperatorBase* op_;
const std::shared_ptr<Scope> scope_;
const DeviceContext* device_context_;
};
/**
* OperatorBase has the basic element that Net will call to do computation.
* Only CreateOperator from OpRegistry will new Operator directly. User
* should always construct a proto message OpDesc and call
* OpRegistry::CreateOp(op_desc) to get an Operator instance.
*/
class OperatorBase {
public:
virtual ~OperatorBase() {}
template <typename T>
inline const T& GetAttr(const std::string& name) const {
PADDLE_ENFORCE(attrs_.count(name) != 0, "%s should be in AttributeMap",
name);
return boost::get<T>(attrs_.at(name));
}
std::string DebugString() const;
/// InferShape infer the size of Variables used by this Operator with
/// information inside scope
virtual void InferShape(const std::shared_ptr<Scope>& scope) const = 0;
/// Net will call this function to Run an op.
virtual void Run(const std::shared_ptr<Scope>& scope,
const DeviceContext* dev_ctx) const = 0;
public:
OpDesc desc_;
std::vector<std::string> inputs_;
std::vector<std::string> outputs_;
AttributeMap attrs_;
};
class OperatorWithKernel : public OperatorBase {
public:
virtual ~OperatorWithKernel() {}
virtual void InferShape(const std::shared_ptr<Scope>& scope) const {}
void Run(const std::shared_ptr<Scope>& scope,
const DeviceContext* dev_ctx) const {
OpRunContext op_ctx(this, scope, dev_ctx);
Run(&op_ctx);
}
/// when implement an Op, your should implement this function.
/// this function should be moved to OpKernel later
virtual void Run(const OpRunContext* context) const = 0;
};
} // namespace framework
} // namespace paddle
/* 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/operator.h"
#include "gtest/gtest.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace framework {
class OperatorTest : public OperatorWithKernel {
public:
void Run(const OpRunContext* ctx) const override {
float scale = ctx->op_->GetAttr<float>("scale");
PADDLE_ENFORCE(ctx->Input(0) == nullptr, "Input(0) should not initialized");
PADDLE_ENFORCE(ctx->Output(0) == nullptr,
"Output(1) should not initialized");
auto output1 = ctx->scope_->CreateVariable("output1");
PADDLE_ENFORCE(output1 != nullptr, "should create output1 from scope");
printf("get attr %s = %f\n", "scale", scale);
printf("%s\n", DebugString().c_str());
}
};
class OperatorTestProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
public:
OperatorTestProtoAndCheckerMaker(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);
AddType("test_operator");
AddComment("This is test op");
}
};
REGISTER_OP(OperatorTest, OperatorTestProtoAndCheckerMaker, test_operator)
TEST(OperatorBase, DebugString) {
OpDesc op_desc;
op_desc.set_type("test_operator");
std::vector<std::string> inputs = {"IN1", "IN2"};
for (auto& input : inputs) {
op_desc.add_inputs(input);
}
std::vector<std::string> outputs = {"OUT1", "OUT2"};
for (auto& output : outputs) {
op_desc.add_outputs(output);
}
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);
DeviceContext device_context;
auto scope = std::make_shared<Scope>();
OperatorBase* op = paddle::framework::OpRegistry::CreateOp(op_desc);
ASSERT_EQ(op->inputs_, inputs);
ASSERT_EQ(op->outputs_, outputs);
ASSERT_EQ(op->GetAttr<float>("scale"), scale);
op->Run(scope, &device_context);
}
} // namespace framework
} // namespace paddle
\ No newline at end of file
---
Language: Cpp
BasedOnStyle: Google
Standard: Cpp11
...
