bu fixes

source/network/Main.cpp

@@ -40,12 +40,7 @@ using namespace transformer;
 
 int main( int argc, const char ** argv )
 {
-    //TransposeTest();
-    //return 0;
-
-    //SumDimTest();
-    //return 0;
-
+    //_CrtSetBreakAlloc(896);
 
     if(argc > 1 && !strcmp(argv[1], "-fnnlm"))
         FNNLMMain(argc - 1, argv + 1);
@@ -58,37 +53,6 @@ int main( int argc, const char ** argv )
         fprintf(stderr, "Or run this program with \"-fnnlm\" for sample FNNLM!\n");
     }
 
-    return 0;
-
-    XNet net;
-    XTensor a;
-    XTensor b;
-    XTensor c;
-
-    InitTensor2D(&a, 2, 2);
-    InitTensor2D(&b, 2, 4);
-    InitTensor2D(&c, 2, 4);
-
-    a.SetZeroAll();
-    b.SetZeroAll();
-    c.SetZeroAll();
-
-    SetDataFixed(a, 0.1F);
-    a.Set2D(0.3F, 1, 0);
-    a.Set2D(0.4F, 1, 1);
-
-    b = Merge(a, a, 1);
-    c = HTanH(MMul(a, b));
-
-    a.Dump(stderr, "a:");
-    b.Dump(stderr, "b:");
-    c.Dump(stderr, "c:");
-    XLink::ShowNetwork(stderr, &c);
-
-    net.Backward(c);
-
-    net.Dump(stderr);
-    
     //_CrtDumpMemoryLeaks();
     
     return 0;

source/network/XNet.cpp

@@ -46,6 +46,11 @@ unsigned int MakeNetID()
     return id;
 }
 
+void XNetClearAll()
+{
+    MUTEX_DELE(netMutex);
+}
+
 /* constructor */
 XNet::XNet()
 {

source/network/XNet.h

@@ -95,6 +95,7 @@ struct XNet
 extern unsigned int netIDGlobal;
 extern MUTEX_HANDLE netMutex;
 extern unsigned int MakeNetID();
+extern void XNetClearAll();
 
 }
 

source/sample/fnnlm/FNNLM.cpp

@@ -240,6 +240,7 @@ void Check(FNNModel &model)
 {
     CheckErrors(model.n > 0 && model.n <= MAX_N_GRAM, "The LM order is out of range (use -n)!");
     CheckErrors(model.vSize > 0, "no vocabulary size found (use -vsize)!");
+    CheckErrors(model.eSize > 0, "no embedding size found (use -esize)!");
 }
 
 /* make a hard copy of the fnn model */
@@ -632,8 +633,10 @@ int LoadNGrams(FILE * file, int n, NGram * ngrams, int sentNum, int wordNum)
         if(pin <= 0){
             int len = (int)strlen(lineBuf);
 
-            if(lineBuf[len - 1] == '\r')
+            while(lineBuf[len - 1] == '\r' || lineBuf[len - 1] == '\n'){
                 lineBuf[len - 1] = 0;
+                len--;
+            }
 
             len = (int)strlen(lineBuf);
             if(len == 0)
@@ -644,10 +647,11 @@ int LoadNGrams(FILE * file, int n, NGram * ngrams, int sentNum, int wordNum)
         
             /* how many words are in the sentence */
             int wNum = 0;
+            int i = 0;
 
-            for(int i = pin; i < len; i++){
+            for(i = pin; i < len; i++){
                 /* load word (id) seperated by space or tab */
-                if((lineBuf[i] == ' ' || lineBuf[i] == '\t' || i == len - 1) && wSize > 0){
+                if((lineBuf[i] == ' ' || lineBuf[i] == '\t') && wSize > 0){
                     lineBuf[i] = 0;
                     wordBuf[wNum++] = atoi(lineBuf + i - wSize);
                     wSize = 0;
@@ -656,6 +660,9 @@ int LoadNGrams(FILE * file, int n, NGram * ngrams, int sentNum, int wordNum)
                     wSize++;
             }
 
+            if(wSize > 0)
+                wordBuf[wNum++] = atoi(lineBuf + i - wSize);
+
             wordBufCount = wNum;
             lineNum++;
         }

source/sample/transformer/T2TAttention.cpp

@@ -80,16 +80,16 @@ make the network
 >> v - values
 << return - multi-attention result
 */
-XTensor * T2TAttention::Make(XTensor * k, XTensor * q, XTensor * v)
+XTensor T2TAttention::Make(XTensor &k, XTensor &q, XTensor &v)
 {
     XTensor k2;
     XTensor q2;
     XTensor v2;
 
