https://github.com/Microsoft/CNTK
Tip revision: 865d71c0c654d758bc0824d57f9cefee8dae9cdc authored by kaituoxu on 09 June 2018, 04:32:46 UTC
add bidirectional FSMN node and make it work in NDL/BS and add FSMN CPU forward
add bidirectional FSMN node and make it work in NDL/BS and add FSMN CPU forward
Tip revision: 865d71c
Value.cpp
//
// Copyright (c) Microsoft. All rights reserved.
// Licensed under the MIT license. See LICENSE.md file in the project root for full license information.
//
#include "stdafx.h"
#ifdef _WIN32
#define _SCL_SECURE_NO_WARNINGS
#endif
#include "CNTKLibrary.h"
#include "CompositeFunction.h"
#include "Utils.h"
#include "Value.h"
#include "Matrix.h"
#include "CommonMatrix.h"
#include "CPUSparseMatrix.h"
#include "RecurrentNodes.h"
namespace CNTK
{
Value::Value(const NDArrayViewPtr& data)
: Value(data, nullptr)
{
}
Value::Value(const NDArrayViewPtr& data, const NDMaskPtr& mask)
: m_data(data), m_mask(mask)
{
if (mask != nullptr)
{
auto dataShape = data->Shape();
auto maskShape = mask->Shape();
if (maskShape.Rank() > dataShape.Rank())
InvalidArgument("The rank (%zu) of the mask of a Value object cannot exceed the rank (%zu) of the data NDArrayView object", maskShape.Rank(), dataShape.Rank());
if (dataShape.SubShape(dataShape.Rank() - maskShape.Rank()) != maskShape)
InvalidArgument("Invalid Value object: data and mask are incompatible. The %s dimensions of the data with shape '%S' "
"do not match the dimensions of the mask with shape '%S'",
Internal::IsReversingTensorShapesInErrorMessagesEnabled() ? "leading" : "trailing",
dataShape.AsString().c_str(), maskShape.AsString().c_str());
}
}
//
// Create NDMask for the 'sequences' if the 'sequences' do not have the same length.
// It returns null if all the 'sequences' have the same length.
//
template <typename T>
static NDMaskPtr CreateMask(size_t numElementsPerSample, const std::vector<std::vector<T>>& sequences, const std::vector<bool>& sequenceStartFlags, const DeviceDescriptor& device)
{
size_t numSequences = sequences.size();
std::vector<size_t> sequenceLengths(numSequences);
for (size_t i = 0; i < numSequences; ++i)
sequenceLengths[i] = sequences[i].size() / numElementsPerSample;
return CreateMask(sequenceLengths, sequenceStartFlags, device);
}
template <typename ElementType>
/*static*/ ValuePtr Value::Create(const NDShape& sampleShape, const std::vector<std::vector<size_t>>& oneHotSequences, const std::vector<bool>& sequenceStartFlags, const DeviceDescriptor& device, bool readOnly/* = false*/)
{
if (oneHotSequences.size() == 0)
InvalidArgument("Value::Create:: The number of sequences must be > 0");
if (sampleShape.Rank() < 1)
InvalidArgument("Value::Create:: The sample rank must be > 0");
auto dimension = sampleShape[0];
auto numElementsPerSample = sampleShape.SubShape(1).TotalSize();
NDMaskPtr deviceValueMask = CreateMask(numElementsPerSample, oneHotSequences, sequenceStartFlags, DeviceDescriptor::CPUDevice());
// If deviceValueMask is null, all the sequences have the same length.
size_t maxSequenceLength = (deviceValueMask == nullptr) ? (oneHotSequences[0].size() / numElementsPerSample) : deviceValueMask->Shape()[0];
size_t maxSequenceNumCols = maxSequenceLength * numElementsPerSample;
size_t numSequences = oneHotSequences.size();
NDShape valueDataShape = sampleShape.AppendShape({ maxSequenceLength, numSequences });
size_t numCSCCols = valueDataShape.SubShape(1).TotalSize() + 1;
std::vector<SparseIndexType> colStarts(numCSCCols);
std::vector<ElementType> nonZeroValues;
std::vector<SparseIndexType> rowIndices;
for (size_t i = 0; i < numSequences; ++i)
{
size_t currentSequenceNumCols = oneHotSequences[i].size();
size_t j = 0;
for (; j < currentSequenceNumCols; ++j)
{
colStarts[(i * maxSequenceNumCols) + j] = (SparseIndexType)nonZeroValues.size();
size_t oneHotIdx = oneHotSequences[i][j];
if (oneHotIdx == OneHotSkip)
{
nonZeroValues.push_back(0);
rowIndices.push_back(0);
}
else
{
nonZeroValues.push_back(1);
if (oneHotIdx >= dimension)
InvalidArgument("Value::Create: one-hot index value (%zu) exceeds vocabulary size (%zu).", oneHotSequences[i][j], dimension);
rowIndices.push_back((SparseIndexType)(oneHotSequences[i][j]));
}
}
for (; j < maxSequenceNumCols; ++j)
colStarts[(i * maxSequenceNumCols) + j] = (SparseIndexType)(nonZeroValues.size());
}
colStarts[numCSCCols - 1] = (SparseIndexType)(nonZeroValues.size());
NDArrayViewPtr deviceValueData = MakeSharedObject<NDArrayView>(AsDataType<ElementType>(), valueDataShape, colStarts.data(), rowIndices.data(), nonZeroValues.data(), nonZeroValues.size(), device, readOnly);
return MakeSharedObject<Value>(deviceValueData, deviceValueMask);
}
template <typename ElementType>
