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 // @(#)root/tmva/tmva/dnn:$Id$
 // Author: Vladimir Ilievski
 
 /**********************************************************************************
  * Project: TMVA - a Root-integrated toolkit for multivariate data analysis       *
  * Package: TMVA                                                                  *
  * Class  : TTensorDataLoader                                                     *
  *                                             *
  *                                                                                *
  * Description:                                                                   *
  *      Tensor Data Loader Class                                                  *
  *                                                                                *
  * Authors (alphabetical):                                                        *
  *      Vladimir Ilievski      <ilievski.vladimir@live.com>  - CERN, Switzerland  *
  *                                                                                *
  * Copyright (c) 2005-2015:                                                       *
  *      CERN, Switzerland                                                         *
  *      U. of Victoria, Canada                                                    *
  *      MPI-K Heidelberg, Germany                                                 *
  *      U. of Bonn, Germany                                                       *
  *                                                                                *
  * Redistribution and use in source and binary forms, with or without             *
  * modification, are permitted according to the terms listed in LICENSE           *
  * (see tmva/doc/LICENSE)                                          *
  **********************************************************************************/
 
 #ifndef TMVA_DNN_TENSORDATALOADER
 #define TMVA_DNN_TENSORDATALOADER
 
 #include "TMatrix.h"
 #include "TMVA/Event.h"
 #include <algorithm>
 #include <vector>
 #include <utility>
 
 namespace TMVA {
    class DataSetInfo;
 namespace DNN {
 
 //
 // Input Data Types
 //______________________________________________________________________________
 using TensorInput =
    std::tuple<const std::vector<TMatrixT<Double_t>> &, const TMatrixT<Double_t> &, const TMatrixT<Double_t> &>;
 
 using TMVAInput_t =  std::tuple<const std::vector<Event *> &, const DataSetInfo &>;
 using IndexIterator_t = typename std::vector<size_t>::iterator;
 
 /** TTensorBatch
  *
  * Class representing training batches consisting of a vector of matrices as input data
  * and a matrix of output data. The input and output data can be accessed using
  * the GetInput() and GetOutput() member functions.
  *
  * \tparam Architecture_t The underlying architecture.
  */
 
 template <typename Architecture_t>
 class TTensorBatch {
 public:
    using Matrix_t = typename Architecture_t::Matrix_t;
    using Tensor_t = typename Architecture_t::Tensor_t;
 
 private:
    Tensor_t  fInputTensor;         ///< The input tensor batch, one matrix one input.
    Matrix_t fOutputMatrix;         ///< The output matrix representing the ground truth.
    Matrix_t fWeightMatrix;         ///< The event/example weights
 
 public:
    TTensorBatch(Tensor_t &, Matrix_t &, Matrix_t &);
    TTensorBatch(const TTensorBatch &) = default;
    TTensorBatch(TTensorBatch &&) = default;
    TTensorBatch &operator=(const TTensorBatch &) = default;
    TTensorBatch &operator=(TTensorBatch &&) = default;
 
    /** Return the tensor representing the input data */
    Tensor_t &GetInput() { return fInputTensor; }
    /** Return the matrix representing the output data. */
    Matrix_t &GetOutput() { return fOutputMatrix; }
    /** Return the matrix holding the event weights. */
    Matrix_t &GetWeights() { return fWeightMatrix; }
 };
 
 template <typename Data_t, typename Architecture_t>
 class TTensorDataLoader;
 
 /** TTensorBatchIterator
  *
  * Simple iterator class for the iterations over the training batches in
  * a given data set represented by a TTensorDataLoader object.
  *
  * \tparam Data_t         The input data type.
  * \tparam Architecture_t The underlying architecture type.
  */
 template <typename Data_t, typename Architecture_t>
 class TTensorBatchIterator {
 private:
    TTensorDataLoader<Data_t, Architecture_t> &fTensorDataLoader;
    size_t fBatchIndex;
 
 public:
    TTensorBatchIterator(TTensorDataLoader<Data_t, Architecture_t> &tensorDataLoader, size_t index = 0)
       : fTensorDataLoader(tensorDataLoader), fBatchIndex(index)
    {
       // Nothing to do here.
    }
 
    TTensorBatch<Architecture_t> operator*() { return fTensorDataLoader.GetTensorBatch(); }
    TTensorBatchIterator operator++()
    {
       fBatchIndex++;
       return *this;
    }
    bool operator!=(const TTensorBatchIterator &other) { return fBatchIndex != other.fBatchIndex; }
 };
 
