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NiuTrans.Tensor
Commit 86adc288 by linye
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update float16 datatype of Sign, Sub, SubDim, SumDim

parent bfaee8f9
正在显示 包含 949 行增加 和 127 行删除
doc/manual.md
......@@ -2631,94 +2631,3 @@ void Backward(XTensor inputs[], XTensor &output, XTensor &gold, LOSS_FUNCTION_NA
* 胡驰
NiuTrans.Tensor张量计算库由东北大学自然语言处理实验室小牛开源团队开发，成员来自东北大学自然语言处理实验室、小牛翻译、小牛雅智，致力于为深度学习相关研究及工业系统的开发提供完整的张量定义及计算功能。
\ No newline at end of file
## 附录
在XTensor.h头文件中定义的成员变量说明：
| 成员变量 | 功能 |
| - | - |
| int id | 张量标识 |
| XMem * mem | 张量所使用的内存池 |
| void * data | 保存元素的数据数组 |
| void * dataHost | 主机内存上的数据副本，只在GPU上运行时被激活 |
| void ** dataP | 指向数据地址的指针 |
| int devID | 设备ID，指张量所申请的空间所在CPU或者GPU设备的编号，-1表示CPU |
| int order | 张量的维度，例如：一个矩阵（维度为2）是一个二维张量 |
| int dimSize[ ] | 张量中每一维度的大小，索引0表示第1维 |
| int dimSizeRDI[ ] | 转置模式下张量中每一维度的大小，索引0表示第1维 |
| TENSOR_DATA_TYPE dataType | 每个数据单元的数据类型 |
| int unitSize | 数据单元的大小，类似于sizeof() |
| int unitNum | 数据单元的数量 |
| bool isSparse | 是否稠密，一个n * m稠密矩阵的数据量大小为n * m,而稀疏（非稠密）矩阵的数据量大小则取决于矩阵中非零元素个数。|
| int unitNumNonZero | 稀疏矩阵中非零元素个数 |
| float denseRatio | 稠密度，指非零单元的比例，是介于0和1之间的一个实数，0表示所有单元全为零，1表示全为非零单元。|
| bool isShared | 标志数据数组是否被其他张量所共享 |
| bool isDefaultDType | 矩阵中使用的数据类型是否是属于默认数据类型 |
| bool isInGlobalMem | 标志数据是否在全局内存而不是内存池中 |
| bool isAllValued[ ] | 标志稀疏矩阵中是否每个维度都具有非零元素 |
| bool isInit | 张量是否被初始化 |
| bool isTmp | 张量是否为临时创建 |
| bool isGrad | 当使用模型参数时张量是否保持梯度 |
| unsigned int visitMark | 节点访问标志 |
| XTensor * grad | 反向传播的梯度 |
| XLink income | 超边的入边 |
| XLink outgo | 超边的出边 |
在XTensor.h头文件中定义的方法说明：
| 功能 | 函数 | 参数 |
| - | - | - |
| 构造函数 | XTensor() | N/A |
| 析构函数 | ~XTensor() | N/A |
| 初始化成员变量 | void Init() | N/A |
| 销毁数据 | void DestroyData() | N/A |
| 张量的浅层复制 | void ShallowCopy(<br>const XTensor &tensor) | tensor - 进行复制的张量 |
| 重载等于符号 | XTensor& operator= (<br>const XTensor &tensor) | tensor - 重载的张量 |
| 重载加法符号 | XTensor operator+ (<br>const XTensor &tensor) | tensor - 重载的张量 |
| 重载乘法符号 | XTensor operator* (<br>const XTensor &tensor) | tensor - 重载的张量 |
| 线性变换 | XTensor Lin(<br>DTYPE scale, DTYPE shift = 0) | scale - 缩放参数 <br> shift - 偏移参数 |
| 判断两个张量数据类型<br>和大小是否相同 | static bool IsIdentical(<br> XTensor * a, XTensor * b) | a - 进行比较的第一个张量 <br> b - 进行比较的第二个张量 |
| 判断三个张量数据类型<br>和大小是否相同 | static bool IsIdentical(<br> XTensor * a, XTensor * b, XTensor * c) | a - 进行比较的第一个张量 <br> b - 进行比较的第二个张量 <br> c - 进行比较的第三个张量 |
| 设置张量每一维度的大小 | void SetDim(<br>int * myDimSize) |myDimSize - 张量每一维度的大小 |
| 得到张量中给定的维度大小 | int GetDim(<br>const int dim) | dim - 张量的维度 |
| 重新调整矩阵维度 | void Reshape(<br> const int order, const int * myDimSize) | order - 张量的维度 <br> myDimSize - 张量每一维的大小 |
| 得到张量中元素数量 | int GetSize() | N/A |
| 得到内存使用大小 | int GetDataSizeInChar() | N/A |
| 得到所给数据类型的数据<br> 单元大小 | int GetUnitSize(<br> TENSOR_DATA_TYPE myDataType) | myDataType - 所给数据类型 |
| 张量中所有元素设置为0 | void SetZeroAll(XStream * stream = NULL) | stream - 多线程流|
| 用数组赋值张量 | void SetData(<br> const void * d, int num, int beg = 0) | d - 赋值数组 <br> num - 数组大小 <br> beg - 赋值时从张量的第几位开始 |
| 设置张量服从均匀分布 | void SetDataRand(<br> DTYPE lower, DTYPE upper) | lower - 最小值 <br> upper - 最大值 |
| 设置张量服从正态分布 | void SetDataRandn(<br> DTYPE mean, DTYPE standardDeviation) | mean - 均值 <br> standardDeviation - 标准差 |
| 检查张量中元素是否相同 | bool CheckData(<br> const void * answer, int num, int beg = 0) | answer - 给定数组 <br> num - 数组大小 <br> beg - 赋值时从张量的第几位开始 |
| 设置数据指针 | void SetDataPointer() | N/A |
| 将给定维度中元素<br> 设置为升序 | void SetAscendingOrder(<br>int dim) | dim - 给定维度 |
| 得到索引指向的单元的值 | DTYPE Get(int index[], int size = -1) | index - 给定索引 <br> size-矩阵大小 |
| 获取张量中元素指针 | void * GetCell(<br>int * index, int size) | index - 元素位置 <br> size-矩阵大小 |
| 获取一维张量中元素的<br>默认类型值 | DTYPE Get1D(<br>int i) | i - 第一维 |
| 获取二维张量中元素的<br>默认类型值 | DTYPE Get2D(<br>int ni, int mi) const | ni - 第一维 <br> mi - 第二维 |
| 获取三维张量中元素的<br>默认类型值 | DTYPE Get3D(<br>int d0, int d1, int d2) | d0 - 第一维 <br> d1 - 第二维 <br> d2 - 第三维 |
| 获取一维张量中元素的<br>整形值 |int Get1DInt(<br>int i) | i - 第一维 |
| 获取二维张量中元素的<br>整形值 | int Get2DInt(<br>int ni, int mi) | ni - 第一维 <br> mi - 第二维 |
| 获取三维张量中元素的整形值 | int Get3DInt(<br>int d0, int d1, int d2) | d0 - 第一维 <br> d1 - 第二维 <br> d2 - 第三维 |
| 获取稀疏张量的值 | DTYPE GetInSparse(int i) | i - 稀疏矩阵中非0元素位置 |
