remove gradMap and redefine XWorkerUpdate

source/train/XLeader.cpp

@@ -41,7 +41,6 @@ XLeader::XLeader()
 {
     id = -1;
     paramMap = NULL;
-    gradMap = NULL;
     modelNum = 0;
 }
 
@@ -385,11 +384,8 @@ void XLeader::DestroyParamMap()
     for(int i = 0; i < serverModel.paramNum; i++){
         if(paramMap != NULL)
             delete[] paramMap[i];
-        if(gradMap != NULL)
-            delete[] gradMap[i];
     }
     delete[] paramMap;
-    delete[] gradMap;
     modelNum = 0;
 }
 
@@ -411,13 +407,11 @@ void XLeader::MakeParamMap()
     if(modelCount != modelNum){
         DestroyParamMap();
         paramMap = new XTensorKeeper*[serverModel.paramNum];
-        gradMap  = new XTensorKeeper*[serverModel.paramNum];
     }
     
     for(int i = 0; i < serverModel.paramNum; i++){
         if(modelCount != modelNum){
             paramMap[i] = new XTensorKeeper[modelCount];
-            gradMap[i]  = new XTensorKeeper[modelCount];
         }
         
         for (int j = 0, c = 0; j < jworkers.count; j++) {
@@ -425,11 +419,9 @@ void XLeader::MakeParamMap()
             if (worker->GetWorkerType() == XWORKER_TYPE_JOB) {
                 XModel * model = ((XWorkerJob*)jworkers[j])->GetModel();
                 paramMap[i][c].tensor = model->params[i].tensor;
+                paramMap[i][c].grad = model->params[i].tensor->grad;
                 paramMap[i][c].flag = PARAM_STATE_NOT_READY;
-                paramMap[i][c].trainFlag = PARAM_STATE_NOT_READY;
-                gradMap[i][c].tensor = model->params[i].tensor->grad;
-                gradMap[i][c].flag = PARAM_STATE_NOT_READY;
-                gradMap[i][c].trainFlag = PARAM_STATE_NOT_READY;
+                paramMap[i][c].trainFlag = PARAM_STATE_NOT_READY;;
                 c++;
             }
             else {
@@ -600,33 +592,36 @@ void XLeader::RunUpdate(XConfig * config, XOptimizer * optimizer, const int * ac
             XTensorKeeper &paramServer = serverModel.params[j];
 
             /* isGradFinished is true only if the model finishes the computation
-            (in another process) */
+               (in another thread) */
             if (paramServer.flag != PARAM_STATE_NOT_READY || !paramServer.tensor->isGradFinished)
                 continue;
 
+            /* set the gradient tensor */
+            if (paramServer.grad != paramServer.tensor->grad)
+                paramServer.grad = paramServer.tensor->grad;
+
             /* check if all the models (or part of them) are ready */
             for (int n = 0, i = 0; n < jworkers.count; n++) {
                 XWorkerJob * worker = (XWorkerJob*)jworkers[n];
                 for (int m = 0; m < worker->GetModelNum(); m++, i++) {
-                    XTensorKeeper &paramSource = paramMap[j][i];
-                    XTensorKeeper &gradSource  = gradMap[j][i];
+                    XTensorKeeper &paramWorker = paramMap[j][i];
 
                     /* isGradFinished is true only if the model finishes the computation
-                    (in another process) */
-                    if (paramSource.flag == PARAM_STATE_NOT_READY && paramSource.tensor->isGradFinished) {
+                      (in another thread) */
+                    if (paramWorker.flag == PARAM_STATE_NOT_READY && paramWorker.tensor->isGradFinished) {
                         
                         /* get the gradient */
-                        gradSource.tensor = paramSource.tensor->grad;
+                        paramWorker.grad = paramWorker.tensor->grad;
                         
                         /* the job queue of updating parameter j */
                         XQueue* jobQueue = (XQueue*)jobQueues.GetItem(j);
 
                         /* data transmit */
-                        collecter->AddJobCollectDataP2P(jobQueue, gradSource.tensor, paramServer.tensor->grad);
+                        collecter->AddJobCollectDataP2P(jobQueue, paramWorker.grad, paramServer.grad);
                         collecter->AddJobEnqueueFinished(jobQueue);
 
                         /* reset the flag */
-                        paramSource.flag = PARAM_STATE_COLLECTED;
+                        paramWorker.flag = PARAM_STATE_COLLECTED;
                         finished++;
                         finishedCount[j]++;
 
@@ -638,10 +633,10 @@ void XLeader::RunUpdate(XConfig * config, XOptimizer * optimizer, const int * ac
                             if (updater != NULL) {
 
                                 /* update the parameters */
-                                updater->AddJobUpdate(jobQueue, &serverModel, j, optimizer);
+                                updater->AddJobUpdate(jobQueue, &paramServer, optimizer);
                                 updater->AddJobEnqueueFinished(jobQueue);
 
-                                /* broadcast the new parameter to other models*/
+                                /* broadcast the new parameter to other models */
                                 broadcaster->AddJobBroadcast(jobQueue, &serverModel, &membersAll, j);
                                 broadcaster->AddJobEnqueueFinished(jobQueue);
                             }

source/train/XLeader.h

@@ -101,10 +101,6 @@ protected:
        gradient of the loss with respect to the parameters. */
     XTensorKeeper ** paramMap;
     