/*static*/ void Value::AppendSparseSequenceData(const NDArrayViewPtr& sequenceData, std::vector<SparseIndexType>& colStarts, std::vector<SparseIndexType>& rowIndices, std::vector<char>& nonZeroValues, size_t maxSequenceLengthInCols)
{
size_t existingNumNonZeroValues = nonZeroValues.size() / sizeof(ElementType);
std::vector<SparseIndexType> currentSequencePaddedColStarts(maxSequenceLengthInCols);
auto matrix = sequenceData->GetMatrix<ElementType>();
matrix->TransferToDeviceIfNotThere(AsCNTKImplDeviceId(DeviceDescriptor::CPUDevice()), true);
auto cpuSparseMatrix = matrix->m_CPUSparseMatrix;
auto currentSequenceNumCols = matrix->GetNumCols();
auto currentSequenceColStarts = cpuSparseMatrix->SecondaryIndexLocation();
auto currentSequenceNumNonZeroValues = currentSequenceColStarts[currentSequenceNumCols] - currentSequenceColStarts[0];
std::copy(cpuSparseMatrix->MajorIndexLocation(), cpuSparseMatrix->MajorIndexLocation() + currentSequenceNumNonZeroValues, std::back_inserter(rowIndices));
std::copy((char*)(cpuSparseMatrix->Data()), (char*)(cpuSparseMatrix->Data() + currentSequenceNumNonZeroValues), std::back_inserter(nonZeroValues));
for (size_t j = 0; j < currentSequenceNumCols; ++j)
currentSequencePaddedColStarts[j] = existingNumNonZeroValues + (currentSequenceColStarts[j] - currentSequenceColStarts[0]);
for (size_t j = currentSequenceNumCols; j < maxSequenceLengthInCols; ++j)
currentSequencePaddedColStarts[j] = existingNumNonZeroValues + currentSequenceNumNonZeroValues;
std::copy(currentSequencePaddedColStarts.begin(), currentSequencePaddedColStarts.end(), std::back_inserter(colStarts));
}
/*static*/ ValuePtr Value::Create(const NDShape& sampleShape, const std::vector<NDArrayViewPtr>& sequences, const std::vector<bool>& sequenceStartFlags, const DeviceDescriptor& device, bool readOnly, bool createNewCopy)
{
auto numSequences = sequences.size();
if (numSequences == 0)
InvalidArgument("Value::Create:: The number of sequences must be > 0");
std::vector<size_t> sequenceLengths(numSequences);
size_t maxSequenceLength = 0;
auto dataType = sequences[0]->GetDataType();
auto storageFormat = sequences[0]->GetStorageFormat();
NDShape fullyDefinedSampleShape = sampleShape;
for (size_t i = 0; i < numSequences; ++i)
{
auto currentSequenceData = sequences[i];
if (currentSequenceData->GetDataType() != dataType)
InvalidArgument("Value::Create: The data for all sequences/samples must have the same data type");
if (currentSequenceData->GetStorageFormat() != storageFormat)
InvalidArgument("Value::Create: All NDArrayView objects must have the same storage format");
if ((numSequences > 1) && (currentSequenceData->Device() != DeviceDescriptor::CPUDevice()))
InvalidArgument("Value::Create: All NDArrayView objects must be located on the CPU");
auto currentSequenceDataShape = currentSequenceData->Shape();
// Since scalar samples can be rank=1 with dim=1, we automatically pad the sequence data shape with a leading axis
// of dim=1 if the sequence data shape's leading axis's dimensionality is not 1
if ((fullyDefinedSampleShape.Rank() == 1) && !fullyDefinedSampleShape.HasUnboundDimension() && (fullyDefinedSampleShape.TotalSize() == 1) && (currentSequenceDataShape.Rank() > 0) && (currentSequenceDataShape[0] != 1))
currentSequenceDataShape = NDShape(1, 1).AppendShape(currentSequenceDataShape);
if ((currentSequenceDataShape.Rank() < fullyDefinedSampleShape.Rank()) || (currentSequenceDataShape.Rank() > (fullyDefinedSampleShape.Rank() + 1)))
InvalidArgument("Value::Create: The shape '%S' of sequence #%zu is not compatible with the sample shape '%S'.", currentSequenceData->Shape().AsString().c_str(), i, sampleShape.AsString().c_str());
auto sequenceValueVarSubshape = currentSequenceDataShape.SubShape(0, fullyDefinedSampleShape.Rank());
if (sequenceValueVarSubshape != fullyDefinedSampleShape)
{
for (size_t k = 0; k < fullyDefinedSampleShape.Rank(); ++k)
{
if (fullyDefinedSampleShape[k] == NDShape::FreeDimension)
fullyDefinedSampleShape[k] = sequenceValueVarSubshape[k];
else if (fullyDefinedSampleShape[k] != sequenceValueVarSubshape[k])
InvalidArgument("Value::Create: The shape '%S' of sequence #%zu is not compatible with the sample shape '%S'.", currentSequenceData->Shape().AsString().c_str(), i, sampleShape.AsString().c_str());
}
}
sequenceLengths[i] = currentSequenceDataShape.SubShape(fullyDefinedSampleShape.Rank()).TotalSize();
maxSequenceLength = std::max(maxSequenceLength, sequenceLengths[i]);
}
bool isDataSparse = sequences[0]->IsSparse();
NDMaskPtr deviceValueMask = CreateMask(sequenceLengths, sequenceStartFlags, DeviceDescriptor::CPUDevice());
NDArrayViewPtr valueData;
NDShape valueDataShape = fullyDefinedSampleShape.AppendShape({ maxSequenceLength, numSequences });
