https://github.com/Microsoft/CNTK
Tip revision: a737b4741a7ff6fffdb7756876e4042bdf3be093 authored by unknown on 02 June 2016, 19:53:17 UTC
Merge branch 'master' into issaca
Merge branch 'master' into issaca
Tip revision: a737b47
gammacalculation.h
#pragma once
#include <unordered_map>
#include "simplesenonehmm.h"
#include "latticearchive.h"
#include "latticesource.h"
#include "ssematrix.h"
#include "Matrix.h"
#include "CUDAPageLockedMemAllocator.h"
#include <memory>
#include <vector>
#pragma warning(disable : 4127) // conditional expression is constant
namespace msra { namespace lattices {
struct SeqGammarCalParam
{
double amf;
double lmf;
double wp;
double bMMIfactor;
bool sMBRmode;
SeqGammarCalParam()
{
amf = 14.0;
lmf = 14.0;
wp = 0.0;
bMMIfactor = 0.0;
sMBRmode = false;
}
};
template <class ElemType>
class GammaCalculation
{
bool cpumode;
public:
GammaCalculation()
: cpumode(false)
{
initialmark = false;
lmf = 7.0f; // Note that 9 was best for Fisher --these should best be configurable
wp = 0.0f;
amf = 7.0f;
boostmmifactor = 0.0f;
seqsMBRmode = false;
}
~GammaCalculation()
{
}
// ========================================
// Sec. 1 init functions
// ========================================
void init(msra::asr::simplesenonehmm hset, int DeviceId)
{
m_deviceid = DeviceId;
if (!initialmark)
{
m_hset = hset;
m_maxframenum = 0;
// prep for parallel implementation (CUDA)
parallellattice.setdevice(DeviceId);
if (parallellattice.enabled()) // send hmm set to GPU if GPU computation enabled
parallellattice.entercomputation(m_hset, mbrclassdef); // cache senone2classmap if mpemode
initialmark = true;
}
}
// ========================================
// Sec. 2 set functions
// ========================================
void SetGammarCalculationParams(const SeqGammarCalParam& gammarParam)
{
lmf = (float) gammarParam.lmf;
amf = (float) gammarParam.amf;
wp = (float) gammarParam.wp;
seqsMBRmode = gammarParam.sMBRmode;
boostmmifactor = (float) gammarParam.bMMIfactor;
}
// ========================================
// Sec. 3 calculation functions
// ========================================
void calgammaformb(Microsoft::MSR::CNTK::Matrix<ElemType>& functionValues,
std::vector<std::shared_ptr<const msra::dbn::latticepair>>& lattices,
const Microsoft::MSR::CNTK::Matrix<ElemType>& loglikelihood,
Microsoft::MSR::CNTK::Matrix<ElemType>& labels,
Microsoft::MSR::CNTK::Matrix<ElemType>& gammafromlattice,
std::vector<size_t>& uids, std::vector<size_t>& boundaries,
size_t samplesInRecurrentStep, /* numParallelUtterance ? */
std::shared_ptr<Microsoft::MSR::CNTK::MBLayout> pMBLayout,
std::vector<size_t>& extrauttmap,
bool doreferencealign)
{
// check total frame number to be added ?
// int deviceid = loglikelihood.GetDeviceId();
size_t boundaryframenum;
std::vector<size_t> validframes; // [s] cursor pointing to next utterance begin within a single parallel sequence [s]
validframes.assign(samplesInRecurrentStep, 0);
ElemType objectValue = 0.0;
// convert from Microsoft::MSR::CNTK::Matrix to msra::math::ssematrixbase
size_t numrows = loglikelihood.GetNumRows();
size_t numcols = loglikelihood.GetNumCols();
Microsoft::MSR::CNTK::Matrix<ElemType> tempmatrix(m_deviceid);
// copy loglikelihood to pred
if (numcols > pred.cols())
{
pred.resize(numrows, numcols);
dengammas.resize(numrows, numcols);
}
if (doreferencealign)
labels.SetValue((ElemType)(0.0f));
size_t T = numcols / samplesInRecurrentStep; // number of time steps in minibatch
if (samplesInRecurrentStep > 1)
{
assert(extrauttmap.size() == lattices.size());
assert(T == pMBLayout->GetNumTimeSteps());
}
size_t mapi = 0; // parallel-sequence index for utterance [i]
// cal gamma for each utterance
size_t ts = 0;
for (size_t i = 0; i < lattices.size(); i++)
{
const size_t numframes = lattices[i]->getnumframes();
msra::dbn::matrixstripe predstripe(pred, ts, numframes); // logLLs for this utterance
msra::dbn::matrixstripe dengammasstripe(dengammas, ts, numframes); // denominator gammas
if (samplesInRecurrentStep == 1) // no sequence parallelism
{
tempmatrix = loglikelihood.ColumnSlice(ts, numframes);
// if (m_deviceid == CPUDEVICE)
{
CopyFromCNTKMatrixToSSEMatrix(tempmatrix, numframes, predstripe);
}
if (m_deviceid != CPUDEVICE)
parallellattice.setloglls(tempmatrix);
}
else // multiple parallel sequences
{
// get number of frames for the utterance
mapi = extrauttmap[i]; // parallel-sequence index; in case of >1 utterance within this parallel sequence, this is in order of concatenation
// scan MBLayout for end of utterance
size_t mapframenum = SIZE_MAX; // duration of utterance [i] as determined from MBLayout
for (size_t t = validframes[mapi]; t < T; t++)
{
// TODO: Adapt this to new MBLayout, m_sequences would be easier to work off.
