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NiuTrans.Tensor
Commit 58181c8d by liyinqiao
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1. Clean the codes; 

2. Merge with Xiao branch.
parent d1714e17
正在显示 包含 72 行增加 和 369 行删除
source/sample/transformer/T2TBatchLoader.cpp
......@@ -166,6 +166,8 @@ int T2TBatchLoader::LoadBuf(FILE * file, bool isSorted, int step)
if(wordCount >= bufSize - MAX_SEQUENCE_LENGTH)
break;
CheckNTErrors(seqCount % step == 0, "Wrong number of sequences!");
}
nseqBuf = seqCount;
......
source/sample/transformer/T2TSearch.cpp
......@@ -293,10 +293,10 @@ void T2TSearch::Generate(T2TStateBundle * beam)
CopyValues(index, preID);
/* "preID" represents the id (or the offset) of previous state used to make the current
/* "preID" represents the id (or the offset) of the previous state used to make the current
hypothesis. Note that we reshape the "score" tensor into a matrix where each
row means a previous state. The column number is size-of-beam * vocab-size. We,
therefore, divide entries of the top-k index by vocab-size to compute the id of
row means a previous state. The column number is size-of-beam \times vocab-size. We,
therefore, divide entries of the top-k index by vocab-size to compute the id of the
previous state for each hypothesis in the top-k list. */
Descale(preID, sizeVocab);
......
source/tensor/XDevice.cpp
......@@ -201,7 +201,8 @@ void XDevice::SetGPUDevice(int devID)
cudaError_t error = cudaSetDevice(devID);
if (error != cudaSuccess){
fprintf(stderr, "Error! Calling cudaSetDevice(%d) fails(%d:%s)\n", devID, error, cudaGetErrorString(error));
fprintf(stderr, "Error! Calling cudaSetDevice(%d) fails(%d:%s)\n",
devID, error, cudaGetErrorString(error));
exit(1);
}
#else
......@@ -216,7 +217,7 @@ void XDevice::SetGPUDeviceFast(int devID)
SetFastFlags();
}
/* switch to a get current dev */
/* get the id of the current GPU device */
int XDevice::GetGPUDevice()
{
#ifdef USE_CUDA
......@@ -224,7 +225,8 @@ int XDevice::GetGPUDevice()
cudaError_t error = cudaGetDevice(&devID);
if (error != cudaSuccess){
fprintf(stderr, "Error! Calling cudaGetDevice(%d) fails(%d:%s)\n", devID, error, cudaGetErrorString(error));
fprintf(stderr, "Error! Calling cudaGetDevice(%d) fails(%d:%s)\n",
devID, error, cudaGetErrorString(error));
exit(1);
}
......@@ -248,7 +250,7 @@ void XDevice::SetFastFlags()
#endif
}
/* reset cuda flag for more efficient cuda execution (all devices) */
/* reset the cuda flag for more efficient cuda execution (all devices) */
void XDevice::SetFastFlagsAllDevices()
{
#ifdef USE_CUDA
......@@ -274,7 +276,7 @@ XDevManager::~XDevManager()
}
/* initialize it and get the CPU and GPU information */
/* initialization */
void XDevManager::Init()
{
srand((unsigned int)time(NULL));
......@@ -318,7 +320,7 @@ void XDevManager::Clear()
#ifdef USE_CUDA
/* get the handle of GPU */
/* get the handle of a given GPU */
cublasHandle_t * XDevManager::GetCudaHandle(const int devID)
{
CheckNTErrors(devID < nGPU, "index of GPU is out of range.");
......@@ -326,7 +328,7 @@ cublasHandle_t * XDevManager::GetCudaHandle(const int devID)
return GPUs[devID].GetCublasHandle();
}
/* get the stream of cuda */
/* get the stream of a given GPU */
cudaStream_t * XDevManager::GetCudaStream(const int devID)
{
CheckNTErrors(devID < nGPU, "index of GPU is out of range.");
......@@ -523,7 +525,7 @@ get device ids for the given device information
devInfo = "0:CPU-1 1:GPU-0 2:CPU-1"
means that the first device is CPU, the second device
is GPU-0, the third device is CPU.
