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NiuTrans.Tensor
Commit 554bdfd6 by xiaotong
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updates of XLeader

parent a864edb1
正在显示 包含 460 行增加 和 25 行删除
source/train/XLeader.cpp
...@@ -50,10 +50,17 @@ XLeader::~XLeader() ...@@ -50,10 +50,17 @@ XLeader::~XLeader()
/* intialize the leader */ /* intialize the leader */
void XLeader::Init() void XLeader::Init()
{ {
for (int i = 0; i < jworkers.count; i++) { for (int i = 0; i < jworkers.count; i++)
delete (XWorkerJob*)jworkers.GetItem(i); delete (XWorkerJob*)jworkers.GetItem(i);
}
jworkers.Clear(); jworkers.Clear();
for (int i = 0; i < cworkers.count; i++)
delete (XWorkerCollect*)cworkers.GetItem(i);
cworkers.Clear();
for (int i = 0; i < uworkers.count; i++)
delete (XWorkerUpdate*)uworkers.GetItem(i);
uworkers.Clear();
} }
/* set id */ /* set id */
...@@ -76,6 +83,24 @@ void XLeader::SetMode(XLEADER_MODE myMode) ...@@ -76,6 +83,24 @@ void XLeader::SetMode(XLEADER_MODE myMode)
{ {
mode = myMode; mode = myMode;
} }
/* start the workers */
void XLeader::Start()
{
for (int i = 0; i < jworkers.count; i++) {
XWorkerJob * worker = (XWorkerJob*)jworkers.GetItem(i);
worker->Start();
}
for (int i = 0; i < cworkers.count; i++) {
XWorkerJob * worker = (XWorkerJob*)cworkers.GetItem(i);
worker->Start();
}
for (int i = 0; i < uworkers.count; i++) {
XWorkerJob * worker = (XWorkerJob*)uworkers.GetItem(i);
worker->Start();
}
}
/* /*
add a number of job workers (given their device ids) add a number of job workers (given their device ids)
...@@ -87,25 +112,55 @@ void XLeader::AddJobWorker(XModel * model, int n, int * ids) ...@@ -87,25 +112,55 @@ void XLeader::AddJobWorker(XModel * model, int n, int * ids)
{ {
/* we keep the input model */ /* we keep the input model */
if (n >= 1) { if (n >= 1) {
jworkers.Add(model); XWorkerJob * worker = new XWorkerJob();
worker->SetModel(model);
jworkers.Add(worker);
} }
/* we clone the input model */ /* we clone the input model */
for (int i = 0; i < n - 1; i++) { for (int i = 0; i < n - 1; i++) {
jworkers.Add(model->Clone(ids[i])); XWorkerJob * worker = new XWorkerJob();
worker->SetModel(model->Clone(ids[i]));
jworkers.Add(worker);
} }
} }
/* /*
add a data-collecting worker
>> mode - the data-transfer mode of the worker
*/
void XLeader::AddJobCollectWorker(DATA_COLLECT_TYPE mode)
{
XWorkerCollect * worker = new XWorkerCollect();
worker->SetCollectMode(mode);
cworkers.Add(worker);
}
/*
add a model-update worker
>> model - the model
>> optimizer - the optimizer
*/
void XLeader::AddJobUpdateWorker(XModel * model, XOptimizer * optimizer)
{
XWorkerUpdate * worker = new XWorkerUpdate();
worker->SetOptimizer(optimizer);
uworkers.Add(worker);
}
/*
run the model (for one time) run the model (for one time)
>> config - the configuration >> config - the configuration
>> dataDistributor - data distributor >> dataDistributor - data distributor
>> model - the neural network that we want to run >> model - the neural network that we want to run
>> optimizer - the optimization method >> optimizer - the optimization method
<< return - if we can fetch the new data
*/ */
void XLeader::Run(XConfig * config, DataDistributeBase * dataDistributor, bool XLeader::Run(XConfig * config, DataDistributeBase * dataDistributor,
XModel * model, XOptimizer * optimizer) XModel * model, XOptimizer * optimizer)
{ {
bool isDataOK = true;
/* Feed the input to each worker and geneate the output. /* Feed the input to each worker and geneate the output.
For each worker, we define a job queue and enqueue jobs For each worker, we define a job queue and enqueue jobs
into it. into it.
