//
// 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 "CPUSparseMatrix.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 (%d) of the mask of a Value object cannot exceed the rank (%d) of the data NDArrayView object", (int)maskShape.Rank(), (int)dataShape.Rank());

            if (dataShape.SubShape(dataShape.Rank() - maskShape.Rank()) != maskShape)
                InvalidArgument("Invalid Value object; the data and mask are incompatible. The trailing dimensions of the data with shape %S do not match the dimensions of the mask with shape %S", AsStringForErrorReporting(dataShape).c_str(), AsStringForErrorReporting(maskShape).c_str());
        }
    }

    static NDMaskPtr CreateMask(const std::vector<size_t>& sequenceLengths, const std::vector<bool>& sequenceStartFlags, const DeviceDescriptor& device)
    {
        size_t numSequences = sequenceLengths.size();

        if (!sequenceStartFlags.empty() && (sequenceStartFlags.size() != numSequences))
            InvalidArgument("Value::Create:: The number of sequence start flags does not match the number of sequences");

        std::vector<bool> actualStarts = sequenceStartFlags;
        if (actualStarts.empty())
            actualStarts.resize(numSequences, true);

        size_t maxSequenceLength = 0;
        for (size_t i = 0; i < numSequences; ++i)
            maxSequenceLength = std::max(maxSequenceLength, sequenceLengths[i]);

        bool needsMask = (std::find(actualStarts.begin(), actualStarts.end(), false) != actualStarts.end());
        needsMask = needsMask || (std::find_if(sequenceLengths.begin(), sequenceLengths.end(), [maxSequenceLength](const size_t& currentSequenceLength) {
            return (currentSequenceLength != maxSequenceLength);
        }) != sequenceLengths.end());

        // If needed, create a mask to account for variability in lengths of specified sequences
        NDMaskPtr deviceValueMask;
        if (needsMask)
        {
            NDShape valueMaskShape = { maxSequenceLength, numSequences };
            deviceValueMask = MakeSharedObject<NDMask>(valueMaskShape, device);
            for (size_t i = 0; i < numSequences; ++i)
            {
                if (actualStarts[i])
                    deviceValueMask->MarkSequenceBegin({ 0, i });
                deviceValueMask->InvalidateSection({ sequenceLengths[i], i }, { NDShape::InferredDimension, 1 });
            }
        }

        return deviceValueMask;
    }

    //
    // 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(size_t vocabularySize, const std::vector<std::vector<size_t>>& oneHotSequences, const std::vector<bool>& sequenceStartFlags, const DeviceDescriptor& device, bool readOnly/* = false*/)
    {
        NDMaskPtr deviceValueMask = CreateMask(1, oneHotSequences, sequenceStartFlags, DeviceDescriptor::CPUDevice());
        // If deviceValueMask is null, all the sequences have the same length.
        size_t maxSequenceLength = (deviceValueMask == nullptr) ? oneHotSequences[0].size() : deviceValueMask->Shape()[0];

        size_t numSequences = oneHotSequences.size();
        NDShape sampleShape = { vocabularySize };
        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 currentSequenceLength = oneHotSequences[i].size();
            size_t j = 0;
            for (; j < currentSequenceLength; ++j)
            {
                colStarts[(i * maxSequenceLength) + j] = (SparseIndexType)nonZeroValues.size();
                nonZeroValues.push_back(1);
                if (oneHotSequences[i][j] >= vocabularySize)
                    InvalidArgument("Value::Create: one-hot data exceeds vocabulary size");
                rowIndices.push_back((SparseIndexType)(oneHotSequences[i][j]));
            }

            for (; j < maxSequenceLength; ++j)
                colStarts[(i * maxSequenceLength) + j] = (SparseIndexType)(nonZeroValues.size());
        }

        colStarts[numSequences * maxSequenceLength] = (SparseIndexType)(nonZeroValues.size());
        NDArrayViewPtr deviceValueData = MakeSharedObject<NDArrayView>(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 maxSequenceLength)
    {
        size_t existingNumNonZeroValues = nonZeroValues.size() / sizeof(ElementType);
        std::vector<SparseIndexType> currentSequencePaddedColStarts(maxSequenceLength);

        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 < maxSequenceLength; ++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 is 0");

        std::vector<size_t> sequenceLengths(numSequences);
        size_t maxSequenceLength = 0;
        auto dataType = sequences[0]->GetDataType();
        auto storageFormat = sequences[0]->GetStorageFormat();
        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 ((sampleShape.Rank() == 1) && (sampleShape.TotalSize() == 1) && (currentSequenceDataShape[0] != 1))
                currentSequenceDataShape = NDShape(1, 1).AppendShape(currentSequenceDataShape);

            if ((currentSequenceDataShape.Rank() < sampleShape.Rank()) || (currentSequenceDataShape.Rank() > (sampleShape.Rank() + 1)) || (currentSequenceDataShape.SubShape(0, sampleShape.Rank()) != sampleShape))
                InvalidArgument("Value::Create:: The shape of the sequence %lu (%S) is not compatible with the sample shape (%S)", (unsigned long)i, AsStringForErrorReporting(currentSequenceData->Shape()).c_str(), AsStringForErrorReporting(sampleShape).c_str());

            sequenceLengths[i] = currentSequenceDataShape.SubShape(sampleShape.Rank()).TotalSize();
            maxSequenceLength = std::max(maxSequenceLength, sequenceLengths[i]);
        }

        bool isDataSparse = sequences[0]->IsSparse();
        if (isDataSparse && (sampleShape[0] != sampleShape.TotalSize()))
            InvalidArgument("Value::Create:: The sample shape's leading axis dimensionality must equal the total size of the sample for sparse data");

        NDMaskPtr deviceValueMask = CreateMask(sequenceLengths, sequenceStartFlags, DeviceDescriptor::CPUDevice());

