probability of one step

source/sample/transformer/T2TPredictor.cpp

@@ -135,6 +135,8 @@ void T2TPredictor::Predict(T2TStateBundle * next, XTensor * encoding, XTensor *
 
 
     /* prediction probabilities */
     /* prediction probabilities */
     XTensor &output = next->prob;
     XTensor &output = next->prob;
+    XTensor decoding;
+    XTensor decodingStep;
     
     
     XTensor paddingDec;
     XTensor paddingDec;
     InitTensor3D(&paddingDec, inputDec.GetDim(0), inputDec.GetDim(1), m->outputLayer->vSize, X_INT);
     InitTensor3D(&paddingDec, inputDec.GetDim(0), inputDec.GetDim(1), m->outputLayer->vSize, X_INT);
@@ -146,8 +148,33 @@ void T2TPredictor::Predict(T2TStateBundle * next, XTensor * encoding, XTensor *
     /* decoder mask */
     /* decoder mask */
     m->MakeMTMaskDec(*inputEnc, inputDec, *paddingEnc, paddingDec, maskDec, maskEncDec);
     m->MakeMTMaskDec(*inputEnc, inputDec, *paddingEnc, paddingDec, maskDec, maskEncDec);
 
 
-    /* make the decoding network and generate the output probabilities */
-    output = decoder.Make(inputDec, *encoding, maskDec, maskEncDec, false);
+    /* make the decoding network */
+    decoding = decoder.Make(inputDec, *encoding, maskDec, maskEncDec, false);
+
+    XTensor selectSrc;
+    XTensor selectTgt;
+
+    CheckNTErrors(decoding.order >= 2, "The tensor must be of order 2 or larger!");
+
+    int dims[MAX_TENSOR_DIM_NUM];
+    for(int i = 0; i < decoding.order - 1; i++)
+        dims[i] = decoding.GetDim(i);
+    dims[decoding.order - 2] = 1;
+
+    InitTensor(&selectSrc, decoding.order - 1, dims, X_INT);
+    InitTensor(&selectTgt, decoding.order - 1, dims, X_INT);
+
+    int stride = decoding.GetDim(decoding.order - 2);
+    for(int i = 0; i < selectSrc.unitNum; i++){
+        selectSrc.SetInt(i * stride + stride - 1, i);
+        selectTgt.SetInt(i, i);
+    }
+
+    /* the decoder output of the last position */
+    decodingStep = CopyIndexed(decoding, decoding.order - 2, selectSrc, selectTgt);
+
+    /* generate the output probabilities */
+    m->outputLayer->Make(decodingStep, output);
     
     
     next->layersEnc.AddList(&s->layersEnc);
     next->layersEnc.AddList(&s->layersEnc);
     next->layersDec.Add(&inputDec);
     next->layersDec.Add(&inputDec);

source/sample/transformer/T2TSearch.cpp

@@ -319,10 +319,17 @@ void T2TSearch::Collect(T2TStateBundle * beam)
 
 
 /* 
 /* 
 save the output sequences in a tensor 
 save the output sequences in a tensor 
->> beam - the beam that keeps a number of states
+>> input - input sequences
+>> output - output sequences (for return)
 */
 */
-void T2TSearch::Dump(T2TStateBundle * beam, XTensor * output)
+void T2TSearch::Dump(XTensor * input, XTensor * output)
 {
 {
+    int dims[MAX_TENSOR_DIM_NUM];
+    for(int i = 0; i < input->order - 1; i++)
+        dims[i] = input->GetDim(i);
+    dims[input->order - 1] = maxLength;
+
+    InitTensor(output, input->order, dims, X_INT);
 }
 }
 
 
 /* 
 /* 

source/sample/transformer/T2TSearch.h

@@ -88,7 +88,7 @@ public:
     void Collect(T2TStateBundle * beam);
     void Collect(T2TStateBundle * beam);
 
 
     /* save the output sequences in a tensor */
     /* save the output sequences in a tensor */
-    void Dump(T2TStateBundle * beam, XTensor * output);
+    void Dump(XTensor * input, XTensor * output);
 
 
     /* check if the token is an end symbol */
     /* check if the token is an end symbol */
     bool IsEnd(int token);
     bool IsEnd(int token);