| 获取稀疏张量中<br> 元组的键值 | int GetKeyInSparse(int i) | i - 稀疏矩阵中非0元素位置 |
| 设置单元中的值 | bool Set(<br>DTYPE value, int index[], int size = -1) | value - 值 <br> index - 元素位置 <br> size-矩阵大小 |
| 设置一维张量中的单元值 | bool Set1D(<br>DTYPE value, int i) | value - 值 <br> i - 第一维 |
| 设置二维张量中的单元值 | bool Set2D(<br>DTYPE value, int ni, int mi) | value - 值 <br> ni - 第一维 <br> mi - 第二维 |
| 设置三维张量中的单元值 | bool Set3D(<br>DTYPE value, int d0, int d1, int d2) | value - 值 <br> d0 - 第一维 <br> d1 - 第二维 <br> d2 - 第三维 |
| 增加二维张量中<br> 的单元值 | bool Add2D(<br>DTYPE value, int ni, int mi = 0) | value - 单元值 <br> ni - 行值 <br> mi - 列值 |
| 获取稀疏矩阵中<br> 非零元素数量 | int GetNonzeroSize() | N/A |
| 设置张量为临时变量 | void SetTMP(<br>bool myIsTmp = true) | myIsTmp - 是否为临时变量 |
| 张量是否保持梯度 | void SetGrad(<br>bool myIsGrad = true) | myIsTmp - 是否保持梯度 |
| 将矩阵重置为特定大小 | bool Resize(<br> const int myOrder, <br> const int * myDimSize, <br> const TENSOR_DATA_TYPE myDataType = DEFAULT_DTYPE, <br> const float myDenseRatio = 1.0F) | myOrder - 张量的维度 <br> myDimSize - 张量每一维的大小,索引0表示第一维 <br> myDataType - 张量的数据类型 <br> myDenseRatio - 张量的稠密度，1表示稠密张量 |
| 将矩阵重置为特定大小<br>并不申请新空间 | bool ResizeWithNoData(<br> const int myOrder, <br> const int * myDimSize, <br> const TENSOR_DATA_TYPE myDataType = DEFAULT_DTYPE, <br> const float myDenseRatio = 1.0F) | myOrder - 张量的维度 <br> myDimSize - 张量每一维的大小,索引0表示第一维 <br> myDataType - 张量的数据类型 <br> myDenseRatio - 张量的稠密度，1表示稠密张量 |
| 将矩阵重置为<br> 另一矩阵大小 | bool Resize(<br> const XTensor * myTensor) | myTensor - 重置矩阵大小的参考矩阵 |
| 用二值搜索方法<br> 找到稀疏矩阵中元素 | bool BinarySearch(<br> int key, DTYPE &value, void * &position) | key - 稀疏矩阵中元素位置 <br> value - 元素值 <br> position - 元素坐标位置 |
| 将数据刷新到<br> 目标设备中 | void FlushToMem(XMem * targetMem) | targetMem - 目标设备 |
| 在全局内存中<br> 申请矩阵的内存空间 | static void AllocateData(<br> XTensor * matrix, <br> XMem * myMem = NULL, <br> bool useBuf = false) | matrix - 申请内存空间的矩阵 <br> myMem - 是否在内存池中申请空间 <br> useBuf - 是否使用缓冲区 |
| 在全局内存中<br> 释放矩阵的内存空间 | static void FreeData(<br> XTensor * matrix, <br> XMem * myMem = NULL, <br> bool useBuf = false) | matrix - 申请内存空间的矩阵 <br> myMem - 是否在内存池中申请空间 <br> useBuf - 是否使用缓冲区 |
| 在缓冲区创建张量 | XTensor * NewTensorBuf( <br> const int myOrder, <br> const int * myDimSize, XMem * myMem, <br> const TENSOR_DATA_TYPE myDataType = <br> X_FLOAT, const float myDenseRatio = 1.0F) | myOrder - 张量的维度 <br> myDimSize - 张量每一维的大小,索引0表示第一维 <br> myMem - 张量所使用的内存池 <br> myDataType - 张量的数据类型 <br> myDenseRatio - 张量的稠密度，1表示稠密张量 |
| 依据给定张量<br>复制一个新的张量 | XTensor * NewTensor(<br>XTensor * a, bool isFilledData = true) | a - 给定张量 <br> isFilledData - 是否申请张量中的数据空间 |
| 依据给定张量<br>释放数据空间 | void DelTensor(<br>const XTensor * tensor) | tensor - 给定张量 |
| 依据给定张量<br>在缓存中释放数据空间 | void DelTensorBuf(<br>const XTensor * tensor) | tensor - 给定张量 |
\ No newline at end of file
source/network/Main.cpp
......@@ -48,6 +48,7 @@ void ReduceSumFP16Test();
void LogSoftmaxFP16Test();
void ClipFP16Test();
void ScaleAndShiftFP16Test();
void InitTensorFP16Test();
using namespace nts;
using namespace fnnlm;
......@@ -87,6 +88,8 @@ int main(int argc, const char ** argv )
//return 0;
//ScaleAndShiftFP16Test();
//return 0;
//InitTensorFP16Test();
//return 0;
if (argc > 1 && !strcmp(argv[1], "-test"))
Test();
......@@ -106,6 +109,20 @@ int main(int argc, const char ** argv )
return 0;
}
void InitTensorFP16Test() {
XTensor a;
InitTensor2D(&a, 1, 10, X_FLOAT, 0);
a.SetDataRand(-10.0F, 10.0F);
XTensor halfA;
halfA = ConvertDataType(a, X_FLOAT16);
halfA.Dump(&halfA, stderr, "halfA:");
XTensor b;
InitTensor2D(&b, 1, 10, X_FLOAT16, 0);
_SetDataRand(&b, -10.0F, 10.0F);
b.Dump(&b, stderr, "b:");
}
void ScaleAndShiftFP16Test() {
XTensor a;
XTensor intA;
......
source/network/XNet.cpp
......@@ -189,7 +189,6 @@ void XNet::Backward(XList &roots, XList &golds, XList &paddings, LOSS_FUNCTION_N
}
//XLossGrad lossGrad;
///* we start with the gradient with respect to the loss for output layers */
//for (int i = 0; i < roots.count; i++) {
// XTensor * root = (XTensor*)roots.Get(i);
......@@ -198,11 +197,9 @@ void XNet::Backward(XList &roots, XList &golds, XList &paddings, LOSS_FUNCTION_N
// XLink &income = root->income;
// int funcID = income.typeID;
// void * params = income.params;
// /* we compute dE/dx if the output is generated by an activation function y = f(x).