if (numSequences == 1)
{
if (createNewCopy)
valueData = sequences[0]->DeepClone();
else
valueData = sequences[0];
// We can use the original buffer directly but need to reshape to the valueDataShape
valueData = valueData->AsShape(valueDataShape);
}
else
{
if (isDataSparse)
{
if (storageFormat != StorageFormat::SparseCSC)
LogicError("Value::Create currently only SparseCSC format sparse data is supported");
auto numColsPerSample = fullyDefinedSampleShape.SubShape(ShapeRowColSplitPoint(fullyDefinedSampleShape, isDataSparse, /*noDynamicAxes =*/ false)).TotalSize();
std::vector<SparseIndexType> colStarts;
std::vector<SparseIndexType> rowIndices;
std::vector<char> nonZeroValues;
for (size_t i = 0; i < numSequences; ++i)
{
switch (dataType)
{
case DataType::Float:
AppendSparseSequenceData<float>(sequences[i], colStarts, rowIndices, nonZeroValues, maxSequenceLength * numColsPerSample);
break;
case DataType::Double:
AppendSparseSequenceData<double>(sequences[i], colStarts, rowIndices, nonZeroValues, maxSequenceLength * numColsPerSample);
break;
default:
NOT_IMPLEMENTED;
}
}
auto totalNumNonZeroValues = nonZeroValues.size() / DataTypeSize(dataType);
colStarts.push_back(totalNumNonZeroValues);
valueData = MakeSharedObject<NDArrayView>(dataType, valueDataShape, colStarts.data(), rowIndices.data(), (void*)nonZeroValues.data(), totalNumNonZeroValues, device, readOnly);
}
else
{
valueData = MakeSharedObject<NDArrayView>(dataType, valueDataShape, DeviceDescriptor::CPUDevice());
auto maxSequenceSizeInElements = fullyDefinedSampleShape.TotalSize() * maxSequenceLength;
switch (dataType)
{
case DataType::Float:
{
float* dataBuffer = valueData->WritableDataBuffer<float>();
for (size_t i = 0; i < numSequences; ++i)
{
const float* currentSequenceBuffer = sequences[i]->DataBuffer<float>();
auto currentSequenceSizeInElements = sequences[i]->Shape().TotalSize();
std::copy(currentSequenceBuffer, currentSequenceBuffer + currentSequenceSizeInElements, dataBuffer + (maxSequenceSizeInElements * i));
}
break;
}
case DataType::Double:
{
double* dataBuffer = valueData->WritableDataBuffer<double>();
for (size_t i = 0; i < numSequences; ++i)
{
const double* currentSequenceBuffer = sequences[i]->DataBuffer<double>();
auto currentSequenceSizeInElements = sequences[i]->Shape().TotalSize();
std::copy(currentSequenceBuffer, currentSequenceBuffer + currentSequenceSizeInElements, dataBuffer + (maxSequenceSizeInElements * i));
}
break;
}
default:
NOT_IMPLEMENTED;
}
}
}
NDArrayViewPtr deviceValueData;
if (device == valueData->Device())
{
if (readOnly)
deviceValueData = valueData->Alias(readOnly);
else
deviceValueData = valueData;
}
else
deviceValueData = valueData->DeepClone(device, readOnly);
return MakeSharedObject<Value>(deviceValueData, deviceValueMask);
}
template <typename ElementType>
/*static*/ ValuePtr Value::Create(const NDShape& sampleShape, const std::vector<std::vector<ElementType>>& sequences, const std::vector<bool>& sequenceStartFlags, const DeviceDescriptor& device, bool readOnly)
{
// Create a NDArrayView object wrapping each of the vectors representing a sequence
size_t numElementsPerSample = sampleShape.TotalSize();
size_t numSequences = sequences.size();
std::vector<NDArrayViewPtr> sequencesData;
for (size_t i = 0; i < numSequences; ++i)
{
auto& currentSequence = sequences[i];
if ((currentSequence.size() % numElementsPerSample) != 0)
InvalidArgument("Value::Create: The number of elements (%zu) in the vector containing sequence data must be a multiple of the size (%zu) of specified sample shape '%S'",
currentSequence.size(), numElementsPerSample, sampleShape.AsString().c_str());
auto sequenceLength = currentSequence.size() / numElementsPerSample;
auto sequenceDataShape = sampleShape.AppendShape({ sequenceLength });
sequencesData.push_back(MakeSharedObject<NDArrayView>(sequenceDataShape, currentSequence));
}
return Create(sampleShape, sequencesData, sequenceStartFlags, device, readOnly, /*createNewCopy =*/ true);
}
template <typename ElementType>
/*static*/ ValuePtr Value::CreateBatch(const NDShape& sampleShape, const std::vector<ElementType>& batchData, const DeviceDescriptor& device, bool readOnly /*= false */)
{
auto shapeSize = sampleShape.TotalSize();
if (batchData.size() % shapeSize != 0)
InvalidArgument("The number of elements (%zu) in the vector containing batch data must be a multiple of the size (%zu) of the sample shape '%S'.",
batchData.size(), shapeSize, sampleShape.AsString().c_str());
auto numOfSequences = batchData.size() / shapeSize;
std::vector<NDArrayViewPtr> sequencesView(numOfSequences);
for (size_t i = 0; i < numOfSequences; i++)
{
// Sequence length is 1.
