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NiuTrans.Tensor
Commit 102db468 by xuchen
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better code for dropout function (by broadcasting)

parent 4336f2f9
正在显示 包含 263 行增加 和 184 行删除
source/sample/transformer/T2TAttention.cpp
......@@ -125,17 +125,8 @@ XTensor T2TAttention::Make(XTensor &k, XTensor &q, XTensor &v, XTensor &mask)
dot = Linear(dot, 1.0F/(float)sqrt((float)dk));
//if(llnum == 1)
// dot.Dump(tf, "dot:");
scalar = Softmax(dot, -1);
//if(llnum == 1)
// scalar.Dump(tf, "scalar:");
//if(ignored > 0)
// _SetDataDim(&scalar, 0, ignored, scalar.order - 2, 1e-9F);
att = BMMul(scalar, vheads);
/* concatenate the heads */
......
source/sample/transformer/T2TAttention.h
......@@ -73,6 +73,9 @@ public:
special design for the attention model. */
int ignored;
/* indicates whether the model is used for training */
bool isTraining;
public:
/* constructor */
T2TAttention();
......
source/sample/transformer/T2TEncoder.cpp
......@@ -63,6 +63,7 @@ void AttEncoder::InitModel(int argc, const char ** argv,
LoadParamInt(argc, argv, "hsize", &hSize, DEFAULT_EMBEDDING_SIZE);
LoadParamInt(argc, argv, "esize", &eSize, DEFAULT_EMBEDDING_SIZE);
LoadParamInt(argc, argv, "vsize", &vSize, -1);
LoadParamFloat(argc, argv, "dropout", &dropoutP, 0);
CheckNTErrors(nlayer >= 1, "We have one encoding layer at least!");
CheckNTErrors(vSize > 1, "set vocabulary size by \"-vsize\"");
......@@ -89,29 +90,34 @@ make the encoding network
>> input - the input tensor of the encoder
>> mask - the mask that indicate each position is valid
>> skipInputRes - indicates whether we skip the residual connection of the first layer
>> isTraining - indicates whether the model is for training
<< return - the output tensor of the encoder
*/
XTensor AttEncoder::Make(XTensor &input, XTensor &mask, bool skipInputRes)
XTensor AttEncoder::Make(XTensor &input, XTensor &mask, bool skipInputRes, bool isTraining)
{
XTensor x;
x = embedder.Make(input);
/* dropout */
if(isTraining && dropoutP > 0)
x = Dropout(x, dropoutP);
for(int i = 0; i < nlayer; i++){
XTensor att;
XTensor ln;
XTensor fnn;
XTensor res;
llnum = -1;
/* we skip the residual connection for the first layer if
the encoder is used in language modeling. */
if(skipInputRes && i == 0){
/* self attention */
att = attentions[i].Make(x, x, x, mask);
/* TODO: dropout */
/* dropout */
if(isTraining && dropoutP > 0)
att = Dropout(att, dropoutP);
/* layer normalization */
x = attLayerNorms[i].Make(att);
......@@ -121,27 +127,32 @@ XTensor AttEncoder::Make(XTensor &input, XTensor &mask, bool skipInputRes)
/* self attention */
att = attentions[i].Make(x, x, x, mask);
/* dropout */
if(isTraining && dropoutP > 0)
att = Dropout(att, dropoutP);
/* residual connection */
res = Sum(att, x);
/* TODO: dropout */
/* layer normalization */
x = attLayerNorms[i].Make(res);
llnum = -1;
}
/* fnn */
fnn = fnns[i].Make(x);
/* dropout */
if(isTraining && dropoutP > 0)
fnn = Dropout(fnn, dropoutP);
/* residual connection */
res = Sum(fnn, x);
/* TODO: dropout */
/* layer normalization */
x = fnnLayerNorms[i].Make(res);
if(isTraining && dropoutP > 0)
x = Dropout(x, dropoutP);
}
return x;
......