#pragma once
#include "paddle/framework/op_registry.h"
using namespace paddle::framework;
namespace paddle {
namespace operators {
class CosineOp : public OperatorWithKernel {
public:
void Run(const OpRunContext *context) const override {
printf("%s\n", DebugString().c_str());
}
};
class CosineOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
public:
CosineOpProtoAndCheckerMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("input", "input of cosine op");
AddOutput("output", "output of cosine op");
AddAttr<float>("scale", "scale of cosine op")
.SetDefault(1.0)
.LargerThan(0.0);
AddType("cos");
AddComment("This is cos op");
}
};
REGISTER_OP(CosineOp, CosineOpProtoAndCheckerMaker, cos_sim)
class MyTestOp : public OperatorWithKernel {
public:
void Run(const OpRunContext *context) const override {
printf("%s\n", DebugString().c_str());
}
};
class MyTestOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
public:
MyTestOpProtoAndCheckerMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("input", "input of cosine op");
AddOutput("output", "output of cosine op");
auto my_checker = [](int i) {
PADDLE_ENFORCE(i % 2 == 0, "'test_attr' must be even!");
};
AddAttr<int>("test_attr", "a simple test attribute")
.AddCustomChecker(my_checker);
AddType("my_test_op");
AddComment("This is my_test op");
}
};
REGISTER_OP(MyTestOp, MyTestOpProtoAndCheckerMaker, my_test_op)
} // namespace operators
} // namespace operators
......@@ -4,3 +4,5 @@ nv_test(cuda_test SRCS cuda_test.cu)
cc_library(place SRCS place.cc)
cc_test(place_test SRCS place_test.cc DEPS place glog gflags)
nv_test(device_context_test SRCS device_context_test.cc DEPS dynamic_loader place eigen3 glog gflags)
/* 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/enforce.h"
#ifndef PADDLE_ONLY_CPU
#include "paddle/platform/cuda.h"
#include "paddle/platform/dynload/cublas.h"
#include "paddle/platform/dynload/cudnn.h"
#include "paddle/platform/dynload/curand.h"
#define EIGEN_USE_GPU
#endif
#include "paddle/platform/place.h"
#include "unsupported/Eigen/CXX11/Tensor"
namespace paddle {
namespace platform {
class DeviceContext {
public:
virtual ~DeviceContext() {}
};
class CPUDeviceContext : public DeviceContext {};
#ifndef PADDLE_ONLY_CPU
class GPUPlaceGuard {
public:
explicit GPUPlaceGuard(GPUPlace new_place) : previous_(GetCurrentDeviceId()) {
if (previous_ != new_place) {
paddle::platform::SetDeviceId(new_place.device);
}
}
~GPUPlaceGuard() { paddle::platform::SetDeviceId(previous_.device); }
private:
GPUPlace previous_;
};
class CUDADeviceContext : public DeviceContext {
public:
explicit CUDADeviceContext(const GPUPlace gpu_place) : gpu_place_(gpu_place) {
GPUPlaceGuard guard(gpu_place_);
paddle::platform::throw_on_error(cudaStreamCreate(&stream_),
"cudaStreamCreate failed");
eigen_stream_ = new Eigen::CudaStreamDevice(&stream_);
eigen_device_ = new Eigen::GpuDevice(eigen_stream_);
}
void Wait() {
paddle::platform::throw_on_error(cudaStreamSynchronize(stream_),
"cudaStreamSynchronize failed");
}
cudaStream_t stream() { return stream_; }
Eigen::GpuDevice eigen_device() { return *eigen_device_; }
cublasHandle_t cublas_handle() {
if (!blas_handle_) {
GPUPlaceGuard guard(gpu_place_);
PADDLE_ENFORCE(paddle::platform::dynload::cublasCreate(&blas_handle_) ==
