Clean the local memory codes in Transformer.

source/sample/transformer/T2TAttention.cpp

@@ -51,14 +51,12 @@ initialize the model
 >> myIgnored - number of position ignored in attention (from the begining)
 >> myIsMasked - indicates whether the attention is with a mask
 >> myDevID - device id
->> myMem - the memory pool
 */
 void T2TAttention::InitModel(int argc, char ** argv, 
                              bool myIsMasked, int myIgnored, 
-                             int myDevID, XMem * myMem)
+                             int myDevID)
 {
     devID = myDevID;
-    mem = myMem;
     isMasked = myIsMasked;
     ignored = myIgnored;
     

source/sample/transformer/T2TAttention.h

@@ -42,9 +42,6 @@ public:
     /* device id */
     int devID;
     
-    /* memory pool */
-    XMem * mem;
-    
     /* head number */
     int nhead;
 
@@ -94,7 +91,7 @@ public:
     /* initialize the model */
     void InitModel(int argc, char ** argv, 
                    bool myIsMasked, int myIgnored, 
-                   int myDevID = -1, XMem * myMem = NULL);
+                   int myDevID = -1);
 
     /* make the network */
     XTensor Make(XTensor &k, XTensor &q, XTensor &v, XTensor &mask, bool isTraining);

source/sample/transformer/T2TBatchLoader.cpp

@@ -280,7 +280,6 @@ load a batch of sequences
 >> isSorted - indicates whether the sequences are sorted by length
 >> wCount - word count
 >> devID - device id
->> mem - memory pool
 >> isTraining - indicates whether we are training the model
 */
 int T2TBatchLoader::LoadBatch(FILE * file, bool isLM, 
@@ -290,18 +289,17 @@ int T2TBatchLoader::LoadBatch(FILE * file, bool isLM,
                           int * seqs,
                           int vsEnc, int vsDec, int sBatch, int wBatch, 
                           bool isSorted, int &ws, int &wCount,
-                          int devID, XMem * mem, 
-						  bool isTraining)
+                          int devID, bool isTraining)
 {
     if(isLM){
         return LoadBatchLM(file, batchEnc, paddingEnc, batchDec, paddingDec, gold, label,
                            seqs, vsEnc, sBatch, wBatch, 
-                           isSorted, wCount, devID, mem, isTraining);
+                           isSorted, wCount, devID, isTraining);
     }
     else{
         return LoadBatchMT(file, batchEnc, paddingEnc, batchDec, paddingDec, gold, label,
                            seqs, vsEnc, vsDec, sBatch, wBatch, 
-                           isSorted, ws, wCount, devID, mem, isTraining);
+                           isSorted, ws, wCount, devID, isTraining);
     }
 }
 
@@ -322,7 +320,6 @@ load a batch of sequences (for LM)
 >> isSorted - indicates whether the sequences are sorted by length
 >> wCount - word count
 >> devID - device id
->> mem - memory pool
 >> isTraining - indicates whether we are training the model
 */
 int T2TBatchLoader::LoadBatchLM(FILE * file, 
@@ -332,8 +329,7 @@ int T2TBatchLoader::LoadBatchLM(FILE * file,
                             int * seqs,
                             int vSize, int sBatch, int wBatch, 
                             bool isSorted, int &wCount,
-                            int devID, XMem * mem,
-							bool isTraining)
+                            int devID, bool isTraining)
 {
     if(nextSeq < 0 || nextSeq >= nseqBuf)
         LoadBuf(file, isSorted, 1);
@@ -481,7 +477,6 @@ load a batch of sequences (for MT)
 >> isSorted - indicates whether the sequences are sorted by length
 >> wCount - word count
 >> devID - device id
->> mem - memory pool
 >> isTraining - indicates whether we are training the model
 */
 int T2TBatchLoader::LoadBatchMT(FILE * file, 
@@ -491,8 +486,7 @@ int T2TBatchLoader::LoadBatchMT(FILE * file,
                             int * seqs,
                             int vSizeEnc, int vSizeDec, int sBatch, int wBatch, 
                             bool isSorted, int &ws, int &wCount,
-                            int devID, XMem * mem, 
-							bool isTraining)
+                            int devID, bool isTraining)
 {
     if (nextBatch < 0 || nextBatch >= bufBatchSize) {
         LoadBuf(file, isSorted, 2);

source/sample/transformer/T2TBatchLoader.h

@@ -131,8 +131,7 @@ public:
                   int * seqs,
                   int vsEnc, int vsDec, int sBatch, int wBatch, 
                   bool isSorted, int &ws, int &wCount,
-                  int devID, XMem * mem, 
-				  bool isTraining);
+                  int devID, bool isTraining);
 