source/sample/transformer/T2TEncoder.h
......@@ -40,7 +40,7 @@ class T2TEncoder
{
public:
virtual
XTensor Make(XTensor &input, XTensor &mask, bool skipInputRes) = 0;
XTensor Make(XTensor &input, XTensor &mask, bool skipInputRes, bool isTraining) = 0;
};
/*
......@@ -49,7 +49,7 @@ the encoder based on RNN
class RNNEncoder : T2TEncoder
{
public:
XTensor Make(XTensor &input, XTensor &mask, bool skipInputRes);
XTensor Make(XTensor &input, XTensor &mask, bool skipInputRes, bool isTraining);
};
......@@ -77,6 +77,9 @@ public:
/* vocabulary size */
int vSize;
/* dropout probability */
DTYPE dropoutP;
/* some positions can be ignored in attention. this is useful in lm where the first position needs
special design for the attention model. */
int ignored;
......@@ -115,7 +118,7 @@ public:
int myDevID = -1, XMem * myMem = NULL);
/* make the encoding network */
XTensor Make(XTensor &input, XTensor &mask, bool skipInputRes);
XTensor Make(XTensor &input, XTensor &mask, bool skipInputRes, bool isTraining);
};
......
source/sample/transformer/T2TFNN.cpp
......@@ -58,7 +58,7 @@ void T2TFNN::InitModel(int argc, const char ** argv, int myDevID, XMem * myMem)
LoadParamInt(argc, argv, "d", &inSize, DEFAULT_EMBEDDING_SIZE);
LoadParamInt(argc, argv, "d", &outSize, DEFAULT_EMBEDDING_SIZE);
LoadParamInt(argc, argv, "fnnh", &hSize, DEFAULT_EMBEDDING_SIZE);
LoadParamInt(argc, argv, "fnnh", &hSize, DEFAULT_EMBEDDING_SIZE * 4);
LoadParamFloat(argc, argv, "fnnminmax", &minmax, 0.1F);
InitTensor2D(&w1, inSize, hSize, X_FLOAT, devID, mem);
......
source/sample/transformer/T2TModel.cpp
......@@ -77,11 +77,12 @@ make the encoding network
>> input - input tensor
>> mask - the mask for positions that are/not involved in computation
>> skipInputRes - indicates whether we skip the residual connection of the first layer
>> isTraining - indicates whether we are training the model
<< return - encoding result
*/
XTensor T2TModel::MakeEncoding(XTensor &input, XTensor &mask, bool skipInputRes)
XTensor T2TModel::MakeEncoding(XTensor &input, XTensor &mask, bool skipInputRes, bool isTraining)
{
return encoder.Make(input, mask, skipInputRes);
return encoder.Make(input, mask, skipInputRes, isTraining);
}
/*
......@@ -89,8 +90,9 @@ make the entire network (with the output softmax layer)
>> input - input tensor
>> output - output tensor (distribution)
>> padding - padding of the sequences
>> isTraining - indicates whether the model is for training
*/
void T2TModel::Make(XTensor &input, XTensor &output, XTensor &padding)
void T2TModel::Make(XTensor &input, XTensor &output, XTensor &padding, bool isTraining)
{
XTensor encoding;
......@@ -134,7 +136,7 @@ void T2TModel::Make(XTensor &input, XTensor &output, XTensor &padding)
//_Sum(&mask, padding3, &mask);
encoding = MakeEncoding(input, mask, true);
encoding = MakeEncoding(input, mask, true, isTraining);
outputLayer.Make(encoding, output);
delete[] dims;
......
source/sample/transformer/T2TModel.h
......@@ -69,10 +69,10 @@ public:
void InitModel(int argc, const char ** argv);
/* make the encoding network */
XTensor MakeEncoding(XTensor &input, XTensor &mask, bool skipInputRes);
XTensor MakeEncoding(XTensor &input, XTensor &mask, bool skipInputRes, bool isTraining);
/* make the entire network (with the output softmax layer) */
void Make(XTensor &input, XTensor &output, XTensor &padding);
void Make(XTensor &input, XTensor &output, XTensor &padding, bool isTraining);
/* get parameter matrics */
void GetParams(XList &list);
......
