new code of mem

source/sample/transformer/T2TTrainer.cpp

@@ -31,8 +31,6 @@ namespace transformer
 /* constructor */
 T2TTrainer::T2TTrainer()
 {
-    devID = -1;
-    mem = NULL;
     seqLen = NULL;
     nseqBuf = 0;
     nextSeq = -1;
@@ -56,7 +54,6 @@ void T2TTrainer::Init(int argc, const char ** argv)
     bool useMem = false;
 
     LoadParamBool(argc, argv, "mem", &useMem, useMem);
-    LoadParamInt(argc, argv, "dev", &devID, -1);
     LoadParamFloat(argc, argv, "lrate", &lrate, 0.001F);
     LoadParamInt(argc, argv, "sbatch", &sBatchSize, 1);
     LoadParamInt(argc, argv, "wbatch", &wBatchSize, 1);
@@ -72,10 +69,6 @@ void T2TTrainer::Init(int argc, const char ** argv)
     seqLen = new int[bufSize];
     seqOffset = new int[bufSize];
 
-    if(useMem){
-        delete mem;
-        mem = new XMem(devID, UNI_FREE, MILLION * 64, 1024, MILLION * 64);
-    }
 }
 
 /* 
@@ -94,7 +87,8 @@ void T2TTrainer::Train(const char * fn, T2TModel * model)
     float loss = 0;
     float lr = 0;
 
-    model->mem->SetPin();
+    int devID = model->devID;
+    XMem * mem = model->mem;
     mem->SetPin();
 
     XNet net;
@@ -108,7 +102,6 @@ void T2TTrainer::Train(const char * fn, T2TModel * model)
         
         wordCount = 0;
 
-        model->mem->BackToPin();
         mem->BackToPin();
         
         /* batch of input sequences */
@@ -117,7 +110,7 @@ void T2TTrainer::Train(const char * fn, T2TModel * model)
         /* padding */
         XTensor padding;
         
-        while(LoadBatch(file, &batch, &padding, 1, vSize, sBatchSize, wBatchSize, isLenSorted, wc)){
+        while(LoadBatch(file, &batch, &padding, 1, vSize, sBatchSize, wBatchSize, isLenSorted, wc, devID, mem)){
             
             /* output probabilities */
             XTensor output;
@@ -157,7 +150,6 @@ void T2TTrainer::Train(const char * fn, T2TModel * model)
                         lr, elapsed, step, epoch + 1, wordCountTotal, exp(loss / wordCount));
             }
 
-            model->mem->BackToPin();
             mem->BackToPin();
         }
         
@@ -261,10 +253,13 @@ load a batch of sequences
 >> wBatch - batch size of words
 >> isSorted - indicates whether the sequences are sorted by length
 >> wCount - word count
+>> devID - device id
+>> mem - memory pool
 */
 int T2TTrainer::LoadBatch(FILE * file, XTensor * batch, XTensor * padding, 
                           int step, int vs, int sBatch, int wBatch, 
-                          bool isSorted, int &wCount)
+                          bool isSorted, int &wCount,
+                          int devID, XMem * mem)
 {
     if(nextSeq < 0 || nextSeq >= nseqBuf)
         LoadBuf(file);
@@ -295,15 +290,15 @@ int T2TTrainer::LoadBatch(FILE * file, XTensor * batch, XTensor * padding,
         dims[1] = max;
         dims[2] = vs;
 
-        if(batch->order != 3 || batch->GetDim(0) != dims[0] || 
-           batch->GetDim(1) != dims[1] || batch->GetDim(2) != dims[2]){
+        //if(batch->order != 3 || batch->GetDim(0) != dims[0] || 
+        //   batch->GetDim(1) != dims[1] || batch->GetDim(2) != dims[2]){
                InitTensor(batch, 3, dims, X_FLOAT, 1.0F, devID, mem);
-        }
+        //}
 
-        if(padding->order != 2 || padding->GetDim(0) != sc || 
-           padding->GetDim(1) != max){
+        //if(padding->order != 2 || padding->GetDim(0) != sc || 
+        //   padding->GetDim(1) != max){
                InitTensor2D(padding, sc, max, X_FLOAT, devID, mem);
-        }
+        //}
 
         batch->SetZeroAll();
         padding->SetZeroAll();

source/sample/transformer/T2TTrainer.h

@@ -37,12 +37,6 @@ namespace transformer
 class T2TTrainer
 {
 public:
-    /* device id */
-    int devID;
-
-    /* memory pool */
-    XMem * mem;
-
     /* buffer for loading words */
     int * buf;
 
@@ -107,7 +101,8 @@ public:
     /* load a batch of sequences */
     int LoadBatch(FILE * file, XTensor * batch, XTensor * padding, 
                   int step, int vs, int sBatch, int wBatch, 
-                  bool isSorted, int &wCount);
+                  bool isSorted, int &wCount,
+                  int devID, XMem * mem);
     
     /* get word probabilities for a batch of sequences */
     float GetProb(XTensor * output, XTensor * gold, XTensor * wordProbs);

source/tensor/XMem.cpp

@@ -394,7 +394,8 @@ void * XMem::AllocDynamic(int myDevID, MTYPE mySize)
             CheckNTErrors(cudaMemset(mem, 0, b->size + 2 * CUDA_PITCH) == cudaSuccess, "Cannot update the memory.");
             SetDevice(devIDBackup);
 #else
-            ShowNTErrors("Please specify USE_CUDA for compiling this program.");
+            ShowNTErrors("Please specify USE_CUDA for compiling this program.");>	NiuTrans.Network.exe!nts::XMem::AllocDynamic(int myDevID, unsigned __int64 mySize)  387	C++
+
 #endif
         }