CUBLAS_STATUS_SUCCESS,
"cublasCreate failed");
PADDLE_ENFORCE(paddle::platform::dynload::cublasSetStream(
blas_handle_, stream_) == CUBLAS_STATUS_SUCCESS,
"cublasSetStream failed");
}
return blas_handle_;
}
cudnnHandle_t cudnn_handle() {
if (!dnn_handle_) {
GPUPlaceGuard guard(gpu_place_);
PADDLE_ENFORCE(paddle::platform::dynload::cudnnCreate(&dnn_handle_) ==
CUDNN_STATUS_SUCCESS,
"cudnnCreate failed");
PADDLE_ENFORCE(paddle::platform::dynload::cudnnSetStream(
dnn_handle_, stream_) == CUDNN_STATUS_SUCCESS,
"cudnnSetStream failed");
}
return dnn_handle_;
}
curandGenerator_t curand_generator() {
if (!rand_generator_) {
GPUPlaceGuard guard(gpu_place_);
PADDLE_ENFORCE(paddle::platform::dynload::curandCreateGenerator(
&rand_generator_, CURAND_RNG_PSEUDO_DEFAULT) ==
CURAND_STATUS_SUCCESS,
"curandCreateGenerator failed");
PADDLE_ENFORCE(
paddle::platform::dynload::curandSetPseudoRandomGeneratorSeed(
rand_generator_, random_seed_) == CURAND_STATUS_SUCCESS,
"curandSetPseudoRandomGeneratorSeed failed");
PADDLE_ENFORCE(paddle::platform::dynload::curandSetStream(
rand_generator_, stream_) == CURAND_STATUS_SUCCESS,
"curandSetStream failed");
}
return rand_generator_;
}
~CUDADeviceContext() {
Wait();
if (blas_handle_) {
PADDLE_ENFORCE(paddle::platform::dynload::cublasDestroy(blas_handle_) ==
CUBLAS_STATUS_SUCCESS,
"cublasDestroy failed");
}
if (dnn_handle_) {
PADDLE_ENFORCE(paddle::platform::dynload::cudnnDestroy(dnn_handle_) ==
CUDNN_STATUS_SUCCESS,
"cudnnDestroy failed");
}
if (rand_generator_) {
PADDLE_ENFORCE(paddle::platform::dynload::curandDestroyGenerator(
rand_generator_) == CURAND_STATUS_SUCCESS,
"curandDestroyGenerator failed");
}
delete eigen_stream_;
delete eigen_device_;
paddle::platform::throw_on_error(cudaStreamDestroy(stream_),
"cudaStreamDestroy failed");
}
private:
GPUPlace gpu_place_;
cudaStream_t stream_;
Eigen::CudaStreamDevice* eigen_stream_;
Eigen::GpuDevice* eigen_device_;
cublasHandle_t blas_handle_{nullptr};
cudnnHandle_t dnn_handle_{nullptr};
int random_seed_;
curandGenerator_t rand_generator_{nullptr};
};
#endif
} // namespace platform
} // namespace paddle
/* 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/platform/device_context.h"
#include "gtest/gtest.h"
TEST(CUDADeviceContext, Init) {
int count = paddle::platform::GetDeviceCount();
for (int i = 0; i < count; i++) {
paddle::platform::CUDADeviceContext* device_context =
new paddle::platform::CUDADeviceContext(i);
Eigen::GpuDevice gpu_device = device_context->eigen_device();
ASSERT_NE(nullptr, gpu_device.stream());
cudnnHandle_t cudnn_handle = device_context->cudnn_handle();
ASSERT_NE(nullptr, cudnn_handle);
cublasHandle_t cublas_handle = device_context->cublas_handle();
ASSERT_NE(nullptr, cublas_handle);
curandGenerator_t curand_handle = device_context->curand_generator();
ASSERT_NE(nullptr, curand_handle);
delete device_context;
}
}
cc_library(paddle_pybind SHARED SRCS pybind.cc DEPS pybind python)
/* 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/scope.h>
#include <pybind11/pybind11.h>
namespace py = pybind11;
namespace pd = paddle::framework;
PYBIND11_PLUGIN(core) {
py::module m("core", "C++ core of Paddle Paddle");
py::class_<pd::Variable>(m, "Variable", R"DOC(Variable Class.
All parameter, weight, gradient are variables in Paddle.