source/sample/transformer/T2TTrainer.cpp
......@@ -149,7 +149,7 @@ void T2TTrainer::Train(const char * fn, T2TModel * model)
XTensor output;
/* make the network */
model->Make(batch, output, padding);
model->Make(batch, output, padding, true);
/* make paddings for the output */
if(output.GetDim(0) > 1)
......@@ -167,16 +167,6 @@ void T2TTrainer::Train(const char * fn, T2TModel * model)
/* get probabilities */
float prob = GetProb(&output, &gold, NULL);
MTYPE totalUsed = 0;
MTYPE totalSize = 0;
for (int i = 0; i <= mem->curBlockID; i++) {
totalSize += mem->blocks[i].size;
totalUsed += mem->blocks[i].used;
}
//fprintf(stderr, "%d(%ld,%ld,%f)\n", mem->curBlockID, totalUsed, totalSize, (float)totalUsed/totalSize);
loss += -prob;
wordCount += wc;
wordCountTotal += wc;
......@@ -209,6 +199,8 @@ void T2TTrainer::Train(const char * fn, T2TModel * model)
fclose(tf);
epoch = MIN(epoch, nepoch);
XPRINT6(0, stderr, "[INFO] lr=%.2e, elapsed=%.1fs, step=%d, epoch=%d, word=%d, ppl=%.3f\n",
lr, elapsed, step, epoch, wordCountTotal, exp(loss / wordCount));
XPRINT3(0, stderr, "[INFO] training finished (took %.1fs, step=%d and epoch=%d)\n",
......@@ -271,7 +263,7 @@ void T2TTrainer::Test(const char * fn, const char * ofn, T2TModel * model)
XTensor output;
/* make the network */
model->Make(batch, output, padding);
model->Make(batch, output, padding, false);
int bSize = batch.GetDim(0);
int length = batch.GetDim(1);
......@@ -333,11 +325,19 @@ void T2TTrainer::Test(const char * fn, const char * ofn, T2TModel * model)
char line[MAX_SEQUENCE_LENGTH];
struct SampleNode
{
int id;
int size;
};
/*
load data to buffer
>> file - where to load data
>> isSorted - indicates whether the samples are sorted by length
>> step - the number of sequences we go over when move to the next sample
*/
int T2TTrainer::LoadBuf(FILE * file)
int T2TTrainer::LoadBuf(FILE * file, bool isSorted, int step)
{
int lineCount = 0;
int seqCount = 0;
......@@ -403,6 +403,17 @@ int T2TTrainer::LoadBuf(FILE * file)
nseqBuf = seqCount;
nextSeq = 0;
if (isSorted) {
SampleNode * nodes = new SampleNode[seqCount];
int count = 0;
for (int i = 0; i < seqCount; i += step) {
nodes[count].id = count;
nodes[count].size = seqLen[i];
count++;
}
delete[] nodes;
}
return lineCount;
}
......@@ -438,7 +449,7 @@ int T2TTrainer::LoadBatch(FILE * file, bool isLM,
int devID, XMem * mem)
{
if(nextSeq < 0 || nextSeq >= nseqBuf)
LoadBuf(file);
LoadBuf(file, isSorted);
int seq = MAX(nextSeq, 0);
int wc = 0;
......
source/sample/transformer/T2TTrainer.h
......@@ -118,7 +118,7 @@ public:
void Test(const char * fn, const char * ofn, T2TModel * model);
/* load data to buffer */
int LoadBuf(FILE * file);
int LoadBuf(FILE * file, bool isSorted, int step);
/* clear data buffer */
void ClearBuf();
......