>> devIDs - device sequence specified by devInfo
>> devIDs - device IDs specified by devInfo
<< return - number of devices
*/
int XDevManager::GetDeviceIDs(char * devInfo, int * devIDs)
......@@ -565,7 +567,7 @@ int XDevManager::GetDeviceIDs(char * devInfo, int * devIDs)
return devCount;
}
/* show id sequence */
/* show device IDs */
void XDevManager::ShowDeviceIDs(char * devInfo, char * msg)
{
msg[0] = 0;
......
source/tensor/XMem.cpp
......@@ -63,7 +63,7 @@ constructor
>> myMode - mode of running the memory pool
UNI_FREE: free all the space at the end of using the memory pool
FREE_ON_THE_FLY: normal "malloc" and "free" mode
>> myBlockSize - size of memory block
>> myBlockSize - size of a memory block
>> myBlockNum - number of memory blocks
>> myBufSize - size of buffer
*/
......@@ -108,7 +108,7 @@ initialize it
>> myMode - mode of running the memory pool
UNI_FREE: free all the space at the end of using the memory pool
FREE_ON_THE_FLY: normal "malloc" and "free" mode
>> myBlockSize - size of memory block
>> myBlockSize - size of a memory block
>> myBlockNum - number of memory blocks
>> myBufSize - size of buffer
*/
......@@ -222,8 +222,8 @@ void XMem::Free(int myDevID, void * mem)
}
/*
get signature
<< return - return the signature
get the signature
<< return - the signature
*/
MTYPE XMem::GetSignature()
{
......@@ -231,7 +231,7 @@ MTYPE XMem::GetSignature()
}
/*
use string as the name of the memory pool
set the name of the memory pool
>> myName - name of the memory pool
*/
void XMem::SetName(const char * myName)
......@@ -264,7 +264,7 @@ void XMem::SetDevice(int myDevID)
}
/*
switch to the device (with fast cuda execution mode) we want to work
switch to the device (with fast cuda execution mode) we intend to work on
>> myDevID - device id(-1: CPU memory, >=0: GPU device ID)
*/
void XMem::SetDeviceFast(int myDevID)
......@@ -280,7 +280,7 @@ void XMem::SetDeviceFast(int myDevID)
}
/*
run in static mode
run in the static mode
>> myIsStatic - specify if the memory allocation is static
*/
void XMem::SetStaticMode(bool myIsStatic)
......
source/tensor/XQueue-李垠桥的MacBook Pro.cpp deleted 100644 → 0
/* NiuTrans.Tensor - an open-source tensor library
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, 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.
*/
/*
*
* This is an implementation of queue. Actually we intend to use it to maintain
* a priority job list
*
* $Created by: XIAO Tong (xiaotong@mail.neu.edu.cn) 2017-04-05
*
*/
#include <stdio.h>
#include <stdlib.h>
#include "XQueue.h"
#include "XDevice.h"
#include "XList.h"
#include "XUtility.h"
/* the nts (NiuTrans.Tensor) namespace */
namespace nts{
/**************************************
job item used in queues
*/
/* constructor */
JobQueueNode::JobQueueNode()
{
job = NULL;
args = new TensorList(1);
}
/* de-constructor */
JobQueueNode::~JobQueueNode()
{
delete args;
}
/**************************************
This class provides standard utilities of Queue.