// Note that we do not need to obtain dE/dy here because it is no use in the
// folloing process of back-propagation */
// if (gold != NULL && income.tailNum == 1 && (funcID & FUNCTION_BASE)) {
// if (funcID == FUNC_LOGSOFTMAX || funcID == FUNC_SOFTMAX) {
// XTensor * x = income.tails[0];
......
source/sample/fnnlm/FNNLM.cpp
......@@ -481,7 +481,7 @@ void Train(const char * train, bool isShuffled, FNNModel &model)
/* this is implemented by gather function */
ForwardAutoDiff(ngrams, ngramNum, output, model);
/* this is implemented by multiply function */
///* this is implemented by multiply function */
//ForwardAutoDiff(inputs, output, model);
/* automatic differentiation */
......
source/tensor/core/arithmetic/Sign.cu
......@@ -17,6 +17,7 @@
/*
* $Created by: LI Yinqiao (li.yin.qiao.2012@hotmail.com) 2018-7-11
* $Update by: Lin Ye (email: linye2015@outlook.com) 2019-07-24 float16 added
*/
#include "../../XDevice.h"
......@@ -33,15 +34,16 @@ set each entry to its sign value (CUDA Kernel)
>> b - pointer to output data array
>> size - size of the data array
*/
template<class T>
__global__
void KernelSign(DTYPE * a, DTYPE * b, int size)
void KernelSign(T * a, T * b, int size)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < size) {
if (a[i] > 0)
if (i < size){
if (a[i] > (T)0)
b[i] = 1.0F;
else if (a[i] == 0)
else if (a[i] == (T)0)
b[i] = 0.0F;
else
b[i] = -1.0F;
......@@ -49,19 +51,6 @@ void KernelSign(DTYPE * a, DTYPE * b, int size)
}
/*
set each entry to its sign value with float16 data type value (CUDA Kernel)
This is for float16 computation
>> a - pointer to input data array
>> b - pointer to output data array
>> size - size of the data array
*/
__global__
void KernelSign(__half * a, __half * b, int size)
{
return;
}
/*
set each entry to its sign value
>> a - input tensor we are processing
>> b - output tensor we are processing
......@@ -83,10 +72,10 @@ void _CudaSign(const XTensor * a, XTensor * b)
ProtectCudaDev(a->devID, devIDBackup);
if (a->dataType == DEFAULT_DTYPE) {
KernelSign << <blocks, threads >> >((DTYPE*)a->data, (DTYPE*)b->data, a->unitNum);
KernelSign<<<blocks, threads>>>((DTYPE*)a->data, (DTYPE*)b->data, a->unitNum);
}
else if (a->dataType == X_FLOAT16) {
KernelSign << <blocks, threads >> >((__half*)a->data, (__half*)b->data, a->unitNum);
KernelSign<<<blocks, threads>>>((__half*)a->data, (__half*)b->data, a->unitNum);
}
else {
ShowNTErrors("TODO!");
......
source/tensor/core/arithmetic/Sign.cuh
......@@ -29,12 +29,9 @@ namespace nts { // namespace nts(NiuTrans.Tensor)
#ifdef USE_CUDA
/* set each entry to its sign value (CUDA Kernel) */
template<class T>
__global__
void KernelSign(DTYPE * a, DTYPE * b, int size);
/* set each entry to its sign value (CUDA Kernel) with float16 data type*/
__global__
void KernelSign(__half * a, __half * b, int size);
void KernelSign(T * a, T * b, int size);
/* set each entry to its sign value */
void _CudaSign(const XTensor * a, XTensor * b);
......
source/tensor/core/arithmetic/Sub.cu
......@@ -17,6 +17,7 @@
/*
* $Created by: Xu Chen (email: hello_master1954@163.com) 2018-08-01
* $Update by: Lin Ye (email: linye2015@outlook.com) 2019-07-24 float16 added
*/
#include "../../XDevice.h"
......@@ -36,8 +37,9 @@ c = a - b * \beta
>> size - the size of a/b/c
>> beta - the coefficient
*/
template<class T>
__global__
void KernelSUB(DTYPE * a, DTYPE * b, DTYPE * c, int size, DTYPE beta)
void KernelSUB(T * a, T * b, T * c, int size, T beta)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
......@@ -77,7 +79,20 @@ void _CudaSub(const XTensor * a, const XTensor * b, XTensor * c, DTYPE beta)
GDevs.GetCudaThread(a->devID, a->unitNum, gridSize, blockSize);
dim3 blocks(gridSize[0]);
dim3 threads(blockSize[0]);
KernelSUB << <blocks, threads >> >((DTYPE*)a->data, (DTYPE*)b->data, (DTYPE*)c->data, a->unitNum, beta);
KernelSUB<<<blocks, threads>>>((DTYPE*)a->data, (DTYPE*)b->data, (DTYPE*)c->data, a->unitNum, beta);
}
else if (a->dataType == X_FLOAT16 &&
b->dataType == X_FLOAT16 &&
c->dataType == X_FLOAT16)
{
int gridSize[3], blockSize[3];
GDevs.GetCudaThread(a->devID, a->unitNum, gridSize, blockSize);
dim3 blocks(gridSize[0]);
dim3 threads(blockSize[0]);
half beta1 = __float2half(beta);
KernelSUB<<<blocks, threads>>>((__half*)a->data, (__half*)b->data, (__half*)c->data, a->unitNum, (__half)beta1);
}
else {
// TODO!!
......
source/tensor/core/arithmetic/Sub.cuh
......@@ -29,8 +29,9 @@ namespace nts { // namespace nts(NiuTrans.Tensor)
#ifdef USE_CUDA
/* subtraction of data arrays (CUDA Kernel) */
template<class T>
__global__
void KernelSUB(DTYPE * a, DTYPE * b, DTYPE * c, int size, DTYPE beta = (DTYPE)1.0);
void KernelSUB(T * a, T * b, T * c, int size, T beta = (T)1.0);
/* tensor subtraction c = a - b * \beta (cuda version) */
void _CudaSub(const XTensor * a, const XTensor * b, XTensor * c = NULL, DTYPE beta = (DTYPE)1.0);
......
source/tensor/core/arithmetic/SubDim.cu
......@@ -17,6 +17,7 @@
/*
* $Created by: Lin Ye (email: linye2015@outlook.com) 2018-08-13
* $Update by: Lin Ye (email: linye2015@outlook.com) 2019-07-24 float16 added
*/
#include "SubDim.cuh"
......@@ -168,6 +169,34 @@ void _CudaSubDim(const XTensor * a, const XTensor * b, XTensor * c, int n, DTYPE
ShowNTErrors("Something is wrong!");
}
}
else if (a->dataType == X_FLOAT16) {
half beta1 = __float2half(beta);
if (stride > 1) {
GDevs.GetCudaThread2D(a->devID, stride * blockNum, blockSize, MAX_INT, cudaGrids, cudaBlocks);
if (beta == (DTYPE)1.0F)
KernelSubWithCol<__half, false> <<<dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1])>>>
((__half*)a->data, (__half*)b->data, (__half*)c->data,
blockSize, stride, blockSize * stride, blockNum, beta1);
else
KernelSubWithCol<__half, true> <<<dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1])>>>
((__half*)a->data, (__half*)b->data, (__half*)c->data,
blockSize, stride, blockSize * stride, blockNum, beta1);
}
else if (stride == 1) {
GDevs.GetCudaThread2D(a->devID, blockSize, blockNum, MAX_INT, cudaGrids, cudaBlocks);
if (beta == (DTYPE)1.0F)
KernelSubWithRow<__half, false> <<<dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1])>>>
((__half*)a->data, (__half*)b->data, (__half*)c->data,
blockNum, blockSize, beta1);
else
KernelSubWithRow<__half, true> <<<dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1])>>>
((__half*)a->data, (__half*)b->data, (__half*)c->data,
blockNum, blockSize, beta1);
}
else {
ShowNTErrors("Something is wrong!");
}
}
else {
ShowNTErrors("TODO!");
}
......
source/tensor/core/arithmetic/SumDim.cu
......@@ -19,6 +19,7 @@
* $Created by: XIAO Tong (email: xiaotong@mail.neu.edu.cn) 2018-07-29
* &Updated by: XIAO Tong (email: xiaotong@mail.neu.edu.cn) 2018-12-26
* Add summation by broadcasting.
