code clean

source/sample/transformer/Utility.cpp

@@ -192,7 +192,9 @@ IntList SplitInt(const string& s, const string& delimiter)
     IntList values;
     auto indices = SplitToPos(s, delimiter);
     for (int i = 0; i < indices.Size(); i++) {
-        values.Add(strtol(s.data() + indices[i], nullptr, 10));
+        
+        /* this line is with problem. Why do we need an IntList to keep an int64*/
+        values.Add((int)strtol(s.data() + indices[i], nullptr, 10));
     }
     return values;
 }
@@ -208,4 +210,4 @@ FloatList SplitFloat(const string& s, const string& delimiter)
     return values;
 }
 
-}
\ No newline at end of file
+}

source/sample/transformer/submodel/Attention.cpp

@@ -226,7 +226,6 @@ XTensor Attention::MakeRPRAttention(XTensor& k, XTensor& q, XTensor& v,
     XTensor qheads;
     XTensor vheads;
 
-    const int batchSize = q.GetDim(0);
     const int lenQ = q.GetDim(1);
     const int lenKV = k.GetDim(1);
 
@@ -402,4 +401,4 @@ void Cache::Reorder(XTensor& reorder)
         value = AutoGather(value, reorder);
     }
 }
-}
\ No newline at end of file
+}

source/sample/transformer/submodel/GLU.cpp

@@ -48,8 +48,6 @@ void GLU::InitModel(Config& config)
 {
     devID = config.devID;
 
-    float minmax = 0;
-
     inSize = config.modelSize;
     outSize = config.modelSize;
 
@@ -84,4 +82,4 @@ XTensor GLU::Make(XTensor& input)
     return t1 * Sigmoid(t2);
 }
 
-}
\ No newline at end of file
+}

source/sample/transformer/train/Trainer.cpp

@@ -334,7 +334,7 @@ void Trainer::Validate(const char* fn, const char* ofn, Model* model)
         }
 
         int bSize = output.GetDim(0);
-        int length = output.GetDim(1);
+        //int length = output.GetDim(1);
 
         labelOnehot = IndexToOnehot(label, vSizeTgt, 0);
         lossTensor = CrossEntropy(output, labelOnehot, paddingDec);
@@ -479,4 +479,4 @@ void Trainer::PrepareModel(Model* model)
     adamBeta2T = 1.0F;
 }
 
-}
\ No newline at end of file
+}

source/sample/transformer/translate/Search.cpp

@@ -322,7 +322,7 @@ void BeamSearch::Generate(StateBundle* prev, StateBundle* beam)
     /* keep the most promising candidates in the beam */
     TopK(score, scoreTopK, index, -1, beamSize, true);
 
-    float lp = LengthPenalizer::GNMT(beam->nstep, alpha);
+    //float lp = LengthPenalizer::GNMT(beam->nstep, alpha);
 
     CopyValues(index, indexCPU);
     CopyValues(index, preID);
@@ -493,8 +493,8 @@ void BeamSearch::Collect(StateBundle* beam)
 
         /* check if this is the first end symbol. It is false
            if there have been end symbols in previously generated words. */
-        bool isCompleted = state.isCompleted && 
-             (state.last == NULL || !state.last->isCompleted);
+        //bool isCompleted = state.isCompleted &&
+        //     (state.last == NULL || !state.last->isCompleted);
 
         /* we push the hypothesis into the heap when it is completed */
         if ((state.isEnd || state.isCompleted)) {
@@ -557,7 +557,6 @@ void BeamSearch::Dump(IntList* output, XTensor* score)
             }
         }
 
-        int count = 0;
         bool isCompleted = true;
 
         /* we track the state from the end to the beginning */
@@ -873,4 +872,4 @@ void GreedySearch::Search(Model* model, XTensor& input,
     delete[] finishedFlags;
 }
 
-}
\ No newline at end of file
+}

source/sample/transformer/translate/Translator.cpp

@@ -155,7 +155,7 @@ void Translator::Translate(const char* ifn, const char* sfn,
         batchLoader.outputBuffer.Add(emptyRes);
     }
 
-    double startDump = GetClockSec();
+    //double startDump = GetClockSec();
 
