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NiuTrans.Tensor
Commit 454bd870 by xiaotong
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rename SumFilled as SumDim and add the new code

parent dcba416c
正在显示 包含 445 行增加 和 137 行删除
source/network/Main.cpp
...@@ -31,6 +31,7 @@ ...@@ -31,6 +31,7 @@
//#include <crtdbg.h> //#include <crtdbg.h>
void TransposeTest(); void TransposeTest();
void SumDimTest();
using namespace nts; using namespace nts;
using namespace samplefnnlm; using namespace samplefnnlm;
...@@ -40,6 +41,9 @@ int main( int argc, const char ** argv ) ...@@ -40,6 +41,9 @@ int main( int argc, const char ** argv )
//TransposeTest(); //TransposeTest();
//return 0; //return 0;
SumDimTest();
return 0;
if(argc > 1 && !strcmp(argv[1], "-test")) if(argc > 1 && !strcmp(argv[1], "-test"))
1;//Test(); 1;//Test();
else if(argc > 1 && !strcmp(argv[1], "-fnnlm")) else if(argc > 1 && !strcmp(argv[1], "-fnnlm"))
...@@ -94,6 +98,7 @@ void TransposeTest() ...@@ -94,6 +98,7 @@ void TransposeTest()
//XMem mem1(1, UNI_FREE, MILLION * 64, 1024, MILLION * 64); //XMem mem1(1, UNI_FREE, MILLION * 64, 1024, MILLION * 64);
XTensor x; XTensor x;
XTensor y; XTensor y;
XTensor z;
int loops = 2000; int loops = 2000;
...@@ -106,19 +111,25 @@ void TransposeTest() ...@@ -106,19 +111,25 @@ void TransposeTest()
InitTensor3D(&x, B, N, H, X_FLOAT, 0); InitTensor3D(&x, B, N, H, X_FLOAT, 0);
InitTensor4D(&y, K, B, N, H/K, X_FLOAT, 0); InitTensor4D(&y, K, B, N, H/K, X_FLOAT, 0);
InitTensor3D(&z, B, N, H, X_FLOAT, 0);
cudaEvent_t ctime0; cudaEvent_t ctime0;
cudaEvent_t ctime1; cudaEvent_t ctime1;
cudaEvent_t ctime2; cudaEvent_t ctime2;
cudaEvent_t ctime3; cudaEvent_t ctime3;
cudaEvent_t ctime4;
cudaEvent_t ctime5;
float elapsedSplit = 0.0; float elapsedSplit = 0.0;
float elapsedMerge = 0.0; float elapsedMerge = 0.0;
float elapsedSum = 0.0;
cudaEventCreate(&ctime0); cudaEventCreate(&ctime0);
cudaEventCreate(&ctime1); cudaEventCreate(&ctime1);
cudaEventCreate(&ctime2); cudaEventCreate(&ctime2);
cudaEventCreate(&ctime3); cudaEventCreate(&ctime3);
cudaEventCreate(&ctime4);
cudaEventCreate(&ctime5);
cudaEventRecord(ctime0, 0); cudaEventRecord(ctime0, 0);
...@@ -142,7 +153,57 @@ void TransposeTest() ...@@ -142,7 +153,57 @@ void TransposeTest()
cudaEventSynchronize(ctime3); cudaEventSynchronize(ctime3);
cudaEventElapsedTime(&elapsedMerge, ctime2, ctime3); cudaEventElapsedTime(&elapsedMerge, ctime2, ctime3);
fprintf(stderr, "split:%f merge:%f\n", time1 - time0, time3 - time2); cudaEventRecord(ctime4, 0);
fprintf(stderr, "split:%f merge:%f\n", elapsedSplit, elapsedMerge);
double time4 = GetClock();