)DOC")
.def("is_int", [](const pd::Variable& var) { return var.IsType<int>(); })
.def("set_int",
[](pd::Variable& var, int val) -> void {
*var.GetMutable<int>() = val;
})
.def("get_int",
[](const pd::Variable& var) -> int { return var.Get<int>(); });
py::class_<pd::Scope, std::shared_ptr<pd::Scope>>(m, "Scope")
.def(py::init<const std::shared_ptr<pd::Scope>&>())
.def("get_var",
&pd::Scope::GetVariable,
py::return_value_policy::reference)
.def("create_var",
&pd::Scope::CreateVariable,
py::return_value_policy::reference);
return m.ptr();
}
\ No newline at end of file
......@@ -22,7 +22,8 @@ DECLARE_string(save_dir);
namespace paddle {
NewRemoteParameterUpdater::NewRemoteParameterUpdater(
const OptimizationConfig &config, const std::string pserverSpec)
: parameterClient_(-1),
: trainerConfig_(config),
parameterClient_(-1),
newParameters_(nullptr),
newGradients_(nullptr),
pserverSpec_(pserverSpec) {}
......@@ -51,7 +52,22 @@ void NewRemoteParameterUpdater::init(
LOG(INFO) << "paddle_begin_init_params start";
for (int i = 0; i < parameterSize(); ++i) {
auto paramConfig = parameters_[i]->getConfig();
std::string bytes = paramConfig.SerializeAsString();
LOG(INFO) << "old param config: " << paramConfig.DebugString();
// FIXME(typhoonzero): convert old paramConfig to optimizerConfig
OptimizerConfig optimizeConfigV2;
auto sgdConfigV2 = optimizeConfigV2.mutable_sgd();
sgdConfigV2->set_momentum(paramConfig.momentum());
sgdConfigV2->set_decay(paramConfig.decay_rate());
optimizeConfigV2.set_lr_policy(paddle::OptimizerConfig::Const);
auto constlr = optimizeConfigV2.mutable_const_lr();
constlr->set_learning_rate(paramConfig.learning_rate());
if (trainerConfig_.algorithm() == "sgd") {
optimizeConfigV2.set_optimizer(paddle::OptimizerConfig::SGD);
// FIXME: config all algorithms
} else {
optimizeConfigV2.set_optimizer(paddle::OptimizerConfig::SGD);
}
std::string bytes = optimizeConfigV2.SerializeAsString();
const char *array = bytes.data();
int size = (int)bytes.size();
paddle_init_param(
......@@ -83,4 +99,4 @@ void NewRemoteParameterUpdater::finishBatch(real cost) {
void NewRemoteParameterUpdater::startPass() {}
bool NewRemoteParameterUpdater::finishPass() { return true; }
}
} // namespace paddle
......@@ -16,6 +16,7 @@ limitations under the License. */
#include <functional>
#include <thread>
#include "OptimizerConfig.pb.h"
#include "ParameterUpdater.h"
#include "libpaddle_pserver_cclient.h"
#include "paddle/pserver/ParameterClient2.h"
......@@ -101,6 +102,7 @@ private:
}
protected:
const OptimizationConfig& trainerConfig_;
/// internal parameter client object for exchanging data with pserver
paddle_pserver_client parameterClient_;
/// the parameters for new pserver client
......
......@@ -26,10 +26,17 @@ endif(WITH_GOLANG)
configure_file(${CMAKE_CURRENT_SOURCE_DIR}/setup.py.in
${CMAKE_CURRENT_BINARY_DIR}/setup.py)
add_custom_command(OUTPUT ${PROJ_ROOT}/python/paddle/v2/framework/core.so
COMMAND cmake -E copy $<TARGET_FILE:paddle_pybind> ${PROJ_ROOT}/python/paddle/v2/framework/core.so
DEPENDS paddle_pybind)
add_custom_target(copy_paddle_pybind ALL DEPENDS ${PROJ_ROOT}/python/paddle/v2/framework/core.so)
add_custom_command(OUTPUT ${OUTPUT_DIR}/.timestamp
COMMAND env ${py_env} ${PYTHON_EXECUTABLE} setup.py bdist_wheel
COMMAND ${CMAKE_COMMAND} -E touch ${OUTPUT_DIR}/.timestamp
DEPENDS gen_proto_py framework_py_proto ${PY_FILES} ${external_project_dependencies} ${COPY_PADDLE_MASTER})
DEPENDS gen_proto_py copy_paddle_pybind framework_py_proto ${PY_FILES} ${external_project_dependencies} ${COPY_PADDLE_MASTER})
add_custom_target(paddle_python ALL DEPENDS
${OUTPUT_DIR}/.timestamp)
......