* $Update by: Lin Ye (email: linye2015@outlook.com) 2019-07-24 float16 added
*/
#include "SumDim.cuh"
......@@ -170,6 +171,34 @@ void _CudaSumDim(const XTensor * a, const XTensor * b, XTensor * c, int n, DTYPE
ShowNTErrors("Something is wrong!");
}
}
else if (a->dataType == X_FLOAT16) {
half beta1 = __float2half(beta);
if (stride > 1) {
GDevs.GetCudaThread2D(a->devID, stride * blockNum, blockSize, MAX_INT, cudaGrids, cudaBlocks);
if (beta == (DTYPE)1.0F)
KernelAddWithCol<__half, false> <<<dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1])>>>
((__half*)a->data, (__half*)b->data, (__half*)c->data,
blockSize, stride, blockSize * stride, blockNum, beta1);
else
KernelAddWithCol<__half, true> <<<dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1])>>>
((__half*)a->data, (__half*)b->data, (__half*)c->data,
blockSize, stride, blockSize * stride, blockNum, beta1);
}
else if (stride == 1) {
GDevs.GetCudaThread2D(a->devID, blockSize, blockNum, MAX_INT, cudaGrids, cudaBlocks);
if (beta == (DTYPE)1.0F)
KernelAddWithRow<__half, false> <<<dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1])>>>
((__half*)a->data, (__half*)b->data, (__half*)c->data,
blockNum, blockSize, beta1);
else
KernelAddWithRow<__half, true> <<<dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1])>>>
((__half*)a->data, (__half*)b->data, (__half*)c->data,
blockNum, blockSize, beta1);
}
else {
ShowNTErrors("Something is wrong!");
}
}
else {
ShowNTErrors("TODO!");
}
......
source/tensor/core/math/ScaleAndShift.cu
......@@ -108,7 +108,7 @@ void _CudaScaleAndShift(const XTensor * a, XTensor * b, DTYPE scale, DTYPE shift
else
KernelScaleAndShift<__half, false, false> << <blocks, threads >> >((__half*)a->data, (__half*)b->data, a->unitNum, scale1, shift1);
}
else if (a->dataType == X_INT) {
else if (a->dataType == X_INT){
int scale2 = int(scale);
int shift2 = int(shift);
......@@ -121,7 +121,7 @@ void _CudaScaleAndShift(const XTensor * a, XTensor * b, DTYPE scale, DTYPE shift
else
KernelScaleAndShift<int, false, false><<<blocks, threads>>>((int *)a->data, (int *)b->data, a->unitNum, scale2, shift2);
}
else if (a->dataType == X_INT8) {
else if (a->dataType == X_INT8){
__int8 scale2 = __int8(scale);
__int8 shift2 = __int8(shift);
......
source/tensor/test/TSign.cpp
......@@ -17,9 +17,11 @@
/*
* $Created by: Xu Chen (email: hello_master1954@163.com) 2018-07-12
* $Update by: Lin Ye (email: linye2015@outlook.com) 2019-07-24 float16 added
*/
#include "TSign.h"
#include "../core/getandset/ConvertDataType.h"
namespace nts { // namespace nts(NiuTrans.Tensor)
......@@ -110,6 +112,88 @@ bool TestSign1()
#endif // USE_CUDA
}
/*
case 2: float16 test Sign function.
Set every entry to its sign value.
*/
bool TestSign2()
{
/* a tensor of size (3, 2) */
int aOrder = 2;
int * aDimSize = new int[aOrder];
aDimSize[0] = 3;
aDimSize[1] = 2;
int aUnitNum = 1;
for (int i = 0; i < aOrder; i++)
aUnitNum *= aDimSize[i];
DTYPE aData[3][2] = { {1.0F, -2.0F},
{0.0F, 4.0F},
{5.0F, -6.0F} };
DTYPE answer[3][2] = { {1.0F, -1.0F},
{0.0F, 1.0F},
{1.0F, -1.0F} };
/* CPU test */
bool cpuTest = true;
#ifdef USE_CUDA
/* GPU test */
bool gpuTest = true;
/* create tensor */
XTensor * aGPU = NewTensor(aOrder, aDimSize, X_FLOAT, 1.0F, 0);
XTensor * bGPU = NewTensor(aOrder, aDimSize, X_FLOAT, 1.0F, 0);
XTensor * aMeGPU = NewTensor(aOrder, aDimSize, X_FLOAT, 1.0F, 0);
XTensor bUserGPU;
/* create float16 tensor */
XTensor aHalfGPU;
XTensor bHalfGPU;
XTensor aMeHalfGPU;
XTensor bUserHalfGPU;
/* Initialize variables */
aGPU->SetData(aData, aUnitNum);
aMeGPU->SetData(aData, aUnitNum);
/* convert data type from float to float16 */
aHalfGPU = ConvertDataType(*aGPU, X_FLOAT16);
aMeHalfGPU = ConvertDataType(*aMeGPU, X_FLOAT16);
bHalfGPU = ConvertDataType(*bGPU, X_FLOAT16);
/* call Sign function */
_Sign(&aHalfGPU, &bHalfGPU);
_SignMe(&aMeHalfGPU);
bUserHalfGPU = Sign(aHalfGPU);
/* convert data type from float16 to float */
_ConvertDataType(&bHalfGPU, bGPU);
_ConvertDataType(&aMeHalfGPU, aMeGPU);
bUserGPU = ConvertDataType(bUserHalfGPU, X_FLOAT);
/* check results */
gpuTest = bGPU->CheckData(answer, aUnitNum, 1e-4F) &&
aMeGPU->CheckData(answer, aUnitNum, 1e-4F) &&
bUserGPU.CheckData(answer, aUnitNum, 1e-4F);
/* destroy variables */
delete aGPU;
delete bGPU;
delete aMeGPU;
delete[] aDimSize;
return cpuTest && gpuTest;
#else
/* destroy variables */
delete[] aDimSize;
return cpuTest;
#endif // USE_CUDA
}
/* other cases */
/*
TODO!!
......@@ -131,6 +215,16 @@ bool TestSign()
else
XPRINT(0, stdout, ">> case 1 passed!\n");
/* case 2 test */
caseFlag = TestSign2();
if (!caseFlag) {
returnFlag = false;
XPRINT(0, stdout, ">> case 2 failed!\n");
}
else
XPRINT(0, stdout, ">> case 2 passed!\n");
/* other cases test */
/*
TODO!!