for(int i = 0; i < loops; i++)
_Sum(&x, &z, &x);
double time5 = GetClock();
cudaEventRecord(ctime5, 0);
cudaEventSynchronize(ctime5);
cudaEventElapsedTime(&elapsedSum, ctime4, ctime5);
fprintf(stderr, "split:%f merge:%f sum:%f\n", time1 - time0, time3 - time2, time5 - time4);
fprintf(stderr, "split:%f merge:%f sum:%f\n", elapsedSplit, elapsedMerge, elapsedSum);
#endif #endif
} }
void SumDimTest()
{
XTensor x;
XTensor y;
XTensor z;
int loops = 2000;
int a = 5;
int b = 7;
int c = 3;
XDevice::SetGPUDevice(0);
InitTensor3D(&x, a, b, c, X_FLOAT, 0);
InitTensor1D(&y, c, X_FLOAT, 0);
InitTensor3D(&z, a, b, c, X_FLOAT, 0);
x.SetZeroAll();
y.SetZeroAll();
z.SetZeroAll();
float * data = new float[x.unitNum];
for(int i = 0; i < x.unitNum; i++)
data[i] = (DTYPE)i;
x.SetData(data, x.unitNum);
for(int i = 0; i < y.unitNum; i++)
data[i] = -(DTYPE)i;
y.SetData(data, y.unitNum);
_SumDim(&x, &y, &z, 2);
z.Dump(stderr, "z:");
delete[] data;
}
source/tensor/XTensor.h
...@@ -45,7 +45,7 @@ namespace nts{ ...@@ -45,7 +45,7 @@ namespace nts{
struct XLink; struct XLink;
/* define the maximum number of dimensions in a tensor */ /* define the maximum number of dimensions in a tensor */
#define MAX_TENSOR_DIM_NUM 6 #define MAX_TENSOR_DIM_NUM 8
#define USE_BATCHED_STRIDED_MAT_MUL #define USE_BATCHED_STRIDED_MAT_MUL
#define MIN_TENSOR_SPLIT_NUM 0 #define MIN_TENSOR_SPLIT_NUM 0
#define MIN_TENSOR_SPLIT_LIST_NUM 1024 #define MIN_TENSOR_SPLIT_LIST_NUM 1024
......
source/tensor/XUtility.cpp
...@@ -175,29 +175,38 @@ void XMemCopy(void * t, int devIDT, const void * s, int devIDS, size_t size) ...@@ -175,29 +175,38 @@ void XMemCopy(void * t, int devIDT, const void * s, int devIDS, size_t size)
return; return;
} }
#ifdef USE_CUDA #ifdef USE_CUDA
else if(devIDT >= 0 && devIDS < 0){
cudaError_t error = cudaMemcpy(t, s, size, cudaMemcpyHostToDevice);
if(error != cudaSuccess){
ShowNTErrors("cudaMemcpy error (cudaMemcpyHostToDevice)");
}
}
else if(devIDT < 0 && devIDS >= 0){
cudaError_t error = cudaMemcpy(t, s, size, cudaMemcpyDeviceToHost);
if(error != cudaSuccess){
ShowNTErrors("cudaMemcpy error (cudaMemcpyDeviceToHost)");
}
}
else{ else{
//if(devIDT == devIDS){ int devID = devIDT < 0 ? devIDS : devIDT;
cudaError_t error = cudaMemcpy(t, s, size, cudaMemcpyDeviceToDevice); int devIDBackup = 0;
cudaGetDevice(&devIDBackup);
cudaSetDevice(devID);
if(devIDT >= 0 && devIDS < 0){
cudaError_t error = cudaMemcpy(t, s, size, cudaMemcpyHostToDevice);
if(error != cudaSuccess){ if(error != cudaSuccess){
ShowNTErrors("cudaMemcpy error (cudaMemcpyDeviceToDevice)"); ShowNTErrors("cudaMemcpy error (cudaMemcpyHostToDevice)");
}
}
else if(devIDT < 0 && devIDS >= 0){