auto sequenceDataShape = sampleShape.AppendShape({ 1 });
sequencesView[i] = MakeSharedObject<NDArrayView>(sequenceDataShape, batchData.data() + i * shapeSize, shapeSize, DeviceDescriptor::CPUDevice());
}
// Pass the empty seqStartFlags means all sequences have the start flag with true.
return Create(sampleShape, sequencesView, {}, device, readOnly, /*createNewCopy =*/ true);
}
template <typename ElementType>
/*static*/ ValuePtr Value::CreateSequence(const NDShape& sampleShape, const std::vector<ElementType>& sequenceData, bool sequenceStartFlag, const DeviceDescriptor& device, bool readOnly /*= false */)
{
auto shapeSize = sampleShape.TotalSize();
if (sequenceData.size() % shapeSize != 0)
InvalidArgument("The number of elements (%zu) in the sequence data must be a multiple of the size (%zu) of the sample shape '%S'",
sequenceData.size(), shapeSize, sampleShape.AsString().c_str());
auto sequenceLength = sequenceData.size() / shapeSize;
std::vector<NDArrayViewPtr> sequencesView(1);
auto sequenceDataShape = sampleShape.AppendShape({ sequenceLength });
sequencesView[0] = MakeSharedObject<NDArrayView>(sequenceDataShape, sequenceData);
return Create(sampleShape, sequencesView, { sequenceStartFlag }, device, readOnly, /*createNewCopy =*/ true);
}
template <typename ElementType>
/*static*/ ValuePtr Value::CreateBatch(size_t dimension, const std::vector<size_t>& batchData, const DeviceDescriptor& device, bool readOnly/* = false*/)
{
//TODO: avoid data copy.
std::vector<std::vector<size_t>> input(batchData.size());
for (size_t i = 0; i < batchData.size(); i++)
{
input[i] = {batchData[i]};
}
// Pass the empty seqStartFlags means all sequences have the start flag with true.
return Create<ElementType>(dimension, input, {}, device, readOnly);
}
template <typename ElementType>
/*static*/ ValuePtr Value::CreateSequence(size_t dimension, const std::vector<size_t>& sequenceData, bool sequenceStartFlag, const DeviceDescriptor& device, bool readOnly/* = false*/)
{
//TODO: avoid data copy.
std::vector<std::vector<size_t>> input = { sequenceData };
return Create<ElementType>(dimension, input, {sequenceStartFlag}, device, readOnly);
}
template <typename ElementType>
/*static*/ ValuePtr Value::CreateSequence(const NDShape& sampleShape, size_t sequenceLength, const SparseIndexType* colStarts, const SparseIndexType* rowIndices, const ElementType* nonZeroValues, size_t numNonZeroValues, bool sequenceStartFlag, const DeviceDescriptor& device, bool readOnly/* = false*/)
{
auto sequenceShape = sampleShape.AppendShape({sequenceLength});
auto sequenceData = MakeSharedObject<NDArrayView>(AsDataType<ElementType>(), sequenceShape, colStarts, rowIndices, nonZeroValues, numNonZeroValues, device, readOnly);
return Create(sampleShape, {sequenceData}, {sequenceStartFlag}, device, readOnly, false);
}
/*virtual*/ Value::~Value()
{
}
/*virtual*/ void Value::Erase()
{
m_data = nullptr;
m_mask = nullptr;
}
/*virtual*/ NDArrayViewPtr Value::Data() const
{
if (!m_data)
{
RuntimeError("This Value object is invalid and can no longer be accessed. This usually happens when a temporary Value object returned by the CNTK library"
" is not cloned and accessed later after it has been erased by the library. The Value objects created inside and returned by the library from APIs "
"like Forward, Backward etc. are temporary and are only guaranteed to be valid until the next Forward/Backward call. If you want to access the Values "
"later, you must explicitly clone them.");
}
// TODO: Check if this is a derived type and throw an exception in that case
return m_data;
}
/*virtual*/ NDMaskPtr Value::Mask() const
{
// TODO: Check if this is a derived type and throw an exception in that case
return m_mask;
}
/*virtual*/ ValuePtr Value::DeepClone(bool readOnly/* = false*/) const
{
// TODO: Check if this is a derived type and throw an exception in that case
return MakeSharedObject<Value>(Data()->DeepClone(readOnly), (Mask() != nullptr) ? Mask()->DeepClone() : nullptr);
}
/*virtual*/ ValuePtr Value::Alias(bool readOnly/* = false*/) const
{
// TODO: Check if this is a derived type and throw an exception in that case
return MakeSharedObject<Value>(Data()->Alias(readOnly), (Mask() != nullptr) ? Mask()->Alias() : nullptr);
}
/*virtual*/ void Value::CopyFrom(const Value& source)
{
// TODO: Check if this is a derived type and throw an exception in that case
Data()->CopyFrom(*source.Data());
if ((Mask() == nullptr) && (source.Mask() != nullptr))
InvalidArgument("Value::CopyFrom: Invalid source object; Cannot copy a Value with a mask into 'this' Value which does not have a mask.");
if (source.Mask() != nullptr)
Mask()->CopyFrom(*source.Mask());
else
{
if (Mask() != nullptr)
{
// Clear the mask
Mask()->Clear();
}
}
}