     /* linear transofmration before self-attention */
-    k2 = MMul(*k, wk);
-    q2 = MMul(*q, wq);
-    v2 = MMul(*v, wv);
+    k2 = MMul(k, wk);
+    q2 = MMul(q, wq);
+    v2 = MMul(v, wv);
 
     XTensor kheads;
     XTensor qheads;
@@ -107,12 +107,8 @@ XTensor * T2TAttention::Make(XTensor * k, XTensor * q, XTensor * v)
     scalar = Softmax(Linear(BMMul(qheads, X_NOTRANS, kheads, X_TRANS), 1/sqrt((float)dk)), -1);
     att = BMMul(scalar, vheads);
 
-    XTensor * result = new XTensor();
-
     /* concatenate the heads */
-    *result = Merge(att, att.order - 1);
-
-    return result;
+    return Merge(att, att.order - 1);
 }
 
 }

source/sample/transformer/T2TAttention.h

@@ -77,7 +77,7 @@ public:
     void InitModel(int argc, const char ** argv, int myDevID = -1, XMem * myMem = NULL);
 
     /* make the network */
-    XTensor * Make(XTensor * k, XTensor * q, XTensor * v);
+    XTensor Make(XTensor &k, XTensor &q, XTensor &v);
 };
 
 }

source/sample/transformer/T2TEmbedding.cpp

@@ -101,21 +101,21 @@ void T2TEmbedder::MakePosEmbedding(int eSize, int d, int length)
 /* 
 make the network 
 */
-XTensor * T2TEmbedder::Make(XTensor * input)
+XTensor T2TEmbedder::Make(XTensor &input)
 {
-    CheckNTErrors(input->GetDim(-1) == vSize, "Wrong vocabulary size!");
-    CheckNTErrors(input->order > 1, "Wrong input tensor size!");
-    CheckNTErrors(input->dimSize[input->order - 2] < maxLength, "The sequence is too long!");
+    CheckNTErrors(input.GetDim(-1) == vSize, "Wrong vocabulary size!");
+    CheckNTErrors(input.order > 1, "Wrong input tensor size!");
+    CheckNTErrors(input.dimSize[input.order - 2] < maxLength, "The sequence is too long!");
     CheckNTErrors(vSize > 0, "set vocabulary size by \"-vsize\"");
     CheckNTErrors(eSize > 0, "set embedding size by \"-esize\"");
 
     int dims[MAX_TENSOR_DIM_NUM];
-    memcpy(dims, input->dimSize, input->order * sizeof(int));
-    dims[input->order - 1] = eSize;
+    memcpy(dims, input.dimSize, input.order * sizeof(int));
+    dims[input.order - 1] = eSize;
 
-    bool match = (posEmbedding.order == input->order);
+    bool match = (posEmbedding.order == input.order);
     if(match){
-        for(int i = 0; i < input->order; i++){
+        for(int i = 0; i < input.order; i++){
             if(dims[i] != posEmbedding.GetDim(i))
                 match = false;
         }
@@ -123,7 +123,7 @@ XTensor * T2TEmbedder::Make(XTensor * input)
 
     /* we make positional embeddings first */
     if(!match){
-        InitTensor(&posEmbedding, input->order, dims, X_FLOAT, 1.0F, devID, mem);
+        InitTensor(&posEmbedding, input.order, dims, X_FLOAT, 1.0F, devID, mem);
         XTensor * posTMP = NewTensorBuf(2, dims + 1, X_FLOAT, 1.0F, devID, mem);
 
         _CopyValues(&posEmbeddingBase, 0, posTMP->unitNum, posTMP, 0);
@@ -135,14 +135,10 @@ XTensor * T2TEmbedder::Make(XTensor * input)
     XTensor wordEmbedding;
 
     /* then we make word embeddings */
-    wordEmbedding = MMul(*input, w);
-
-    XTensor * result = new XTensor();
+    wordEmbedding = MMul(&input, w);
 
     /* we sum over the two embeddings */
-    *result = wordEmbedding + posEmbedding;
-
-    return result;
+    return wordEmbedding + posEmbedding;
 }
 