*/
/* constuctor */
XQueue::XQueue(int mySize)
{
queue = new void*[mySize];
memset(queue, 0, sizeof(void*) * mySize);
size = mySize;
itemCount = 0;
head = 0;
tail = 0;
isJobQueue = false;
jobDequeuerArgs = new TensorList(1);
jobDequeuerBreak = false;
runningJobCount = 0;
jobStream = NULL;
jobStream1 = NULL;
jobStream2 = NULL;
MUTEX_INIT(enqueueMutex);
MUTEX_INIT(dequeueMutex);
COND_INIT(queueCond);
MUTEX_INIT(jobQueueMutex);
}
/* deconstructor */
XQueue::~XQueue()
{
delete[] queue;
delete jobDequeuerArgs;
delete jobStream;
delete jobStream1;
delete jobStream2;
//if(isJobQueue)
// StopJobConsumer();
MUTEX_DELE(enqueueMutex);
MUTEX_DELE(dequeueMutex);
COND_DELE(queueCond);
MUTEX_DELE(jobQueueMutex);
}
/*
put an item in the tail of the queue
>> item - the item we intend to add into the queue
*/
void XQueue::Enqueue(void * item)
{
MUTEX_LOCK(enqueueMutex);
MUTEX_LOCK(dequeueMutex);
CheckNTErrors((itemCount < size), "Put too many items into the queue!");
queue[tail] = item;
tail = (tail + 1) % size;
itemCount++;
COND_SIGNAL(queueCond);
MUTEX_UNLOCK(dequeueMutex);
MUTEX_UNLOCK(enqueueMutex);
}
/*
fetch an item from head of the queue
<< return - the head item of the queue
*/
void * XQueue::Dequeue()
{
MUTEX_LOCK(dequeueMutex);
while(itemCount == 0)
{
#ifdef WIN32
MUTEX_UNLOCK(dequeueMutex);
#endif
COND_WAIT(queueCond, dequeueMutex);
#ifdef WIN32
MUTEX_LOCK(dequeueMutex);
#endif
}
void * r = queue[head];
head = (head + 1) % size;
itemCount--;
MUTEX_UNLOCK(dequeueMutex);
return r;
}
/* return if the queue is empty */
bool XQueue::IsEmpty()
{
return itemCount == 0;
}
/* wait until the queue is empty */
void XQueue::WaitForEmptyJobQueue()
{
while(runningJobCount > 0){
XSleep(10);
}
if(jobStream != NULL){
CheckNTErrors((jobStream->IsFinished()), "None fineished jobs remain");
jobStream->Clear();
}
if(jobStream1 != NULL){
CheckNTErrors((jobStream1->IsFinished()), "None fineished jobs remain");
jobStream1->Clear();
}
if(jobStream2 != NULL){
CheckNTErrors((jobStream2->IsFinished()), "None fineished jobs remain");
jobStream2->Clear();
}
}
int devids[16] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15};
int cpuid = -1;
/*
run job consumer (in another thread)
>> jobDevID - id of the device for running the jobs
*/
void XQueue::RunJobConsumer(int jobDevID)
{
CheckNTErrors((jobDevID < 16), "device id is out of scope!");
isJobQueue = true;
jobDequeuerArgs->Clear();
jobDequeuerArgs->Add(this);
jobDequeuerArgs->Add(jobDevID >= 0 ? devids + jobDevID : &cpuid);
jobDequeuer.function = (TFunction)DequeueJobs;
jobDequeuer.argv = jobDequeuerArgs;
jobDequeuer.Start();
jobDequeuer.LetItGo();
}
/* stop the job consumer */
void XQueue::StopJobConsumer()
{
jobDequeuerBreak = true;
XSleep(10);
EnqueueJob(NULL, NULL);
jobDequeuer.End();
isJobQueue = false;
}
/* add a job item to process */
void XQueue::EnqueueJob(void * job, TensorList * jobArgs)
{
MUTEX_LOCK(jobQueueMutex);
runningJobCount++;
MUTEX_UNLOCK(jobQueueMutex);
JobQueueNode * node = new JobQueueNode();
node->job = job;
if(jobArgs != NULL)
node->args->AddList(jobArgs);
Enqueue(node);
}
/* job item consumer */
void XQueue::DequeueJobs(TensorList * args)
{
CheckNTErrors((args->count == 2), "Illegal arguments!");
XQueue * q = (XQueue*)args->GetItem(0);
int devID = *(int*)args->GetItem(1);
int devIDBackup = XDevice::GetGPUDevice();
if(devID >= 0)
XDevice::SetGPUDevice(devID);
while(1){
JobQueueNode * node = (JobQueueNode*)q->Dequeue();
if(q->GetJobBreak())
break;
CheckNTErrors((node != NULL), "Illegal job!");
/* process a job */
((TFunction)node->job)(node->args);
delete node;
MUTEX_LOCK(q->jobQueueMutex);
q->runningJobCount--;
MUTEX_UNLOCK(q->jobQueueMutex);