cudaError_t error = cudaMemcpy(t, s, size, cudaMemcpyDeviceToHost);
if(error != cudaSuccess){
ShowNTErrors("cudaMemcpy error (cudaMemcpyDeviceToHost)");
} }
/*} }
else{ else{
CheckNTErrors((cudaMemcpyPeer(t, devIDT, s, devIDS, size) == cudaSuccess), //if(devIDT == devIDS){
"cudaMemcpy error (cudaMemcpyDeviceToDevice)"); cudaError_t error = cudaMemcpy(t, s, size, cudaMemcpyDeviceToDevice);
}*/ if(error != cudaSuccess){
ShowNTErrors("cudaMemcpy error (cudaMemcpyDeviceToDevice)");
}
/*}
else{
CheckNTErrors((cudaMemcpyPeer(t, devIDT, s, devIDS, size) == cudaSuccess),
"cudaMemcpy error (cudaMemcpyDeviceToDevice)");
}*/
}
cudaSetDevice(devIDBackup);
} }
#else #else
ShowNTErrors("Please specify USE_CUDA and recompile the code!"); ShowNTErrors("Please specify USE_CUDA and recompile the code!");
...@@ -270,23 +279,32 @@ void XMemCopy2D(void * t, size_t tPitch, int devIDT, const void * s, size_t sPit ...@@ -270,23 +279,32 @@ void XMemCopy2D(void * t, size_t tPitch, int devIDT, const void * s, size_t sPit
return; return;
} }
#ifdef USE_CUDA #ifdef USE_CUDA
else if (devIDT >= 0 && devIDS < 0) { else{
cudaError_t error = cudaMemcpy2D(t, tPitch, s, sPitch, mSize, n, cudaMemcpyHostToDevice); int devID = devIDT < 0 ? devIDS : devIDT;
if(error != cudaSuccess){ int devIDBackup = 0;
ShowNTErrors("cudaMemcpy2D error (cudaMemcpyHostToDevice)"); cudaGetDevice(&devIDBackup);
cudaSetDevice(devID);
if (devIDT >= 0 && devIDS < 0) {
cudaError_t error = cudaMemcpy2D(t, tPitch, s, sPitch, mSize, n, cudaMemcpyHostToDevice);
if(error != cudaSuccess){
ShowNTErrors("cudaMemcpy2D error (cudaMemcpyHostToDevice)");
}
} }
} else if (devIDT < 0 && devIDS >= 0) {
else if (devIDT < 0 && devIDS >= 0) { cudaError_t error = cudaMemcpy2D(t, tPitch, s, sPitch, mSize, n, cudaMemcpyDeviceToHost);
cudaError_t error = cudaMemcpy2D(t, tPitch, s, sPitch, mSize, n, cudaMemcpyDeviceToHost); if(error != cudaSuccess){
if(error != cudaSuccess){ ShowNTErrors("cudaMemcpy error (cudaMemcpyDeviceToHost)");
ShowNTErrors("cudaMemcpy error (cudaMemcpyDeviceToHost)"); }
} }
} else {
else { cudaError_t error = cudaMemcpy2D(t, tPitch, s, sPitch, mSize, n, cudaMemcpyDeviceToDevice);
cudaError_t error = cudaMemcpy2D(t, tPitch, s, sPitch, mSize, n, cudaMemcpyDeviceToDevice); if (error != cudaSuccess) {
if (error != cudaSuccess) { ShowNTErrors("cudaMemcpy error (cudaMemcpyDeviceToDevice)");
ShowNTErrors("cudaMemcpy error (cudaMemcpyDeviceToDevice)"); }
} }
cudaSetDevice(devIDBackup);
} }
#else #else
ShowNTErrors("Please specify USE_CUDA and recompile the code!"); ShowNTErrors("Please specify USE_CUDA and recompile the code!");
......
