new code for split

source/network/XBackwardShape.cpp

@@ -43,6 +43,10 @@ void XShapeGrad::MakeGrad(XTensor * node)
         GradMergeList(node);
         GradMergeList(node);
     else if(operID == SHAPE_UNSQUEEZE)
     else if(operID == SHAPE_UNSQUEEZE)
         GradUnsqueeze(node);
         GradUnsqueeze(node);
+    else if(operID == SHAPE_SPLIT)
+        GradSplit(node);
+    else if(operID == SHAPE_SPLIT_LIST)
+        GradSplitList(node);
     else{
     else{
         ShowNTErrors("TODO!");
         ShowNTErrors("TODO!");
     }
     }
@@ -234,7 +238,7 @@ we have
 dE/da = merge(dE/dc)
 dE/da = merge(dE/dc)
 >> node - the node (c) for backward computation
 >> node - the node (c) for backward computation
 */
 */
-void GradSplit(XTensor * node)
+void XShapeGrad::GradSplit(XTensor * node)
 {
 {
     XLink &income = node->income;
     XLink &income = node->income;
     XTensor * input = income.tails[0];
     XTensor * input = income.tails[0];
@@ -256,17 +260,10 @@ void GradSplit(XTensor * node)
     /* if the tensor is used somewhere else, we need another SUM
     /* if the tensor is used somewhere else, we need another SUM
        for gradient accumulation */
        for gradient accumulation */
     else{
     else{
-        int * dims = new int[input->order];
-        memcpy(dims, input->dimSize, sizeof(int) * input->order);
-        dims[0] = -dims[0];
-        XTensor inputGradTMP(input->order, dims,
-                             input->dataType, input->denseRatio,
-                             input->devID, input->mem);
+        XTensor inputGradTMP(input);
 
 
         _Merge(node->grad, &inputGradTMP, whereToSplit + 1, 0);
         _Merge(node->grad, &inputGradTMP, whereToSplit + 1, 0);
         _Sum(input->grad, &inputGradTMP, input->grad);
         _Sum(input->grad, &inputGradTMP, input->grad);
-
-        delete[] dims;
     }
     }
 
 
     node->visitMark = NODE_FINISHED;
     node->visitMark = NODE_FINISHED;
@@ -293,7 +290,7 @@ void XShapeGrad::GradSplitList(XTensor * node)
 }
 }
 
 
 /*
 /*
-gradient computation for spliting where we return 
+gradient computation for spliting. We return 
 the list of the splits : list(c_1, ...) = split(a).
 the list of the splits : list(c_1, ...) = split(a).
 this method is called only when all nodes of spliting 
 this method is called only when all nodes of spliting 
 have been processed. We do this in a post-processing
 have been processed. We do this in a post-processing
@@ -303,6 +300,46 @@ one time. This is good for system speed up.
 */
 */
 void XShapeGrad::GradSplitListPost(XTensor * node)
 void XShapeGrad::GradSplitListPost(XTensor * node)
 {
 {
+    /* we compute the gradient for current node, rather than for
+       child node, i.e., we use the outgoing edge here */
+    XLink &outgo = node->outgo;
+    XList splits(outgo.tailNum);
+    int whereToSplit = -1;
+    int splitNum = 0;
+
+    for(int i = 0; i < outgo.tailNum; i++){
+        XTensor * parent = (XTensor*)outgo.tails[i];
+        XLink &income = parent->income;
+        if(income.typeID == SHAPE_SPLIT_LIST){
+            int w = income.GetParamInt(0);
+            int splitID = income.GetParamInt(1);
+            if(whereToSplit < 0)
+                whereToSplit = w;
+            splitNum++;
+
+            CheckNTErrors(whereToSplit == w, "Wrong dimension for spliting");
+            CheckNTErrors(income.tailNum == 1, "Something wrong with outgoing edge!");
+            CheckNTErrors(splitNum - 1 == splitID, "Wrong split id!");
+
+            splits.Add(parent);
+        }
+    }
+
+    /* we can simply merge the gradient tensor 
+       if the node is used in spliting only */
+    if(outgo.tailNum == splitNum){
+        _Merge(&splits, node->grad, whereToSplit + 1);
+    }
+
+    /* if the tensor is used as input to other nodes
+       somewhere else, we need another SUM for gradient 
+       accumulation */
+    else{
+        XTensor nodeGradTMP(node);
+
+        _Merge(&splits, &nodeGradTMP, whereToSplit + 1);
+        _Sum(node->grad, &nodeGradTMP, node->grad);
+    }
 }
 }
 