     /* load a batch of sequences (for language modeling) */
     int LoadBatchLM(FILE * file, 
@@ -141,8 +140,7 @@ public:
                     XTensor * gold, XTensor * label,
                     int * seqs, int vs, int sBatch, int wBatch, 
                     bool isSorted, int &wCount,
-                    int devID, XMem * mem, 
-					bool isTraining);
+                    int devID, bool isTraining);
 
     /* load a batch of sequences (for machine translation) */
     int LoadBatchMT(FILE * file, 
@@ -151,8 +149,7 @@ public:
                     XTensor * gold, XTensor * label,
                     int * seqs, int vsEnc, int vsDec, int sBatch, int wBatch, 
                     bool isSorted, int &ws, int &wCount,
-                    int devID, XMem * mem, 
-					bool isTraining);
+                    int devID, bool isTraining);
 
     /* shuffle the data file */
     void Shuffle(const char * srcFile, const char * tgtFile);

source/sample/transformer/T2TDecoder.cpp

@@ -57,16 +57,14 @@ initialize the model
 >> myIsMasked - indicates whether the masked attention is employed
 >> myIgnored - number of positions ignored in attention (from the start)
 >> myDevID - device id
->> myMem - the memory pool
 */
 void AttDecoder::InitModel(int argc, char ** argv, 
                            bool myIsMasked, int myIgnored, 
-                           int myDevID, XMem * myMem)
+                           int myDevID)
 {
-    //AttEncoder::InitModel(argc, argv, myIsMasked, myIgnored, myDevID, myMem);
+    //AttEncoder::InitModel(argc, argv, myIsMasked, myIgnored, myDevID);
 
     devID = myDevID;
-    mem = myMem;
     ignored = myIgnored;
 
     LoadParamInt(argc, argv, "nlayer", &nlayer, 6);
@@ -79,7 +77,7 @@ void AttDecoder::InitModel(int argc, char ** argv,
     CheckNTErrors(vSize > 1, "set vocabulary size by \"-vsizetgt\"");
 
     /* embedding model */
-    embedder.InitModel(argc, argv, devID, mem, false);
+    embedder.InitModel(argc, argv, devID, false);
 
     attentions = new T2TAttention[nlayer];
     fnns = new T2TFNN[nlayer];
@@ -90,12 +88,12 @@ void AttDecoder::InitModel(int argc, char ** argv,
 
     /* initialize the stacked layers */
     for (int i = 0; i < nlayer; i++) {
-        attentions[i].InitModel(argc, argv, myIsMasked, myIgnored, myDevID, myMem);
-        fnns[i].InitModel(argc, argv, myDevID, myMem);
-        attLayerNorms[i].InitModel(argc, argv, myDevID, myMem);
-        fnnLayerNorms[i].InitModel(argc, argv, myDevID, myMem);
-        attentionsEnde[i].InitModel(argc, argv, true, myIgnored, myDevID, myMem);
-        attEndeLayerNorms[i].InitModel(argc, argv, myDevID, myMem);
+        attentions[i].InitModel(argc, argv, myIsMasked, myIgnored, myDevID);
+        fnns[i].InitModel(argc, argv, myDevID);
+        attLayerNorms[i].InitModel(argc, argv, myDevID);
+        fnnLayerNorms[i].InitModel(argc, argv, myDevID);
+        attentionsEnde[i].InitModel(argc, argv, true, myIgnored, myDevID);
+        attEndeLayerNorms[i].InitModel(argc, argv, myDevID);
     }
 }
 

source/sample/transformer/T2TDecoder.h

@@ -37,9 +37,6 @@ public:
     /* device id */
     int devID;
 
-    /* memory pool */
-    XMem * mem;
-
     /* layer number */
     int nlayer;
 
@@ -95,7 +92,7 @@ public:
     /* initialize the model */
     void InitModel(int argc, char ** argv, 
                    bool myIsMasked, int myIgnored, 
-                   int myDevID = -1, XMem * myMem = NULL);
+                   int myDevID = -1);
 