source/tensor/core/CHeader.h
...@@ -64,7 +64,7 @@ ...@@ -64,7 +64,7 @@
#include "arithmetic/Sum.h" #include "arithmetic/Sum.h"
#include "arithmetic/SumByColumnTV.h" #include "arithmetic/SumByColumnTV.h"
#include "arithmetic/SumByColumnVT.h" #include "arithmetic/SumByColumnVT.h"
#include "arithmetic/SumFilled.h" #include "arithmetic/SumDim.h"
#include "sort/TopK.h" #include "sort/TopK.h"
#include "shape/Transpose.h" #include "shape/Transpose.h"
#include "shape/Unsqueeze.h" #include "shape/Unsqueeze.h"
......
source/tensor/core/arithmetic/Sum.cpp
...@@ -67,7 +67,7 @@ void _Sum(const XTensor * a, const XTensor * b, XTensor * c, DTYPE beta) ...@@ -67,7 +67,7 @@ void _Sum(const XTensor * a, const XTensor * b, XTensor * c, DTYPE beta)
} }
else { else {
if (!a->isSparse && !b->isSparse) { if (!a->isSparse && !b->isSparse) {
CheckNTErrors(!c->isSparse, "Illegal use of sparse matrix in addition!"); CheckNTErrors(!c->isSparse, "Illegal use of sparse tensor in addition!");
if (a->dataType == DEFAULT_DTYPE && if (a->dataType == DEFAULT_DTYPE &&
b->dataType == DEFAULT_DTYPE && b->dataType == DEFAULT_DTYPE &&
......
source/tensor/core/arithmetic/SumDim.cpp 0 → 100644
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2018, Natural Language Processing Lab, Northestern University.
* All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/*
* $Created by: XIAO Tong (email: xiaotong@mail.neu.edu.cn) 2018-07-29
*/
#include "Sum.h"
#include "SumDim.h"
#include "SumDim.cuh"
#include "../movement/CopyValues.h"
namespace nts { // namespace nts(NiuTrans.Tensor)
/*
tensor summation
c = a + b * \beta
where the size of b is equal to the n-th dimension of a,
i.e., a is summed with b by broadcasting
>> a - a tensor
>> b - another tensor whose size is equal to that of dimension n of a
>> c - where we put a+b*\beta. we save it in a if c is NULL
>> n - the dimension index
>> beta - the scaling factor
*/
void _SumDim(const XTensor * a, const XTensor * b, XTensor * c, int n, DTYPE beta)
{
CheckNTErrors(a && b && c, "Empty tensor input!");
CheckNTErrors(a->unitNum == c->unitNum, "Unmatched tensors in addition!");
CheckNTErrors(a->dataType == b->dataType && a->dataType == c->dataType,
"Unmatched data types in addition!");
CheckNTErrors(a->order == c->order, "The input tensors do not have the same order in addition!");
CheckNTErrors(!a->isSparse && !b->isSparse && !c->isSparse, "Dense tensors are required!");
CheckNTErrors(a->dimSize[n] == b->unitNum, "Wrong tensor size!");
if(beta == 0){
_CopyValues(a, c);
return;
}
if(XTensor::IsSameShaped(a, b)){
_Sum(a, b, c, beta);
return;
}
if(a->devID >= 0 || b->devID >= 0 || c->devID >= 0){
_CudaSumDim(a, b, c, n, beta);
}
else{
int stride = 1;
int blockSize = a->dimSize[n];
int blockNum = 1;
for(int i = a->order - 1; i >= 0; i--){
if(i > n)
stride *= a->dimSize[i];
else if(i < n)
blockNum *= a->dimSize[i];
}
if (a->dataType == DEFAULT_DTYPE){
int num = a->unitNum;
if(stride > 1){
for(int i = 0, j = 0; i < num; i += stride, j++){
DTYPE * ap = (DTYPE*)a->data + i;
DTYPE bv = *((DTYPE*)b->data + j % blockSize) * beta;
DTYPE * cp = (DTYPE*)c->data + i;
for(int k = 0; k < stride; k++)
cp[k] = ap[k] + bv;
}
}
else if(stride == 1){
DTYPE * bp = (DTYPE*)b->data;
for(int i = 0; i < num; i += blockSize){
DTYPE * ap = (DTYPE*)a->data + i;
DTYPE * cp = (DTYPE*)c->data + i;
if(beta == 1.0F){
for(int j = 0; j < blockSize; j++)
cp[j] = ap[j] + bp[j];
}
else{
for(int j = 0; j < blockSize; j++)
cp[j] = ap[j] + bp[j] * beta;
}
}
}
else{
ShowNTErrors("Something is wrong!");
}
}
else {
ShowNTErrors("TODO!");
}
}
}
}
source/tensor/core/arithmetic/SumDim.cu 0 → 100644
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2018, Natural Language Processing Lab, Northestern University.
