remove \"extern "c"\"

source/tensor/core/arithmetic/SumByColumnTV.h

@@ -27,9 +27,8 @@
 namespace nts { // namespace nts(NiuTrans.Tensor)
 
 /* sum of a tensor and a (column) vector */
-extern "C"
 void _SumByColumnTV(const XTensor * a, const XTensor * b, XTensor * c = NULL, DTYPE beta = (DTYPE)1.0);
 
 } // namespace nts(NiuTrans.Tensor)
 
-#endif // __SUMBYCOLUMNTV_H__
\ No newline at end of file
+#endif // __SUMBYCOLUMNTV_H__

source/tensor/core/arithmetic/SumByColumnVT.h

@@ -27,9 +27,8 @@
 namespace nts { // namespace nts(NiuTrans.Tensor)
 
 /* sum of a (column) vector and a tensor */
-extern "C"
 void _SumByColumnVT(const XTensor * a, const XTensor * b, XTensor * c = NULL, DTYPE beta = (DTYPE)1.0);
 
 } // namespace nts(NiuTrans.Tensor)
 
-#endif // __SUMBYCOLUMNVT_H__
\ No newline at end of file
+#endif // __SUMBYCOLUMNVT_H__

source/tensor/core/getandset/Select.h

@@ -27,7 +27,6 @@
 namespace nts{ // namespace nts(NiuTrans.Tensor)
 
 /* generate a tensor with selected data c = select(a) */
-extern "C"
 void _Select(const XTensor * a, XTensor * c, XTensor * indexCPU);
 
 /* 
@@ -40,7 +39,6 @@ XTensor Select(const XTensor &a, XTensor &indexCPU);
 generate a tensor with selected data in range[low,high] along the given dimension 
 c = select(a)
 */
-extern "C" 
 void _SelectRange(const XTensor * a, XTensor * c, int dim, int low, int high);
 
 /* 
@@ -52,4 +50,4 @@ XTensor SelectRange(const XTensor &a, int dim, int low, int high);
 
 } // namespace nts(NiuTrans.Tensor)
 
-#endif // __SELECT_H__
\ No newline at end of file
+#endif // __SELECT_H__

source/tensor/core/math/Normalize.h

@@ -31,7 +31,6 @@ normalized the data with normal distribution.
 For an input x, y = a * (x-mean)/sqrt(variance+\epsilon) + b
 where a and b are the scalar and bias respectively, and \epsilon is the adjustment parameter.
 */
-extern "C"
 void _Normalize(const XTensor * input, XTensor * output, int dim, const XTensor * mean, const XTensor * var, const XTensor * a, const XTensor * b, DTYPE epsilon);
 
 /*
@@ -40,7 +39,6 @@ keep the result in the input tenosr and return nothing
 For an input x, x = a * (x-mean)/sqrt(variance+\epsilon) + b
 where a and b are the scalar and bias respectively, and \epsilon is the adjustment parameter.
 */
-extern "C"
 void _NormalizeMe(XTensor * input, int dim, const XTensor * mean, const XTensor * var, const XTensor * a, const XTensor * b, DTYPE epsilon);
 
 /*

source/tensor/core/movement/CopyBlocksInGrid.h

@@ -27,7 +27,6 @@
 namespace nts { // namespace nts(NiuTrans.Tensor)
 
 /* copy a number of blocks in grid */
-extern "C"
 void _CopyBlocksInGrid(void * source, int blockSize, int blockNum, int gridNum, void * target, int * index, int unitSize, bool isIndexOnDev, XMem * myMem);
 
 } // namespace nts(NiuTrans.Tensor)

source/tensor/core/movement/CopyData2D.h

@@ -27,7 +27,6 @@
 namespace nts { // namespace nts(NiuTrans.Tensor)
 
 /* copy data blocks by 2d layout */
-extern "C"
 void _CopyData2D(void ** s, int sPitch, void ** t, int tPitch, int count, int mSize, int n, XMem * myMem);
 
 } // namespace nts(NiuTrans.Tensor)

source/tensor/core/movement/CopyInGrid.h

@@ -27,9 +27,8 @@
 namespace nts { // namespace nts(NiuTrans.Tensor)
 
 /* copy a number of blocks in grid. i.e., reorder the data blocks in the same memory piece*/
-extern "C"
 void _CopyInGrid(const XTensor * s, XTensor * t, int * index, int blockDim, int blockNumInGrid, bool isIndexOnDev = false);
 
 } // namespace nts(NiuTrans.Tensor)
 
-#endif // __COPYINGRID_H__
\ No newline at end of file
+#endif // __COPYINGRID_H__

source/tensor/core/movement/CopyValues.h

@@ -27,7 +27,6 @@
 namespace nts { // namespace nts(NiuTrans.Tensor)
 