     /* make the decoding network */
     XTensor Make(XTensor &inputDec, XTensor &outputEnc, XTensor &mask, XTensor &maskEncDec, bool isTraining);

source/sample/transformer/T2TEmbedding.cpp

@@ -31,7 +31,6 @@ namespace transformer
 T2TEmbedder::T2TEmbedder()
 {
     devID = -1;
-    mem = NULL;
     vSize = -1;
     maxLength = -1;
 }
@@ -46,12 +45,10 @@ initialize the model
 >> argc - number of arguments
 >> argv - list of pointers to the arguments
 >> myDevID - device id
->> myMem - the memory pool
 */
-void T2TEmbedder::InitModel(int argc, char ** argv, int myDevID, XMem * myMem, bool isEnc)
+void T2TEmbedder::InitModel(int argc, char ** argv, int myDevID, bool isEnc)
 {
     devID = myDevID;
-    mem = myMem;
     
     if(isEnc){
         LoadParamInt(argc, argv, "vsize", &vSize, -1);

source/sample/transformer/T2TEmbedding.h

@@ -41,9 +41,6 @@ public:
     /* device id */
     int devID;
     
-    /* memory pool */
-    XMem * mem;
-    
     /* vocabulary size */
     int vSize;
 
@@ -71,7 +68,7 @@ public:
     ~T2TEmbedder();
 
     /* initialize the model */
-    void InitModel(int argc, char ** argv, int myDevID = -1, XMem * myMem = NULL, bool isEnc = true);
+    void InitModel(int argc, char ** argv, int myDevID = -1, bool isEnc = true);
 
     /* make positional embeddings */
     void MakePosEmbedding(int eSize, int d, int length);

source/sample/transformer/T2TEncoder.cpp

@@ -52,15 +52,12 @@ initialize the model
 >> argv - list of pointers to the arguments
 >> myIsMasked - indicates whether the masked attention is employed
 >> myIgnored - number of positions ignored in attention (from the start)
->> myDevID - device id
->> myMem - the memory pool
-*/
+>> myDevID - device id*/
 void AttEncoder::InitModel(int argc, char ** argv, 
                            bool myIsMasked, int myIgnored, 
-                           int myDevID, XMem * myMem)
+                           int myDevID)
 {
     devID = myDevID;
-    mem = myMem;
     ignored = myIgnored;
     
     LoadParamInt(argc, argv, "nlayer", &nlayer, 6);
@@ -73,7 +70,7 @@ void AttEncoder::InitModel(int argc, char ** argv,
     CheckNTErrors(vSize > 1, "set vocabulary size by \"-vsize\"");
 
     /* embedding model */
-    embedder.InitModel(argc, argv, devID, mem);
+    embedder.InitModel(argc, argv, devID);
 
     attentions = new T2TAttention[nlayer];
     fnns = new T2TFNN[nlayer];
@@ -82,10 +79,10 @@ void AttEncoder::InitModel(int argc, char ** argv,
 
     /* initialize the stacked layers */
     for(int i = 0; i < nlayer; i++){
-        attentions[i].InitModel(argc, argv, myIsMasked, myIgnored, myDevID, myMem);
-        fnns[i].InitModel(argc, argv, myDevID, myMem);
-        attLayerNorms[i].InitModel(argc, argv, myDevID, myMem);
-        fnnLayerNorms[i].InitModel(argc, argv, myDevID, myMem);
+        attentions[i].InitModel(argc, argv, myIsMasked, myIgnored, myDevID);
+        fnns[i].InitModel(argc, argv, myDevID);
+        attLayerNorms[i].InitModel(argc, argv, myDevID);
+        fnnLayerNorms[i].InitModel(argc, argv, myDevID);
     }
 }
 

source/sample/transformer/T2TEncoder.h

@@ -65,9 +65,6 @@ public:
     /* device id */
     int devID;
 
-    /* memory pool */
-    XMem * mem;
-
     /* layer number */
     int nlayer;
 
@@ -118,7 +115,7 @@ public:
     /* initialize the model */
     void InitModel(int argc, char ** argv, 
                    bool myIsMasked, int myIgnored, 
-                   int myDevID = -1, XMem * myMem = NULL);
+                   int myDevID = -1);
 
