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NiuTrans.Tensor
Commit a8304bed by xiaotong
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broadcasting

parent 14f245fa
正在显示 包含 194 行增加 和 10 行删除
source/tensor/XName.cpp
...@@ -67,6 +67,8 @@ const char * GetOPName(int type) ...@@ -67,6 +67,8 @@ const char * GetOPName(int type)
return "M_MULTIPLY"; return "M_MULTIPLY";
else if (type == MATH_MULTIPLYDIM) else if (type == MATH_MULTIPLYDIM)
return "M_MULTIPLYDIM"; return "M_MULTIPLYDIM";
else if (type == MATH_MULTIPLYBROADCAST)
return "M_MULTIPLYBROADCAST";
else if (type == MATH_NEGATE) else if (type == MATH_NEGATE)
return "M_NEGATE"; return "M_NEGATE";
else if (type == MATH_NORMALIZE) else if (type == MATH_NORMALIZE)
...@@ -85,6 +87,8 @@ const char * GetOPName(int type) ...@@ -85,6 +87,8 @@ const char * GetOPName(int type)
return "M_SUM"; return "M_SUM";
else if (type == MATH_SUMDIM) else if (type == MATH_SUMDIM)
return "M_SUMDIM"; return "M_SUMDIM";
else if (type == MATH_SUMBROADCAST)
return "M_SUMBROADCAST";
else if (type == REDUCE_REDUCEMAX) else if (type == REDUCE_REDUCEMAX)
return "R_REDUCEMAX"; return "R_REDUCEMAX";
else if (type == REDUCE_REDUCEMEAN) else if (type == REDUCE_REDUCEMEAN)
......
source/tensor/XName.h
...@@ -52,7 +52,8 @@ namespace nts { // namespace nts(NiuTrans.Tensor) ...@@ -52,7 +52,8 @@ namespace nts { // namespace nts(NiuTrans.Tensor)
#define MATH_MATRIXMULBATCHED MATH_MATRIXMUL + 1 #define MATH_MATRIXMULBATCHED MATH_MATRIXMUL + 1
#define MATH_MULTIPLY MATH_MATRIXMULBATCHED + 1 #define MATH_MULTIPLY MATH_MATRIXMULBATCHED + 1
#define MATH_MULTIPLYDIM MATH_MULTIPLY + 1 #define MATH_MULTIPLYDIM MATH_MULTIPLY + 1
#define MATH_NEGATE MATH_MULTIPLYDIM + 1 #define MATH_MULTIPLYBROADCAST MATH_MULTIPLYDIM + 1
#define MATH_NEGATE MATH_MULTIPLYBROADCAST + 1
#define MATH_NORMALIZE MATH_NEGATE + 1 #define MATH_NORMALIZE MATH_NEGATE + 1
#define MATH_POWER MATH_NORMALIZE + 1 #define MATH_POWER MATH_NORMALIZE + 1
#define MATH_SCALEANDSHIFT MATH_POWER + 1 #define MATH_SCALEANDSHIFT MATH_POWER + 1
...@@ -61,8 +62,9 @@ namespace nts { // namespace nts(NiuTrans.Tensor) ...@@ -61,8 +62,9 @@ namespace nts { // namespace nts(NiuTrans.Tensor)
#define MATH_SUBDIM MATH_SUB + 1 #define MATH_SUBDIM MATH_SUB + 1
#define MATH_SUM MATH_SUBDIM + 1 #define MATH_SUM MATH_SUBDIM + 1
#define MATH_SUMDIM MATH_SUM + 1 #define MATH_SUMDIM MATH_SUM + 1
#define MATH_SUMBROADCAST MATH_SUMDIM + 1
#define REDUCE MATH_SUMDIM + 1 #define REDUCE MATH_SUMBROADCAST + 1
#define REDUCE_REDUCEMAX REDUCE + 1 #define REDUCE_REDUCEMAX REDUCE + 1
#define REDUCE_REDUCEMEAN REDUCE_REDUCEMAX + 1 #define REDUCE_REDUCEMEAN REDUCE_REDUCEMAX + 1
#define REDUCE_REDUCESUM REDUCE_REDUCEMEAN + 1 #define REDUCE_REDUCESUM REDUCE_REDUCEMEAN + 1
......