* All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/*
* $Created by: XIAO Tong (email: xiaotong@mail.neu.edu.cn) 2018-07-29
*/
#include "SumDim.cuh"
#include "../../XDevice.h"
namespace nts { // namespace nts(NiuTrans.Tensor)
/*
tensor summation of a tensor and a row vector
c = a + b * \beta
where a is a tensor and b is a row vector
>> a - pointer to the data array of a
>> b - pointer to the data array of b
>> c - pointer to the data array of c
>> rowNum - number of rows of a and c
>> colNum - number of columns of a and c (i.e., the size of b)
>> beta - the scaling factor
*/
template <class T, bool betaFired>
__global__
void KernelAddWithRow(T * a, T * b, T * c, int rowNum, int colNum, T beta)
{
__shared__ T bv[MAX_CUDA_THREAD_NUM_PER_BLOCK];
int col = blockDim.x * blockIdx.x + threadIdx.x;
int row = blockDim.y * blockIdx.y + threadIdx.y;
if(col >= colNum || row >= rowNum)
return;
if(threadIdx.y == 0)
bv[threadIdx.x] = b[col];
__syncthreads();
int offset = colNum * row + col;
if(betaFired)
c[offset] = a[offset] + bv[threadIdx.x] * beta;
else
c[offset] = a[offset] + bv[threadIdx.x];
}
/*
tensor summation of a tensor and a colum vector
c = a + b * \beta
where a is a tensor and b is a colum vector
>> a - pointer to the data array of a
>> b - pointer to the data array of b
>> c - pointer to the data array of c
>> rowNum - number of rows of a and c (i.e., the size of b)
>> colNum - number of columns of a and c
>> blockNum - size of a block (matrix), i.e., rowNum * colNum
>> blockNum - number of matrics
>> beta - the scaling factor
*/
template <class T, bool betaFired>
__global__
void KernelAddWithCol(T * a, T * b, T * c, int rowNum, int colNum, int blockSize, int blockNum, T beta)
{
__shared__ T bv[MAX_CUDA_THREAD_NUM_PER_BLOCK];
int colIndex = blockDim.x * blockIdx.x + threadIdx.x;
int row = blockDim.y * blockIdx.y + threadIdx.y;
int col = colIndex % colNum;
int block = colIndex / colNum;
if(row >= rowNum || block >= blockNum)
return;
if(threadIdx.x == 0)
bv[threadIdx.y] = b[row];
__syncthreads();
int offset = block * blockSize + row * colNum + col;
if(betaFired)
c[offset] = a[offset] + bv[threadIdx.y] * beta;
else
c[offset] = a[offset] + bv[threadIdx.y];
}
/*
tensor summation (cuda version)
c = a + b * \beta
where the size of b is equal to the n-th dimension of a,
i.e., a is summed with b by broadcasting
>> a - a tensor
>> b - another tensor whose size is equal to that of dimension n of a
>> c - where we put a+b*\beta. we save it in a if c is NULL
>> n - the dimension index
>> beta - the scaling factor
*/
void _CudaSumDim(const XTensor * a, const XTensor * b, XTensor * c, int n, DTYPE beta)
{
CheckNTErrors(a && b && c, "Empty tensor input!");
CheckNTErrors(a->unitNum == c->unitNum, "Unmatched tensors in addition!");
CheckNTErrors(a->dataType == b->dataType && a->dataType == c->dataType,
"Unmatched data types in addition!");
CheckNTErrors(a->order == c->order, "The input tensors do not have the same order in addition!");
CheckNTErrors(!a->isSparse && !b->isSparse && !c->isSparse, "Dense tensors are required!");
CheckNTErrors(a->dimSize[n] == b->unitNum, "Wrong tensor size!");