     /* make the encoding network */
     XTensor Make(XTensor &input, XTensor &mask, XTensor &maskEncDec, bool isTraining);

source/sample/transformer/T2TFNN.cpp

@@ -47,12 +47,10 @@ initialize the model
 >> argc - number of arguments
 >> argv - list of pointers to the arguments
 >> myDevID - device id
->> myMem - the memory pool
 */
-void T2TFNN::InitModel(int argc, char ** argv, int myDevID, XMem * myMem)
+void T2TFNN::InitModel(int argc, char ** argv, int myDevID)
 {
     devID = myDevID;
-    mem = myMem;
     
     float minmax = 0;
 
@@ -63,10 +61,10 @@ void T2TFNN::InitModel(int argc, char ** argv, int myDevID, XMem * myMem)
     LoadParamFloat(argc, argv, "dropoutfnn", &dropoutP, 0);
 
     InitTensor2DV2(&w1, inSize, hSize, X_FLOAT, devID);
-    InitTensor1D(&b1, hSize, X_FLOAT, devID, mem);
+    InitTensor1DV2(&b1, hSize, X_FLOAT, devID);
 
     InitTensor2DV2(&w2, hSize, outSize, X_FLOAT, devID);
-    InitTensor1D(&b2, outSize, X_FLOAT, devID, mem);
+    InitTensor1DV2(&b2, outSize, X_FLOAT, devID);
 
     float scale = 1.0F;
     float finfout1 = (float)sqrt(6.0F * scale/(inSize + hSize));

source/sample/transformer/T2TFNN.h

@@ -36,9 +36,6 @@ public:
     /* device id */
     int devID;
 
-    /* memory pool */
-    XMem * mem;
-
     /* size of input vector */
     int inSize;
 
@@ -72,7 +69,7 @@ public:
     ~T2TFNN();
 
     /* initialize the model */
-    void InitModel(int argc, char ** argv, int myDevID = -1, XMem * myMem = NULL);
+    void InitModel(int argc, char ** argv, int myDevID = -1);
 
     /* make the network */
     XTensor Make(XTensor &input, bool isTraining);

source/sample/transformer/T2TLayerNormal.cpp

@@ -32,7 +32,6 @@ namespace transformer
 T2TLN::T2TLN()
 {
     devID = -1;
-    mem = NULL;
     d = 0;
 }
 
@@ -46,12 +45,10 @@ initialize the model
 >> argc - number of arguments
 >> argv - list of pointers to the arguments
 >> myDevID - device id
->> myMem - the memory pool
 */
-void T2TLN::InitModel(int argc, char ** argv, int myDevID, XMem * myMem)
+void T2TLN::InitModel(int argc, char ** argv, int myDevID)
 {
     devID = myDevID;
-    mem = myMem;
 
     d = 0;
     LoadParamInt(argc, argv, "d", &d, DEFAULT_EMBEDDING_SIZE);

source/sample/transformer/T2TLayerNormal.h

@@ -36,9 +36,6 @@ class T2TLN
 public:
     /* device id */
     int devID;
-    
-    /* memory pool */
-    XMem * mem;
 
     /* the transformation matrix w */
     XTensor w;
@@ -57,7 +54,7 @@ public:
     ~T2TLN();
     
     /* initialize the model */
-    void InitModel(int argc, char ** argv, int myDevID = -1, XMem * myMem = NULL);
+    void InitModel(int argc, char ** argv, int myDevID = -1);
     
     /* make the network */
     XTensor Make(XTensor &input);

source/sample/transformer/T2TModel.cpp

@@ -32,7 +32,6 @@ namespace transformer
 T2TModel::T2TModel()
 {
     devID = -1;
-    mem = NULL;
     isLM = false;
     isMT = false;
     nhead = 1;
@@ -48,10 +47,6 @@ T2TModel::~T2TModel()
     delete encoder;
     delete decoder;
     delete outputLayer;
-    
-    /* we delete "mem" at the end because other members are using it and we must
-       remove the memory space before all tensors are destroyed. */
-    delete mem;
 }
 
