coding of predictor

source/sample/transformer/T2TPredictor.cpp

@@ -28,6 +28,36 @@ namespace transformer
 {
 {
 
 
 /* constructor */
 /* constructor */
+T2TStateBundle::T2TStateBundle()
+{
+    states = NULL;
+}
+
+/* de-constructor */
+T2TStateBundle::~T2TStateBundle()
+{
+    if(states != NULL)
+        delete[] states;
+}
+
+/* 
+create states 
+>> num - number of states
+*/
+void T2TStateBundle::MakeStates(int num)
+{
+    CheckNTErrors(num > 0, "invalid number");
+
+    if(states != NULL)
+        delete[] states;
+
+    states = new T2TState[num];
+
+    for(int i = 0; i < num; i++)
+        states[i].last = NULL;
+}
+
+/* constructor */
 T2TPredictor::T2TPredictor()
 T2TPredictor::T2TPredictor()
 {
 {
 }
 }

source/sample/transformer/T2TPredictor.h

@@ -65,6 +65,19 @@ public:
 
 
     /* layers on the decoder side */
     /* layers on the decoder side */
     XList layersDec;
     XList layersDec;
+
+    /* list of states */
+    T2TState * states;
+
+public:
+    /* constructor */
+    T2TStateBundle();
+
+    /* de-constructor */
+    ~T2TStateBundle();
+
+    /* create states */
+    void MakeStates(int num);
 };
 };
 
 
 /* The predictor reads the current state and then predicts the next. 
 /* The predictor reads the current state and then predicts the next. 

source/sample/transformer/T2TSearch.cpp

@@ -118,12 +118,31 @@ void T2TSearch::Generate(T2TStateBundle * beam)
 
 
     score.Reshape(order, dimsBeam);
     score.Reshape(order, dimsBeam);
     
     
+    /* keep the most promissing candidates in the beam */
     TopK(score, scoreTopK, index, 0, beamSize);
     TopK(score, scoreTopK, index, 0, beamSize);
 
 
     score.Reshape(order, dims);
     score.Reshape(order, dims);
 }
 }
 
 
 /* 
 /* 
+expand the search graph 
+>> beam - the beam that keeps a number of states
+*/
+void T2TSearch::Expand(T2TStateBundle * prev, T2TStateBundle * beam)
+{
+    beam->MakeStates(beam->prediction.unitNum);
+
+    T2TState * states = beam->states;
+    XTensor &predict = beam->prediction;
+    XTensor index = *NewTensorBuf(predict.order - 1, predict.dimSize, X_FLOAT, 1.0F, 
+                                  predict.devID, predict.mem);
+
+    index.SetAscendingOrder(-1);
+
+    DelTensorBuf(&index);
+}
+
+/* 
 save the output sequences in a tensor 
 save the output sequences in a tensor 
 >> beam - the beam that keeps a number of states
 >> beam - the beam that keeps a number of states
 */
 */

source/sample/transformer/T2TSearch.h

@@ -60,6 +60,9 @@ public:
     /* generate token indices via beam pruning */
     /* generate token indices via beam pruning */
     void Generate(T2TStateBundle * beam);
     void Generate(T2TStateBundle * beam);
 
 
+    /* expand the search graph */
+    void Expand(T2TStateBundle * prev, T2TStateBundle * beam);
+
     /* save the output sequences in a tensor */
     /* save the output sequences in a tensor */
     void DumpOutput(T2TStateBundle * beam, XTensor * output);
     void DumpOutput(T2TStateBundle * beam, XTensor * output);
 };
 };

source/tensor/XTensor.cpp

@@ -969,8 +969,20 @@ set the cell to the ascending order along a given dimension
 */
 */
 void XTensor::SetAscendingOrder(int dim)
 void XTensor::SetAscendingOrder(int dim)
 {
 {
-    CheckNTErrors((dim >= 0 && dim < order), "Wrong dimension specified!");
-    CheckNTErrors((dataType == X_INT), "TODO!");
+    CheckNTErrors(dim < order, "Wrong dimension specified!");
+    CheckNTErrors(dataType == X_INT, "TODO!");
+
+    if(dim < 0){        
+        int o = order;
+        int ds[MAX_TENSOR_DIM_NUM];
+        memcpy(ds, dimSize, sizeof(int) * order);
+
+        Reshape(unitNum);
+        SetAscendingOrder(0);
+        Reshape(o, ds);
+
+        return;
+    }
 
 
     int dimRDI = order - dim - 1;
     int dimRDI = order - dim - 1;
     if(devID >= 0){
     if(devID >= 0){