Skip to content

N
NiuTrans.Tensor
Commit 569cb2dd by Tianzhi
Options

finish reduce sum

parent e5f95479
正在显示 包含 277 行增加 和 105 行删除
niutrans.network.cpu
No preview for this file type
niutrans.tensor.cpu
No preview for this file type
source/network/XNet.o
No preview for this file type
source/tensor/XBLAS.o
No preview for this file type
source/tensor/XHeap.o
No preview for this file type
source/tensor/XLink.o
No preview for this file type
source/tensor/XList.o
No preview for this file type
source/tensor/XMem.o
No preview for this file type
source/tensor/XName.o
No preview for this file type
source/tensor/core/reduce/ReduceSum.cpp
......@@ -24,6 +24,7 @@
#include "ReduceSum.cuh"
#include "../../XName.h"
#include "../../XBLAS.h"
#include "./VectorBuffer.h"
#include "../arithmetic/XTensorBLAS.h"
#include <iostream>
......@@ -73,8 +74,8 @@ void _ReduceSum(const XTensor * input, XTensor * output, int dim, const XTensor
else{
CheckNTErrors((input->dataType == DEFAULT_DTYPE), "TODO!");
int stride = 1;
int strideNum = input->dimSizeRDI[dimRDI];
int stride = 1;
int blockSize = 1;
int blockNum = 1;
for (int i = 0; i < input->order; i++) {
......@@ -85,131 +86,188 @@ void _ReduceSum(const XTensor * input, XTensor * output, int dim, const XTensor
}
blockSize = stride * strideNum;
for(int k = 0; k < blockNum; k++){
DTYPE * ip = (DTYPE*)input->data + blockSize * k;
DTYPE * op = (DTYPE*)output->data + stride * k;
DTYPE * sp = shift != NULL ? (DTYPE*)shift->data + stride * k : NULL;
for(int i = 0; i < stride; i++){
DTYPE sum = 0;
DTYPE bias = shift != NULL ? *(sp + i) : 0;
DTYPE * ipe = ip + blockSize;
if(isExp){
if(bias == 0){
if(power == (DTYPE)1.0){
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride)
sum += (DTYPE)exp(*ipb);
}
else if(power == (DTYPE)2.0){
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride){
DTYPE value = (*ipb);
sum += (DTYPE)exp(value * value);
}
}
else if(power == (DTYPE)0.5){
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride){
DTYPE value = (*ipb);
sum += (DTYPE)exp(sqrt(value));
}
}
else{
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride){
DTYPE value = (*ipb);
sum += (DTYPE)exp(pow(value, power));
}
}
if(input->dimSizeRDI[0] % (4 * 32 / sizeof(DTYPE)) == 0 && input->dimSizeRDI[0] >= 32){
int vecBufLength = 32 / sizeof(DTYPE);
if(dimRDI == 0){
//data is contiguous in dim 0
for(int i = 0; i < blockNum; i++){
// stride = 1
DTYPE * ip = (DTYPE*)input->data + blockSize * i;
DTYPE * op = (DTYPE*)output->data + i;
DTYPE * sp = shift != NULL ? (DTYPE*)shift->data + i : NULL;
DTYPE bias[32 / sizeof(DTYPE)] = {0};
if(shift != NULL){
for(int k = 0; k < 32 / sizeof(DTYPE); k++)
bias[k] = *(sp);
}
else{
if(power == (DTYPE)1.0){
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride)
sum += (DTYPE)exp(*ipb - bias);
VectorBuffer vecBuf[4];
for(int j = 0; j < 4; j++){
vecBuf[j] = VectorBuffer::loadu((DTYPE*)(ip) + j * vecBufLength, isExp, power, bias);
}
for(int j = 1; j < strideNum / 32; j++){
const DTYPE* ptr = (DTYPE*)(ip + j * vecBufLength);
vecBuf[0] = vecBuf[0] + VectorBuffer::loadu(ptr + 0 * vecBufLength, isExp, power, bias);
vecBuf[1] = vecBuf[1] + VectorBuffer::loadu(ptr + 1 * vecBufLength, isExp, power, bias);
vecBuf[2] = vecBuf[2] + VectorBuffer::loadu(ptr + 2 * vecBufLength, isExp, power, bias);
vecBuf[3] = vecBuf[3] + VectorBuffer::loadu(ptr + 3 * vecBufLength, isExp, power, bias);
}