     /* reorder the result */
     batchLoader.SortOutput();
@@ -163,7 +163,7 @@ void Translator::Translate(const char* ifn, const char* sfn,
     /* print the result to a file */
     batchLoader.DumpRes(ofn);
 
-    double elapsed = GetClockSec() - startDump;
+    //double elapsed = GetClockSec() - startDump;
 
     LOG("translation completed (word=%d, sent=%zu)", 
         wordCountTotal, batchLoader.inputBuffer.Size() + batchLoader.emptyLines.Size());
@@ -196,4 +196,4 @@ void Translator::Dump(FILE* file, XTensor* output)
     }
 }
 
-}
\ No newline at end of file
+}

source/sample/transformer/translate/Vocab.cpp

@@ -34,14 +34,14 @@ void Vocab::Load(const string& src)
 
     /* get the vocab size and the start id */
     f >> vsz >> sid;
-    startID = stol(sid);
-    vocabSize = stol(vsz);
+    startID = (int)stol(sid);
+    vocabSize = (int)stol(vsz);
 
     string word, id;
     for (int i = 0; i < vocabSize - startID; i++) {
         f >> word >> id;
-        word2id[word] = stol(id);
-        id2word[stol(id)] = word;
+        word2id[word] = (int)stol(id);
+        id2word[(int)stol(id)] = word;
     }
 
     f.close();
@@ -75,4 +75,4 @@ void Vocab::CopyFrom(const Vocab& v)
         id2word.insert(i2w);
 }
 
-}
\ No newline at end of file
+}

source/tensor/XDevice.cpp

@@ -182,10 +182,11 @@ void XDevice::Reset()
     XMem * mem = GMems.GetMem(devID);
     mem->Free();
 
+#ifdef USE_CUDA
     int devIDReset = devID;
+    
     Clear();
-
-#ifdef USE_CUDA
+    
     if (devIDReset >= 0) {
         int devIDBackup = -1;
         cudaGetDevice(&devIDBackup);
@@ -195,6 +196,8 @@ void XDevice::Reset()
 
         cudaSetDevice(devIDBackup);
     }
+#else
+    Clear();
 #endif
 }
 

source/tensor/core/arithmetic/MulAndShift.cpp

@@ -121,8 +121,8 @@ XTensor MulAndShift(const XTensor& x, MATRIX_TRANS_TYPE transposedX,
     CheckNTErrors(x.order >= 2 && w.order >= 2, "Input tensors must have a order >= 2!");
 
     int xn = transposedX == X_TRANS ? x.dimSize[x.order - 1] : x.dimSize[x.order - 2];
-    int xm = transposedX == X_TRANS ? x.dimSize[x.order - 2] : x.dimSize[x.order - 1];
-    int wn = transposedW == X_TRANS ? w.dimSize[w.order - 1] : w.dimSize[w.order - 2];
+    //int xm = transposedX == X_TRANS ? x.dimSize[x.order - 2] : x.dimSize[x.order - 1];
+    //int wn = transposedW == X_TRANS ? w.dimSize[w.order - 1] : w.dimSize[w.order - 2];
     int wm = transposedW == X_TRANS ? w.dimSize[w.order - 2] : w.dimSize[w.order - 1];
 
     int order = x.order + w.order - 2;
@@ -179,4 +179,4 @@ XTensor MulAndShift(const XTensor& x, MATRIX_TRANS_TYPE transposedX,
     return c;
 }
 
-}
\ No newline at end of file
+}

source/tensor/core/movement/Spread.cpp

@@ -231,8 +231,8 @@ And this is a special spread function for backward computation of gather functio
 */
 void _SpreadForGather(XTensor * source, XTensor * collection, XTensor * index)
 {
-    int dim = 0;
-    int order = source->order;
+    //int dim = 0;
+    //int order = source->order;
 
     CheckNTErrors(source->dataType == DEFAULT_DTYPE, "TODO!");
     CheckNTErrors(collection->GetDim(-1) == source->GetDim(-1), "Illegal dimension!");
@@ -272,4 +272,4 @@ void _SpreadForGather(XTensor * source, XTensor * collection, XTensor * index)
     }
 }
 