 /* copy s to t */
-extern "C"
 void _CopyValues(const XTensor * s, XTensor * t, XStream * stream = NULL);
 
 /* 
@@ -38,4 +37,4 @@ XTensor CopyValues(const XTensor &s, XStream * stream = NULL);
 
 } // namespace nts(NiuTrans.Tensor)
 
-#endif // __COPYVALUES_H__
\ No newline at end of file
+#endif // __COPYVALUES_H__

source/tensor/core/reduce/ReduceMax.h

@@ -27,7 +27,6 @@
 namespace nts{ // namespace nts(NiuTrans.Tensor)
 
 /* get the max value of the items along a dimension of the tensor. */
-extern "C"
 void _ReduceMax(const XTensor * input, XTensor * output, int dim);
 
 /* 
@@ -38,4 +37,4 @@ XTensor ReduceMax(const XTensor &input, int dim);
 
 } // namespace nts(NiuTrans.Tensor)
 
-#endif // __REDUCEMAX_H__
\ No newline at end of file
+#endif // __REDUCEMAX_H__

source/tensor/core/reduce/ReduceMean.h

@@ -30,7 +30,6 @@ namespace nts{ // namespace nts(NiuTrans.Tensor)
 get the mean value along a dimension of the tensor
 For a 1-dimensional data array a, mean = (1/n) * sum_i input_i
 */
-extern "C"
 void _ReduceMean(const XTensor * input, XTensor * output, int dim);
 
 /* 
@@ -42,4 +41,4 @@ XTensor ReduceMean(const XTensor &input, int dim);
 
 } // namespace nts(NiuTrans.Tensor)
 
-#endif // __REDUCEMEAN_H__
\ No newline at end of file
+#endif // __REDUCEMEAN_H__

source/tensor/core/reduce/ReduceSum.h

@@ -32,7 +32,6 @@ For a 1-dimensional data array a,
 sum = \sum_i (a_i - shift) if isExp == false
 sum = \sum_i exp(a_i - shift) if isExp == true
 */
-extern "C"
 void _ReduceSum(const XTensor * input, XTensor * output, int dim, const XTensor * shift = NULL,
     DTYPE power = (DTYPE)1.0F, bool isExp = false);
 
@@ -56,4 +55,4 @@ XTensor ReduceSum(const XTensor &input, int dim, DTYPE power = (DTYPE)1.0F, bool
 
 } // namespace nts(NiuTrans.Tensor)
 
-#endif // __REDUCESUM_H__
\ No newline at end of file
+#endif // __REDUCESUM_H__

source/tensor/core/reduce/ReduceSumSquared.h

@@ -31,7 +31,6 @@ squared sum of the items along a dimension of the tensor
 For a 1-dimensional data array a,
 sum = \sum_i (a_i - shift)^2
 */
-extern "C"
 void _ReduceSumSquared(const XTensor * input, XTensor * output, int dim, const XTensor * shift);
 
 /* 

source/tensor/core/reduce/ReduceVariance.h

@@ -30,7 +30,6 @@ namespace nts{ // namespace nts(NiuTrans.Tensor)
 variance of the items along a dimension of the tensor
 For a 1-dimensional data array a, variance = 1/n * \sum_i (a_i - mean)^2
 */
-extern "C"
 void _ReduceVariance(const XTensor * input, XTensor * output, int dim, const XTensor * mean);
 
 /* 

source/tensor/core/shape/Unsqueeze.h

@@ -27,7 +27,6 @@
 namespace nts { // namespace nts(NiuTrans.Tensor)
 
 /* insert a dimension by copying the blocks for x times (where x is the size of the inerted dimension) */
-extern "C"
 void _Unsqueeze(const XTensor * a, XTensor * b, int dim, int dSize);
 
 /*
@@ -39,4 +38,4 @@ XTensor Unsqueeze(const XTensor &a, int dim, int dSize);
 
 } // namespace nts(NiuTrans.Tensor)
 
-#endif // __UNSQUEEZE_H__
\ No newline at end of file
+#endif // __UNSQUEEZE_H__

source/tensor/core/sort/TopK.h

@@ -27,13 +27,11 @@
 namespace nts { // namespace nts(NiuTrans.Tensor)
 
 /* get the top-k items along a given dimension */
-extern "C"
 void _TopK(const XTensor * a, XTensor * b, XTensor * index, int dim, int k);
 
 /* get the top-k items along a given dimension */
-extern "C"
 void TopK(XTensor &a, XTensor &b, XTensor &index, int dim, int k);
 
 } // namespace nts(NiuTrans.Tensor)
 
-#endif // __TOPK_H__
\ No newline at end of file
+#endif // __TOPK_H__