 }

source/sample/transformer/T2TEmbedding.h

@@ -77,7 +77,7 @@ public:
     void MakePosEmbedding(int eSize, int d, int length);
 
     /* make the network */
-    XTensor * Make(XTensor * input);
+    XTensor Make(XTensor &input);
 };
 
 }

source/sample/transformer/T2TEncoder.cpp

@@ -82,26 +82,28 @@ make the encoding network
 >> input - the input tensor of the encoder
 << return - the output tensor of the encoder
 */
-XTensor * AttEncoder::Make(XTensor * input)
+XTensor AttEncoder::Make(XTensor &input)
 {
-    XTensor * x = embedder.Make(input);
+    XTensor x;
+
+    x = embedder.Make(input);
 
     for(int i = 0; i < nlayer; i++){
-        XTensor * att;
-        XTensor * ln;
-        XTensor * fnn;
+        XTensor att;
+        XTensor ln;
+        XTensor fnn;
         XTensor res;
 
         /* self attention */
         att = attentions[i].Make(x, x, x);
 
         /* residual connection */
-        res = Sum(*att, *x);
+        res = Sum(att, x);
 
         /* TODO: dropout */
 
         /* layer normalization */
-        ln = layerNorms[i].Make(&res);
+        ln = layerNorms[i].Make(res);
 
         /* input of next layer */
         x = ln;
@@ -110,12 +112,12 @@ XTensor * AttEncoder::Make(XTensor * input)
         fnn = fnns[i].Make(x);
 
         /* residual connection */
-        res = Sum(*fnn, *x);
+        res = Sum(fnn, x);
 
         /* TODO: dropout */
 
         /* layer normalization */
-        ln = layerNorms[i].Make(&res);
+        ln = layerNorms[i].Make(res);
 
         /* input of next layer */
         x = ln;

source/sample/transformer/T2TEncoder.h

@@ -40,7 +40,7 @@ class T2TEncoder
 {
 public:
     virtual
-    XTensor * Make(XTensor * input) = 0;
+    XTensor Make(XTensor &input) = 0;
 };
 
 /* 
@@ -49,7 +49,7 @@ the encoder based on RNN
 class RNNEncoder : T2TEncoder
 {
 public:
-    XTensor * Make(XTensor * input);
+    XTensor Make(XTensor &input);
 };
 
 
@@ -106,7 +106,7 @@ public:
     void InitModel(int argc, const char ** argv, int myDevID = -1, XMem * myMem = NULL);
 
     /* make the encoding network */
-    XTensor * Make(XTensor * input);
+    XTensor Make(XTensor &input);
 };
 
 

source/sample/transformer/T2TFNN.cpp

@@ -78,18 +78,15 @@ y = max(0, x * w1 + b1) * w2 + b2
 >> input - the input tensor
 >> return - the output tensor 
 */
-XTensor * T2TFNN::Make(XTensor * input)
+XTensor T2TFNN::Make(XTensor &input)
 {
     XTensor t1;
-    XTensor * result = new XTensor();
 
     /* t1 = max(0, x * w1 + b1) */
-    t1 = Rectify(MMul(*input, X_NOTRANS, w1, X_NOTRANS) + b1);
+    t1 = Rectify(MMul(input, X_NOTRANS, w1, X_NOTRANS) + b1);
 
     /* result = t1 * w2 + b2 */
-    *result = MMul(t1, X_NOTRANS, w2, X_NOTRANS) + b2;
-
-    return result;
+    return MMul(t1, X_NOTRANS, w2, X_NOTRANS) + b2;
 }
 
 

source/sample/transformer/T2TFNN.h

@@ -72,7 +72,7 @@ public:
     void InitModel(int argc, const char ** argv, int myDevID = -1, XMem * myMem = NULL);
 
     /* make the network */
-    XTensor * Make(XTensor * input);
+    XTensor Make(XTensor &input);
 
 };
 

source/sample/transformer/T2TLayerNormal.cpp

@@ -57,15 +57,14 @@ y =
 >> input - the input tensor
 >> return - layer normalization output
 */
-XTensor * T2TLN::Make(XTensor * input)
+XTensor T2TLN::Make(XTensor &input)
 {
-    XTensor &x = *input;
+    XTensor &x = input;
     XTensor mean;
     XTensor variance;
     XTensor standard;
     XTensor meanFilled;
     XTensor standardFilled;
-    XTensor * result = new XTensor();
 