-} // namespace nts(NiuTrans.Tensor)
\ No newline at end of file
+} // namespace nts(NiuTrans.Tensor)

source/tensor/core/reduce/ReduceSumAll.cpp

@@ -72,7 +72,6 @@ sum all the items of the tensor (It should be optimized!)
 void _ReduceSumAll(const XTensor * source, DTYPE * value)
 {
     int * dimSize = new int[MAX_TENSOR_DIM_NUM];
-    float dr = (!source->isSparse) ? 1.0F : source->denseRatio;
     XTensor * target = NewTensorBufV2(0, dimSize, source->dataType, source->denseRatio, source->devID, source->mem);
     target->SetTMPFlag();
 
@@ -122,4 +121,4 @@ DTYPE ReduceSumAllValue(const XTensor & source)
     return target.Get0D();
 }
 
-} // namespace nts(NiuTrans.Tensor)
\ No newline at end of file
+} // namespace nts(NiuTrans.Tensor)

source/tensor/core/shape/Stack.cpp

@@ -43,13 +43,11 @@ void _Stack(const TensorList * smalls, XTensor * t, int dim)
 
     int blockSize = 1;
     int blockNum = 1;
-    int gridSize = 1;
     int gridNum = 1;
 
     XTensor * smallsItem0 = smalls->GetItem(0);
-    int unitNum = smallsItem0->unitNum;
+    //int unitNum = smallsItem0->unitNum;
     int unitSize = smallsItem0->unitSize;
-    int itemSize = unitNum * unitSize;
 
     for (int i = 0; i < smallsItem0->order; i++) {
         if (i >= dim)
@@ -129,7 +127,7 @@ bool CheckStackShape(const TensorList &smalls, XTensor &t, int dim)
     XTensor * tensor = (XTensor*)smalls.GetItem(0);
     int order = tensor->order;
 
-    for (int i = 0; i < tensor->order; i++) {
+    for (int i = 0; i < order; i++) {
         if (i < dim) {
             if (t.GetDim(i) != tensor->GetDim(i)) 
                 return false;

source/tensor/test/TConvertDataType.cpp

@@ -215,12 +215,12 @@ bool TestConvertDataType3()
                           {0.5F, -4.0F},
                           {0.0F, 6.0F} };
     
-    DTYPE data2[2][3] = { {1.0F, 2.0F, 3.0F},
+    /*DTYPE data2[2][3] = { {1.0F, 2.0F, 3.0F},
                           {0.0F, 4.0F, 5.0F} };
     
     DTYPE answer[3][3] = { {1.0F, -6.0F, -7.0F},
                            {0.5F, -15.0F, -18.5F}, 
-                           {0.0F, 24.0F, 30.0F} };
+                           {0.0F, 24.0F, 30.0F} };*/
 
     /* CPU test */
     bool cpuTest = true;

source/tensor/test/TGather.cpp

@@ -67,7 +67,6 @@ bool TestGather1()
     DTYPE answer[2][3] = { {0.0F, -1.0F, 2.0F},
                            {1.0F, 2.0F, 4.0F} };
 
-    int dim = 0;
     int indexSize = 2;
     int srcIndex[2] = {0, 2};
 

source/tensor/test/TSetData.cpp

@@ -422,7 +422,7 @@ bool TestSetData6()
     for (int i = 0; i < order; i++)
         unitNum *= dimSize[i];
 
-    DTYPE answer[5] = {5.2F, 3.2F, 1.2F, -0.8F, -2.8F};
+    //DTYPE answer[5] = {5.2F, 3.2F, 1.2F, -0.8F, -2.8F};
 
     /* CPU test */
     bool cpuTest = true;

source/train/XWorkerJob.cpp

@@ -51,7 +51,7 @@ bool XWorkerJob::AddJobRefresh(XModel * paramKeeper)
     XList args(1);
     args.Add(paramKeeper);
 
-    queue.EnqueueJob(&XModel::Refresh, &args);
+    queue.EnqueueJob((void*)&paramKeeper->Refresh, &args);
 
     return true;
 }
@@ -79,4 +79,4 @@ bool XWorkerJob::AddJobNeuralNet(void * func, void * net, XList * inputs, XList
     return true;
 }
 
-}  /* end of the nts (NiuTrans.Tensor) namespace */
\ No newline at end of file
+}  /* end of the nts (NiuTrans.Tensor) namespace */