source/tensor/XMem.cpp
......@@ -747,6 +747,64 @@ void * XMem::AllocStandard(int myDevID, MTYPE mySize, bool myIsRebuiltIndex)
CheckNTErrors(nodeNumUsed < nodeNum, "No enough index nodes for the memory pool!");
}
/*if(testxmemid == 30){
recordp = result;
}
if(curBlockID >= 25){
MHeader * head = blocks[25].head;
while(head != NULL){
fprintf(stderr, "head: %ld %ld\n", head->indexNode->pReal, head->indexNode->size);
head = head->next;
}
}
if(testxmemid == 32){
int nnn = 0;
}
if(recordp != NULL){
MTYPE size = mySize;
if(size <= minSizeIndex[0])
size = minSizeIndex[0];
MPieceNode * entry = NULL;
MPieceNode * node = NULL;
MPieceNode * hit = NULL;
MPieceNode * last = NULL;
entry = memIndex + indexEntryNum + FindIndexEntry(size);
last = entry;
node = entry->next;
while(node != NULL){
CheckNTErrors(node->pre == last, "Something is wrong!");
CheckNTErrors(last->next == node, "Something is wrong!");
CheckNTErrors(node->head.state == 2, "Something is wrong!");
last = node;
if(node->size == 0){
MPieceNode * next = node->next;
RemoveFreeIndexNode(node, entry);
node = next;
ShowNTErrors("Something is wrong!");
}
else{
CheckNTErrors(node->pReal != NULL, "Illegal pointer!");
if(node->pReal == recordp){
hit = node;
break;
}
node = node->next;
}
}
if(hit == NULL){
int nnn = 0;
}
}*/
return result;
}
......@@ -918,6 +976,8 @@ void XMem::ReleaseStandard(int myDevID, void * p, MTYPE size)
hit->head.state = 1;
RemoveAllocIndexNode(hit);
hit->size = (char*)hit->p + hit->head.size - (char*)GetPitchedAddress((char*)hit->p, MY_PITCH);
AddFreeIndexNode(hit);
blocks[hit->head.blockID].used -= hit->head.size;
......@@ -981,8 +1041,9 @@ void XMem::RebuildIndex()
/* make a new index node */
MPieceNode * newNode = memIndex2 + nodeNumUsed2++;
newNode->p = p;
newNode->size = (char*)p + head->size -
( head->state == 1 ? (char*)GetPitchedAddress((char*)p, MY_PITCH) : (char*)head->indexNode->pReal);
newNode->size = node->size;
//newNode->size = (char*)p + head->size -
// ( head->state == 1 ? (char*)GetPitchedAddress((char*)p, MY_PITCH) : (char*)head->indexNode->pReal);
newNode->pre = NULL;
newNode->next = NULL;
......
source/tensor/XTensor.cpp
......@@ -553,10 +553,16 @@ void XTensor::SetZeroAll(XStream * stream)
#ifdef USE_CUDA
int size = sizeof(int) + (sizeof(int)+sizeof(DTYPE)) * unitNumNonZero;
int devIDBackup = 0;
cudaGetDevice(&devIDBackup);
cudaSetDevice(devID);
if(stream == NULL)
cudaMemset(data, 0, size);
else
cudaMemsetAsync(data, 0, size, stream->stream);
cudaSetDevice(devIDBackup);
#endif
}
else
......@@ -567,10 +573,16 @@ void XTensor::SetZeroAll(XStream * stream)
else{
if(devID >= 0){
#ifdef USE_CUDA
int devIDBackup = 0;
cudaGetDevice(&devIDBackup);
cudaSetDevice(devID);
if(stream == NULL)
cudaMemset(data, 0, unitNum * unitSize);
else
cudaMemsetAsync(data, 0, unitNum * unitSize, stream->stream);
cudaSetDevice(devIDBackup);
#endif
}
else
......
source/tensor/core/arithmetic/Multiply.cu
......@@ -171,14 +171,12 @@ void _CudaMultiply(const XTensor * a, const XTensor * b, XTensor * c, DTYPE alph
if (alpha == 0) {
KernelMulElementWiseTensorDynamic<0> << <blocks, threads >> >
((DTYPE*)a->data, (DTYPE*)b->data, (DTYPE*)c->data, 0,
stride, dimensionSizeA, dimensionSizeB, dimensionSizeC,
blockNum);
stride, dimensionSizeA, dimensionSizeB, dimensionSizeC, blockNum);
}
else {
KernelMulElementWiseTensorDynamic<1> << <blocks, threads >> >
((DTYPE*)a->data, (DTYPE*)b->data, (DTYPE*)c->data, alpha,
stride, dimensionSizeA, dimensionSizeB, dimensionSizeC,
blockNum);
stride, dimensionSizeA, dimensionSizeB, dimensionSizeC, blockNum);
}
}
}
......