     /* \mu = (sum_i x_i)/m */
     mean = ReduceSum(x, x.order - 1);
@@ -82,9 +81,7 @@ XTensor * T2TLN::Make(XTensor * input)
     standardFilled = Unsqueeze(standard, x.order - 1, x.GetDim(-1));
 
     /* x' = (x - \mu)/standard */
-    *result = (x - meanFilled)/standardFilled;
-
-    return result;
+    return (x - meanFilled)/standardFilled;
 }
 
 }

source/sample/transformer/T2TLayerNormal.h

@@ -49,7 +49,7 @@ public:
     void InitModel(int argc, const char ** argv, int myDevID = -1, XMem * myMem = NULL);
     
     /* make the network */
-    XTensor * Make(XTensor * input);
+    XTensor Make(XTensor &input);
 };
 
 }

source/sample/transformer/T2TModel.cpp

@@ -69,7 +69,7 @@ make the encoding network
 >> input - input tensor
 << return - encoding result
 */
-XTensor * T2TModel::MakeEncoding(XTensor * input)
+XTensor T2TModel::MakeEncoding(XTensor &input)
 {
     return encoder.Make(input);
 }
@@ -79,10 +79,12 @@ make the entire network (with the output softmax layer)
 >> input - input tensor
 >> output - output tensor (distribution)
 */
-void T2TModel::Make(XTensor * input, XTensor * output)
+void T2TModel::Make(XTensor &input, XTensor &output)
 {
     if(isLM){
-        XTensor * encoding = MakeEncoding(input);
+        XTensor encoding;
+
+        encoding = MakeEncoding(input);
         outputLayer.Make(encoding, output);
     }
     else{

source/sample/transformer/T2TModel.h

@@ -66,10 +66,10 @@ public:
     void InitModel(int argc, const char ** argv);
 
     /* make the encoding network */
-    XTensor * MakeEncoding(XTensor * input);
+    XTensor MakeEncoding(XTensor &input);
 
     /* make the entire network (with the output softmax layer) */
-    void Make(XTensor * input, XTensor * output);
+    void Make(XTensor &input, XTensor &output);
 };
 
 }

source/sample/transformer/T2TOutput.cpp

@@ -53,11 +53,15 @@ void T2TOutput::InitModel(int argc, const char ** argv, int myDevID, XMem * myMe
     devID = myDevID;
     mem = myMem;
 
+    float minmax = 0;
+
     LoadParamInt(argc, argv, "vsize", &vSize, -1);
     LoadParamInt(argc, argv, "d", &inSize, DEFAULT_BEDDING_SIZE);
     LoadParamInt(argc, argv, "d", &hSize, DEFAULT_BEDDING_SIZE);
+    LoadParamFloat(argc, argv, "outputminmax", &minmax, 0.08F);
 
     InitTensor2D(&w, hSize, vSize, X_FLOAT, devID, mem);
+    w.SetDataRand(-minmax, minmax);
 }
 
 /* 
@@ -66,14 +70,11 @@ y = softmax(x * w)
 >> input - input tensor
 << return - output tensor 
 */
-XTensor * T2TOutput::Make(XTensor * input)
+XTensor T2TOutput::Make(XTensor &input)
 {
-    XTensor &x = *input;
-    XTensor * result = new XTensor();
-
-    *result = LogSoftmax(MMul(x, w), -1);
+    XTensor &x = input;
 
-    return result;
+    return LogSoftmax(MMul(x, w), -1);
 }
 
 /* 
@@ -81,11 +82,11 @@ make the network (redefined output tensor)
 >> input - input tensor
 >> output - output tensor 
 */
-void T2TOutput::Make(XTensor * input, XTensor * output)
+void T2TOutput::Make(XTensor &input, XTensor &output)
 {
-    XTensor &x = *input;
+    XTensor &x = input;
 
-    *output = LogSoftmax(MMul(x, w), -1);
+    output = LogSoftmax(MMul(x, w), -1);
 }
 
 }
\ No newline at end of file

source/sample/transformer/T2TOutput.h

@@ -62,10 +62,10 @@ public:
     void InitModel(int argc, const char ** argv, int myDevID = -1, XMem * myMem = NULL);
 