source/tensor/core/arithmetic/MultiplyDim.cpp
...@@ -22,7 +22,9 @@ ...@@ -22,7 +22,9 @@
#include "Multiply.h" #include "Multiply.h"
#include "MultiplyDim.h" #include "MultiplyDim.h"
#include "MultiplyDim.cuh" #include "MultiplyDim.cuh"
#include "../shape/Unsqueeze.h"
#include "../../XName.h" #include "../../XName.h"
#include "../../XUtility.h"
#include "../movement/CopyValues.h" #include "../movement/CopyValues.h"
namespace nts { // namespace nts(NiuTrans.Tensor) namespace nts { // namespace nts(NiuTrans.Tensor)
...@@ -135,28 +137,167 @@ void _MultiplyDimMe(XTensor * a, const XTensor * b, int n, DTYPE alpha) ...@@ -135,28 +137,167 @@ void _MultiplyDimMe(XTensor * a, const XTensor * b, int n, DTYPE alpha)
tensor multiplication (return an XTensor structure and make tensor connections) tensor multiplication (return an XTensor structure and make tensor connections)
make a new tensor to keep the result and return it make a new tensor to keep the result and return it
c = a * b + \alpha * c c = a * b
where the size of b is equal to the n-th dimension of a, where the size of b is equal to the n-th dimension of a,
i.e., a is multiplied with b by broadcasting i.e., a is multiplied with b by broadcasting
>> a - a tensor >> a - a tensor
>> b - another tensor whose size is equal to that of dimension n of a >> b - another tensor whose size is equal to that of dimension n of a
>> n - the dimension index >> n - the dimension index
>> alpha - the scaling factor
<< return - the result tensor by tensor multiplication << return - the result tensor by tensor multiplication
*/ */
XTensor MultiplyDim(const XTensor &a, const XTensor &b, int n, DTYPE alpha) XTensor MultiplyDim(const XTensor &a, const XTensor &b, int n)
{ {
XTensor c(&a); XTensor c(&a);
c.SetTMPFlag(); c.SetTMPFlag();
/* call _Multiply function */ /* call _Multiply function */
_MultiplyDim(&a, &b, &c, n, alpha); _MultiplyDim(&a, &b, &c, n, 0);
/* tensor connections */ /* tensor connections */
XLink::MakeLink(&a, &b, &c, MATH_MULTIPLYDIM); XLink::MakeLink(&a, &b, &c, MATH_MULTIPLYDIM);
XLink::AddParamToHeadInt(&c, n); XLink::AddParamToHeadInt(&c, n);
XLink::AddParamToHead(&c, alpha); XLink::AddParamToHead(&c, 0);
return c;
}
/*
tensor broadcast multiplication
c = a * b + c * \beta
where some of dimensions of b can be of size 1
>> a - a tensor
>> b - another tensor that would be broadcasted
>> c - the resulting tensor
>> beta - the scaling factor
*/
void _MultiplyBroadcast(const XTensor * a, const XTensor * b, XTensor * c, DTYPE beta)
{
CheckNTErrors(a->order == b->order, "Wrong tensor orders!");
CheckNTErrors(a->order == c->order, "Wrong tensor orders!");
CheckNTErrors(a->order > 0, "TODO!");
int order = a->order;
int count = 0;
void * source = 0;
void * target = 0;
for(int i = 0; i < order; i++){
if(a->GetDim(i) == b->GetDim(i))
continue;
if(b->GetDim(i) == 1){
int fitSize = a->GetDim(i);
int j = i + 1;
/* we define a range over dimensions. It is to be unsqueezed */
for(; j < order; j++){
if(a->GetDim(j) == b->GetDim(j))
break;
fitSize *= a->GetDim(j);
}
int dimsS[MAX_TENSOR_DIM_NUM];
int dimsT[MAX_TENSOR_DIM_NUM];
for(int k = 0; k < i; k++){
dimsS[k] = a->GetDim(k);
dimsT[k] = a->GetDim(k);
}
dimsT[i] = fitSize;
bool isLast = true;
for(int k = j; k < order; k++){
dimsS[i + k - j + 0] = b->GetDim(k);
dimsT[i + k - j + 1] = b->GetDim(k);
if(a->GetDim(k) != b->GetDim(k)){
if(b->GetDim(k) == 1)
isLast = false;
else{
ShowNTErrors("Wrong dimension size!")