vecBuf[0] = ((vecBuf[0] + vecBuf[1]) + (vecBuf[2] + vecBuf[3]));
DTYPE sum = (DTYPE) 0.0;
for(int k = 0; k < vecBufLength; k++){
sum = sum + vecBuf[0][k];
}
*op = sum;
}
} else{
//data is separated
for(int i = 0; i < blockNum; i++){
for(int j = 0; j < input->dimSizeRDI[0] / 32; j++){
DTYPE * ip = (DTYPE*)input->data + blockSize * i;
DTYPE * op = (DTYPE*)output->data + stride * i;
DTYPE * sp = shift != NULL ? (DTYPE*)shift->data + stride * i : NULL;
DTYPE bias[4 * 32 / sizeof(DTYPE)] = {0};
if(shift != NULL){
for(int k = 0; k < 4 * 32 / sizeof(DTYPE); k++)
bias[k] = *(sp + k);
}
else if(power == (DTYPE)2.0){
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride){
DTYPE value = (*ipb) - bias;
sum += (DTYPE)exp(value * value);
}
VectorBuffer vecBuf[4];
for(int k = 0; k < 4; k++){
vecBuf[k] = VectorBuffer::loadu((DTYPE*)(ip) + (j * 4 + k) * 32 / sizeof(DTYPE), isExp, power, bias + j * 32 / sizeof(DTYPE));
}
else if(power == (DTYPE)0.5){
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride){
DTYPE value = (*ipb) - bias;
sum += (DTYPE)exp(sqrt(value));
}
for(int k = 1; k < strideNum; k++){
DTYPE * ptr = ip + k * stride + (j * 4) * vecBufLength;
vecBuf[0] = vecBuf[0] + VectorBuffer::loadu(ptr + 0 * vecBufLength, isExp, power, bias);
vecBuf[1] = vecBuf[1] + VectorBuffer::loadu(ptr + 1 * vecBufLength, isExp, power, bias + 1 * vecBufLength);
vecBuf[2] = vecBuf[2] + VectorBuffer::loadu(ptr + 2 * vecBufLength, isExp, power, bias + 2 * vecBufLength);
vecBuf[3] = vecBuf[3] + VectorBuffer::loadu(ptr + 3 * vecBufLength, isExp, power, bias + 3 * vecBufLength);
}
else{
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride){
DTYPE value = (*ipb) - bias;
sum += (DTYPE)exp(pow(value, power));
}
for(int k = 0; k < 4; k++){
for(int l = 0; l < vecBufLength; l++)
*(op + j * 32 + 8 * k + l) = vecBuf[k][l];
}
}
}
else{
if(bias == 0){
if(power == (DTYPE)1.0){
//#if defined(USE_BLAS)
// sum = ASUM(strideNum, ip + i, stride);
//#else
}
}//run vector buffer
else{
for(int k = 0; k < blockNum; k++){
DTYPE * ip = (DTYPE*)input->data + blockSize * k;
DTYPE * op = (DTYPE*)output->data + stride * k;
DTYPE * sp = shift != NULL ? (DTYPE*)shift->data + stride * k : NULL;
for(int i = 0; i < stride; i++){
DTYPE sum = 0;
DTYPE bias = shift != NULL ? *(sp + i) : 0;
DTYPE * ipe = ip + blockSize;
if(isExp){
if(bias == 0){
if(power == (DTYPE)1.0){
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride)
sum += *ipb;
//#endif
}
else if(power == (DTYPE)2.0){
//#if defined(USE_BLAS)
// sum = NRM2(strideNum, ip + i, stride);
// sum = sum * sum;
//#else
sum += (DTYPE)exp(*ipb);
}
else if(power == (DTYPE)2.0){
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride){
DTYPE value = (*ipb);
sum += value * value;
sum += (DTYPE)exp(value * value);
}
}
else if(power == (DTYPE)0.5){
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride){
DTYPE value = (*ipb);
sum += (DTYPE)exp(sqrt(value));
}
}
else{
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride){
DTYPE value = (*ipb);
sum += (DTYPE)exp(pow(value, power));
}
//#endif
}
else if(power == (DTYPE)0.5){
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride){
DTYPE value = (*ipb);
sum += (DTYPE)sqrt(value);
}
}
else{
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride){
DTYPE value = (*ipb);
sum += (DTYPE)pow(value, power);
if(power == (DTYPE)1.0){
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride)