......@@ -1395,7 +1395,7 @@ def inputs(layers, *args):
if len(args) != 0:
layers.extend(args)
Inputs(* [l.name for l in layers])
Inputs(*[l.name for l in layers])
def outputs(layers, *args):
......@@ -1408,6 +1408,8 @@ def outputs(layers, *args):
:return:
"""
traveled = set()
def __dfs_travel__(layer,
predicate=lambda x: x.layer_type == LayerType.DATA):
"""
......@@ -1419,6 +1421,11 @@ def outputs(layers, *args):
:type layer: LayerOutput
:return:
"""
if layer in traveled:
return []
else:
traveled.add(layer)
assert isinstance(layer, LayerOutput), "layer is %s" % (layer)
retv = []
if layer.parents is not None:
......@@ -1438,7 +1445,7 @@ def outputs(layers, *args):
assert len(layers) > 0
if HasInputsSet(): # input already set
Outputs(* [l.name for l in layers])
Outputs(*[l.name for l in layers])
return # just return outputs.
if len(layers) != 1:
......
......@@ -6,6 +6,7 @@ img_layers img_trans_layers util_layers simple_rnn_layers unused_layers test_cos
test_rnn_group shared_fc shared_lstm shared_gru test_cost_layers_with_weight
test_spp_layer test_bilinear_interp test_maxout test_bi_grumemory math_ops
test_seq_concat_reshape test_pad test_smooth_l1 test_multiplex_layer
test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_layer)
test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_layer
test_recursive_topology)
export whole_configs=(test_split_datasource)
......@@ -131,6 +131,7 @@ input_layer_names: "weight"
input_layer_names: "multi_class_label"
output_layer_names: "__cost_0__"
output_layer_names: "__mse_cost_0__"
output_layer_names: "__nce_layer_0__"
evaluators {
name: "classification_error_evaluator"
type: "classification_error"
......@@ -154,6 +155,7 @@ sub_models {
input_layer_names: "multi_class_label"
output_layer_names: "__cost_0__"
output_layer_names: "__mse_cost_0__"
output_layer_names: "__nce_layer_0__"
evaluator_names: "classification_error_evaluator"
is_recurrent_layer_group: false
}
......
type: "nn"
layers {
name: "data"
type: "data"
size: 100
active_type: ""
}
layers {
name: "__addto_0__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "data"
}
inputs {
input_layer_name: "data"
}
}
layers {
name: "__addto_1__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_0__"
}
inputs {
input_layer_name: "__addto_0__"
}
}
layers {
name: "__addto_2__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_1__"
}
inputs {
input_layer_name: "__addto_1__"
}
}
layers {
name: "__addto_3__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_2__"
}
inputs {
input_layer_name: "__addto_2__"
}
}
layers {
name: "__addto_4__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_3__"
}
inputs {
input_layer_name: "__addto_3__"
}
}
layers {
name: "__addto_5__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_4__"
}
inputs {
input_layer_name: "__addto_4__"
}
}
layers {
name: "__addto_6__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_5__"
}
inputs {
input_layer_name: "__addto_5__"
}
}
layers {
name: "__addto_7__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_6__"
}
inputs {
input_layer_name: "__addto_6__"
}
}
layers {
name: "__addto_8__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_7__"
}
inputs {
input_layer_name: "__addto_7__"
}
}
layers {
name: "__addto_9__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_8__"
}
inputs {
input_layer_name: "__addto_8__"
}
}
layers {
name: "__addto_10__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_9__"
}
inputs {
input_layer_name: "__addto_9__"
}
}
layers {
name: "__addto_11__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_10__"
}
inputs {
input_layer_name: "__addto_10__"
}
}
layers {
name: "__addto_12__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_11__"
}
inputs {
input_layer_name: "__addto_11__"
}
}
layers {
name: "__addto_13__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_12__"
}
inputs {
input_layer_name: "__addto_12__"
}
}
layers {
name: "__addto_14__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_13__"
}
inputs {
input_layer_name: "__addto_13__"
}
}
layers {
name: "__addto_15__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_14__"
}
inputs {
input_layer_name: "__addto_14__"
}
}
layers {
name: "__addto_16__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_15__"
}
inputs {
input_layer_name: "__addto_15__"
}
}
layers {
name: "__addto_17__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_16__"
}
inputs {
input_layer_name: "__addto_16__"
}
}