}
if(devID >= 0)
XDevice::SetGPUDevice(devIDBackup);
}
/* get the break flag */
bool XQueue::GetJobBreak()
{
return jobDequeuerBreak;
}
/* get job stream */
XStream * XQueue::GetJobStream(int n)
{
if(n == 0)
return jobStream;
else if(n == 1)
return jobStream1;
else if(n == 2)
return jobStream2;
else{
ShowNTErrors("invalid stream id!");
}
return NULL;
}
/* make job streams */
void XQueue::MakeJobStreams(int devID, int devID1, int devID2)
{
if(devID != INVALID_DEVICE_ID)
jobStream = new XStream(0, devID);
if(devID1 != INVALID_DEVICE_ID)
jobStream1 = new XStream(0, devID1);
if(devID2 != INVALID_DEVICE_ID)
jobStream2 = new XStream(0, devID2);
}
} /* end of the nts (NiuTrans.Tensor) namespace */
source/tensor/XTensor.cpp
......@@ -81,11 +81,7 @@ int MakeTensorID()
return id;
}
/*
constructor
>> myOrder - order of the tensor
>> myMem - memory pool used to allocating the data array
*/
/* constructor */
XTensor::XTensor()
{
Init();
......@@ -130,9 +126,9 @@ XTensor::XTensor(const int myOrder, int myDevID, XMem * myMem)
/*
constructor
>> myOrder - order of the tensor
>> myDimSize - the size of each dimension
>> myDimSize - size of each dimension
>> myDataType - unit size (e.g., int, float, and double)
>> myDenseRatio - how often an element has non-zero value
>> myDenseRatio - how often an element has a non-zero value
>> myDevID - device id
>> myMem - memory pool used to allocating the data array
*/
......@@ -168,10 +164,10 @@ XTensor::XTensor(const XTensor &reference)
signature = reference.signature;
/* what we really want to do is "reference.data = NULL;"
As "reference" is constant, we cannot reset reference.data
here. So we save the ADDRESS of reference.data in
reference.dataP, and do this work by updating "*reference.dataP".
This is VERY tricky and might not be the best solution :) */
As "reference" is constant, we cannot reset "reference.data"
here. So we save the ADDRESS of "reference.data" in
"reference.dataP", and do this work by updating "*reference.dataP".
This is VERY tricky and there might be better solutions :) */
*reference.dataP = NULL;
}
else{
......@@ -208,10 +204,10 @@ XTensor::XTensor(const XTensor &&reference)
signature = reference.signature;
/* what we really want to do is "reference.data = NULL;"
As "reference" is constant, we cannot reset reference.data
here. So we save the ADDRESS of reference.data in
reference.dataP, and do this work by updating "*reference.dataP".
This is VERY tricky and might not be the best solution :) */
As "reference" is constant, we cannot reset "reference.data"
here. So we save the ADDRESS of "reference.data" in
"reference.dataP", and do this work by updating "*reference.dataP".
This is VERY tricky and there might be better solutions :) */
*reference.dataP = NULL;
XLink::Replace(&reference, this);
......@@ -305,7 +301,7 @@ void XTensor::DestroyData()
}
/*
shallow copy of tensor
shallow copy of the tensor
Note that we do not copy data array here
>> tensor - the source tensor
*/
......@@ -353,7 +349,7 @@ XTensor& XTensor::operator= (const XTensor& tensor)
}
if(false && !tensor.isTmp){
/* NOTE: this might lead to additional data copy on Mac machines */
/* NOTE: this might lead to additional data copy by Mac LLVM compilers */
/* we make an identity transformation here */
if(outgo.tailNum > 0)
......@@ -440,10 +436,10 @@ XTensor& XTensor::operator= (const XTensor&& tensor)
signature = tensor.signature;
/* what we really want to do is "reference.data = NULL;"
As "reference" is constant, we cannot reset reference.data
here. So we save the ADDRESS of reference.data in
reference.dataP, and do this work by updating "*reference.dataP".