sum += (DTYPE)exp(*ipb - bias);
}
else if(power == (DTYPE)2.0){
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride){
DTYPE value = (*ipb) - bias;
sum += (DTYPE)exp(value * value);
}
}
else if(power == (DTYPE)0.5){
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride){
DTYPE value = (*ipb) - bias;
sum += (DTYPE)exp(sqrt(value));
}
}
else{
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride){
DTYPE value = (*ipb) - bias;
sum += (DTYPE)exp(pow(value, power));
}
}
}
}
else{
if(power == (DTYPE)1.0){
//#if defined(USE_BLAS)
// sum = ASUM(strideNum, ip + i, stride);
//#else
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride)
sum += *ipb;
//#endif
sum -= strideNum * bias;
}
else if(power == (DTYPE)2.0){
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride){
DTYPE value = (*ipb) - bias;
sum += value * value;
if(bias == 0){
if(power == (DTYPE)1.0){
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride)
sum += *ipb;
}
}
else if(power == (DTYPE)0.5){
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride){
DTYPE value = (*ipb) - bias;
sum += (DTYPE)sqrt(value);
else if(power == (DTYPE)2.0){
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride){
DTYPE value = (*ipb);
sum += value * value;
}
}
else if(power == (DTYPE)0.5){
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride){
DTYPE value = (*ipb);
sum += (DTYPE)sqrt(value);
}
}
else{
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride){
DTYPE value = (*ipb);
sum += (DTYPE)pow(value, power);
}
}
}
else{
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride){
DTYPE value = (*ipb) - bias;
sum += (DTYPE)pow(value, power);
if(power == (DTYPE)1.0){
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride)
sum += *ipb;
sum -= strideNum * bias;
}
else if(power == (DTYPE)2.0){
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride){
DTYPE value = (*ipb) - bias;
sum += value * value;
}
}
else if(power == (DTYPE)0.5){
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride){
DTYPE value = (*ipb) - bias;
sum += (DTYPE)sqrt(value);
}
}
else{
for(DTYPE * ipb = ip + i; ipb < ipe; ipb += stride){
DTYPE value = (*ipb) - bias;
sum += (DTYPE)pow(value, power);
}
}
}
}
*(op + i) = sum;
}
*(op + i) = sum;
}
}
}
}
......
source/tensor/core/reduce/VectorBuffer.h 0 → 100644
#include <cstring>
#include <cmath>
#include "../../XGlobal.h"
class VectorBuffer{
private:
DTYPE values[32 / sizeof(DTYPE)] = {0};
public:
static int size() {
return 32 / sizeof(DTYPE);
}
VectorBuffer() {}
VectorBuffer(DTYPE val) {
for (int i = 0; i != size(); i++) {
values[i] = val;
}
}
static VectorBuffer loadu(const DTYPE* ptr, bool isExp = false, DTYPE power = (DTYPE)1.0F, DTYPE* bias = NULL) {
int count = 32 / sizeof(DTYPE);
VectorBuffer vec;
if(isExp){
if(bias == 0){
if(power == (DTYPE)1.0){
for (int i = 0; i != count; i++) {
vec.values[i] = (DTYPE)std::exp(*(ptr + i));
}
} else if(power == (DTYPE)2.0){
for (int i = 0; i != count; i++) {
vec.values[i] = (DTYPE)std::exp((*(ptr + i)) * (*(ptr + i)));
}
} else if(power == (DTYPE)0.5){
for (int i = 0; i != count; i++) {
vec.values[i] = (DTYPE)std::exp(std::sqrt(*(ptr + i)));
}
} else{
for (int i = 0; i != count; i++) {
vec.values[i] = (DTYPE)std::exp(std::pow(*(ptr + i), power));
}
}
}//is bias == 0
else{
if(power == (DTYPE)1.0){
for (int i = 0; i != count; i++) {
vec.values[i] = (DTYPE)std::exp(*(ptr + i) - bias[i]);