}
}
}
dimsS[0] = -dimsS[0];
dimsT[0] = -dimsT[0];
XTensor * s = NewTensor(order - (j - i), dimsS, a->dataType, a->denseRatio, a->devID, a->mem);
XTensor * t = NewTensor(order - (j - i) + 1, dimsT, b->dataType, b->denseRatio, b->devID, b->mem);
if(count == 0)
source = b->data;
else{
source = target;
}
target = t->mem != NULL ?
t->mem->AllocBuf(t->devID, t->unitNum * t->unitSize):
XMemAlloc(t->devID, t->unitNum * t->unitSize);
s->data = source;
t->data = target;
_Unsqueeze(s, t, i, fitSize);
/* free the memory space of the one before the last allocation */
if(count > 0){
int size = s->unitNum * s->unitSize;
if(t->mem != NULL)
t->mem->ReleaseBuf(t->devID, size);
else
XMemFree(t->devID, source);
}
/* we do multiplication here */
if(isLast){
CheckNTErrors(t->unitNum == c->unitNum, "Wrong tensor size!");
_Multiply(a, t, c, beta);
if(t->mem != NULL)
t->mem->ReleaseBuf(t->devID, t->unitNum * t->unitSize);
else
XMemFree(t->devID, target);
target = NULL;
}
s->data = NULL;
t->data = NULL;
DelTensor(s);
DelTensor(t);
i = j;
count++;
}
}
if(count == 0)
_Multiply(a, b, c, beta);
CheckNTErrors(target == NULL, "Something is wrong!");
}
/*
tensor broadcast multiplication
c = a * b
where some of dimensions of b can be of size 1
>> a - a tensor
>> b - another tensor that would be broadcasted
<< return - the resulting tensor c
*/
XTensor MultiplyBroadcast(const XTensor &a, const XTensor &b)
{
XTensor c(&a);
c.SetTMPFlag();
/* call _SumBroadcast function */
_MultiplyBroadcast(&a, &b, &c, 0);
/* tensor connections */
XLink::MakeLink(&a, &b, &c, MATH_MULTIPLYBROADCAST);
XLink::AddParamToHead(&c, 0);
return c; return c;
} }
......
source/tensor/core/arithmetic/MultiplyDim.h
...@@ -34,9 +34,16 @@ void _MultiplyDim(const XTensor * a, const XTensor * b, XTensor * c, int n, DTYP ...@@ -34,9 +34,16 @@ void _MultiplyDim(const XTensor * a, const XTensor * b, XTensor * c, int n, DTYP
i.e., a is multiplied with b by broadcasting. we keep the result in the input tensor a and return nothing */ i.e., a is multiplied with b by broadcasting. we keep the result in the input tensor a and return nothing */
void _MultiplyDimMe(XTensor * a, const XTensor * b, int n, DTYPE alpha = 0.0); void _MultiplyDimMe(XTensor * a, const XTensor * b, int n, DTYPE alpha = 0.0);
/* tensor multiplication c = a * b + \alpha * c where the size of b is equal to the n-th dimension of a, /* tensor multiplication c = a * b where the size of b is equal to the n-th dimension of a,
i.e., a is multiplied with b by broadcasting. We make a new tensor c to keep the result and return it */ i.e., a is multiplied with b by broadcasting. We make a new tensor c to keep the result and return it */
XTensor MultiplyDim(const XTensor &a, const XTensor &b, int n, DTYPE alpha = 0.0); XTensor MultiplyDim(const XTensor &a, const XTensor &b, int n);
/* tensor multiplication summation c = a * b + c * \beta where some of dimensions of b can be of size 1 */
void _MultiplyBroadcast(const XTensor * a, const XTensor * b, XTensor * c, DTYPE beta = (DTYPE)1.0);
/* tensor broadcast multiplication c = a * b where some of dimensions of b can be of size 1.
