generation via beam pruning

source/sample/transformer/T2TSearch.cpp

@@ -75,35 +75,52 @@ void T2TSearch::Search(T2TModel * model, XTensor * input, XTensor * padding, XTe
         /* predict the next state */
         predictor.Predict(next, &encoding, input, padding);
 
-        /* pruning */
-        Prune(next);
+        /* beam pruning */
+        Generate(next);
     }
     
     delete[] states;
 }
 
 /* 
-beam pruning 
+generate tokens for the next state via beam pruning
 >> beam - the beam that keeps a number of states
 */
-void T2TSearch::Prune(T2TStateBundle * beam)
+void T2TSearch::Generate(T2TStateBundle * beam)
 {
     int dims[MAX_TENSOR_DIM_NUM];
+    int dimsBeam[MAX_TENSOR_DIM_NUM];
+    int dimsTopK[MAX_TENSOR_DIM_NUM];
     
     XTensor scoreTopK;
     XTensor &score = beam->score;
     XTensor &index = beam->prediction;
+    int order = score.order;
 
-    for(int i = 0; i < score.order; i++)
+    CheckNTErrors(order >= 2, "The tensor must be of order 2 or larger.");
+    CheckNTErrors(dimsBeam[order - 2] % beamSize == 0, "Wrong dimension size!");
+    
+    for (int i = 0; i < order; i++) {
         dims[i] = score.GetDim(i);
-    dims[score.order - 1] = beamSize;
+        dimsBeam[i] = score.GetDim(i);
+        dimsTopK[i] = score.GetDim(i);
+    }
+
+    dimsBeam[order - 2] /= beamSize;
+    dimsBeam[order - 1] *= beamSize;
+    dimsTopK[order - 2] =  dimsBeam[order - 2];
+    dimsTopK[order - 1] =  beamSize;
     
-    InitTensor(&scoreTopK, score.order, score.dimSize, score.dataType,
+    InitTensor(&scoreTopK, order, dimsTopK, score.dataType,
                1.0F, score.devID, score.mem);
-    InitTensor(&index, score.order, score.dimSize, X_INT,
+    InitTensor(&index, order, dimsTopK, X_INT,
                1.0F, score.devID, score.mem);
 
+    score.Reshape(order, dimsBeam);
+    
     TopK(score, scoreTopK, index, 0, beamSize);
+
+    score.Reshape(order, dims);
 }
 
 /* 

source/sample/transformer/T2TSearch.h

@@ -28,9 +28,9 @@
 namespace transformer
 {
 
-/* The class orgnizes the search process. It calls “predictors” to generate
+/* The class orgnizes the search process. It calls "predictors" to generate
    distributions of the predictions and prunes the search space by beam pruning.
-   It results in a graph where each path respresents a translation hypothsis.
+   This makes a graph where each path respresents a translation hypothsis.
    The output can be the path with the highest model score. */
 class T2TSearch
 {
@@ -57,8 +57,8 @@ public:
     /* search for the most promising states */
     void Search(T2TModel * model, XTensor * input, XTensor * padding, XTensor * output);
 
-    /* beam pruning */
-    void Prune(T2TStateBundle * beam);
+    /* generate token indices via beam pruning */
+    void Generate(T2TStateBundle * beam);
 
     /* save the output sequences in a tensor */
     void DumpOutput(T2TStateBundle * beam, XTensor * output);