     /* make the network */
-    XTensor * Make(XTensor * input);
+    XTensor Make(XTensor &input);
 
     /* make the network (redefined output tensor) */
-    void Make(XTensor * input, XTensor * output);
+    void Make(XTensor &input, XTensor &output);
 };
 
 

source/sample/transformer/T2TTrainer.cpp

@@ -43,6 +43,7 @@ T2TTrainer::~T2TTrainer()
 {
     delete[] buf;
     delete[] seqLen;
+    delete[] seqOffset;
 }
 
 /* 
@@ -96,18 +97,19 @@ void T2TTrainer::Train(const char * fn, T2TModel * model)
         /* batch of input sequences */
         XTensor batch;
         
+        while(LoadBatch(file, &batch, 1, vSize, sBatchSize, wBatchSize, isLenSorted, wc)){
+            
             /* output probabilities */
             XTensor output;
             
-        while(LoadBatch(file, &batch, 1, vSize, sBatchSize, wBatchSize, isLenSorted, wc)){
-            
             /* make the network */
-            model->Make(&batch, &output);
+            model->Make(batch, output);
 
             /* back-propagation for obtaining gradients */
             net.Backward(output, batch, CROSSENTROPY);
             
-            /* TODO: update the model!!!! */
+            /* update the parameters */
+            Update(model);
             
             /* get probabilities */
             float prob = GetProb(&output, &batch, NULL);
@@ -121,7 +123,7 @@ void T2TTrainer::Train(const char * fn, T2TModel * model)
                 break;
             }
             
-            if (step % 100 == 0) {
+            if (step % 1 == 0) {
                 double elapsed = GetClockSec() - startT;
                 XPRINT5(0, stderr, "[INFO] elapsed=%.1fs, step=%d, epoch=%d, ngram=%d, ppl=%.3f\n",
                         elapsed, step, epoch + 1, wordCountTotal, exp(loss / wordCount));
@@ -153,8 +155,10 @@ int T2TTrainer::LoadBuf(FILE * file)
     while(fgets(line, MAX_SEQUENCE_LENGTH - 1, file)){
         int len = (int)strlen(line);
 
-        if(line[len - 1] == '\r')
+        while(line[len - 1] == '\r' || line[len - 1] == '\n'){
             line[len - 1] = 0;
+            len--;
+        }
 
         len = (int)strlen(line);
         if(len == 0)
@@ -166,10 +170,11 @@ int T2TTrainer::LoadBuf(FILE * file)
         /* how many words are in the sentence */
         int wNum = 0;
         int wNumLocal = 0;
+        int i = 0;
 
-        for(int i = 0; i < len; i++){
+        for(i = 0; i < len; i++){
             /* load word (id) seperated by space or tab */
-            if((line[i] == ' ' || line[i] == '\t' || i == len - 1) && wSize > 0){
+            if((line[i] == ' ' || line[i] == '\t') && wSize > 0){
                 line[i] = 0;
 
                 if(wSize == 3 && line[i - 1] == '|' && line[i - 2] == '|' && line[i - 3] == '|'){
@@ -179,7 +184,7 @@ int T2TTrainer::LoadBuf(FILE * file)
                     wNumLocal = 0;
                 }
                 else{
-                    buf[wNum++] = atoi(line + i - wSize);
+                    buf[wordCount + wNum++] = atoi(line + i - wSize);
                     wNumLocal++;
                 }
 
@@ -189,6 +194,11 @@ int T2TTrainer::LoadBuf(FILE * file)
                 wSize++;
         }
 
+        if(wSize > 0){
+            buf[wordCount + wNum++] = atoi(line + i - wSize);
+            wNumLocal++;
+        }
+
         seqLen[seqCount] = wNumLocal;
         seqOffset[seqCount] = wordCount + wNum - wNumLocal;
         seqCount++;
@@ -305,4 +315,35 @@ float T2TTrainer::GetProb(XTensor * output, XTensor * gold, XTensor * wordProbs)
     return result.Get1D(0);
 }
 