 /* 
@@ -61,29 +56,16 @@ initialize the model
 */
 void T2TModel::InitModel(int argc, char ** argv)
 {
-    bool useMem = false;
-    int memSize = 0;
-    bool isMemFreeOTF = false;
-
     LoadParamInt(argc, argv, "dev", &devID, -1);
-    LoadParamBool(argc, argv, "mem", &useMem, useMem);
-    LoadParamInt(argc, argv, "memsize", &memSize, 1024);
     LoadParamBool(argc, argv, "mt", &isMT, false);
     LoadParamBool(argc, argv, "lm", &isLM, !isMT);
     LoadParamInt(argc, argv, "nhead", &nhead, 8);
-    LoadParamBool(argc, argv, "freeotf", &isMemFreeOTF, false);
-
-    if(useMem){
-        delete mem;
-        mem = new XMem(devID, FREE_ON_THE_FLY, (MTYPE)MILLION * 256, 1024, MILLION * 128);
-        mem->SetDesiredSize(devID, 0, (MTYPE)memSize * MILLION);
-    }
 
-    encoder->InitModel(argc, argv, true, 0, devID, mem);
-    outputLayer->InitModel(argc, argv, devID, mem);
+    encoder->InitModel(argc, argv, true, 0, devID);
+    outputLayer->InitModel(argc, argv, devID);
 
     if(isMT)
-        decoder->InitModel(argc, argv, true, 0, devID, mem);
+        decoder->InitModel(argc, argv, true, 0, devID);
 
     TensorList params(10);
     GetParams(params);
@@ -149,7 +131,8 @@ void T2TModel::MakeLM(XTensor &input, XTensor &output, XTensor &padding, bool is
         dims[i + 1] = input.GetDim(i);
     dims[0] = nhead;
     dims[input.order + 1] = len;
-    XTensor mask(input.order + 2, dims, X_FLOAT, 1.0F, padding.devID, padding.mem);
+    XTensor mask;
+    InitTensorV2(&mask, input.order + 2, dims, X_FLOAT, padding.devID);
 
     /* a upper triangular matrix where the cells of the upper triangular are set to -1e-9.
         this matrix can be used to prevent the attention to current or following words in

source/sample/transformer/T2TModel.h

@@ -40,9 +40,6 @@ public:
     /* device id */
     int devID;
 
-    /* memory pool */
-    XMem * mem;
-
     /* the encoder */
     AttEncoder * encoder;
 

source/sample/transformer/T2TOutput.cpp

@@ -31,7 +31,6 @@ namespace transformer
 T2TOutput::T2TOutput()
 {
     devID = -1;
-    mem = NULL;
     vSize = -1;
     inSize = -1;
     hSize = -1;
@@ -47,12 +46,10 @@ initialize the model
 >> argc - number of arguments
 >> argv - list of pointers to the arguments
 >> myDevID - device id
->> myMem - the memory pool
 */
-void T2TOutput::InitModel(int argc, char ** argv, int myDevID, XMem * myMem)
+void T2TOutput::InitModel(int argc, char ** argv, int myDevID)
 {
     devID = myDevID;
-    mem = myMem;
 
     float minmax = 0;
 

source/sample/transformer/T2TOutput.h

@@ -38,9 +38,6 @@ public:
     /* device id */
     int devID;
 
-    /* memory pool */
-    XMem * mem;
-
     /* vocabulary size */
     int vSize;
 
@@ -61,7 +58,7 @@ public:
     ~T2TOutput();
 
     /* initialize the model */
-    void InitModel(int argc, char ** argv, int myDevID = -1, XMem * myMem = NULL);
+    void InitModel(int argc, char ** argv, int myDevID = -1);
 
     /* make the network */
     XTensor Make(XTensor &input);

source/sample/transformer/T2TPredictor.cpp

@@ -179,7 +179,7 @@ void T2TPredictor::Predict(T2TStateBundle * next, XTensor * encoding,
     }
     else{
         inputDec = GeneratePaths(s);
-        inputDec.SetDevice(inputEnc->devID, inputEnc->mem);
+        inputDec.SetDevice(inputEnc->devID);
 
         inputDec = Concatenate(first, inputDec, inputDec.order - 1);
     }
@@ -219,8 +219,8 @@ void T2TPredictor::Predict(T2TStateBundle * next, XTensor * encoding,
     selectSrc.SetInt(stride - 1, 0);
     selectTgt.SetInt(0, 0);
 