source/tensor/function/Dropout.cpp
......@@ -25,120 +25,59 @@
#include "Dropout.h"
#include "Dropout.cuh"
#include "../core/arithmetic/Multiply.h"
#include "../core/arithmetic/MultiplyDim.h"
#include "../core/math/ScaleAndShift.h"
namespace nts{ // namespace nts(NiuTrans.Tensor
/*
generate a random bernoulli number
*/
DTYPE RandomBernoulli(DTYPE prob)
{
return (DTYPE)rand()/(DTYPE)RAND_MAX > prob ? (DTYPE)1.0 : (DTYPE)0.0;
}
/*
dropout function
It randomly zeroes some of the elements of the input tensor
with probability p via a Bernoulli distribution.
During training, randomly zeroes some of the elements of the input tensor
with probability p using samples from a Bernoulli distribution.
The elements to zero are randomized on every forward call.
See "Improving neural networks by preventing co-adaptation of feature detectors"
for more details.
This has proven to be an effective technique for regularization and
preventing the co-adaptation of neurons as described in the paper
"Improving neural networks by preventing co-adaptation of feature detectors".
Here, the output is scaled by a factor of \frac{1}{1-p} so that we do not need
to mark the tensor with probability p in the inference phase. Instead we perform
the same inference procedure as that with no use of dropout on the test data.
Furthermore, the outputs are scaled by a factor of \frac{1}{1-p} during training.
This means that during evaluation the module simply computes an identity function.
>> x - input tensor
>> y - output tensor
>> prob - probability to set an element zero
>> seed - random seed
>> dropProb - probability to set an element to zero
>> leadingDim - the dimension which we generate the random numbers and perform broadcasting
*/
void _Dropout(const XTensor *x, XTensor *y, unsigned int seed, DTYPE prob)
void _Dropout(const XTensor * x, XTensor * y, unsigned int seed, DTYPE dropProb, int leadingDim)
{
CheckNTErrors(prob >= 0.0 && prob <= 1.0, "The probability must be 0-1!");
DTYPE scaleFactor = (DTYPE)1.0 / ((DTYPE)1.0 - prob);
/* generate a mask tensor again with special probability */
srand(seed);
int unitNum = x->unitNum;
DTYPE * maskArray = new DTYPE[unitNum];
for (int i = 0; i < unitNum; i++)
maskArray[i] = RandomBernoulli(prob);
XTensor * maskTensor = NewTensorBuf(x, x->devID, x->mem);
maskTensor->SetData(maskArray, unitNum);
#ifdef USE_CUDA
if(x->devID >=0 || y->devID >= 0){
_CudaDropout(x, y, maskTensor, scaleFactor);
DelTensorBuf(maskTensor);
delete[] maskArray;
return;
}
#endif
XTensor * inter = NewTensorBuf(x, x->devID, x->mem);
_Multiply(x, maskTensor, inter);
_ScaleAndShift(inter, y, scaleFactor, 0);
DelTensorBuf(inter);
DelTensorBuf(maskTensor);
delete[] maskArray;
}
CheckNTErrors(dropProb >= 0.0 && dropProb <= 1.0, "The probability must be 0-1!");
/*
dropout function (return a XTensor structure)
make a new tensor to keep the result and return it
int n = leadingDim < 0 ? x->order - 1 : leadingDim;
During training, randomly zeroes some of the elements of the input tensor
with probability p using samples from a Bernoulli distribution.
The elements to zero are randomized on every forward call.
CheckNTErrors(n >= 0 && n < x->order, "Wrong leadingDim!");
This has proven to be an effective technique for regularization and
preventing the co-adaptation of neurons as described in the paper
"Improving neural networks by preventing co-adaptation of feature detectors".
Furthermore, the outputs are scaled by a factor of \frac{1}{1-p} during training.
This means that during evaluation the module simply computes an identity function.