layers {
name: "__addto_18__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_17__"
}
inputs {
input_layer_name: "__addto_17__"
}
}
layers {
name: "__addto_19__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_18__"
}
inputs {
input_layer_name: "__addto_18__"
}
}
layers {
name: "__addto_20__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_19__"
}
inputs {
input_layer_name: "__addto_19__"
}
}
layers {
name: "__addto_21__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_20__"
}
inputs {
input_layer_name: "__addto_20__"
}
}
layers {
name: "__addto_22__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_21__"
}
inputs {
input_layer_name: "__addto_21__"
}
}
layers {
name: "__addto_23__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_22__"
}
inputs {
input_layer_name: "__addto_22__"
}
}
layers {
name: "__addto_24__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_23__"
}
inputs {
input_layer_name: "__addto_23__"
}
}
layers {
name: "__addto_25__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_24__"
}
inputs {
input_layer_name: "__addto_24__"
}
}
layers {
name: "__addto_26__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_25__"
}
inputs {
input_layer_name: "__addto_25__"
}
}
layers {
name: "__addto_27__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_26__"
}
inputs {
input_layer_name: "__addto_26__"
}
}
layers {
name: "__addto_28__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_27__"
}
inputs {
input_layer_name: "__addto_27__"
}
}
layers {
name: "__addto_29__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_28__"
}
inputs {
input_layer_name: "__addto_28__"
}
}
layers {
name: "__addto_30__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_29__"
}
inputs {
input_layer_name: "__addto_29__"
}
}
layers {
name: "__addto_31__"
type: "addto"
size: 100
active_type: ""
inputs {
input_layer_name: "__addto_30__"
}
inputs {
input_layer_name: "__addto_30__"
}
}
layers {
name: "__fc_layer_0__"
type: "fc"
size: 32
active_type: "relu"
inputs {
input_layer_name: "__addto_31__"
input_parameter_name: "___fc_layer_0__.w0"
}
bias_parameter_name: "___fc_layer_0__.wbias"
}
layers {
name: "__fc_layer_1__"
type: "fc"
size: 10
active_type: "softmax"
inputs {
input_layer_name: "__fc_layer_0__"
input_parameter_name: "___fc_layer_1__.w0"
}
bias_parameter_name: "___fc_layer_1__.wbias"
}
parameters {
name: "___fc_layer_0__.w0"
size: 3200
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 32
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___fc_layer_0__.wbias"
size: 32
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 32
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___fc_layer_1__.w0"
size: 320
initial_mean: 0.0
initial_std: 0.176776695297
dims: 32
dims: 10
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___fc_layer_1__.wbias"
size: 10
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 10
initial_strategy: 0
initial_smart: false
}
input_layer_names: "data"
output_layer_names: "__fc_layer_1__"
sub_models {
name: "root"
layer_names: "data"
layer_names: "__addto_0__"
layer_names: "__addto_1__"
layer_names: "__addto_2__"
layer_names: "__addto_3__"
layer_names: "__addto_4__"
layer_names: "__addto_5__"
layer_names: "__addto_6__"
layer_names: "__addto_7__"
layer_names: "__addto_8__"
layer_names: "__addto_9__"
layer_names: "__addto_10__"
layer_names: "__addto_11__"
layer_names: "__addto_12__"
layer_names: "__addto_13__"
layer_names: "__addto_14__"
layer_names: "__addto_15__"
layer_names: "__addto_16__"
layer_names: "__addto_17__"
layer_names: "__addto_18__"
layer_names: "__addto_19__"
layer_names: "__addto_20__"
layer_names: "__addto_21__"
layer_names: "__addto_22__"
layer_names: "__addto_23__"
layer_names: "__addto_24__"
layer_names: "__addto_25__"
layer_names: "__addto_26__"
layer_names: "__addto_27__"
layer_names: "__addto_28__"
layer_names: "__addto_29__"
layer_names: "__addto_30__"
layer_names: "__addto_31__"
layer_names: "__fc_layer_0__"
layer_names: "__fc_layer_1__"
input_layer_names: "data"
output_layer_names: "__fc_layer_1__"
is_recurrent_layer_group: false
}
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
din = data_layer(name='data', size=100)
enc = din
for i in range(32):
enc = addto_layer([enc, enc])
pred = fc_layer(
input=fc_layer(
input=enc, size=32, act=ReluActivation()),
size=10,
act=SoftmaxActivation())
outputs(pred)
......@@ -32,9 +32,9 @@ MD5_DEV_TEST = '7d7897317ddd8ba0ae5c5fa7248d3ff5'