 /** TTensorDataLoader
  *
  * Service class managing the streaming of the training data from the input data
  * type to the accelerator device or the CPU. A TTensorDataLoader object manages
  * a number of host and device buffer pairs that are used in a round-robin manner
  * for the transfer of batches to the device.
  *
  * Each TTensorDataLoader object has an associated batch size and a number of total
  * samples in the dataset. One epoch is the number of buffers required to transfer
  * the complete training set. Using the begin() and end() member functions allows
  * the user to iterate over the batches in one epoch.
  *
  * \tparam Data_t The input data type.
  * \tparam Architecture_t The architecture class of the underlying architecture.
  */
 template <typename Data_t, typename Architecture_t>
 class TTensorDataLoader {
 private:
    using HostBuffer_t = typename Architecture_t::HostBuffer_t;
    using DeviceBuffer_t = typename Architecture_t::DeviceBuffer_t;
    using Matrix_t = typename Architecture_t::Matrix_t;
    using Tensor_t = typename Architecture_t::Tensor_t;
    using Shape_t = typename Architecture_t::Tensor_t::Shape_t;
    using BatchIterator_t = TTensorBatchIterator<Data_t, Architecture_t>;
 
    const Data_t &fData;     ///< The data that should be loaded in the batches.
    size_t fNSamples;        ///< The total number of samples in the dataset.
    size_t fBatchSize;       ///< The size of a batch.
    Shape_t    fInputLayout; ///< The input data layout  (does not include batch size)
    size_t fBatchDepth;      ///< The number of matrices in the tensor.
    size_t fBatchHeight;     ///< The number od rows in each matrix.
    size_t fBatchWidth;      ///< The number of columns in each matrix.
    size_t fNOutputFeatures; ///< The number of outputs from the classifier/regressor.
    size_t fBatchIndex;      ///< The index of the batch when there are multiple batches in parallel
 
 
    size_t fNStreams;                           ///< Number of buffer pairs.
    std::vector<DeviceBuffer_t> fDeviceBuffers; ///< The device buffers used to keep the input, output and weight data.
    std::vector<HostBuffer_t> fHostBuffers;     ///< The host buffers used to load the input, output and weight data.
 
    std::vector<size_t> fSampleIndices; ///< Ordering of the samples in the epoch.
 
 public:
    /*! Constructor. */
    TTensorDataLoader(const Data_t &data, size_t nSamples, size_t batchSize, const Shape_t & inputLayout,
        const Shape_t & batchLayout, size_t nOutputFeatures, size_t nStreams = 1);
 
    TTensorDataLoader(const TTensorDataLoader &) = default;
    TTensorDataLoader(TTensorDataLoader &&) = default;
    TTensorDataLoader &operator=(const TTensorDataLoader &) = default;
    TTensorDataLoader &operator=(TTensorDataLoader &&) = default;
 
    /** Copy input tensor into the given host buffer. Function to be specialized by
     *  the architecture-specific backend. */
    void CopyTensorInput(HostBuffer_t &buffer, IndexIterator_t begin);
    /** Copy output matrix into the given host buffer. Function to be specialized
     * by the architecture-specific backend. */
    void CopyTensorOutput(HostBuffer_t &buffer, IndexIterator_t begin);
    /** Copy weight matrix into the given host buffer. Function to be specialized
     * by the architecture-specific backend. */
    void CopyTensorWeights(HostBuffer_t &buffer, IndexIterator_t begin);
 
    BatchIterator_t begin() { return TTensorBatchIterator<Data_t, Architecture_t>(*this); }
    BatchIterator_t end() { return TTensorBatchIterator<Data_t, Architecture_t>(*this, fNSamples / fBatchSize); }
 
    /** Shuffle the order of the samples in the batch. The shuffling is indirect,
     *  i.e. only the indices are shuffled. No input data is moved by this
     * routine. */
    template<typename RNG>
    void Shuffle(RNG & rng);
 
    /** Return the next batch from the training set. The TTensorDataLoader object
     *  keeps an internal counter that cycles over the batches in the training
     *  set. */
    TTensorBatch<Architecture_t> GetTensorBatch();
 };
 
 //
 // TTensorBatch Class.
 //______________________________________________________________________________
 template <typename Architecture_t>
 TTensorBatch<Architecture_t>::TTensorBatch(Tensor_t &inputTensor, Matrix_t &outputMatrix,
                                            Matrix_t &weightMatrix)
    : fInputTensor(inputTensor), fOutputMatrix(outputMatrix), fWeightMatrix(weightMatrix)
 {
    // Nothing to do here.
 }
 