...@@ -115,19 +170,47 @@ void XLeader::Run(XConfig * config, DataDistributeBase * dataDistributor, ...@@ -115,19 +170,47 @@ void XLeader::Run(XConfig * config, DataDistributeBase * dataDistributor,
XModel * model = worker->GetModel(); XModel * model = worker->GetModel();
/* get a batch of samples */ /* get a batch of samples */
dataDistributor->GetBatch(worker->GetInput()); bool fetched = dataDistributor->GetBatch(worker->GetInput());
/* job in the queue: refresh the model */ /* job in queue 1: refresh the model */
worker->AddJobRefresh(model); worker->AddJobRefresh(model);
/* job in the queue: run the model */ /* job in queue 1: run the model */
worker->AddJobNeuralNet(model, worker->GetInput(), worker->GetOutput()); worker->AddJobNeuralNet(model, worker->GetInput(), worker->GetOutput());
/* clear it */ /* clear it */
worker->Clear(); worker->Clear();
if (!fetched)
isDataOK = false;
}
XList members(jworkers.count);
for (int i = 0; i < jworkers.count; i++) {
XWorkerJob * worker = (XWorkerJob*)jworkers[i];
if (worker->GetModel() != model)
members.Add(worker->GetModel());
}
/* job in queue 2: collect the (gradient) data */
if (cworkers.count > 0) {
XWorkerCollect * collecter = (XWorkerCollect*)cworkers.GetItem(0);
collecter->AddJobCollect(&members, model);
}
else {
ShowNTErrors("No data-collecting workers!");
}
/* job in queue 3: update the model */
if (uworkers.count > 0) {
XWorkerUpdate * updater = (XWorkerUpdate*)uworkers.GetItem(0);
updater->AddJobUpdate(model, optimizer);
}
else {
ShowNTErrors("No model-update workers!");
} }
/* collect the (gradient) data and update the model */ return isDataOK;
} }
} /* end of the nts (NiuTrans.Tensor) namespace */ } /* end of the nts (NiuTrans.Tensor) namespace */
source/train/XLeader.h
...@@ -39,6 +39,8 @@ ...@@ -39,6 +39,8 @@
#include "XOptimizer.h" #include "XOptimizer.h"
#include "XBaseTemplate.h" #include "XBaseTemplate.h"
#include "XWorkerJob.h" #include "XWorkerJob.h"
#include "XWorkerCollect.h"
#include "XWorkerUpdate.h"
#include "../tensor/XConfig.h" #include "../tensor/XConfig.h"
#include "../tensor/XList.h" #include "../tensor/XList.h"
...@@ -65,9 +67,15 @@ protected: ...@@ -65,9 +67,15 @@ protected:
/* communication mode */ /* communication mode */
XLEADER_MODE mode; XLEADER_MODE mode;
/* job workers of the leader */ /* job workers */
XList jworkers; XList jworkers;
/* data-collecting workers */
XList cworkers;
/* model-update workers */
XList uworkers;
public: public:
/* constructor */ /* constructor */
XLeader(); XLeader();
...@@ -84,14 +92,23 @@ public: ...@@ -84,14 +92,23 @@ public:
/* get id */ /* get id */
int GetID(); int GetID();
/* start the workers */
void Start();
/* set the communication mode */ /* set the communication mode */
void SetMode(XLEADER_MODE myMode); void SetMode(XLEADER_MODE myMode);
/* add a number of job workers (given their device ids) */ /* add a number of job workers (given their device ids) */
void AddJobWorker(XModel * model, int n, int * ids); void AddJobWorker(XModel * model, int n, int * ids);
/* add a data-collecting worker */
void AddJobCollectWorker(DATA_COLLECT_TYPE mode = DATA_COLLECT_P2P);
/* add a model-update worker */
void AddJobUpdateWorker(XModel * model, XOptimizer * optimizer);
/* run the model (for one time) */ /* run the model (for one time) */
void Run(XConfig * config, DataDistributeBase * dataDistributor, bool Run(XConfig * config, DataDistributeBase * dataDistributor,
XModel * model, XOptimizer * optimizer); XModel * model, XOptimizer * optimizer);
}; };
......
source/train/XModel.h
...@@ -43,8 +43,12 @@ parameter state ...@@ -43,8 +43,12 @@ parameter state
1) not ready 1) not ready
2) ready 2) ready
3) the parameter has been collected from other models 3) the parameter has been collected from other models
4) the updated parameter
*/ */
enum PARAM_STATE { PARAM_STATE_NOT_READY, PARAM_STATE_READY, PARAM_STATE_COLLECTED }; enum PARAM_STATE { PARAM_STATE_NOT_READY,
PARAM_STATE_READY,
PARAM_STATE_COLLECTED,
PARAM_STATE_UPDATED };
/* a model template for training */ /* a model template for training */
class XModel class XModel
......