This is VERY tricky and might not be the best solution :) */
As "reference" is constant, we cannot reset "reference.data"
here. So we save the ADDRESS of "reference.data" in
"reference.dataP", and do this work by updating "*reference.dataP".
This is VERY tricky and there might be better solutions :) */
*tensor.dataP = NULL;
XLink::Replace(&tensor, this);
......@@ -526,7 +522,7 @@ void XTensor::SetDevice(int myDevId, XMem * myMem)
}
/*
judge whether the two matrices are in the same type and size
check whether the two matrices are in the same type and size
>> a - input tensor
>> b - anther tensor to compare with
<< return - whether the two input tensors are identical
......@@ -556,6 +552,18 @@ bool XTensor::IsSameShaped(const XTensor * a, const XTensor * b)
return true;
}
/*
check whether the three matrices are in the same type and size
>> a - input tensor
>> b - anther tensor to compare with
>> c - a tensor again
<< return - whether the two input tensors are identical
*/
bool XTensor::IsSameShaped(const XTensor * a, const XTensor * b, const XTensor * c)
{
return IsSameShaped(a, b) && IsSameShaped(a, c);
}
bool XTensor::IsReduceShaped(const XTensor * a, const XTensor * b, int dim)
{
if (a == NULL || b == NULL)
......@@ -588,18 +596,6 @@ bool XTensor::IsReduceShaped(const XTensor * a, const XTensor * b, int dim)
}
/*
judge whether the three matrices are in the same type and size
>> a - input tensor
>> b - anther tensor to compare with
>> c - a tensor again
<< return - whether the two input tensors are identical
*/
bool XTensor::IsSameShaped(const XTensor * a, const XTensor * b, const XTensor * c)
{
return IsSameShaped(a, b) && IsSameShaped(a, c);
}
/*
set the size of each dimension
>> myDimSize - size of each dimension
*/
......@@ -630,7 +626,7 @@ int XTensor::GetDim(const int dim) const
/*
reshape the tensor
>> myOrder - order of the tensor
>> myDimSize - the size of each dimension
>> myDimSize - size of each dimension
*/
void XTensor::Reshape(const int myOrder, const int * myDimSize)
{
......@@ -652,7 +648,7 @@ void XTensor::Reshape(const int myOrder, const int * myDimSize)
}
/*
reshape the tensor to a vector
reshape the tensor into a vector
>> num - number of elements
*/
void XTensor::Reshape(const int num)
......@@ -662,7 +658,7 @@ void XTensor::Reshape(const int num)
}
/*
reshape the tensor to a matrix
reshape the tensor into a matrix
>> rowNum - number of rows
>> colNum - number of columns
*/
......@@ -708,7 +704,7 @@ int XTensor::GetSize() const
return unitNum;
}
/* get size of the memory used */
/* get the size of the memory space used */
int XTensor::GetDataSizeInChar()
{
if(isSparse){
......@@ -826,7 +822,7 @@ void XTensor::SetZeroAll(XStream * stream)
/* set the tensor with an data array
>> d - input data. it must be on CPU
>> num - number of data items
>> beg - where we start this in the data array of the tensor
>> beg - where we start the data copy in the data array of the tensor
*/
void XTensor::SetData(const void * d, int num, int beg)
{
......@@ -846,7 +842,7 @@ set the tensor items by a uniform distribution in range [lower, upper]
*/
void XTensor::SetDataRand(DTYPE lower, DTYPE upper)
{
// TODO: cuda code!!!!!!!
// TODO: GPU code!!!!!!!
if (data == NULL)
return;
......@@ -884,7 +880,7 @@ void XTensor::SetDataRand(DTYPE lower, DTYPE upper)
/* a gauss distribution (Box-Muller method) */
double GaussRand(DTYPE mean, DTYPE standardDeviation)
{
// TODO: cuda code!!!!!!!