# this is a small set of data for test. The original data is too large and will be add later.
URL_TRAIN = 'http://paddlepaddle.cdn.bcebos.com/demo/wmt_shrinked_data/wmt14.tgz'
MD5_TRAIN = '0791583d57d5beb693b9414c5b36798c'
# this is the pretrained model, whose bleu = 26.92
# BLEU of this trained model is 26.92
URL_MODEL = 'http://paddlepaddle.bj.bcebos.com/demo/wmt_14/wmt14_model.tar.gz'
MD5_MODEL = '4ce14a26607fb8a1cc23bcdedb1895e4'
MD5_MODEL = '0cb4a5366189b6acba876491c8724fa3'
START = "<s>"
END = "<e>"
......
add_python_test(test_framework test_protobuf.py)
add_python_test(test_framework test_protobuf.py test_scope.py)
......@@ -24,3 +24,7 @@ class TestFrameworkProto(unittest.TestCase):
attr.type = attr_type_lib.FLOAT
op_proto.type = "cos"
self.assertTrue(op_proto.IsInitialized())
if __name__ == "__main__":
unittest.main()
import paddle.v2.framework.core
import unittest
class TestScope(unittest.TestCase):
def test_create_destroy(self):
paddle_c = paddle.v2.framework.core
scope = paddle_c.Scope(None)
self.assertIsNotNone(scope)
scope_with_parent = paddle_c.Scope(scope)
self.assertIsNotNone(scope_with_parent)
def test_none_variable(self):
paddle_c = paddle.v2.framework.core
scope = paddle_c.Scope(None)
self.assertIsNone(scope.get_var("test"))
def test_create_var_get_var(self):
paddle_c = paddle.v2.framework.core
scope = paddle_c.Scope(None)
var_a = scope.create_var("var_a")
self.assertIsNotNone(var_a)
self.assertIsNotNone(scope.get_var('var_a'))
scope2 = paddle_c.Scope(scope)
self.assertIsNotNone(scope2.get_var('var_a'))
def test_var_get_int(self):
paddle_c = paddle.v2.framework.core
scope = paddle_c.Scope(None)
var = scope.create_var("test_int")
var.set_int(10)
self.assertTrue(var.is_int())
self.assertEqual(10, var.get_int())
if __name__ == '__main__':
unittest.main()
......@@ -66,6 +66,8 @@ class Optimizer(object):
if use_sparse_remote_updater:
gradient_machine.prefetch(in_args)
parameter_updater.getParametersRemote()
:param pserver_spec: pserver location, eg: localhost:3000
:return: parameter_updater
"""
if is_local:
......
......@@ -41,6 +41,7 @@ class SGD(object):
:type parameters: paddle.v2.parameters.Parameters
:param extra_layers: Some layers in the neural network graph are not
in the path of cost layer.
:param pserver_spec: pserver location, eg: localhost:3000
:type extra_layers: paddle.v2.config_base.Layer
"""
......
......@@ -29,7 +29,9 @@ setup(name='paddle',
description='Parallel Distributed Deep Learning',
install_requires=setup_requires,
packages=packages,
package_data={'paddle.v2.master': ['libpaddle_master.so'], },
package_data={'paddle.v2.master': ['libpaddle_master.so'],
'paddle.v2.framework': ['core.so']
},
package_dir={
'': '${CMAKE_CURRENT_SOURCE_DIR}',
# The paddle.v2.framework.proto will be generated while compiling.
......
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