>> x - input tensor
>> y - output tensor
>> prob - probability to set an element zero
*/
XTensor Dropout(const XTensor &x, DTYPE prob)
{
XTensor y(&x);
y.SetTMP();
DTYPE scaleFactor = (DTYPE)1.0 / ((DTYPE)1.0 - prob);
DTYPE scaleFactor = (DTYPE)1.0 / ((DTYPE)1.0 - dropProb);
/* generate a mask tensor again with special probability */
srand((unsigned int)time(NULL));
int unitNum = x.unitNum;
int unitNum = x->dimSize[n];
DTYPE * maskArray = new DTYPE[unitNum];
srand(seed);
for (int i = 0; i < unitNum; i++)
maskArray[i] = RandomBernoulli(prob);
maskArray[i] = RandomBernoulli(dropProb, scaleFactor);
XTensor maskTensor(&x);
maskTensor.SetData(maskArray, unitNum);
XTensor * mask = NewTensor1D(unitNum, x->dataType, x->devID, x->mem);
mask->SetData(maskArray, unitNum);
XTensor inter;
inter = Multiply(x, maskTensor);
y = ScaleAndShift(inter, scaleFactor, 0);
/* call Multiply function for mask */
_MultiplyDim(x, mask, y, n, 0);
delete mask;
delete[] maskArray;
///* tensor connection */
//XLink::MakeLink(&x, NULL, &y, FUNC_DROPOUT);
//XLink::AddParamToHead(&y, prob);
return y;
}
/*
backward computation of dropout function
backward computation of the dropout function
dE/dx = dE/dy * dy/dx
......@@ -146,48 +85,86 @@ dE/dx = dE/dy * dy/dx
>> x - input of the dropout function
>> dedy - dE/dy
>> dedx - dE/dx
>> prob - probability to set an element zero
>> seed - random seed
>> dropProb - probability to set an element to zero
>> leadingDim - the dimension which we generate the random numbers and perform broadcasting
*/
void _DropoutBackward(const XTensor * y, const XTensor * x,
const XTensor * dedy, XTensor * dedx,
unsigned int seed, DTYPE prob)
unsigned int seed, DTYPE dropProb, int leadingDim)
{
CheckNTErrors(dropProb >= 0.0 && dropProb <= 1.0, "The probability must be 0-1!");
int n = leadingDim < 0 ? x->order - 1 : leadingDim;
CheckNTErrors(n >= 0 && n < x->order, "Wrong leadingDim!");
if(x->dataType == DEFAULT_DTYPE && y->dataType == DEFAULT_DTYPE)
{
int unitNum = y->unitNum;
DTYPE scaleFactor = (DTYPE)1.0F / ((DTYPE)1.0F - prob);
DTYPE scaleFactor = (DTYPE)1.0F / ((DTYPE)1.0F - dropProb);
/* generate a mask tensor again with special probability */
srand(seed);
int unitNum = x->dimSize[n];
DTYPE * maskArray = new DTYPE[unitNum];
srand(seed);
for (int i = 0; i < unitNum; i++)
maskArray[i] = RandomBernoulli(prob);
maskArray[i] = RandomBernoulli(dropProb, scaleFactor);
XTensor * maskTensor = NewTensorBuf(x, x->devID, x->mem);
maskTensor->SetData(maskArray, unitNum);
XTensor * mask = NewTensor1D(unitNum, x->dataType, x->devID, x->mem);
mask->SetData(maskArray, unitNum);
#ifdef USE_CUDA
if(x->devID >= 0 || y->devID >= 0){
_CudaDropoutBackward(y, x, dedy, dedx, maskTensor, scaleFactor);
/* call MultiplyDim function for mask */
_MultiplyDim(dedy, mask, dedx, n, 0);
DelTensorBuf(maskTensor);
delete mask;
delete[] maskArray;
return;
}
#endif
else
ShowNTErrors("TODO!");
}
/*
dropout function (we make tensor connections here)
It randomly zeroes some of the elements of the input tensor
with probability p via a Bernoulli distribution.
See "Improving neural networks by preventing co-adaptation of feature detectors"
for more details.