......
source/tensor/test/TSub.cpp
......@@ -17,9 +17,11 @@
/*
* $Created by: Xu Chen (email: hello_master1954@163.com) 2018-08-01
* $Update by: Lin Ye (email: linye2015@outlook.com) 2019-07-24 float16 added
*/
#include "TSub.h"
#include "../core/getandset/ConvertDataType.h"
namespace nts { // namespace nts(NiuTrans.Tensor)
......@@ -214,6 +216,177 @@ bool TestSub2()
#endif // USE_CUDA
}
/* case 3: float16 tensor subtraction c = a - b * \beta */
bool TestSub3()
{
/* a tensor of size (2, 4) */
int order = 2;
int * dimSize = new int[order];
dimSize[0] = 2;
dimSize[1] = 4;
int unitNum = 1;
for (int i = 0; i < order; i++)
unitNum *= dimSize[i];
DTYPE aData[2][4] = { {0.0F, 1.0F, 2.0F, 3.0F},
{4.0F, 5.0F, 6.0F, 7.0F} };
DTYPE bData[2][4] = { {1.0F, -1.0F, -3.0F, -5.0F},
{-7.0F, -9.0F, -11.0F, -13.0F} };
DTYPE answer[2][4] = { {-1.0F, 2.0F, 5.0F, 8.0F},
{11.0F, 14.0F, 17.0F, 20.0F} };
/* CPU test */
bool cpuTest = true;
#ifdef USE_CUDA
/* GPU test */
bool gpuTest = true;
/* create tensor */
XTensor * aGPU = NewTensor(order, dimSize, X_FLOAT, 1.0F, 0);
XTensor * bGPU = NewTensor(order, dimSize, X_FLOAT, 1.0F, 0);
XTensor * cGPU = NewTensor(order, dimSize, X_FLOAT, 1.0F, 0);
XTensor * cMeGPU = NewTensor(order, dimSize, X_FLOAT, 1.0F, 0);
XTensor cUserGPU;
/* create float16 tensor */
XTensor aHalfGPU;
XTensor bHalfGPU;
XTensor cHalfGPU;
XTensor cMeHalfGPU;
XTensor cUserHalfGPU;
/* Initialize variables */
aGPU->SetData(aData, unitNum);
cMeGPU->SetData(aData, unitNum);
bGPU->SetData(bData, unitNum);
cGPU->SetZeroAll();
/* convert data type from float to float16 */
aHalfGPU = ConvertDataType(*aGPU, X_FLOAT16);
bHalfGPU = ConvertDataType(*bGPU, X_FLOAT16);
cHalfGPU = ConvertDataType(*cGPU, X_FLOAT16);
cMeHalfGPU = ConvertDataType(*cMeGPU, X_FLOAT16);
/* call Sub function */
_Sub(&aHalfGPU, &bHalfGPU, &cHalfGPU);
_SubMe(&cMeHalfGPU, &bHalfGPU);
cUserHalfGPU = Sub(aHalfGPU, bHalfGPU);
/* convert data type from float16 to float */
_ConvertDataType(&cHalfGPU, cGPU);
_ConvertDataType(&cMeHalfGPU, cMeGPU);
cUserGPU = ConvertDataType(cUserHalfGPU, X_FLOAT);
/* check results */
gpuTest = cGPU->CheckData(answer, unitNum, 1e-4F) &&
cMeGPU->CheckData(answer, unitNum, 1e-4F) &&
cUserGPU.CheckData(answer, unitNum, 1e-4F);
/* destroy variables */
delete aGPU;
delete bGPU;
delete cGPU;
delete cMeGPU;
delete[] dimSize;
return cpuTest && gpuTest;
#else
/* destroy variables */
delete[] dimSize;
return cpuTest;
#endif // USE_CUDA
}
/* case 4: float16 tensor subtraction c = a - b * \beta */
bool TestSub4()
{
/* a tensor of size (2, 4) */
int order = 2;
int * dimSize = new int[order];
dimSize[0] = 2;
dimSize[1] = 4;
int unitNum = 1;
for (int i = 0; i < order; i++) {
unitNum *= dimSize[i];
}
DTYPE aData[2][4] = { {0.0F, 1.0F, 2.0F, 3.0F},
{4.0F, 5.0F, 6.0F, 7.0F} };
DTYPE bData[2][4] = { {1.0F, -1.0F, -3.0F, -5.0F},
{-7.0F, -9.0F, -11.0F, -13.0F} };
DTYPE answer[2][4] = { {-0.5F, 1.5F, 3.5F, 5.5F},
{7.5F, 9.5F, 11.5F, 13.5F} };
float beta = 0.5F;
/* CPU test */
bool cpuTest = true;
#ifdef USE_CUDA
/* GPU test */
bool gpuTest = true;
/* create tensor */
XTensor * aGPU = NewTensor(order, dimSize, X_FLOAT, 1.0F, 0);
XTensor * bGPU = NewTensor(order, dimSize, X_FLOAT, 1.0F, 0);
XTensor * cGPU = NewTensor(order, dimSize, X_FLOAT, 1.0F, 0);
XTensor * cMeGPU = NewTensor(order, dimSize, X_FLOAT, 1.0F, 0);
XTensor cUserGPU;
/* create float16 tensor */
XTensor aHalfGPU;
XTensor bHalfGPU;
XTensor cHalfGPU;
XTensor cMeHalfGPU;
XTensor cUserHalfGPU;
/* Initialize variables */
aGPU->SetData(aData, unitNum);
cMeGPU->SetData(aData, unitNum);
bGPU->SetData(bData, unitNum);
cGPU->SetZeroAll();
/* convert data type from float to float16 */
aHalfGPU = ConvertDataType(*aGPU, X_FLOAT16);
bHalfGPU = ConvertDataType(*bGPU, X_FLOAT16);
cHalfGPU = ConvertDataType(*cGPU, X_FLOAT16);
cMeHalfGPU = ConvertDataType(*cMeGPU, X_FLOAT16);
/* call Sub function */
_Sub(&aHalfGPU, &bHalfGPU, &cHalfGPU, beta);
_SubMe(&cMeHalfGPU, &bHalfGPU, beta);
cUserHalfGPU = Sub(aHalfGPU, bHalfGPU, beta);
/* convert data type from float16 to float */
_ConvertDataType(&cHalfGPU, cGPU);
_ConvertDataType(&cMeHalfGPU, cMeGPU);
cUserGPU = ConvertDataType(cUserHalfGPU, X_FLOAT);
/* check results */
gpuTest = cGPU->CheckData(answer, unitNum, 1e-4F) &&
cMeGPU->CheckData(answer, unitNum, 1e-4F) &&
cUserGPU.CheckData(answer, unitNum, 1e-4F);
/* destroy variables */
delete aGPU;
delete bGPU;
delete cGPU;
delete cMeGPU;
delete[] dimSize;
return cpuTest && gpuTest;
#else
/* destroy variables */
delete[] dimSize;
return cpuTest;
#endif // USE_CUDA
}
/* other cases */
/*
TODO!!
......@@ -243,6 +416,24 @@ bool TestSub()
else
XPRINT(0, stdout, ">> case 2 passed!\n");
/* case 3 test */
caseFlag = TestSub3();
if (!caseFlag) {
returnFlag = false;
XPRINT(0, stdout, ">> case 3 failed!\n");
}
else
XPRINT(0, stdout, ">> case 3 passed!\n");
/* case 4 test */
caseFlag = TestSub4();
if (!caseFlag) {
returnFlag = false;
XPRINT(0, stdout, ">> case 4 failed!\n");
}
else
XPRINT(0, stdout, ">> case 4 passed!\n");
/* other cases test */
/*
TODO!!
