updates of XTensorKeeper

source/train/XLeader.cpp

@@ -174,6 +174,17 @@ void XLeader::ResetParamGrad()
 }
 
 /* 
+prepare for running 
+>> config - the configuration
+>> model - the model that we run
+*/
+void XLeader::MakeAll(XConfig * config, XModel * model)
+{
+    SetServerModel(config, model);
+    ResetParamGrad();
+}
+
+/*
 wait for finished states (i.e., all workers finish their jobs)
 >> activeJobWorkers - indicates whether each job worker is active
 >> isToUpdate - indicates whether the model is updated

source/train/XLeader.h

@@ -139,6 +139,9 @@ public:
     /* set grad = 0 */
     void ResetParamGrad();
 
+    /* prepare for running */
+    void MakeAll(XConfig * config, XModel * model);
+
     /* wait for finished states (i.e., all workers finish their jobs) */
     void WaitForFinishing(const int * activeJobWorkers, const int isToUpdate);
 

source/train/XModel.cpp

@@ -33,24 +33,6 @@
 /* the nts (NiuTrans.Tensor) namespace */
 namespace nts {
 
-
-/* constructor */
-XTensorKeeper::XTensorKeeper()
-{
-    tensor = NULL;
-    flag = PARAM_STATE_NOT_READY;
-    trainFlag = PARAM_STATE_NOT_READY;
-    MUTEX_INIT(accessLock);
-    MUTEX_INIT(trainLock);
-}
-
-/* constructor */
-XTensorKeeper::~XTensorKeeper()
-{
-    MUTEX_DELE(accessLock);
-    MUTEX_DELE(trainLock);
-}
-
 /* constructor */
 XModel::XModel()
 {

source/train/XModel.h

@@ -32,53 +32,13 @@
 #ifndef __XMODEL_H__
 #define __XMODEL_H__
 
+#include "XTensorKeeper.h"
 #include "../network/XNet.h"
 #include "../tensor/XQueue.h"
 #include "../tensor/XList.h"
 
 namespace nts { // namespace nts(NiuTrans.Tensor)
 
-/* 
-parameter state
-1) not ready 
-2) ready 
-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, 
-                   PARAM_STATE_UPDATED };
-
-/* tensor keeper */
-class XTensorKeeper
-{
-public:
-    /* the parameter */
-    XTensor * tensor;
-
-    /* the parameter state */
-    PARAM_STATE flag;
-
-    /* the state of the entire training process 
-      (choosing from PARAM_STATE_NOT_READY and 
-      PARAM_STATE_UPDATED */
-    PARAM_STATE trainFlag;
-
-    /* a mutex for locking and unlocking the parameter */
-    MUTEX_HANDLE accessLock;
-
-    /* a mutex of the overall training */
-    MUTEX_HANDLE trainLock;
-
-public:
-    /* constructor */
-    XTensorKeeper();
-
-    /* constructor */
-    ~XTensorKeeper();
-};
-
 /* a model template for training */
 class XModel
 {

source/train/XTensorKeeper.cpp

+/*
+* NiuTrans.Tensor - an open-source tensor library
+* Copyright (C) 2016-2021
+* Natural Language Processing Lab, Northeastern University
+* and
+* NiuTrans Research
+* All rights reserved.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*   http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+/*
+* We define a class that keeps a tensor (could be either a parameter or
+* gradient).
+*
+* $Created by: XIAO Tong (xiaotong@mail.neu.edu.cn) 2021-03-25
+*/
+
+#include "XTensorKeeper.h"
+
+namespace nts { // namespace nts(NiuTrans.Tensor)
+
+/* constructor */
+XTensorKeeper::XTensorKeeper()
+{
+    tensor = NULL;
+    flag = PARAM_STATE_NOT_READY;
+    trainFlag = PARAM_STATE_NOT_READY;
+    MUTEX_INIT(accessLock);
+    MUTEX_INIT(trainLock);
+}
+
+/* constructor */
+XTensorKeeper::~XTensorKeeper()
+{
+    MUTEX_DELE(accessLock);
+    MUTEX_DELE(trainLock);
+}
+
+}
\ No newline at end of file

source/train/XTensorKeeper.h

+/*
+* NiuTrans.Tensor - an open-source tensor library
+* Copyright (C) 2016-2021
+* Natural Language Processing Lab, Northeastern University
+* and
+* NiuTrans Research
+* All rights reserved.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*   http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+/*
+* We define a class that keeps a tensor (could be either a parameter or 
+* gradient).
+*
+* $Created by: XIAO Tong (xiaotong@mail.neu.edu.cn) 2021-03-25
+* I will take the first shot of the COVID-19 vaccine this afternoon.
+*/
+
+#ifndef __XTENSORKEEPER_H__
+#define __XTENSORKEEPER_H__
+
+#include "../network/XNet.h"
+
+namespace nts { // namespace nts(NiuTrans.Tensor)
+
+/*
+parameter state
+1) not ready
+2) ready
+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,
+    PARAM_STATE_UPDATED
+};
+
+/* tensor keeper */
+class XTensorKeeper
+{
+public:
+    /* the parameter */
+    XTensor * tensor;
+
+    /* the parameter state */
+    PARAM_STATE flag;
+
+    /* the state of the entire training process
+    (choosing from PARAM_STATE_NOT_READY and
+    PARAM_STATE_UPDATED */
+    PARAM_STATE trainFlag;
+
+    /* a mutex for locking and unlocking the parameter */
+    MUTEX_HANDLE accessLock;
+
+    /* a mutex of the overall training */
+    MUTEX_HANDLE trainLock;
+
+public:
+    /* constructor */
+    XTensorKeeper();
+
+    /* constructor */
+    ~XTensorKeeper();
+};
+}
+
+#endif
\ No newline at end of file

source/train/XTrainer.cpp

@@ -120,7 +120,7 @@ void XTrainer::Run(XConfig * config, DataDistributeBase * dataDistributor,
     leader.AddJobBroadcastWorker();
     leader.AddJobParamterWorker(model->paramNum);
     //leader.SetInstantRun();
-    leader.SetServerModel(config, model);
+    leader.MakeAll(config, model);
     leader.Start();
 
     /* learning rate scheduler */