we return the resulting tensor here */
XTensor MultiplyBroadcast(const XTensor &a, const XTensor &b);
} // namespace nts(NiuTrans.Tensor) } // namespace nts(NiuTrans.Tensor)
......
source/tensor/core/arithmetic/SumDim.cpp
...@@ -170,7 +170,7 @@ XTensor SumDim(const XTensor &a, const XTensor &b, int n, DTYPE beta) ...@@ -170,7 +170,7 @@ XTensor SumDim(const XTensor &a, const XTensor &b, int n, DTYPE beta)
XTensor c(&a); XTensor c(&a);
c.SetTMPFlag(); c.SetTMPFlag();
/* call _Sum function */ /* call _SumDim function */
_SumDim(&a, &b, &c, n, beta); _SumDim(&a, &b, &c, n, beta);
/* tensor connections */ /* tensor connections */
...@@ -296,4 +296,30 @@ void _SumBroadcast(const XTensor * a, const XTensor * b, XTensor * c, DTYPE beta ...@@ -296,4 +296,30 @@ void _SumBroadcast(const XTensor * a, const XTensor * b, XTensor * c, DTYPE beta
CheckNTErrors(target == NULL, "Something is wrong!"); CheckNTErrors(target == NULL, "Something is wrong!");
} }
/*
tensor broadcast summation c = a + b * \beta where some of dimensions of b can be of size 1
c = a + b * \beta
we return c here
>> a - a tensor
>> b - another tensor that would be broadcasted
>> beta - the scaling factor
<< return - the resulting tensor c
*/
XTensor SumBroadcast(const XTensor &a, const XTensor &b, DTYPE beta)
{
XTensor c(&a);
c.SetTMPFlag();
/* call _SumBroadcast function */
_SumBroadcast(&a, &b, &c, beta);
/* tensor connections */
XLink::MakeLink(&a, &b, &c, MATH_SUMBROADCAST);
XLink::AddParamToHead(&c, beta);
return c;
}
} }
source/tensor/core/arithmetic/SumDim.h
...@@ -45,6 +45,10 @@ XTensor SumDim(const XTensor &a, const XTensor &b, int n, DTYPE beta = (DTYPE)1. ...@@ -45,6 +45,10 @@ XTensor SumDim(const XTensor &a, const XTensor &b, int n, DTYPE beta = (DTYPE)1.
/* tensor broadcast summation c = a + b * \beta where some of dimensions of b can be of size 1 */ /* tensor broadcast summation c = a + b * \beta where some of dimensions of b can be of size 1 */
void _SumBroadcast(const XTensor * a, const XTensor * b, XTensor * c, DTYPE beta = (DTYPE)1.0); void _SumBroadcast(const XTensor * a, const XTensor * b, XTensor * c, DTYPE beta = (DTYPE)1.0);
/* tensor broadcast summation c = a + b * \beta where some of dimensions of b can be of size 1.
we return the resulting tensor here */
XTensor SumBroadcast(const XTensor &a, const XTensor &b, DTYPE beta = (DTYPE)1.0);
} // namespace nts(NiuTrans.Tensor) } // namespace nts(NiuTrans.Tensor)
#endif // __SUMDIM_H__ #endif // __SUMDIM_H__
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