-    selectSrc.SetDevice(decoding.devID, decoding.mem);
-    selectTgt.SetDevice(decoding.devID, decoding.mem);
+    selectSrc.SetDevice(decoding.devID);
+    selectTgt.SetDevice(decoding.devID);
     
     /* the decoder output of the last position */
     decodingStep = CopyIndexed(decoding, decoding.order - 2, selectSrc, selectTgt);

source/sample/transformer/T2TSearch.cpp

@@ -595,7 +595,7 @@ XTensor T2TSearch::MakeFirstMask(T2TStateBundle * beam)
             mask.Set(-1e9, i);
     }
 
-    mask.SetDevice(prob.devID, prob.mem);
+    mask.SetDevice(prob.devID);
 
     return mask;
 }

source/sample/transformer/T2TTester.cpp

@@ -75,7 +75,6 @@ void T2TTester::Test(const char * fn, const char * ofn, T2TModel * model)
     CheckNTErrors(ofile, "Cannot open the output file");
 
     int devID = model->devID;
-    XMem * mem = model->mem;
 
     XNet net;
     
@@ -106,7 +105,7 @@ void T2TTester::Test(const char * fn, const char * ofn, T2TModel * model)
     while(batchLoader.LoadBatch(file, model->isLM, 
                                 &batchEnc, &paddingEnc, &paddingDec, &paddingDec, &gold, &label,
                                 seqs, vSize, vSizeTgt,
-                                1, 1, false, ws, wc, devID, mem, false))
+                                1, 1, false, ws, wc, devID, false))
     {
         CheckNTErrors(batchEnc.order == 2, "wrong tensor order of the sequence batch!");
         CheckNTErrors(!model->isLM, "Only MT model is supported!");

source/sample/transformer/T2TTrainer.cpp

@@ -75,9 +75,6 @@ void T2TTrainer::Init(int argc, char ** argv)
         strcpy(argArray[i], argv[i]);
     }
 
-    bool useMem = false;
-
-    LoadParamBool(argc, argv, "mem", &useMem, useMem);
     LoadParamFloat(argc, argv, "lrate", &lrate, 1.0F);
     LoadParamFloat(argc, argv, "lrbias", &lrbias, 0);
     LoadParamInt(argc, argv, "sbatch", &sBatchSize, 1);
@@ -142,7 +139,6 @@ void T2TTrainer::Train(const char * fn, const char * validFN, const char * model
 #endif
 
     int devID = model->devID;
-    XMem * mem = model->mem;
     XNet net;
 
     if(isDebugged)
@@ -184,7 +180,7 @@ void T2TTrainer::Train(const char * fn, const char * validFN, const char * model
         while (batchLoader.LoadBatch(file, model->isLM, 
                                      &batchEnc, &paddingEnc, &batchDec, &paddingDec, &gold, &label,
                                      NULL, vSize, vSizeTgt,
-                                     sBatchSize, wBatchSize, isLenSorted, ws, wc, devID, mem, true)) 
+                                     sBatchSize, wBatchSize, isLenSorted, ws, wc, devID, true)) 
         {
 
             CheckNTErrors(batchEnc.order == 2, "wrong tensor order of the sequence batch");
@@ -321,7 +317,6 @@ void T2TTrainer::Test(const char * fn, const char * ofn, T2TModel * model)
     CheckNTErrors(ofile, "Cannot open the output file");
 
     int devID = model->devID;
-    XMem * mem = model->mem;
 
     XNet net;
     
@@ -351,7 +346,7 @@ void T2TTrainer::Test(const char * fn, const char * ofn, T2TModel * model)
     while(batchLoader.LoadBatch(file, model->isLM, 
                                 &batchEnc, &paddingEnc, &batchDec, &paddingDec, &gold, &label,
                                 seqs, vSize, vSizeTgt,
-                                1, 1, false, ws, wc, devID, mem, false))
+                                1, 1, false, ws, wc, devID, false))
     {
         CheckNTErrors(batchEnc.order == 2, "wrong tensor order of the sequence batch");
             

source/tensor/XTensor.cpp

@@ -517,7 +517,7 @@ void XTensor::SetDevice(int myDevId, XMem * myMem)
         isInGlobalMem = false;
     }
     else {
-        ShowNTErrors("TODO!");
+        myMem = GMems.GetMem(myDevId);
     }
 }