......
source/tensor/test/TSubDim.cpp
......@@ -17,11 +17,13 @@
/*
* $Created by: Lin Ye (email: linye2015@outlook.com) 2018-08-13
* $Update by: Lin Ye (email: linye2015@outlook.com) 2019-07-24 float16 added
*/
#include "TSubDim.h"
#include "../core/arithmetic/SubDim.h"
#include "../XTensor.h"
#include "../core/getandset/ConvertDataType.h"
namespace nts { // namespace nts(NiuTrans.Tensor)
......@@ -249,6 +251,206 @@ bool TestSubDim2()
#endif // USE_CUDA
}
/*
case 3: float16 tensor subtraction c = a - b * \beta
where the size of b is equal to the n-th dimension of a,
i.e., a is subtracted with b by broadcasting
*/
bool TestSubDim3()
{
/* a tensor of size (2, 4) */
int aOrder = 2;
int * aDimSize = new int[aOrder];
aDimSize[0] = 2;
aDimSize[1] = 4;
int aUnitNum = 1;
for (int i = 0; i < aOrder; i++)
aUnitNum *= aDimSize[i];
/* a tensor of size (2) */
int bOrder = 1;
int * bDimSize = new int[bOrder];
bDimSize[0] = 2;
int bUnitNum = 1;
for (int i = 0; i < bOrder; i++)
bUnitNum *= bDimSize[i];
DTYPE aData[2][4] = { {0.0F, 1.0F, 2.0F, 3.0F},
{4.0F, 5.0F, 6.0F, 7.0F} };
DTYPE bData[2] = {1.0F, -1.0F};
DTYPE answer[2][4] = { {-1.0F, 0.0F, 1.0F, 2.0F},
{5.0F, 6.0F, 7.0F, 8.0F} };
/* CPU test */
bool cpuTest = true;
#ifdef USE_CUDA
/* GPU test */
bool gpuTest = true;
/* create tensor */
XTensor * aGPU = NewTensor(aOrder, aDimSize, X_FLOAT, 1.0F, 0);
XTensor * bGPU = NewTensor(bOrder, bDimSize, X_FLOAT, 1.0F, 0);
XTensor * cGPU = NewTensor(aOrder, aDimSize, X_FLOAT, 1.0F, 0);
XTensor * cMeGPU = NewTensor(aOrder, aDimSize, X_FLOAT, 1.0F, 0);
XTensor cUserGPU;
/* create float16 tensor */
XTensor aHalfGPU;
XTensor bHalfGPU;
XTensor cHalfGPU;
XTensor cMeHalfGPU;
XTensor cUserHalfGPU;
/* Initialize variables */
aGPU->SetData(aData, aUnitNum);
cMeGPU->SetData(aData, aUnitNum);
bGPU->SetData(bData, bUnitNum);
cGPU->SetZeroAll();
/* convert data type from float to float16 */
aHalfGPU = ConvertDataType(*aGPU, X_FLOAT16);
bHalfGPU = ConvertDataType(*bGPU, X_FLOAT16);
cHalfGPU = ConvertDataType(*cGPU, X_FLOAT16);
cMeHalfGPU = ConvertDataType(*cMeGPU, X_FLOAT16);
/* call sub function */
_SubDim(&aHalfGPU, &bHalfGPU, &cHalfGPU, 0);
_SubDim(&cMeHalfGPU, &bHalfGPU, 0);
cUserHalfGPU = SubDim(aHalfGPU, bHalfGPU, 0);
/* convert data type from float16 to float */
_ConvertDataType(&cHalfGPU, cGPU);
_ConvertDataType(&cMeHalfGPU, cMeGPU);
cUserGPU = ConvertDataType(cUserHalfGPU, X_FLOAT);
/* check results */
gpuTest = cGPU->CheckData(answer, aUnitNum) &&
cMeGPU->CheckData(answer, aUnitNum) &&
cUserGPU.CheckData(answer, aUnitNum);
/* destroy variables */
delete aGPU;
delete bGPU;
delete cGPU;
delete cMeGPU;
delete[] aDimSize;
delete[] bDimSize;
return cpuTest && gpuTest;
#else
/* destroy variables */
delete[] aDimSize;
delete[] bDimSize;
return cpuTest;
#endif // USE_CUDA
}
/*
case 4: float16 tensor subtraction c = a - b * \beta
where the size of b is equal to the n-th dimension of a,
i.e., a is subtracted with b by broadcasting
*/
bool TestSubDim4()
{
/* a tensor of size (2, 4) */
int aOrder = 2;
int * aDimSize = new int[aOrder];
aDimSize[0] = 2;
aDimSize[1] = 4;
int aUnitNum = 1;
for (int i = 0; i < aOrder; i++)
aUnitNum *= aDimSize[i];
/* a tensor of size (2, 2) */
int bOrder = 2;
int * bDimSize = new int[bOrder];
bDimSize[0] = 2;
bDimSize[1] = 2;
int bUnitNum = 1;
for (int i = 0; i < bOrder; i++)
bUnitNum *= bDimSize[i];
DTYPE aData[2][4] = { {0.0F, 1.0F, 2.0F, 3.0F},
{4.0F, 5.0F, 6.0F, 7.0F} };
DTYPE bData[2][2] = { {1.0F, -1.0F},
{-1.0F, 1.0F} };
DTYPE answer[2][4] = { {-1.0F, 2.0F, 3.0F, 2.0F},
{3.0F, 6.0F, 7.0F, 6.0F} };
/* CPU test */
bool cpuTest = true;
#ifdef USE_CUDA
/* GPU test */
bool gpuTest = true;
/* create tensor */
XTensor * aGPU = NewTensor(aOrder, aDimSize, X_FLOAT, 1.0F, 0);
XTensor * bGPU = NewTensor(bOrder, bDimSize, X_FLOAT, 1.0F, 0);
XTensor * cGPU = NewTensor(aOrder, aDimSize, X_FLOAT, 1.0F, 0);
XTensor * cMeGPU = NewTensor(aOrder, aDimSize, X_FLOAT, 1.0F, 0);
XTensor cUserGPU;
/* create float16 tensor */
XTensor aHalfGPU;
XTensor bHalfGPU;
XTensor cHalfGPU;
XTensor cMeHalfGPU;
XTensor cUserHalfGPU;
/* Initialize variables */
aGPU->SetData(aData, aUnitNum);
cMeGPU->SetData(aData, aUnitNum);
bGPU->SetData(bData, bUnitNum);
cGPU->SetZeroAll();
/* convert data type from float to float16 */
aHalfGPU = ConvertDataType(*aGPU, X_FLOAT16);
bHalfGPU = ConvertDataType(*bGPU, X_FLOAT16);
cHalfGPU = ConvertDataType(*cGPU, X_FLOAT16);
cMeHalfGPU = ConvertDataType(*cMeGPU, X_FLOAT16);
/* call sub function */
_SubDim(&aHalfGPU, &bHalfGPU, &cHalfGPU, 1);
_SubDim(&cMeHalfGPU, &bHalfGPU, 1);
cUserHalfGPU = SubDim(aHalfGPU, bHalfGPU, 1);
/* convert data type from float16 to float */
_ConvertDataType(&cHalfGPU, cGPU);
_ConvertDataType(&cMeHalfGPU, cMeGPU);
cUserGPU = ConvertDataType(cUserHalfGPU, X_FLOAT);
/* check results */
gpuTest = cGPU->CheckData(answer, aUnitNum) &&
cMeGPU->CheckData(answer, aUnitNum) &&
cUserGPU.CheckData(answer, aUnitNum);
/* destroy variables */
delete aGPU;
delete bGPU;
delete cGPU;
delete cMeGPU;
delete[] aDimSize;
delete[] bDimSize;
return cpuTest && gpuTest;
#else
/* destroy variables */
delete[] aDimSize;
delete[] bDimSize;
return cpuTest;
#endif // USE_CUDA
}
/* other cases */
/*
TODO!!