+/* 
+update the model by delta rule 
+>> model - the t2t model
+*/
+void T2TTrainer::Update(T2TModel * model)
+{
+    XList ws(100);
+
+    ws.Add(&model->outputLayer.w);
+    
+    for(int i = 0; i < model->encoder.nlayer; i++){
+        ws.Add(&model->encoder.fnns[i].w1);
+        ws.Add(&model->encoder.fnns[i].b1);
+        ws.Add(&model->encoder.fnns[i].w2);
+        ws.Add(&model->encoder.fnns[i].b2);
+    }
+
+    ws.Add(&model->encoder.embedder.w);
+
+    for(int i = 0; i < ws.count; i++){
+        XTensor * para = (XTensor*)ws.Get(i);
+        XTensor * paraGrad = para->grad;
+
+        CheckNTErrors(para != NULL, "NULL parameter tensor!");
+        CheckNTErrors(paraGrad != NULL, "NULL gradient tensor!");
+
+        /* the delta rule */
+        _Sum(para, paraGrad, para, -lrate);
+    }
+}
+
 }

source/sample/transformer/T2TTrainer.h

@@ -103,6 +103,9 @@ public:
     
     /* get word probabilities for a batch of sequences */
     float GetProb(XTensor * output, XTensor * gold, XTensor * wordProbs);
+
+    /* update the model by delta rule */
+    void Update(T2TModel * model);
 };
 
 

source/sample/transformer/Transformer.cpp

@@ -23,6 +23,7 @@
 #include "T2TModel.h"
 #include "T2TUtility.h"
 #include "T2TTrainer.h"
+#include "../../tensor/XDevice.h"
 
 namespace transformer
 {

source/tensor/XTensor.cpp

@@ -1042,11 +1042,11 @@ set the value of a cell in a 3d tensor in default type
 */
 bool XTensor::Set3D(DTYPE value, int d0, int d1, int d2)
 {
-    CheckNTErrors((order == 3), "Cannot get a 2d cell for a tensor whose order is not 2!");
-    CheckNTErrors((d0 >= 0 && d0 < dimSize[0]), "dimension 0 is out of range!");
-    CheckNTErrors((d2 >= 0 && d1 < dimSize[1]), "dimension 1 is out of range!");
-    CheckNTErrors((d2 >= 0 && d2 < dimSize[2]), "dimension 1 is out of range!");
-    CheckNTErrors((dataType == DEFAULT_DTYPE), "The tensor is not in default type.");
+    CheckNTErrors(order == 3, "Cannot get a 2d cell for a tensor whose order is not 2!");
+    CheckNTErrors(d0 >= 0 && d0 < dimSize[0], "dimension 0 is out of range!");
+    CheckNTErrors(d1 >= 0 && d1 < dimSize[1], "dimension 1 is out of range!");
+    CheckNTErrors(d2 >= 0 && d2 < dimSize[2], "dimension 1 is out of range!");
+    CheckNTErrors(dataType == DEFAULT_DTYPE, "The tensor is not in default type.");
 
     int dims[3] = {d0, d1, d1};
 

source/tensor/core/arithmetic/MatrixMulBatched.cpp

@@ -117,6 +117,9 @@ void _MatrixMulBatchedGPU(const XTensor * a, MATRIX_TRANS_TYPE transposedA,
         blockNum *= a->dimSizeRDI[i];
     }
 
+    int devIDBackup = 0;
+    ProtectCudaDev(a->devID, devIDBackup);
+
     cublasHandle_t * handle = a->mem != NULL ? a->mem->GetCublasHandle() : GDevs.GetCudaHandle(a->devID);
     _CudaBLASMatrixMULBatchedStrided(handle,
                                      a->data, transposedA, a->dataType, aBlockSize,
@@ -125,6 +128,8 @@ void _MatrixMulBatchedGPU(const XTensor * a, MATRIX_TRANS_TYPE transposedA,
                                      a->dimSizeRDI[1], a->dimSizeRDI[0],
                                      b->dimSizeRDI[1], b->dimSizeRDI[0],
                                      c->dimSizeRDI[1], c->dimSizeRDI[0], alpha, beta);
+
+    BacktoCudaDev(a->devID, devIDBackup);
 #endif
 }
 

source/tensor/core/arithmetic/Sum.cpp

@@ -22,6 +22,7 @@
 #include "../../XTensor.h"
 #include "../../XName.h"
 #include "../../XUtility.h"
+#include "../movement/CopyValues.h"
 #include "Sum.h"
 #include "Sum.cuh"
 #include "SumDim.h"
@@ -44,8 +45,12 @@ void _Sum(const XTensor * a, const XTensor * b, XTensor * c, DTYPE beta)
     CheckNTErrors(a->dataType == b->dataType && a->dataType == c->dataType,
                   "Unmatched tensors in addition!");
 