if (pMBLayout->IsEnd(mapi, t))
{
mapframenum = t - validframes[mapi] + 1;
break;
}
}
// must match the explicit information we get from the reader
if (numframes != mapframenum)
LogicError("gammacalculation: IsEnd() not working, numframes (%d) vs. mapframenum (%d)", (int) numframes, (int) mapframenum);
assert(numframes == mapframenum);
if (numframes > tempmatrix.GetNumCols())
tempmatrix.Resize(numrows, numframes);
Microsoft::MSR::CNTK::Matrix<ElemType> loglikelihoodForCurrentParallelUtterance = loglikelihood.ColumnSlice(mapi + (validframes[mapi] * samplesInRecurrentStep), ((numframes - 1) * samplesInRecurrentStep) + 1);
tempmatrix.CopyColumnsStrided(loglikelihoodForCurrentParallelUtterance, numframes, samplesInRecurrentStep, 1);
// if (doreferencealign || m_deviceid == CPUDEVICE)
{
CopyFromCNTKMatrixToSSEMatrix(tempmatrix, numframes, predstripe);
}
if (m_deviceid != CPUDEVICE)
{
parallellattice.setloglls(tempmatrix);
}
}
array_ref<size_t> uidsstripe(&uids[ts], numframes);
if (doreferencealign)
{
boundaryframenum = numframes;
}
else
boundaryframenum = 0;
array_ref<size_t> boundariesstripe(&boundaries[ts], boundaryframenum);
double numavlogp = 0;
foreach_column (t, dengammasstripe) // we do not allocate memory for numgamma now, should be the same as numgammasstripe
{
const size_t s = uidsstripe[t];
numavlogp += predstripe(s, t) / amf;
}
numavlogp /= numframes;
// auto_timer dengammatimer;
double denavlogp = lattices[i]->second.forwardbackward(parallellattice,
(const msra::math::ssematrixbase&) predstripe, (const msra::asr::simplesenonehmm&) m_hset,
(msra::math::ssematrixbase&) dengammasstripe, (msra::math::ssematrixbase&) gammasbuffer /*empty, not used*/,
lmf, wp, amf, boostmmifactor, seqsMBRmode, uidsstripe, boundariesstripe);
objectValue += (ElemType)((numavlogp - denavlogp) * numframes);
if (samplesInRecurrentStep == 1)
{
tempmatrix = gammafromlattice.ColumnSlice(ts, numframes);
}
// copy gamma to tempmatrix
if (m_deviceid == CPUDEVICE)
{
CopyFromSSEMatrixToCNTKMatrix(dengammas, numrows, numframes, tempmatrix, gammafromlattice.GetDeviceId());
}
else
parallellattice.getgamma(tempmatrix);
// set gamma for multi channel
if (samplesInRecurrentStep > 1)
{
Microsoft::MSR::CNTK::Matrix<ElemType> gammaFromLatticeForCurrentParallelUtterance = gammafromlattice.ColumnSlice(mapi + (validframes[mapi] * samplesInRecurrentStep), ((numframes - 1) * samplesInRecurrentStep) + 1);
gammaFromLatticeForCurrentParallelUtterance.CopyColumnsStrided(tempmatrix, numframes, 1, samplesInRecurrentStep);
}
if (doreferencealign)
{
for (size_t nframe = 0; nframe < numframes; nframe++)
{
size_t uid = uidsstripe[nframe];
if (samplesInRecurrentStep > 1)
labels(uid, (nframe + validframes[mapi]) * samplesInRecurrentStep + mapi) = 1.0;
else
labels(uid, ts + nframe) = 1.0;
}
}
if (samplesInRecurrentStep > 1)
validframes[mapi] += numframes; // advance the cursor within the parallel sequence
fprintf(stderr, "dengamma value %f\n", denavlogp);
ts += numframes;
}
functionValues.SetValue(objectValue);
}
private:
// Helper methods for copying between ssematrix objects and CNTK matrices
void CopyFromCNTKMatrixToSSEMatrix(const Microsoft::MSR::CNTK::Matrix<ElemType>& src, size_t numCols, msra::math::ssematrixbase& dest)
{
if (!std::is_same<ElemType, float>::value)
{
LogicError("Cannot copy between a SSE matrix and a non-float type CNTK Matrix object!");
}
size_t numRows = src.GetNumRows();
const Microsoft::MSR::CNTK::Matrix<ElemType> srcSlice = src.ColumnSlice(0, numCols);
if ((m_intermediateCUDACopyBuffer == nullptr) || (m_intermediateCUDACopyBufferSize < srcSlice.GetNumElements()))
{