......@@ -278,6 +480,24 @@ bool TestSubDim()
else
XPRINT(0, stdout, ">> case 2 passed!\n");
/* case 3 test */
caseFlag = TestSubDim3();
if (!caseFlag) {
returnFlag = false;
XPRINT(0, stdout, ">> case 3 failed!\n");
}
else
XPRINT(0, stdout, ">> case 3 passed!\n");
/* case 4 test */
caseFlag = TestSubDim4();
if (!caseFlag) {
returnFlag = false;
XPRINT(0, stdout, ">> case 4 failed!\n");
}
else
XPRINT(0, stdout, ">> case 4 passed!\n");
/* other cases test */
/*
TODO!!
......
source/tensor/test/TSumDim.cpp
......@@ -17,12 +17,14 @@
/*
* $Created by: Xu Chen (email: hello_master1954@163.com) 2018-07-30
* $Update by: Lin Ye (email: linye2015@outlook.com) 2019-07-24 float16 added
*/
#include "TSumDim.h"
#include "../XTensor.h"
#include "../core/arithmetic/SumDim.h"
#include "../core/getandset/SetData.h"
#include "../core/getandset/ConvertDataType.h"
namespace nts { // namespace nts(NiuTrans.Tensor)
......@@ -471,6 +473,310 @@ bool TestSumDim4()
#endif // USE_CUDA
}
/*
case 5: float16 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.
In this case, (2, 4) + (2) = (2, 4), n = 0.
*/
bool TestSumDim5()
{
/* a tensor of size (2, 4) */
int aOrder = 2;
int * aDimSize = new int[aOrder];
aDimSize[0] = 2;
aDimSize[1] = 4;
int aUnitNum = 1;
for (int i = 0; i < aOrder; i++)
aUnitNum *= aDimSize[i];
/* a tensor of size (2) */
int bOrder = 1;
int * bDimSize = new int[bOrder];
bDimSize[0] = 2;
int bUnitNum = 1;
for (int i = 0; i < bOrder; i++)
bUnitNum *= bDimSize[i];
DTYPE aData[2][4] = { {0.0F, 1.0F, 2.0F, 3.0F},
{4.0F, 5.0F, 6.0F, 7.0F} };
DTYPE bData[2] = {1.0F, -1.0F};
DTYPE answer[2][4] = { {1.0F, 2.0F, 3.0F, 4.0F},
{3.0F, 4.0F, 5.0F, 6.0F} };
/* CPU test */
bool cpuTest = true;
#ifdef USE_CUDA
/* GPU test */
bool gpuTest = true;
/* create tensor */
XTensor * aGPU = NewTensor(aOrder, aDimSize, X_FLOAT, 1.0F, 0);
XTensor * bGPU = NewTensor(bOrder, bDimSize, X_FLOAT, 1.0F, 0);
XTensor * cGPU = NewTensor(aOrder, aDimSize, X_FLOAT, 1.0F, 0);
XTensor * cMeGPU = NewTensor(aOrder, aDimSize, X_FLOAT, 1.0F, 0);
XTensor cUserGPU;
/* create float16 tensor */
XTensor aHalfGPU;
XTensor bHalfGPU;
XTensor cHalfGPU;
XTensor cMeHalfGPU;
XTensor cUserHalfGPU;
/* Initialize variables */
aGPU->SetData(aData, aUnitNum);
cMeGPU->SetData(aData, aUnitNum);
bGPU->SetData(bData, bUnitNum);
cGPU->SetZeroAll();
/* convert data type from float to float16 */
aHalfGPU = ConvertDataType(*aGPU, X_FLOAT16);
bHalfGPU = ConvertDataType(*bGPU, X_FLOAT16);
cHalfGPU = ConvertDataType(*cGPU, X_FLOAT16);
cMeHalfGPU = ConvertDataType(*cMeGPU, X_FLOAT16);
/* call sum function */
_SumDim(&aHalfGPU, &bHalfGPU, &cHalfGPU, 0);
_SumDim(&cMeHalfGPU, &bHalfGPU, 0);
cUserHalfGPU = SumDim(aHalfGPU, bHalfGPU, 0);
/* convert data type from float16 to float */
_ConvertDataType(&cHalfGPU, cGPU);
_ConvertDataType(&cMeHalfGPU, cMeGPU);
cUserGPU = ConvertDataType(cUserHalfGPU, X_FLOAT);
/* check results */
gpuTest = cGPU->CheckData(answer, aUnitNum) &&
cMeGPU->CheckData(answer, aUnitNum) &&
cUserGPU.CheckData(answer, aUnitNum);
/* destroy variables */
delete aGPU;
delete bGPU;
delete cGPU;
delete cMeGPU;
delete[] aDimSize;
delete[] bDimSize;
return cpuTest && gpuTest;
#else
/* destroy variables */
delete[] aDimSize;
delete[] bDimSize;
return cpuTest;
#endif // USE_CUDA
}
/*
case 6: float16 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.
In this case, (2, 4) + (2, 2) = (2, 4), n = 1.
*/
bool TestSumDim6()
{
/* a tensor of size (2, 4) */
int aOrder = 2;
int * aDimSize = new int[aOrder];
aDimSize[0] = 2;
aDimSize[1] = 4;
int aUnitNum = 1;
for (int i = 0; i < aOrder; i++)
aUnitNum *= aDimSize[i];
/* a tensor of size (2, 2) */
int bOrder = 2;
int * bDimSize = new int[bOrder];
bDimSize[0] = 2;
bDimSize[1] = 2;
int bUnitNum = 1;
for (int i = 0; i < bOrder; i++)
bUnitNum *= bDimSize[i];
DTYPE aData[2][4] = { {0.0F, 1.0F, 2.0F, 3.0F},
{4.0F, 5.0F, 6.0F, 7.0F} };
DTYPE bData[2][2] = { {1.0F, -1.0F},
{-1.0F, 1.0F} };
DTYPE answer[2][4] = { {1.0F, 0.0F, 1.0F, 4.0F},
{5.0F, 4.0F, 5.0F, 8.0F} };
/* CPU test */
bool cpuTest = true;
#ifdef USE_CUDA
/* GPU test */
bool gpuTest = true;
/* create tensor */
XTensor * aGPU = NewTensor(aOrder, aDimSize, X_FLOAT, 1.0F, 0);
XTensor * bGPU = NewTensor(bOrder, bDimSize, X_FLOAT, 1.0F, 0);
XTensor * cGPU = NewTensor(aOrder, aDimSize, X_FLOAT, 1.0F, 0);
XTensor * cMeGPU = NewTensor(aOrder, aDimSize, X_FLOAT, 1.0F, 0);
XTensor cUserGPU;
/* create float16 tensor */
XTensor aHalfGPU;
XTensor bHalfGPU;
XTensor cHalfGPU;
XTensor cMeHalfGPU;
XTensor cUserHalfGPU;
/* Initialize variables */
aGPU->SetData(aData, aUnitNum);
cMeGPU->SetData(aData, aUnitNum);
bGPU->SetData(bData, bUnitNum);
cGPU->SetZeroAll();
/* convert data type from float to float16 */
aHalfGPU = ConvertDataType(*aGPU, X_FLOAT16);
bHalfGPU = ConvertDataType(*bGPU, X_FLOAT16);
cHalfGPU = ConvertDataType(*cGPU, X_FLOAT16);
cMeHalfGPU = ConvertDataType(*cMeGPU, X_FLOAT16);
/* call sum function */
_SumDim(&aHalfGPU, &bHalfGPU, &cHalfGPU, 1);
_SumDim(&cMeHalfGPU, &bHalfGPU, 1);
cUserHalfGPU = SumDim(aHalfGPU, bHalfGPU, 1);
/* convert data type from float16 to float */
_ConvertDataType(&cHalfGPU, cGPU);
_ConvertDataType(&cMeHalfGPU, cMeGPU);
cUserGPU = ConvertDataType(cUserHalfGPU, X_FLOAT);
/* check results */
gpuTest = cGPU->CheckData(answer, aUnitNum) &&
cMeGPU->CheckData(answer, aUnitNum) &&
cUserGPU.CheckData(answer, aUnitNum);
/* destroy variables */
delete aGPU;
delete bGPU;
delete cGPU;
delete cMeGPU;
delete[] aDimSize;
delete[] bDimSize;
return cpuTest && gpuTest;
#else
/* destroy variables */
delete[] aDimSize;
delete[] bDimSize;
return cpuTest;
#endif // USE_CUDA
}
/*
case 7: float16 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.