Here, the output is scaled by a factor of \frac{1}{1-p} so that we do not need
to mark the tensor with probability p in the inference phase. Instead we perform
the same inference procedure as that with no use of dropout on the test data.
DTYPE * dedyp = (DTYPE*)dedy->data;
DTYPE * dedxp = (DTYPE*)dedx->data;
>> x - input tensor
>> dropProb - probability to set an element to zero
>> leadingDim - the dimension which we generate the random numbers and perform broadcasting
*/
XTensor Dropout(const XTensor &x, DTYPE dropProb, int leadingDim)
{
CheckNTErrors(dropProb >= 0.0 && dropProb <= 1.0, "The probability must be 0-1!");
int n = leadingDim < 0 ? x.order - 1 : leadingDim;
/* dE/dx = dE/dy * dy/dx */
for(int i = 0; i < unitNum; i++)
dedxp[i] = dedyp[i] * maskArray[i] * scaleFactor;
CheckNTErrors(n >= 0 && n < x.order, "Wrong leadingDim!");
DTYPE scaleFactor = (DTYPE)1.0 / ((DTYPE)1.0 - dropProb);
/* generate a mask tensor with probability p */
int unitNum = x.dimSize[n];
DTYPE * maskArray = new DTYPE[unitNum];
srand((unsigned int)time(NULL));
for (int i = 0; i < unitNum; i++)
maskArray[i] = RandomBernoulli(dropProb, scaleFactor);
XTensor mask;
InitTensor1D(&mask, unitNum, x.dataType, x.devID, x.mem);
mask.SetData(maskArray, unitNum);
DelTensorBuf(maskTensor);
delete[] maskArray;
}
else
ShowNTErrors("TODO!");
return MultiplyDim(x, mask, n, 0);
}
} // namespace nts(NiuTrans.Tensor)
\ No newline at end of file
source/tensor/function/Dropout.h
......@@ -27,16 +27,22 @@
namespace nts{ // namespace nts(NiuTrans.Tensor)
/* dropout function */
void _Dropout(const XTensor * x, XTensor * y, unsigned int seed, DTYPE prob = 0.5);
/* generate a random bernoulli number */
inline DTYPE RandomBernoulli(DTYPE dropProb, DTYPE value)
{
return (DTYPE)rand()/(DTYPE)RAND_MAX >= dropProb ? (DTYPE)value : 0;
}
/* dropout function */
XTensor Dropout(const XTensor &x, DTYPE prob = 0.5);
void _Dropout(const XTensor * x, XTensor * y, unsigned int seed, DTYPE dropProb, int leadingDim = -1);
/* de/dx */
void _DropoutBackward(const XTensor * y, const XTensor * x,
const XTensor * dedy, XTensor * dedx,
unsigned int seed, DTYPE prob = 0.5);
unsigned int seed, DTYPE dropProb, int leadingDim = -1);
/* dropout function */
XTensor Dropout(const XTensor &x, DTYPE dropProb, int leadingDim = -1);
} // namespace nts(NiuTrans.Tensor)
......
source/tensor/function/FHeader.h
......@@ -26,6 +26,7 @@
#include "../XTensor.h"
#include "Dropout.h"
#include "HardTanH.h"
#include "Identity.h"
#include "LogSoftmax.h"
......
source/tensor/test/TDropout.cpp
......@@ -31,10 +31,11 @@ case 1: test Dropout function.