int stride = 1;
int blockSize = a->dimSize[n];
int blockNum = 1;
for(int i = a->order - 1; i >= 0; i--){
if(i > n)
stride *= a->dimSize[i];
else if(i < n)
blockNum *= a->dimSize[i];
}
int cudaGrids[3];
int cudaBlocks[3];
if (a->dataType == DEFAULT_DTYPE){
if(stride > 1){
GDevs.GetCudaThread2D(a->devID, stride * blockNum, blockSize, MAX_INT, cudaGrids, cudaBlocks);
if(beta == (DTYPE)1.0F)
KernelAddWithCol<DTYPE, false> <<<dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1])>>>
((DTYPE*)a->data, (DTYPE*)b->data, (DTYPE*)c->data,
blockSize, stride, blockSize * stride, blockNum, beta);
else
KernelAddWithCol<DTYPE, true> <<<dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1])>>>
((DTYPE*)a->data, (DTYPE*)b->data, (DTYPE*)c->data,
blockSize, stride, blockSize * stride, blockNum, beta);
}
else if(stride == 1){
GDevs.GetCudaThread2D(a->devID, blockSize, blockNum, MAX_INT, cudaGrids, cudaBlocks);
if(beta == (DTYPE)1.0F)
KernelAddWithRow<DTYPE, false> <<<dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1])>>>
((DTYPE*)a->data, (DTYPE*)b->data, (DTYPE*)c->data,
blockNum, blockSize, beta);
else
KernelAddWithRow<DTYPE, true> <<<dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1])>>>
((DTYPE*)a->data, (DTYPE*)b->data, (DTYPE*)c->data,
blockNum, blockSize, beta);
}
else{
ShowNTErrors("Something is wrong!");
}
}
else {
ShowNTErrors("TODO!");
}
}
} // namespace nts(NiuTrans.Tensor)
source/tensor/core/arithmetic/SumFilled.cu → source/tensor/core/arithmetic/SumDim.cuh
...@@ -16,13 +16,20 @@ ...@@ -16,13 +16,20 @@
*/ */
/* /*
* $Created by: XIAO Tong (email: xiaotong@mail.neu.edu.cn) 2018-07-28 * $Created by: XIAO Tong (email: xiaotong@mail.neu.edu.cn) 2018-07-29
*/ */
#include "SumFilled.cuh" #ifndef __SUMDIM_CUH__
#define __SUMDIM_CUH__
#include "../../XTensor.h"
namespace nts { // namespace nts(NiuTrans.Tensor) namespace nts { // namespace nts(NiuTrans.Tensor)
/* tensor summation c = a + b * \beta where the size of b is equal to the n-th dimension of a,
i.e., a is summed with b by broadcasting (cuda version) */
void _CudaSumDim(const XTensor * a, const XTensor * b, XTensor * c, int n, DTYPE beta = (DTYPE)1.0);
} // namespace nts(NiuTrans.Tensor) } // namespace nts(NiuTrans.Tensor)
#endif // __SUMDIM_CUH__
source/tensor/core/arithmetic/SumFilled.cuh → source/tensor/core/arithmetic/SumDim.h
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2018, Natural Language Processing Lab, Northestern University. * Copyright (C) 2018, Natural Language Processing Lab, Northestern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License. * you may not use this file except in compliance with the License.
* You may obtain a copy of the License at * You may obtain a copy of the License at
* *
* http://www.apache.org/licenses/LICENSE-2.0 * http://www.apache.org/licenses/LICENSE-2.0
* *
* Unless required by applicable law or agreed to in writing, software * Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, * distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and * See the License for the specific language governing permissions and
* limitations under the License. * limitations under the License.