void Value::GetSequenceStartsAndLengths(const NDMaskPtr& mask, std::vector<ptrdiff_t>& sequenceBeginIndices, std::vector<size_t>& sequenceLengths, size_t numDynamicAxes)
{
if (!mask)
return;
auto cpuMask = mask;
if (mask->Device() != DeviceDescriptor::CPUDevice())
cpuMask = mask->DeepClone(DeviceDescriptor::CPUDevice());
const MaskKind* maskBuffer = cpuMask->DataBuffer();
size_t maxNumTimeSteps, numSequences;
std::tie(maxNumTimeSteps, numSequences) = GetNumTimeStepsAndSequences(mask->Shape(), numDynamicAxes);
assert(sequenceLengths.size() == numSequences);
assert(sequenceBeginIndices.size() == numSequences);
for (size_t i = 0; i < numSequences; ++i)
{
MaskKind firstMaskEntry = maskBuffer[i * maxNumTimeSteps];
if (firstMaskEntry == MaskKind::SequenceBegin)
sequenceBeginIndices[i] = 0;
else if (firstMaskEntry == MaskKind::Valid)
sequenceBeginIndices[i] = Microsoft::MSR::CNTK::SentinelValueIndicatingUnspecifedSequenceBeginIdx;
else
LogicError("The first entry of a Value mask must be Valid or SequenceBegin");
size_t currentSequenceLength = 1;
bool currentSequenceEndAlreadyFound = false;
for (size_t j = 1; j < maxNumTimeSteps; ++j)
{
if (maskBuffer[(i * maxNumTimeSteps) + j] == MaskKind::Invalid)
currentSequenceEndAlreadyFound = true;
else
{
if (currentSequenceEndAlreadyFound)
InvalidArgument("Invalid Value object; only trailing steps of a sequence can be masked.");
currentSequenceLength++;
}
}
sequenceLengths[i] = currentSequenceLength;
}
}
template <typename ElementType, typename DestType>
void DirectCopy(const ElementType *source, size_t elementCount, std::vector<DestType>& dest);
template <typename ElementType, typename DestType>
void CopyDenseToOneHot(const ElementType *source, const size_t sampleCount, const size_t sampleSize, std::vector<DestType>& dest);
template <typename ElementType>
void Value::CopyVariableValueToVector(const Variable& outputVariable, std::vector<std::vector<ElementType>>& sequences)
{
// Check the data type matches
if (AsDataType<ElementType>() != GetDataType())
InvalidArgument("The specified ElementType %s does not match the DataType %s", typeid(ElementType).name(), DataTypeName(GetDataType()));
CopyVariableValueToImpl<ElementType, ElementType>(outputVariable, sequences);
}
template <typename ElementType>
void Value::CopyVariableValueToVector(const Variable& outputVariable, std::vector<std::vector<size_t>>& sequences)
{
if (outputVariable.Shape()[0] != outputVariable.Shape().TotalSize())
InvalidArgument("For sparse data, the outputVariable's leading axis dimensionality (%zu) must equal the total size (%zu) of the Variable '%S'.",
outputVariable.Shape()[0], outputVariable.Shape().TotalSize(), outputVariable.AsString().c_str());
CopyVariableValueToImpl<ElementType, size_t>(outputVariable, sequences);
}
template <typename ValueType, typename DestType>
void Value::CopyVariableValueToImpl(const Variable& outputVariable, std::vector<std::vector<DestType>>& sequences)
{
// PackedValue should be automatically unpacked when accessing Data() and Mask().
NDShape inferredVarShape;
size_t numOfSequences;
size_t maxSequenceLen;
// Verify compatibility of 'this' value and outputVariable, get sequence and batch length, and get the inferred shape if the variable has a free dimension.
std::tie(maxSequenceLen, numOfSequences) = GetSequenceAndBatchLength(outputVariable, &inferredVarShape);
if (sequences.size() < numOfSequences)
RuntimeError("The size of output buffer (%zu) is smaller than the number (%zu) of sequences.", sequences.size(), numOfSequences);
// Copy data to the CPU device if required.
const ValueType *valueData;
NDArrayViewPtr cpuArrayView;
if (Device().Type() == DeviceKind::GPU)
{
// TODO: leverage sparse if the original NDArrayView is in spase.
cpuArrayView = MakeSharedObject<NDArrayView>(GetDataType(), Shape(), DeviceDescriptor::CPUDevice());
cpuArrayView->CopyFrom(*Data());
}
else if (Device().Type() == DeviceKind::CPU)
{
// TODO: direct process sparse data without copy
if (GetStorageFormat() != StorageFormat::Dense)
{
cpuArrayView = MakeSharedObject<NDArrayView>(GetDataType(), Shape(), DeviceDescriptor::CPUDevice());
cpuArrayView->CopyFrom(*Data());
}
else
{
cpuArrayView = Data();
}
}
else
{
LogicError("Invalid device type (%u).", (unsigned int)Device().Type());
}
valueData = cpuArrayView->DataBuffer<ValueType>();
auto sampleSize = inferredVarShape.TotalSize();
for (auto seqIndex = 0; seqIndex < numOfSequences; seqIndex++)
{
size_t seqStart = seqIndex * maxSequenceLen;
// The assumption here is that a sequence always start at 0 (no invaid mark at the beginning),
// and ends at the first invalid mask.
// Therefore, no need to check NDMask again.
// And the sequences has been resized to match the number of sequences and the length of each sequence in the Value object.