source/train/XOptimizer.cpp
...@@ -23,11 +23,68 @@ ...@@ -23,11 +23,68 @@
* This class define the template of the update rule in gradient based methods * This class define the template of the update rule in gradient based methods
* *
* $Created by: XIAO Tong (xiaotong@mail.neu.edu.cn) 2021-03-01 * $Created by: XIAO Tong (xiaotong@mail.neu.edu.cn) 2021-03-01
* March comes but there was a snow last night.
*/ */
#include "XOptimizer.h" #include "XOptimizer.h"
#include "../tensor/core/CHeader.h"
namespace nts { // namespace nts(NiuTrans.Tensor) namespace nts { // namespace nts(NiuTrans.Tensor)
/* constructor */
XOptimizer::XOptimizer()
{
Clear();
}
/* de-constructor */
XOptimizer::~XOptimizer()
{
}
/*
initialize the optimizer
>> config - the configuration
*/
void XOptimizer::Init(XConfig * config)
{
}
/* clear the optimizer */
void XOptimizer::Clear()
{
nstep = 0;
nepoch = 0;
lrate = 0;
}
/*
prepare for the update
>> model - the model that we want to update
*/
void XOptimizer::Prepare(XModel * model)
{
}
/*
record the update
>> model - the model that we want to update
*/
void XOptimizer::Note(XModel * model)
{
nstep++;
}
/*
update a parameter matrix
>> param - the parameter matrix
>> gard - the gradient
>> pid - the id of the parameter matrix
*/
void XOptimizer::UpdateParam(XTensor * param, XTensor * grad, int pid)
{
/* the delta rule
\theta_new = \theta_old - \grad * \lrate */
Sum(param, grad, param, -lrate);
}
} }
source/train/XOptimizer.h
...@@ -23,17 +23,57 @@ ...@@ -23,17 +23,57 @@
* This class define the template of the update rule in gradient based methods * This class define the template of the update rule in gradient based methods
* *
* $Created by: XIAO Tong (xiaotong@mail.neu.edu.cn) 2021-03-01 * $Created by: XIAO Tong (xiaotong@mail.neu.edu.cn) 2021-03-01
* March comes but there was a snow last night. * March came finally but there was a snow last night.
*/ */
#ifndef __XOPTIMIZER_H__ #ifndef __XOPTIMIZER_H__
#define __XOPTIMIZER_H__ #define __XOPTIMIZER_H__
#include "XModel.h"
#include "../tensor/XConfig.h"
namespace nts { // namespace nts(NiuTrans.Tensor) namespace nts { // namespace nts(NiuTrans.Tensor)
/* this class defines a template of the optimizer and
implement the simple delta-rule in SGD. */
class XOptimizer class XOptimizer
{ {
public:
/* update step number */
int nstep;
/* training epoch number */
int nepoch;
/* learning rate */
float lrate;
public:
/* constructor */
XOptimizer();
/* de-constructor */
~XOptimizer();
/* initialize the optimizer */
virtual
void Init(XConfig * config);
/* clear the optimizer */
virtual
void Clear();
/* prepare for the update */
virtual
void Prepare(XModel * model);
/* record the update */
virtual
void Note(XModel * model);
/* update a parameter matrix */
virtual
void UpdateParam(XTensor * param, XTensor * grad, int pid);
}; };
} }
......