// TODO: GPU code!!!!!!!
static double u, v;
static int phase = 0;
......@@ -947,7 +943,7 @@ void XTensor::SetDataRandn(DTYPE mean, DTYPE standardDeviation)
/*
set tensor items with an array of offsets
>> offsets - offset for each data item
>> value - value for data items
>> value - value for the data items
>> num - number of the data items
*/
void XTensor::SetDataBatched(MTYPE * offsets, DTYPE value, int num)
......@@ -967,7 +963,7 @@ void XTensor::SetDataBatchedWithValues(MTYPE * offsets, void * values, int num)
}
/* check whether the data array is the same as the answer
>> d - input data. it must be on CPU
>> d - input data (it must be on CPUs)
>> num - number of data items
>> beg - where we start this in the data array of the tensor
*/
......@@ -1001,7 +997,7 @@ void XTensor::SetDataPointer()
dataP = &data;
}
/* compare two number */
/* compare two numbers */
bool IsFloatEqual(DTYPE a, DTYPE b, float absError, float relError)
{
if(a == b)
......@@ -1014,7 +1010,7 @@ bool IsFloatEqual(DTYPE a, DTYPE b, float absError, float relError)
return (fabs((a - b) / a) < relError) ? true : false;
}
/* check whether the data array is the same as the answer */
/* check whether the data array is the same as the "answer" */
bool XTensor::CheckData(const void * d, int num, float tolerance, int beg)
{
if (data == NULL || d == NULL)
......@@ -1088,7 +1084,7 @@ void XTensor::SetAscendingOrder(int dim)
/*
get the value of a cell with the index
>> index - index of each dimension
>> size - size of index
>> size - size of the index
<< return - cell value
*/
DTYPE XTensor::Get(int index[], int size)
......@@ -1099,7 +1095,7 @@ DTYPE XTensor::Get(int index[], int size)
}
/*
get the value of a cell with the offset
get the value of a cell with its offset
>> offset - offset in the array
<< return - cell value
*/
......@@ -1689,7 +1685,7 @@ bool XTensor::Resize(const int myOrder, const int * myDimSize,
}
/*
resize a tensor by another one
resize a tensor by another
>> myTensor - tensor for reference
*/
bool XTensor::Resize(const XTensor * myTensor)
......@@ -1711,7 +1707,7 @@ binary search to find an element in a sparse tensor
>> value - value for return
>> position - the position of the tuple.
it is the previous one if there is no hit
<< return - find it or not?
<< return - found it or not?
*/
bool XTensor::BinarySearch(int key, DTYPE &value, void * &position) const
{
......@@ -1880,10 +1876,10 @@ void XTensor::Dump(FILE * file, const char * label, const int n, const int beg,
/*
dump data to a file
>> tensor - tensor whose data is dumped
>> tensor - the tensor for dumping
>> file - where to domp the data
>> label - label of the tensor
>> n - number of items to dump
>> n - number of the items to dump
>> beg - the first item id
>> verbose - verbose level
*/
......@@ -2050,7 +2046,7 @@ void XTensor::FlushToMem(XMem * targetMem)
allocate the memory space of the tensor (in the global memory)
>> tensor - the tensor we intend to process
>> myMem - the memory pool we are using
>> useBuf - use the buffer in the memory pool
>> useBuf - indicates whether we use the buffer in the memory pool
*/
void XTensor::AllocateData(XTensor * tensor, XMem * myMem, bool useBuf)
{
......@@ -2082,7 +2078,7 @@ void XTensor::AllocateData(XTensor * tensor, XMem * myMem, bool useBuf)
free the memory space of the tensor (in the global memory)
>> tensor - the tensor we intend to process
>> myMem - the memory pool we are using
>> useBuf - use the buffer in the memory pool
>> useBuf - indicates whether we use the buffer in the memory pool
*/
void XTensor::FreeData(XTensor * tensor, XMem * myMem, bool useBuf)
{
......
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