*/ */
/* /*
* $Created by: XIAO Tong (email: xiaotong@mail.neu.edu.cn) 2018-07-28 * $Created by: XIAO Tong (email: xiaotong@mail.neu.edu.cn) 2018-07-29
*/ * It reached to 39 centigrade around 3:00 pm in Shenyang
*/
#ifndef __SUMFILLED_CUH__ #ifndef __SUMDIM_H__
#define __SUMFILLED_CUH__ #define __SUMDIM_H__
#include "../../XTensor.h" #include "../../XTensor.h"
namespace nts { // namespace nts(NiuTrans.Tensor) namespace nts { // namespace nts(NiuTrans.Tensor)
/* tensor summation c = a + b * \beta where each dimension of b is equal to that of a or has /* tensor summation c = a + b * \beta where the size of b is equal to the n-th dimension of a,
a value of 1, i.e., a is summed with b by broadcasting */ i.e., a is summed with b by broadcasting */
void _CudaSumFilled(const XTensor * a, const XTensor * b, XTensor * c, DTYPE beta = (DTYPE)1.0); void _SumDim(const XTensor * a, const XTensor * b, XTensor * c, int n, DTYPE beta = (DTYPE)1.0);
/* tensor summation c = a + b * \beta where the size of b is equal to the n-th dimension of a,
i.e., a is summed with b by broadcasting. we keep the result in the input tensor a and return nothing */
void _SumDim(XTensor * a, const XTensor * b, int n, DTYPE beta = (DTYPE)1.0);
/* tensor summation c = a + b * \beta where the size of b is equal to the n-th dimension of a,
i.e., a is summed with b by broadcasting. We make a new tensor c to keep the result and return it */
XTensor SumDim(const XTensor &a, const XTensor &b, int n, DTYPE beta = (DTYPE)1.0);
} // namespace nts(NiuTrans.Tensor) } // namespace nts(NiuTrans.Tensor)
#endif // __SUMFILLED_CUH__ #endif // __SUMDIM_H__
source/tensor/core/arithmetic/SumFilled.cpp deleted 100644 → 0
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2018, Natural Language Processing Lab, Northestern University.
* All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/*
* $Created by: XIAO Tong (email: xiaotong@mail.neu.edu.cn) 2018-07-28
*/
#include "SumFilled.h"
#include "SumFilled.cuh"
namespace nts { // namespace nts(NiuTrans.Tensor)
}
source/tensor/core/arithmetic/SumFilled.h deleted 100644 → 0
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2018, Natural Language Processing Lab, Northestern University.
* All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/*
* $Created by: XIAO Tong (email: xiaotong@mail.neu.edu.cn) 2018-07-28
*/
#ifndef __SUMFILLED_H__
#define __SUMFILLED_H__
#include "../../XTensor.h"
namespace nts { // namespace nts(NiuTrans.Tensor)
/* tensor summation c = a + b * \beta where each dimension of b is equal to that of a or has
a value of 1, i.e., a is summed with b by broadcasting */
void _SumFilled(const XTensor * a, const XTensor * b, XTensor * c, DTYPE beta = (DTYPE)1.0);
/* tensor summation c = a + b * \beta where each dimension of b is equal to that of a or has
a value of 1, i.e., a is summed with b by broadcasting
keep the result in the input tensor a and return nothing */
void _SumFilledMe(XTensor * a, const XTensor * b, DTYPE beta = (DTYPE)1.0);
/* tensor summation c = a + b * \beta where each dimension of b is equal to that of a or has
a value of 1, i.e., a is summed with b by broadcasting
make a new tensor c to keep the result and return it */
XTensor SumFilled(const XTensor &a, const XTensor &b, DTYPE beta = (DTYPE)1.0);
} // namespace nts(NiuTrans.Tensor)
#endif // __SUMFILLED_H__
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