source/train/XTrainer.cpp
...@@ -40,14 +40,55 @@ XTrainer::~XTrainer() ...@@ -40,14 +40,55 @@ XTrainer::~XTrainer()
{ {
} }
/*
get the device ids of the jobs
>> config - configuration
>> ids - the array of device ids
>> num - number of the jobs
>> maxDevNum - the maximum number of devices
*/
void XTrainer::GetDevIDs(XConfig * config, int * ids, int & num, int maxDevNum)
{
CheckNTErrors(maxDevNum > 0, "No data array for input!");
num = 0;
for (int i = 0; i < maxDevNum; i++) {
char dev[16];
sprintf(dev, "jobdev%d", i);
int id = config->GetInt(dev, -128);
if (id != -128) {
ids[num++] = id;
}
else
break;
}
if (num == 0) {
char dev[16];
sprintf(dev, "jobdev");
int id = config->GetInt(dev, -128);
if (id != -128)
ids[num++] = id;
}
if (num == 0) {
char dev[16];
sprintf(dev, "dev");
int id = config->GetInt(dev, -128);
if (id != -128)
ids[num++] = id;
}
}
/* /*
run the trainer (this is the core process) run the trainer (this is the core process)
>> config - configuration >> config - configuration
>> dataDistributor - the data distributor that generates an input for the net each time >> dataDistributor - the data distributor that generates an input for the net each time
>> model - the neural network >> model - the neural network
>> optimizer - the optimizer
*/ */
void XTrainer::Run(XConfig * config, DataDistributeBase * dataDistributor, void XTrainer::Run(XConfig * config, DataDistributeBase * dataDistributor,
XModel * model) XModel * model, XOptimizer * optimizer)
{ {
CheckNTErrors(config != NULL, "No input config!"); CheckNTErrors(config != NULL, "No input config!");
CheckNTErrors(dataDistributor != NULL, "No input data distributor!"); CheckNTErrors(dataDistributor != NULL, "No input data distributor!");
...@@ -57,11 +98,41 @@ void XTrainer::Run(XConfig * config, DataDistributeBase * dataDistributor, ...@@ -57,11 +98,41 @@ void XTrainer::Run(XConfig * config, DataDistributeBase * dataDistributor,
int nstep = config->GetInt("nstep", 100000); int nstep = config->GetInt("nstep", 100000);
int epoch = 0; int epoch = 0;
int step = 0; int step = 0;
int jobNum = 0;
int * ids = new int[MAX_DEVICE_NUM_TRAINING];
GetDevIDs(config, ids, jobNum, MAX_DEVICE_NUM_TRAINING);
/* create the server and workers */
XLeader leader;
leader.Init();
leader.AddJobWorker(model, jobNum, ids);
leader.AddJobCollectWorker();
leader.AddJobUpdateWorker(model, optimizer);
leader.Start();
/* train the model */
for (int epoch = 0; epoch < nepoch; epoch++) { for (int epoch = 0; epoch < nepoch; epoch++) {
if (step++ >= nstep)
bool ok = true;
dataDistributor->Start();
while (ok) {
/* one step of udpate */
ok = leader.Run(config, dataDistributor, model, optimizer);
if (step++ >= nstep)
break;
}
dataDistributor->End();
if (step >= nstep)
break; break;
} }
delete[] ids;
} }
} /* end of the nts (NiuTrans.Tensor) namespace */ } /* end of the nts (NiuTrans.Tensor) namespace */
source/train/XTrainer.h
...@@ -38,6 +38,8 @@ ...@@ -38,6 +38,8 @@
namespace nts { // namespace nts(NiuTrans.Tensor) namespace nts { // namespace nts(NiuTrans.Tensor)
#define MAX_DEVICE_NUM_TRAINING 128
/* /*
Training of neural networks with gradient methods. Here we suppose that we Training of neural networks with gradient methods. Here we suppose that we
are training NLP models. The routine could be: are training NLP models. The routine could be:
...@@ -59,8 +61,6 @@ the job to the workers and maintain the model. ...@@ -59,8 +61,6 @@ the job to the workers and maintain the model.
*/ */
class XTrainer class XTrainer
{ {
private:
public: public:
/* constructor */ /* constructor */
XTrainer(); XTrainer();
...@@ -68,9 +68,13 @@ public: ...@@ -68,9 +68,13 @@ public:
/* de-constructor */ /* de-constructor */
~XTrainer(); ~XTrainer();
/* get the device ids of the jobs */
void GetDevIDs(XConfig * config, int * ids, int & num, int maxDevNum);
/* run the leader (this is the core process) */ /* run the leader (this is the core process) */
virtual virtual
void Run(XConfig * config, DataDistributeBase * dataDistributor, XModel * model); void Run(XConfig * config, DataDistributeBase * dataDistributor,
XModel * model, XOptimizer * optimizer);
}; };
} }
#endif // __XTRAINER_H__ #endif // __XTRAINER_H__
\ No newline at end of file
source/train/XWorker.cpp
...@@ -39,6 +39,7 @@ XWorker::XWorker() ...@@ -39,6 +39,7 @@ XWorker::XWorker()
/* de-constructor */ /* de-constructor */
XWorker::~XWorker() XWorker::~XWorker()
{ {
Stop();
} }
/* set device id */ /* set device id */
...@@ -78,7 +79,7 @@ void XWorker::AddJob(void * job, XList * jobArgs) ...@@ -78,7 +79,7 @@ void XWorker::AddJob(void * job, XList * jobArgs)
/* start the work */ /* start the work */
void XWorker::Start() void XWorker::Start()
{ {
queue.StopJobConsumer(); queue.RunJobConsumer();
} }
/* stop the work */ /* stop the work */
......