-    if (a->devID >= 0 || b->devID >= 0 || c->devID >= 0) {
+    if(beta == 0){
+        _CopyValues(a, c);
+        return;
+    }
 
+    if (a->devID >= 0 || b->devID >= 0 || c->devID >= 0) {
 #ifdef USE_CUDA
         if (a == c) {
             int P2PAccesible = 0;

source/tensor/core/shape/MergeBlockLists.cu

@@ -109,6 +109,9 @@ void _CudaMergeBlockLists(const XList * sourceList, int * blockSizes, int blockN
     CheckNTErrors((maxBlockSize % sizeof(DTYPE) == 0), "Unsupported block size!");
     realMaxBlockSize = maxBlockSize / sizeof(DTYPE);
 
+    int devIDBackup;
+    ProtectCudaDev(myMem->devID, devIDBackup);
+
     int cudaGridSizes[3];
     int cudaBlockSizes[3];
 
@@ -135,6 +138,8 @@ void _CudaMergeBlockLists(const XList * sourceList, int * blockSizes, int blockN
     delete[] targetArrays;
     delete[] sizes;
     delete[] offsets;
+
+    BacktoCudaDev(myMem->devID, devIDBackup);
 }
 #endif // USE_CUDA
 

source/tensor/function/LogSoftmax.cpp

@@ -150,11 +150,10 @@ void _LogSoftmax(const XTensor * x, XTensor * y, int leadDim)
             }
         }
 
-        if (x->devID < 0) {
         DelTensorBuf(max);
         DelTensorBuf(sum);
-        }
-        else {
+
+        if (x->devID >= 0) {
             delete blockx;
             delete blocky;
             delete blockMax;

source/tensor/function/Softmax.cu

@@ -239,6 +239,9 @@ void _CudaSoftmaxBackward(XTensor * gold, XTensor * y, XTensor * x,
     CheckNTErrors((x->devID == y->devID), "Matrices used in log softmax are not on the same GPU.");
     CheckNTErrors((y->order >= 1), "Empty tensor!");
 
+    int devIDBackup;
+    ProtectCudaDev(x->devID, devIDBackup);
+
     if(x->dataType == DEFAULT_DTYPE && y->dataType == DEFAULT_DTYPE){
         
         CheckNTErrors((lossName == CROSSENTROPY || 
@@ -284,8 +287,14 @@ void _CudaSoftmaxBackward(XTensor * gold, XTensor * y, XTensor * x,
             /* make a matrix to keep \beta */
             XTensor * beta = new XTensor(y->order - 1, dimSize, y->dataType, y->denseRatio, y->devID, mem);
 
+            if(mem != NULL){
                 ytmp->data = mem->AllocBuf(mem->devID, y->unitNum * y->unitSize);
                 beta->data = mem->AllocBuf(mem->devID, beta->unitNum * beta->unitSize);
+            }
+            else{
+                ytmp->data = XMemAlloc(y->devID, y->unitNum * y->unitSize);
+                beta->data = XMemAlloc(y->devID, beta->unitNum * beta->unitSize);
+            }
 
             /* \beta = \sum_i (dE/dy_i * y_i) */
             _Multiply(dedy, y, ytmp, 0, 0);
@@ -298,8 +307,18 @@ void _CudaSoftmaxBackward(XTensor * gold, XTensor * y, XTensor * x,
             /* dE/ds_j = y_j * ytmp = y_j * (dE/dy_j - \beta) */
             _Multiply(y, ytmp, dedx, 0, 0);
 
+
+            if(mem != NULL){
                 mem->ReleaseBuf(mem->devID, y->unitNum * y->unitSize);
                 mem->ReleaseBuf(mem->devID, beta->unitNum * beta->unitSize);
+            }
+            else{
+                XMemFree(y->devID, ytmp->data);
+                XMemFree(y->devID, beta->data);
+            }
+
+            ytmp->data = NULL;
+            beta->data = NULL;
 
             delete[] dimSize;
             delete ytmp;
@@ -311,6 +330,8 @@ void _CudaSoftmaxBackward(XTensor * gold, XTensor * y, XTensor * x,
     }
     else
         ShowNTErrors("TODO!");
+
+    BacktoCudaDev(x->devID, devIDBackup);
 }
 
 #endif