bool TestDropout1()
{
/* a input tensor of size (4, 5) */
int order = 2;
int order = 3;
int * dimSize = new int[order];
dimSize[0] = 40;
dimSize[1] = 50;
dimSize[2] = 60;
int unitNum = 1;
for (int i = 0; i < order; i++)
......@@ -49,14 +50,14 @@ bool TestDropout1()
XTensor yUser;
/* initialize variables */
x->SetDataRand(0, 1);
_SetDataFixedFloat(x, 1.0F);
y->SetZeroAll();
/* call Dropout function */
float prob = 0.2F;
float drop_prob = 0.2F;
int seed = 20;
_Dropout(x, y, seed, prob);
yUser = Dropout(*x);
_Dropout(x, y, seed, drop_prob);
yUser = Dropout(*x, drop_prob);
/* check result */
int zeroNum1 = 0;
......@@ -73,9 +74,9 @@ bool TestDropout1()
}
printf("CPU Test:\n");
printf("In tensor y, there are %d units.\n", unitNum);
printf("There are %d zero units by Dropout layer with probability %.2f.\n", zeroNum1, prob);
printf("There are %d zero units by Dropout layer with probability %.2f.\n", zeroNum1, drop_prob);
printf("In tensor yUser, there are %d units.\n", unitNum);
printf("There are %d zero units by Dropout layer with default probability %.2f.\n", zeroNum2, 0.5F);
printf("There are %d zero units by Dropout layer with default probability %.2f.\n", zeroNum2, drop_prob);
#ifdef USE_CUDA
/* GPU test */
......@@ -87,12 +88,12 @@ bool TestDropout1()
XTensor yUserGPU;
/* initialize variables */
xGPU->SetDataRand(0, 1);
_SetDataFixedFloat(xGPU, 1.0F);
yGPU->SetZeroAll();
/* call Dropout function */
_Dropout(xGPU, yGPU, seed, prob);
yUserGPU = Dropout(*xGPU);
_Dropout(xGPU, yGPU, seed, drop_prob);
yUserGPU = Dropout(*xGPU, drop_prob);
/* check result */
zeroNum1 = 0;
......@@ -109,9 +110,9 @@ bool TestDropout1()
}
printf("CPU Test:\n");
printf("In tensor y, there are %d units.\n", unitNum);
printf("There are %d zero units by Dropout layer with probability %.2f.\n", zeroNum1, prob);
printf("There are %d zero units by Dropout layer with probability %.2f.\n", zeroNum1, drop_prob);
printf("In tensor yUser, there are %d units.\n", unitNum);
printf("There are %d zero units by Dropout layer with default probability %.2f.\n", zeroNum2, 0.5F);
printf("There are %d zero units by Dropout layer with default probability %.2f.\n", zeroNum2, drop_prob);
/* destroy variables */
delete x;
......@@ -159,13 +160,13 @@ bool TestDropout2()
_SetDataFixedFloat(x, 1.0F);
y->SetZeroAll();
dedx->SetZeroAll();
_SetDataFixedFloat(dedy, 1.0F);
_SetDataFixedFloat(dedy, 1.5F);
/* call Dropout function */
float prob = 0.5F;
float drop_prob = 0.5F;
int seed = 1;
_Dropout(x, y, seed, prob);
_DropoutBackward(y, x, dedy, dedx, 1, prob);
_Dropout(x, y, seed, drop_prob);
_DropoutBackward(y, x, dedy, dedx, 1, drop_prob);
/* check result */
y->Dump(stderr, "y");
......@@ -185,11 +186,11 @@ bool TestDropout2()
_SetDataFixedFloat(xGPU, 1.0F);
yGPU->SetZeroAll();
dedxGPU->SetZeroAll();
_SetDataFixedFloat(dedyGPU, 1.0F);
_SetDataFixedFloat(dedyGPU, 1.5F);
/* call Dropout function */
_Dropout(xGPU, yGPU, seed, prob);
_DropoutBackward(yGPU, xGPU, dedyGPU, dedxGPU, 1, prob);
_Dropout(xGPU, yGPU, seed, drop_prob);
_DropoutBackward(yGPU, xGPU, dedyGPU, dedxGPU, 1, drop_prob);
/* check result */
yGPU->Dump(stderr, "yGPU");
......
source/tensor/test/TXMem.cpp
......@@ -65,9 +65,10 @@ bool TestXMemCase1()
for (int i = 0; i < testNum * scalar; i++) {
testxmemid++;
//fprintf(stderr, "%d %d\n", testxmemid, ok);
int j = rand() % caseNum;
//fprintf(stderr, "%d %d %d\n", testxmemid, j, ok);
if (p[j] == NULL) {
p[j] = (int*)mem.AllocStandard(mem.devID, size[j] * sizeof(int));
for (int k = 0; k < size[j]; k++)
......
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