// TODO: if function pointer or lambda could support template, switch to use them.
if (std::is_same<DestType, size_t>::value)
{
// If the output is of the one-hot vector format, each value in sequences[seqIndex] is an index which represents a sample of sampleSize elements.
CopyDenseToOneHot<ValueType, DestType>(valueData + seqStart * sampleSize, sequences[seqIndex].size(), sampleSize, sequences[seqIndex]);
}
else
{
// If the output is of the dense format, each value in sequences[seqIndex] represents an element of a sample.
DirectCopy<ValueType, DestType>(valueData + seqStart * sampleSize, sequences[seqIndex].size(), sequences[seqIndex]);
}
}
}
std::pair<size_t, size_t> Value::GetSequenceAndBatchLength(const Variable& outputVariable, NDShape* inferredVarShape)
{
Utils::VerifyVariableValueCompatibility(outputVariable, shared_from_this(), inferredVarShape);
size_t varRank = outputVariable.Shape().Rank();
size_t maxSequenceLength = 1;
size_t numSequences = 1;
std::tie(maxSequenceLength, numSequences) = GetNumTimeStepsAndSequences(Shape().SubShape(varRank), outputVariable.DynamicAxes().size());
return std::pair<size_t, size_t>(maxSequenceLength, numSequences);
}
template <typename ElementType>
std::tuple<size_t, size_t, size_t> Value::ValidateSparseCSCAndGetIndexBufferSizes(const Variable& outputVariable)
{
auto varShape = outputVariable.Shape();
if (varShape.IsUnknown() || varShape.HasInferredDimension())
InvalidArgument("The outputVariable '%S' shape '%S' is of unknown shape or has inferred dimension for at least one axis.",
outputVariable.AsString().c_str(), varShape.AsString().c_str());
if (!outputVariable.IsSparse())
InvalidArgument("The outputVariable '%S' must be in the sparse format.", outputVariable.AsString().c_str());
size_t numOfSequences;
size_t maxSequenceLen;
std::tie(maxSequenceLen, numOfSequences) = GetSequenceAndBatchLength(outputVariable);
// Only support sequence without batch
if (numOfSequences != 1)
InvalidArgument("The Value cannot be copied to buffers in sparse format, since it contains multiple sequences. Only a single sequence is supported.");
if (MaskedCount() != 0)
RuntimeError("There should not be any masks for a Value containing only one single sequence.");
auto numNonZeroValues = std::get<3>(Data()->SparseCSCDataBuffers<ElementType>());
auto numOfColsInMatrix = GetMatrixDimensions(Shape()).second + 1;
return std::tuple<size_t, size_t, size_t>(maxSequenceLen, numOfColsInMatrix, numNonZeroValues);
}
template <typename ElementType>
void Value::CopyVariableValueToCSCSparse(size_t sequenceLength, std::vector<SparseIndexType>& colStarts, std::vector<SparseIndexType>& rowIndices, std::vector<ElementType>& nonZeroValues, size_t& numNonZeroValues)
{
// All sanity check has been done in ValidateSparseCSCAndGetIndexSizes().
NDArrayViewPtr cpuView;
if (Device().Type() == DeviceKind::GPU)
{
// Todo: GPUSparseMatrix to CPUSparseMatrix is not implemented in matrix, as a workaround the dense matrix is used as intermediate presentation.
// However, it is possible that data value very close to 0 could treated as 0 after transformation between dense and sparse.
auto cpuDenseView = MakeSharedObject<NDArrayView>(GetDataType(), StorageFormat::Dense, Shape(), DeviceDescriptor::CPUDevice());
cpuDenseView->CopyFrom(*Data());
cpuView = MakeSharedObject<NDArrayView>(GetDataType(), GetStorageFormat(), Shape(), DeviceDescriptor::CPUDevice());
cpuView->CopyFrom(*cpuDenseView);
}
else
cpuView = Data();
auto numOfColsInMatrix = GetMatrixDimensions(cpuView->Shape()).second + 1;
const ElementType* rawNonZeroValues;
const SparseIndexType* rawColStarts;
const SparseIndexType* rawRowIndices;
std::tie(rawNonZeroValues, rawColStarts, rawRowIndices, numNonZeroValues) = cpuView->SparseCSCDataBuffers<ElementType>();
memcpy(colStarts.data(), rawColStarts, numOfColsInMatrix * sizeof(SparseIndexType));
memcpy(nonZeroValues.data(), rawNonZeroValues, numNonZeroValues * sizeof(ElementType));
memcpy(rowIndices.data(), rawRowIndices, numNonZeroValues * sizeof(SparseIndexType));
}
template <typename ElementType>
ElementType Value::AsScalar() const
{
if (Mask())
LogicError("Value::AsScalar: Scalar Value object must not have an associated mask");
return Data()->AsScalar<ElementType>();
}
/* virtual */ bool Value::IsValid() const
{
return !!m_data;
}
std::wstring Value::AsString() const
{
wstringstream wss;
if (IsValid())
wss << L"Value(" << Shape().AsString() << ", " << DeviceKindName(Device().Type()) << L")";
else
wss << L"Value(###)";
return wss.str();
}
void PackedValue::Unpack() const
{
if (m_packedDataLayout && (m_packedDataLayout->GetNumTimeSteps() != 1) && (m_packedDataLayout->GetNumSequences() != 1) && Internal::IsAutomaticUnpackingOfPackedValuesDisabled())
LogicError("PackedValue::Unpack: Automatic unpacking of PackedValue objects is disabled");
if (m_isPacked)
{
ValuePtr valueObject;
auto dataType = m_packedData->GetDataType();