source/train/XWorkerCollect.cpp
...@@ -91,7 +91,7 @@ void XWorkerCollect::CollectData(XList * sourceList, XModel * target, long sleep ...@@ -91,7 +91,7 @@ void XWorkerCollect::CollectData(XList * sourceList, XModel * target, long sleep
if (source->flags[j] != PARAM_STATE_COLLECTED && sp[j]->isGradFinished) { if (source->flags[j] != PARAM_STATE_COLLECTED && sp[j]->isGradFinished) {
/* data transmit */ /* data transmit */
CollectP2P(sp.GetItem(j), tp.GetItem(j)); CollectP2P(sp.GetItem(j)->grad, tp.GetItem(j)->grad);
/* reset the flag */ /* reset the flag */
source->flags[j] = PARAM_STATE_COLLECTED; source->flags[j] = PARAM_STATE_COLLECTED;
...@@ -134,11 +134,11 @@ void XWorkerCollect::CollectData(XList * sourceList, XModel * target, long sleep ...@@ -134,11 +134,11 @@ void XWorkerCollect::CollectData(XList * sourceList, XModel * target, long sleep
for (int i = 0; i < sourceList->count; i++) { for (int i = 0; i < sourceList->count; i++) {
XModel * source = (XModel*)sourceList->GetItem(i); XModel * source = (XModel*)sourceList->GetItem(i);
TensorList & sp = source->params; TensorList & sp = source->params;
tensorList.Add(sp.GetItem(j)); tensorList.Add(sp.GetItem(j)->grad);
} }
/* data transmit */ /* data transmit */
CollectReduceSum(&tensorList, tp.GetItem(j)); CollectReduceSum(&tensorList, tp.GetItem(j)->grad);
/* reset the flags */ /* reset the flags */
for (int i = 0; i < sourceList->count; i++) { for (int i = 0; i < sourceList->count; i++) {
...@@ -146,6 +146,7 @@ void XWorkerCollect::CollectData(XList * sourceList, XModel * target, long sleep ...@@ -146,6 +146,7 @@ void XWorkerCollect::CollectData(XList * sourceList, XModel * target, long sleep
source->flags[j] = PARAM_STATE_COLLECTED; source->flags[j] = PARAM_STATE_COLLECTED;
} }
target->flags[j] = PARAM_STATE_COLLECTED;
finished += sourceList->count; finished += sourceList->count;
} }
} }
...@@ -236,4 +237,25 @@ void XWorkerCollect::CollectAllReduce(XList * all) ...@@ -236,4 +237,25 @@ void XWorkerCollect::CollectAllReduce(XList * all)
ShowNTErrors("TODO!"); ShowNTErrors("TODO!");
} }
/*
add a new job of collecting data
>> sourceList - the list of models that we want collect data from
>> target - the destination of the collection
*/
bool XWorkerCollect::AddJobCollect(XList * sourceList, XModel * target)
{
CheckNTErrors(sourceList != NULL, "no input source model list!");
CheckNTErrors(target != NULL, "no input target model!");
XList args;
args.Add(this);
args.AddInt(sourceList->count);
args.AddList(sourceList);
args.Add(target);
queue.EnqueueJob((void*)(char*)XWorkerCollect::Collect, &args);
return true;
}
} }
source/train/XWorkerCollect.h
...@@ -22,7 +22,8 @@ ...@@ -22,7 +22,8 @@
/* /*
* The worker that collects data from workers. * The worker that collects data from workers.
* *
* $Created by: XIAO Tong (xiaotong@mail.neu.edu.cn) 2021-03-01 * $Created by: XIAO Tong (xiaotong@mail.neu.edu.cn) 2021-03-02
* minus 10 degrees centigrade comes again!