 
 /* 
 /* 

source/network/XBackwardShape.h

@@ -57,11 +57,11 @@ private:
     static
     static
     void GradSplit(XTensor * node);
     void GradSplit(XTensor * node);
 
 
-    /* gradient computation for spliting where we return the list of the splits : list(c_1, ...) = split(a) */
+    /* gradient computation for spliting. we return the list of the splits : list(c_1, ...) = split(a) */
     static
     static
     void GradSplitList(XTensor * node);
     void GradSplitList(XTensor * node);
 
 
-    /* gradient computation for spliting where we return the list of the splits : list(c_1, ...) = split(a).
+    /* gradient computation for spliting. we return the list of the splits : list(c_1, ...) = split(a).
        this method is called only when all nodes of spliting have been processed. We do this in a post-processing
        this method is called only when all nodes of spliting have been processed. We do this in a post-processing
        manner because we can fuze multiple memory copy jobs one time. This is good for system speed up. */
        manner because we can fuze multiple memory copy jobs one time. This is good for system speed up. */
     static
     static

source/sample/fnnlm/FNNLM.cpp

@@ -492,15 +492,15 @@ void Update(FNNModel &model, FNNModel &grad, float epsilon, bool isNodeGrad)
         gradList.Add(&grad.embeddingW);
         gradList.Add(&grad.embeddingW);
     }
     }
     else{
     else{
-        paraList.Add(model.outputW.grad);
-        paraList.Add(&model.outputB.grad);
+        gradList.Add(model.outputW.grad);
+        gradList.Add(model.outputB.grad);
 
 
         for (int i = 0; i < model.hDepth; i++) {
         for (int i = 0; i < model.hDepth; i++) {
-            paraList.Add(&model.hiddenW[i].grad);
-            paraList.Add(&model.hiddenB[i].grad);
+            gradList.Add(model.hiddenW[i].grad);
+            gradList.Add(model.hiddenB[i].grad);
         }
         }
 
 
-        paraList.Add(&model.embeddingW.grad);
+        gradList.Add(model.embeddingW.grad);
     }
     }
 
 
     for (int i = 0; i < paraList.count; i++) {
     for (int i = 0; i < paraList.count; i++) {

source/tensor/XList.cpp

@@ -208,22 +208,16 @@ void XList::Insert(int pos, void * item)
 /* get the item at position i */
 /* get the item at position i */
 void * XList::GetItem(int i) const
 void * XList::GetItem(int i) const
 {
 {
-    if( i >= 0 && i < count )
-        return items[i];
-    else
-        return NULL;
+    CheckNTErrors(i >= 0 && i < count, "Index of a list item is out of scope!");
+    return items[i];
 }
 }
 
 
 /* get the integer-typed item at position i */
 /* get the integer-typed item at position i */
 int XList::GetItemInt(int i)
 int XList::GetItemInt(int i)
 {
 {
     CheckNTErrors(isIntList, "An int list is required!");
     CheckNTErrors(isIntList, "An int list is required!");
-
-    if( i >= 0 && i < count ){
-        return *(int*)(items[i]);
-    }
-    else
-        return 0;
+    CheckNTErrors(i >= 0 && i < count, "Index of a list item is out of scope!");
+    return *(int*)(items[i]);
 }
 }
 
 
 /* set the item at position i */
 /* set the item at position i */