-    /* map of parameter gradients. (x,y) indexes the
-       gradient of parameter x of worker y. */
-    XTensorKeeper ** gradMap;
-    
     /* number of model copies for paramMap and gradMap */
     int modelNum;
 

source/train/XOptimizer.cpp

@@ -87,9 +87,8 @@ void XOptimizer::Note(XModel * model)
 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)
+void XOptimizer::UpdateParam(XTensor * param, XTensor * grad)
 {
     /* the delta rule
        \theta_new = \theta_old - \grad * \lrate */

source/train/XOptimizer.h

@@ -77,7 +77,7 @@ public:
 
     /* update a parameter matrix */
     virtual
-    void UpdateParam(XTensor * param, XTensor * grad, int pid);
+    void UpdateParam(XTensor * param, XTensor * grad);
 
     /* get learning rate */
     float GetLearningRate();

source/train/XTensorKeeper.cpp

@@ -34,6 +34,7 @@ namespace nts { // namespace nts(NiuTrans.Tensor)
 XTensorKeeper::XTensorKeeper()
 {
     tensor = NULL;
+    grad = NULL;
     flag = PARAM_STATE_NOT_READY;
     trainFlag = PARAM_STATE_NOT_READY;
     MUTEX_INIT(accessLock);

source/train/XTensorKeeper.h

@@ -55,6 +55,9 @@ public:
     /* the parameter */
     XTensor * tensor;
 
+    /* the gradient */
+    XTensor * grad;
+
     /* the parameter state */
     PARAM_STATE flag;
 

source/train/XWorkerUpdate.cpp

@@ -55,26 +55,24 @@ XOptimizer * XWorkerUpdate::GetOptimizer()
 
 /* 
 update a parameter of a model 
->> model - the model that we want to update (on the server side)
->> pid - the parameter index
+>> paramKeeper -  the parameter keeper
 >> optimizer - the optimizer
 */
-void XWorkerUpdate::UpdateParameter(XModel * server, int pid,
-                                    XOptimizer * optimizer)
+void XWorkerUpdate::UpdateParameter(XTensorKeeper * paramKeeper, XOptimizer * optimizer)
 {
 
-    CheckNTErrors(server->params[pid].flag == PARAM_STATE_COLLECTED, "The state of the parameter is wrong!");
+    CheckNTErrors(paramKeeper->flag == PARAM_STATE_COLLECTED, "The state of the parameter is wrong!");
 
-    XTensor * param = server->params[pid].tensor;
-    XTensor * grad = param->grad;
+    XTensor * param = paramKeeper->tensor;
+    XTensor * grad = paramKeeper->grad;
 
     CheckNTErrors(grad != NULL, "No gradient!");
 
     /* update the parameter */
-    optimizer->UpdateParam(param, grad, pid);
+    optimizer->UpdateParam(param, grad);
 
     /* set the flag */
-    server->params[pid].flag = PARAM_STATE_UPDATED;
+    paramKeeper->flag = PARAM_STATE_UPDATED;
 }
 
 /* 
@@ -85,37 +83,32 @@ void XWorkerUpdate::Update(XList * args)
 {
     int paramCount = 0;
 
-    CheckNTErrors(args != NULL && args->count >= 4, "Illegal argument list!");
+    CheckNTErrors(args != NULL && args->count == 3, "Illegal argument list!");
 
     XWorkerUpdate * updater = (XWorkerUpdate*)args->GetItem(paramCount++);
-    XModel * server = (XModel*)args->GetItem(paramCount++);
-
-    int pid = args->GetInt(paramCount++);
+    XTensorKeeper * paramKeeper = (XTensorKeeper*)args->GetItem(paramCount++);
     XOptimizer * optimizer = (XOptimizer*)args->GetItem(paramCount++);
 
     if(updater != NULL)
-        updater->UpdateParameter(server, pid, optimizer);
+        updater->UpdateParameter(paramKeeper, optimizer);
 }
 
 /* 
 add a new job of model update (for a parameter) 
 >> jobQueue - the queue for sub-jobs executed in the job
->> model - the model that we want to update (on the server side)
->> pid - the parameter index
+>> paramKeeper -  the parameter keeper
 >> optimizer - the optimizer
 */
 bool XWorkerUpdate::AddJobUpdate(XQueue * jobQueue,
-                                 XModel * model, int pid,
+                                 XTensorKeeper * paramKeeper,
                                  XOptimizer * optimizer)
 {
-    CheckNTErrors(model != NULL, "No input model!");
+    CheckNTErrors(paramKeeper != NULL, "No input parameter keeper!");
     CheckNTErrors(optimizer != NULL, "No optimizer!");
-    CheckNTErrors(pid >= 0 && pid < model->paramNum, "Illegal parameter index!");
 
     XList args;
     args.Add(this);
-    args.Add(model);
-    args.AddInt(pid);
+    args.Add(paramKeeper);
     args.Add(optimizer);
     
     XQueue& queueRun = jobQueue != NULL ? *jobQueue : queue;

source/train/XWorkerUpdate.h

@@ -57,16 +57,14 @@ public:
     XOptimizer * GetOptimizer();
 
     /* update the parameter */
-    void UpdateParameter(XModel * server, int pid, XOptimizer * optimizer);
-
+    void UpdateParameter(XTensorKeeper * paramKeeper, XOptimizer * optimizer);
 
     /* wrapper of UpdateParameter */
     static
     void Update(XList * args);
 
-
     /* add a new job of model update (for a parameter) */
-    bool AddJobUpdate(XQueue * jobQueue, XModel * model, int pid, XOptimizer * optimizer);
+    bool AddJobUpdate(XQueue * jobQueue, XTensorKeeper * paramKeeper, XOptimizer * optimizer);
 };
 
 }