switch (dataType)
{
case DataType::Float:
valueObject = Utils::GetValueObjectFromCNTKImplMatrixAndMBLayout(m_sampleShape, m_sampleDynamicAxes, *(m_packedData->GetMatrix<float>()), m_packedDataLayout, m_isReadOnly);
break;
case DataType::Double:
valueObject = Utils::GetValueObjectFromCNTKImplMatrixAndMBLayout(m_sampleShape, m_sampleDynamicAxes, *(m_packedData->GetMatrix<double>()), m_packedDataLayout, m_isReadOnly);
break;
case DataType::Float16:
valueObject = Utils::GetValueObjectFromCNTKImplMatrixAndMBLayout(m_sampleShape, m_sampleDynamicAxes, *(m_packedData->GetMatrix<half>()), m_packedDataLayout, m_isReadOnly);
break;
default:
LogicError("Unsupported DataType %s", DataTypeName(dataType));
}
m_data = valueObject->Data();
m_mask = valueObject->Mask();
m_packedData = nullptr;
m_packedDataLayout = nullptr;
m_isPacked = false;
if (m_unpackedShape != m_data->Shape())
LogicError("The computed unpacked shape '%S' of the PackedValue object does not match the actual Data NDArrayView's shape '%S' after unpacking.",
m_unpackedShape.AsString().c_str(), m_data->Shape().AsString().c_str());
}
}
template <typename ElementType, typename DestType>
void DirectCopy(const ElementType *source, const size_t elementCount, std::vector<DestType>& dest)
{
if (!std::is_same<ElementType, DestType>::value)
RuntimeError("Copy: Source and destination must be the same data type.");
DestType *destData = dest.data();
if (elementCount > dest.size())
RuntimeError("Copy: The output buffer size (%zu) is smaller than the number (%zu) of source elements to copy.", dest.size(), elementCount);
std::copy(source, source + elementCount, reinterpret_cast<ElementType *>(destData));
}
template <typename ElementType, typename DestType>
void CopyDenseToOneHot(const ElementType *source, const size_t sampleCount, const size_t sampleSize, std::vector<DestType>& dest)
{
if (!std::is_same<DestType, size_t>::value)
RuntimeError("Copy: The destination data type must be size_t.");
const ElementType *currentp = source;
const ElementType *lastp = source + sampleCount * sampleSize;
size_t destIndex = 0;
while (currentp < lastp)
{
size_t index = sampleSize;
bool found = false;
for (size_t i = 0; i < sampleSize; i++)
{
if (*currentp == (ElementType)1)
{
if (found)
RuntimeError("CopyDenseToOneHot: Cannot convert to onehot vector; more than one non-zero value in the sample.");
index = i;
found = true;
}
else if (*currentp != (ElementType)0)
RuntimeError("CopyDenseToOneHot: Cannot convert to onehot vector; contains value other than 0/1.");
currentp++;
}
if (!found)
RuntimeError("CopyDenseToOneHot: Cannot convert to onehot vector; the sample does not have any non-zero value.");
assert(index != sampleSize);
dest[destIndex++] = static_cast<DestType>(index);
}
assert(currentp == lastp);
}
// Explicit template instantiations
template /*static*/ CNTK_API ValuePtr Value::Create<float>(const NDShape& sampleShape, const std::vector<std::vector<float>>& sequences, const std::vector<bool>& sequenceStartFlags, const DeviceDescriptor& device, bool readOnly/* = false*/);
template /*static*/ CNTK_API ValuePtr Value::Create<double>(const NDShape& sampleShape, const std::vector<std::vector<double>>& sequences, const std::vector<bool>& sequenceStartFlags, const DeviceDescriptor& device, bool readOnly/* = false*/);
template /*static*/ CNTK_API ValuePtr Value::Create<float16>(const NDShape& sampleShape, const std::vector<std::vector<float16>>& sequences, const std::vector<bool>& sequenceStartFlags, const DeviceDescriptor& device, bool readOnly/* = false*/);
template /*static*/ CNTK_API ValuePtr Value::Create<float>(const NDShape& sampleShape, const std::vector<std::vector<size_t>>& oneHotSequences, const std::vector<bool>& sequenceStartFlags, const DeviceDescriptor& device, bool readOnly/* = false*/);
template /*static*/ CNTK_API ValuePtr Value::Create<double>(const NDShape& sampleShape, const std::vector<std::vector<size_t>>& oneHotSequences, const std::vector<bool>& sequenceStartFlags, const DeviceDescriptor& device, bool readOnly/* = false*/);
template /*static*/ CNTK_API ValuePtr Value::Create<float16>(const NDShape& sampleShape, const std::vector<std::vector<size_t>>& oneHotSequences, const std::vector<bool>& sequenceStartFlags, const DeviceDescriptor& device, bool readOnly/* = false*/);
template /*static*/ CNTK_API ValuePtr Value::CreateBatch<float>(const NDShape& sampleShape, const std::vector<float>& batchData, const DeviceDescriptor& device, bool readOnly /*= false */);
template /*static*/ CNTK_API ValuePtr Value::CreateBatch<double>(const NDShape& sampleShape, const std::vector<double>& batchData, const DeviceDescriptor& device, bool readOnly /*= false */);
template /*static*/ CNTK_API ValuePtr Value::CreateBatch<float16>(const NDShape& sampleShape, const std::vector<float16>& batchData, const DeviceDescriptor& device, bool readOnly /*= false */);