 //
 // TTensorDataLoader Class.
 //______________________________________________________________________________
 template <typename Data_t, typename Architecture_t>
 TTensorDataLoader<Data_t, Architecture_t>::TTensorDataLoader(const Data_t &data, size_t nSamples, size_t batchSize,
                                                              const Shape_t & inputLayout,  const Shape_t & batchLayout,
                                                              size_t nOutputFeatures, size_t nStreams)
    : fData(data), fNSamples(nSamples), fBatchSize(batchSize), fInputLayout(inputLayout), fBatchDepth(batchLayout[0]), fBatchHeight(batchLayout[1]),
      fBatchWidth(batchLayout[2]), fNOutputFeatures(nOutputFeatures), fBatchIndex(0), fNStreams(nStreams), fDeviceBuffers(),
      fHostBuffers(), fSampleIndices()
 {
    size_t inputTensorSize = fBatchDepth * fBatchHeight * fBatchWidth;
    size_t outputMatrixSize = fBatchSize * fNOutputFeatures;
    size_t weightMatrixSize = fBatchSize;
 
    for (size_t i = 0; i < fNStreams; i++) {
       fHostBuffers.push_back(HostBuffer_t(inputTensorSize + outputMatrixSize + weightMatrixSize));
       fDeviceBuffers.push_back(DeviceBuffer_t(inputTensorSize + outputMatrixSize + weightMatrixSize));
    }
 
    fSampleIndices.reserve(fNSamples);
    for (size_t i = 0; i < fNSamples; i++) {
       fSampleIndices.push_back(i);
    }
 }
 
 //______________________________________________________________________________
 template <typename Data_t, typename Architecture_t>
 TTensorBatch<Architecture_t> TTensorDataLoader<Data_t, Architecture_t>::GetTensorBatch()
 {
    fBatchIndex %= (fNSamples / fBatchSize); // Cycle through samples.
 
    size_t inputTensorSize =  fBatchDepth * fBatchHeight * fBatchWidth;
    size_t outputMatrixSize = fBatchSize * fNOutputFeatures;
    size_t weightMatrixSize = fBatchSize;
 
    size_t streamIndex = fBatchIndex % fNStreams;
    HostBuffer_t &hostBuffer = fHostBuffers[streamIndex];
    DeviceBuffer_t &deviceBuffer = fDeviceBuffers[streamIndex];
 
    HostBuffer_t inputHostBuffer = hostBuffer.GetSubBuffer(0, inputTensorSize);
    HostBuffer_t outputHostBuffer = hostBuffer.GetSubBuffer(inputTensorSize, outputMatrixSize);
    HostBuffer_t weightHostBuffer = hostBuffer.GetSubBuffer(inputTensorSize + outputMatrixSize, weightMatrixSize);
 
    DeviceBuffer_t inputDeviceBuffer = deviceBuffer.GetSubBuffer(0, inputTensorSize);
    DeviceBuffer_t outputDeviceBuffer = deviceBuffer.GetSubBuffer(inputTensorSize, outputMatrixSize);
    DeviceBuffer_t weightDeviceBuffer = deviceBuffer.GetSubBuffer(inputTensorSize + outputMatrixSize, weightMatrixSize);
 
    // here sample index has batch size as offset , while in
    // copy tensor input has batch depth.
    // We support then now two cases: batchdepth = 1  batchHeight = batch size
    //   or batch depth = batch
    size_t sampleIndex = fBatchIndex * fBatchSize;
    IndexIterator_t sampleIndexIterator = fSampleIndices.begin() + sampleIndex;
 
    CopyTensorInput(inputHostBuffer, sampleIndexIterator);
    CopyTensorOutput(outputHostBuffer, sampleIndexIterator);
    CopyTensorWeights(weightHostBuffer, sampleIndexIterator);
 
    deviceBuffer.CopyFrom(hostBuffer);
 
    assert(fInputLayout.size() == 3);
    Tensor_t inputTensor = Architecture_t::CreateTensor( inputDeviceBuffer, fBatchSize, fInputLayout[0], fInputLayout[1], fInputLayout[2] );
    // in case of dense layers
    if (fBatchDepth == 1 && fBatchHeight == fBatchSize && fInputLayout[0] == 1 && fInputLayout[1] == 1){
       inputTensor = Tensor_t( inputDeviceBuffer, {fBatchSize, fInputLayout.back() }, Tensor_t::MemoryLayout::ColumnMajor );
    }
 
    Matrix_t outputMatrix(outputDeviceBuffer, fBatchSize, fNOutputFeatures);
    Matrix_t weightMatrix(weightDeviceBuffer, fBatchSize, 1);
 
    fBatchIndex++;
 
 
    return TTensorBatch<Architecture_t>(inputTensor, outputMatrix, weightMatrix);
 }
 
 //______________________________________________________________________________
 template <typename Data_t, typename Architecture_t>
 template <typename RNG>
 void TTensorDataLoader<Data_t, Architecture_t>::Shuffle(RNG & rng)
 {
    std::shuffle(fSampleIndices.begin(), fSampleIndices.end(), rng);
 }
 
 } // namespace DNN
 } // namespace TMVA
 
 #endif

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