*/ */
#ifndef __XWORKERCOLLECT_H__ #ifndef __XWORKERCOLLECT_H__
...@@ -74,6 +75,9 @@ public: ...@@ -74,6 +75,9 @@ public:
/* all-reduce */ /* all-reduce */
void CollectAllReduce(XList * all); void CollectAllReduce(XList * all);
/* add a new job of collecting data */
bool AddJobCollect(XList * sourceList, XModel * target);
}; };
} }
......
source/train/XWorkerUpdate.cpp
...@@ -27,5 +27,107 @@ ...@@ -27,5 +27,107 @@
#include "XWorkerUpdate.h" #include "XWorkerUpdate.h"
namespace nts { // namespace nts(NiuTrans.Tensor) namespace nts { // namespace nts (NiuTrans.Tensor)
/* constructor */
XWorkerUpdate::XWorkerUpdate()
{
optimizer = NULL;
}
/* de-constructor */
XWorkerUpdate::~XWorkerUpdate()
{
}
/* set the optimizer */
void XWorkerUpdate::SetOptimizer(XOptimizer * myOptimizer)
{
optimizer = myOptimizer;
}
/* get the optimizer */
XOptimizer * XWorkerUpdate::GetOptimizer()
{
return optimizer;
}
/*
update the model
>> model - the model that we want to update
>> optimizer - the optimizer
>> sleepTime - waiting time in each update
*/
void XWorkerUpdate::UpdateModel(XModel * model, XOptimizer * optimizer, long sleepTime)
{
int finished = 0;
TensorList & params = model->params;
PARAM_STATE * flags = model->flags;
optimizer->Prepare(model);
while (1) {
for (int i = 0; i < params.count; i++) {
if (flags[i] == PARAM_STATE_COLLECTED) {
XTensor * param = params.GetItem(i);
XTensor * grad = param->grad;
/* update the parameter */
optimizer->UpdateParam(param, grad, i);
/* set the flag */
flags[i] = PARAM_STATE_UPDATED;
finished++;
}
}
if (finished == params.count)
break;
#ifdef _WIN32
Sleep((DWORD)sleepTime);
#else
sleep((unsigned)sleepTime / 1000);
#endif
}
optimizer->Note(model);
}
/*
wrapper of UpdateModel
>> args - arguments of the update
*/
void XWorkerUpdate::Update(XList * args)
{
CheckNTErrors(args != NULL && args->count > 3, "Illegal argument list!");
XWorkerUpdate * updater = (XWorkerUpdate*)args->GetItem(0);
XModel * model = (XModel*)args->GetItem(1);
XOptimizer * optimizer = (XOptimizer*)args->GetItem(2);
updater->UpdateModel(model, optimizer, SLEEP_TIME_IN_MODEL_UPDATE);
}
/*
add a new job of model update
>> model - the model that we want to update
>> optimizer - the optimizer
*/
bool XWorkerUpdate::AddJobUpdate(XModel * model, XOptimizer * optimizer)
{
CheckNTErrors(model != NULL, "No input model!");
CheckNTErrors(optimizer != NULL, "No optimizer!");
XList args;
args.Add(this);
args.Add(model);
args.Add(optimizer);
queue.EnqueueJob((void*)(char*)XWorkerUpdate::Update, &args);
return true;
}
} }
\ No newline at end of file
source/train/XWorkerUpdate.h
...@@ -29,13 +29,41 @@ ...@@ -29,13 +29,41 @@
#define __XWORKERUPDATE_H__ #define __XWORKERUPDATE_H__
#include "XWorker.h" #include "XWorker.h"
#include "XOptimizer.h"
namespace nts { // namespace nts(NiuTrans.Tensor) namespace nts { // namespace nts(NiuTrans.Tensor)
#define SLEEP_TIME_IN_MODEL_UPDATE 10
/* The class defines the model-update worker */ /* The class defines the model-update worker */
class XWorkerUpdate : public XWorker class XWorkerUpdate : public XWorker
{ {
protected:
/* the optimizer */
XOptimizer * optimizer;
public: public:
/* constructor */
XWorkerUpdate();
/* de-constructor */
~XWorkerUpdate();
/* set the optimizer */
void SetOptimizer(XOptimizer * myOptimizer);
/* get the optimizer */
XOptimizer * GetOptimizer();
/* update the model */
void UpdateModel(XModel * model, XOptimizer * optimizer, long sleepTime);
/* wrapper of UpdateModel */
static
void Update(XList * args);
/* add a new job of model update */
bool AddJobUpdate(XModel * model, XOptimizer * optimizer);
}; };
} }
......
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