m_intermediateCUDACopyBuffer = AllocateIntermediateBuffer(srcSlice.GetDeviceId(), srcSlice.GetNumElements());
m_intermediateCUDACopyBufferSize = srcSlice.GetNumElements();
}
ElemType* pBuf = m_intermediateCUDACopyBuffer.get();
srcSlice.CopyToArray(pBuf, m_intermediateCUDACopyBufferSize);
if (pBuf != m_intermediateCUDACopyBuffer.get())
{
LogicError("Unexpected re-allocation of destination CPU buffer in Matrix::CopyToArray!");
}
if ((dest.getcolstride() == dest.rows()) && (numRows == dest.rows()))
{
memcpy(&dest(0, 0), (float*) pBuf, sizeof(ElemType) * numRows * numCols);
}
else
{
// We need to copy columnwise
for (size_t i = 0; i < numCols; ++i)
{
memcpy(&dest(0, i), (float*) (pBuf + (i * numRows)), sizeof(ElemType) * numRows);
}
}
}
void CopyFromSSEMatrixToCNTKMatrix(const msra::math::ssematrixbase& src, size_t numRows, size_t numCols, Microsoft::MSR::CNTK::Matrix<ElemType>& dest, int deviceId)
{
if (!std::is_same<ElemType, float>::value)
{
LogicError("Cannot copy between a SSE matrix and a non-float type CNTK Matrix object!");
}
size_t numElements = numRows * numCols;
if ((m_intermediateCUDACopyBuffer == nullptr) || (m_intermediateCUDACopyBufferSize < numElements))
{
m_intermediateCUDACopyBuffer = AllocateIntermediateBuffer(deviceId, numElements);
m_intermediateCUDACopyBufferSize = numElements;
}
if ((src.getcolstride() == src.rows()) && (numRows == src.rows()))
{
memcpy((float*) m_intermediateCUDACopyBuffer.get(), &src(0, 0), sizeof(float) * numRows * numCols);
}
else
{
// We need to copy columnwise
for (size_t i = 0; i < numCols; ++i)
{
memcpy((float*) (m_intermediateCUDACopyBuffer.get() + (i * numRows)), &src(0, i), sizeof(float) * numRows);
}
}
dest.SetValue(numRows, numCols, deviceId, m_intermediateCUDACopyBuffer.get(), 0);
}
// TODO: This function is duplicate of the one in HTLMLFReader.
// This should be moved to a common utils library and removed from here as well as HTLMLFReader
std::unique_ptr<Microsoft::MSR::CNTK::CUDAPageLockedMemAllocator>& GetCUDAAllocator(int deviceID)
{
if (m_cudaAllocator != nullptr)
{
if (m_cudaAllocator->GetDeviceId() != deviceID)
{
m_cudaAllocator.reset(nullptr);
}
}
if (m_cudaAllocator == nullptr)
{
m_cudaAllocator.reset(new Microsoft::MSR::CNTK::CUDAPageLockedMemAllocator(deviceID));
}
return m_cudaAllocator;
}
// TODO: This function is duplicate of the one in HTLMLFReader.
// This should be moved to a common utils library and removed from here as well as HTLMLFReader
std::shared_ptr<ElemType> AllocateIntermediateBuffer(int deviceID, size_t numElements)
{
if (deviceID >= 0)
{
// Use pinned memory for GPU devices for better copy performance
size_t totalSize = sizeof(ElemType) * numElements;
return std::shared_ptr<ElemType>((ElemType*) GetCUDAAllocator(deviceID)->Malloc(totalSize), [this, deviceID](ElemType* p)
{
this->GetCUDAAllocator(deviceID)->Free((char*) p);
});
}
else
{
return std::shared_ptr<ElemType>(new ElemType[numElements], [](ElemType* p)
{
delete[] p;
});
}
}
protected:
msra::asr::simplesenonehmm m_hset;
msra::lattices::lattice::parallelstate parallellattice;
msra::lattices::mbrclassdefinition mbrclassdef = msra::lattices::senone; // defines the unit for minimum bayesian risk
bool initialmark;
msra::dbn::matrix dengammas;
msra::dbn::matrix pred;
int m_deviceid; // -1: cpu
size_t m_maxframenum;
float lmf; // Note that 9 was best for Fisher --these should best be configurable
float wp;
float amf;
msra::dbn::matrix gammasbuffer;
std::vector<size_t> boundary;
float boostmmifactor;
bool seqsMBRmode;
private:
std::unique_ptr<Microsoft::MSR::CNTK::CUDAPageLockedMemAllocator> m_cudaAllocator;
std::shared_ptr<ElemType> m_intermediateCUDACopyBuffer;
size_t m_intermediateCUDACopyBufferSize;
};
}}