        NDArrayViewPtr valueData;
        if (numSequences == 1)
        {
            if (createNewCopy)
                valueData = sequences[0]->DeepClone();
            else
                valueData = sequences[0];
        }
        else
        {
            NDShape valueDataShape = sampleShape.AppendShape({ maxSequenceLength, numSequences });
            if (isDataSparse)
            {
                if (storageFormat != StorageFormat::SparseCSC)
                    LogicError("Value::Create currently only SparseCSC format data is supported");

                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);
                        break;
                    case DataType::Double:
                        AppendSparseSequenceData<double>(sequences[i], colStarts, rowIndices, nonZeroValues, maxSequenceLength);
                        break;
                    default:
                        NOT_IMPLEMENTED;
                    }
                }

                auto totalNumNonZeroValues = nonZeroValues.size() / DataTypeSize(dataType);
                colStarts.push_back(totalNumNonZeroValues);

                switch (dataType)
                {
                case DataType::Float:
                    // TODO: In case of sparse we can directly create on target device
                    valueData = MakeSharedObject<NDArrayView>(valueDataShape, colStarts.data(), rowIndices.data(), (float*)nonZeroValues.data(), totalNumNonZeroValues, device, readOnly);
                    break;
                case DataType::Double:
                    valueData = MakeSharedObject<NDArrayView>(valueDataShape, colStarts.data(), rowIndices.data(), (double*)nonZeroValues.data(), totalNumNonZeroValues, device, readOnly);
                    break;
                default:
                    NOT_IMPLEMENTED;
                }
            }
            else
            {
                valueData = MakeSharedObject<NDArrayView>(dataType, valueDataShape, DeviceDescriptor::CPUDevice());
                auto maxSequenceSizeInElements = sampleShape.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 in the vector containing sequence data must be a multiple of the size of specified sampel shape");

            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);
    }

    /*virtual*/ Value::~Value()
    {
    }

    /*virtual*/ NDArrayViewPtr Value::Data() const
    {
        // 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 that does not have a mask.");

        if (source.Mask() != nullptr)
            Mask()->CopyFrom(*source.Mask());
        else
        {
            if (Mask() != nullptr)
            {
                // Clear the mask
                Mask()->Clear();
            }
        }
    }

    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("The outputVariable's leading axis dimensionality must equal the total size of the variable for sparse data.");
        }
        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().
        size_t numOfSequences;
        size_t maxSequenceLen;
        std::tie(maxSequenceLen, numOfSequences) = GetSequenceAndBatchLength(outputVariable);

        if (sequences.size() < numOfSequences)
            RuntimeError("The size of output buffer is too small");

        // Copy data to the CPU device if required.
        const ValueType *valueData;
        NDArrayViewPtr cpuArrayView;
        if (Device().Type() != DeviceKind::CPU)
        {
            // TODO: leverage sparse if the original NDArrayView is in spase.
            cpuArrayView = MakeSharedObject<NDArrayView>(GetDataType(), Shape(), DeviceDescriptor::CPUDevice());
            cpuArrayView->CopyFrom(*Data());
        }
        else
        {
            // TODO: direct process sparse data without copy
            if (GetStorageFormat() != StorageFormat::Dense)
            {
                cpuArrayView = MakeSharedObject<NDArrayView>(GetDataType(), Shape(), DeviceDescriptor::CPUDevice());
                cpuArrayView->CopyFrom(*Data());
            }
            else
            {
                cpuArrayView = Data();
            }
        }
        valueData = cpuArrayView->DataBuffer<ValueType>();

        auto sampleSize = outputVariable.Shape().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)
    {
        size_t varRank = outputVariable.Shape().Rank();
        size_t maxSequenceLength = 1;
        size_t numSequences = 1;

        if (Shape().Rank() < varRank)
            RuntimeError("The Value'rank should be greater than or equal to the variable's rank.");

        size_t maskRank = Shape().Rank() - varRank;
        if (outputVariable.Shape() != Shape().SubShape(0, varRank))
            RuntimeError("The shape of the outputVariable does not match the Value shape.");

        if (outputVariable.DynamicAxes().size() > 2)
            LogicError("More than 2 dynamic axis for a variable is currently unsupported");

        if (maskRank > 2)
            LogicError("Value rank which is larger than the output variable rank by more than 2 dynamic axes is currently unsupported.");

        std::tie(maxSequenceLength, numSequences) = GetNumTimeStepsAndSequences(Shape().SubShape(varRank), outputVariable.DynamicAxes().size());

        return std::pair<size_t, size_t>(maxSequenceLength, numSequences);
    }


    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_packedData->GetMatrix<float>()), m_packedDataLayout, m_isReadOnly);
                break;
            case DataType::Double:
                valueObject = Utils::GetValueObjectFromCNTKImplMatrixAndMBLayout(m_sampleShape, *(m_packedData->GetMatrix<double>()), 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 of the PackedValue object does not match the actual Data NDArrayView's shape after unpacking");
        }
    }

    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("Source and destination must be the same data type.");

        DestType *destData = dest.data();
        if (elementCount > dest.size())
            RuntimeError("The output buffer is too small.");
        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("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 == 1)
                {
                    if (found)
                        RuntimeError("Cannot convert to onehot vector: more than one non-zero value in the sample.");
                    index = i;
                    found = true;
                }
                else if (*currentp != 0)
                    RuntimeError("Cannot convert to onehot vector: contain value other than 0 and 1.");
                currentp++;
            }
            if (!found)
                RuntimeError("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<float>(size_t vocabSize, 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>(size_t vocabSize, const std::vector<std::vector<size_t>>& oneHotSequences, const std::vector<bool>& sequenceStartFlags, 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<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);
}