In this case,
(20, 40, 4000) + (40) = (20, 40, 4000), dim = 1.
*/
bool TestSumDim7()
{
/* a tensor of size (20, 40, 4000) */
int aOrder = 3;
int * aDimSize = new int[aOrder];
aDimSize[0] = 20;
aDimSize[1] = 40;
aDimSize[2] = 4000;
int aUnitNum = 1;
for (int i = 0; i < aOrder; i++)
aUnitNum *= aDimSize[i];
/* a tensor of size (40) */
int bOrder = 1;
int * bDimSize = new int[bOrder];
bDimSize[0] = 40;
int bUnitNum = 1;
for (int i = 0; i < bOrder; i++)
bUnitNum *= bDimSize[i];
/* CPU test */
bool cpuTest = true;
/* create tensors */
XTensor * answer = NewTensor(aOrder, aDimSize);
/* initialize variables */
_SetDataFixed(answer, 1.0F);
#ifdef USE_CUDA
/* GPU test */
bool gpuTest = true;
/* create tensor */
XTensor * aGPU = NewTensor(aOrder, aDimSize, X_FLOAT, 1.0F, 0);
XTensor * bGPU = NewTensor(bOrder, bDimSize, X_FLOAT, 1.0F, 0);
XTensor * cGPU = NewTensor(aOrder, aDimSize, X_FLOAT, 1.0F, 0);
XTensor * cMeGPU = NewTensor(aOrder, aDimSize, X_FLOAT, 1.0F, 0);
XTensor cUserGPU;
/* create float16 tensor */
XTensor aHalfGPU;
XTensor bHalfGPU;
XTensor cHalfGPU;
XTensor cMeHalfGPU;
XTensor cUserHalfGPU;
/* Initialize variables */
aGPU->SetZeroAll();
cMeGPU->SetZeroAll();
_SetDataFixed(bGPU, 1.0F);
/* convert data type from float to float16 */
aHalfGPU = ConvertDataType(*aGPU, X_FLOAT16);
bHalfGPU = ConvertDataType(*bGPU, X_FLOAT16);
cHalfGPU = ConvertDataType(*cGPU, X_FLOAT16);
cMeHalfGPU = ConvertDataType(*cMeGPU, X_FLOAT16);
/* call sum function */
_SumDim(&aHalfGPU, &bHalfGPU, &cHalfGPU, 1);
_SumDim(&cMeHalfGPU, &bHalfGPU, 1);
cUserHalfGPU = SumDim(aHalfGPU, bHalfGPU, 1);
/* convert data type from float16 to float */
_ConvertDataType(&cHalfGPU, cGPU);
_ConvertDataType(&cMeHalfGPU, cMeGPU);
cUserGPU = ConvertDataType(cUserHalfGPU, X_FLOAT);
/* check results */
gpuTest = cGPU->CheckData(answer->data, aUnitNum) &&
cMeGPU->CheckData(answer->data, aUnitNum) &&
cUserGPU.CheckData(answer->data, aUnitNum);
/* destroy variables */
delete answer;
delete aGPU;
delete bGPU;
delete cGPU;
delete cMeGPU;
delete[] aDimSize;
delete[] bDimSize;
return cpuTest && gpuTest;
#else
/* destroy variables */
delete answer;
delete[] aDimSize;
delete[] bDimSize;
return cpuTest;
#endif // USE_CUDA
}
/* other cases */
/*
TODO!!
......@@ -518,6 +824,33 @@ bool TestSumDim()
//else
// XPRINT(0, stdout, ">> case 4 passed!\n");
/* case 5 test */
caseFlag = TestSumDim5();
if (!caseFlag) {
returnFlag = false;
XPRINT(0, stdout, ">> case 5 failed!\n");
}
else
XPRINT(0, stdout, ">> case 5 passed!\n");
/* case 6 test */
caseFlag = TestSumDim6();
if (!caseFlag) {
returnFlag = false;
XPRINT(0, stdout, ">> case 6 failed!\n");
}
else
XPRINT(0, stdout, ">> case 6 passed!\n");
/* case 7 test */
caseFlag = TestSumDim7();
if (!caseFlag) {
returnFlag = false;
XPRINT(0, stdout, ">> case 7 failed!\n");
}
else
XPRINT(0, stdout, ">> case 7 passed!\n");
/* other cases test */
/*
TODO!!
......
source/tensor/test/Test.cpp
......@@ -63,17 +63,18 @@ bool Test()
//wrong = !TestScaleAndShift() || wrong;
//wrong = !TestSelect() || wrong;
//wrong = !TestSetAscendingOrder() || wrong;
wrong = !TestSetData() || wrong;
//wrong = !TestSetData() || wrong;
//wrong = !TestSign() || wrong;
//wrong = !TestSin() || wrong;
//wrong = !TestSort() || wrong;
//wrong = !TestSplit() || wrong;
//wrong = !TestSpread() || wrong;
//wrong = !TestSub() || wrong;
//wrong = !TestSubDim() || wrong;
//wrong = !TestSum() || wrong;
//wrong = !TestSumByColumnTV() || wrong;
//wrong = !TestSumByColumnVT() || wrong;
//wrong = !TestSumDim() || wrong;
wrong = !TestSumDim() || wrong;
//wrong = !TestTan() || wrong;
//wrong = !TestTranspose() || wrong;
//wrong = !TestTopK() || wrong;
......
source/tensor/test/Test.h
......@@ -63,6 +63,7 @@
#include "TSplit.h"
#include "TSpread.h"
#include "TSub.h"
#include "TSubDim.h"
#include "TSum.h"
#include "TSumByColumnTV.h"
#include "TSumByColumnVT.h"
......
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