add a new class XLearningRate

source/train/XLearningRate.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.
+*/
+
+/*
+*
+* $Created by: XIAO Tong (xiaotong@mail.neu.edu.cn) 2021-03-16
+* I wore my coat again after the rain yesterday.
+*/
+
+#include "XLearningRate.h"
+#include <math.h>
+
+namespace nts { // namespace nts(NiuTrans.Tensor)
+
+/* constructor */
+XLearningRate::XLearningRate()
+{
+}
+
+/* de-constructor */
+XLearningRate::~XLearningRate()
+{
+}
+
+/* a Transformer-style scheduler. For more details, see
+"Attention is all need" by Vaswani at al. 
+>> lrate - the learning rate
+>> nstep - the update step number 
+>> nwarmup - the warmup step number 
+*/
+float XLearningRate::MakeLRTransformer(const float lrate, const int nstep, const int nwarmup)
+{
+    float lr = 0;
+    float warmupEndLR = lrate;
+    float warmupInitLR = 1e-7F;
+    float lrStep = (warmupEndLR - warmupInitLR) / nwarmup;
+    float decayFactor = warmupEndLR * (float)pow(float(nwarmup), 0.5F);
+
+    /* learning rate, scheduled by inverse square root */
+    if (nstep < nwarmup)
+        lr = warmupInitLR + nstep * lrStep;
+    else
+        lr = decayFactor * (float)pow((float)nstep, -0.5F);
+
+    return lr;
+}
+
+}
\ No newline at end of file

source/train/XLearningRate.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.
+*/
+
+/*
+* This a learning rate generator. E.g., one can adjust learning rate as
+* the training process proceeds.
+*
+* $Created by: XIAO Tong (xiaotong@mail.neu.edu.cn) 2021-03-16
+* I wore my coat again after the rain yesterday.
+*/
+
+#ifndef __XLEARNINGRATE_H__
+#define __XLEARNINGRATE_H__
+
+namespace nts { // namespace nts(NiuTrans.Tensor)
+
+/* Learning rate scheduler */
+class XLearningRate
+{
+public:
+    /* constructor */
+    XLearningRate();
+
+    /* de-constructor */
+    ~XLearningRate();
+
+    /* a Transformer-style scheduler */
+    float MakeLRTransformer(const float lrate, const int nstep, const int nwarmup);
+};
+
+}
+
+#endif
\ No newline at end of file

source/train/XOptimizer.cpp

@@ -96,4 +96,19 @@ void XOptimizer::UpdateParam(XTensor * param, XTensor * grad, int pid)
     _Sum(param, grad, param, -lrate);
     _Sum(param, grad, param, -lrate);
 }
 }
 
 
+/* get learning rate */
+float XOptimizer::GetLearningRate()
+{
+    return lrate;
+}
+
+/* 
+set learning rate 
+>> myLRate - the learning rate that we want to use
+*/
+void XOptimizer::SetLearningRate(float myLRate)
+{
+    lrate = myLRate;
+}
+
 }
 }

source/train/XOptimizer.h

@@ -78,6 +78,12 @@ public:
     /* update a parameter matrix */
     /* update a parameter matrix */
     virtual
     virtual
     void UpdateParam(XTensor * param, XTensor * grad, int pid);
     void UpdateParam(XTensor * param, XTensor * grad, int pid);
+
+    /* get learning rate */
+    float GetLearningRate();
+
+    /* set learning rate */
+    void SetLearningRate(float myLRate);
 };
 };
 
 
 }
 }

source/train/XTrainer.cpp

@@ -26,6 +26,7 @@
 */
 */
 
 
 #include "XTrainer.h"
 #include "XTrainer.h"
+#include "XLearningRate.h"
 
 
 /* the nts (NiuTrans.Tensor) namespace */
 /* the nts (NiuTrans.Tensor) namespace */
 namespace nts {
 namespace nts {
@@ -99,6 +100,8 @@ void XTrainer::Run(XConfig * config, DataDistributeBase * dataDistributor,
     int stepAll = 0;
     int stepAll = 0;
     int jobNum = 0;
     int jobNum = 0;
     int accumulation = config->GetInt("accumulation", 1);
     int accumulation = config->GetInt("accumulation", 1);
+    int nwarmup = config->GetInt("nwarmup", 0);
+    int lrate = optimizer->GetLearningRate();
 
 
     CheckNTErrors(accumulation >= 1, "accumulation must be larger than 0!");
     CheckNTErrors(accumulation >= 1, "accumulation must be larger than 0!");
 
 
@@ -119,6 +122,9 @@ void XTrainer::Run(XConfig * config, DataDistributeBase * dataDistributor,
     leader.SetServerModel(config, model);
     leader.SetServerModel(config, model);
     leader.Start();
     leader.Start();
 
 
+    /* learning rate scheduler */
+    XLearningRate LRScheduler;
+
     double startT = GetClockSec();
     double startT = GetClockSec();
 
 
     XPRINT(1, stderr, "[INFO] Initializing the model ... [DONE]\n");
     XPRINT(1, stderr, "[INFO] Initializing the model ... [DONE]\n");
@@ -132,6 +138,10 @@ void XTrainer::Run(XConfig * config, DataDistributeBase * dataDistributor,
         while (ok) {
         while (ok) {
             if (++stepAll % accumulation == 0) {
             if (++stepAll % accumulation == 0) {
 
 
+                /* learning rate scheduling */
+                if (nwarmup > 0)
+                    optimizer->SetLearningRate(LRScheduler.MakeLRTransformer(lrate, step + 1, nwarmup));
+
                 /* one step of udpate */
                 /* one step of udpate */
                 ok = leader.Run(config, dataDistributor, model, optimizer);
                 ok = leader.Run(config, dataDistributor, model, optimizer);