class of predictors

source/sample/transformer/T2TDecoder.cpp

@@ -68,7 +68,7 @@ void AttDecoder::InitModel(int argc, char ** argv,
     LoadParamFloat(argc, argv, "dropout", &dropoutP, 0);
 
     CheckNTErrors(nlayer >= 1, "We have one encoding layer at least!");
-    CheckNTErrors(vSize > 1, "set vocabulary size by \"-vsize\"");
+    CheckNTErrors(vSize > 1, "set vocabulary size by \"-vsizetgt\"");
 
     /* embedding model */
     embedder.InitModel(argc, argv, devID, mem, false);

source/sample/transformer/T2TModel.h

@@ -31,6 +31,9 @@
 namespace transformer
 {
 
+/* a transformer model that keeps parameters of the encoder,
+   the decoder and the output layer (softmax). Also, it creates
+   the network used in transformer. */
 class T2TModel
 {
 public:

source/sample/transformer/T2TSearcher.cpp → source/sample/transformer/T2TPredictor.cpp

 /* NiuTrans.Tensor - an open-source tensor library
- * Copyright (C) 2018, Natural Language Processing Lab, Northestern University.
+ * Copyright (C) 2019, Natural Language Processing Lab, Northestern University.
  * All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
@@ -19,5 +19,9 @@
  * $Created by: XIAO Tong (xiaotong@mail.neu.edu.cn) 2019-03-13
  */
 
-#include "T2TSearcher.h"
+#include "T2TPredictor.h"
+
+namespace transformer
+{
+}
 

source/sample/transformer/T2TSearcher.h → source/sample/transformer/T2TPredictor.h

 /* NiuTrans.Tensor - an open-source tensor library
- * Copyright (C) 2018, Natural Language Processing Lab, Northestern University.
+ * Copyright (C) 2019, Natural Language Processing Lab, Northestern University.
  * All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
@@ -20,7 +20,52 @@
  * This is the first source file I create in 2019 - new start!
  */
 
-#ifndef __T2TSEARCHER_H__
-#define __T2TSEARCHER_H__
+#ifndef __T2TPREDICTOR_H__
+#define __T2TPREDICTOR_H__
+
+#include "T2TModel.h"
+
+namespace transformer
+{
+
+/* state in decoder - it keeps all previously-generated words and their 
+   hidden states */
+class T2TState
+{
+    /* layers on the encoder side. We actually use the encoder output instead
+       of all hidden layers. */
+    XList * encoderLayers;
+
+    /* layers on the decoder side */
+    XList * decoderLayers;
+
+    /* */
+};
+
+/* The predictor reads the current state and then predicts the next. 
+   It is exactly the same procedure of MT inference -
+   we get the state of previous words and then generate the next word.
+   Here, a state can be regared as the representation of words (word 
+   indices, hidden states, embeddings and etc.). */
+class T2TPredictor
+{
+    /* pointer to the transformer model */
+    T2TModel * model;
+
+public:
+    /* constructor */
+    T2TPredictor();
+
+    /* de-constructor */
+    ~T2TPredictor();
+
+    /* read a state */
+    void Read(T2TModel * model, T2TState * current);
+
+    /* predict the next state */
+    void Predict(T2TState * next);
+};
+
+}
 
 #endif

source/sample/transformer/Transformer.cpp

@@ -25,7 +25,7 @@
 #include "T2TModel.h"
 #include "T2TUtility.h"
 #include "T2TTrainer.h"
-#include "T2TSearcher.h"
+#include "T2TPredictor.h"
 #include "../../tensor/XDevice.h"
 #include "../../tensor/XUtility.h"
 #include "../../tensor/XGlobal.h"