code cleaning and better management of device

source/sample/transformer/T2TBatchLoader.cpp

@@ -302,9 +302,10 @@ load a batch of sequences (for LM)
 >> paddingEnc - padding of the input sequences
 >> batchDec - the batch of the output sequences
 >> paddingDec - padding of the output sequences
->> gold - gold standard
+>> gold - gold standard (distribution of every position)
+>> label - (gold standard) label index of every position
 >> seqs - keep the sequences in an array
->> vs - vocabulary size
+>> vSize - vocabulary size
 >> sBatch - batch size of sequences
 >> wBatch - batch size of words
 >> isSorted - indicates whether the sequences are sorted by length
@@ -318,7 +319,7 @@ int T2TBatchLoader::LoadBatchLM(FILE * file,
                             XTensor * batchDec, XTensor * paddingDec,
                             XTensor * gold, XTensor * label,
                             int * seqs,
-                            int vs, int sBatch, int wBatch, 
+                            int vSize, int sBatch, int wBatch, 
                             bool isSorted, int &wCount,
                             int devID, XMem * mem,
 							bool isTraining)
@@ -355,7 +356,7 @@ int T2TBatchLoader::LoadBatchLM(FILE * file,
     int dims[MAX_TENSOR_DIM_NUM];
     dims[0] = sc;
     dims[1] = max;
-    dims[2] = vs;
+    dims[2] = vSize;
 
     InitTensor2D(batchEnc, sc, max, X_INT, devID, mem);
     InitTensor2D(label, sc, max, X_INT, devID, mem);
@@ -459,10 +460,11 @@ load a batch of sequences (for MT)
 >> paddingEnc - padding of the input sequences
 >> batchDec - the batch of the output sequences
 >> paddingDec - padding of the output sequences
->> gold - gold standard
+>> gold - gold standard (distribution of every position)
+>> label - (gold standard) label index of every position
 >> seqs - keep the sequences in an array
->> vsEnc - size of the encoder vocabulary
->> vsDec - size of the decoder vocabulary
+>> vSizeEnc - size of the encoder vocabulary
+>> vSizeDec - size of the decoder vocabulary
 >> sBatch - batch size of sequences
 >> wBatch - batch size of words
 >> isSorted - indicates whether the sequences are sorted by length
@@ -476,7 +478,7 @@ int T2TBatchLoader::LoadBatchMT(FILE * file,
                             XTensor * batchDec, XTensor * paddingDec,
                             XTensor * gold, XTensor * label,
                             int * seqs,
-                            int vsEnc, int vsDec, int sBatch, int wBatch, 
+                            int vSizeEnc, int vSizeDec, int sBatch, int wBatch, 
                             bool isSorted, int &ws, int &wCount,
                             int devID, XMem * mem, 
 							bool isTraining)

source/sample/transformer/T2TPredictor.cpp

@@ -160,7 +160,7 @@ void T2TPredictor::Predict(T2TStateBundle * next, XTensor * encoding, XTensor *
     dims[inputDec.order - 1] = inputDec.GetDim(-1);
     
     XTensor paddingDec;
-    InitTensor(&paddingDec, inputDec.order, dims, X_INT);
+    InitTensor(&paddingDec, inputDec.order, dims, X_INT, 1.0F, paddingEnc->devID, paddingEnc->mem);
     SetDataFixedInt(paddingDec, 1);
     
     XTensor maskDec;
@@ -190,6 +190,9 @@ void T2TPredictor::Predict(T2TStateBundle * next, XTensor * encoding, XTensor *
         selectTgt.SetInt(i, i);
     }
 
+    selectSrc.SetDevice(decoding.devID, decoding.mem);
+    selectTgt.SetDevice(decoding.devID, decoding.mem);
+    
     /* the decoder output of the last position */
     decodingStep = CopyIndexed(decoding, decoding.order - 2, selectSrc, selectTgt);
 

source/sample/transformer/T2TTrainer.cpp

@@ -344,7 +344,7 @@ void T2TTrainer::Test(const char * fn, const char * ofn, T2TModel * model)
     batchLoader.ClearBuf();
 
     while(batchLoader.LoadBatch(file, model->isLM, 
-                                &batchEnc, &paddingEnc, &paddingDec, &paddingDec, &gold, &label,
+                                &batchEnc, &paddingEnc, &batchDec, &paddingDec, &gold, &label,
                                 seqs, vSize, vSizeTgt,
                                 1, 1, false, ws, wc, devID, mem, false))
     {
@@ -369,8 +369,12 @@ void T2TTrainer::Test(const char * fn, const char * ofn, T2TModel * model)
         XTensor probs;
         InitTensor1D(&probs, bSize * length);
 
+        XTensor labelOnehot;
+
+        labelOnehot = IndexToOnehot(label, vSizeTgt, 0);
+
         /* get probabilities */
-        float prob = GetProb(&output, &gold, &probs);
+        float prob = GetProb(&output, &labelOnehot, &probs);
 
         /* dump the test result */
         for(int s = 0; s < bSize; s++){

source/sample/transformer/Transformer.cpp

@@ -81,11 +81,8 @@ int TransformerMain(int argc, const char ** argv)
     if(strcmp(modelFN, ""))
         model.Read(modelFN);
 
-    
-
     /* test the model on the new data */
-    if(strcmp(testFN, "") && strcmp(outputFN, ""))
-    {
+    if(strcmp(testFN, "") && strcmp(outputFN, "")){
         /* beam search */
         if(isBeamSearch){
             T2TTester searcher;

source/tensor/XTensor.cpp

@@ -512,6 +512,21 @@ XTensor XTensor::Lin(DTYPE scale, DTYPE shift) const
 }
 
 /* 
+relocate the data on the target device 
+>> myDevId - target device id
+>> myMem - memory pool on the target device
+*/
+void XTensor::SetDevice(int myDevId, XMem * myMem)
+{
+    if (myMem != NULL) {
+        FlushToMem(myMem);
+    }
+    else {
+        ShowNTErrors("TODO!");
+    }
+}
+
+/* 
 judge whether the two matrices are in the same type and size 
 >> a - input tensor
 >> b - anther tensor to compare with

source/tensor/XTensor.h

@@ -248,6 +248,9 @@ public:
     /* linear transformation */
     XTensor Lin(DTYPE scale, DTYPE shift = 0) const;
 
+    /* relocate the data on the target device */
+    void SetDevice(int myDevId, XMem * myMem = NULL);
+
     /* judge whether the two matrices are in the same type and size */
     static
     bool IsSameShaped(const XTensor * a, const XTensor * b);