template /*static*/ CNTK_API ValuePtr Value::CreateSequence<float>(const NDShape& sampleShape, const std::vector<float>& sequenceData, bool sequenceStartFlag, const DeviceDescriptor& device, bool readOnly /*= false */);
template /*static*/ CNTK_API ValuePtr Value::CreateSequence<double>(const NDShape& sampleShape, const std::vector<double>& sequenceData, bool sequenceStartFlag, const DeviceDescriptor& device, bool readOnly /*= false */);
template /*static*/ CNTK_API ValuePtr Value::CreateSequence<float16> (const NDShape& sampleShape, const std::vector<float16>& sequenceData, bool sequenceStartFlag, const DeviceDescriptor& device, bool readOnly /*= false */);
template /*static*/ CNTK_API ValuePtr Value::CreateBatch<float>(size_t dimension, const std::vector<size_t>& batchData, const DeviceDescriptor& device, bool readOnly/* = false*/);
template /*static*/ CNTK_API ValuePtr Value::CreateBatch<double>(size_t dimension, const std::vector<size_t>& batchData, const DeviceDescriptor& device, bool readOnly/* = false*/);
template /*static*/ CNTK_API ValuePtr Value::CreateBatch<float16> (size_t dimension, const std::vector<size_t>& batchData, const DeviceDescriptor& device, bool readOnly/* = false*/);
template /*static*/ CNTK_API ValuePtr Value::CreateSequence<float>(size_t dimension, const std::vector<size_t>& sequenceData, bool sequenceStartFlag, const DeviceDescriptor& device, bool readOnly/* = false*/);
template /*static*/ CNTK_API ValuePtr Value::CreateSequence<double>(size_t dimension, const std::vector<size_t>& sequenceData, bool sequenceStartFlag, const DeviceDescriptor& device, bool readOnly/* = false*/);
template /*static*/ CNTK_API ValuePtr Value::CreateSequence<float16>(size_t dimension, const std::vector<size_t>& sequenceData, bool sequenceStartFlag, const DeviceDescriptor& device, bool readOnly/* = false*/);
template /*static*/ CNTK_API ValuePtr Value::CreateSequence<float>(const NDShape& sampleShape, size_t sequenceLength, const SparseIndexType* colStarts, const SparseIndexType* rowIndices, const float* nonZeroValues, size_t numNonZeroValues, bool sequenceStartFlag, const DeviceDescriptor& device, bool readOnly/* = false*/);
template /*static*/ CNTK_API ValuePtr Value::CreateSequence<double>(const NDShape& sampleShape, size_t sequenceLength, const SparseIndexType* colStarts, const SparseIndexType* rowIndices, const double* nonZeroValues, size_t numNonZeroValues, bool sequenceStartFlag, const DeviceDescriptor& device, bool readOnly/* = false*/);
template /*static*/ CNTK_API ValuePtr Value::CreateSequence<float16>(const NDShape& sampleShape, size_t sequenceLength, const SparseIndexType* colStarts, const SparseIndexType* rowIndices, const float16* nonZeroValues, size_t numNonZeroValues, bool sequenceStartFlag, const DeviceDescriptor& device, bool readOnly/* = false*/);
template CNTK_API void Value::CopyVariableValueToVector<float>(const Variable& outputVariable, std::vector<std::vector<float>>& sequences);
template CNTK_API void Value::CopyVariableValueToVector<double>(const Variable& outputVariable, std::vector<std::vector<double>>& sequences);
template CNTK_API void Value::CopyVariableValueToVector<float16>(const Variable& outputVariable, std::vector<std::vector<float16>>& sequences);
template CNTK_API void Value::CopyVariableValueToVector<float>(const Variable& outputVariable, std::vector<std::vector<size_t>>& sequences);
template CNTK_API void Value::CopyVariableValueToVector<double>(const Variable& outputVariable, std::vector<std::vector<size_t>>& sequences);
template CNTK_API void Value::CopyVariableValueToVector<float16>(const Variable& outputVariable, std::vector<std::vector<size_t>>& sequences);
template CNTK_API std::tuple<size_t, size_t, size_t> Value::ValidateSparseCSCAndGetIndexBufferSizes<float>(const Variable& outputVariable);
template CNTK_API std::tuple<size_t, size_t, size_t> Value::ValidateSparseCSCAndGetIndexBufferSizes<double>(const Variable& outputVariable);
template CNTK_API std::tuple<size_t, size_t, size_t> Value::ValidateSparseCSCAndGetIndexBufferSizes<float16>(const Variable& outputVariable);
template CNTK_API void Value::CopyVariableValueToCSCSparse<float>(size_t sequenceLength, std::vector<SparseIndexType>& colStarts, std::vector<SparseIndexType>& rowIndices, std::vector<float>& nonZeroValues, size_t& numNonZeroValues);
template CNTK_API void Value::CopyVariableValueToCSCSparse<double>(size_t sequenceLength, std::vector<SparseIndexType>& colStarts, std::vector<SparseIndexType>& rowIndices, std::vector<double>& nonZeroValues, size_t& numNonZeroValues);
template CNTK_API void Value::CopyVariableValueToCSCSparse<float16>(size_t sequenceLength, std::vector<SparseIndexType>& colStarts, std::vector<SparseIndexType>& rowIndices, std::vector<float16>& nonZeroValues, size_t& numNonZeroValues);
template float Value::AsScalar<float>() const;
template double Value::AsScalar<double>() const;
template float16 Value::AsScalar<float16>() const;
}