}
} else if(power == (DTYPE)2.0){
for (int i = 0; i != count; i++) {
DTYPE value = *(ptr + i) - bias[i];
vec.values[i] = (DTYPE)std::exp(value * value);
}
} else if(power == (DTYPE)0.5){
for (int i = 0; i != count; i++) {
vec.values[i] = (DTYPE)std::exp(std::sqrt(*(ptr + i) - bias[i]));
}
} else{
for (int i = 0; i != count; i++) {
vec.values[i] = (DTYPE)std::exp(std::pow(*(ptr + i) - bias[i], power));
}
}
}
}//isExp
else{
if(bias == 0){
if(power == (DTYPE)1.0){
std::memcpy(vec.values, ptr, count * sizeof(DTYPE));
} else if(power == (DTYPE)2.0){
for (int i = 0; i != count; i++) {
vec.values[i] = (*(ptr + i)) * (*(ptr + i));
}
} else if(power == (DTYPE)0.5){
for (int i = 0; i != count; i++) {
vec.values[i] = (DTYPE)std::sqrt(*(ptr + i));
}
} else{
for (int i = 0; i != count; i++) {
vec.values[i] = (DTYPE)std::pow(*(ptr + i), power);
}
}
}// if bias == 0
else{
if(power == (DTYPE)1.0){
for (int i = 0; i != count; i++) {
vec.values[i] = *(ptr + i) - bias[i];
}
} else if(power == (DTYPE)2.0){
for (int i = 0; i != count; i++) {
DTYPE value = *(ptr + i) - bias[i];
vec.values[i] = value * value;
}
} else if(power == (DTYPE)0.5){
for (int i = 0; i != count; i++) {
vec.values[i] = (DTYPE)std::sqrt(*(ptr + i) - bias[i]);
}
} else{
for (int i = 0; i != count; i++) {
vec.values[i] = (DTYPE)std::pow(*(ptr + i) - bias[i], power);
}
}
}
}
return vec;
}
const DTYPE& operator[](int idx) const {
return values[idx];
}
VectorBuffer operator+(const VectorBuffer &a) {
for (int i = 0; i != a.size(); i++) {
this->values[i] = a[i] + this->values[i];
}
return *this;
}
};
\ No newline at end of file
source/tensor/test/TReduceSum.cpp
......@@ -37,7 +37,7 @@ bool TestReduceSum1()
int sOrder = 2;
int * sDimSize = new int[sOrder];
sDimSize[0] = 2;
sDimSize[1] = 4;
sDimSize[1] = 32;
int sUnitNum = 1;
for (int i = 0; i < sOrder; i++)
......@@ -46,7 +46,7 @@ bool TestReduceSum1()
/* a tensor of size (4) */
int tOrder1 = 1;
int * tDimSize1 = new int[tOrder1];
tDimSize1[0] = 4;
tDimSize1[0] = 32;
int tUnitNum1 = 1;
for (int i = 0; i < tOrder1; i++)
......@@ -61,10 +61,10 @@ bool TestReduceSum1()
for (int i = 0; i < tOrder2; i++)
tUnitNum2 *= tDimSize2[i];
DTYPE sData[2][4] = { {0.0F, 1.0F, -2.0F, 3.0F},
{4.0F, 5.0F, 6.0F, 7.0F} };
DTYPE answer1[4] = {4.0F, 6.0F, 4.0F, 10.0F};
DTYPE answer2[2] = {2.0F, 22.0F};
DTYPE sData[2][32] = { {0.0F, 1.0F, 2.0F, 3.0F, 4.0F, 5.0F, 6.0F, 7.0F,0.0F, 1.0F, 2.0F, 3.0F, 4.0F, 5.0F, 6.0F, 7.0F,0.0F, 1.0F, 2.0F, 3.0F, 4.0F, 5.0F, 6.0F, 7.0F,0.0F, 1.0F, 2.0F, 3.0F, 4.0F, 5.0F, 6.0F, 7.0F},
{4.0F, 5.0F, 6.0F, 7.0F, 8.0F, 9.0F, 10.0F, 11.0F,4.0F, 5.0F, 6.0F, 7.0F, 8.0F, 9.0F, 10.0F, 11.0F,4.0F, 5.0F, 6.0F, 7.0F, 8.0F, 9.0F, 10.0F, 11.0F,4.0F, 5.0F, 6.0F, 7.0F, 8.0F, 9.0F, 10.0F, 11.0F} };
DTYPE answer1[32] = {4.0F, 6.0F, 8.0F, 10.0F, 12.0F, 14.0F, 16.0F, 18.0F, 4.0F, 6.0F, 8.0F, 10.0F, 12.0F, 14.0F, 16.0F, 18.0F, 4.0F, 6.0F, 8.0F, 10.0F, 12.0F, 14.0F, 16.0F, 18.0F, 4.0F, 6.0F, 8.0F, 10.0F, 12.0F, 14.0F, 16.0F, 18.0F};
DTYPE answer2[2] = {112.0F, 240.0F};
/* CPU test */
bool cpuTest = true;
......
source/tensor/test/Test.cpp
......@@ -78,7 +78,7 @@ bool Test()
wrong = !TestTopK() || wrong;
wrong = !TestUnsqueeze() || wrong;
wrong = !TestXMem() || wrong;
wrong = !TestCrossEntropy() || wrong;
wrong = !TestDropout() || wrong;
wrong = !TestHardTanH() || wrong;
......
Markdown 格式
0% 或
您添加了 0 人 到此讨论。请谨慎行事。
请先完成此评论的编辑！
请 注册 或者 后发表评论