Clean the local memory codes in FNNLM.

source/sample/fnnlm/FNNLM.cpp

@@ -68,8 +68,8 @@ void Read(const char * fn, FNNModel &model);
 void Test(const char * test, const char * result, FNNModel &model);
 void Test(const char * test, const char * result, FNNModel &model);
 int  LoadNGrams(FILE * file, int n, NGram * ngrams, int sentNum, int wordNum);
 int  LoadNGrams(FILE * file, int n, NGram * ngrams, int sentNum, int wordNum);
 void InitZeroOneTensor2D(XTensor &tensor, int rowNum, int colNum, int * rows, int * cols, 
 void InitZeroOneTensor2D(XTensor &tensor, int rowNum, int colNum, int * rows, int * cols, 
-                         int itemNum, int devID, XMem * mem);
-void MakeWordBatch(XTensor &batch, NGram * ngrams, int ngramNum, int n, int vSize, int devID, XMem * mem);
+                         int itemNum, int devID);
+void MakeWordBatch(XTensor &batch, NGram * ngrams, int ngramNum, int n, int vSize, int devID);
 void Forward(XTensor inputs[], XTensor &output, FNNModel &model, FNNNet &net);
 void Forward(XTensor inputs[], XTensor &output, FNNModel &model, FNNNet &net);
 void Backward(XTensor inputs[], XTensor &output, XTensor &gold, LOSS_FUNCTION_NAME loss, 
 void Backward(XTensor inputs[], XTensor &output, XTensor &gold, LOSS_FUNCTION_NAME loss, 
               FNNModel &model, FNNModel &grad, FNNNet &net);
               FNNModel &model, FNNModel &grad, FNNNet &net);
@@ -229,11 +229,6 @@ void LoadArgs(int argc, const char ** argv, FNNModel &model)
             fprintf(stderr, " -dev=%d\n", model.devID);
             fprintf(stderr, " -dev=%d\n", model.devID);
         }
         }
     }
     }
-
-    for(int i = 0; i < argc; i++){
-        if (!strcmp(argv[i], "-mempool"))
-            model.mem = new XMem(model.devID);
-    }
 }
 }
 
 
 /* check model settings */
 /* check model settings */
@@ -262,11 +257,6 @@ void Copy(FNNModel &tgt, FNNModel &src)
     tgt.vSize = src.vSize;
     tgt.vSize = src.vSize;
     tgt.devID = src.devID;
     tgt.devID = src.devID;
     tgt.useMemPool = src.useMemPool;
     tgt.useMemPool = src.useMemPool;
-    if(src.mem != NULL){
-        tgt.mem = new XMem(src.mem->devID, src.mem->mode, 
-                           src.mem->maxBlockSize, src.mem->blockNum, 
-                           src.mem->bufSize);
-    }
 }
 }
 
 
 /* 
 /* 
@@ -459,10 +449,10 @@ void Train(const char * train, bool isShuffled, FNNModel &model)
 
 
             /* make the input tensor for position i */
             /* make the input tensor for position i */
             for(int i = 0; i < model.n - 1; i++)
             for(int i = 0; i < model.n - 1; i++)
-                MakeWordBatch(inputs[i], ngrams, ngramNum, i, model.vSize, model.devID, model.mem);
+                MakeWordBatch(inputs[i], ngrams, ngramNum, i, model.vSize, model.devID);
 
 
             /* make the gold tensor */
             /* make the gold tensor */
-            MakeWordBatch(gold, ngrams, ngramNum, model.n - 1, model.vSize, model.devID, model.mem);
+            MakeWordBatch(gold, ngrams, ngramNum, model.n - 1, model.vSize, model.devID);
 
 
             if(!autoDiff){
             if(!autoDiff){
                 /* prepare an empty network for building the fnn */
                 /* prepare an empty network for building the fnn */
@@ -474,8 +464,6 @@ void Train(const char * train, bool isShuffled, FNNModel &model)
                 /* forward computation */
                 /* forward computation */
                 Forward(inputs, output, model, net);
                 Forward(inputs, output, model, net);
 
 
-
-
                 /* backward computation to obtain gradients */
                 /* backward computation to obtain gradients */
                 Backward(inputs, output, gold, CROSSENTROPY, model, grad, net);
                 Backward(inputs, output, gold, CROSSENTROPY, model, grad, net);
 
 
@@ -727,10 +715,9 @@ The indexed cell is set to 1, and 0 otherwise.
 >> cols - column index
 >> cols - column index
 >> itemNum - number of non-zero items
 >> itemNum - number of non-zero items
 >> devID - device id
 >> devID - device id
->> mem - memory pool
 */
 */
 void InitZeroOneTensor2D(XTensor &tensor, int rowNum, int colNum, int * rows, int * cols, 
 void InitZeroOneTensor2D(XTensor &tensor, int rowNum, int colNum, int * rows, int * cols, 
-                         int itemNum, int devID, XMem * mem)
+                         int itemNum, int devID)
 {
 {
     InitTensor2DV2(&tensor, rowNum, colNum, X_FLOAT, devID);
     InitTensor2DV2(&tensor, rowNum, colNum, X_FLOAT, devID);
 
 
@@ -749,9 +736,8 @@ make a tensor that encodes a batch of words
 >> n - indicate which word is encode for each ngram
 >> n - indicate which word is encode for each ngram
 >> vSize - vocabulary size
 >> vSize - vocabulary size
 >> devID - device id
 >> devID - device id
->> mem - memory pool
 */
 */
-void MakeWordBatch(XTensor &batch, NGram * ngrams, int ngramNum, int n, int vSize, int devID, XMem * mem)
+void MakeWordBatch(XTensor &batch, NGram * ngrams, int ngramNum, int n, int vSize, int devID)
 {
 {
     int * rows = new int[ngramNum];
     int * rows = new int[ngramNum];
     int * cols = new int[ngramNum];
     int * cols = new int[ngramNum];
@@ -761,7 +747,7 @@ void MakeWordBatch(XTensor &batch, NGram * ngrams, int ngramNum, int n, int vSiz
         cols[i] = ngrams[i].words[n];
         cols[i] = ngrams[i].words[n];
     }
     }
 
 
-    InitZeroOneTensor2D(batch, ngramNum, vSize, rows, cols, ngramNum, devID, mem);
+    InitZeroOneTensor2D(batch, ngramNum, vSize, rows, cols, ngramNum, devID);
 
 
     delete[] rows;
     delete[] rows;
     delete[] cols;
     delete[] cols;
@@ -1170,10 +1156,10 @@ void Test(const char * test, const char * result, FNNModel &model)
         
         
         /* make the input tensor for position i */
         /* make the input tensor for position i */
         for (int i = 0; i < model.n - 1; i++)
         for (int i = 0; i < model.n - 1; i++)
-            MakeWordBatch(inputs[i], ngrams, ngramNum, i, model.vSize, model.devID, model.mem);
+            MakeWordBatch(inputs[i], ngrams, ngramNum, i, model.vSize, model.devID);
 
 
         /* make the gold tensor */
         /* make the gold tensor */
-        MakeWordBatch(gold, ngrams, ngramNum, model.n - 1, model.vSize, model.devID, model.mem);
+        MakeWordBatch(gold, ngrams, ngramNum, model.n - 1, model.vSize, model.devID);
 
 
         if (!autoDiff) {
         if (!autoDiff) {
             /* prepare an empty network for building the fnn */
             /* prepare an empty network for building the fnn */